Reviewerly/exHarmony huggingface dataset:
TutorNet: Towards Flexible Knowledge Distillation for End-to-End Speech Recognition

In recent years, there has been a great deal of research in developing end-to-end speech recognition models, which enable simplifying the traditional pipeline and achieving promising results. Despite their remarkable performance improvements, end-to-end models typically require expensive computational cost to show successful performance. To reduce this computational burden, knowledge distillation (KD), which is a popular model compression method, has been used to transfer knowledge from a deep and complex model (teacher) to a shallower and simpler model (student). Previous KD approaches have commonly designed the architecture of the student by reducing the width per layer or the number of layers of the teacher. This structural reduction scheme might limit the flexibility of model selection since the student model structure should be similar to that of the given teacher. To cope with this limitation, we propose a KD method for end-to-end speech recognition, namely TutorNet, that applies KD techniques across different types of neural networks at the hidden representation-level as well as the output-level. For concrete realizations, we firstly apply representation-level knowledge distillation (RKD) during the initialization step, and then apply the softmax-level knowledge distillation (SKD) combined with the original task learning. When the student is trained with RKD, we make use of frame weighting that points out the frames to which the teacher pays more attention. Through a number of experiments, it is verified that TutorNet not only distills the knowledge between networks with different topologies but also significantly contributes to improving the performance of the distilled student.
Four-mode squeezed states: two-field quantum systems and the symplectic group $$\mathrm {Sp}(4,{\mathbb {R}})$$

Abstract We construct the four-mode squeezed states and study their physical properties. These states describe two linearly-coupled quantum scalar fields, which makes them physically relevant in various contexts such as cosmology. They are shown to generalise the usual two-mode squeezed states of single-field systems, with additional transfers of quanta between the fields. To build them in the Fock space, we use the symplectic structure of the phase space. For this reason, we first present a pedagogical analysis of the symplectic group $$\mathrm {Sp}(4,{\mathbb {R}})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>Sp</mml:mi> <mml:mo>(</mml:mo> <mml:mn>4</mml:mn> <mml:mo>,</mml:mo> <mml:mi>R</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> and its Lie algebra, from which we construct the four-mode squeezed states and discuss their structure. We also study the reduced single-field system obtained by tracing out one of the two fields. This procedure being easier in the phase space, it motivates the use of the Wigner function which we introduce as an alternative description of the state. It allows us to discuss environmental effects in the case of linear interactions. In particular, we find that there is always a range of interaction coupling for which decoherence occurs without substantially affecting the power spectra (hence the observables) of the system.
Parametrized entanglement monotone

Entanglement concurrence has been widely used for featuring entanglement in quantum experiments. As an entanglement monotone it is related to specific quantum Tsallis entropy. Our goal in this paper is to propose a parametrized bipartite entanglement monotone which is named as $q$-concurrence inspired by general Tsallis entropy. We derive an analytical lower bound for the $q$-concurrence of any bipartite quantum entanglement state by employing positive partial transposition criterion and realignment criterion, which shows an interesting relationship to the strong separability criteria. The parametrized entanglement monotone is used to characterize bipartite isotropic states. Finally, we provide a computational method to estimate the $q$-concurrence for any entanglement by superposing two bipartite pure states. It shows that the superposition operations can at most increase one ebit for the $q$-concurrence in the case that the two states being superposed are biorthogonal or one-sided orthogonal. These results reveal a series of phenomena about the entanglement, which may be interesting in quantum communication and quantum information processing.
iGCACS-IoD: An Improved Certificate-Enabled Generic Access Control Scheme for Internet of Drones Deployment

Due to wide-spread use of the Information and Communications Technology (ICT) and Internet of Things (IoT) enabled smart devices, called unmanned aerial vehicles (UAVs) (popularly known as drones), a lot of potential applications of Internet of Drones (IoD) are available ranging from the military to civilian applications.Access control mechanism is an important potential security service that is needed to secure communication among the drones in their respective flying zones, and also among the drones and the Ground Service Station (GSS).In 2021, Chaudhry et al. proposed a certificate based generic access control scheme for IoD environment, called GCACS-IoD.Their claims about the possible security attacks resistant of GCACS-IoD is not justified.In fact, we first prove that GCACS-IoD is unable to protect the disclosure of the private key r CR of the trusted control room (CR), which is extremely unfortunate careless design flaw and it leads to compromise the entire network.Using the disclosed private key r CR , we further show that GCACS-IoD is completely insecure against other serious attacks, such as malicious drones deployment attack, drone/GSS impersonation attacks and Ephemeral Secret Leakage (ESL) attack, which lead to compromise the session key between any two drones communicating in a particular flying zone.We thus feel that there is a strong need to remedy such serious weaknesses found in Chaudhry et al.'s GCACS-IoD.An improved certificate-enabled generic access control scheme for IoD deployment, called as iGCACS-IoD, has been suggested, which overcomes the weaknesses found in the previous GCACS-IoD.The practical demonstration of iGCACS-IoD has been done through formal security verification and also through NS2 simulation study.
Higher Classification of Fake Political News Using Decision Tree Algorithm Over Naive Bayes Algorithm

Aim: The main aim of the study proposed is to perform higher classification of fake political news by implementing fake news detectors using machine learning classifiers by comparing their performance. Materials and Methods: By considering two groups such as Decision Tree algorithm and Naive Bayes algorithm. The algorithms have been implemented and tested over a dataset which consists of 44,000 records. Through the programming experiment which is performed using N=10 iterations on each algorithm to identify various scales of fake news and true news classification. Result: After performing the experiment the mean accuracy of 99.6990 by using Decision Tree algorithm and the accuracy of 95.3870 by using Naive Bayes algorithm for fake political news in. There is a statistical significant difference in accuracy for two algorithms is p&lt;0.05 by performing independent samples t-tests. Conclusion: This paper is intended to implement the innovative fake news detection approach on recent Machine Learning Classifiers for prediction of fake political news. By testing the algorithms performance and accuracy on fake political news detection and other issues. The comparison results shows that the Decision Tree algorithm has better performance when compared to Naive Bayes algorithm.
Skin strata delineation in reflectance confocal microscopy images using recurrent convolutional networks with attention

Reflectance confocal microscopy (RCM) is an effective non-invasive tool for cancer diagnosis. However, acquiring and reading RCM images requires extensive training and experience, and novice clinicians exhibit high discordance in diagnostic accuracy. Quantitative tools to standardize image acquisition could reduce both required training and diagnostic variability. To perform diagnostic analysis, clinicians collect a set of RCM mosaics (RCM images concatenated in a raster fashion to extend the field view) at 4-5 specific layers in skin, all localized in the junction between the epidermal and dermal layers (dermal-epidermal junction, DEJ), necessitating locating that junction before mosaic acquisition. In this study, we automate DEJ localization using deep recurrent convolutional neural networks to delineate skin strata in stacks of RCM images collected at consecutive depths. Success will guide to automated and quantitative mosaic acquisition thus reducing inter operator variability and bring standardization in imaging. Testing our model against an expert labeled dataset of 504 RCM stacks, we achieved [Formula: see text] classification accuracy and nine-fold reduction in the number of anatomically impossible errors compared to the previous state-of-the-art.
Versatile and Concurrent FPGA-Based Architecture for Practical Quantum Communication Systems

This work presents a hardware and software architecture which can be used in those systems that implement practical Quantum Key Distribution (QKD) and Quantum Random Number Generation (QRNG) schemes. This architecture fully exploits the capability of a System-on-a-Chip (SoC) which comprehends both a Field Programmable Gate Array (FPGA) and a dual core CPU unit. By assigning the time-related tasks to the FPGA and the management to the CPU, we built a flexible system with optimized resource sharing on a commercial off-the-shelf (COTS) evaluation board which includes a SoC. Furthermore, by changing the dataflow direction, the versatile system architecture can be exploited as a QKD transmitter, QKD receiver and QRNG control-acquiring unit. Finally, we exploited the dual core functionality and realized a concurrent stream device to implement a practical QKD transmitter where one core continuously receives fresh data at a sustained rate from an external QRNG source while the other operates with the FPGA to drive the qubits transmission to the QKD receiver. The system was successfully tested on a long-term run proving its stability and security. This demonstration paves the way towards a more secure QKD implementation, with fully unconditional security as the QKD states are entirely generated by a true random process and not by deterministic expansion algorithms. Eventually, this enables the realization of a standalone quantum transmitter, including both the random numbers and the qubits generation.
Shortcuts to adiabaticity for open systems in circuit quantum electrodynamics

Abstract Shortcuts to adiabaticity are powerful quantum control methods, allowing quick evolution into target states of otherwise slow adiabatic dynamics. Such methods have widespread applications in quantum technologies, and various shortcuts to adiabaticity protocols have been demonstrated in closed systems. However, realizing shortcuts to adiabaticity for open quantum systems has presented a challenge due to the complex controls in existing proposals. Here, we present the experimental demonstration of shortcuts to adiabaticity for open quantum systems, using a superconducting circuit quantum electrodynamics system. By applying a counterdiabatic driving pulse, we reduce the adiabatic evolution time of a single lossy mode from 800 ns to 100 ns. In addition, we propose and implement an optimal control protocol to achieve fast and qubit-unconditional equilibrium of multiple lossy modes. Our results pave the way for precise time-domain control of open quantum systems and have potential applications in designing fast open-system protocols of physical and interdisciplinary interest, such as accelerating bioengineering and chemical reaction dynamics.
ESL Learners’ Challenges in Speaking English in Malaysian Classroom

Speaking is a useful language ability that allows speakers and listeners to engage in oral communication for the purpose of transferring and sharing information and developing long-term relationships.English is one of those languages that has the global demand for learners from varied L1 origins at all levels of communication.However, ESL students' developing speaking skills are always regarded as complex and challenging, requiring a great deal of effort to maintain.This article presents a comprehensive overview of published studies of the challenges of ESL learners speaking English from 2014 to 2021 in the Malaysian classroom.This systemic review addresses two questions of research, i.e., what are the challenges faced by ESL learners in Malaysia to speak English and what are the effective strategies to overcome the speaking challenges among the ESL learners?A total of fifteen studies were reviewed in depth.The findings show that ESL students experience both internal and external challenges to speaking English.The findings also reveal useful strategies for overcoming learners' difficulties in speaking the language.The limitations of previous studies were examined in greater depth, as well as future research and practice directions.The study may provide useful information for ESL teachers, English supervisors, administrators, Ministry of Education officials, and, most importantly, learners in the development of speaking abilities.
Scalable and High-Fidelity Quantum Random Access Memory in Spin-Photon Networks

A quantum random access memory (qRAM) is considered an essential computing unit to enable polynomial speedups in quantum information processing. Proposed implementations include the use of neutral atoms and superconducting circuits to construct a binary tree but these systems still require demonstrations of the elementary components. Here, we propose a photonic-integrated-circuit (PIC) architecture integrated with solid-state memories as a viable platform for constructing a qRAM. We also present an alternative scheme based on quantum teleportation and extend it to the context of quantum networks. Both implementations realize the two key qRAM operations, (1) quantum state transfer and (2) quantum routing, with already demonstrated components: electro-optic modulators, a Mach-Zehnder interferometer (MZI) network, and nanocavities coupled to artificial atoms for spin-based memory writing and retrieval. Our approaches furthermore benefit from built-in error detection based on photon heralding. Detailed theoretical analysis of the qRAM efficiency and query fidelity shows that our proposal presents viable near-term designs for a general qRAM.9 MoreReceived 16 March 2021Revised 11 June 2021Accepted 6 July 2021DOI:https://doi.org/10.1103/PRXQuantum.2.030319Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasLight-matter interactionQuantum information architectures & platformsQuantum networksQuantum InformationAtomic, Molecular & Optical
The Potential of Machine Learning Algorithms for Sentiment Classification of Students’ Feedback on MOOC

Students’ feedback assessment became a hot topic in recent years with growing e-learning platforms coupled with an ongoing pandemic outbreak. Many higher education institutes were compelled to shift on-campus physical classes to online mode, utilizing various online teaching tools and massive open online courses (MOOCs). For many institutes, including both teachers and students, it was a unique and challenging experience conducting lectures and taking classes online. Therefore, analyzing students’ feedback in this crucial time is inevitable for effective teaching and monitoring learning outcomes. Thus, in this paper, we propose and conduct a study to evaluate various machine learning models for aspect-based opinion mining to address this challenge effectively. The proposed approach is trained and validated on a large-scale dataset consisting of manually labeled students’ comments collected from the Coursera online platform. Various conventional machine learning algorithms, namely Random Forest (RF), Support Vector Machine (SVM), and Decision Tree (DT), along with deep-learning methods, are employed to identify teaching-related aspects and predict opinions/attitudes of students towards those aspects. The obtained results are very promising, with an F1 score of 98.01% and 99.43% achieved from RF on the aspect identification and the aspect sentiment classification task, respectively.
Certifying Choreography Compilation

Choreographic programming is a paradigm for developing concurrent and distributed systems, where programs are choreographies that define, from a global viewpoint, the computations and interactions that communicating processes should enact. Choreography compilation translates choreographies into the local definitions of process behaviours, given as terms in a process calculus. Proving choreography compilation correct is challenging and error-prone, because it requires relating languages in different paradigms (global interactions vs local actions) and dealing with a combinatorial explosion of proof cases. We present the first certified program for choreography compilation for a nontrivial choreographic language supporting recursion.
Perception! Immersion! Empowerment! Superpowers as Inspiration for Visualization

We explore how the lens of fictional superpowers can help characterize how visualizations empower people and provide inspiration for new visualization systems. Researchers and practitioners often tout visualizations' ability to "make the invisible visible" and to "enhance cognitive abilities." Meanwhile superhero comics and other modern fiction often depict characters with similarly fantastic abilities that allow them to see and interpret the world in ways that transcend traditional human perception. We investigate the intersection of these domains, and show how the language of superpowers can be used to characterize existing visualization systems and suggest opportunities for new and empowering ones. We introduce two frameworks: The first characterizes seven underlying mechanisms that form the basis for a variety of visual superpowers portrayed in fiction. The second identifies seven ways in which visualization tools and interfaces can instill a sense of empowerment in the people who use them. Building on these observations, we illustrate a diverse set of "visualization superpowers" and highlight opportunities for the visualization community to create new systems and interactions that empower new experiences with data Material and illustrations are available under CC-BY 4.0 at osf.io/8yhfz.
Non-Markovian Effects of Two-Level Systems in a Niobium Coaxial Resonator with a Single-Photon Lifetime of 10 milliseconds

Understanding and mitigating loss channels due to two-level systems (TLS) is one of the main cornerstones in the quest of realizing long photon lifetimes in superconducting quantum circuits. Typically, the TLS to which a circuit couples are modeled as a large bath without any coherence. Here we demonstrate that the coherence of TLS has to be considered to accurately describe the ring-down dynamics of a coaxial quarter-wave resonator with an internal quality factor of $0.5\ifmmode\times\else\texttimes\fi{}{10}^{9}$ at the single-photon level. The transient analysis reveals effective non-Markovian dynamics of the combined TLS and cavity system, which we can accurately fit by introducing a comprehensive TLS model. The fit returns an average coherence time of around ${T}_{2}^{\ensuremath{\ast}}\ensuremath{\approx}0.3\phantom{\rule{0.2em}{0ex}}\ensuremath{\mu}\mathrm{s}$ for a total of $N\ensuremath{\approx}{10}^{9}$ TLS with power-law distributed coupling strengths. Despite the shortly coherent TLS excitations, we observe long-term effects on the cavity decay due to coherent elastic scattering between the resonator field and the TLS. Moreover, this model provides an accurate prediction of the internal quality factor's temperature dependence.
Industrial Scene Text Detection With Refined Feature-Attentive Network

Detecting the marking characters of industrial metal parts remains challenging due to low visual contrast, uneven illumination, corroded surfaces, and cluttered background of metal part images.Affected by these factors, bounding boxes generated by most existing methods could not locate low-contrast text areas very well.In this paper, we propose a refined featureattentive network (RFN) to solve the inaccurate localization problem.Specifically, we first design a parallel feature integration mechanism to construct an adaptive feature representation from multi-resolution features, which enhances the perception of multi-scale texts at each scale-specific level to generate a highquality attention map.Then, an attentive proposal refinement module is developed by the attention map to rectify the location deviation of candidate boxes.Besides, a re-scoring mechanism is designed to select text boxes with the best rectified location.To promote the research towards industrial scene text detection, we contribute two industrial scene text datasets, including a total of 102156 images and 1948809 text instances with various character structures and metal parts.Extensive experiments on our dataset and four public datasets demonstrate that our proposed method achieves the state-of-the-art performance.Both code and dataset are available at:
Single-state multi-party semiquantum key agreement protocol based on multi-particle GHZ entangled states

In this paper, we put forward a novel single-state three-party semiquantum key agreement (SQKA) protocol with three-particle GHZ entangled states first. Different with previous quantum key agreement (QKA) protocols, the proposed single-state three-party SQKA protocol can realize the goal that a quantum party and two classical parties who only possess limited quantum capabilities equally contribute to the generation of a shared private key over quantum channels. Detailed security analysis turns out that the proposed single-state three-party SQKA protocol is secure against several famous attacks from an outside eavesdropper, such as the Trojan horse attack, the entangle-measure attack, the measure-resend attack and the intercept-resend attack. Moreover, it can resist the participant attack, which means that the shared private key cannot be determined fully by any nontrivial subset of three parties. The proposed single-state three-party SQKA protocol has the following nice features: (1) it only employs one kind of three-particle GHZ entangled states as initial quantum resource; (2) it does not need pre-shared keys among different parties; (3) it does not need unitary operations or quantum entanglement swapping. Finally, we generalize the proposed single-state three-party SQKA protocol into the case of $$N$$ -party by only employing one kind of $$N$$ -particle GHZ entangled states as initial quantum resource, which inherits the nice features of its three-party counterpart.