Reviewerly/exHarmony huggingface dataset:
Agriculture Assistant Chatbot Using Artificial Neural Network

As India has an agro-based economy, 58&#x0025; of its population relies on agriculture as its primary method of livelihood. In spite of this, the economic survey for 2019&#x2013;2020 indicates that agriculture growth in India has stagnated around 2.9&#x0025; annually for the past 6 years. Considering the number of people in India still relying on it, it is a real concern. One of the prevailing issues is lack of right information. This problem can be solved by providing farmers with expert advice and relevant information (e.g. determine when to irrigate, how to sow seeds, and which pesticides to use effectively to increase the yields). In this paper, the proposed chatbot called AgroBot is a multi-user chat application. AgroBot can overcome this problem by allowing farmers to obtain the information they need to succeed in an ever-changing market and to enlarge with new technology and market demand in an easy-to-use manner. Farmers can communicate easily with the chatbot since the system uses NLP (Natural Language Processing) to identify and parse farmer inquiries, identify the main key words and their questions, identify the main keywords and compare them to the Knowledge Base, and provide the best possible results. The development of such a system would benefit farmers by allowing them to gain better information about agricultural practices and, as a result, increase agricultural productivity.
Remote justice: information rights as a tool of empowerment

The coronavirus pandemic has resulted in a compulsory retreat from public spaces. While, for some, this displacement has brought about engagement with digital technologies in new and interesting ways, for others, digital technologies have proved to be the site of technology-facilitated abuse (TFA). Consequently, there are renewed calls for regulation of TFA, with a great deal of this discussion focussing on the design and enforcement of criminal law. However, the scope of behaviour perpetrated with, or through, digital technologies is much broader and demands a range of responses that offer access to justice. This paper argues information rights offer significant potential to enable victims/survivors to gain control over personal information, feel empowered, and improve their mental health and wellbeing. First, it defines information rights and how they are accessed from an EU perspective. Second, it addresses the relationship between legal rights and empowerment in this context. It reflects on if, and how, information rights have been used within the UK specifically, to provide reflections on harnessing their potential. And lastly, explores the viability of advocacy in this area.
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si40.svg"><mml:mrow><mml:msub><mml:mrow><mml:mi mathvariant="script">H</mml:mi></mml:mrow><mml:mrow><mml:mi>∞</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math> state estimation for T-S fuzzy reaction-diffusion delayed neural networks with randomly occurring gain uncertainties and semi-Markov jump parameters

This paper is concerned with the H∞ state estimation issue for Takagi-Sugeno (T-S) fuzzy reaction-diffusion delayed neural networks (RDNNs) with randomly occurring gain uncertainties and semi-Markov jump parameters (sMJP). The considered gain perturbations are assumed to occur in a random manner and are modeled by a random variable with the Bernoulli distribution. Furthermore, different from the existing T-S fuzzy neural networks (NNs), as the first attempt, the reaction-diffusion phenomenon, the T-S fuzzy rules, and the sMJP are taken into account in the unified framework, which makes the proposed models more applicable. By utilizing the Lyapunov functional method and introducing a suitable free weight matrix, sufficient critered to guarantee the exponential stability and H∞ performance of estimation error. In order to improve the tolerance of the proposed estimator to gain variations, a fuzzy resilient estimator design scheme is presented with the aid of some decoupling techniques. Finally, two numerical simulations verify the effectiveness and superiority of the proposed scheme.
Performance comparison of Docker and Podman container-based virtualization

The central topic of this paper is the performance comparison and potential issues of the container-based virtualization, with Docker and Podman as typical representatives. The measurements validity is managed with the use of the same testing environment, the identical hardware and software components. The testing procedure is conducted by the means of the Filebench, on CentOS Linux 7. The goal is to evaluate an impact of one and more container-based virtual machines on the file system performances. The obtained results are thoroughly explained and further presented in graphical form.
Deep Learning of Biomechanical Dynamics With Spatial Variability for Lifestyle Management

Human Physical Activity Detection (PAD) is essential for advanced lifestyle management. Nowadays, many devices like smartwatches and phones have motion sensors. And the placement of devices, especially phones, is diverse. Understanding the optimal sensor location is important for robust PAD applications. However, the biomechanical dynamics are highly complex, making signal processing very challenging. In this study, we propose to leverage deep learning to perform intelligent PAD, and comprehensively compare seven different body locations for optimal sensor placement recommendation. Multi-stage deep learning has been proposed and developed, which firstly leverages convolutional layers to abstract spatial features from the motion data, and then generates the physical activity type prediction with fully connected layers. Evaluated on the real-world database, the proposed deep learning model is very effective on PAD tasks, and successfully determines the thigh location as the optimal sensor placement method. This study will greatly advance deep learning-driven biomechanical dynamics mining for advanced lifestyle management.
Syntactic Knowledge Embedding Network for Aspect-Based Sentiment Classification

An aspect-based sentiment classification task is a fine-grained sentiment analysis task, which is aimed at identifying the sentiment polarity of a given aspect in subjective sentences. In recent years, some researchers have applied pretrained BERT models to this task. However, existing research only uses the BERT output layer and ignores the syntactic features in the middle layers, leading to deviations in the prediction results. In order to solve above problems, we propose a new model BERT-SFE. Firstly, we explicitly utilize the middle layers of BERT to capture the underlying syntactic features. Secondly, we construct a syntactic feature extraction unit based on Star-Transformer, which uses an auxiliary vector and the star network structure to capture both local and global syntactic information in a sentence. Finally, we merge the syntactic features with the semantic features from the BERT output layer in the feature fusion layer, obtaining a more accurate sentiment representation of the aspect. The experimental results on three public ABSA datasets show that using the syntactic feature extraction unit based on Star-Transformer to mine the syntactic knowledge in the middle layers of BERT can effectively improve the accuracy of sentiment classification. BERT-SFE achieves the best performance compared with existing models.
Enhancing the Performance of POS based Features using Generalization for Sentiment Classification

The task of evaluating the polarity of an opinionated text as positive or negative is known as sentiment classification. Companies nowadays are interested in learning how their consumers feel about their products by studying their views on review pages, blogs, tweets, discussion boards and web portals. Politicians and governments are also interested in sentiment classification for defining campaign plans and policies. The aim of this work is to use Part of Speech (POS) based knowledge in a machine learning approach to decide whether an opinionated document is positive or negative. In order to have a more effective feature space and to reduce the sparsity of the feature vector, generalization of bigrams is done by backing-off the first word or the second word to their respective POS cluster. Experiments conducted show that the use of combined POS features of unigrams and generalized bigrams outperform other features in terms of accuracy using Multinomial Naive Bayes (MNB) classifier.
Joint Multiple Fine-grained feature for Vehicle Re-Identification

The process of recognizing the same vehicle in different scenes is called vehicle re-identification. However, due to the different locations of the surveillance cameras, there may be obstacles in the captured vehicle pictures and multiple viewpoints may make the same vehicle look different. In order to effectively reduce the interference of obstacle occlusion, multiple viewpoints, and other factors on vehicle re-identification, in this paper, we propose a multi-fine-grained feature extraction network. While retaining the global information of vehicles, we extract the finegrained features of vehicles precisely by segmenting the vehicle feature map. In addition, we introduce a new evaluation metric mean Inverse Negative Penalty (mINP) to evaluate the vehicle re-identification model more comprehensively. Our method achieves superior accuracy over the state-of-the-art methods on the challenging vehicle datasets: VeRi-776, VehicleID, and VRIC.
Monitored Access Distribution Method for Finding the Abnormality in the Internet of Things Based Smart City Application

The support of the Internet of Things (IoT) in smart city applications improves the scalability of services regardless of the users’ varying densities. The densely populated users demand heterogeneous security measures for reliable and robust application services. Here, the Permanent Denial of Service (PDoS) problem arises from improper user identification. In this authentication method, the application security is ensured by using monitored access distribution. The monitored access distribution exploits the user application’s communication features and its synchronization with the user device. The abnormality in linking user device, application, and authentication is observed in BackPropagation (BP) learning. BP learning reduces the assigned weights based on the abnormality that is trained during the access distribution process. The proposed authentication method’s performance is verified using the metrics authentication delay, false rate, synchronization failures, and hit rate.
Dataset complexity assessment based on cumulative maximum scaled area under Laplacian spectrum

Dataset complexity assessment aims to predict classification performance on a dataset with complexity calculation before training a classifier, which can also be used for classifier selection and dataset reduction. The training process of deep convolutional neural networks (DCNNs) is iterative and time-consuming because of hyperparameter uncertainty and the domain shift introduced by different datasets. Hence, it is meaningful to predict classification performance by assessing the complexity of datasets effectively before training DCNN models. This paper proposes a novel method called cumulative maximum scaled Area Under Laplacian Spectrum (cmsAULS), which can achieve state-of-the-art complexity assessment performance on six datasets.
The effects of Social Media User Behavioural Factors on Hate Speech Detection

Social networking sites have grown in popularity, and many people have incorporated them into their daily routines. Most importantly, these platforms allow people to freely express themselves. However, the enforcement of threatening behaviours leading to societal risks is also prominent with the provisions of hate posts on social networking sites. Although there are rules and regulations to safeguard the community, the necessity to reduce the exposure of such hate posts in the user&#x2019;s timeline in every possible way is encouraged. Facebook users communicate through three behaviors: reactions, comments, and shares where those have an individual psychological inference towards the social emotion of a post as a significant proportion of natural behaviours of the user are deemed to be manifested by what is posted and how they have reacted to them. Thus, this proposed model identifies the social media behavioural factors which positively support hate and non-hate posts using the Binary Logistic Regression Statistical Model in a multilingual context. The experiment shows that social media behavioural factors such as "angry", "haha", "sad", and the number of shares positively support hate posts and can be applied as a parameter to reduce the engagement of hate posts as well to use them for detecting hate content which is a challenging task even the state-of-the-art models struggle with. Experimentally, this study shows that incorporating social media behavioural factors significantly improves classification performance by obtaining 88% accuracy when compared to similar methods. Finally, the results also showed that the developed system is good enough to classify hate speech detection methods providing a solution to the challenge of natural language processing for low-resourced languages.
Blindfold: Keeping private keys in PKIs and CDNs out of sight

Public key infrastructure (PKI) is a certificate-based technology that helps in authenticating systems identities. HTTPS/TLS relies mainly on PKI to minimize fraud over the Internet. Nowadays, websites utilize CDNs to improve user experience, performance, and resilience against cyber attacks. However, combining HTTPS/TLS with CDNs has raised new security challenges. In any PKI system, keeping private keys private is of utmost importance. However, it has become the norm for CDN-powered websites to violate that fundamental assumption. Several solutions have been proposed to make HTTPS CDN-friendly. However, protection of private keys from the very instance of generation; and how they can be made secure against exposure by malicious (CDN) administrators and malware remain unexplored. We utilize trusted execution environments to protect private keys by never exposing them to human operators or untrusted software. We design Blindfold to protect private keys in HTTPS/TLS infrastructures, including CAs, website on-premise servers, and CDNs. We implemented a prototype to assess Blindfold's performance and performed several experiments on both the micro and macro levels. We found that Blindfold slightly outperforms SoftHSM in key generation by 1% while lagging by 0.01% for certificate issuance operations.
Analysis on Image Restoration of Ancient Paintings

The Indian paintings are mostly inspired from Indian mythology depicting religious deities and kings. The earliest Indian painting was rock painting. Tanjore painting is an important South-Indian painting which started around an early 9th century. These are centuries old which are portrayed on the walls and ceiling of the temples, that illustrates the stories of the people, their culture and religion. Poor maintenance, climatic conditions, and rituals followed in the temple, have a great impact on the loss of the paintings and murals. These paintings represents rich cultural heritage of India. Preserving these paintings and murals are essential in order to protect the heritage. This paper focuses towards the non destructive restoration methodologies adopted in preserving the ancient paintings.
Application of Unsupervised Transfer Technique Based on Deep Learning Model in Physical Training

The research purpose is to study the standardization and scientizing of physical training actions. Stacking denoising auto encoder (SDAE), a BiLSTM deep network model (SDAL-DNM) (a kind of training action model), and an unsupervised transfer model are used to deeply study the action problem of physical training. Initially, the physical training action discrimination model adopted here is a combination of stacked noise reduction self-encoder and bidirectional depth network model. Then, this model can collect data for five actions in physical training and further analyze the importance of action standardization for physical training. Afterward, the SDAL-DNM implemented here fully integrates the advantages of SDAE and BiLSTM. Finally, the unsupervised transfer model adopted here is based on SDAL-DNM deep learning (DL). The movement data of the physical training crowd are collected, and then the unsupervised transfer model is trained. According to the movement characteristics of physical training, the data difference between trainers is calculated so that the actions of each trainer can be continuously adapted according to the model, and finally, the benefits of effectively distinguishing the training actions can be achieved. The research shows that before and after unsupervised learning, the average decline of the model used is 1.69%, while the average decline of extreme learning machine (ELM) is 5.5%. The conclusion is that the unsupervised transfer model can improve the discrimination accuracy of physical training actions and provide theoretical support to effectively correct mistakes in physical training actions.
Research on the Design and Operation Mechanism of the Opening and Sharing Platform for Scientific and Technological Resources

Abstract: Science and technology resources are not only the foundation of scientific and technological development, but also the motive power behind social and economic development. Taking universities, scientific research institutions and enterprises as the main entities, based on the needs of the entities in scientific and technological activities for the sharing and transformation of scientific and technological resources, as well as the requirements of the sharing and transformation of scientific and technological resources on opening and sharing platforms, this paper constructs the functional modules and basic elements of the opening and sharing platform for scientific and technological resources, and discusses the responsibilities, rights and interests of platform constructors and the related mechanisms that guarantee the platform for efficient operation. The paper also makes detailed design for the science and technology resources sharing platform and sub platform. Through the opening and sharing of scientific and technological resources, the utilization rate of such resources in universities will be greatly increased. Some large scientific research instruments and equipment that were previously idle will be used rationally. On the other hand, enterprises will be more proactive in scientific and technological innovation, and researchers from universities and scientific research institutions will also be able to walk out of laboratories and enter into enterprises to verify and transform existing research fruits through communication and cooperation with enterprises’ employees.
Study of scintillation detector fault diagnosis based on ELM method

This paper proposes a method for scintillation detector fault diagnosis based on the Extreme Learning Machine (ELM) algorithm. A related characteristics database is established including the falling edge time, signal amplitude of nuclear pulse signals, and energy spectrum peak channel, low channel address count of energy spectrum signal under various operating conditions. Then a diagnosis model for scintillation detector is created by improved ELM algorithm which have changed the activation function of the hidden layers and modified the number of hidden layers. An experiment system is also constructed for collecting and validating fault data, the diagnostic experiment results show that this method can not only classify the faults of the failed detector, but also distinguish intelligently the severity of various faults.
Penetration Testing of Wireless EncryptionProtocols

Due to its flexibility, portability, and simplicity of accessibility, the use of wireless networks as a communication medium has skyrocketed. As more devices join a wireless network, valuable and sensitive data is sent over the air between users, making it easy for attackers to sniff and seize data packets. The goal of this work is to use penetration testing to identify weaknesses in the WEP, WPA, and 802.11i WPA2 security protocols, as well as to conduct attacks against them being anonymous with the use of macchanger script. Kali Linux and its Aircrack-ng tools were used to perform penetration testing in WEP &#x0026; exploited a 4-way handshake for WPA/WPA2.
A Vanka-type multigrid solver for complex-shifted Laplacian systems from diagonalization-based parallel-in-time algorithms

We propose and analyze a Vanka-type multigrid solver for solving a sequence of complex-shifted Laplacian systems arising in diagonalization-based parallel-in-time algorithms for evolutionary equations. Under suitable assumption, local Fourier analysis shows the proposed Vanka-type smoother achieves a uniform smoothing factor, which is verified by several numerical examples.
Anomaly Detection Using a <scp>SCADA</scp> Feature Extractor and Machine Learning to Detect Lightning Damage on Wind Turbine Blades

Many wind turbines in Japan are damaged by lightning strikes. In particular, blades that are already damaged by lightning strikes are further damaged by continuous blade rotation. To prevent such additional damage, an emergency stop device that is triggered by lightning detection is required to be installed on wind turbines in winter lightning areas. Normally, the wind turbine restarts after soundness is confirmed by inspection. However, it is often difficult to inspect the turbine visually because of bad weather, and consequently, the wind turbine downtime is prolonged. This type of downtime is a reason for the reduced availability of wind turbines. Therefore, in this study, we consider technologies that would allow quick restart of wind turbines and improve availability, based on understanding the soundness of blades after a lightning strike, using a machine‐learning model based on supervisory control and data acquisition system data. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
Microblogging for Engagement: Effects of Prior Company Involvement, Communication Strategy, and Emojis on Western and Chinese Users

Companies’ social networking platforms can contribute to engaged stakeholder relationships, but many companies struggle to actually make that happen. An online experiment with IT companies’ microblogs investigated effects of communication strategy, emoji use, and prior company involvement on the appreciation and online engagement of Western and Chinese users. The exogenous variables had strong effects. Chinese users showed more appreciation and online engagement than Western users. Prior company involvement contributed to appreciation and online engagement. The message characteristics did not have the expected effects. Action- or community-focused microblogs resulted in lower appreciation. Emojis did not affect appreciation and online engagement.