Reviewerly/exHarmony huggingface dataset:
A Presidential Debate: Exploiting Agreement in an Adversarial Context

In this Chapter, the first presidential debate in the history of US elections, opposing Kennedy as challenging candidate to Nixon as incumbent Vice-President, is analyzed. Argument reconstruction is combined with a discourse perspective, against the backdrop of both contextual factors and generic traits. The main argumentative schemes exploited by the candidates are singled out, namely arguments from: alternatives, value-based practical reasoning, interaction of act and person, and group-member relation. Special emphasis is placed on the choice of premises and their rebuttal by the adversary. Two different strategies seem to characterize the discourse of either candidate. Nixon seeks agreement with as broad an audience as possible on the needs, often acknowledging a unity of judgment with his competitor, while emphasising differences from the adversary on the means that must be used to address them. Kennedy, on his part, stresses differences between the parties that he and Nixon represent. In the rhetorical perspective, the advantage Kennedy obtained from this debate may stem not only from his look or from his ability to play with the medium, but crucially also from his capacity of constructing the right audience. He showed a big view, appealed to widely shared values, well aware of speaking to people not familiar with the technicalities of politics or the economy– the large prime time TV audience. Conversely, Nixon lost not only for his inadequate approach to TV broadcasting, for his being underweight and pale, nor for his perspiration. Actually, he did not consider the nature of his potential audience, speaking to a small fraction of that big viewership, who were able to understand and to follow him in his punctual rebuttal of his adversary’s convictions.
Wearing Detection by Multi-Scale Information Fusion Network for Live-Line Working Scenarios

In live-line working scenarios, properly wearing the insulating protective personal equipment (PPE) is an important means of ensuring the safety of the operator. However, there are many challenges in applying universal object detection for wearing detection in live-line working scenarios. Different cameras are located at different distances from the operator. During the operation, the insulating bucket where the operator stands will keep moving. These can lead to a large range of scale changes of objects in the image, causing a decrease in detection accuracy. In order to solve the above problems, we design a PPE dataset about live-line working and propose a multi-scale information fusion network based on Yolov5. To be specific, in order to suppress the inconsistency between different scales, we employ the adaptive spatial feature fusion module. Then we integrate coordinate attention to make the network pay more attention to the location information, alleviating the problem of small target miss detection. Moreover, we also adopt Varifocal loss to mitigate the category imbalance problem to improve accuracy. The experiments prove that our method outperforms most object detection methods. And the results show that our multiscale information fusion network achieves 90.3% AP50 and 61.4% AP50:95 on our dataset. Compared to Yolov5, our method improves 2.2% AP50 and 2.1% AP50:95. For the shawl category, AP50 improves by 7.4%.
A Security-Enhanced Pairing-Free Certificateless Aggregate Signature for Vehicular Ad-Hoc Networks

As self-organizing, highly heterogeneous and multi-interactive networks, vehicular ad-hoc networks (VANETs) are rapidly improving the efficiency of modern traffic. This promising technology is significant for supporting current intelligent transportation by cooperative data sharing and processing. Considering requirements for secure interactions and the limitation of resources in VANETs, protecting data authenticity while enhancing computational efficiency has become the main challenge. A certificateless aggregate signature (CLAS) can not only ensure data security, but also reduce communication costs, which is suitable for a resource-constrained environment. However, most existing CLAS schemes either own serious security vulnerabilities or result in tremendous computational overhead. In this article, we propose an efficient, privacy-preserving, and pairing-free CLAS scheme with enhanced security. We prove that it is existentially unforgeable against adaptive chosen message attacks in the random oracle model. Performance evaluation illustrates that our proposed scheme is more appropriate to the VANETs scenario.
Interactive Mongolian Question Answer Matching Model Based on Attention Mechanism in the Law Domain

Mongolian question answer matching task is challenging, since Mongolian is a kind of low-resource language and its complex morphological structures lead to data sparsity. In this work, we propose an Interactive Mongolian Question Answer Matching Model (IMQAMM) based on attention mechanism for Mongolian question answering system. The key parts of the model are interactive information enhancement and max-mean pooling matching. Interactive information enhancement contains sequence enhancement and multi-cast attention. Sequence enhancement aims to provide a subsequent encoder with an enhanced sequence representation, and multi-cast attention is designed to generate scalar features through multiple attention mechanisms. Max-Mean pooling matching is to obtain the matching vectors for aggregation. Moreover, we introduce Mongolian morpheme representation to better learn the semantic feature. The model experimented on the Mongolian corpus, which contains question-answer pairs of various categories in the law domain. Experimental results demonstrate that our proposed Mongolian question answer matching model significantly outperforms baseline models.
The Design and Construction of Smart Classroom of the “Diplomatic Etiquette” Course

With an aim to improve the teaching result of the “Diplomatic Etiquette” course, this article develops a study on the design and construction of smart classroom of the “Diplomatic Etiquette” course on account of the absence of smart classroom presently. Firstly, this article puts forward four functions of the smart classroom of the “Diplomatic Etiquette” course based on the introduction of the “Diplomatic Etiquette” course. Afterwards, this article has a discussion on about the core sections, i.e., how to achieve the software system of teaching management system, classroom interactive system, electronic school bag system and etc. The conclusion of this article has certain theoretical significance and practice value to improve the teaching result of the “Diplomatic Etiquette” course and to enhance students’ ability to interact with foreign countries.
Matrix-Based Secret Sharing for Reversible Data Hiding in Encrypted Images

Traditional schemes for reversible data hiding in encrypted images (RDH-EI) focus on one data hider and cannot resist the single point of failure. Besides, the image security is determined by one party, rather than multiple parties. Thus, it is valuable to design RDH-EI schemes with multiple data hiders for stronger security. In this article, we propose a multiple data hiders-based RDH-EI scheme using a new secret sharing technique. First, we devise an <inline-formula><tex-math notation="LaTeX">$(r,n)$</tex-math></inline-formula> -threshold <inline-formula><tex-math notation="LaTeX">$(r\leq n)$</tex-math></inline-formula> matrix-based secret sharing (MSS) using matrix theory, and theoretically verify its efficacy and security properties. Then, using the MSS, we propose an <inline-formula><tex-math notation="LaTeX">$(r,n)$</tex-math></inline-formula> -threshold RDH-EI scheme called MSS-RDHEI. The content owner encrypts an image to be <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> encrypted images using the MSS with an encryption key, and outsources these encrypted images to <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> data hiders. Each data hider can embed some data, e.g., copyright and identification information, into the encrypted image for the purposes of storage, management, or other processing, and these data can also be losslessly extracted. An authorized receiver can recover the confidential image from <inline-formula><tex-math notation="LaTeX">$r$</tex-math></inline-formula> encrypted images. By designing, our MSS-RDHEI scheme can withstand <inline-formula><tex-math notation="LaTeX">$n-r$</tex-math></inline-formula> points of failure. Experimental results show that it ensures the image content confidentiality and achieves a much larger embedding capacity than state-of-the-art schemes.
Graphic Characters as Twitter Age Group Identifiers

The twenty-first century has been a time of significant changes in the lives of many societies worldwide. The constantly increasing access to the web has changed many aspects of social life, including the availability of information and opinions expressed by hundreds of millions of people around the globe. Social networking sites have become the most important forum for exchanging views on virtually any topic. Twitter has a dominant position here, transmitting millions of posts from their users every day. The researchers analysing the content of the data derived from social networks often experience difficulties in identifying the age of the published texts’ authors. Such information, with accuracy to specific age groups, can be very useful in e.g., marketing research, which provides knowledge on purchasing preference differences. In many cases, users do not publish any information about their age, which significantly limits user profiling possibilities, for instance. Linguistic research shows that representatives of different age groups use distinct vocabulary and grammatical forms. It seems that they are also distinguished by the use of different graphical symbols such as emoticons, emojis, pictograms, and other graphics used in text. The research presented aims to distinguish Twitter user age groups in distribution by the typical emojis used in the messages published. An attempt will be made to answer the question of whether the type and the number of the graphical symbols used in tweets can indicate the age group of their authors. Text mining methods and artificial intelligence were used to carry out the research.
Traffic signs detection, recognition and tracking for roads mapping

Traffic signs detection and recognition are well-known problems that are being solved for multiple decades already. There is significant progress in this research area in the last decade. This problem has plenty of solutions that are even deployed in business from driver assistant systems to autonomous vehicles. Nevertheless, we still focus on this area but from a different perspective utilizing the already achieved advances either in traffic signs detection and recognition or in computer vision in general. We want to empower our research group by focusing on the intelligent road infrastructure of which traffic signs are a part. Our contribution to the research group is in composing a solution for real-time mapping of traffic signs. This solution is going to be deployed on real vehicles collecting data about traffic signs like their location, side of the road, visibility, detection distance, or the direction of a road where the sign matters. Finally, this data is going to be crowdsourced by the vehicle agents and collected on a server creating a robust mapping of traffic signs on real roads.
Hierarchical Neural Network with Bidirectional Selection Mechanism for Sentiment Analysis

Document-level sentiment classification is a process that aims to predict the sentiment rating of a particular document. The most popular methods take word embeddings as the inputs of a neural network. However, there are many different senses that coexist in a word embedding; such ambiguous word representation will cause models to misunderstand a text, which will result in predicting the sentiment incorrectly. Accordingly, to make the information in the text representation more explicit, we propose a Bidirectional Selection Mechanism (Bi-SM), which filters out redundancy from different perspectives by alternately selecting salient information contained in the text representation and the category representation. Bi-SM mainly contains two parts-Selection and Back Selection. At the word level, the Selection module uses the category of each word to “filter” out redundant senses. Meanwhile, the category will be enriched in this module for further being used in the Back Selection module. Subsequently, the Back Selection module uses the “filtered” word information to “select” the salient “enriched category”, which is further used at the sentence level to “filter” out redundancy in the sentence representation. The same thing happened at the sentence level. Our experimental results demonstrate that our proposed models can achieve consistent improvements compared to state-of-the-art methods.
Meta-strategies of referencing basic level categories in elaborative response generation in cognitive agents

This paper introduces a set of meta-strategies utilising basic-level categories in a cognitive agent capable of answering question using autoepistemic natural statements grounded in its experience and hierarchical knowledge structures. The proposed approach thrives in combing several natural phenomena, as observed in human language users, with inherent notion of uncertainty about categorisation of observable objects. Using built-in hierarchical structures between available concepts we show how a set of basic meta-strategies can be defined to determine the focus of the response (responding agent) in the context of particular distribution of certainty (established through past individual experiences) in the hierarchy, current focus of the question (querying agent) and basic-level in the hierarchy. Such meta-strategies can be further implemented as more natural extensions of agent's internal organisation of linguistic activities.
Guided Data Discovery in Interactive Visualizations via Active Search

Recent advances in visual analytics have enabled us to learn from user interactions and uncover analytic goals. These innovations set the foundation for actively guiding users during data exploration. Providing such guidance will become more critical as datasets grow in size and complexity, precluding exhaustive investigation. Mean-while, the machine learning community also struggles with datasets growing in size and complexity, precluding exhaustive labeling. Active learning is a broad family of algorithms developed for actively guiding models during training. We will consider the intersection of these analogous research thrusts. First, we discuss the nuances of matching the choice of an active learning algorithm to the task at hand. This is critical for performance, a fact we demonstrate in a simulation study. We then present results of a user study for the particular task of data discovery guided by an active learning algorithm specifically designed for this task.
Wireless Communication Systems: Confidentiality

In common communication systems confidentiality are ensured exclusively by encryption of the messages. This chapter covers the security procedures in wireless communication systems. The goal of physical layer security is to make use of the properties of the physical layer to enable critical security aspects. Moreover, wireless communication services are rapidly increasing because of the proliferation of mobile devices and the advent of the Internet of Things (IoT). Authentication is an important issue in wireless communications because the open nature of the wireless medium provides more security vulnerabilities. Most of existing wireless communication systems achieve the authentication goal via upper-layer authentication mechanisms.
Research Trend in the International Literatures on Machine Translation Based on CiteSpace

With the continuous development of computer, machine translation has experienced from “mechanical brain” to neural network machine translation based on deep learning. It is constantly innovating in technology and upgrading in algorithm. With the attempt to clarify the development characteristics, development status, advantages and limitations of the machine translation system at each stage, this study collects the machine translation research data from the databases of SCI and SSCI since the 1990s and analyzes them with the assist of CiteSpace so as to looking forward to the future of machine translation.
Design and Implementation of Text Processing System Based on Summarization Algorithm

With the popularization and development of the Internet, text information has shown an exponential growth trend. This information overload phenomenon affects the ability of users to receive critical information, and people’s demand for quick access to information is increasing. Text summarization technology uses computers to automatically extract the key information of text, which helps to grasp the text content accurately and quickly, so it has a good application prospect. The traditional rule-based method simply counts the word frequency and lacks the consideration of the semantic information of the text, so the results are not accurate enough. To this end, this paper proposes an extractive text summarization algorithm based on multiple feature weighting, which comprehensively considers the global information, surface information, structural information, and semantic information of the text by weighting the sentence position, the total amount of keyword information, keyword distribution, and semantic similarity. This method retains the advantages of the rule-based approach from not requiring data annotation and saving computational resources while improving the text understanding capability of the model. Experimental results show that the model improves the evaluation results of the datasets, improving the quality and accuracy of text summarization. And when the model is applied to the text processing system, the user can quickly obtain the required information, effectively speeding up the process of obtaining and processing information.
The Incorporation of NID into Digital Libraries and Its Benefits

This paper addresses the topic of the ongoing digitization of previously analogue knowledge collections, primarily libraries, i.e. mainly but not exclusively written materials in text format and additionally materials in images, video, and audio formats. A short introduction to digital books explains the extended ways of using them in contrast to their analogue predecessors and their incorporation into various systems such as NID (Net Interactive Documents). In one section, the topic of digital libraries is reviewed. A few of the most important representatives are briefly introduced. The 3.2 chapter applies to the software, which can be used for digital libraries. In the main part, an introduction is given to NID (Net Interactive Documents) and its current applications as well as projects planned for the future. Finally, different features of NID are compared with a similar system - Hypothes.is, and the advantages and disadvantages are explained.
A Noise-Robust Partial Blur Detection Algorithm Combining Walsh Transform and Sequency Truncation

Blur detection is an important yet challenging task in computer vision. The previous algorithms are mostly difficult to achieve a cost-benefit balance and their performance is largely limited when faced with the blur image polluted by noise. To address these issues, a fast and unsupervised blur detection algorithm is proposed, which is robust to noise. First, a re-blur strategy and Walsh transform are utilized to analyze the input image in sequency domain. Meanwhile, the low-sequency zone of sequency spectrum is adaptively truncated to eliminate the noise interference. Then, a noise-robust local blur metric is constructed and pixel-wise blurriness is calculated to obtain the blur map. Finally, the blur region is segmented using the clustering-guided multi-scale growth framework. Experimental results on CUHK and DUT datasets demonstrate that the proposed algorithm can detect the image blur effectively and efficiently, and achieves the state-of-the-art performance on multiple indicators like precision, recall, <italic>F</italic><sub>1</sub>-measure, mean absolute error, and mean runtime. Especially on noise-polluted conditions, the proposed algorithm significantly surpasses other competitive algorithms.
Rapid Preparation of Bell States Based on an Exponential Quantum Projection Filter

The exponential quantum projection filtering algorithm is applied in measurement-based quantum feedback control and a specific switching control law is designed to realize the rapid preparation of a target Bell state. First, we use an exponential projection filter to obtain an approximate estimate of the system state, which can reduce the calculation time spent in the measurement process. Then, the quantum state space is partitioned into two subsets to separate the target state from other equilibrium states, two Lyapunov functions are constructed and the corresponding control laws are designed in the two subsets. It is shown that the switching control scheme in this paper ensures that the system trajectory crosses the boundary of the two subsets no more than twice from any initial state, which effectively reduces the time that the system converges to the target Bell state. Numerical simulation on a two-qubit system verifies the effectiveness of the proposed control scheme.
Immersive Technologies for Vestibular Rehabilitation

Vestibular vertigo accounts for about a quarter of dizziness complaints. Loss of vestibular function is a debilitating condition that causes nausea, spontaneous nystagmus, or disequilibrium, which is known to highly influence day-to-day tasks. Therefore, recovery should start as soon as possible, targeting the affected side of the vestibular apparatus, by performing a set of prescribed exercises. Aside from being a long and tiresome process, patients must perform these exercises, while trying to stimulate optokinetic, angular vestibulo-ocular, and vestibulospinal reflexes. The article presents the development of virtual reality serious games that can be played at home or in a rehabilitation clinic. The main objective is to increase patients' motivation, specifically during telerehabilitation which is essential for a faster recovery process. A preliminary evaluation was carried out to compare the users' experiences using a smartphone-based headset and a standalone commercial head-mounted display, the Oculus Quest.
Using Electronic Evidence in Civil and Commercial Dispute Resolution: Challenges and Opportunities

Starting from the shortcomings regarding the main issues of electronic evidence and the use of electronic evidence in civil and commercial dispute resolution identified in the previous chapters, this final chapter explores the challenges and opportunities for the use of electronic evidence in civil and commercial dispute resolution. The significant development of information technology has brought advantages to the use of electronic evidence but also challenged not only the involved parties, the judges and arbitrators in all stages of using electronic evidence. This chapter identifies the challenges of using electronic evidence in civil and commercial dispute resolution from both technical and legal views. After briefly explaining the challenges of using electronic evidence, the chapter makes proposal on improving the use of electronic evidence in civil and arbitral proceedings to ensure the legal framework for electronic evidence.