Reviewerly/exHarmony huggingface dataset:
A Secure Multicontroller SDN Blockchain Model for IoT Infrastructure

IoTK.Janani Dr.S.Ramamoorthy is making significant progress in a variety of fields, including health care, smart grids, supply chain, and so on. It also makes people’s daily lives easier and improves their interactions with one another and their surroundings and environment. There is a variety of research on decentralized computing for IoT develops a decentralized IoT-based biometric facial recognition solution for COVID-19 lockdown cities. They propose a three-layer architecture (application layer, control layer, and data layer) and then create a blockchain framework on top of it to entirely restrict public movements. The software-defined network is the most widely utilized solution for establishing secure network interaction and building secure IoT infrastructures. They give a solid and dependable framework for dealing with dangers and issues like security, scalability, and confidentiality. This study provides a blockchain-based software-defined IoT framework for smart networks that are optimized for energy efficiency and security. Indeed, multicontroller SDN blockchain (MC-SDNBC) has been extensively used to manage vast-scale networks which are, though, subject to a variety of attacks, include false data injection, which causes regulator topology inconsistencies. Every software definition network domain is administered with a single master controller who communicates with both the masters of the other Internet via blockchain. The controller unit generates blocks of dynamic network modifications, which are subsequently evaluated by redundant controllers using a reputation technique given by the control system. The popularity system uses continuous and coupled reactive fading repute algorithms to score the controllers, for example, the voter’s maker and block, during each voting activity. The analysis findings show that false flow rule insertion may be detected quickly and efficiently, keeping more secured IoT Systems.
Digitalization of the Railway Transport System - The Applicability of Blockchain Technology in the Context of the Industrial Revolution 4.0

This work aims to expose a detailed analysis of the main characteristics of blockchain technology, its benefits and applicability in the rail transport system. The need for research in this regard lies in the system’s need to adapt to new challenges, to increase the quality of services and attractiveness. Since this technology is still in an early stage, this paper opens new horizons of knowledge, opens new directions of research and helps to understand the phenomenon.
Intelligent interactive accompaniment platform to support the learning process for highly vulnerable children

Access to information and digital tools worldwide is a reality through the internet; Data in the cloud, connectivity applications, work networks, however, in the area of ​​Education we find problems when implementing a strategy of dissemination, use and application of platforms in the vulnerable sector.We speak of the vulnerable sector as the group of families that do not have access to coverage or equipment that allows access to “Information and Communication Technologies”, the statement made by UNESCO shows through an international study that; approximately 1.5 billion students could not attend school due to the COVID19 pandemic, 706 million do not have access to computers, 56 million students live where there is no mobile network coverage, with which they proposed alternatives such as radio station programs and community televisions, in addition 63 million teachers do not have the means to reach students since the doors of schools were closed in 191 countries.It is necessary to accept the educational proposals that exist and translate them into the collective reality without affecting equality of opportunity, a precise case in our proposal taking as input the educational material by the Ministry of Education of Ecuador to implement an intelligent interactive support platform as a support in the learning process directed for highly vulnerable children of the preparatory sublevel, for which the guidelines have been revised and work will be carried out in the form of knowledge modules identified as: Module scope of logical-mathematical relationships, module scope of corporal expression and motor skills , module area understanding and expression of language, module area of ​​artistic expression. These modules will be programmed with content that can be interrelated with interactive books that include a specific game so that the child interacts with the information and activities proposed, giving them instant positive feedback, an incentive to continue in the game and reach complete a unit of study, likewise intelligent interactive multimedia material will be programmed, leaving a set of tools so that the teacher can include other activities with the same virtues mentioned
New Federation Algorithm Based on End-Edge-Cloud Architecture for Smart Grid

In the smart grid, the underlying data often contains a large amount of private information and cannot be shared, which further limits the effect of large-scale data analysis and utilization. Federated learning, as a decentralized learning strategy with a privacy protection function, can learn a wider range of knowledge through model aggregation without touching local data and is suitable for smart grid scenarios. Considering the limited computing and storage capabilities of the underlying terminal of the grid system, this paper proposes a three-tier federation architecture of the end-side-cloud, using model segmentation technology to only retain the optimized feature extraction model on the local client. The back-end models that need to be repeatedly iteratively trained are allocated to edge servers and interact with the cloud to learn collaboratively, thereby reducing system overhead. We conducted extensive experiments to verify the superiority of the federated model compared to the independent training model and further analyzed the feasibility of edge computing.
A Methodology for Enabling NLP Capabilities on Edge and Low-Resource Devices

Conversational assistants with increasing NLP capabilities are becoming commodity functionality for most new devices. However, the underlying language models responsible for language-related intelligence are typically characterized by a large number of parameters and high demand for memory and resources. This makes them a no-go for edge and low-resource devices, forcing them to be cloud-hosted, hence experiencing delays. To this end, we design a systematic language-agnostic methodology to develop powerful lightweight NLP models using knowledge distillation techniques, this way building models suitable for such low resource devices. We follow the steps of the proposed approach for the Greek language and build the first - to the best of our knowledge - lightweight Greek language model, which we make publicly available. We train and evaluate GloVe word embeddings in Greek and efficiently distill Greek-BERT into various BiLSTM models, without considerable loss in performance. Experiments indicate that knowledge distillation and data augmentation can improve the performance of simple BiLSTM models for two NLP tasks in Modern Greek, i.e., Topic Classification and Natural Language Inference, making them suitable candidates for low-resource devices.
Reversible Data Hiding for JPEG Image Based on Paillier Encryption

With the convenient and rapid development of cloud storage, data privacy violations have become increasingly prominent. Reversible Data Hiding (RDH) can be used to embed additional data in encrypted data stored in the cloud. Most encrypted RDH algorithms are performed in uncompressed images and are not widely employed in popular JPEG images. Therefore, an effective RDH scheme in encrypted JPEG images is proposed in the paper. This algorithm first extracts the distribution characteristics of DCT coefficients, and chaotic encryption is utilized to encrypt the DC coefficients. Then the number of AC coefficients “0” is calculated in each DCT block to generate the embedded sequence in descending order. The paillier encryption and the homomorphic encryption is employed the multiplication feature to embed for the AC coefficient. During the embedding process, the range of the encrypted ciphertexts is determined according to the paillier parameter and the embedding threshold. It ensures that the ciphertexts do not out the range of the DCT coefficient while maintaining a small file increment. The experimental results show that the scheme effectively improves the data embedding capacity and image encryption quality compared to other state-of-the-art encrypted RDH schemed. The scheme also realizes the synchronization of data extraction between the ciphertext image and the original image. The experimental analysis further shows that this scheme has high embedding capacity and good image quality. The scheme can be applied to application scenarios with high embedded capacity and high-security requirements.
Evaluation of Optimal Feature Transformation Using Particle Swarm Optimization

Feature reduction is one of the essential steps for machine learning applications. It reduces redundancy in the feature set, which reduces the computational cost in the learning phase. The success of the reduction stage depends on the size of the feature selected and the separability of the transformed matrix. In most of the work, the feature transformation matrix is determined mathematically and most of the time depends on eigenvectors and eigenvalues. If the feature space is high, it is difficult to calculate the eigen matrix in smaller devices. Hence, this work proposes a method to generate an optimum transformation matrix using heuristic optimization approach that leads to better classification accuracy with less feature size. This study uses Bhattacharyya distance as an objective function to evaluate the separability of the transformed feature matrix. Moreover, to select a proper subset of features, a penalty parameter is added with respect to number of features in transformation matrix. This work proposes a modified version of particle swarm optimization that can satisfy the objective. The study shows the ability to learn a transformation matrix with competitive results in classification task. The proposed method is evaluated on various freely available public datasets, namely Fisher’s IRIS, Wine, Wisconsin Breast Cancer, Ionosphere, Sonar, MNIST, NIT-R Bangla Numeral, and ISI-K Bangla Numeral datasets.
Human Activity Recognition with Smart Watches Using Federated Learning

Federated learning (FL) is an emerging technique for training machine learning models in a distributed manner and offers advantages in terms of data privacy and saving communication costs. Wearable devices, such as smart watches, are personal devices and generate lots of personal sensor data. In this respect, FL can offer advantages; hence, their data requires more privacy, and saving communication costs is essential. In this paper, we investigate the performance of FL compared to centralized learning for the domain of human activity recognition (HAR) using wearable devices. We used a dataset from the literature composed of sensor data collected from smart watches and trained three different deep learning algorithms. We compare the performance of the centralized and FL models in terms of model accuracy and observe that FL performs equivalent to the centralized approach.
Video Consumption on Smartphones: Does a Vertical Video Format Affect the Immersive Experience?

Even though smaller screen sizes are discussed to be related to negative effects on the attention span and the working memory [3] or as leading to a negative impact on viewing experience [19], recent developments of video design and consumption [6], as well as the general usage of smartphones as video streaming devices [5, 12] imply a shift in general usage behavior, which seems to demand streaming provider to adapt to stand up to the competition. Simultaneously to this movement, a shift from horizontal video formats (landscape) to vertical video formats (portrait) can be registered [11]. The present study aimed to verify the assumption that both video formats have a high acceptance and therefore there can be measured no significant difference in the perceived immersion effect between two groups watching the identical film extract in a vertical or a horizontal video format. For the measurement of the immersion, the Film Immersive Experience Questionnaire by Rigby et al. [19] was used. The data analyses performing a two-sided independent samples t-test revealed that there was no statistically significant difference between the two experimental groups. Therefore, the null hypothesis was maintained.
Redesign of Alarm Clock through Usability Evaluations

This research seeks to contribute in the construction of evidence concerning the importance of usability for design and processes to ensure that products, once they are in the context of use, have a performance consistent with the conditions and characteristics of the user. Above, it is intended to make clear that today's technology gives us basic tools that can be used to simulate an environment of use and test design proposals which, in the end, will be a more reliable and effective product. To achieve the above, assessed existing and from the problems of interaction detected electronic product, two proposals were generated of redesign, in order to recognize if there were improvements over the original artifact. These proposals were evaluated from the simulation of its operation and use to an Adobe Flash program and with a touchscreen platform. The results reflected significant improvements with the redesign in multiple aspects, in particular with the #1 proposal and it became apparent that even though in some ways the simulation does not replace the functional prototypes, it is a very useful tool for the design.
Automatic Annotation for Weakly Supervised Pedestrian Detection

Pedestrian detection is an important task addressed in computer vision given its direct application in video surveillance, autonomous driving and biomechanics among many others. The advent of deep neural networks has meant a breakthrough in its resolution. The major problem is the need for very large labeled datasets, which is usually difficult to obtain, either because it is not publicly available or it is not suitable for the particular problem. To solve it, we design a method capable of self-labeling a detection dataset using only small manually labeled portion of it. Results show an autolabeled dataset of 10342 images from a preliminary set of 1312 manually labeled images.
TSM: An Efficient Time-Sensitive Data Sharing Scheme in Cloud Storage

Because of the ever-increasing need of data sharing among multiple parties, data security in cloud storage becomes a huge challenge. Consider data sharing as a complicated scenario and the time is a key factor in many access control applications, more flexible access control policies are needed. Our work proposes a new method named as TSM, which belongs to time-sensitive multi-authority attribute-based encryption, adding a time-domain attribute to attribute-based encryption. Data owners can pre-set the specific time of each file and upload ciphertext to the cloud storage platform, and data users cannot decrypt the ciphertext until the specific time reaches. Reaching a tradeoff of efficiency and security, TSM is efficient and can update time attributes with low storage cost, which is suitable for data sharing in time-sensitive access control scenarios. This paper finally proves that TSM is secure under q-BDHE. Both theoretical analysis and experimental results show that TSM can indeed enhance data security efficiently with low cost.
Multicultural Science Education: Preparing Elementary Teachers to Support the Academic Language Needs of ELLs in Science

The academic language (oral and written language used for academic purposes) associated with science is often abstract. As such, learning the language of science can present multiple challenges for English language learners (ELLs). Along with being embedded in individual vocabulary words, this abstraction is found in the language demands that teachers need to consider as they plan for, and support students’ learning of science. In order to assist ELLs in becoming more proficient with the academic language of science, and thus broadening their participation in STEM-related fields, teacher education programs must better prepare elementary teachers to support academic language development. This chapter will examine instructional practices that positively influence the academic language knowledge of English language learners as it relates to science.
Image Segmentation Losses with Modules Expressing a Relationship Between Predictions

We focus on semantic image segmentation with the usage of deep neural networks and give emphasis on the loss functions used for training the networks. Considering region-based losses, Dice loss, and Tversky loss, we propose two independent modules that easily modify the loss functions to take into account the relationship between the class predictions and increase the slope of the gradient. The first module expresses the ambiguity between classes and the second module utilizes a differentiable soft argmax function. Each of the modules is used before the standard loss is computed and remains untouched. In the benchmark, we involved two neural network architectures with two different backbones, selected two loss functions, and examined separately two scenarios for softmax and sigmoid top activation functions. In the experiment, we demonstrate the usefulness of our modules by improving the IOU and F1 coefficients on the test dataset for all scenarios tested. Moreover, the usage of the modules decreases overfitting. The proposed modules are easy to integrate into existing solutions and add near-zero computational overhead.
Modeling for Car Quality Complaint Classification based on Machine Learning

Cars play an important role in many aspects of people's social life, and the effective handling of car quality complaints is of great significance to the proper running of cars and the reputation maintenance of car brands; effective classification of car quality complaint texts is the basis of the efficient handling of corresponding quality complaints, while relying on manual classification has disadvantages such as heavy workload, experience dependence, and error proneness; machine learning methods have been quite widely used in the automatic classification modeling for different types of natural language texts.It is of great practical significance to construct the automatic classification model of car quality complaints based on machine learning.Based on the characteristics of car quality complaint texts, this study vectorized the texts after word segmentation, performed feature selection and dimension reduction based on correlation analysis, and combined the progressive model training method and support vector machine to construct the classification model; in model reliability analysis, it was evaluated based on the effect of data amount on the modeling and the effect of text length on the prediction probability distribution.The results show that based on the method in this study, effective automatic classification model of car quality complaint texts could be constructed.
Distance Learning For Schoolchildren: Readiness For Challenges

The stability of the school education system depends on many factors, and one of the most significant is the professionalism of the teaching staff, the willingness of the teaching staff to respond to new challenges and effectively carry out professional activities in the most difficult conditions. The coronavirus pandemic has led to the need to make significant changes in the functioning of the education system as soon as possible, including the transfer of the educational process to a remote mode. Despite the timely measures taken at the federal and regional levels to ensure the continuity of the educational process and preserve a single educational space, while maintaining the health of all participants in educational relations remained an absolute priority, the school education system faced serious difficulties. The situation was complicated not only by the presence of certain problems in the provision of educational organizations and households with the necessary technical means, but also by the fact that a significant part of the teaching staff did not have the practice of teaching remotely and by the beginning of the pandemic were not ready to organize the educational process using distance learning technologies. The article presents the results of a sociological study on the readiness of teachers to carry out professional pedagogical activities in the conditions of forced restrictions caused by the pandemic, based on a comparison of the monitoring data carried out on the eve of the pandemic with the data of 2021.
Information System Security Risk Assessment Based on Entropy Weight Method - Bayesian Network

Traditional information security risk assessment models usually rely on expert analysis to obtain prior knowledge, which leads to a greater impact on the results of security risk assessment by subjective bias. To solve this problem, we added the entropy weight method to the traditional Bayesian network-based information security risk assessment model. Entropy weight method is used to compute the weight coefficients of each risk factor involved in a risk event. Compared with traditional evaluation models, weighting risk factors during risk evaluation can effectively reduce the impact of excessive reliance on expert information, that is, excessive subjective factors, and theoretically improve the accuracy of the evaluation results. Finally, an instance of the risk assessment approach on the model is analyzed, which demonstrates the rationality and feasibility of this method.
Separate then Constrain: A Hierarchical Network for End-to-End Triples Extraction

In recent years, end-to-end triples extraction based on multi-task learning has achieved promising performance. The existing methods typically use the same sentence representation generated by pretrained language models to address different subtasks. They are either hard to capture the subtask-specific features, or hard to make deep associations among different subtasks. In this paper, we propose a Separate then Constrain Network (SCN) that contains two main layers, i.e., separation layer and constraint layer. Specifically, separation layer first transfers the sentence representation into three different subtask spaces, respectively. Then, constraint layer further refines all sentence representations by simulating the inherent dependencies among three parts of a triple. In addition, to alleviate the negative impact of the error entity prediction on relation classification, we design a simple but effective way, called Entity-Derivate Checker. On three public datasets, SCN shows significant improvement over existing methods.
Retraction Note to: Boosting Approach for Multiclass Fake News Detection

The Editors have retracted this chapter, because it inadvertently repeats a previous publication [1] by the sameauthors in the previous proceedings of the same conference, resulting in a duplicate publication. We apologise tothe authors and to readers. Dr. Pradeep Singh agrees to this retraction. Dr. Rajkamal Kareddula has not responded to any correspondence fromthe publisher about this retraction. [1] Kareddula R., Singh P. (2021) Boosting Approach for Multiclass Fake News Detection. In: Gunjan V.K., Zurada J.M.(eds) Proceedings of International Conference on Recent Trends in Machine Learning, IoT, Smart Cities andApplications. Advances in Intelligent Systems and Computing, vol 1245. Springer, Singapore. https://doi.org/10.1007/978-981-15-7234-0_35