Reviewerly/exHarmony huggingface dataset:
Secure multiparty quantum key agreement against collusive attacks

Quantum key agreement enables remote participants to fairly establish a secure shared key based on their private inputs. In the circular-type multiparty quantum key agreement mode, two or more malicious participants can collude together to steal private inputs of honest participants or to generate the final key alone. In this work, we focus on a powerful collusive attack strategy in which two or more malicious participants in particular positions, can learn sensitive information or generate the final key alone without revealing their malicious behaviour. Many of the current circular-type multiparty quantum key agreement protocols are not secure against this collusive attack strategy. As an example, we analyze the security of a recently proposed multiparty key agreement protocol to show the vulnerability of existing circular-type multiparty quantum key agreement protocols against this collusive attack. Moreover, we design a general secure multiparty key agreement model that would remove this vulnerability from such circular-type key agreement protocols and describe the necessary steps to implement this model. The proposed model is general and does not depend on the specific physical implementation of the quantum key agreement.
A new vocabulary revision technique using WhatsApp: Peer-chain

The purpose of this mixed-method study was to examine the effect of a new vocabulary revision technique "peer-chain" developed by the researchers on vocabulary learning and retention among English as a foreign language (EFL) university students. A total of 46 participants (21 in the control group and 25 in the experimental group) completed the study, and the effectiveness of the treatment was checked via conducting a pre-test post-test/retention test control group design for the quantitative part. The study was eight weeks in duration, and the students were assessed with a 42-question, multiple-choice vocabulary achievement test (VAT). An analysis of ANOVA for the quantitative data showed a significant difference in students' vocabulary achievement between the students who used peer-chain and the students who used traditional word cards. Also, the study explored learner perceptions through interviews with the experimental group students. The qualitative content analysis of the interview data showed that most learners had positive perceptions of the technique. Both quantitative and qualitative outcomes suggest that integrating WhatsApp with the peer-chain technique in teaching vocabulary is a promising technique and can be more successful than the traditional paper-based word cards for vocabulary learning and retention.
Statistical Models for the Analysis of Optimization Algorithms With Benchmark Functions

Frequentist statistical methods, such as hypothesis testing, are standard practices in studies that provide benchmark comparisons. Unfortunately, these methods have often been misused, e.g., without testing for their statistical test assumptions or without controlling for familywise errors in multiple group comparisons, among several other problems. Bayesian data analysis (BDA) addresses many of the previously mentioned shortcomings but its use is not widely spread in the analysis of empirical data in the evolutionary computing community. This article provides three main contributions. First, we motivate the need for utilizing BDA and provide an overview of this topic. Second, we discuss the practical aspects of BDA to ensure that our models are valid and the results are transparent. Finally, we provide five statistical models that can be used to answer multiple research questions. The online Appendix provides a step-by-step guide on how to perform the analysis of the models discussed in this article, including the code for the statistical models, the data transformations, and the discussed tables and figures.
Quantum metrology with one auxiliary particle in a correlated bath and its quantum simulation

In realistic metrology, entangled probes are more sensitive to noise, especially for a correlated environment. The precision of parameter estimation with entangled probes is even lower than that of the unentangled ones in a correlated environment. In this paper we propose a measurement scheme with only one auxiliary qubit, which can selectively offset the impact of environmental noise under this situation. We analyze the estimation precision of our scheme and find out that it approaches the Heisenberg limit when prepared in a proper auxiliary state. We further discuss employing auxiliary states to improve the precision of measurement in other environment models such as a partially correlated environment. In order to verify our scheme, we apply a recently developed quantum algorithm to simulate the quantum dynamics of our proposal and show that it outperforms the other proposals with less resources.
A Guidance and Maneuvering Control System Design With Anti-Collision Using Stream Functions With Vortex Flows for Autonomous Marine Vessels

Autonomous marine vessels are expected to avoid inter-vessel collisions and comply with the international regulations for safe voyages. This paper presents a stepwise path planning method using stream functions. The dynamic flow of fluids is used as a guidance model, where the collision avoidance in static environments is achieved by applying the circular theorem in the sink flow. We extend this method to dynamic environments by adding vortex flows in the flow field. The stream function is recursively updated to enable on the fly waypoint decisions. The vessel avoids collisions and also complies with several rules of the Convention on the International Regulations for Preventing Collisions at Sea. The method is conceptually and computationally simple and convenient to tune, and yet versatile to handle complex and dense marine traffic with multiple dynamic obstacles. The ship dynamics are taken into account, by using B\'{e}zier curves to generate a sufficiently smooth path with feasible curvature. Numerical simulations are conducted to verify the proposed method.
Is Online and Distance Learning the Future in Global Higher Education?

This timely volume explores how colleges and universities around the world have managed the shifts to online and distance education as a result of the worldwide coronavirus disease 2019 (COVID-19) pandemic. The need to understand online teaching and learning is more urgent as institutions have responded to COVID-19 restrictions and how technology has been deployed in higher education. This book investigates how the use of information and communication technologies impacted students, faculty, and staff experiences during the COVID-19 lockdown. Collectively, all contributors offer unique innovative insights and perspectives on the opportunities and challenges of remote work, while reflecting on new strategies and initiatives that may enhance the quality of online teaching and learning in higher education. As more colleges and universities around the world continue to be affected by the COVID-19 outbreak, we hope that this book will help teacher-scholars and practitioners redesign curricula, cocurricular experiences, and physical campus spaces in order to prepare them for the rising complexity of digitalization in higher education. Ultimately, we hope that this book will serve as a tool for further discussion and reflection that can advance pedagogical transformation in all spaces (face-to-face, online, synchronous, asynchronous) and support student learning (study, work, teach, language, volunteer abroad, international student services, and career services) in the post-COVID-19 era.
Higher criticism to compare two large frequency tables, with sensitivity to possible rare and weak differences

We adapt Higher Criticism (HC) to the comparison of two frequency tables which may -- or may not -- exhibit moderate differences between the tables in some unknown, relatively small subset out of a large number of categories. Our analysis of the power of the proposed HC test quantifies the rarity and size of assumed differences and applies moderate deviations-analysis to determine the asymptotic powerfulness/powerlessness of our proposed HC procedure. Our analysis considers the null hypothesis of no difference in underlying generative model against a rare/weak perturbation alternative, in which the frequencies of $N^{1-\beta}$ out of the $N$ categories are perturbed by $r(\log N)/2n$ in the Hellinger distance; here $n$ is the size of each sample. Our proposed Higher Criticism (HC) test for this setting uses P-values obtained from $N$ exact binomial tests. We characterize the asymptotic performance of the HC-based test in terms of the rarity parameter $\beta$ and the perturbation intensity parameter $r$. Specifically, we derive a region in the $(\beta,r)$-plane where the test asymptotically has maximal power, while having asymptotically no power outside this region. Our analysis distinguishes between cases in which the counts in both tables are low, versus cases in which counts are high, corresponding to the cases of sparse and dense frequency tables. The phase transition curve of HC in the high-counts regime matches formally the curve delivered by HC in a two-sample normal means model.
Fast and Robust Online Inference with Stochastic Gradient Descent via Random Scaling

We develop a new method of online inference for a vector of parameters estimated by the Polyak-Ruppert averaging procedure of stochastic gradient descent (SGD) algorithms. We leverage insights from time series regression in econometrics and construct asymptotically pivotal statistics via random scaling. Our approach is fully operational with online data and is rigorously underpinned by a functional central limit theorem. Our proposed inference method has a couple of key advantages over the existing methods. First, the test statistic is computed in an online fashion with only SGD iterates and the critical values can be obtained without any resampling methods, thereby allowing for efficient implementation suitable for massive online data. Second, there is no need to estimate the asymptotic variance and our inference method is shown to be robust to changes in the tuning parameters for SGD algorithms in simulation experiments with synthetic data.
Analysis of Excitability in Resonant Tunneling Diode-Photodetectors

We investigate the dynamic behaviour of resonant tunneling diode-photodetectors (RTD-PDs) in which the excitability can be activated by either electrical noise or optical signals. In both cases, we find the characteristics of the stochastic spiking behavior are not only dependent on the biasing positions but also controlled by the intensity of the input perturbations. Additionally, we explore the ability of RTD-PDs to perform optical signal transmission and neuromorphic spike generation simultaneously. These versatile functions indicate the possibility of making use of RTD-PDs for innovative applications, such as optoelectronic neuromorphic circuits for spike-encoded signaling and data processing.
A transactive energy model for smart metering systems using blockchain

The lowering cost of electricity generation technologies by renewable means has caused the role of end-users to change from being a simple consumer to a producer of their energy. This new role has been called a prosumer and has contributed to modifying electricity markets, by allowing prosumers to merchandize the energy surplus to electricity companies and other prosumers. The fundamental key to any economic transaction is trust between the parties. Recently, new technologies, such as blockchain, are transforming transaction security, and allowing decentralization of the smart power grid. This study presents the development of a transactional model of electrical energy using the infrastructure of smart metering and blockchain systems to allow the commercialization of electrical energy by prosumers. The results show that for the Mexican electricity market, they can be good alternatives by reducing subsidies and being more profitable than current rates.
SEA-BREW: A scalable Attribute-Based Encryption revocable scheme for low-bitrate IoT wireless networks

Attribute-Based Encryption (ABE) is an emerging cryptographic technique that allows one to embed a fine-grained access control mechanism into encrypted data. In this paper we propose a novel ABE scheme called SEA-BREW (Scalable and Efficient Abe with Broadcast REvocation for Wireless networks), which is suited for Internet of Things (IoT) and Industrial IoT (IIoT) applications. In contrast to state-of-the-art ABE schemes, ours is capable of securely performing key revocations with a single short broadcast message, instead of a number of unicast messages that is linear with the number of nodes. This is desirable for low-bitrate Wireless Sensor and Actuator Networks (WSANs) which often are the heart of (I)IoT systems. In SEA-BREW, sensors, actuators, and users can exchange encrypted data via a cloud server, or directly via wireless if they belong to the same WSAN. We formally prove that our scheme is secure also in case of an untrusted cloud server that colludes with a set of users, under the generic bilinear group model. We show by simulations that our scheme requires a constant computational overhead on the cloud server with respect to the complexity of the access control policies. This is in contrast to state-of-the-art solutions, which require instead a linear computational overhead.
Dual‐view 3D human pose estimation without camera parameters for action recognition

The purpose of 3D human pose estimation is to estimate the 3D coordinates of key points of the human body directly from images. Although multi-view based methods have better performance and higher precision of coordinate estimation than a single-view based, they need to know the camera parameters. In order to effectively avoid the restriction of this constraint and improve the generalizability of the model, a dual-view single-person 3D pose estimation method without camera parameters is proposed. This method first uses the 2D pose estimation network HR-net to estimate the 2D joint point coordinates from two images with different views, and then inputs them into the 3D regression network to generate the final 3D joint point coordinates. In order to make the 3D regression network fully learn the spatial structure relationship of the human body and the transformation projection relationship between different views, a self-supervised training method is designed based on a 3D human pose orthogonal projection model to generate the virtual views. In the pose estimation experiments on the Human3.6 dataset, this method achieves a significantly improved estimation error of 34.5 mm. Furthermore, an action recognition based on the human poses extracted by the proposed method is conducted, and an accuracy of 83.19% is obtained.
Web-archiving and social media: an exploratory analysis

The archived web provides an important footprint of the past, documenting online social behaviour through social media, and news through media outlets websites and government sites. Consequently, web archiving is increasingly gaining attention of heritage institutions, academics and policy makers. The importance of web archives as data resources for (digital) scholars has been acknowledged for investigating the past. Still, heritage institutions and academics struggle to ‘keep up to pace’ with the fast evolving changes of the World Wide Web and with the changing habits and practices of internet users. While a number of national institutions have set up a national framework to archive ‘regular’ web pages, social media archiving (SMA) is still in its infancy with various countries starting up pilot archiving projects. SMA is not without challenges; the sheer volume of social media content, the lack of technical standards for capturing or storing social media data and social media’s ephemeral character can be impeding factors. The goal of this article is three-fold. First, we aim to extend the most recent descriptive state-of-the-art of national web archiving, published in the first issue of International Journal of Digital Humanities (March 2019) with information on SMA. Secondly, we outline the current legal, technical and operational (such as the selection and preservation policy) aspects of archiving social media content. This is complemented with results from an online survey to which 15 institutions responded. Finally, we discuss and reflect on important challenges in SMA that should be considered in future archiving projects.
Cellular-Assisted COVID-19 Contact Tracing

The coronavirus disease (COVID-19) pandemic has caused social and economic upheaval around the world. Contact tracing is a proven effective way that health authorities may contain the spread of COVID-19, but is challenging for airborne disease. In this paper, we propose LTESafe, a cellular-assisted privacy-preserving COVID-19 contact tracing system. LTESafe leverages a deep neural network based feature extractor to map the cellular CSI to a high-dimensional feature space, within which the Euclidean distance between points indicates the proximity of devices. By doing so, we preserve user privacy by hiding the physical locations of smartphones and at the same time achieve high accuracy. Our preliminary experimental results demonstrate that LTESafe achieves an overall accuracy of 92.79% in determining whether two devices are within six feet proximity or not, and only misses 1.35% of close contacts.
Teleportation with a cubic phase gate

We propose a modified quantum teleportation scheme to increase the teleportation accuracy by applying a cubic phase gate to the displaced squeezed state. We have described the proposed scheme in Heisenberg's language, evaluating it from the point of view of adding an error in teleportation, and have shown that it allows achieving less error than the original scheme. Repeating the description in the language of wave functions, we have found the range of the displacement values, at which our conclusions will be valid. Using the example of teleportation of the vacuum state, we have shown that the scheme allows one to achieve high fidelity values.
Novel quantum group signature scheme based on orthogonal product states

In this paper, we present a quantum group signature (QGS) scheme based on orthogonal product states (OPSs) that cannot be perfectly distinguished by local operations and classical communication. Our scheme has all the properties of QGS, including unforgeability, undeniability, traceability, verifiability and anonymity. These properties can guarantee the security of the scheme. More importantly, different particles of a product state that comes from a nonlocal set are transmitted separately, thus the information that is encoded in the product state will not be leaked. Security and efficiency analysis of the scheme show that our scheme is secure and efficient.
The Influence of Avatar Embodiment on Time Perception - Towards VR for Time-Based Therapy

Psycho-pathological conditions, such as depression or schizophrenia, are often accompanied by a distorted perception of time. People suffering from this conditions often report that the passage of time slows down considerably and that they are “ stuck in time. ” Virtual Reality (VR) could potentially help to diagnose and maybe treat such mental conditions. However, the conditions in which a VR simulation could correctly diagnose a time perception deviation are still unknown. In this paper, we present an experiment investigating the difference in time experience with and without a virtual body in VR, also known as avatar. The process of substituting a person’s body with a virtual body is called avatar embodiment. Numerous studies demonstrated interesting perceptual, emotional, behavioral, and psychological effects caused by avatar embodiment. However, the relations between time perception and avatar embodiment are still unclear. Whether or not the presence or absence of an avatar is already influencing time perception is still open to question. Therefore, we conducted a between-subjects design with and without avatar embodiment as well as a real condition ( avatar vs. no-avatar vs. real ). A group of 105 healthy subjects had to wait for seven and a half minutes in a room without any distractors (e.g., no window, magazine, people, decoration) or time indicators (e.g., clocks, sunlight). The virtual environment replicates the real physical environment. Participants were unaware that they will be asked to estimate their waiting time duration as well as describing their experience of the passage of time at a later stage. Our main finding shows that the presence of an avatar is leading to a significantly faster perceived passage of time. It seems to be promising to integrate avatar embodiment in future VR time-based therapy applications as they potentially could modulate a user’s perception of the passage of time. We also found no significant difference in time perception between the real and the VR conditions ( avatar , no-avatar ), but further research is needed to better understand this outcome.
The<i>G</i>-Wishart Weighted Proposal Algorithm: Efficient Posterior Computation for Gaussian Graphical Models

Gaussian graphical models can capture complex dependency structures among variables. For such models, Bayesian inference is attractive as it provides principled ways to incorporate prior information and to quantify uncertainty through the posterior distribution. However, posterior computation under the conjugate G-Wishart prior distribution on the precision matrix is expensive for general non-decomposable graphs. We therefore propose a new Markov chain Monte Carlo (MCMC) method named the G-Wishart weighted proposal algorithm (WWA). WWA's distinctive features include delayed acceptance MCMC, Gibbs updates for the precision matrix and an informed proposal distribution on the graph space that enables embarrassingly parallel computations. Compared to existing approaches, WWA reduces the frequency of the relatively expensive sampling from the G-Wishart distribution. This results in faster MCMC convergence, improved MCMC mixing and reduced computing time. Numerical studies on simulated and real data show that WWA provides a more efficient tool for posterior inference than competing state-of-the-art MCMC algorithms.
Explaining Counterexamples with Giant-Step Assertion Checking

Identifying the cause of a proof failure during deductive verification of programs is hard: it may be due to an incorrectness in the program, an incompleteness in the program annotations, or an incompleteness of the prover.The changes needed to resolve a proof failure depend on its category, but the prover cannot provide any help on the categorisation.When using an SMT solver to discharge a proof obligation, that solver can propose a model from a failed attempt, from which a possible counterexample can be derived.But the counterexample may be invalid, in which case it may add more confusion than help.To check the validity of a counterexample and to categorise the proof failure, we propose the comparison between the run-time assertion-checking (RAC) executions under two different semantics, using the counterexample as an oracle.The first RAC execution follows the normal program semantics, and a violation of a program annotation indicates an incorrectness in the program.The second RAC execution follows a novel "giant-step" semantics that does not execute loops nor function calls but instead retrieves return values and values of modified variables from the oracle.A violation of the program annotations only observed under giant-step execution characterises an incompleteness of the program annotations.We implemented this approach in the Why3 platform for deductive program verification and evaluated it using examples from prior literature.