Reviewerly/exHarmony huggingface dataset:
Digital Paradigm in Educational Management: The Case of Construction Education Based on Emerging Technologies

The effective use of emerging technologies demands the training of highly quali-fied personnel with digital literacy professional skills. The aim of the study is to characterize the virtual construction site as a digital resource for pedagogical man-agement of the educational process in the construction university. The principles of tradition and innovation have become the methodological basis for linking best practices in pedagogical management of the learning process with qualitative changes based on modern digital technologies. A virtual construction site entails a digital resource imitating professional activities and providing integration of sci-ence, technology, pedagogical management of educational process on the basis of common standards of data description. The content of the virtual construction site represents a complex multi-level menu corresponding to the construction stages. Efficiency criteria (metaproject, activity, digital, relevant, complex) allow to find out the level of formation of ability to work with digital technologies. The princi-ples of selection and structuring of content ensure its validity and updating. The practical significance of the research is provided by the didactic functions of the virtual construction site, providing for the involvement of students in design and construction activities, establishment of interdisciplinary relations. The social sig-nificance consists in personalisation of the educational process and formation of the students' professional individuality. The originality of the study lies in the jus-tification of the digital paradigm of higher education, which forms a pedagogical community that promotes and actively integrates digital content into the educa-tional process.
The Mediating Effect of Crisis Management on Leadership Styles and Hotel Performance in Jordan

Research scholars have argued that the emergence of the crisis in the Middle East have led to significant changes in business performance. This has been most notable in the hotels operating in the Jordanian tourist sector, thus, creating a need to investigate leadership styles, crisis management, and hotel performance in a holistic framework. However, this is quite lacking in the literature, and this needs to be addressed. Transformational leadership style and transactional leadership styles are the independent variables, crisis management is the mediating variable and hotel performance is the dependent variable. A quantitative study that used a self-administered questionnaire was carried out on 3-star to 5-star hotels operating in Jordan. The Partial Least Squares (PLS) method as part of Structural Equation Modelling (SEM) was applied to analyse the causal relationships between constructs using the software application Smart-PLS 3.0. The Partial Least Squares approach was chosen according to the research’s exploratory nature. In order to analyze data two-step approach was utilized; the first step was to test the reliability and validity of the constructs in the measurement models, the second step was to test research hypotheses in the structural models. The results show that transformational leadership has significant positive effects on hotel performance and crisis management. Transactional leadership had a significant positive effect on crisis management and crisis management had significant positive effects on hotel performance. Moreover, crisis management partially mediated the relationship between transformational leadership and hotel performance; while the relationship between transactional leadership and hotel performance was fully mediated by crisis management. These findings will help to strengthen the discussion in the extant literature.
Stabilizing entanglement in two-mode Gaussian states

We analyze the stabilizability of entangled two-mode Gaussian states in three benchmark dissipative models: local damping, dissipators engineered to preserve two-mode squeezed states, and cascaded oscillators. In the first two models, we determine principal upper bounds on the stabilizable entanglement, while in the last model arbitrary amounts of entanglement can be stabilized. All three models exhibit a tradeoff between state entanglement and purity in the entanglement maximizing limit. Our results are derived from the Hamiltonian-independent stabilizability conditions for Gaussian systems. Here, we sharpen these conditions with respect to their applicability.
Gaussian Conversion Protocols for Cubic Phase State Generation

Universal quantum computing with continuous variables requires non-Gaussian resources, in addition to a Gaussian set of operations. A known resource enabling universal quantum computation is the cubic phase state, a non-Gaussian state whose experimental implementation has so far remained elusive. In this paper, we introduce two Gaussian conversion protocols that allow for the conversion of a non-Gaussian state that has been achieved experimentally, namely the trisqueezed state [Sandbo Changet al., Phys. Rev. X10, 011011 (2020)],to a cubic phase state. The first protocol is deterministic and it involves active (in-line) squeezing, achieving large fidelities that saturate the bound for deterministic Gaussian protocols. The second protocol is probabilistic and it involves an auxiliary squeezed state, thus removing the necessity of in-line squeezing but still maintaining significant success probabilities and fidelities even larger than for the deterministic case. The success of these protocols provides strong evidence for using trisqueezed states as resources for universal quantum computation.
Edge-Aware Superpixel Generation for SAR Imagery With One Iteration Merging

Most of the existing superpixel generation methods are based on local iterative clustering. However, such methods have the following shortcomings: 1) these methods require several iterations and the number of iterations is difficult to determine and 2) the generated superpixel lacks explicit connectivity without a postprocessing step. Aiming to overcome the limitations, we propose an edge-aware superpixel generation with one iteration merging (ESOM) for synthetic aperture radar (SAR) imagery. In specific, we introduce a ratio-based edge detector with a Gaussian-shaped window to extract the edge information and an edge-aware dissimilarity is defined. Then, a new merging method termed as one iteration merging is proposed, which leverages the continuity of the adjacent pixels and ensures the connectivity of superpixel. Furthermore, instead of iterative clustering, the one iteration merging is achieved in only one iteration without determining the number of iterations and hence efficient in computation. Experiments on two real SAR images demonstrate that the proposed method yields substantially better performance than some state-of-the-art methods.
Chapter 2 Knowledge Graphs: The Layered Perspective

Knowledge Graphs (KGs) are one of the key trends among the next wave of technologies. Many definitions exist of what a Knowledge Graph is, and in this chapter, we are going to take the position that precisely in the multitude of definitions lies one of the strengths of the area. We will choose a particular perspective, which we will call the layered perspective, and three views on Knowledge Graphs.
COVID-19 DAN E-LEARNING: PERUBAHAN STRATEGI PEMBELAJARAN SAINS DAN LINGKUNGAN DI SMP

Corona Virus Diseases (Covid-19) has become an epidemic in Indonesia and even has become a global pandemic in 2020. Various impacts of Covid-19 is one of them changes learning strategy. One of them is the change from conventional learning to internet-based learning or electronic learning (e-learning). The purpose of this study was to describe the e-learning in natural science and environmental learning during the Covid-19 outbreak. The research method used descriptive method and data collection techniques using a survey through Google Form. The study was conducted during March 2020 in Bekasi city and Bandung city, when the Covid-19 outbreak began in Indonesia. The results of this study indicate that there are many limitations. Findings showed that Whatsapp was the highest e-learning media (65.54%). Related to the biggest obstacle is about internet quota in a cellular phone (27.03%). Then students think the video (35.14%) was the best type of file to be shared in e-learning. Then the majority of students (54.73%) thought that the most effective duration for e-learning was 2-3 hours. The results of this study indicate that e-learning was not yet perfect and needs to be improved. The conclusion of this study was e-learning needs to be improved and need innovation.
Classical and Fuzzy Two-Layered Modal Logics for Uncertainty: Translations and Proof-Theory

This paper is a contribution to the study of two distinct kinds of logics for modelling uncertainty.Both approaches use logics with a two-layered modal syntax, but while one employs classical logic on both levels and infinitely-many multimodal operators, the other involves a suitable system of fuzzy logic in the upper layer and only one monadic modality.We take two prominent examples of the former approach, the probability logics Pr lin and Pr pol (whose modal operators correspond to all possible linear/polynomial inequalities with integer coefficients), and three logics of the latter approach: Pr Ł , Pr Ł △ and Pr PŁ △ (given by the Łukasiewicz logic and its expansions by the Baaz-Monteiro projection connective △ and also by the product conjunction).We describe the relation between the two approaches by giving faithful translations of Pr lin and Pr pol into, respectively, Prand Pr PŁ △ , and vice versa.We also contribute to the proof theory of two-layered modal logics of uncertainty by introducing a hypersequent calculus HPr Ł for the logic Pr Ł .Using this formalism, we obtain a translation of Pr lin into the logic Pr Ł , seen as a logic on hypersequents of relations, and give an alternative proof of the axiomatization of Pr lin .
Beetle Colony Optimization Algorithm and its Application

Massive data sets and complex scheduling processes have high-dimensional and non-convex features bringing challenges on various applications.With deep insight into the bio-heuristic opinion, we propose a novel Beetle Colony Optimization (BCO) being able to adapt NP-hard issues to meet growing application demands.Two important mechanisms are introduced into the proposed BCO algorithm.The first one is Beetle Antennae Search (BAS), which is a mechanism of random search along the gradient direction but not use gradient information at all.The second one is swarm intelligence, which is a collective mechanism of decentralized and self-organized agents.Both of them have reached a performance balance to elevate the proposed algorithm to maintain a wide search horizon and high search efficiency.Finally, our algorithm is applied to traveling salesman problem, and quadratic assignment problem and possesses excellent performance, which also shows that the algorithm has good applicability from the side.The effectiveness of the algorithm is also substantiated by comparing the results with the original ant colony optimization (ACO) algorithm in 3D simulation model experimental path planning.
Invisible Backdoor Attacks on Deep Neural Networks via Steganography and Regularization

Deep neural networks (DNNs) have been proven vulnerable to backdoor attacks, where hidden features (patterns) trained to a normal model, which is only activated by some specific input (called triggers), trick the model into producing unexpected behavior. In this paper, we create covert and scattered triggers for backdoor attacks, invisible backdoors, where triggers can fool both DNN models and human inspection. We apply our invisible backdoors through two state-of-the-art methods of embedding triggers for backdoor attacks. The first approach on Badnets embeds the trigger into DNNs through steganography. The second approach of a trojan attack uses two types of additional regularization terms to generate the triggers with irregular shape and size. We use the Attack Success Rate and Functionality to measure the performance of our attacks. We introduce two novel definitions of invisibility for human perception; one is conceptualized by the Perceptual Adversarial Similarity Score (PASS) and the other is Learned Perceptual Image Patch Similarity (LPIPS). We show that the proposed invisible backdoors can be fairly effective across various DNN models as well as four datasets MNIST, CIFAR-10, CIFAR-100, and GTSRB, by measuring their attack success rates for the adversary, functionality for the normal users, and invisibility scores for the administrators. We finally argue that the proposed invisible backdoor attacks can effectively thwart the state-of-the-art trojan backdoor detection approaches, such as Neural Cleanse and TABOR.
Does domain name encryption increase users' privacy?

Knowing domain names associated with traffic allows eavesdroppers to profile users without accessing packet payloads. Encrypting domain names transiting the network is, therefore, a key step to increase network confidentiality. Latest efforts include encrypting the TLS Server Name Indication (eSNI extension) and encrypting DNS traffic, with DNS over HTTPS (DoH) representing a prominent proposal. In this paper, we show that an attacker able to observe users' traffic relying on plain-text DNS can uncover the domain names of users relying on eSNI or DoH. By relying on large-scale network traces, we show that simplistic features and off-the-shelf machine learning models are sufficient to achieve surprisingly high precision and recall when recovering encrypted domain names. The triviality of the attack calls for further actions to protect privacy, in particular considering transient scenarios in which only a fraction of users will adopt these new privacy-enhancing technologies.
Reversible data hiding scheme based on pixel-value differencing in dual images

In this article, a new reversible data hiding scheme using pixel-value differencing in dual images is proposed. The proposed pixel-value differencing method can embed more secret data as the difference value of adjacent pixels is increased. In the proposed scheme, the cover image is divided into non-overlapping blocks and the maximum difference value is calculated to hide secret bits. On the sender side, the length of embeddable secret data is calculated by using the maximum difference value and the log function, and the decimal secret data are embedded into the two stego-images after applying the ceil function and floor function. On the receiver side, the secret data extraction and the cover image restoration can be performed by using the correlation between two stego-images. After recovering the cover image from two stego-images, the secret data can be extracted using the maximum difference value and the log function. The experimental results show that the proposed scheme has a higher embedding capacity and the proposed scheme differs in embedding the secret data depending on the characteristics of the cover image with less distortion. Also, the proposed scheme maintains the degree of image distortion that cannot be perceived by the human visual system.
Waveguide band-gap <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>N</mml:mi></mml:math> -qubit array with a tunable transparency resonance

We study a single photon transmission through 1D N- qubit chain. The qubits are supposed to be identical with equal distance between neighbors. We express the transfer matrix of N- qubit chain in terms of Chebyshev polynomials, which allows us to obtain simple expressions for the transmission and reflection amplitudes for arbitrarily large N. If the distance between neighbor qubits is equal to half wavelength, the transmission spectrum exhibits a flat bandgap structure with very steep walls. We show that for odd N the tuning of the excitation frequency of a central qubit gives rise to the appearance within a bandgap of a narrow resonance with a full transmission. The position of the resonance and its width can be controlled by the frequency of a central qubit. We show that the formation of the bandgap and of the transmission resonance is conditioned by the overlapping the widths of individual qubits which results from the strong coupling between qubits and waveguide photons.
Beyond empowerment, experimentation and reasoning: The public discourse around the Quantified Self movement

Abstract This article presents the results of a discourse analysis of press coverage on the Quantified Self (QS) movement in the German and British (online) press between 2007 and 2018. The analysis is driven by two questions: What discursive patterns can be discerned within this coverage? And, what characterizes the translation of the experimental practices and imaginaries of this pioneer community into an overall societal reflection of deep mediatization? In essence, the article shows that the QS movement becomes a ‘general signifier’ for a dystopian view of deep mediatization. So, while the QS movement itself understands its practices and community as self-empowering, self-reasoning, and experimental, the constructions of the QS movement in public discourse suggest the opposite. Paradoxically, however, another basic imaginary of the pioneer community is adopted and confirmed, namely that of the (simple) mutability of society as a consequence of digital media technologies.
Data Augmentation for Training Dialog Models Robust to Speech Recognition Errors

Speech-based virtual assistants, such as Amazon Alexa, Google assistant, and Apple Siri, typically convert users’ audio signals to text data through automatic speech recognition (ASR) and feed the text to downstream dialog models for natural language understanding and response generation. The ASR output is error-prone; however, the downstream dialog models are often trained on error-free text data, making them sensitive to ASR errors during inference time. To bridge the gap and make dialog models more robust to ASR errors, we leverage an ASR error simulator to inject noise into the error-free text data, and subsequently train the dialog models with the augmented data. Compared to other approaches for handling ASR errors, such as using ASR lattice or end-to-end methods, our data augmentation approach does not require any modification to the ASR or downstream dialog models; our approach also does not introduce any additional latency during inference time. We perform extensive experiments on benchmark data and show that our approach improves the performance of downstream dialog models in the presence of ASR errors, and it is particularly effective in the low-resource situations where there are constraints on model size or the training data is scarce.
CNOT circuit extraction for topologically-constrained quantum memories

Many physical implementations of quantum computers impose stringent memory constraints in which 2-qubit operations can only be performed between qubits which are nearest neighbours in a lattice or graph structure. Hence, before a computation can be run on such a device, it must be mapped onto the physical architecture. That is, logical qubits must be assigned physical locations in the quantum memory, and the circuit must be replaced by an equivalent one containing only operations between nearest neighbours. In this paper, we give a new technique for quantum circuit mapping (a.k.a. routing), based on Gaussian elimination constrained to certain optimal spanning trees called Steiner trees. We give a reference implementation of the technique for CNOT circuits and show that it significantly out-performs general-purpose routines on CNOT circuits. We then comment on how the technique can be extended straightforwardly to the synthesis of CNOT+Rz circuits and as a modification to a recently-proposed circuit simplification/extraction procedure for generic circuits based on the ZX-calculus.
An Enriched Customer Journey Map: How to Construct and Visualize a Global Portrait of Both Lived and Perceived Users’ Experiences?

Design is about understanding the system and its users. Although User Experience (UX) research methodologies aim to explain the benefits of a holistic measurement approach including explicit (e.g., self-reported) and implicit (e.g., automatic and unconscious biophysiological reactions) data to better understand the global user experience, most of the personas and customer journey maps (CJM) seen in the literature and practice are mainly based on perceived and self-reported users’ responses. This paper aims to answer a call for research by proposing an experimental design based on the collection of both explicit and implicit data in the context of an authentic user experience. Using an inductive clustering approach, we develop a data driven CJM that helps understand, visualize, and communicate insights based on both data typologies. This novel tool enables the design development team the possibility of acquiring a broad portrait of both experienced (implicit) and perceived (explicit) users’ experiences.
Disentangled Speaker and Nuisance Attribute Embedding for Robust Speaker Verification

Over the recent years, various deep learning-based embedding methods have been proposed and have shown impressive performance in speaker verification. However, as in most of the classical embedding techniques, the deep learning-based methods are known to suffer from severe performance degradation when dealing with speech samples with different conditions (e.g., recording devices, emotional states). In this paper, we propose a novel fully supervised training method for extracting a speaker embedding vector disentangled from the variability caused by the nuisance attributes. The proposed framework was compared with the conventional deep learning-based embedding methods using the RSR2015 and VoxCeleb1 dataset. Experimental results show that the proposed approach can extract speaker embeddings robust to channel and emotional variability.
UC-Secure OT from LWE, Revisited

We build a two-round, UC-secure oblivious transfer protocol (OT) in the common reference string (CRS) model under the Learning with Errors assumption (LWE) with super-polynomial modulus-to-noise ratio. We do so by instantiating the dual-mode encryption framework of Peikert, Vaikuntanathan and Waters (CRYPTO’08). The resulting OT can be instantiated in either one of two modes: one providing statistical sender security, and the other statistical receiver security. Furthermore, our scheme allows the sender and the receiver to reuse the CRS across arbitrarily many executions of the protocol. To our knowledge, this is the first construction of an UC-secure OT from LWE that achieves either statistical receiver security or unbounded reusability of the CRS. For comparison, the construction of UC-secure OT from LWE of Peikert, Vaikuntanathan and Waters only provides computational receiver security and bounded reusability of the CRS. Our main technical contribution is a public-key encryption scheme from LWE where messy public keys (under which encryptions hide the underlying message statistically) can be tested in time essentially independent of the LWE modulus q.