Reviewerly/exHarmony huggingface dataset:
Development of IoT smart cities and optimization of English education systems based on 5G networks

In recent years, smart cities have become a popular research direction in the fields of natural and social sciences. This article analyzes the impact of 5G networks and the Internet of Things on smart life, speculates on the potential trends of the 5G era in people’s daily lives, and explores methods based on the multiple situational changes in 5G life and how people can respond to these situations. Then, this article explores English education. In contemporary society, due to the rapid development of information technology and economic globalization, the importance of the universal language—English—is increasingly being valued. At present, the competition in English education in China is becoming more intense, and the requirements for English teachers and universities are becoming increasingly strict. This article adopts the methods of questionnaire survey and in-depth interview to manage and analyze the problems existing in the pre class, during class, and post class stages of English learning for college students. Attempt to use learning methods is suitable for English universities to manage information systems, in order to improve the quality of teaching services and optimize English learning process management in various universities, in order to enhance industry competitiveness. This article aims to accelerate the development of smart cities by studying people's life experiences with 5G networks and the Internet of Things. It also analyzes the optimization of computer systems and applies them to English education systems.
Application of artificial intelligence to build a security control software system in local military units

This paper introduces the application of artificial intelligence to build a security control software system in local military units. This software system uses state-of-the-art convolutional neural networks (CNN SOTA) for facial recognition by testing two of the best facial recognition models currently available: the FaceNet model and the VGGFace model. Through testing on the proposed hardware, the FaceNet model meets the accuracy and speed requirements for practical application. The software includes multiple identity management categories to ensure information security and monitor the access of soldiers and other individuals. Additionally, the software features access management functions for protected areas, allowing for audio and visual alerts to ensure safety and security in those areas. The software also enables users to set up connections with other devices for efficient data collection and processing. Simultaneously, it supports synchronized data connection to help users save time and effort in managing information. Moreover, the software includes user-friendly interfaces and customizable settings, ensuring ease of use and adaptability to the specific needs of each military unit. this software system provides a comprehensive and effective solution for ensuring security and monitoring access in local military units. By leveraging artificial intelligence, the system can adapt and improve over time, offering enhanced performance and capabilities to meet the evolving security needs of military organizations. The innovative approach presented in this paper has the potential to significantly improve the overall security and efficiency of local military units, contributing to the safety and well-being of both military personnel and the communities they serve.
Detecting and Measuring Security Risks of Hosting-Based Dangling Domains

Public hosting services offer a convenient and secure option for creating web applications. However, adversaries can take over a domain by exploiting released service endpoints, leading to hosting-based domain takeover. This threat has affected numerous popular websites, including the subdomains of microsoft.com. However, no effective detection system for identifying vulnerable domains at scale exists to date. This paper fills the research gap by presenting a novel framework, HostingChecker, for detecting domain takeovers. HostingChecker expands detection scope and improves efficiency compared to previous work by: (i) identifying vulnerable hosting services using a semi-automated method; and (ii) detecting vulnerable domains through passive reconstruction of domain dependency chains. The framework enables us to detect the subdomains of Tranco sites on a daily basis. It discovers 10,351 vulnerable subdomains under Tranco Top-1M apex domains, which is over 8× more than previous findings, demonstrating its effectiveness. Furthermore, we conduct an in-depth security analysis on the affected vendors (e.g., Amazon, Alibaba) and gain a suite of new insights, including flawed domain ownership validation implementation. In the end, we have reported the issues to the security response centers of affected vendors, and some (e.g., Baidu and Tencent) have adopted our mitigation. The full paper is provided in [2].
Explainable acceptance in probabilistic and incomplete abstract argumentation frameworks

Dung's Argumentation Framework (AF) has been extended in several directions, including the possibility of representing uncertainty about the existence of arguments and attacks. In this regard, two main proposals have been introduced in the literature: Probabilistic Argumentation Framework (PrAF) and Incomplete Argumentation Framework (iAF). PrAF is an extension of AF with probability theory, thus representing quantified uncertainty. In contrast, iAF represents unquantified uncertainty, that is it can be seen as a special case where we only know that some elements (arguments or attacks) are uncertain. In this paper, we first address the problem of computing the probability that a given argument is accepted in PrAF. This is carried out by introducing the concept of probabilistic explanation for any given (probabilistic) extension. We show that the complexity of the problem is FP#P-hard and propose polynomial approximation algorithms with bounded additive error for PrAFs where odd-length cycles are forbidden. We investigate the approximate complexity of the related FP#P-hard problems of credulous and skeptical acceptance in PrAF, showing that they are generally harder than the problem of computing the probability that a given argument is accepted. Next we consider iAF and, after showing some equivalence properties among classes of iAFs, we study iAF as a special case of PrAF where uncertain elements have associated a probability equal to 1/2. Finally, given this result, we investigate the relationships between iAF acceptance problems and probabilistic acceptance in PrAF.
An Approach to Multi-Party Privacy Conflict Resolution for Co-owned Images on Content Sharing Platforms

The popularity of content-sharing platforms such as Online Social Networks (OSNs) is due primarily to advances in information-sharing technologies. One feature that makes content-sharing platforms particularly attractive for users is the possibility of capturing and sharing images. Image sharing however raises a privacy issue in the case of jointly owned images. Jointly owned images might involve users with varying privacy concerns, ranging from very strict (no disclosure) to very relaxed (share everything). So, sharing such images requires obtaining explicit permission to avoid privacy violations. However, obtaining explicit permission from the stakeholders (users) involved is a time-consuming process that additionally raises multi-party privacy conflicts (MPC). In this paper, we present a privacy inference model that operates by considering user preferences in order to automate the decision-making process involved in resolving Multi-Party Privacy Conflicts and inferring user permissions with respect to sharing co-owned images on content-sharing platforms such as OSNs. Our results demonstrate that our privacy inference model performs with 79.60% accuracy.
Are Attention blocks better than BiLSTM for text recognition? 

This paper studies the impact of using Sequential Attention blocks versus Bidirectional Long-Short-Term Memory (BiLSTM) layers for Optical Character Recognition (OCR). The main target is to improve the inference time – specifically on CPU – of state-of-the-art OCRs, with also the additional constraint of being trainable with only a restricted amount of data. While OCR research often focuses on improving recognition accuracy, there has been little emphasis on optimizing processing speed and model weights. In this context, experimental results presented in this paper show the superiority of Attention blocks compared to BiLSTM layers. Attention blocks appear to be up to 5x faster on CPU, while achieving better and similar decoding rates on a typical industrial dataset of identity document text fields and publicly available Scene Text Recognition (STR) datasets, respectively. Also, in addition to being faster and accurate, which was the primary goal, it appears that Attention blocks lead to lighter models.
Integration of Vision IoT, AI-based OCR and Blockchain Ledger for Immutable Tracking of Vehicle's Departure and Arrival Times

Vehicle based logistics are hinged on their ability to timely deliver goods, services, and people. The classical expression of "time is money" comes alive in the logistics industry yielding potentially huge financial and health consequences in case of missing deadlines. This is especially the case for time sensitive pharmaceuticals, delivery of perishable goods, delivery of people travelling, delivery of services in fault fixing/recovery sector. All these use cases motivate the need for an immutable, secure, and immortalized process of tracking time. To solve this challenge, this paper presents prototype-based research that integrates the 4th industrial revolution technologies of vision Internet of Things (IoT), Artificial Intelligence (AI)-based Optical Character Recognition (OCR) and blockchain. The developed prototype features a Raspberry-PI board embedding a camera, an Artificial Intelligence (AI) model to recognize plate letters from the image and a crypto wallet to sign the logging of plate number and time events on the NEAR blockchain, an emerging sharded, proof-of-stake, layer-one blockchain that is simple to use, secure and scalable. The effective operation of the developed prototype has been validated inside a campus parking and shows an accuracy of 80%. The benefits of transparency, security, and immutability of the blockchain combined with the intelligence, data capture, and processing of IoT will enable to develop accountability solutions trusted by all different logistic stakeholders.
Energy-aware Tiny Machine Learning for Sensor-based Hand-washing Recognition

Tiny wearable devices are nowadays one of the most popular and used devices in everyday life. At the same time, machine learning techniques have reached a level of maturity such that they can be used in the most varied fields. The union of these two technologies represents a valuable opportunity for the development of pervasive computing applications. On the other hand, pushing the machine learning inference on a wearable device can lead to nontrivial issues. In fact, devices with small size and low-energy availability, like those dedicated to wearable platforms, pose strict computational, memory, and power requirements which result in challenging issues to be addressed by designers. The main purpose of this study is to empirically explore the trade-off between energy consumption and classification accuracy of a machine learning-based hand-washing recognition task deployed on a real wearable device. Through extensive experimental results, obtained on a public human activity recognition dataset, we demonstrated that given an identical level of classification performance, a classic SVM classifier can save about 40% of energy with respect to a more complex LSTM network. Moreover, reducing the LSTM complexity, by lowering the number of its internal unit, can make the LSTM network energy cost-effective (with a savings of about 30%) at the cost of a reduction in accuracy of only 2%.
Impact of Sentiment Analysis for the 2020 U.S. Presidential Election on Social Media Data

The increased number of social media platforms allows different groups of people around the globe to express their thoughts and sentiments on such media. Such ideas and opinions might implicate the user's social and political perspectives. Those perspectives are subject-specific inputs that have been useful to data science researchers to do community detection and sentiment analysis studies. Research studies utilize the rich information from social media, such as Twitter, to gain a better knowledge of public opinion in societies. In our study, we proposed a machine learning-based algorithm to predict and classify the textual data from Twitter about the 2020 US election campaign. The opinion poll of the presidential elections might be inaccurate or bias, resulting in providing significant negative effects with voters. The dataset was collected during the 2020 presidential election campaign toward the voting day. In addition to the original tweets, we generated fake tweets added to the dataset. Before organizing the comprehensive data and validation, we have done data cleaning and screening to get a reasonable and sensible correlation. For comparison, we used a total of five classification algorithms, such as Linear Support Vector Classification (LSVC), Random Forest (RF), Decision Tree (DT), Logistic Regression, and Naive Bayes. The study found that Trump was more popular than Biden on Twitter. Even though he was more famous than his rival, the study suggested that Joe Biden has a higher average percentage of positive sentiments than Donald Trump nationally. On the other hand, Donald Trump has a higher average percentage of negative sentiments than Joe Biden. In favor of Joe Biden compared to Donald Trump during the 2020 US presidential election, which led to the prediction that Joe Biden would outperform Donald Trump in the 2020 election. Furthermore, adding fake tweets into the dataset has slightly decreased Naive Bayes, Decision Tree, and LinearSVC model performances, but it slightly improved the Logistics Regression model classifier. Retrospectively, the Twitter data could have successfully predicted the US presidential election outcome by factoring in the proportion of positive and negative sentiment for the leading candidates. The study concludes that social media data-based sentiment analysis provides an accurate prediction model for American presidential elections.
"When Can I Trust It?" Contextualising Explainability Methods for Classifiers

The need for artificial intelligence systems to expose reasons for promoted decisions grows with the prevalence of these systems in society.In this work, we study, for carefully selected images, how an end user's trust is affected by visual explanations.Additionally, we complement our work by probing the pretrained neural network's consistency for the selected images.Our research approach exposes the brittleness in these systems pointing toward a need to develop benchmarking methods connecting visual explanations to training data distribution and, additionally, move away from a flat output hierarchy toward including a concept ontology that matches the target domain.Additional material and code to reproduce experiments can be found at https://github.com/k3larra/IKR.
An approach to detecting extremely small or large objects based on an improved scale variation in YOLOv3

YOLOv3 is a state-of-the-art object detection algorithm mainly used to solve real-time tasks. However, this design struggles with scale variation, especially for extremely small or large objects. To improve scale variation, we re-design the Feature Pyramid Network by replacing the stride-2 operation pooling layer with a dilated convolution and bilateral filter. Thus, reducing the network complexity. This helps to improve the network's ability to detect smaller and bigger scale objects. Lastly, the network performance is improved with respect to speed and accuracy.
QLDT+: Efficient Construction of a Quantum Logic Decision Tree

The quantum-logic inspired decision tree (QLDT) is based on quantum logic concepts and input values from the unit interval whereas the traditional decision tree is based on Boolean values. The logic behind the QLDT obeys the rules of a Boolean algebra. The QLDT is appropriate for classification problems where for a class decision several input values interact gradually with each other. The QLDT construction for a classification problem with n input attributes requires the computation of 2n minterms. The QLDT+ method, however, uses a heuristic for obtaining a QLDT with much smaller computational complexity. As result, the QLDT+ method can be applied to classification problems with a higher number of input attributes.
Multi-Supervised LSTM for Bengali Text Sentiment Analysis

Bangla text sentiment analysis is one of the challenges of recent years. Various machine learning and deep learning approaches have been made to further improve the classification models' performance to achieve state-of-the art results. Since text sentiment analysis is a sequential task, Long Short-term memory have provided great results in these cases. In this paper, we propose a methodology to tweak an existing LSTM model to gain further improvement in results. As Bengali Text Sentiment analysis is a sequence-to-one task, we discuss how using the features of the entire sequence generated from LSTM along with the last hidden state's output can improve the results. We compare with a single LSTM layer where the last hidden state is used for producing the final result with our methodology where also a single LSTM layer is used but with additional feature engineering and multi-supervision is applied on the generated sequence and final hidden state to produce the results. Our final results show that adding these tweaks to an existing LSTM layer can increase the overall performance of the model.
SCALA: Scaling algorithm for multi-class imbalanced classification

Most of the existing techniques for solving data imbalance problems are geared towards binary classification problems, hence a novel strategy capable of natively handling multi-class classification problems is required. Existing implementations mainly employ a one-versus-rest approach to support multi-class problems and this generalisation hinders its effectiveness in datasets with multiple minority classes. On the contrary, a one-versus-one approach avoids such generalisation and provides finer control over the balancing strategy. In this paper, we propose a novel SCALA algorithm capable of handling imbalanced data with multiple minority class labels with a multi-class output. We introduce a user-defined set of scaling factors which are then integrated with a one-versus-one balancing strategy. Our results show that SCALA demonstrated a significant improvement compared to ADASYN and SMOTE in model performance metrics used to validate balancing techniques. SCALA can balance these datasets without allowing minority classes to overshadow other minority classes. This preserves the information needed by the training algorithm to distinguish between the classes to a high precision.
A History of Cyber Risk Transfer

Cyber risk management involves balancing risk acceptance, avoidance, reduction and transfer. Yet academic researchers—under the banner of computer, information and/or cyber security—have focused predominantly on risk reduction measures. Studies of cyber risk transfer are less common, mainly centering on cyber insurance. This ignores a flowering of real-world cyber risk transfer products in the last decade. Our study describes the historical emergence of products including: warranties, cloud computing partnerships, parametric insurance, reinsurance, and cyber cat bonds. We characterize how these solutions addressed four core challenges: (1) tailoring coverage to the threat landscape; (2) maintaining profitability; (3) data collection for risk assessment; and (4) creating incentives for risk reduction. The result is an integrated history of cyber risk transfer describing how novel products and partnerships emerged to address failings in prevailing business models. Our descriptive study can help other researchers to understand problems faced in the real-world, providing a foundation for future research.
Development of Telugu Language Software’s

In the olden days initiation of literacy was done by making the child write ‘om namaha’ with his right finger in the sand. Later slates and slate pencils took their place. Today there is no need for either slates or books and we have digital methods for the same thing. If one just switches on the computer it shows how to write om namaha and we also hear a melodious voice saying om namaha. This is the way to teach lessons with the help of multimedia in today’s technological world. In the previous days for a marriage invitation card various blocks were manually arranged, proofreading was done and then the invitation card was printed. But today there are beautiful fonts like potana and srilipi and they have changed the field of printing as never before. Today there are various fonts that support various software, the anusaraka translation machine which helps in translation, E- Dictionary and spell checkers to know the spelling mistakes, Telugu searching to browse various things on the Internet, Telugu bloggers for various reviews, and Encyclopedia have also become available. The work done by C- DOC, Modular InfoTech, IIIT, and Hyderabad Central University is highly commendable in the development of this software.
Task-specific pre-training improves models for paraphrase generation

Paraphrase generation is a fundamental and longstanding problem in the Natural Language Processing field. With the huge success of transfer learning, the pre-train → fine-tune approach has become a standard choice. At the same time, popular task-agnostic pre-trainings usually require gigabyte datasets and hundreds of GPUs, while available pre-trained models are limited by fixed architecture and size (i.e. base, large). We propose a simple and efficient pre-training approach specifically for paraphrase generation, which noticeably boosts model quality and matches the performance of general-purpose pre-trained models. We also investigate how this procedure influences the scores across different architectures and show that it works for all of them.
Vietnamese Text Summarization Based on Elementary Discourse Units

This paper presents text summarization models based on elementary discourse units (EDUs) to construct extractive and abstractive summarization for Vietnamese documents. First, we introduce algorithms using the POS information for constructing EDUs in Vietnamese. Then, the EDUs created are fed into an extractive summarization model using a pointer network and an abstractive summarization model using a pointer generator model. A reinforcement learning method is used to improve the quality of the models. We perform experiments on the CTUNLPSUM dataset, including 1,053,702 Vietnamese documents extracted from online magazines. The extractive summarization models based on EDUs outperform other extractive summarization models based on words or sentences. The ROUGE-1, ROUGE-2, and ROUGE-L of the best extractive and abstractive summarization models are 0.567, 0.241, 0.461; and 0.530, 0.213, 0.394, respectively.