Reviewerly/exHarmony huggingface dataset:
MNIST Handwritten Digit Classification Based on Convolutional Neural Network with Hyperparameter Optimization

Accurate handwriting recognition has been a challenging computer vision problem, because static feature analysis of the text pictures is often inadequate to account for high variance in handwriting styles across people and poor image quality of the handwritten text. Recently, by introducing machine learning, especially convolutional neural networks (CNNs), the recognition accuracy of various handwriting patterns is steadily improved. In this paper, a deep CNN model is developed to further improve the recognition rate of the MNIST handwritten digit dataset with a fast-converging rate in training. The proposed model comes with a multi-layer deep arrange structure, including 3 convolution and activation layers for feature extraction and 2 fully connected layers (i.e., dense layers) for classification. The model's hyperparameters, such as the batch sizes, kernel sizes, batch normalization, activation function, and learning rate are optimized to enhance the recognition performance. The average classification accuracy of the proposed methodology is found to reach 99.82% on the training dataset and 99.40% on the testing dataset, making it a nearly error-free system for MNIST recognition.
Learning-Related Sentiment Detection, Classification, and Application for a Quality Education Using Artificial Intelligence Techniques

Quality education is one of the primary objectives of any nation-building strategy and is one of the seventeen Sustainable Development Goals (SDGs) by the United Nations. To provide quality education, delivering top-quality content is not enough. However, understanding the learners’ emotions during the learning process is equally important. However, most of this research work uses general data accessed from Twitter or other publicly available databases. These databases are generally not an ideal representation of the actual learning process and the learners’ sentiments about the learning process. This research has collected real data from the learners, mainly undergraduate university students of different regions and cultures. By analyzing the emotions of the students, appropriate steps can be suggested to improve the quality of education they receive. In order to understand the learning emotions, the XLNet technique is used. It investigated the transfer learning method to adopt an efficient model for learners’ sentiment detection and classification based on real data. An experiment on the collected data shows that the proposed approach outperforms aspect enhanced sentiment analysis and topic sentiment analysis in the online learning community.
Metaheuristics with Vector Quantization Enabled Codebook Compression Model for Secure Industrial Embedded Environment

At the present time, the Industrial Internet of Things (IIoT) has swiftly evolved and emerged, and picture data that is collected by terminal devices or IoT nodes are tied to the user's private data. The use of image sensors as an automation tool for the IIoT is increasingly becoming more common. Due to the fact that this organisation transfers an enormous number of photographs at any one time, one of the most significant issues that it has is reducing the total quantity of data that is sent and, as a result, the available bandwidth, without compromising the image quality. Image compression in the sensor, on the other hand, expedites the transfer of data while simultaneously reducing bandwidth use. The traditional method of protecting sensitive data is rendered less effective in an environment dominated by IoT owing to the involvement of third parties. The image encryption model provides a safe and adaptable method to protect the confidentiality of picture transformation and storage inside an IIoT system. This helps to ensure that image datasets are kept safe. The Linde–Buzo–Gray (LBG) methodology is an example of a vector quantization algorithm that is extensively used and a relatively new form of picture reduction known as vector quantization (VQ). As a result, the purpose of this research is to create an artificial humming bird optimization approach that combines LBG-enabled codebook creation and encryption (AHBO-LBGCCE) for use in an IIoT setting. In the beginning, the AHBO-LBGCCE method used the LBG model in conjunction with the AHBO algorithm in order to construct the VQ. The Burrows-Wheeler Transform (BWT) model is used in order to accomplish codebook compression. In addition, the Blowfish algorithm is used in order to carry out the encryption procedure so that security may be attained. A comprehensive experimental investigation is carried out in order to verify the effectiveness of the proposed algorithm in comparison to other algorithms. The experimental values ensure that the suggested approach and the outcomes are examined in a variety of different perspectives in order to further enhance them.
Embedded System Based Raspberry Pi 4 for Text Detection and Recognition

Detecting and recognizing text from natural scene images presents a challenge because the image quality depends on the conditions in which the image is captured, such as viewing angles, blurring, sensor noise, etc. However, in this paper, a prototype for text detection and recognition from natural scene images is proposed. This prototype is based on the Raspberry Pi 4 and the Universal Serial Bus (USB) camera and embedded our text detection and recognition model, which was developed using the Python language. Our model is based on the deep learning text detector model through the Efficient and Accurate Scene Text Detector (EAST) model for text localization and detection and the Tesseract-OCR, which is used as an Optical Character Recognition (OCR) engine for text recognition. Our prototype is controlled by the Virtual Network Computing (VNC) tool through a computer via a wireless connection. The experiment results show that the recognition rate for the captured image through the camera by our prototype can reach 99.75% with low computational complexity. Furthermore, our prototype is more performant than the Tesseract software in terms of the recognition rate. Besides, it provides the same performance in terms of the recognition rate with a huge decrease in the execution time by an average of 89% compared to the EasyOCR software on the Raspberry Pi 4 board.
Guided Dropout: Improving Deep Networks Without Increased Computation

Deep convolution neural networks are going deeper and deeper. However, the complexity of models is prone to overfitting in training. Dropout, one of the crucial tricks, prevents units from co-adapting too much by randomly dropping neurons during training. It effectively improves the performance of deep networks but ignores the importance of the differences between neurons. To optimize this issue, this paper presents a new dropout method called guided dropout, which selects the neurons to switch off according to the differences between the convolution kernel and preserves the informative neurons. It uses an unsupervised clustering algorithm to cluster similar neurons in each hidden layer, and dropout uses a certain probability within each cluster. Thereby this would preserve the hidden layer neurons with different roles while maintaining the model’s scarcity and generalization, which effectively improves the role of the hidden layer neurons in learning the features. We evaluated our approach compared with two standard dropout networks on three well-established public object detection datasets. Experimental results on multiple datasets show that the method proposed in this paper has been improved on false positives, precision-recall curve and average precision without increasing the amount of computation. It can be seen that the increased performance of guided dropout is thanks to shallow learning in the networks. The concept of guided dropout would be beneficial to the other vision tasks.
Multi-Task Deep Learning with Task Attention for Post-Click Conversion Rate Prediction

Online advertising has gained much attention on various platforms as a hugely lucrative market. In promoting content and advertisements in real life, the acquisition of user target actions is usually a multi-step process, such as impression→click→conversion, which means the process from the delivery of the recommended item to the user’s click to the final conversion. Due to data sparsity or sample selection bias, it is difficult for the trained model to achieve the business goal of the target campaign. Multi-task learning, a classical solution to this problem, aims to generalize better on the original task given several related tasks by exploiting the knowledge between tasks to share the same feature and label space. Adaptively learned task relations bring better performance to make full use of the correlation between tasks. We train a general model capable of capturing the relationships between various tasks on all existing active tasks from a meta-learning perspective. In addition, this paper proposes a Multi-task Attention Network (MAN) to identify commonalities and differences between tasks in the feature space. The model performance is improved by explicitly learning the stacking of task relationships in the label space. To illustrate the effectiveness of our method, experiments are conducted on Alibaba Click and Conversion Prediction (Ali-CCP) dataset. Experimental results show that the method outperforms the state-of-the-art multi-task learning methods.
Docurity: A New Cryptographic Primitive for Collaborative Cloud Systems

Recently, there has been a sudden shift from using traditional office applications to the collaborative cloud-based office suite such as Microsoft Office 365. Such cloud-based systems allow users to work together on the same document stored in a cloud server at once, by which users can effectively collaborate with each other. However, there are security concerns unsolved in using cloud collaboration. One of the major concerns is the security of data stored in cloud servers, which comes from the fact that data that multiple users are working together cannot be stored in encrypted form because of the dynamic characteristic of cloud collaboration. In this paper, we propose a novel mode of operation, DL-ECB, for AES by which we can modify, insert, and delete the ciphertext based on changes in plaintext. Therefore, we can use encrypted data in collaborative cloud-based platforms. To demonstrate that the DL-ECB mode can preserve the confidentiality, integrity, and auditability of data used in collaborative cloud systems from adversaries, we implement and evaluate the prototype of the DL-ECB mode.
DCRL-KG: Distributed Multi-Modal Knowledge Graph Retrieval Platform Based on Collaborative Representation Learning

The knowledge graph with relational abundant information has been widely used as the basic data support for the retrieval platforms. Image and text descriptions added to the knowledge graph enrich the node information, which accounts for the advantage of the multi-modal knowledge graph. In the field of cross-modal retrieval platforms, multi-modal knowledge graphs can help to improve retrieval accuracy and efficiency because of the abundant relational information provided by knowledge graphs. The representation learning method is significant to the application of multi-modal knowledge graphs. This paper proposes a distributed collaborative vector retrieval platform (DCRL-KG) using the multimodal knowledge graph VisualSem as the foundation to achieve efficient and high-precision multimodal data retrieval. Firstly, use distributed technology to classify and store the data in the knowledge graph to improve retrieval efficiency. Secondly, this paper uses BabelNet to expand the knowledge graph through multiple filtering processes and increase the diversification of information. Finally, this paper builds a variety of retrieval models to achieve the fusion of retrieval results through linear combination methods to achieve high-precision language retrieval and image retrieval. The paper uses sentence retrieval and image retrieval experiments to prove that the platform can optimize the storage structure of the multi-modal knowledge graph and have good performance in multi-modal space.
A Novel Deep Learning Representation for Industrial Control System Data

Feature extraction plays an important role in constructing artificial intelligence (AI) models of industrial control systems (ICSs). Three challenges in this field are learning effective representation from high-dimensional features, data heterogeneity, and data noise due to the diversity of data dimensions, formats and noise of sensors, controllers and actuators. Hence, a novel unsupervised learning autoencoder model is proposed for ICS data in this paper. Although traditional methods only capture the linear correlations of ICS features, our deep industrial representation learning model (DIRL) based on a convolutional neural network can mine high-order features, thus solving the problem of high-dimensional and heterogeneous ICS data. In addition, an unsupervised denoising autoencoder is introduced for noisy ICS data in DIRL. Training the denoising autoencoder allows the model to better mitigate the sensor noise problem. In this way, the representative features learned by DIRL could help to evaluate the safety state of ICSs more effectively. We tested our model with absolute and relative accuracy experiments on two large-scale ICS datasets. Compared with other popular methods, DIRL showed advantages in four common indicators of AI algorithms: accuracy, precision, recall, and F1-score. This study contributes to the effective analysis of large-scale ICS data, which promotes the stable operation of ICSs.
Blockchain Technology Based Security for UAV IoT Environment

Recent emerging technologies are being utilized for the quench of connectivity in real time scenarios. A push is coming to make the information available to humans from the real-time environmental data collected through small sensing devices. Wireless ad-hoc network is a base architecture for Internet of Things (IoT), Unmanned Arial Vehicle (UAV) and drones etc. In this series, IoDTIoDT (Internet of Drone Things) came as the future of drones backend via the Internet of Things, smart vision, cloud computing, enhanced communication, big data, and advanced security approaches. Rapid growth in sensing devices connected to the Internet with intelligence and capabilities also opens the door for attackers because more devices are connected means more chances of security vulnerabilities. Since data authentication is handled only by the central station, which may lead to the chances of device spoofing and false authentication brings less reliability after all. Blockchain (BC) technology is introduced to address such security concerns by eliminating the role of central authority. Blockchain Technology gives decentralized and non-tamperable solutions for the most demanding security service i.e. Authentication. This paper starts with unique characteristics and security challenges in such IOT environment and further covers the authentication process by blockchain with its potential benefits. The multilayer ecosystem is illustrated to fulfill the requirement of the UAV IoT environment, where multiple devices are equally working in a cooperative manner to perform an authorized action. The paper presented a complete study about the integration of blockchain in IoT enabled UAV.
Simple Proofs of Two Axiomatic Characterizations of State-Salient Decision Rules

We present simple proofs of two well-known axiomatic characterization of state-salient decision rules, using Weak Dominance Criterion and Independence of Irrelevant Alternatives. The first axiomatic characterization assumes that the choice function is resolute whereas the second does not.
Application of KNN for Fall Detection on Qualcomm SoCs

A fall of an elderly person often leads to serious injuries, even death. Many falls occur in the home environment, and hence, a reliable fall detection system that can raise alarms immediately is a necessity. Wrist-worn accelerometer-based fall detection systems have been developed, and there are various data sets available, but the accuracy and precision have not been standardized or compared; even where comparison does exist, it has been run on GPUs. No analysis of the workability of the models and the data sets on SoCs has been previously attempted. Though over the last few years, ML and DL algorithms have been increasingly used in fall detection, and there have also been some suggestions for the use of compressed modelling, there are no concrete statistics available to form this conclusion. In this paper, we attempt to understand why ML algorithms cannot run as-is on existing SoCs; We are using Snapdragon 410c to do our analytics as it is primarily used in Biomedical and IoT applications, has low power consumption and small form factor making it ideal for wearables. In this paper, we have used KNN to prove that ML cannot be used directly on SoCs. We are using KNN as it does not have any pre-training period, and it is a very simple algorithm that gives good accuracy. In this paper, we establish the need for model and data compression for fall detection if we must use ML or DL algorithms on SOCs. We have done this with statistical analysis across different data sets.
UAV to Cadastral Parcel Boundary Translation and Synthetic UAV Image Generation Using Conditional-Generative Adversarial Network

The precise boundaries of the cadastral parcels from the Unmanned Aerial Vehicle (UAV) data are essential for any eGovernance application. The pix2pix, image-to-image translation using the conditional Generative Adversarial Network (cGAN) models, has emerged as an alternative to the traditional machine learning and image processing algorithms. It has been used and demonstrated for productive purposes in different domains without any change in the pix2pix network model and loss functions. The pix2pix model is implemented in this research for extracting the cadastral parcel boundaries using the existing UAV data set, and the corresponding digitised data. The input data set is prepared using the python modules. The model is also used to predict the synthetic UAV data from the map data. The predicted boundary of the model is very useful. The proposed model can reduce the manual labour and human interventions in outlining the parcel boundary from UAV data.
Corporate Governance Data Storage Tort Liability for Artificial Intelligence and Emotion Recognition

The rapid development of information technology in the 21st century has realized the transformation of people's production and lifestyle.At the same time, the current research on data infringement is increasing.At present, the legal field has gradually begun to apply informatization, using its advanced data exploration methods and collection technologies.This paper aimed to realize the research on tort liability of corporate governance data storage under the background of artificial intelligence and emotion recognition, so as to promote the ecological development of tort liability and the protection of rights and interests in the era of big data.The principles and laws of neural network technology in artificial intelligence were used for reference to promote the development of data infringement tracking technology.In the experiment of numerical information extraction under the neural network, the recognition rate of the system algorithm only exceeded 98% in time, and the highest recognition rate in location type was only 95.39%.Therefore, it showed that the method proposed in this paper achieved a certain recall rate, and the method was flexible and feasible.Due to the limited training sample size, the accuracy of subjects and attributes was relatively low.However, accuracy topics and attributes had relatively little impact on search results.The subsequent search module was based on a full-text search engine.Therefore, it is of great significance to use neural network technology to study the tort liability of data storage in the context of corporate governance.
Signal processing and thermal performance analysis of motor heat recovery system

In order to realize the condition monitoring of the motor ?anytime, anywhere?, improve the detection accuracy and shorten the detection time, the author proposes a fault signal processing and diagnosis system for the motor heat recovery system based on the IoT. That is, based on the IoT technology, a mobile terminal oriented motor remote monitoring and fault diagnosis system, the sensing layer of the system collects real-time motor operation status data, and the transmission layer realizes data transmission, cloud storage and response to data requests from the application layer, finally, at the mobile end, the motor running status and diagnosis results are displayed through charts and text, so as to realize remote monitoring and fault diagnosis of the motor. The experimental results show that the accuracy of fault diagnosis test of GA-SVM in mobile terminal is more than 90%, and the running time is less than 30 ms, and the running time is very short. It proves that the mobile terminal uses the fault detection method based on GA-SVM model with high accuracy and short detection time, that is, the fault signal processing and diagnosis accuracy of the motor heat recovery system of the IoT is high and the detection time is short.
Towards Design Recommendations for Social Engagement Platforms Supporting Volunteerism Targeting Older People in Local Communities

COVID-19 showed the need for community support networks to help vulnerable individuals that were forced to stay in their homes during extended periods. While the pandemic is slowly passing, there is still a need for this type of help, especially for elderly who nowadays want to live longer in their own home. Several social engagement platforms are already offering support for this type of services. However, some became rather inactive, while others are successful but not always in the context of offering help to vulnerable elderly. In this paper we present 16 design recommendations to consider when designing (or to improve) social engagement platforms focused on volunteerism for helping older people. These recommendations are based on the evaluation of 10 digital social engagement platforms that allow asking for assistance and/or volunteering to provide help.
Experimentation of a Nighttime Wandering Assistance System Based on AAL Solutions to Foster Aging at Home

To promote the aging in place of elderly people with cognitive impairment, it is desirable to make their homes more intelligent. With Ambient Assisted Living (AAL) systems, it is possible to define intelligent environments that can take into account the needs of these people. This article presents the results of the experimentation of a Nighttime Assistance System (NAS) for elderly people with Alzheimer’s disease. The NAS uses AAL technologies to non-intrusively monitor the Activities of Daily Living (ADL) of an elderly person living alone, to contextualize and adapt the help that will be offered to them. For this experiment, we collected data for five weeks on a person’s activities. This work describes how this experimentation was carried out with an emphasis on user needs. It also shows the different patterns of activity observed, with particular emphasis on nocturnal ambulation. Finally, it describes the contextual model set up to support the person in the event of an episode of nocturnal wandering.