Reviewerly/exHarmony huggingface dataset:
Individual Cryptocurrency Investors: Evidence From A Population Survey

Cryptocurrencies, such as Bitcoin, are a highly volatile asset class where very high returns are offset by large losses. This study examines the financial success of individual investments in cryptocurrencies and analyzes whether it relates to similar explanatory factors as for investments in other asset classes. For this purpose, a nationally representative survey data set of 3,864 German citizens is used, of which 354 (9.2%) reported owning cryptocurrencies in March 2019. We analyze the subpopulation of 225 cryptocurrency owners who classify as investors. 56% of them experienced positive returns, while 29% had negative results. The remaining respondents broke even. The average investment was €1,773 in a portfolio of two cryptocurrencies. At the time of the survey, the average portfolio value had risen to €7094 — an average gain of 300%. While nearly half of the investors (44%) outperformed Bitcoin market returns, not a single one of the early investors (2009–2012) did. We find that net income, the degree of cryptocurrency knowledge and the degree of ideological motivation for owning cryptocurrency have positive effects on returns. This first scientific analysis of individual investment in cryptocurrencies provides a basis for future research and for regulatory decision-making.
Enhancing LSB Using Binary Message Size Encoding for High Capacity, Transparent and Secure Audio Steganography–An Innovative Approach

We propose a novel LSB-BMSE method that enhances LSB audio steganography.It uses an innovative mechanism, Binaries of Message Size Encoding (BMSE), to embed a secret message after hiding its size in random samples.First, the secret message is compressed using Huffman coding, then encrypted by AES-128.The audio cover is split into number of blocks depending on secret message size.A secure key BMSE , output from the BMSE mechanism, is used to embed the secret message in random blocks and bytes adaptively according to its size.It is implemented using MATLAB and standard parameters: Perceptual Evaluation of Speech Quality and NIST Statistical Test Suite were used to measure imperceptibility between cover & stego audios and randomness of BMSE mechanism respectively.The fidelity was tested using Mean Square Error, Peak Signal to Noise Ratio and Signal-to-Noise Ratio.Comprehensive experiments on widely used metrics demonstrate that LSB-BMSE significantly surpasses existing methods in terms of hiding capacity and imperceptibility.Moreover, LSB-BMSE shows resistance to brute force attacks and statistical analysis.Although it was robust towards re-sampling attacks, nevertheless was not vigorous towards noise nor LSB attacks.The key BMSE complies with Kerckhoff's principle and exhibits avalanche criteria.The tested proposed LSB-BMSE proclaimed its effectiveness.
Perspectives of quantum computing for chemical engineering

Abstract Quantum computing has been attracting public attention recently. This interest is driven by the advancements in hardware, software, and algorithms required for its successful usage and the promise that it entails the potential acceleration of computational tasks compared to classical computing. This perspective article presents a short review on quantum computing, how this computational approach solves problems, and three fields that quantum computing can potentially impact the most while relevant to chemical engineering: computational chemistry, optimization, and machine learning. Here, we present a series of chemical engineering applications, the developments, potential improvements with respect to classical computing, and challenges that quantum computing faces for each of these fields. This article intends to provide a clear picture of the challenges and potential advantages that quantum technology may yield for chemical engineering, together with an invitation for our colleagues to join us in the adoption and development of quantum computing.
GCN-based document representation for keyphrase generation enhanced by maximizing mutual information

Keyphrase generation is an important fundamental task of natural language processing, which can help users quickly obtain valuable information from a large number of documents especially when they are facing with informal social media text. Existing Recurrent Neural Network (RNN) based keyphrase generation approaches cannot properly model the dependency structure of the informal text, which is often implicit between those distant words and plays an important role in extracting salient information. To obtain core features of text, we apply Graph Convolutional Network (GCN) on document-level graph to capture dependency structure information. The GCN-based node representations are further fed into a predictor network to provide potential candidates for copying mechanism. Moreover, we utilize a novel variational selector network to determine the final selection probability of each word in a phrase, which relies on its probabilities of copying from a given document and being generated from a vocabulary. Eventually, we introduce an enhancement mechanism to maximize the mutual information between document and generated keyphrase, thus ensuring the consistency between them. Experiment results show that our model outperforms previous state-of-the-art baselines on three social datasets, including Weibo, Twitter and StackExchange.
Radio-Frequency Fingerprint Extraction Based on Feature Inhomogeneity

With the popularization of the Internet of Things (IoT), its security has become increasingly prominent. Radio-frequency fingerprinting (RFF) is a promising approach to identify a specific emitter by extracting the intrinsic physical layer characteristics from transmitted signals, adopted as a lightweight noncryptographic access authentication technique. The realization of RFF relies on unintentional modulation of the pulse (UMOP). In this article, we discover the locality and inhomogeneity of RFF, that is, UMOP is concentrating in the radio-frequency fingerprint distribution subregion (RFDR), instead of evenly distributed over the entire feature. This property is demonstrated in the cepstral domain based on the transmitter distortion models. This is the first time that the inhomogeneity of UMOP and the cepstral domain analysis are employed in RFF. First, to automatically search the RFDRs, a method based on the dispersion characteristic index (DCI) and emitter-specific information index (ESI) is proposed, where DCI provides constraints on the scattering characteristics of RFF feature clusters and ESI evaluates the local differences of RFF features. Second, combining the RFDR selection method with the cepstrum analysis, a new feature CepH is obtained. Systematic experiments are conducted on the simulated data sets containing multiple modulation patterns and received data sets from multiple sources, which show that the RFDR is not affected by the modulation pattern and noise, and has high practicability. It also demonstrates that the proposed algorithm outperforms the state-of-art algorithms, especially in the case of a low signal-to-noise ratio (SNR). It can achieve a recognition accuracy of more than 90% under 0 dB.
Protecting quantum coherence and entanglement in a correlated environment

Long-term preservation of quantum coherence and entanglement is indispensable for practical quantum computation. We study the evolution of quantum coherence and genuine multipartite concurrence of extended Werner like states transmitted through correlated amplitude damping (AD), phase damping (PD) and depolarizing (DP) channels. The results show that the decay rate of coherence is curtailed in the presence of correlation between successive actions of the channel. It is shown that the fragility of genuine multipartite entanglement due to decoherence can be protected to some significant amount by using the correlated channels. In fact, for even qubit Werner like states, coherence and entanglement exhibit freezing phenomenon, in which it remains intact against decohering environment in perfectly correlated phase damping and depolarizing channels. For multipartite GHZ-class states in a perfectly correlated channel, it is shown that entanglement sudden death (ESD) is circumvented in amplitude damping channel, and there is an entanglement sudden birth (ESB) for odd qubit systems in depolarizing channel. Further, we have established analytical relation between coherence and entanglement for completely uncorrelated and fully correlated quantum channels.
Robust variational Bayesian method-based SINS/GPS integrated system

SINS/GPS integrated systems are influenced by non-Gaussian noise and unknown measurement noise due to exogenous disturbances and inaccurate noise statistics. To overcome this problem, a robust variational Bayesian method-based SINS/GPS integrated system is designed. First, the variational Bayesian-based Kalman filter is selected to estimate unknown measurement noise covariance. Second, the maximum correntropy criterion is introduced to the nonlinear robust filter to handle interference from non-Gaussian noise. Finally, the robust variational Bayesian method is designed based on the interacting multiple model, which not only fuses the variational Bayesian-based Kalman filter and the robust filter but also avoids non-Gaussian noise interference to the estimation result of measurement noise covariance. The robustness and adaptivity of the robust variational Bayesian method are verified by numerical simulation. Furthermore, the flight test results show improved performance of the SINS/GPS integrated system using the proposed method.
Systematic review on mobile collaborative learning for engineering education

Our daily lives have been transformed by mobile smart devices. Due to the sudden impact of the coronavirus (Covid-19) on education, the importance of mobile devices for communicating with teachers and students has risen to a new level of prominence. The Web of Science and Scopus databases were used to conduct a systematic review of the research on mobile collaborative learning in engineering education. The purpose of this review is to ascertain the degree to which research on mobile collaborative learning has been conducted in the field of engineering education between 2010 and 2020. A total of 48 articles were reviewed to ascertain the research methodologies and area of study, as well as to provide an updated review of studies on mobile collaborative applications, particularly in the field of engineering education. Among the most significant findings is that the majority of publications make use of augmented reality and mobile application development. According to the review, the majority of studies were conducted in the fields of computer sciences, electronic engineering, and artificial intelligence.
Reversible data hiding for JPEG images with minimum additive distortion

Reversible data hiding (RDH) in JPEG images aims at correctly extracting hidden data and correctly recovering the cover image. Existing methods for RDH, however, may not attain a good balance between good visual quality and small file size increment of the embedded image. To address the problem, we design a new RDH scheme by minimizing the additive distortion in JPEG images. Our method firstly designs different additive functions to calculate DCT block cost and DCT frequency cost, and then combines these two cost functions to minimize the additive distortion for a given embedding capacity. After obtaining the optimal embedding positions for each DCT block, we employ an improved two-dimensional histogram shifting strategy for data embedding, which significantly reduces the number of invalid DCT coefficient modifications. Extensive experiments demonstrate that our method outperforms existing JPEG RDH methods with better visual quality and smaller file size increment of the marked image.
S<sup>3</sup>VAADA: Submodular Subset Selection for Virtual Adversarial Active Domain Adaptation

Unsupervised domain adaptation (DA) methods have focused on achieving maximal performance through aligning features from source and target domains without using labeled data in the target domain. Whereas, in the real-world scenario’s it might be feasible to get labels for a small proportion of target data. In these scenarios, it is important to select maximally-informative samples to label and find an effective way to combine them with the existing knowledge from source data. Towards achieving this, we propose S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> VAADA which i) introduces a novel submodular criterion to select a maximally informative subset to label and ii) enhances a cluster-based DA procedure through novel improvements to effectively utilize all the available data for improving generalization on target. Our approach consistently outperforms the competing state-of-the-art approaches on datasets with varying degrees of domain shifts. The project page with additional details is available here: https://sites.google.com/iisc.ac.in/s3vaada-iccv2021/.
How Low Should We Alert? Quantifying Intensity Threshold Alerting Strategies for Earthquake Early Warning in the United States

Abstract We use a suite of historical earthquakes to quantitatively determine earthquake early warning (EEW) alert threshold strategies for a range of shaking intensity targets for EEW along the United States West Coast. The current method for calculating alert regions for the ShakeAlert EEW System does not take into account variabilities and uncertainties in shaking distribution. As a result, if the modified Mercalli intensity (MMI) level used to determine the extent of the alert region (the alert threshold) is the same as the target intensity threshold, the alert region will be too small to include all locations that require alerts even if earthquake source parameters are estimated accurately. Missed alerts can be reduced by using a lower alert threshold than the target threshold. This expands the alert region, increasing the number of precautionary alerts issued to people who experience shaking below the target level. We determine alert thresholds that optimize this tradeoff between missed and precautionary alerts for target thresholds of MMI 4.0–6.0 using 143 M 5.0–7.3 earthquake ShakeMaps as ground truth. We examine the quality of each alerting strategy relative to the target MMI, where we define alert quality metrics in terms of both the area and population alerted. Optimal alert thresholds maximize correct alerts while limiting most precautionary alerts to regions that are likely to still feel some shaking. We find these optimal alert thresholds also maximize warning times. This analysis presents a quantitative framework ShakeAlert can use to communicate alerting strategies and performance expectations to ShakeAlert users.
The Lovelace effect: Perceptions of creativity in machines

This article proposes the notion of the ‘Lovelace Effect’ as an analytical tool to identify situations in which the behaviour of computing systems is perceived by users as original and creative. It contrasts the Lovelace Effect with the more commonly known ‘Lovelace objection’, which claims that computers cannot originate or create anything, but only do what their programmers instruct them to do. By analysing the case study of AICAN – an AI art-generating system – we argue for the need for approaches in computational creativity to shift focus from what computers are able to do in ontological terms to the perceptions of human users who enter into interactions with them. The case study illuminates how the Lovelace effect can be facilitated through technical but also through representational means, such as the situations and cultural contexts in which users are invited to interact with the AI.
Deep Illumination-Enhanced Face Super-Resolution Network for Low-Light Images

Face images are typically a key component in the fields of security and criminal investigation. However, due to lighting and shooting angles, faces taken under low-light conditions are often difficult to recognize. Face super-resolution (FSR) technology can restore high-resolution faces based on low-resolution inputs. However, existing face super-resolution methods typically rely on prior knowledge of inaccurate faces estimated from low-resolution images. Faces restored by low-light inputs may suffer from problems such as low brightness and many missing details. In this article, we proposed an Illumination-Enhanced Face Super-Resolution (IEFSR) model that can progressively super-resolve low-light faces of 32 × 32 pixels by an upscaling factor of 8. While reconstructing the low-light low-resolution face into a clear and high-quality face, we introduce a coarse low-resolution (LR) restoration network to recover the LR face details hidden in the dark. In the generator, we use a series of style blocks with noise to make the generated faces appear to have a more realistic visual aesthetic. Additionally, we introduce spectrum normalization in the discriminator to improve training stability. Extensive experimental evaluations show that the proposed IEFSR yields visually and metrically more attractive results than existing state-of-the-art FSR methods.
A Multi-Agent Formalism Based on Contextual Defeasible Logic for Healthcare Systems

In the last decade, smart computing has garnered much attention, particularly in ubiquitous environments, thus increasing the ease of everyday human life. Users can dynamically interact with the systems using different modalities in a smart computing environment. The literature discussed multiple mechanisms to enhance the modalities for communication using different knowledge sources. Among others, Multi-context System (MCS) has been proven quite significant to interlink various context domains dynamically to a distributed environment. MCS is a collection of different contexts (independent knowledge sources), and every context contains its own set of defined rules and facts and inference systems. These contexts are interlinked via bridge rules. However, the interaction among knowledge sources could have the consequences such as bringing out inconsistent results. These issues may report situations such as the system being unable to reach a conclusion or communication in different contexts becoming asynchronous. There is a need for a suitable framework to resolve inconsistencies. In this article, we provide a framework based on contextual defeasible reasoning and a formalism of multi-agent environment is to handle the issue of inconsistent information in MCS. Additionally, in this work, a prototypal simulation is designed using a simulation tool called NetLogo, and a formalism about a Parkinson's disease patient's case study is also developed. Both of these show the validity of the framework.
Reuse and maintenance practices among divergent forks in three software ecosystems

Abstract With the rise of social coding platforms that rely on distributed version control systems, software reuse is also on the rise. Many software developers leverage this reuse by creating variants through forking, to account for different customer needs, markets, or environments. Forked variants then form a so-called software family; they share a common code base and are maintained in parallel by same or different developers. As such, software families can easily arise within software ecosystems, which are large collections of interdependent software components maintained by communities of collaborating contributors. However, little is known about the existence and characteristics of such families within ecosystems, especially about their maintenance practices. Improving our empirical understanding of such families will help build better tools for maintaining and evolving such families. We empirically explore maintenance practices in such fork-based software families within ecosystems of open-source software. Our focus is on three of the largest software ecosystems existence today: , , and . We identify and analyze software families that are maintained together and that exist both on the official distribution platform (Google play, , and ) as well as on GitHub , allowing us to analyze reuse practices in depth. We mine and identify 38 software families , 526 software families , and 8,837 software families from the ecosystems of , , and , to study their characteristics and code-propagation practices. We provide scripts for analyzing code integration within our families. Interestingly, our results show that there is little code integration across the studied software families from the three ecosystems. Our studied families also show that techniques of direct integration using git outside of GitHub is more commonly used than GitHub pull requests. Overall, we hope to raise awareness about the existence of software families within larger ecosystems of software, calling for further research and better tools support to effectively maintain and evolve them.
Estimation of Photovoltaic Soiling Using Environmental Parameters: A Comparative Analysis of Existing Models

Abstract Soiling is the process where dust, dirt, and organic/inorganic contaminants accumulate on the surface of a photovoltaic (PV) module. It impacts the energy generation of PV systems and causes significant economic losses. It can be monitored through soiling estimation models, which evaluate its impact depending on a number of environmental parameters. The main focus of this study is to compare four existing estimation models in Jaén, Spain, and to analyze their performance in an environment different than that where each of them is developed. The results indicate that all models can reproduce accurately the seasonality of soiling in Jaén, if a cleaning threshold of 0.3 mm day −1 is considered. The models’ performance is found to be influenced not only by the mass concentration but also by the optical properties of each component of the atmospheric particulate matter, with an overestimation up to 4% in high organic carbon concentration periods. The natural cleaning role of rainfalls and its influence on the modeling error are also discussed through the paper.
Low Ambiguity Zone: Theoretical Bounds and Doppler-Resilient Sequence Design in Integrated Sensing and Communication Systems

In radar sensing and communications, designing Doppler resilient sequences (DRSs) with low ambiguity function for delay over the entire signal duration and Doppler shift over the entire signal bandwidth is an extremely difficult task. However, in practice, the Doppler frequency range is normally much smaller than the bandwidth of the transmitted signal, and it is relatively easy to attain quasi-synchronization for delays far less than the entire signal duration. Motivated by this observation, we propose a new concept called low ambiguity zone (LAZ) which is a small area of the corresponding ambiguity function of interest defined by the certain Doppler frequency and delay. Such an LAZ will reduce to a zero ambiguity zone (ZAZ) if the maximum ambiguity values of interest are zero. In this paper, we derive a set of theoretical bounds on periodic LAZ/ZAZ of unimodular DRSs with and without spectral constraints, which include the existing bounds on periodic global ambiguity function as special cases. These bounds may be used as theoretical design guidelines to measure the optimality of sequences against Doppler effect. We then introduce four optimal constructions of DRSs with respect to the derived ambiguity lower bounds based on some algebraic tools such as characters over finite field and cyclic difference sets.