Reviewerly/exHarmony huggingface dataset:
Design and Practice of Multi-level Programming Experimental Teaching for Emerging Engineering Education

Programming courses should not only cultivate the students’ programming ability, but also should guide the students’ ability to analyze and solve complex engineering problems. The Programming Teaching Assistant (PTA) is adopted to design different types of experiments with different levels of difficulty based on output, guided by the course objectives and centered on students. Exploring the multi-level experimental teaching system of Basic Experiment of Programming in Emerging Engineering Education, and analyzing the data collected in the platform, can further improve the teaching methods, enhance the teaching quality, and provide a better basis for the reform of other programming courses in the future.
Block Chain Driven Intelligent Communication System for IoT

Several real-world applications, such as smart transportation and smart cities, are being industrialised by the Internet of Things (IoT). The Internet of Things (IoT) presents a number of difficulties that can be addressed with blockchain technology. In this review paper, they look at how blockchain technology can be used in conjunction with the Internet of Things (IoT). Block chain-based intelligent communication systems for IoT will be examined in this study. First, we introduce IoT and analyse some of its drawbacks. After that, we give a brief introduction to blockchain. Each block in the blockchain is linked to those that came before it, creating a decentralised ledger. Cryptographic hash code, block contents, and the previous block’s hash code are all contained in each block. In the Internet of Things (IoT), BC serves as a mechanism for securely storing and transmitting data between IoT nodes. BC is a safe and open technology that anyone can utilise. Secure communication between IoT nodes is essential for the IoT. The BC for IoT could be able to assist in enhancing the safety of communication. From 2016 to 2022, this article undertakes a comprehensive assessment of 51 reference papers in the subject of blockchain.
Adaptation of Agile Development in Medical Industry

Over the past number of years, the medical care sector has actually undergone an improvement and it continues to progress rapidly. The formerly underserved sections such as assisted living, senior citizen therapy, persistent illness surveillance, along with wearables have actually made outstanding strides in an international market, which deserves above $300 billion. This boom is partly fueled by the advent of consumerism in the health care market and also the comprehensive adoption of totally expanded gadget systems by customers. Consequently, these systems are increasingly being made use of as adaptation of systems from professional devices. The selection for these systems has actually caused the Food along with Drug Management (FDA) to give an official advisory for clinical wellness applications. To develop such medically advantageous devices there are different approaches to design and develop. Agile methodology is one among it. Which will have major and significant positive impact on the development cycle. Few of the examples of adaptations and advantages are discussed in this white paper.
Differential Privacy and Its Challenges: A Literature Review

Now a days educational data has been produced in large amount by learners. Educational data contains online as well as offline learning resources of individuals, learning experience, attendance record, assignment records and many other records. Some useful information can be extracted from this available data which can be further used in improving teaching practises and learning experience of learners. It can also help in increasing the success rate of students. Sharing and analysis of data introduces risk of privacy. This is the responsibility of data curator to provide privacy to individuals data. There are many exiting privacy-preserving algorithms which are used by researchers to sustain privacy of the data. Differential privacy is one of the popular privacy-preserving techniques which tries to reduce privacy leakage of data by adding noise to data. Differential privacy protects individuals’ information from attacker and also maintains accuracy of data. In the proposed work different techniques to implement differential privacy have been explored in detail with their comparative analysis. Different types of differential privacy, sequential decomposition and their comparison with other privacy-preserving techniques is also provided.
Granular Reasoning for the Epistemic Situation Calculus

In Chap. 5, we apply granular reasoning to the epistemic situation calculus ES of Lakemeyer and Levesque by interpreting actions as modalities and granules of possible worlds as states. The zoom reasoning proposed by Murai et al. is regarded as an epistemic action and is incorporated into the ES as an abstraction and refinement action by the granularity of the situation.
Performance Analysis and Optimization of Cross Platform Application Development Using React Native

Mobile App Development is scaling rapidly. Creating cross-platform (both android and iOS) mobile apps as well as optimizing the performance is a tedious task. There are multiple cross-application frameworks available in the market but React Native is the most popular framework for cross-application development. Both Android and iOS apps build using the same code base. Developers majorly focus on the User Interface, rather than the performance of the app. The research work presented in this paper focuses on a metric-based evaluation of the debugging features and performance of the React Hybrid App framework.
Conclusions

In Chap. 7, we give our conclusions of the book. First, we summarize the results, Second, we show further technical questions which should be solved in our future work.
Workload Prediction for Resource Scaling and Migration in the Cloud

The resources in cloud computing are provisioned to fulfill the application's computational requirements. The load balancer in a cloud data center assigns resources to virtual machines. However, the fault occurs when the server is under heavy load. As a result, resources must be adjusted. In this study, we are combining the support vector regression technique (SVRT) prediction model with resource scaling and migration. SVRT is used to predict future utilization of multi-attribute resources of a host. The approach is ideal for coping with nonlinear cloud resource workloads. After scaling the resources as required by the expected workload, the migration is in use to attain load balancing in the VMS. This paradigm for cloud computing infrastructures aims to improve system usage, lower costs and power consumption, and meet service level agreements (SLAs). We have evaluated the applicability of this framework using Google Cloud Trace.
High-Speed Emotional Analysis of Free-Descriptive Sentences Using an Emotional-Word Dictionary

Information and Communications Technology (ICT) is greatly used from basic to higher education, and research on ICT education is actively conducted. As part of our educational support research using ICT, we have been conducting research to analyze (classify, search, visualize, etc.) student questionnaire responses in class at high speed. In this paper, we report a technique for high-speed emotional analysis of free-descriptive sentences using an emotional-word dictionary. Each element of the emotional dictionary is a pair that consists of an entry and an 11-dimensional vector that represents the emotional category to which the entry belongs. To date, we have created a Chinese dictionary that contains approximately 13,000 words and a Japanese dictionary that contains approximately 8,000 words. Our emotional expression analysis system that uses these emotional-word dictionaries analyzes the emotions contained in the analyzed sentences at high speed (10,000 sentences in approximately 1.5 s). This system has functions for negative word and degree adverb processing. The emotional expression analysis method using the emotional-word dictionary described in this paper can be applied not only to Chinese and Japanese but also to the emotional analysis of free-descriptive sentences written in many other languages.
Detection of Fake Reviews on Products Using Machine Learning

It is hard to exaggerate how crucial user reviews are in figuring out an organization’s e-commerce income. Before purchasing any goods or service, online customers rely on product and service reviews. As a result, firms must consider how reliable Internet reviews are because they might directly affect their reputation and bottom line. Because of this, some businesses pay spammers to post false reviews. These false reviews profit from consumer buying choices. As a result, during the past twelve years, there has been substantial study into techniques for spotting false reviews. However, there is still a need for a survey that can analyze and summarize the diverse techniques. In this paper, we are going to put the SVM and Naive Bayesian machine learning model system into place that can spot false reviews with an accuracy of 0.801 and 0.687.
Mid-Air Gesture for Hand Control System Using Leap Motion Robot

This paper proposes the design of a model that enables a communication between human hands and the robotic module. This is performed by human–machine interaction which is the interface between people and computers. For this, a leap motion controller is used, a device that can be able to sense and track hand gestures. The direction of the robotic module is controlled by human hand gestures in three directions based on cartesian co-ordinate system. The performed hand gestures are observed through leap motion controller and translated into commands through which the robotic module is controlled. Here, a mobile robot on four wheels is designed which has the ability to detect obstacles during its mobile movement.
Discussion

In Chap. 6, we discuss some aspects of the Frame Problem in the context of granular reasoning. We describe the essential concern behind the Frame Problem and discuss our point of view for the Frame Problem.
Blockchain Technology in Healthcare

Blockchain is really a radical concept which is being used to provide novel ideas in a variety of industries, such as healthcare. A blockchain technology is utilized in the healthcare industry to store and distribute patient data across health facilities, outpatient services, pharmaceutical businesses, and physicians. As a result, it also has the ability to improve the efficacy, privacy, and openness of the healthcare system’s medical data interchange. This technology helps medical institutions get insight and improve medical record analysis. We investigated blockchain technology and its huge potential in healthcare in this paper. Blockchain is crucial in combating clinical trial fraud; this invention has the potential to increase computational efficiency in healthcare. It can help to reduce concerns about data meddling in healthcare by offering a unique centralized data pattern with the highest level of security.
Exploration and Research on the Construction of New Generation Information Technology Specialty Cluster for Application-Oriented Universities

In order to adapt to the vigorous development of the new economy characterized by new technologies, new industries and new models, there is an urgent need for a large number of emerging engineering education application-oriented talents with good industry literacy, comprehensive engineering practice ability and innovation and entrepreneurship ability. Therefore, it is necessary to explore and build a specialty cluster for the new generation of information technology. Strengthen the cross integration of various majors, realize complementary advantages, scientifically and reasonably allocate educational resources, optimize the construction of teachers, and complete the complete connection between the talent training chain and the new generation of information technology industry chain, which will avoid internal friction caused by professional homogeneity and provide talent guarantee for serving regional economic development and industrial cluster layout reform.
Machine Learning-Based Malicious URLs Detection

Malicious URL’s also known as harmful website which is a serious and common thread to network security. World Wide Web (WWW) (Vanhoenshoven et al. in 2016 IEEE symposium series on computational intelligence (SSCI). IEEE, 2016 [1]), which encourages a wide range of illicit actions like financial fraud, malware distribution and e-commerce. It is of vital importance to discover and act on different ventures on time. Several procedures like blacklisting have been carried out to detect malicious Uniform Resource Locators (URLs) (Catak et al. in Malicious URL detection using machine learning. IGI Global, 2021 [2]). To advance the majority of harmful Uniform Resource Locator’s, different machine learning algorithms are executed in the modern years. Here, in this research paper, we are addressing malicious URLs detection as a problem of classification and also to understand the working and functioning of known machine learning classifiers, namely support vector machines (SVMs), K-nearest neighbors (KNNs), random forest (RF) and Naive Bayes (Vanhoenshoven et al. in 2016 IEEE symposium series on computational intelligence (SSCI). IEEE, 2016 [1]).
Performance Evaluation of CNN Models for Face Detection and Recognition with Mask

Face recognition is widely used across many biometric applications. In recent times, where COVID-19 has impacted manufacturing, travel, tourism, hospitality, and crippling the global economy, wearing a mask is necessary for many establishments and public places for its widespread and safety of an individual. Under this scenario, person recognition for security functions is been difficult for the present face recognition systems in which most of the facial features are covered. Our work relies on computer vision and deep learning models which intend to make an impact and solve the real-world problem of safety measures at some significant level. In our work, we propose a framework for masked face recognition using Inception v3 and FaceNet architectures, which can be easily integrated into various embedded devices with limited computational capacity. We aim to detect the face with a mask and recognize the person in images as well as in real-time videos. We demonstrate the results with an overall accuracy of 88% for masked face recognition within the defined scope.
Enhanced AES for Improved Privacy in 5G-Enabled IoT Network

The world is going to be surrounded by smart objects and most of them will be physical entities that are connected and interacting with each other, this will be possible because of the advancements in the Internet of Things (IoT) enabled with 5G. The IoT contains a vast quantity of devices and substances to encompass exceptionally low or fictional processing and communication assets of a high number of devices. The 5G term is very popular in industry because it will provide high speed data transmission, sufficient radio frequency (RF) coverage to interconnect different types of wireless networks and also connects an enormous amount of personal devices. The challenge is to provide security and privacy to these personal devices. In this paper, an adapted encryption standard named as Enhanced Advanced Encryption Standard (E-AES) is proposed, which enhances the security and privacy of IoT-enabled 5G network. The authors of the paper compared the anticipated E-AES algorithm next to the Advanced Encryption Standard (AES) in terms of delay and area, by simulating in the Cooja simulator and implementing both algorithms in field programmable gate array (FPGA) using Verilog hardware descriptive language (Verilog HDL). It is found that the E-AES algorithm performs better than the AES algorithms in provisos of encryption such as conversion time and power consumption.
Business process Execution: a contextual approach

Companies are always in competition with each other, in order to provide appropriate services to internal and external users. Therefore, they must be always innovative in order to keep their place in the market. To do so, new technologies come to help them in this race, by proposing solutions to facilitate the management of their business. Within this scope comes business process execution, which is a crucial step in the business process management life cycle., in this paper we present a new approach for business process execution that is context-awareness. We propose a SOA based architecture with multiple component that we find very important in business process execution and taking into account the context. To validate our approach, we propose a concrete case study about transport system to provide a proof of the applicability as well as the utility of the approach.
SKNetV2: Improved Selective Kernel Networks for Object Detection

The main task of object detection is to simultaneously detect all objects in an image. Detecting objects of different scales often places conflicting demands on neurons’ receptive fields, therefore, a single receptive field in each convolutional layer cannot effectively solve the problem of scale variation. In this paper, we propose SKNetV2, an improved version of Selective Kernel Networks. The realization of Selective Kernel (SK) convolution is an adaptive fusion of convolutional branches with different kernel sizes under the guidance of attention. The SK convolution considers only channel attention and ignores spatial attention, which is equally important, so the receptive fields of the neurons are still spatially fixed for the same input. We propose an SKv2 convolution that simultaneously applies spatial attention and channel attention to fuse branches of different kernel sizes and then unifies the two attention results. The cooperation of these two attention mechanisms achieves fully selectable receptive fields. We derive SKNetV2 from SKNet by replacing the SK building block with a proposed SKv2 block. We demonstrate the effectiveness of SKNetV2 through extensive experiments on the challenging MS COCO dataset. Without bells and whistles, we achieve an AP of 45.5%, which surpasses the most recently proposed detectors.
Design of Process Management Platform for Project Practice Teaching

In order to solve the problems arising during the students’ project practice teaching, a project practice process-oriented management platform is designed by the project team. A process management of students’ project practice is realized by the constructing of project teaching environment and tracking students’ project practice process, thus leading to improvement of the project practice effect in universities.
Incremental Donor Approach for Immune Plasma Algorithm on Solving Path Planning Problem of UCAV

Immune Plasma algorithm (IP algorithm or IPA) that models the implementation details of a medical method popularized with the COVID-19 pandemic again known as the immune or convalescent plasma has been introduced recently and used successfully for solving different engineering optimization problems. In this study, incremental donor (ID) approach was first developed for controlling how many donor individuals will be chosen before the treatment of receivers representing the poor solutions of the population and then a promising IPA variant called ID-IPA was developed as a new path planner. For analyzing the contribution of the ID approach on the solving capabilities of the IPA, a set of experimental studies was carried out and results of the ID-IPA were compared with different well-known meta-heuristic algorithms. Comparative studies showed that controlling the incrementation of donor individuals as described in the ID approach increases the qualities of the final solutions and improves the stability of the IP algorithm.
Using Modified Whale Optimization Algorithm for Solving Traveling Salesman Problem

The main problem of the network is that the wrong designs and incorrect connection lead to major problems such as cost impact problems and network efficiency, the most famous being Fiber Optical network problems that still considered a problem for various types of networking. Travel Salesmen’s Problem (TSP) is one of the most traditional methods for solving this type of problem, depending today on optimization. Modified algorithms may be regarded as one of the most resourceful and effective ways to solve animal-based TSP problems. Whale Optimization Algorithm (WOA) and Genetic Algorithm (GA) focuses this paper on TSPLIB-type issues. This work showed a greatly promising performance with Whale Optimization Algorithm and was better than the genetic algorithm.
Face Recognition System Using Local Binary Pattern with Binary Dragonfly Algorithm to Feature Selection

In this paper, the local binary pattern is used to feature extraction, and the Binary Dragonfly Algorithm (BDA) is exploited to find the optimum features. This is a new methodology for face recognition systems. A proposed face acknowledgment framework was created to be utilized for various purposes. We utilized a local binary pattern for extraction of the important feature of the face that prepares the information and afterward, we utilized BDA to find the best features from feature data. We will execute and assess the proposed strategy on ORL and other datasets with MATLAB 2021a.
Breast Cancer Detection Using Various Classification Models Combined with Transfer Learning

When mortality rates are considered, breast cancer is among the most common forms of cancer worldwide. As with every cancer, early diagnosis and treatment are the most effective method of preventing breast cancer. Artificial intelligence’s development in the health sector has decreased the margin of error compared to the old mammographic and manual methods. It has now begun to be obtained much earlier and with a low margin of error. Several researchers have worked on the segmentation and categorization of breast cancer using various imaging modalities. One of the imaging methods with the highest sensitivity for diagnosis is the ultrasonic imaging modality. For this purpose, ultrasound images are used in this study, and there are three categories of images in the dataset: normal, benign, and malignant images. This study aims to develop a technique for spotting and diagnosing breast cancers using ultrasound images. Deep learning techniques are key alternatives to feature-based approaches for overcoming their numerous drawbacks. Convolutional neural network models that have been previously trained and machine learning classifiers are used together in this study. This research compares six distinct pre-trained models: MobileNetV1, MobileNetV2, DenseNet121, DenseNet169, ResNet50, and ResNet101, and various classifiers such as Support Vector Machine, Adaptive Boosting, K-Nearest Neighbors, Random Forest, Bootstrap Aggregating and Extreme Gradient Boosting. As a result of the experiments, it was seen that the highest accuracy scores are achieved by using the MobileNetV2 pre-trained model when looking at overall accuracy percentages with nine different classifiers. In addition, when these nine different classifier algorithms are examined among themselves in particular MobileNetV2, Support Vector Machine, K-Nearest Neighbor and Long Short-Term Memory gave the best accuracy results.