Reviewerly/exHarmony huggingface dataset:
The Emergence of Web 3 and Its Core Building Blocks

This chapter delves into the core building blocks of Web3, the transformative phase of the internet characterized by decentralized and user-centric digital ecosystems. It explores the ethos of Web3, emphasizing openness, transparency, trustlessness, and user empowerment. The chapter examines blockchain technology for secure transactions, smart contracts revolutionizing agreements, and cryptocurrencies facilitating peer-to-peer value transfer. It discusses decentralized finance (DeFi) as a transformative building block and explores decentralized applications (DApps) and non-fungible tokens (NFTs) for creating user-centric platforms. The importance of interoperability along with governance mechanisms like decentralized autonomous organizations (DAOs) is highlighted. The chapter concludes by envisioning the interconnectedness between Web3 and the metaverse, where virtual and physical realities merge. Overall, it provides a comprehensive overview of Web3's emergence and its transformative impact on decentralization, transparency, and user empowerment.
The Integration of 4IR Technologies in Architectural Education for Upskilling the Workforce in the Nigerian Built Environment

There is an increasing awareness and adoption of Fourth Industrial Revolution (4IR) technologies in the Architectural, Engineering and Construction, Operations and Management (AECOM) industry. This has placed significant pressures on academia to develop and produce graduates who are equipped with literacy skills to seamlessly transit into the world of work after graduation. However, universities in developing nations such as Nigeria have struggled to integrate 4IR technologies such as visualization tools into their curricula. This study filled this gap by exploring the various 4IR technologies that are relevant for teaching architectural designs and 3D visualization in the architecture discipline. This study further explained how these technologies can be employed in architectural education to improve students understanding of the design and building processes, thus equipping them for the world of work. The objectives were achieved by reviewing existing theoretical literature on how 4IR technologies can promote the upskilling of architectural students learning. Findings from this study revealed that the integration of 4IR technologies in architectural education can enhance students understanding of the design and construction processes and upskill them for greater productivity in the industry. This study therefore recommended the integration of 4IR technologies in architecture education and generally in AECO disciplines for improved learning outcomes and employability of the students. This study also offers a reference point as well as directions for future research in exploring other 4IR technologies for technology-enhanced architecture pedagogy.
Toward a Multidimensional Analysis of the National Vulnerability Database

Bring-your-own-device policies, IoT devices, smart appliances are all contributing to increasing diversity of connected devices. It has become imperative to understand the vulnerabilities of these diverse devices (along with traditional compute devices) to appropriately secure their use. In this paper we conduct a detailed analysis of the vulnerabilities reported for the various hardware and software artifacts in the NIST National Vulnerability Database. We analyze the details of vulnerabilities covering the period 2011-2022. We broadly categorize the vulnerabilities into three product categories: networking, IoT, and computing devices. The data is further classified into application, OS, and hardware domains. We analyze the data across the aforementioned categories over four non-overlapping 3-year time periods. The analysis provides insights into salient trends in vulnerabilities across diverse products, and over time. Our work presents interesting findings based on the trends and persistence observed from the analyzed data. Our study points to insights that could lead to improved resource allocation for addressing security concerns.
A Dual RNN Semantic Analysis Framework for Intent Classification and Slot

The research on spoken language understanding (SLU) system has progressed extremely fast during the past decades. Intent detection and slot filling are two main tasks for building a SLU system. Multiple deep learning based models have demonstrated good results on these tasks. The most effective algorithms are based on the structures of sequence to sequence models (or “encoder-decoder” models), and generate the intents and semantic tags either using separate models (Yao K, et al., Spoken language understanding using long short-term memory neural networks, South Lake Tahoe. 189–194, 2014; Mesnil, et al. IEEE/ACM Trans Audio Speech Lang Process, 23:530–539 2014; Peng, et al. Recurrent neural networks with external memory for spoken language understanding. Proceedings of the 2015 Natural Language Processing and Chinese Computing. 9362:25–35, 2015; Kurata, et al. Leveraging sentence-level information with encoder LSTM for semantic slot filling. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. 2077–2083, 2016; Hahn, et al. Inst Electr Electron Eng Trans Audio Speech Lang Process. 19:1569–1583, 2011) or a joint model (Liu and Lane. Attention-based recurrent neural network models for joint intent detection and slot filling. Proceedings of the Interspeech. 685–689, 2016; Hakkani-Tür et al., Multi-domain joint semantic frame parsing using bi-directional RNN-LSTM. Proceedings of the Interspeech. 715-719, 2016; Guo, et al. Joint semantic utterance classification and slot filling with recursive neural networks. Proceedings of the 2014 Institute of Electrical and Electronics Engineers Spoken Language Technology Workshop. 554–559, 2014). Most of the previous studies, however, either treat the intent detection and slot filling as two separate parallel tasks, or use a sequence to sequence model to generate both semantic tags and intent. Most of these approaches use one (joint) NN based model (including encoder-decoder structure) to model two tasks, hence may not fully take advantage of the cross impact between them. In this chapter, new Bi-model based RNN semantic frame parsing network structures are designed to perform the intent detection and slot filling tasks jointly, by considering their cross-impact to each other using two correlated bidirectional LSTMs (BLSTM). The Bi-model structure with a decoder achieves state-of-the-art results on the benchmark ATIS data (Charles, et al. The atis spoken language systems pilot corpus. Proceedings of a Workshop Held at Hidden Valley. 96–101, 1990; Tur G et al. What is left to be understood in atis? IEEE Spoken Language Technology Workshop. 19–24, 2010), with about 0.5% intent accuracy improvement and 0.9% slot filling improvement.
Automated Optical Accelerator Search: Expediting Green and Ubiquitous DNN-Powered Intelligence

Editor’s notes: In this article, the authors develop a systematized framework to achieve automated optical accelerator architecture search. —Mahdi Nikdast, Colorado State University, USA —Miquel Moreto, Barcelona Supercomputing Center, Spain —Masoumeh (Azin) Ebrahimi, KTH Royal Institute of Technology, Sweden —Sujay Deb, IIIT Delhi, India
Decentralized Identity in Web 3

Web 3, or the decentralized web, uses blockchain technology and decentralised principles to revolutionize online interactions and commercial practises. It gives people authority, builds trust, and allows for peer-to-peer interactions. Blockchain is a decentralized and secure database that ensures transparent transactions without the need of middlemen. Web 3 is reshaping the financial, healthcare, supply chain, and entertainment industries. Cryptocurrencies provide safe and borderless transactions. It improves supply chain management, assures ethical sourcing, and gives content producers more influence. Decentralised identification systems overcome the problems associated with centralized identity. Scalability, interoperability, and performance are all difficult issues. The importance of interoperability and standardization cannot be overstated. Regulatory and legal problems must be consistent with the ideas of decentralized identity. Decentralized identification systems provide personalised and transparent experiences that foster trust and consumer loyalty.
Discovering New Intents Via Constrained Deep Adaptive Clustering with Cluster Refinement

Identifying new user intents is an essential task in the dialogue system. However, it is hard to get satisfying clustering results since the definition of intents is strongly guided by prior knowledge. Existing methods incorporate prior knowledge by intensive feature engineering, which not only leads to overfitting but also makes it sensitive to the number of clusters. In this chapter, we introduce constrained deep adaptive clustering with cluster refinement (CDAC+), an end-to-end clustering method that can naturally incorporate pairwise constraints as prior knowledge to guide the clustering process. Moreover, the clusters are refined by forcing the model to learn from the high confidence assignments. After eliminating low confidence assignments, the approach presented is surprisingly insensitive to the number of clusters. Experimental results on the three benchmark datasets show that the method presented can yield significant improvements over strong baselines.
Optimally Displaced Threshold Detection for TPSK Modulated Coherent States

In this paper, the performance of optimally displaced threshold detection (ODTD) for discriminating ternary coherent signals is theoretically investigated. We first establish the receiver model for ternary phase shift keying optical quantum communication system using Kennedy receiver with ODTD in a realistic situation. Then we analytically study the error probability for ODTD and formulate a joint optimization problem to minimize this error probability of detection, which is a challenging mixed-integer programming (MIP) problem due to the discreteness of the detection threshold. We then propose an efficient algorithm to tackle this MIP problem and design the displacement and the threshold on both detection branches. We also discuss the receiving scheme using ODTD in the case of unequal prior probabilities and optimize the signal power distribution between the two branches. Numerical results show that for the discrimination of ternary coherent signals using Kennedy-type receiver, ODTD can mitigate the influence of thermal noise and dark count noise and is robust to imperfect quantum efficiency. Besides, we find that the error probability performance can be improved by optimizing the decoding order of the ternary signals and the reflectance of the beam splitter.
Motion Planning and Pose Control for Flexible Spacecraft Using Enhanced LQR-RRT<sup>*</sup>

The primary difficulty of on-orbit services is autonomous real-time motion planning, especially considering collision avoidance among complex modulars. This study considers the motion planning and control of flexible spacecraft in on-orbit service. Moreover, a typical flexible spacecraft consisting of flexible appendages and a rigid hub is modeled in this article. The rapidly exploring random tree star (RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> ) algorithm and linear quadratic regulator (LQR) are used to generate a path considering both collision avoidance and dynamic performance. A control algorithm is also designed to drive the spacecraft to track the planned path. Furthermore, the calculation difficulty, in which the target state of LQR is nonzero, is addressed in this article, and an algorithm is designed to estimate the node velocity. To show the advantage of the enhanced LQR-RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> method, some numerical motion planning results are presented for flexible spacecraft considering vibration suppression.
The digital literacy of first-year students and its function in an online method of delivery

Purpose This study aimed to acquire a comprehensive understanding of the digital literacy skills possessed by first-year students and how they impact students' participation in online classes. Design/methodology/approach This research was conducted using a quantitative method to investigate first-year students' digital literacy and its effect on their interaction in online learning. Nine hundred and two students from 19 online English writing classes were invited to complete an online survey questionnaire. The sampling approach was conducted by a nonrandom method. Prior to the data collection, the instrument was reviewed by researchers and peers with quantitative and Indonesian language backgrounds. The procedure was conducted to ensure that the instrument covered the objective of the research. Findings The results showed that students digital literacy in the first semester, in general, is still in the low category. The average score for each competency area still varies. The average score on information and data literacy competence was low. The average score for communication and collaboration competence was in the medium category. The average score for the digital content creation competence area is low. Practical implications The authors suggest that educational institutions should consider organizing primary student digital literacy training or optimizing the lecture process to improve students' digital literacy and further research could usefully explore how information and communication technology (ICT) skills affect student performance. Social implications The study reveals the importance of digital literacy of students for online teaching learning. In order to achieve quality teaching in online methods, the digital literacy is a must for students. The study highlights the limitations of the study and indicates the necessity for further research in this area. The findings have immense implications for practice in a distance learning university, where online teaching learning has become a new normal post-Covid-19. Originality/value The authors present new findings on the digital literacy of first-year students and their effect on their interaction in online learning. The authors also make connections between digital literacy and student engagement in online classes, which is a significant contribution to the field. This paper also provides a comprehensive review of the literature on digital literacy and e-learning, which adds to the scholarship in the field.
Managing Ethical Requirements Elicitation

The process of Requirements Elicitation (RE) demands from a software development team the need to communicate and engage with a variety of stakeholders, for numerous purposes regarding many aspects of the project. The aim is to translate the needs of the “customer” into accurate and actionable requirements. In this initial step of the software life cycle process several ethical challenges are invoked, which, if left unresolved, may lead to unintended consequences. Computer Ethics focuses on the questions of right and wrong that arise from the development and deployment of computers. Thus, it urges that the ethical and social impact of computers must be analysed. The purpose of normative ethics is to scrutinise standards about the rightness and wrongness of actions, the goal being the identification of the true human good. A rational appeal can be made to normative ethical principles to arrive at a judicious, ethically justifiable judgement. In software engineering, the Software Process Improvement (SPI) Manifesto was developed by groups of experts in the field, aimed to improve the software produced, through improving the process, the attitudes of software engineers, and the organisational culture and practices. In this position and constructive design research paper, we argue that software developers, in accordance with the SPI Manifesto aim of improving the software produced, address the ethical challenges invoked in the Requirements Elicitation process. The steps taken in this paper are: First we report on the findings of a broad literature review of related research, which refers to the current challenges in RE. Second, we source from ethical theory, generic Deontological and Teleological ethical principles that can serve as normative guidelines for addressing the challenges identified in the initial step. Third, we prescribe a set of ethical rights and duties that must be exercised and fulfilled by software developers for them to exhibit ethical behaviour. Each of these suggested actions are substantiated via an appeal to one, or several normative guidelines, identified in the second step. By identifying and recommending a set of defensible ethical obligations that must be fulfilled in the RE process, software developers can fulfil their ethical duties and thus reduce the number of unintended consequences that plague Requirements Elicitation. Ultimately RE must be underpinned with ethical consideration.
An Explainable Attention Zone Estimation for Level 3 Autonomous Driving

Accurately assessing the driver’s situational awareness is crucial in level 3 ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> ) autonomous driving, where the driver is in the loop. Estimating the attention zone provides essential information about the drivers’ on/off-road visual attention and determines their readiness to take over the control from the autonomous agent in complicated situations. This paper proposes a double-phase pipeline to improve the explainability and accuracy of the attention zone estimation using an intermediate gaze regression layer, where the true relationships between the input images and output zone labels are interpretable. The proposed GazeMobileNet, a lightweight deep neural network, in the first phase, achieved state-of-the-art performance in estimating the gaze vector in the MPIIGaze dataset, with MAE of 2.37 degrees. The model was used to extract the corresponding gaze vectors from the LISA V2, which is a driving dataset with the in-cabin attention zone labels. As LISA V2 does not contain gaze vector labels, an unsupervised clustering approach was proposed in the second phase to categorize the driver’s gaze vectors and map them to the corresponding attention zones. The proposed method demonstrated improved accuracy and robustness in the zone classification task. This model achieved the accuracies of 75.67% and 83.08% for attention zone estimation under "daytime without eyeglasses" and "nighttime without eyeglasses" capture conditions, respectively. Furthermore, the proposed model surpassed the recent research on that dataset by 73.11% and 74.02% accuracies under the "daytime with eyeglasses" and "nighttime with eyeglasses" capture conditions, respectively.
A Clothing Classification Network with Manifold Structure Based on Second-Order Convolution

Currently, AI-based clothing image classification techniques mostly use traditional deep learning methods, which are based on monocular clothing images for classification. However, the diversity of perspectives of realistic clothing images bring great difficulties and challenges to clothing classification. Moreover, deep convolutional networks have limitations of their own. They treat data as vectors in Euclidean space and fail to make full use of the potential low dimensional non-linear geometric structure information within high-dimensional clothing image data. Therefore, this paper explores and exploits the geometric structure information inherent of clothing image data from the perspective of a non-Euclidean manifold learning method, and designs and implements a clothing classification network with manifold structure based on second-order convolution to classify images using the second-order statistics of clothing features for image classification. Firstly, the input clothing image features extracted by the convolution neural network are pooled with the covariance pooling module to obtain the second-order statistical covariance, which is converted into SPD manifold to characterize the feature information of the clothing image set, and then a complete manifold structure neural network is constructed to enhance the feature representation ability of the model on the geometric intrinsic structure of the clothing image set. The experimental results of this method on the multi view clothing image dataset MVC show that it has good effectiveness, robustness, and accuracy.
Experiment Platform for Dialogue Intent Recognition Based on Deep Learning

This chapter propose the first open intent recognition plat form TEXTOIR, which integrates two complete modules: open intent detection and open intent discovery. It provides toolkits for each module with common interfaces and integrates multiple advanced models and benchmark datasets for the convenience of further research. Additionally, it realizes a pipeline framework to combine the advantages of two modules. The overall framework achieves both identifying known intents and discovering open intents. A series of visualized surfaces help users to manage, train, evaluate, and analyze the performance of different methods.
RCS Statistical Modeling of Stealth Targets Based on Fractional-Order Legendre Polynomials

In contrast to the conventional targets, the electromagnetic simulation results of stealth targets usually exhibit the low radar cross section (RCS) scattering properties. Furthermore, electromagnetic simulation results of a typical stealth aircraft model show that the RCS statistical characteristics of stealth targets are skewed toward the low RCS side and typically exhibit the sharp fluctuation and oscillatory decaying characteristics exemplified by a typical stealth aircraft model. In this article, a fractional-order Legendre polynomial series-based (FLP) RCS distribution model is proposed to more precisely fit the RCS statistical properties of stealth targets. Compared with the conventional Legendre polynomial-series-based (LP) RCS distribution model, the biased side of the FLP RCS distribution model can be adjusted to adapt to the RCS statistical characteristics of stealth targets by varying the fractional order <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\alpha$</tex-math></inline-formula> . Based on this characteristic, the FLP RCS distribution model is adaptable to a variety of scenarios. Especially, the shifted LP (SLP) RCS distribution model with lower complexity is derived from the FLP RCS distribution model by setting <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\alpha =1$</tex-math></inline-formula> . In addition, the fundamental statistics of the FLP and SLP RCS distribution models, including the cumulative distribution function, moment generating function, and higher order moments, are derived. Simulation results indicate that the FLP RCS distribution model can obtain better fitting results than the conventional RCS distribution models. SLP RCS distribution model fitting results for low RCS scattering areas of stealth targets are comparable to FLP RCS distribution model results.
Towards a Basic Theory for Partial Differentiation in the Prototype Verification System

This paper presents preliminary work on theories supporting partial differentiation of scalar fields, which will be based upon, and add to, the large library of mathematical theories supported by the Prototype Verification System theorem-proving environment. These theories include mathematical analysis of functions of one real-valued variable, but not, currently, theories on partial differentiation. In this paper, the issue of defining partial derivatives in the strongly typed, higher-order language of PVS is discussed, and a straightforward, pragmatic approach is proposed, introducing the formalizations of some basic concepts.