Reviewerly/exHarmony huggingface dataset:
Reserved Network Software Channels for Data Exchange in Large-Scale Control Systems for APCS of NPP

The task of reserving of network software channels for data exchange in the Top-level subsystem (TLS) of the Automated process control system (APCS) of a Nuclear power plant (NPP) is considering. The methods for solving this problem and the algorithms implemented in APCS NPP software are proposing.
Estimation of Photovoltaic Energy Production from Italian Highways

The increase in electric loads characterized by the growing penetration of electric vehicles and the urgent need to reduce greenhouse gas is giving a strong boost to the deployment of Renewable Energy Sources power plants, in particular Photovoltaic and wind. In this context, the definition of areas suitable for the installation of such plants is a key factor for an efficient and sustainable land usage.The aim of this work is to exploit georeferenced data to estimate the photovoltaic capacity that can be installed in the competence areas of Italian highways.
Transformer-Encoder Generated Context-Aware Embeddings for Spell Correction

In this paper, we propose a novel approach for context-aware spell correction in text documents. We present a deep learning model that learns a context-aware character-level mapping of words to a compact embedding space. In the embedding space, a word and its spelling variations are mapped close to each other in a Euclidean space. After we develop this mapping for all words in the dataset’s vocabulary, it is possible to identify and correct wrongly spelt words by comparing the distances of their mappings with those of the correctly spelt words. The word embeddings are built in a way that captures the context of each word. This makes it easier for our system to identify correctly spelt words that are used out of their contexts (e.g., their/there, your/you’re). The Euclidean distance, between our word embeddings, can thus be deemed as a context-aware string similarity metric. We employ a transformer-encoder model that takes character-level input of words and their context to achieve this. The embeddings are generated as the outputs of the model. The model is then trained to minimize triplet loss, which ensures that spell variants of a word are embedded close to the word, and that unrelated words are embedded farther away. We further improve the efficiency of the training by using a hard triplet mining approach. Our approach was inspired by FaceNet [18], where the authors developed a similar approach for face recognition and clustering using embeddings generated from Convolutional Neural Networks. The results of our experiments show that our approach is effective in spell check applications.
Grammarly deployment (in)efficacy within EFL academic writing classrooms: an attitudinal report from Afghanistan

Automated writing evaluation (AWE) programs have been widely utilized in writing classrooms and have been increasing gradually, yet teachers’ and students’ attitudes in relation to AWE, Grammarly, have still remained uncharted in Afghanistan. The current study explored teachers’ and students’ attitudes toward the efficacy and/or inefficacy of Grammarly deployment in English as a Foreign Language (EFL) academic writing classrooms. This study employed mixed methods, sending a questionnaire to 66 students and interviewing five Afghan English language (EL) teachers and four students from private language schools (PLSs) and public universities (PUs). Also, the ABC model of attitudes has been used to measure teachers’ and students’ attitudes in this study. Statistical analysis showed that students’ attitudes were quite favorable toward Grammarly deployment in writing classrooms. Besides, inferential statistics uncovered that no significant difference exists between students’ attitudes toward Grammarly use and students’ gender, level of proficiency, and institution types. In addition, the results revealed that teachers had almost positive attitudes toward Grammarly deployment in writing classrooms considering students’ writing skills development. Furthermore, the study also unveiled some exhaustive merits, plus some shortages of Grammarly applications. Moreover, the study’s findings indicated that Grammarly can be included in the English department’s curriculum, computer lab, or library, as well as in professors’ and students’ writing courses. This research also suggested that professors facilitate Grammarly feedback and that policymakers hold technology training courses at institutions.
A Vital Fusion of Internet of Medical Things and Blockchain to Transform Data Privacy and Security

There are two emerging technologies which are creating a buzz in the digital world. One is the Internet of Things and the other is blockchain technology. The Internet of Things is the ecosystem where multiple devices are connected and exchange information. In the context of the healthcare industry, we can think of it as internet of medical things where medical equipment is connected to communicate with each other. With blockchain technology, all the transactions between different peers of the network can be recorded in an immutable decentralized ledger. This technology provides a trustless collaborative environment where multiple parties can exchange information. This technology also uses encryption and hashing techniques to maintain privacy and security. Data is very vital in our digital world. If we talk about the medical data of patients then it becomes more worthy than any other data. Medical data of patients can be misused for different unauthorized purposes so a more secure approach is required to maintain privacy and security. In this chapter, we will discuss how these two emerging technologies can be integrated to develop a decentralized access control system to provide privacy and security for the medical data of patients.
Internet of Medical Things Devices: A Review

The Internet of medical things (IoMT) is a requirement in the modern day. It may be highly beneficial in a variety of scenarios, such as when a patient requires surveillance following heart surgery or when a doctor has to monitor the health of patients at regular intervals. Because it is not always possible for older individuals to visit doctors on a regular basis, IoMT can be used to check their health remotely. Patients can receive medical advice in their hometown if they live in a rural location with poor healthcare services. IoT-based healthcare monitoring devices and systems can supply them with healthcare recommendations from far away. This also cuts down on needless doctor visits, hospital stays, and re-admissions. In addition, the system becomes transparent. With continual monitoring, diseases can be recognized at an early stage. The decision-making process is aided by data from Internet of Things (IoT) devices, which helps to decrease system costs, waste, and mistakes. We’ve covered every aspect of healthcare equipment that can be used in the IoT in this chapter.
Crypto turmoil is set to intensify after FTX collapse

Headline INT: Crypto turmoil is set to intensify after FTX fall
RFE-SRN: Image-text similarity reasoning network based on regional feature enhancement

Image-text matching aims to make a connection between visual and natural information. Some of the current methods have made great progress by using global alignment between images and sentences and local alignment between the image region and its corresponding word. However, the importance of the correlation between global alignment and local alignment is ignored to some extends. Therefore, in this paper, we propose a new region feature enhancement-based image text similarity inference network. Firstly, the image region feature is enhanced by graph convolutional neural network, which is used to find the correlation among different image region features with the production of features’ sematic relationship. Secondly, we propose a vector-based similarity representation to describe local and global alignment with a more comprehensive way. Finally, a graph convolution neural network is introduced to construct a similarity graph for propagating correlation between local alignment and global alignment to every part. By testing on the MSCOCO and Flickr30k dataset, our proposed method shows great accuracy performance and competitiveness.
Early Detection and Diagnosis of Oral Cancer Using Fusioned Deep Neural Network

In recent days, oral cancer cases are significantly increasing due to increase in tobacco consumption, along with the combination of consumption of alcohol, poor oral hygiene, and human papilloma virus (HPV) infection. Early detection of this kind of cancers is preventive, or else, it may leads to premature deaths. 50% of cases are detected in advanced stages. For the above reasons, it is important to develop the new model to detect the oral cavity cancer in early stage from the digital data and image processing techniques. The research in detection of oral cancer is highly active from the twentieth century. In this paper, the detection of oral cancer with the fusion model of CNN + RNN is proposed. The proposed model outperforms the state-of-art techniques in detection of oral cancer with 82% of accuracy. The obtained result is analyzed with systematic approach, and assured diagnosis is ensured the diagnosis of the oral cancer we attempt to near future. The intention of the proposed method is to improve the detection accuracy in the early diagnosis of oral cavity cancers.
A note on negation of a probability distribution

Evaluating the negation of an uncertain event is an open issue. Yager (IEEE Trans Fuzzy Syst 23:1899–1902, 2004) suggested a transformation for evaluating the negation of a probability distribution. He used the idea that any event whose outcome is not certain can be negated by supporting the occurrence of other events with no bias or prejudice for any particular outcome. Various authors have tried to generalize the negation transformation proposed by Yager (IEEE Trans Fuzzy Syst 23:1899–1902, 2004). However, we need to focus on developing the basic structure of negation so that the behaviour of the process modelled by negation transformation can be understood in detail. Yager’s negation is based on distribution of maximum entropy. If a probability distribution is uncertain(a state other than maximum entropy), the more the iterations of negation, the more uncertain this probability event becomes, eventually converging to a homogeneous state, i.e. maximum entropy. In other words, it is the realization of the process. What is noted that during each negation, Yager’s method ensures that the negation is intuitive; the next negation weakens the probability of the event occurring in the previous step. Since negation involves reallocation of probabilities at each step in such a way that the reallocation at each step can be determined from the reallocation at the previous step, it is clear that Yager’s negation has various attributes similar to that of a Markov chain. In the present work, we have shown that Yager’s definition of negation can be modelled as a Markov chain which is irreducible, aperiodic with no absorbing states. Two examples have been discussed to strengthen and support the analytical results. Also, we have defined an information generating function (IGF) whose derivative evaluated at specific points gives the moments of the self-information of negation of a probability distribution. The properties of the generating function along with its relationship with the information generating function proposed by S. Golomb (IEEE Trans Inf Theory 12:75–77, 1966) have been explored. A closer look at the properties of IGF confirms the existence of Markovian structure of Yager’s negation.
A Spatio-Temporal Graph Convolutional Network Model for Internet of Medical Things (IoMT)

In order to provide intelligent and efficient healthcare services in the Internet of Medical Things (IoMT), human action recognition (HAR) can play a crucial role. As a result of their stringent requirements, such as high computational complexity and memory efficiency, classical HAR techniques are not applicable to modern and intelligent healthcare services, e.g., IoMT. To address these issues, we present in this paper a novel HAR technique for healthcare services in IoMT. This model, referred to as the spatio-temporal graph convolutional network (STGCN), primarily aims at skeleton-based human-machine interfaces. By independently extracting spatial and temporal features, STGCN significantly reduces information loss. Spatio-temporal information is extracted independently of the exact spatial and temporal point, ensuring the extraction of useful features for HAR. Using only joint data and fewer parameters, we demonstrate that our proposed STGCN achieved 92.2% accuracy on the skeleton dataset. Unlike multi-channel methods, which use a combination of joint and bone data and have a large number of parameters, multi-channel methods use both joint and bone data. As a result, STGCN offers a good balance between accuracy, memory consumption, and processing time, making it suitable for detecting medical conditions.
A Study on the Design of the Teaching-Learning Model for Airline Service Using VR Content

Objectives This study was conducted for the purpose of designing an airline service curriculum by applying VR (Virtual Reality) content to school education. It is for strengthen the vocational capacity required to perform the work of flight attendants for airline service departments’ students. Methods Learner demand analysis was conducted to design the airline service department curriculum using VR content. In addition, for the design of airline service teaching-learning content, areas feasible in school education were selected based on the contents of airline service education presented by the National Competency Standard Learning Module developed by experts. After that, the educational content was selected based on the ability unit to use VR content. Results Based on the analysis of the needs of learners and instructors, the most effective education method in this study is education using VR content, which is consistent with previous studies that respond more quickly to problem situations and reduce problem solving time in the current vocational education environment. Based on this, the most effective airline service teaching-learning model using VR content was presented. Conclusions The aviation industry is a field in which various areas are combined. In the future, it is necessary to develop VR content so that it can be used as practical educational content not only in schools but also in the aviation industry. Therefore, it is expected that it will be able to establish itself as a good educational content that strengthens practical competencies in the educational field.
Different Skin Tone Segmentation from an Image Using KNN for Sign Language Recognition

Color as a feature has advantages like it is invariant to scaling, rotation, and partial occlusion changes. Skin color segmentation has many applications like sign language recognition, hand and face gesture recognition, biometric applications, face detection, and analysis of facial expressions. Due to the importance of an effective skin segmentation method, the Machine Learning (ML)-based skin segmentation approaches are studied in this paper. The objective of the work is to segment the different human skin tones for sign language recognition. The skin segmentation dataset from the UCI machine learning repository is used to evaluate the effect of various supervised learning algorithms. Despite the comparison criteria, KNN is found to be the desirable classifier. There are two color spaces RGB and HSV, considered in experiments, and the HSV representation gives better performance in the segmentation of various skin tones than the RGB color space.
AirObject: A Temporally Evolving Graph Embedding for Object Identification

Object encoding and identification are vital for robotic tasks such as autonomous exploration, semantic scene understanding, and relocalization. Previous approaches have attempted to either track objects or generate descriptors for object identification. However, such systems are limited to a “fixed” partial object representation from a single viewpoint. In a robot exploration setup, there is a requirement for a temporally “evolving” global object representation built as the robot observes the object from multiple viewpoints. Furthermore, given the vast distribution of unknown novel objects in the real world, the object identification process must be class-agnostic. In this context, we propose a novel temporal 3D object encoding approach, dubbed AirObject, to obtain global keypoint graph-based embeddings of objects. Specifically, the global 3D object embeddings are generated using a temporal convolutional network across structural information of multiple frames obtained from a graph attention-based encoding method. We demonstrate that AirObject achieves the state-of-the-art performance for video object identification and is robust to severe occlusion, perceptual aliasing, viewpoint shift, deformation, and scale transform, outperforming the state-of-the-art single-frame and sequential descriptors. To the best of our knowledge, AirObject is one of the first temporal object encoding methods. Source code is available at https://github.com/Nik-v9/AirObject.
Federated Learning-Based Computation Offloading for Low-Bandwidth Edge Internet of Things

Internet of Things (IoT) devices generates massive amounts of data continuously, making it difficult to combine this data at the edge node or central edge server for AI techniques to be applied, especially on low-bandwidth networks. Traditional computation offloading involves extensive resource management, queuing, and high bandwidth usage to send data from an edge device to a server. To mitigate this challenge, federated learning is applied in this paper, to offload the computation of the edge server. Simulation results show the efficiency of the proposed method over the centralized method.
Prediction of User Electricity Consumption based on Adaptive K-Means Algorithm

When predicting the total power load of many users, the computing resources often can’t keep up with the growth rate of the total amount of data, and it is difficult to analyze effectively the data in the actual environment. This paper firstly considers clustering users, then predicts each cluster separately, and finally summarizes the results of each cluster to get the result. This paper firstly performs PCA dimension reduction on user data, and then uses the adaptive K-Means clustering method to determine the number of clusters and the initial cluster center, and then uses the determined parameters to cluster the users, and then builds a model for each cluster user and sum up the forecast results to get the total power load. In order to illustrate the effect of this method under different models, this paper establishes XGBoost, CatBoost and LightGBM models respectively and predicts the total power load of all users. From the experimental results, it can be seen that this method is consistent with the actual data trend, and the prediction effect is better than that of directly modeling all user data.
Floki

Serverless computing emerges as an architecture choice to build and run containerized data-intensive pipelines. It leaves the tedious work of infrastructure management and operations to the cloud provider, allowing developers to focus on their core business logic, decomposing their jobs into small containerized functions. To increase platform scalability and flexibility, providers take advantage of hardware disaggregation and require inter-function communication to go through shared object storage. Despite data persistence and recovery advantages, object storage is expensive in terms of performance and resources when dealing with data-intensive workloads. In this paper, we present Floki, a data forwarding system for direct and inter-function data exchange proactively enabling point-to-point communication between pipeline producer-consumer pairs of containerized functions through fixed-size memory buffers, pipes, and sockets. Compared with state-of-practice object storage, Floki shows up to 74.95× of end-to-end time performance increase, reducing the largest data sharing time from 12.55 to 4.33 minutes, while requiring up to 50,738× fewer disk resources, with up to roughly 96GB space release.
Fuzzing-Based Grammar Inference

In this paper we propose and suggest a novel approach for grammar inference that is based on grammar-based fuzzing. While executing a target program with random inputs, our method identifies the program input language as a human-readable context-free grammar. Our strategy, which integrates machine learning techniques with program analysis of call trees, uses a far smaller set of seed inputs than earlier work. As a further contribution we also combine the processes of grammar inference and grammar-based fuzzing to incorporate random sample information into our inference technique. Our evaluation shows that our technique is effective in practice and that the input languages of tested recursive-descending parser are correctly inferred.
Research on the Integration Strategy of Traditional Advantage Engineering Specialty and Artificial Intelligence Based on TRIZ

To seize the development opportunity of artificial intelligence and deepen the supply-side structural reform of talent cultivation, this study focuses on the integration of the traditional advantage engineering specialty with artificial intelligence. Six key factors affecting the integration of traditional engineering and artificial intelligence were identified by the structural equation model. Based on the innovative problem-solving theory (TRIZ), the key factors were transformed into six key problems, and the TRIZ problem model and solution model were established by taking the key problems as standard factors. Then, with the help of TRIZ contradiction matrix and 40 invention and innovation principle tools, we provide solutions to problems and transform them into professional integration strategies. Finally, it can be an auxiliary decision support tool for the integration strategy-making of traditional engineering specialty and artificial intelligence. Through this tool, decision-makers can understand each link of the decision-making process more clearly, and make the strategy-making process more scientific and closer to the real needs.
Multi-objective Flexible Job-Shop Scheduling with an Ensemble Optimisation Model

Smart factories and intelligent manufacturing systems are the key drivers of knowledge economy in the era of Industry 4.0. One of the critical aspects is a flexible and optimised production planning and scheduling system. An efficient scheduling system can minimise the production cost and maximise machine utilization, leading to improvement in the production rate. In this study, we design an ensemble-based Harris’ Hawk optimisers (EN-HHO) to address the multi-objective flexible job shop problem (MOFJSP), which is an NP-hard and complex combinatorial task. The developed ensemble model is evaluated using well-known benchmark problems, and the results compare favourably with those from similar methods in the literature.