Reviewerly/exHarmony huggingface dataset:
Innovation and Practice of Music Education Paths in Universities under the Popularity of 5G Network

With the development of the 5G mobile Internet, cloud computing, Internet of Things, and other cutting-edge technologies, the era of big data has quietly arrived. The purpose of this paper is to explore the feasibility of the application of new technologies for music teaching in the era of big data in the context of the rapid development of science and technology in the information society, to enlighten and lead music teachers to apply the spontaneous and conscious awareness of new media and fully apply the new achievements of science and technology in the information society for future music classroom teaching, and to analyze the mode, method, trend, characteristics, advantages, and disadvantages of music teaching in the new media environment. The aim is to analyze the advantages and shortcomings of music teaching in colleges and universities and to find solutions and future development strategies for them so that in the future, the 5G Internet can better serve music lovers and better contribute to the cause of music education in colleges and universities. New media, as a product of constantly updated information technology, provides powerful data support for the development of various fields, and the education industry also needs new media to boost the rapid development of education information technology, which of course includes college music teaching. The effective integration of new media technology into the college music classroom can improve the classroom efficiency of music teaching with rich and diverse teaching resources and flexible teaching forms.
Phase Error Analysis and Correction for Crossed-Grating Phase-Shifting Profilometry

Crossed-grating phase-shifting profilometry (CGPSP) has great utility in three-dimensional shape measurement due to its ability to acquire horizontal and vertical phase maps in a single measurement. However, CGPSP is extremely sensitive to the non-linearity effect of a digital fringe projection system, which is not studied in depth yet. In this paper, a mathematical model is established to analyze the phase error caused by the non-linearity effect. Subsequently, two methods used to eliminate the non-linearity error are discussed in detail. To be specific, a double five-step algorithm based on the mathematical model is proposed to passively suppress the second non-linearity. Furthermore, a precoding gamma correction method based on probability distribution function is introduced to actively attenuate the non-linearity of the captured crossed fringe. The comparison results show that the active gamma correction method requires less fringe patterns and can more effectively reduce the non-linearity error compared with the passive method. Finally, employing CGPSP with gamma correction, a faster and reliable inverse pattern projection is realized with less fringe patterns.
A Stress Testing Framework For Autonomous System Verification And Validation (V&amp;V)

Autonomous cyber-physical systems are prone to error and failure. Verification and validation (V&V) is necessary for their safe, secure and resilient operations. Methods to detect faults in aerospace engineering (fault trees) and later adapted for security (attack trees) could capture a wide array of critical risks and we argue how stress testing could be a pragmatic approach to evaluating the assurance of autonomous cyber-physical systems.
Semantic Memory Search and Retrieval in a Novel Cooperative Word Game: A Comparison of Associative and Distributional Semantic Models

Considerable work during the past two decades has focused on modeling the structure of semantic memory, although the performance of these models in complex and unconstrained semantic tasks remains relatively understudied. We introduce a two-player cooperative word game, Connector (based on the boardgame Codenames), and investigate whether similarity metrics derived from two large databases of human free association norms, the University of South Florida norms and the Small World of Words norms, and two distributional semantic models based on large language corpora (word2vec and GloVe) predict performance in this game. Participant dyads were presented with 20-item word boards with word pairs of varying relatedness. The speaker received a word pair from the board (e.g., exam-algebra) and generated a one-word semantic clue (e.g., math), which was used by the guesser to identify the word pair on the board across three attempts. Response times to generate the clue, as well as accuracy and latencies for the guessed word pair, were strongly predicted by the cosine similarity between word pairs and clues in random walk-based associative models, and to a lesser degree by the distributional models, suggesting that conceptual representations activated during free association were better able to capture search and retrieval processes in the game. Further, the speaker adjusted subsequent clues based on the first attempt by the guesser, who in turn benefited from the adjustment in clues, suggesting a cooperative influence in the game that was effectively captured by both associative and distributional models. These results indicate that both associative and distributional models can capture relatively unconstrained search processes in a cooperative game setting, and Connector is particularly suited to examine communication and semantic search processes.
Adaptive Learning Rate and Momentum for Training Deep Neural Networks

Recent progress on deep learning relies heavily on the quality and efficiency of training algorithms. In this paper, we develop a fast training method motivated by the nonlinear Conjugate Gradient (CG) framework. We propose the Conjugate Gradient with Quadratic line-search (CGQ) method. On the one hand, a quadratic line-search determines the step size according to current loss landscape. On the other hand, the momentum factor is dynamically updated in computing the conjugate gradient parameter (like Polak-Ribiere). Theoretical results to ensure the convergence of our method in strong convex settings is developed. And experiments in image classification datasets show that our method yields faster convergence than other local solvers and has better generalization capability (test set accuracy). One major advantage of the paper method is that tedious hand tuning of hyperparameters like the learning rate and momentum is avoided.
Gaussian Elimination versus Greedy Methods for the Synthesis of Linear Reversible Circuits

Linear reversible circuits represent a subclass of reversible circuits with many applications in quantum computing. These circuits can be efficiently simulated by classical computers and their size is polynomially bounded by the number of qubits, making them a good candidate to deploy efficient methods to reduce computational costs. We propose a new algorithm for synthesizing any linear reversible operator by using an optimized version of the Gaussian elimination algorithm coupled with a tuned LU factorization. We also improve the scalability of purely greedy methods. Overall, on random operators, our algorithms improve the state-of-the-art methods for specific ranges of problem sizes: the custom Gaussian elimination algorithm provides the best results for large problem sizes (n > 150) while the purely greedy methods provide quasi optimal results when n < 30. On a benchmark of reversible functions, we manage to significantly reduce the CNOT count and the depth of the circuit while keeping other metrics of importance (T-count, T-depth) as low as possible.
Solar Radiation Resource: Measurement, Modeling, and Methods

Various technical aspects of the measurement and modeling of the solar energy potential are addressed in this chapter to provide a solid understanding of the different steps involved in the practice of solar resource assessments. The information presented here represents the state of the art with regard to the availability of solar resource databases developed from satellite imagery or meteorological reanalysis, and their derivatives, such as solar resource maps or typical meteorological years. The fundamentals that are reviewed include measuring, modeling, and applying solar radiation resource data to meet various needs, such as site selection, solar system design and simulation, or financial projections.
GAC3D: improving monocular 3D object detection with ground-guide model and adaptive convolution

Monocular 3D object detection has recently become prevalent in autonomous driving and navigation applications due to its cost-efficiency and easy-to-embed to existent vehicles. The most challenging task in monocular vision is to estimate a reliable object's location cause of the lack of depth information in RGB images. Many methods tackle this ill-posed problem by directly regressing the object's depth or take the depth map as a supplement input to enhance the model's results. However, the performance relies heavily on the estimated depth map quality, which is bias to the training data. In this work, we propose depth-adaptive convolution to replace the traditional 2D convolution to deal with the divergent context of the image's features. This lead to significant improvement in both training convergence and testing accuracy. Second, we propose a ground plane model that utilizes geometric constraints in the pose estimation process. With the new method, named GAC3D, we achieve better detection results. We demonstrate our approach on the KITTI 3D Object Detection benchmark, which outperforms existing monocular methods.
Lithogeochemistry of various hydrothermal alteration types associated with precious and base metal epithermal deposits in the Tarom-Hashtjin metallogenic province, NW Iran: Implications for regional exploration

In this study, the styles, types, and degrees of hydrothermal alteration associated with Pb-Zn-Cu-Ag-Au epithermal deposit systems in the Tarom-Hashtjin metallogenic province are evaluated using petrographic studies, as well as mass changes and molar elements ratios (MER), in order to examine regional exploration implications. The MER plots show that the hydrothermally altered rocks were most affected by various types of K-metasomatism, mainly characterized by K gains and Na and Ca losses. The intensity of K-metasomatism is related to precious and base-metal mineralization, showing increasing K trends toward the ore veins. The alteration index (AI) and chlorite-carbonate-pyrite index (CCPI) reveal that argillic, sericitic, and propylitic alteration types are the main zones of alteration surrounding ore veins indicating that AI increases and CCPI decreases toward more proximal to the structures linked to these ore systems. Determination of mass changes in altered rocks surrounding selected epithermal deposits suggest Si, K, and Al gains and Na and Ca losses increasing in intensity toward ore veins, consistent with alteration zoning from the silicic and K-metasomatic (sericite and argillic) inner zones to the propylitic (Fe-rich chlorite alteration) outer zone typical of epithermal ore systems. Data from this study show that the intensity of K-metasomatism in the acidic rocks is greater than in the intermediate and mafic rocks, where it is related to high to intermediate sulfidation epithermal systems. We suggest that exploration can be focused on highly altered volcanic rocks (especially andesite-dacite) with both high intensity of K-metasomatism and higher Pb, Zn, Ag, Au, Ba and Rb and lower Sr.
Blockchain-Enabled Equity Crowdfunding for Energy Storage Investments

Driven by techno-economic and climate change concerns, the energy sector is becoming more decentralized and decarbonized. With the increased deployment of intermittent energy sources such as photovoltaics, the need for energy storage is increasing. However, high costs still create a barrier for investing in such options. In this study, the Levelized Cost of Storage for residential scale Lithium-Ion battery is evaluated by comparing traditional and novel financing options such as blockchain-enabled crowdfunding. The proposed Financial Technology tool can allow multiple smaller stakeholders to offer loans with longer dept tenors and lower interests which will allow digital partial ownership of such investments and provide extra incentives for rapid deployment of energy storage solutions. The findings illustrate the economic viability of energy storage systems entering the European energy sector and highlight how blockchain-based FinTech tools can further accelerate the deployment of energy storage systems through energy financing.
Privug: Using Probabilistic Programming for Quantifying Leakage in Privacy Risk Analysis

Disclosure of data analytics results has important scientific and commercial justifications. However, no data shall be disclosed without a diligent investigation of risks for privacy of subjects. Privug is a tool-supported method to explore information leakage properties of data analytics and anonymization programs. In Privug, we reinterpret a program probabilistically, using off-the-shelf tools for Bayesian inference to perform information-theoretic analysis of the information flow. For privacy researchers, Privug provides a fast, lightweight way to experiment with privacy protection measures and mechanisms. We show that Privug is accurate, scalable, and applicable to a range of leakage analysis scenarios.
EDG-Based Question Decomposition for Complex Question Answering over Knowledge Bases

Knowledge base question answering (KBQA) aims at automatically answering factoid questions over knowledge bases (KBs). For complex questions that require multiple KB relations or constraints, KBQA faces many challenges including question understanding, component linking (e.g., entity, relation, and type linking), and query composition. In this paper, we propose a novel graph structure called Entity Description Graph (EDG) to represent the structure of complex questions, which can help alleviate the above issues. By leveraging the EDG structure of given questions, we implement a QA system over DBpedia, called EDGQA. Extensive experiments demonstrate that EDGQA outperforms state-of-the-art results on both LC-QuAD and QALD-9, and that EDG-based decomposition is a feasible way for complex question answering over KBs.
Infrastructure and Population of the OpenBiodiv Biodiversity Knowledge Graph

OpenBiodiv is a biodiversity knowledge graph containing a synthetic linked open dataset, OpenBiodiv-LOD, which combines knowledge extracted from academic literature with the taxonomic backbone used by the Global Biodiversity Information Facility. The linked open data is modelled according to the OpenBiodiv-O ontology integrating semantic resource types from recognised biodiversity and publishing ontologies with OpenBiodiv-O resource types, introduced to capture the semantics of resources not modelled before.We introduce the new release of the OpenBiodiv-LOD attained through information extraction and modelling of additional biodiversity entities. It was achieved by further developments to OpenBiodiv-O, the data storage infrastructure and the workflow and accompanying R software packages used for transformation of academic literature into Resource Description Framework (RDF). We discuss how to utilise the LOD in biodiversity informatics and give examples by providing solutions to several competency questions. We investigate performance issues that arise due to the large amount of inferred statements in the graph and conclude that OWL-full inference is impractical for the project and that unnecessary inference should be avoided.
Application of Paraconsistent Annotated Evidential Logic Eτ for a Terrestrial Mobile Robot to Avoid Obstacles

One of the significant challenges in contemporary robotics concerns navigation systems and autonomous robots’ location, being the detection and avoidance of obstacles an essential part of this system. This article proposed Paraconsistent Annotated Evidential Logic Eτ in the control algorithm for a terrestrial robot, and thus contribute with navigation systems, to avoid possible collisions with obstacles. A prototype terrestrial robot was then built with ultrasonic sensors for obstacle detection and a servomotor in steering control. Several performance tests were performed. It was noticed that by shortening the perception distances of the sensors, the performance of the robot’s displacement proved to be satisfactory.
Rope: Covert Multi-process Malware Execution with Return-Oriented Programming

Distributed execution designs challenge behavioral analyses of anti-malware solutions by spreading seemingly benign chunks of a malicious payload to multiple processes. Researchers have explored methods to chop payloads, spread chunks to victim applications through process injection techniques, and orchestrate the execution. However, these methods can hardly be practical as they exhibit conspicuous features and make use of primitives that anti-malware solutions and operating system mitigations readily detect. In this paper we reason on fundamental requirements and properties for a stealth implementation of distributed malware. We propose a new covert design, Rope, that minimizes its footprint by making use of commodity techniques like transacted files and return-oriented programming for covert communication and payload distribution. We report on how synthetic Rope samples eluded a number of state-of-the-art anti-virus and endpoint security solutions, and bypassed the opt-in mitigations of Windows 10 for hardening applications. We then discuss directions and practical remediations to mitigate such threats.
Optimization of the resonator-induced phase gate for superconducting qubits

The resonator-induced phase gate is a two-qubit operation in which driving a bus resonator induces a state-dependent phase shift on the qubits equivalent to an effective $ZZ$ interaction. In principle, the dispersive nature of the gate offers flexibility for qubit parameters. However, the drive can cause resonator and qubit leakage, the physics of which cannot be fully captured using either the existing Jaynes-Cummings or Kerr models. In this paper, we adopt an ab initio model based on Josephson nonlinearity for transmon qubits. The ab initio analysis agrees well with the Kerr model in terms of capturing the effective $ZZ$ interaction in the weak-drive dispersive regime. In addition, however, it reveals numerous leakage transitions involving high-excitation qubit states. We analyze the physics behind such novel leakage channels, demonstrate the connection with specific qubit-resonator frequency collisions, and lay out a plan toward device parameter optimization. We show that this type of leakage can be substantially suppressed using very weakly anharmonic transmons. In particular, weaker qubit anharmonicity mitigates both collision density and leakage amplitude, while larger qubit frequency moves the collisions to occur only at large anharmonicity not relevant to experiment. Our work is broadly applicable to the physics of weakly anharmonic transmon qubits coupled to linear resonators. In particular, our analysis confirms and generalizes the measurement-induced state transitions noted in Sank et al. [Phys. Rev. Lett. 117, 190503 (2016)] and lays the groundwork for both strong-drive resonator-induced phase gate implementation and strong-drive dispersive qubit measurement.
Solving Expensive Multimodal Optimization Problem by a Decomposition Differential Evolution Algorithm

An expensive multimodal optimization problem (EMMOP) is that the computation of the objective function is time consuming and it has multiple global optima. This article proposes a decomposition differential evolution (DE) based on the radial basis function (RBF) for EMMOPs, called D/REM. It mainly consists of two phases: the promising subregions detection (PSD) and the local search phase (LSP). In PSD, a population update strategy is designed and the mean-shift clustering is employed to predict the promising subregions of EMMOP. In LSP, a local RBF surrogate model is constructed for each promising subregion and each local RBF surrogate model tracks a global optimum of EMMOP. In this way, an EMMOP is decomposed into many expensive global optimization subproblems. To handle these subproblems, a popular DE variant, JADE, acts as the search engine to deal with these subproblems. A large number of numerical experiments unambiguously validate that D/REM can solve EMMOPs effectively and efficiently.
Spell My Name: Keyword Boosted Speech Recognition

Recognition of uncommon words such as names and technical terminology is important to understanding conversations in context. However, the ability to recognise such words remains a challenge in modern automatic speech recognition (ASR) systems.In this paper, we propose a simple but powerful ASR decoding method that can better recognise these uncommon keywords, which in turn enables better readability of the results. The method boosts the probabilities of given keywords in a beam search based on acoustic model predictions. The method does not require any training in advance.We demonstrate the effectiveness of our method on the LibriSpeeech test sets and also internal data of real-world conversations. Our method significantly boosts keyword accuracy on the test sets, while maintaining the accuracy of the other words, and as well as providing significant qualitative improvements. This method is applicable to other tasks such as machine translation, or wherever unseen and difficult keywords need to be recognised in beam search.