Reviewerly/exHarmony huggingface dataset:
Monitoring Low-Visibility on the Expressway Based on Multi-channel Convolutional Neural Network

Atmospheric visibility is a crucial indicator of meteorological observation. Low visibility environments, such as cluster fog or heavy fog, can seriously threaten the safety of vehicles on expressways. With the encryption deployment of expressway monitoring facilities, low visibility detection based on surveillance video is more economical and feasible than visibility detectors. Therefore, a multi-channel convolutional neural network model based on surveillance video images is proposed for visibility monitoring. This algorithm includes three fog concentration channels: Transmittance-Matrix Channel, Scene-Depth Channel and Depth-Vision Channel. Transmittance-Matrix Channel is used to estimate the transmittance matrix information of the input image. Scene-Depth Channel is used to extract scene-depth features. And Depth-Vision Channel is used to learn depth vision features from input images. 4745 images of the fog environment monitored by the Beijing-Taiwan were selected to verify the effectiveness. Results indicate that this algorithm has higher identification precision. The average resolution time is as low as 0.54 s, and after six parameter adjustments, the overall analytical accuracy was adjusted from 74.8% to 93.4%, an increase of 18.6%. The parsing accuracy of visibility for level 0, level 1, level 2, level 3, and level 4 is 96.50%, 93.30%, 88.10%, 91.80% and 94.60%, respectively. The comprehensive visibility monitoring accuracy is 93.400%, and the parsing deviation is kept within one level, which can support the monitoring and warning of low-visibility events on expressways. It is helpful to reduce misjudgment and missed reading behaviour caused by manual monitoring.
COVID-19 Pandemic Study: Ad-hoc Reaction and Premeditated Transition of Project-Based Product Design Courses

This paper examines and compares the ad-hoc reactions of educators in the product development domain to the first wave of the COVID-19 crisis. The aim of this study is to properly understand the difficulties educators struggled with maintaining their education process in a crisis and which practices, new digital tools, and educational methods best supported the transition. The data was collected from the representatives of four universities from different European countries that are offering undergraduate and graduate product development courses. The results showed that all the educators employed online teaching activities in direct response to the COVID-19 situation. The courses initially remained unaltered with respect to their content but were held online via videoconferencing tools for synchronous communication. Educators mentioned a reduced interactivity with students during sessions, making it difficult to objectively monitor students’ learning achievements. Later in 2020, educators systematically digitalised their courses. Initial findings hint at a shift of workload to the beginning of a teaching period as a result of educators preparing online teaching and assessment material in advance.
Algorithmic Design Considerations of Big Data Analytics

An algorithm is evaluated primarily in two metrics—temporal and spatial complexity. This chapter presents algorithm design challenges for remote sensing big data from the five dimensions—Volume, Velocity, Variety, Veracity, and Value. Volume requires algorithm design to consider processing performance, modularity, data sparsity, dimensionality, feature selection/extraction, nonlinearity, Bonferonni’s principle, and balanced variance/bias. Velocity requires algorithm design to consider data streaming, real-time processing, concept drift, and skewed distributions. Variety requires algorithm design to consider data locality and data heterogeneity. Veracity requires algorithm design to consider data provenance and data quality. Value requires algorithm design to consider domain-specific models and knowledge.
A Dynamic Association Analysis Model Based on Maintenance Sequence for Railway Traction Power Supply System

Traction power supply system, as the power source of electrified railway, has high requirements for reliability. The strategy of planned maintenance combined with emergence maintenance is adopted to ensure its safe operation and numerous information monitoring systems have been deployed to collect and analyze its operating data. Recently, researchers began to use association analysis to study system operating data. The existing methods treat the entire historical data as input, which results in that the association rules are an intermixed reflection of all past operating states and it is unable to observe the inevitable evolution of system due to maintenance. This paper aims to propose an association analysis model reflecting system changes. Firstly, the partition method (PMSO) based on maintenance sequence and the organization of maintenance workshop is proposed to reasonably generate transaction database. Secondly, the database is distributed by sequence of overhaul and the unit frequency is applied as threshold to obtain the association rules. Thirdly, we introduced a novel decision-making method based on temporal proximity to guide the maintenance. A case study based on real operating data of railway lines in south-west China is presented, which confirms our dynamic association analysis model based on maintenance sequence (DAAMS) has ability to distinguish the mutation of system and observe the evolution with maintenance. Based on the mining results, relation networks are built and maintenance suggestions are given based on the priority of rules.
A Comprehensive Review of Conversational AI-Based Chatbots: Types, Applications, and Future Trends

It is an evident fact that almost every application area in the present-day world is driven by technology and a core reason for this disruption is Artificial Intelligence (AI) and Machine learning (ML). Among them, chatbot research is one of the core areas that is driven by AI. The shift from simple rule-based chatbots to AI-based conversational agents was all possible because of advancements in conversational AI research. This paper provides an in-depth review of the evolution of chatbot technology on an AI front, its applications in real-world scenarios, and hence the future trends associated with it. Additionally, a critical comparison of state-of-the-art conversational AI frameworks is also incorporated into the study.
Blockchain Based Tourism Recommender

Evolution in technology is playing a vital role in simplifying the life of people to a greater extent. It is done using Blockchain Technology, which is a type of Distributed Ledger Technology (DLT), by making the digital asset transparent and unalterable through the use of cryptographic hashing and decentralization. A simple way for understanding blockchain, where everyone will be given access to everyone at the same time. Tourism is travel for leisure, recreational and business purpose. Tourists generally travel to various places of interest for leisure, business, and other purposes. Tourism is a known affair in human life. It has been an industry of vast dimensions and eventually supports economic and social growth. In this paper, we are going to show how blockchain can help in the field of tourism which is completely location-based.
Why Companies Scale Agile Development of Physical Products: An Empirical Study

Scaled agile development has become a procedure in the software industry to handle the applicability in large-scale projects. Much of the research on scaling agile is industry agnostic and generally valid. However, significant differences in the development of physical products exist. Handling longer internal and supplier lead times, having cross-disciplinary and joint documentation, or achieving the complete system integration of all participating domains, are some of these. Furthermore, a clear understanding of its purpose and application in developing physical products is still missing. This leads to misunderstandings, contradictions, and deviations in the application of scaling agile. Therefore, this paper aims to explore the purpose of scaling agile, especially in the field of product development. This paper presents empirical data collected through an online survey with more than 120 industry participants representing 12 different industrial sectors. The study shows that most manufacturing companies use individual solutions to scale. This contribution further reveals that for the development of larger interdependent mechatronic systems, several collaborating agile teams within the company are necessary to lessen the complexity. Additionally, the agile methods used in smaller teams should be utilized to develop superordinate larger projects and products. Along with this, enabling integration, synchronization, and coordination of subordinate systems in superordinate projects, especially for agile teams, were also described by participants as purposes of scaling agile.
Furniture for House Decor Using Augmented Reality

An augmented reality called (AR) is an improved edition of an existent physical world that is attained through the exploitation of various digital oriented visual-based sound, elements or other sensory-based stimuli which is conveyed by using technology. This is a new rising trend among the various companies that are concerned in mobile-based computing and business-oriented applications in fastidious. This work is about a real-world sample when a customer needs to purchase any products for the interior design and l has a problem with the satisfactory features that the newly purchased products cannot be placed in their own/original place ahead of buying. Due to this purpose, an android-based application called ‘AR Furniture’ was developed with the exploit of augmented reality-based technique for the design with the decoration which will surely help the customers to visualize how the furniture product pieces will appear and fit inside their house. It also provides the details of the products to the support-based customer decision. This created application is a model to discover factors affecting the design and tracking of AR applications. AR-based technology is frequently used in various applications that replicate a furniture arrangement. It clarifies a scheme by augmented-based reality for decoration of home, that chains real-time-based tracking without the identification markers; by the scheme using the FAST bend detection. In order to overcome this issues, this article proposes a technique that can be able to insert virtual items to the real-world environment (Known as Augmented Reality) by means of a camera. An augmented reality-based furniture arrangement model is very useful for screening the furniture in the building or rooms layouts with not having to move or buy the real-time furniture inside. Though such scheme often involve users in the direction of regularly and physically modify their point of view with respect to the physical break, that needs a manual exploitation of the sight and be often inadequate to the 3D phone or tablet interface. In the direction to assist address this difficulty, a scheme has been proposed which automatically computes the majority of appropriate viewpoint to get better indulgent of layouts of room and also the whole which permit the consumer to easily changeover to that particular viewpoint.
Secure Encryption Using Bit Shuffling

Information transfer over the Internet has historically been problematic due to security issues. After and during the pandemic, it was noticed that since there were many more digital transactions, there were also many more intrusions or hacks. Consequently, there is a greater requirement for secure transactions. In this paper, a bit shuffle technique and cryptographic approach for image encryption and decryption have been proposed. Various tools for performance analysis were also used such as the number of pixel change rate (NPCR), the unified average changing intensity (UACI), the entropy analysis, which is a feature of an encryption scheme that demonstrates the randomness of the image and the correlation coefficient to assess the encryption's quality and determine whether it met the target standard. These findings demonstrate how secure the suggested cryptographic algorithm is.
The Impact of Large Sample Datasets on Hand Gesture Recognition by Hand Landmark Classification

Today, almost all aspects of life are influenced by technology to simplify these aspects of life. Researching how humans and computers interact is very important to build a good model for better human-computer interaction in the future. Using technology such as computer vision, we can now collect the information we are looking for directly from a human. Humans can use many kinds of modalities to interact with computers. Hands are perhaps the largest source of body language information after the face. To understand the gesture’s meaning, we can use MediaPipe Hands, developed at Google LLC, as a method to track and recognize human hands. However, if we want to understand some kinds of hand gestures using MediaPipe Hands, we need to create a condition using if-else manners. This research tried to collect the wide variety of each hand gesture using the 21 key points in x, y, and z coordinates as a feature. We chose Support Vector Machine (SVM) and Artificial Neural Network (ANN) as classifiers to validate the impact of large sample datasets. This research found that ANN is the best among all the methods we used as a classifier method for the 3D value of 21 key points from the hand skeleton. The accuracy and F1-score from ANN were 98.4% accuracy and 98.2% F1-Score, respectively, representing the best performance for each class of all the methods we used in this research.
Improved ViT: Rethinking the Self-attention of Vision Transformer

Recently, Vision Transformer and its variants have achieved great success in various computer vision tasks, self-attention supports global content-dependent interactions between different image regions. However, directly dividing the image into a series of patches may lead to the loss of image information. Moreover, the computation cost increases with the quadratic growth of the token length, which limits the application of the vision transformer in some visual tasks. Currently, many works try to reduce computation and memory cost by employing coarse-grained global attention or fine-grained local attention. However, such approaches shown poor capacity for feature representation compared with the original self-attention mechanism of multi-layer transformers, leading to suboptimal solutions. In this paper, we propose an improved ViT transformer to reduce computation cost as well as the classification error. First, a attention module is designed to reduce the time cost. Then, we combines original self-attention and the improved attention module to constitute a new attention module, so as to achieve image global fine-grained attention and global coarse-grained attention and reduce computational cost. The experimental results on the CIFAR10 and MNIST datasets show that the proposed approach outperforms existing approaches such as ViT and TNT, and achieves high classification accuracy and low the computational cost. Also, such experimental results imply that our self-attention mechanism can improve the performance to the existing transformer models.
Dynamic Gesture Recognition for Data Glove Based on Multi-stream One-dimensional Convolution

In order to meet the demand for gesture recognition in the field of human–computer interaction, a new method for gesture recognition based on wearable data gloves is proposed. This method utilizes a data glove to collect specific 10 dynamic gesture data, which includes five finger bending data and six axis inertia data collected by MEMS-IMU on the back of the glove. After filtering and other data preprocessing operations are carried out on the collected data, a multi-feature fusion one-dimensional convolutional neural network (MS-1DCNN) is used to conduct deep learning model training on the gesture data. This allows for the classification and visualization of dynamic gestures under a custom gesture dataset. In real-time gesture classification experiments, a fixed-length window data interception algorithm is used for data sampling, and classification recognition is carried out by the model. The experimental results demonstrate that the one-dimensional convolutional neural network with multi-feature fusion proposed in this paper can accurately classify 10 kinds of dynamic gestures, with a recognition rate of 97.88%. This method can be used for gesture interaction in specific scenes, improving the convenience of human–computer interaction and expanding the usage scenarios.
Face Mask Detection Using Transfer Learning and TensorRT Optimization

TensorRT is a high-performance deep learning inference optimizer and runtime that can be used to speed up the deployment of deep learning models. In this paper, the performance of different neural network architectures when using TensorRT is compared and showed that TensorRT can significantly reduce the inference time of deep learning models on embedded systems. The SARS-CoV-2 virus, which causes the infectious disease COVID-19, has had a significant impact on global health and the economy. Non-pharmaceutical interventions such as wearing face masks are an effective way to reduce the spread of the virus, and automatic detection systems based on CNN’s can help to detect mask-wearing without requiring human intervention which saves resources or manpower deployed. The results demonstrate that TensorRT is a valuable tool for deploying deep learning applications in resource-constrained environments and can help to improve the performance of a wide range of neural network architectures.
Vision-Based Interpretation of Flashing the Upper and the Dipper Headlights of the Vehicle Behind

One of the largest problems that vehicle drivers face is the inability to identify the flashing of the upper and the dipper headlights from the vehicle that is traveling behind. The inability to find a way even after the flashing of the upper dipper to the vehicle traveling ahead leads to the loss of patience by the driver which ends up with rash driving and road mishaps. The paper presents the solution to this problem using computer vision techniques, further helping to reduce the number of accidents as far as possible. Here, SIFT is employed for feature extraction and description and PCA is used for dimensionality reduction. Multiple classifiers have been used to assess the accuracy which includes Decision Tree, KNN, Random Forest, SVM, and Logistic Regression which are used. The maximum achieved accuracy is 91.37% with the KNN classifier model over the test dataset.
Wearable Sensor Based Cloud Data Analytics Using Federated Learning Integrated with Classification by Deep Learning Technique

Wearable sensors and person to person communication stages assume a key part in giving another strategy to gather patient information for productive medical care observing. In any case, persistent patient observing utilizing wearable sensors produces a lot of medical care information. Deep learning (DL) is a potential method for big data analytics since it can be utilized to quickly classify as well as analyse this plethora of data. This research proposes novel method in wearable sensor based cloud data analysis by federated learning as well as classification using DL. Input is collected by cloud architecture based wearable sensor and processed for noise removal as well as normalization. Cloud data is secured based on federated learning and classified using U-Net convolutional auto encoder radial neural networks. Experimental analysis has been carried out in terms of accuracy, precision, recall, F-1 score, RMSE and NSE for different wearable sensor dataset. Proposed method attained accuracy 98%, precision 85%, recall 75%, F-1 score 66%, RMSE 55% and NSE 55%.
Analyzing the Tweets of the Patients During the COVID-19 Pandemic Using Machine Learning Techniques

Text classification is also known as text categorization. Using natural language processing, the required text can be analyzed and classified into various texts depending on the features. The purpose of this study is to measure the length of the characters of every patient tweet regarding COVID. In this COVID data set, linear regression is accurate. The input data were pre-processed, extracted, vectorized and classified using machine learning techniques like the implementation of linear regression, the performance of gradient descent in linear regression, logistic regression, random forest, k nearest neighbors, and polynomial regression. The experiment uses a corona data set with different column elements for text classification. The accuracy varies from one algorithm to the other depending on the data set related to the algorithm.
Remote Sensing Big Data Computing

This chapter covers computing platforms that are fit for remote sensing big data computing. The evolution of the geospatial computing environment is briefly reviewed, that is, stand-alone, centralized, and distributed. The high-performance computing environment is also briefly reviewed, that is, supercomputer, grid computing, and cloud computing. Service-oriented architecture is discussed in detail. Specialized big data processing frameworks are reviewed, including MapReduce/Hadoop, SPARK, and SCALE.
Parallelized Cyber Reconnaissance Automation: A Real-Time and Scheduled Security Scanner

The extraordinary advancement of technology has increased the importance of achieving the required level of information security, which is still difficult to achieve. Recently, network and web application attacks have become more common, causing confidential data to be stolen by exploiting system vulnerabilities. The CIA Triad Model is broken as a result of this. In this work, with the aim of relieving real-world concerns, we present an enhanced schema for the first feature of the security engine we proposed in the previous paper. It is an automated security scanner based on parallelization for the active information-gathering phase. It supports real-time and scheduled system scans in parallel in the phase of active information gathering based on RESTful API allowing easy integration for real-life cases. With the integration of the message-broker software (RabbitMQ) that originally implemented the advanced message queuing protocol (AMQP), the user has the ability to create instant customized scans and check the related results. These features depend on Celery workers using asynchronous task queue which is reliant on distributed message passing to perform multiprocessing and concurrent execution of tasks. The system can be used by penetration testers, IT departments, and system administrators to monitor their system and grant high security and instant alarms in critical threats. An automated IP and port scanning, service-version enumeration, and security vulnerabilities detection system are the core of the proposed scheme project. The accuracy and efficiency of this technique have been demonstrated through a variety of test cases based on real-world events. The average time of scanning a server and detecting the vulnerabilities has been enhanced by 22.73% to become 1.7 minutes instead of 2.2 minutes. Similarly, the improvement ratio for run time, elapsed time and vulnerability detection are 20.40, 90.80, and 7.70% respectively.