Reviewerly/exHarmony huggingface dataset:
Experimental elective in quantum science and technologies for master level course

Quantum science and technology education is in increasing demand from both educational and industrial establishments. Photon- and Cloud-based systems play a vital role in experimental-based education and training. This contribution features our most recent Master's level course, 'Experimental Quantum Technologies,' which was offered in 2022. This unique course provided brief introductions to each topic with minimal theoretical formulation, and emphasized on observing quantum phenomena. Lab instructions contained the physics and mathematics needed to understand the experiments. The reports were succinct and focused on understanding the experimental results. Our contribution will address the course's key outcomes, including experiments done.
Comparative analysis of the acoustical impedance of a set of traditional trombones versus P-Bone

Abstract In this work, the impedance response of a set of trombones is analysed in order to find correlations between their sound and timbre and the main characteristics of the impedance curves. For this specific application, an impedance tube has been designed and manufactured to measure the different trombones’ impedance curves. A plastic trombone (P-Bone) has been considered in the experiments to determine the importance of the material used to fabricate the instrument. In order to parametrise the trombones considered here, the instrument profile has been theoretically studied using a conical approach. More precisely, the tube and bell cone of the trombone has been discretised along its length in a set of small conical segments. Also, the influence of the mouthpiece is considered in the analytical formalism, and all these factors are optimised by employing a genetic algorithm to find the physical parameters that fit the experimental impedance curves. The experimental results are also compared with the opinions obtained from a survey about the subjective assessment of the same music passage recorded with the different instruments considered here. The results show that the impedance curve and some of the fitted parameters obtained correlate with the assessment of trombones in the blind survey. Moreover, the quality of the impedance tube here designed reveals that it is an excellent option to find leakages or dysfunction in this instrument.
Artificial intelligence technology pushes forward the modernization of firepower weapon equipment

In the new era, in the face of the future battlefield with informationization, three-dimensional combat and flexible maneuverability, all major military powers in the world are vigorously promoting the intelligent strategy of traditional firepower weapon equipment, and the militarization application of artificial intelligence technology is gradually becoming a research hotspot of military experts at home and abroad. Firepower weapon equipment is also gradually undergoing a transformation process from "mechanization and automation" to "informatization and intelligence". Under the new combat background, this paper starts from the category of artificial intelligence technology, analyzes the key technologies such as target recognition and automatic speech recognition based on image information in detail, and indepth discussion and prospect of artificial intelligence technology will vigorously promote the future technical changes of traditional firepower weapon equipment.
If you build it, they will come: creating ecosystems to produce the next generation of optics technicians

Precision optics manufacturing companies across the U.S. are suffering from a critical shortage of technicians. This shortage impacts supply chain, delays shipments, and impedes the productivity of any industry reliant on optics. Further, it stifles innovation and requires engineers with advanced degrees to complete the work of technicians. Development of the current and next-generation optics manufacturing workforce is vital. Monroe Community College (MCC) in Rochester, NY offered the nation’s first Optical Systems Technology program. By collaborating with industry, community, and governmental partners, MCC grew the program over several decades to a record-high graduating class today. Unfortunately, MCC alone cannot meet the demand for optics technicians with 500 new graduates needed every year just in the Western NY region. The U.S. Department of Defense sees this critical workforce shortage as a threat to national security and charged the American Center for Optics Manufacturing – AmeriCOM – to increase the number of optics technicians. Recognizing the success of connecting industry, community, and governmental partners with an optics-focused academic institution, AmeriCOM is replicating this “ecosystem” model in regions across the country. The initiative has: 1) Extensively enhanced precision optics technician training with innovative approaches that meet the needs of the optics industry and students; 2) Increased the number and diversity of optics technicians nationwide; and 3) Established opportunities for student and faculty engagement with the optics industry. This paper describes the success of MCC’s optics ecosystem model and illustrates why AmeriCOM is replicating it nationally to strengthen the nation’s precision optics workforce.
Research on near field communication (NFC) technology of E-CNY hard wallet

As the rapid development of digital currency over recent years, the stability of financial markets and monetary policy of the central bank has been greatly affected. There is great significance that The People's Bank of China continues to promote the e-CNY with the strong support of the government under the situation. As one of the important ways to achieve the application of e-CNY, the development and research of NFC hard wallet near-field payment advanced with significant value. This paper focuses on the NFC hard wallet technology combining NFC and e-CNY , firstly describes the relevant policies and the principles of e-CNY, the principles of NFC, the architecture and process of mobile phone NFC, the memory process of NFC hard wallet and the power consumption optimization, and then summarize the available technical solutions, lastly put forward specific improvement measures by comparing users' usage habits and come up beneficial suggestions with the promotion and popularization of e-CNY.
IT Assessment in ARL Libraries

With the rapid pace of change and the high cost invested in information technology, it is important for academic libraries to evaluate the information technology solutions they use in their institutions to determine whether they are meeting the needs of their students, faculty, staff, and other users of their libraries and resources. This study presents the results of an online survey of Association of Research Libraries (ARL) academic library members to benchmark the assessment strategies that they used to evaluate their IT activities. This article compares their responses with earlier studies to identify trends and make recommendations for effective assessment practices.
Quantum optics laboratories with motorized components

Quantum optics laboratories are emerging as suitable educational platforms to complement quantum mechanics instruction and expose students to non-traditional demonstrations of quantum physics involving quantum superposition and measurement. The automation of these experiments offers an interesting alternative paradigm. In this contribution, I present the automation of laboratory experiments through motorized mounts that displace or rotate optical components, enabling remote execution of the experiments. Some additional benefits include improved repeatability and better preparation of photonic quantum states.
Computer-Aided Composition Using a Sound-Based Notation

Abstract This article describes the music data format of the recently introduced Sound Notation system, and how it makes possible computer-aided composition of scores representing sound-based music. The Sound Notation system is an adaptation of Lasse Thoresen's spectromorphological analysis notation, developed for composition and analysis. A detailed description of the data format is followed by two examples of its application in a computer-aided composition process resulting in the sounding interpretation of two score excerpts of an electroacoustic composition. Generating sound structures as symbolic notation data in this way provided possibilities for the creation of sound-based music otherwise limited to works of traditional notation.
Homomorphic Encryption Enables Data and Algorithm Confidentiality for Remote Monitoring and Control: An Application to Data Center Systems

The design of intelligent algorithms used for device monitoring and control can be costly and is an investment that must be protected against reverse engineering by competitors. An algorithm can be safeguarded by running remotely from the cloud instead of locally on the equipment hardware. However, such a setup requires that sensitive data is sent from the device to the cloud. Fully Homomorphic Encryption (FHE) is an emerging technology that offers a solution to this problem since it enables computation on encrypted data. A cloud service using FHE can protect its proprietary algorithms while simultaneously offering customer data confidentiality. The computational overhead for the technology is, however, still very high. This work reports on a practical investigation of using FHE for data center remote control problems: What applications are feasible today? And at what cost?
Improved polar complex exponential transform for robust local image description

Image description via robust local descriptors plays a vital role in a large number of image representation and matching applications. In this paper, we propose a novel distinctive local image descriptor that is based on the phase and amplitude information of Polar Complex Exponential Transform (PCET). The proposed descriptor, called IPCET (Improved PCET), is robust to the common photometric transformations (e.g., illumination, noise, JPEG compression, and blur) and geometric transformations (e.g., scaling, rotation, translation, and significant affine distortion). We perform extensive experiments to compare our IPCET descriptor with six most cutting-edge region descriptors (i.e., SIFT, Zernike Moment, GLOH, PCA-SIFT, SURF, and ORB). Experimental results demonstrate that our IPCET descriptor outperforms cutting-edge moment-based descriptors.
SemanticHadith: An ontology-driven knowledge graph for the hadith corpus

Hadith is an essential and much-celebrated resource for the Islamic domain. It is one of the two primary sources of Islamic legislation. The hadith corpus is quite large, consisting of the collection of sayings, actions and silent approval of the Prophet Muhammad. Minimal efforts have been made to date, towards unified semantic modelling, and knowledge representation of the hadith structure for enhanced interlinking and knowledge discovery. This paper presents the design, development and publishing of the hadith corpus as a knowledge graph. First, we design the SemanticHadith ontology to describe and relate core structural concepts from the hadith. We then publish the six prominent hadith collections as an RDF-Based hadith knowledge graph, which is an effort towards making the available hadith both human and machine-readable. This is the first step in the annotation and linking process of the hadith corpus aimed at enabling semantic search capabilities to support scholars, students, and researchers in the creation, evolution, and consultation of a digital representation of Islamic knowledge. The SemanticHadith knowledge graph is freely accessible at http://www.semantichadith.com.
FedHD: Communication-efficient federated learning from hybrid data

Federated learning (FL) has attracted significant attention in the machine learning community owing to its instinct local privacy awareness. Depending on how the data are distributed over the clients, FL problems can be divided into three categories, namely the horizontal FL (HFL), the vertical FL (VFL) and the hybrid FL (HBFL). Among them, the HBFL problem is the most challenging because each client neither owns the full set of data samples nor knows the complete feature information. While many FL algorithms have been developed for the HFL and VFL problems, unfortunately they cannot handle the HBFL problem. In this paper, we propose a new FL algorithm, termed as FedHD, that can solve the challenging learning task under the HBFL setting. Since the clients cannot perform local optimization on their own under the hybrid data, a tracking variable is introduced to enable the clients to track the global gradient information and update the model based on their local data. FedHD allows the clients to perform multiple steps of local stochastic gradient descent (SGD), and hence has improved communication efficiency. Theoretical analysis is conducted to show that FedHD has a O(1/QT) convergence rate, where Q denotes the local update number and T is the total number of communication rounds. Then we further reveal the insights on how various algorithm parameters impact on the convergence performance. Experiment results show that the proposed FedHD exhibits robust performance on the hybrid data, and is largely superior to the naive local training model.
New Construction of (k + r,k) Systematic MDS Array Codes with r ≤ 4

Given a prime L, we present a new construction of (L−1)-dimensional (k+r,k) systematic array codes with r ≤ 4, and concretely characterize sufficient conditions on the selection of L to guarantee the codes’ MDS property. The largest possible k that can be supported by the new MDS array codes is 2L−4, nearly twice as large as that supported by classical MDS array codes such as EVENODD codes and RDP codes. Moreover, the number of XORs per original data bit required in encoding of the new codes asymptotically approaches r with increasing k and L, same as EVENODD codes and RDP codes. In addition, for the case r = 4, the explicit conditions on L we obtain to guarantee the new codes’ MDS property can also be used to guarantee the MDS property of EVENODD codes and RDP codes, but are more general than the well known ones in the literature.
Truly Bayesian Entropy Estimation

Estimating the entropy rate of discrete time series is a challenging problem with important applications in numerous areas including neuroscience, genomics, image processing and natural language processing. A number of approaches have been developed for this task, typically based either on universal data compression algorithms, or on statistical estimators of the underlying process distribution. In this work, we propose a fully-Bayesian approach for entropy estimation. Building on the recently introduced Bayesian Context Trees (BCT) framework for modelling discrete time series as variable-memory Markov chains, we show that it is possible to sample directly from the induced posterior on the entropy rate. This can be used to estimate the entire posterior distribution, providing much richer information than point estimates. We develop theoretical results for the posterior distribution of the entropy rate, including proofs of consistency and asymptotic normality. The practical utility of the method is illustrated on both simulated and real-world data, where it is found to outperform state-of-the-art alternatives.
Symmetric 4-adic Complexity of Quaternary Sequences of Length pq with Low Autocorrelation

In this paper, we consider quaternary sequences of length pq, where p and q are two different odd primes. These sequences are constructed based on Legendre symbol and have low autocorrelation and high linear complexity. We derive the symmetric 4-adic complexity of these sequences and show that it is good enough to resist the attack of the rational approximation algorithm.
BNNs- and TISTA- Based Signature Code Design for User Identification and Channel Estimation over Multiple-Access Channel with Rayleigh Fading

User identification (UI) and channel estimation (CE) are essential in wireless networks with numerous users. Signature-code-based UI and CE schemes are widely used owing to their high spectral efficiency. Traditional signature code uses a discrete sensing matrix as a dictionary to generate codewords. Subsequently, the sparse vector recovery algorithm is used to recover the user state and channel state information in the received signal to complete the UI and CE. In this study, we propose an end-to-end machine-learning-aided signature code scheme under a multiple-access Rayleigh fading channel called machine-learning signature code (ML-SC). The ML-SC consists of a binarized-neural-networks-based (BNNs-based) trainable encoder and a trainable-iterative-soft-threshold-algorithm-based (TISTA-based) trainable decoder. To improve the accuracy, the dictionary is optimized by minimizing the mean squared error between the original and recovered information. Our proposed scheme achieved better performance and efficiency than the conventional schemes in the simulation. Moreover, it was confirmed that the dictionary generated by the ML-SC is suitable for various conventional decoders. Finally, by analyzing the results of the simulations, we found that ML-SC improves the restricted isometric constants and coherence of the dictionary.
Asymmetric tree correlation testing for graph alignment

We consider the partial graph alignment problem on two correlated sparse Erdős–Rényi graphs with differing edge or node densities. Exploiting that these graphs are locally tree-like, we come to consider a hypothesis testing problem on correlated Galton-Watson trees. To solve this problem, we give several equivalent conditions for the existence of likelihood-ratio tests with vanishing type-I-error and significant power. We then show that these same conditions enable the partial graph alignment algorithm MPAlign to succeed.This paper generalizes recent results from Ganassali L., Massoulié L. and Lelarge M. to the asymmetric edge and node density case. This extension allows for greater applicability of the results and resolves a special case of the subgraph isomorphism problem.
A constructive approach to multimedia codes with complete traceability resistant to δ-noise

This paper presents an explicit construction of multimedia codes with complete traceability resistant to the averaging attack and δ-noise. The obtained code is a combination of a class of signature codes together with a generalization of superimposed codes, for which existence lower bounds, using the Lovász Local Lemma, are obtained. The constructions are a consequence of the Moser-Tardos variable framework.
Generalized low rank parity check codes

In this work we propose a family of ${\mathbb{F}_q}$-linear lowrank parity check (LRPC) codes based on a bilinear product over $\mathbb{F}_q^m$ defined by a generic 3-tensor over ${\mathbb{F}_q}$. A particular choice of this tensor corresponds to the classical ${\mathbb{F}_{{q^m}}}$-linear LRPC codes; and other tensors yield ${\mathbb{F}_q}$-linear codes, which, with some caveats, can be efficiently decoded with the same idea of decoding LRPC codes. The proposed codes contribute to the diversity of rank metric codes for cryptographic applications, particularly for the cases where attacks utilize ${\mathbb{F}_{{q^m}}}$-linearity to reduce decoding complexity.
Database Matching Under Adversarial Column Deletions

The de-anonymization of users from anonymized microdata through matching or aligning with publicly-available correlated databases has been of scientific interest recently. While most of the rigorous analyses of database matching have focused on random-distortion models, the adversarial-distortion models have been wanting in the relevant literature. In this work, motivated by synchronization errors in the sampling of time-indexed microdata, matching (alignment) of random databases under adversarial column deletions is investigated. It is assumed that a constrained adversary, which observes the anonymized database, can delete up to a δ fraction of the columns (attributes) to hinder matching and preserve privacy. Column histograms of the two databases are utilized as permutation-invariant features to detect the column deletion pattern chosen by the adversary. The detection of the column deletion pattern is then followed by an exact row (user) matching scheme. The worst-case analysis of this two-phase scheme yields a sufficient condition for the successful matching of the two databases, under the near-perfect recovery condition. A more detailed investigation of the error probability leads to a tight necessary condition on the database growth rate, and in turn, to a single-letter characterization of the adversarial matching capacity. This adversarial matching capacity is shown to be significantly lower than the "random" matching capacity, where the column deletions occur randomly. Overall, our results analytically demonstrate the privacy-wise advantages of adversarial mechanisms over random ones during the publication of anonymized time-indexed data.
Generic Decoding in the Cover Metric

Properties of random codes endowed with the cover metric are considered. We prove the NP-hardness of the decoding problem and then provide a generic decoder, following the information set decoding idea from Prange’s algorithm in the Hamming metric. Despite the cover metric lying between the Hamming and the rank metric, the complexity analysis of the algorithm reveals a significant difference between the metrics.
Fundamental Limits of Distributed Optimization over Multiple Access Channel

We consider distributed optimization over a d-dimensional space, where K remote clients send coded gradient estimates over an additive Gaussian Multiple Access Channel (MAC) with noise variance $\sigma _z^2$. Furthermore, the codewords from the K clients must satisfy the average power constraint of P, resulting in a signal-to-noise ratio (SNR) of $KP/\sigma _z^2$. In this paper, we study the fundamental limits imposed by MAC on the convergence rate of any distributed optimization algorithm and design optimal communication schemes to achieve these limits. Our first result is a lower bound for the convergence rate showing that compared to the centralized setting, communicating over a MAC imposes a slowdown of $\sqrt {d/\frac{1}{2}\log (1 + {\text{SNR}})}$ on any protocol. Next, we design a computationally tractable digital communication scheme that matches the lower bound to a logarithmic factor in K when combined with a projected stochastic gradient descent algorithm. At the heart of our communication scheme is a careful combination of several compression and modulation ideas such as quantizing along random bases, Wyner-Ziv compression, modulo-lattice decoding, and amplitude shift keying. We also show that analog coding schemes, which are popular due to their ease of implementation, can give close to optimal convergence rates at low SNR but experience a slowdown of roughly $\sqrt d$ at high SNR.
Minimizing the Outage Probability in a Markov Decision Process

Standard Markov decision process (MDP) and reinforcement learning algorithms optimize the policy with respect to the expected gain. We propose an algorithm which enables to optimize an alternative objective: the probability that the gain is greater than a given value. The algorithm can be seen as an extension of the value iteration algorithm. We also show how the proposed algorithm could be generalized to use neural networks, similarly to the deep Q learning extension of Q learning.