Reviewerly/exHarmony huggingface dataset:
Design of a Fuzzy Control Crane type robot arm for EOD application

In this paper a fuzzy logic controller was design and develop to control the crane arm motor torque and arm movement for lifting delicate objects. The model was developed to help crane arm automatically adjust its movement speed and angle position for carrying sensitive and fragile load. The controller will respond depending on the object load and size.
Application of Crypto-Blockchain Technology for Securing Electronic Voting Systems

The emergence of blockchain technology is indispensable and globally noticed as an efficient security countermeasure for online business, banking transactions, networking, and smart system-based Internet of Things (IoT). The blockchain utilized the advantages of the decentralization system and the crypto-hash method to provide unauthorized access among the chain participants. It provides data privacy, is immutable, prevents fraud, and avoids records tampering by an individual in the network. These prospects and possibilities validate blockchain confidence as an adequate security measure in the era of IoT and smart systems. However, this research proposes an integration of security countermeasure to enhance the adoption of smart electronic voting systems for election processes using blockchain technology with bi-factors authentication methods. The private blockchain technique is adopted to provide immutability, avoid records tampering, and ensure records transactions' integrity and reliability. Using the Paillier homomorphic encryption algorithm on the decentralized system-based blockchain and electronic voting system through bi-factors certification and secrecy (Iris and Fingerprint). The system performance was proved better during testing with a false acceptance rate (FAR) of 0.02% and false rejection rate (FRR) of 0.1%. The crypto-blockchain technique using the Paillier homomorphic encryption performs well with moderate latency during certification execution and data retrieval timing.
GAN‐based deep neural networks for graph representation learning

Abstract Graph representation learning has attracted increasing attention in a variety of applications that involve learning on non‐Euclidean data. Recently, generative adversarial networks(GAN) have been increasingly applied to the field of graph representation learning, and large progress has been made. However, most GAN‐based graph representation learning methods use adversarial learning strategies directly on the update of the vector representation instead of the deep embedding mechanism. Compared with deep models, these methods are less capable of learning nonlinear features. To address this problem and take full advantage of the essential advantages of GAN, we propose to use adversarial idea on the reconstruction mechanism of deep autoencoders. Specifically, the generator and the discriminator are the two basic components of the GAN structure. We use the deep autoencoder as the discriminator, which can capture the highly nonlinear structure of the graph. In addition, the generator another generative model is introduced into the adversarial learning system as a competitor. A series of empirical results proved the effectiveness of the new approach.
Negotiating transboundary crises in higher education: Tsinghua University’s shift to online learning during the COVID-19 pandemic

The COVID-19 pandemic posed a major challenge for education leaders and policymakers across the globe. This paper discusses the experience of Tsinghua University’s shift to fully online learning, as the first major university in the world to move all courses online in response to the 2020 pandemic. The article introduces the Institutional Resilience in Higher Education Framework to articulate the factors that drive quality education during times of crisis. The article explores the shift to online learning in the context of pandemic as transboundary crisis and details Tsinghua’s online learning emergency response to distil lessons learnt and identify emerging best practices for further online learning implementation. It extrapolates broader lessons about university response to transboundary crises and highlights the importance of effective crisis management, institutional resilience and leadership for delivering quality higher education in emergencies and beyond.
Extracting Unambiguous Drone Signature Using High-Speed Camera

In recent years, the use of drones for recreational and commercial activities has grown rapidly due to their affordability and performance. This growing use raises concerns about the threats drones pose to the security of sensitive areas such as airports, prisons, industrial and military facilities. In response to these threats, drones detection methods are being actively developed. In particular, most camera-based methods rely on appearance to perform detection. They are therefore prone to error due to the great similarity between drones and some other flying entities such as birds. However, from a kinematic perspective, unlike birds, drones, especially multicopters, have a propeller rotation speed. The method proposed in this paper uses the propeller rotation speed as the key physical parameter on which to rely to unambiguously distinguish drones from other flying entities. The basic idea consists in using discrete Fourier transform to determine the propellers rotation speed from high frame rate videos, and extracting the propellers induced drone signature as a quantitative camera-based drone signature. The proposed algorithm proceeds as follows: flying entities are continuously tracked in the sky; discrete Fourier transform, applied to the video stream within a time window ending at the current instant (frame), is used to extract the propellers induced drone signature which unambiguously confirm each flying entity as being a drone or not. Experimental results obtained using a consumer-grade camera at a frame rate of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$240Hz$ </tex-math></inline-formula> demonstrate the effectiveness and reliability of the proposed method.
A General Cyber Hygiene Approach for Financial Analytical Environment

Ideally, users must have a good level of cyber hygiene. Many people have bad cyber hygiene. They openly exchange passwords and share private information on social networks. This chapter will include a technical survey to investigate the cyber hygiene behaviors of end-users which will promote the creation of more successful approaches in the financial analytical climate. It will include aspects to allow the company to improve its cyber hygiene approaches due to the various forms of access to financial solutions in the area.
Research on the Design of Intelligent Music Teaching System Based on Virtual Reality Technology

With the continuous development and innovation of artificial intelligence technology, its application in the field of music education is also increasing, music classroom has accepted and applied a more efficient and intelligent teaching system. In the reform of teaching, virtual reality (VR) technology has gradually become a new means which occupies a place in the field of education and scientific research. The teaching system based on virtual reality has been focused in all kinds of teaching. Therefore, in this paper, VR is used to build a music teaching system based on model embedding, bread capture, packing capture and camera establishment, so as to implement the music teaching platform based on VR. Through the construction of different virtual elements, it can better achieve the goals of public participation and can effectively stimulate the singer's sensory organs.
Stateful-CCSH: An Efficient Authentication Scheme for High-Resolution Video Surveillance System

Video cameras have been widely deployed for surveillance and smart city. But the authenticity of a video scene faces a great challenge due to the ease of tampering with video data without leaving visible traces. Unfortunately, existing authentication schemes are not efficient for emerging high-resolution videos. In this article, we propose stateful correlation coefficient sampling-based hash (Stateful-CCSH), which adopts correlation coefficient sampling in image hash and learns from previous frames to sample those blocks with more dynamic contents and thus, more likely to be tampered in a video forgery. To decrease the impact of false detection introduced by compression and sampling, we also propose a group smoothing-based authentication scheme. The experimental results show that Stateful-CCSH not only achieves excellent performance in forgery detection, particularly, the detection of moving object removal, but also saves significant computation and communication costs even when the video resolutions are high.
Ship Detection in Complex Environment Using SAR Time Series

Ship detection in complex environment is a challenging task due to strong background inferences, for which various deep-learning-based methods have been proposed. However, they have poor performance on detecting nearshore ships for medium-resolution synthetic aperture radar (SAR) images due to the loss of typical features and the confusion with the land scatterers. The availability of multitemporal SAR images gives the opportunity to separate nearshore ships with land scatterers by using the temporal characteristics. In this article, we propose a ship detection method based on SAR time series. First, we investigate the statistical stability of the SAR time series and propose a preclassification method to identify the potential changed pixel clusters. Then, we discriminate between ship and background pixel candidates in the preclassification by combining a rotating object detector and the transition detection algorithm and generate the corresponding frozen background reference (FBR) image. In addition, a dynamic framework for ship detection is proposed based on the FBR image and a two-stage outlier detection approach. The experiments show that the proposed method enables a dynamic ship monitoring with a high accuracy in ship detection and low false alarm rate for nearshore ship targets.
Treewidth-aware reductions of normal ASP to SAT – Is normal ASP harder than SAT after all?

Answer Set Programming (ASP) is a paradigm for modeling and solving problems for knowledge representation and reasoning. There are plenty of results dedicated to studying the hardness of (fragments of) ASP. So far, these studies resulted in characterizations in terms of computational complexity as well as in fine-grained insights presented in form of dichotomy-style results, lower bounds when translating to other formalisms like propositional satisfiability (SAT), and even detailed parameterized complexity landscapes. A generic parameter in parameterized complexity originating from graph theory is the so-called treewidth, which in a sense captures structural density of a program. Recently, there was an increase in the number of treewidth-based solvers related to SAT. While there are translations from (normal) ASP to SAT, no reduction that preserves treewidth or at least keeps track of the treewidth increase is known. In this paper we propose a novel reduction from normal ASP to SAT that is aware of the treewidth, and guarantees that a slight increase of treewidth is indeed sufficient. Further, we show a new result establishing that, when considering treewidth, already the fragment of normal ASP is slightly harder than SAT (under reasonable assumptions in computational complexity). This also confirms that our reduction probably cannot be significantly improved and that the slight increase of treewidth is unavoidable. Finally, we present an empirical study of our novel reduction from normal ASP to SAT, where we compare treewidth upper bounds that are obtained via known decomposition heuristics. Overall, our reduction works better with these heuristics than existing translations.
Influencing robot influence

Abstract In the near future, robots will function in social roles and attempt to influence the user’s behavior and / or thinking. The current contribution analyses how to influence robot influence: Persuasive robots can be personalized to make them more effective. We present an overview of (1) the user characteristics to which persuasive robots can be personalized, (2) considering the specific current situation of a user; and (3) the robot characteristics that can be personalized. Thereby, we give an overview of how the persuasive robot’s physical appearance, behavior, (perceived) cognition and affect can be influenced to characteristics of the user (personalized) in order to make the robot more persuasive and thereby to understand better how the persuasive power of an embodied artificial social entity can be influenced.
Translating for Canada, eh?

Abstract In a situation such as the COVID-19 pandemic, there is a need to distribute up-to-date public health information quickly and cost effectively in a wide range of languages and language varieties. Open Educational Resources (OER) are materials that are released under an open license and so can be freely accessed, used, adapted and shared with very few restrictions. There are currently few localization-related OER available, although the OER format could be a useful way of supporting localization efforts. This short article introduces the concept of OER, summarizes some key potential benefits of developing OER for the localization community, and presents Translating for Canada, eh? / Vous traduisez pour le Canada? , an OER that was developed to support localization efforts in Canadian English and French.
Let's teach Kibot: Discovering discussion patterns between student groups and two conversational agent designs

Abstract Conversational agents can deepen reasoning and encourage students to build on others' knowledge in collaborative learning. Embedding agents in group work, however, presents challenges where groups may ignore the agents, and this calls for designs where students perceive agents as learning partners. This study examines group interactions with two text‐based agents (ie, chatbots) that posed as an expert and a less knowledgeable peer in a high school marine biology lesson. Student messages ( N = 1764) from 18 groups (52 students ages 14–15) received codes for reasoning, building on prior ideas, and responsiveness to the agents. Results indicate no differences between agents in how often each discussion move occurred. Interestingly, sequential pattern mining suggests that the less‐knowledgeable‐peer agent prompted groups to show questioning and building on others' ideas, similar to how students may act as peer tutors to the agent. Meanwhile, sequences with the expert agent resembled student‐teacher exchange, where groups responded to the agent's nudges and then provided reasoning. Findings illustrate the affordances of embedding humanized features in technology designs to promote discussion. Practitioner notes What is already known about this topic Conversational agents can facilitate group discussions, but can get abused or ignored by student groups. To engage students, agent designs can simulate the characteristics of familiar classroom figures, such as peers or teachers. There are limited explorations of how student groups adapt their interactions to different agent designs in collaborative settings. What the paper adds Illustrations of the utilities of diversifying agent designs in collaborative learning. Insights into the unique interaction patterns that student groups displayed to different designs, such as questioning and building on others' ideas with a less‐knowledgeable‐peer agent. Implications for practice and/or policy Embedding signs of humanness in classroom facilitation and technology design can foster responsiveness among users. Pedagogy can consider adaptive designs to promote interaction sequences that contribute to learning (eg, questioning, expanding on prior ideas) at opportune moments.
MSAL-Net: improve accurate segmentation of nuclei in histopathology images by multiscale attention learning network

The digital pathology images obtain the essential information about the patient's disease, and the automated nuclei segmentation results can help doctors make better decisions about diagnosing the disease. With the speedy advancement of convolutional neural networks in image processing, deep learning has been shown to play a significant role in the various analysis of medical images, such as nuclei segmentation, mitosis detection and segmentation etc. Recently, several U-net based methods have been developed to solve the automated nuclei segmentation problems. However, these methods fail to deal with the weak features representation from the initial layers and introduce the noise into the decoder path. In this paper, we propose a multiscale attention learning network (MSAL-Net), where the dense dilated convolutions block captures more comprehensive nuclei context information, and a newly modified decoder part is introduced, which integrates with efficient channel attention and boundary refinement modules to effectively learn spatial information for better prediction and further refine the nuclei cell of boundaries.Both qualitative and quantitative results are obtained on the publicly available MoNuseg dataset. Extensive experiment results verify that our proposed method significantly outperforms state-of-the-art methods as well as the vanilla Unet method in the segmentation task. Furthermore, we visually demonstrate the effect of our modified decoder part.The MSAL-Net shows superiority with a novel decoder to segment the touching and blurred background nuclei cells obtained from histopathology images with better performance for accurate decoding.
Multi-Domain Networks for Wireless Interference Recognition

In the field of military communications, electromagnetic interference has posed a serious threat to wireless communication systems, and wireless interference recognition (WIR) is considered as one of the most indispensable steps to defend against adversarial attacks for anti-interference communication. Following the success in advancing many disciplines, deep learning (DL) brings revolutionary changes to WIR. However, most conventional DL-based methods only use single domain information of interference signals, such as time domain or frequency domain, resulting in low recognition performance under low interference-to-noise ratio (INR). To this end, we propose to exploit multi-domain information of interference signals to take full advantage of the complementarity between domains for WIR. We propose multi-domain networks (MDN) that consist of convolutional layers and transformers to simultaneously strengthen locality and establish long-range dependencies for extracting the features of each domain information. Additionally, to fuse the extracted features from multiple transformation domains together, we propose novel fusion mechanisms, which can be seamlessly incorporated into the MDN. The experimental results demonstrate that the proposed methods notably boost recognition performance as compared to conventional methods for WIR.
Analyzing the Content of Business Documents Recognized with a Large Number of Errors Using Modified Levenshtein Distance

The chapter discusses methods for analyzing test material of business documents, lists tasks that use word comparison. The features of the analysis of recognized texts are indicated. A mechanism for identifying recognized words based on textual feature points is described. The advantages and disadvantages of Levenshtein position are listed. Other distances between string objects are described: Jaro-Winkler similarity, multiset metric, MFKC metric. The Levenshtein standard distance is compared to other distances between two string objects. A modification of the Levenshtein position is proposed focused on the features of the recognized characters. Experimental results are presented that demonstrate the effect of using the proposed distance in comparison with the normalized Levenshtein distances. The experiments investigate the extraction of data from the document and the classification of documents. We also compared the time spent on calculating the modified Levenshtein metric and the multiset metric. The proposed method can be applied in a modern CAD system in the recognition component to analyze the information of recognized text documents. Also, the method can be in the system of the analysis of the recognized text using the methods of computational linguistics.
A novel hybrid recommender system approach for student academic advising named COHRS, supported by case-based reasoning and ontology

The recent development of theWorldWideWeb, information, and communications technology have transformed the world and moved us into the data era resulting in an overload of data analysis. Students at high school use, most of the time, the internet as a tool to search for universities/colleges, university?s majors, and career paths that match their interests. However, selecting higher education choices such as a university major is a massive decision for students leading them, to surf the internet for long periods in search of needed information. Therefore, the purpose of this study is to assist high school students through a hybrid recommender system (RS) that provides personalized recommendations related to their interests. To reach this purpose we proposed a novel hybrid RS approach named (COHRS) that incorporates the Knowledge base (KB) and Collaborative Filtering (CF) recommender techniques. This hybrid RS approach is supported by the Casebased Reasoning (CBR) system and Ontology. Hundreds of queries were processed by our hybrid RS approach. The experiments show the high accuracy of COHRS based on two criteria namely the ?accuracy of retrieving the most similar cases? and the ?accuracy of generating personalized recommendations?. The evaluation results show the percentage of accuracy of COHRS based on many experiments as follows: 98 percent accuracy for ?retrieving the most similar cases? and 95 percent accuracy for ?generating personalized recommendations?.