Reviewerly/exHarmony huggingface dataset:
Designing Collaborative and Coordinated Virtual Reality Training Integrated with Virtual and Physical Factories

Rapidly changing customer demands, regulations and technologies drive the complexity of products, processes and production systems, as well as shorter product and factory lifecycles. In order to handle such complexity while decreasing the time-to-market, immersive virtual reality (VR) technologies are increasingly being used in industry to support product and factory lifecycle engineering processes, such as (re)design, validation and verification, learning and training. However, the design and development of multi-user VR training for complex and manual production processes remain a challenge for industry. The integration of VR training simulations with virtual and physical factories could support the handling of such obstacles in terms of efficiency and effectiveness by increasing the precision, accuracy and reliability of data used in VR simulations. In this study, we present a collaborative and coordinated VR training model and its data integration with virtual factory tools and manufacturing execution systems for a wind turbine assembly scenario. A demonstration has been performed and evaluated by industry experts. The preliminary evaluation results show that integrated collaborative VR training has significant potential for more efficient and effective training, as well as enabling new use cases for industry.
The Smartphone as a Pedagogic Tool. Student Profiles as related to its Use and Knowledge

The Smartphone has become the device that offers the greatest number of possibilities to Mobile Learning dynamics, and university students are increasingly optimizing its potential. This research offers a classification of student profiles according to their knowledge and use of Smartphones, and analyzes their relationship between their interest towards training, the perceived academic benefits and the time spent using the device as a pedagogic resource. For this, through a quantitative methodology, a cluster analysis was conducted, which allowed for the defining of student profiles, and through non-parametric tests, the significance was analyzed with respect to their interest in training, the academic benefits perceived, and the time spent using the Smartphone. The results showed the existence of three profiles, namely: High degree of knowledge and academic use of the Smartphone; Average degree of knowledge and use of the Smartphone; and Low degree of knowledge and use of the Smartphone. Also, differences between the profiles were found in the time spent using it, the benefits perceived and the interest for being trained on the academic use of the Smartphone. The students with the Low profile were the ones that detected the least number of positive aspects and had the least interest in training towards this aim.
Optimal Space-Depth Trade-Off of CNOT Circuits in Quantum Logic Synthesis

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2020 ACM-SIAM Symposium on Discrete Algorithms (SODA)Optimal Space-Depth Trade-Off of CNOT Circuits in Quantum Logic SynthesisJiaqing Jiang, Xiaoming Sun, Shang-Hua Teng, Bujiao Wu, Kewen Wu, and Jialin ZhangJiaqing Jiang, Xiaoming Sun, Shang-Hua Teng, Bujiao Wu, Kewen Wu, and Jialin Zhangpp.213 - 229Chapter DOI:https://doi.org/10.1137/1.9781611975994.13PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract Due to the decoherence of the state-of-the-art physical implementations of quantum computers, it is essential to parallelize the quantum circuits to reduce their depth. Two decades ago, Moore and Nilsson [1] demonstrated that additional qubits (or ancillae) could be used to design “shallow” parallel circuits for quantum operators. They proved that any n-qubit CNOT circuit could be parallelized to O(log n) depth, with O(n2) ancillae. However, the near-term quantum technologies can only support limited amount of qubits, making space-depth trade-off a fundamental research subject for quantum-circuit synthesis. In this work, we establish an asymptotically optimal space-depth trade-off for the design of CNOT circuits. We prove that for any m ≥ 0, any n-qubit CNOT circuit can be parallelized to depth, with m ancillae. We show that this bound is tight by a counting argument, and further show that even with arbitrary two-qubit quantum gates to approximate CNOT circuits, the depth lower bound still meets our construction, illustrating the robustness of our result. Our work improves upon two previous results, one by Moore and Nilsson [1] for O(log n)-depth quantum synthesis, and one by Patel, Markov, and Hayes [2] for m =0: for the former, we reduce the need for ancillae by a factor of log2 n by showing that m = O(n2 / log2 n) additional qubits — which is asymptotically optimal — suffice to build O(log n)-depth, O(n2 / log n)-size CNOT circuits; for the later, we reduce the depth by a factor of n to the asymptotically optimal bound . Our results can be directly extended to stabilizer circuits using an earlier result by Aaronson and Gottesman [3]. In addition, we provide relevant hardness evidence for synthesis optimization of CNOT circuits in term of both size and depth. Previous chapter Next chapter RelatedDetails Published:2020eISBN:978-1-61197-599-4 https://doi.org/10.1137/1.9781611975994Book Series Name:ProceedingsBook Code:PRDA20Book Pages:xxii + 3011
Low Quality Video Face Recognition: Multi-Mode Aggregation Recurrent Network (MARN)

Face recognition performance deteriorates when face images are of very low quality. For low quality video sequences, however, more discriminative features can be obtained by aggregating the information in video frames. We propose a Multi-mode Aggregation Recurrent Network (MARN) for real-world low-quality video face recognition. Unlike existing recurrent networks (RNNs), MARN is robust against overfitting since it learns to aggregate pre-trained embeddings. Compared with quality-aware aggregation methods, MARN utilizes the video context and learns multiple attention vectors adaptively. Empirical results on three video face recognition datasets, IJB-S, YTF, and PaSC show that MARN significantly boosts the performance on the low quality video dataset while achieves comparable results on high quality video datasets.
Depth Information Enhancement Using Block Matching and Image Pyramiding Stereo Vision Enabled RGB-D Sensor

Depth sensing devices enabled with an RGB camera, can be used to augment conventional images with depth information on a per-pixel basis. Currently available RGB-D sensors include the Asus Xtion Pro, Microsoft Kinect and Intel RealSense <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">™</sup> . However, these sensors have certain limitations. Objects that are shiny, transparent or have an absorbing matte surface, create problems due to reflection. Also, there can be an interference in the IR pattern due to the use of multiple RGB-D cameras and the depth information is correctly interpreted only for short distances between the camera and the object. The proposed system, block matching stereo vision (BMSV) uses an RGB-D camera with rectified/non-rectified block matching and image pyramiding along with dynamic programming for human tracking and capture of accurate depth information from shiny/transparent objects. Here, the IR emitter generates a known IR pattern and the depth information is recovered by comparing the multiple views of the focused object. The depth map of the BMSV RGB-D camera and the resultant disparity map are fused. This fills any void regions that may have emerged due to interference or because of the reflective transparent surfaces and an enhanced dense stereo image is obtained. The proposed method is applied to a 3D realistic head model, a functional magnetic resonance image (fMRI) and the results are presented. Results showed an improvement in speed and accuracy of RGB-D sensors which in turn provided accurate depth information density irrespective of the object surface.
DT-LET: Deep transfer learning by exploring where to transfer

Previous transfer learning methods based on deep network assume the knowledge should be transferred between the same hidden layers of the source domain and the target domains. This assumption doesn’t always hold true, especially when the data from the two domains are heterogeneous with different resolutions. In such case, the most suitable numbers of layers for the source domain data and the target domain data would differ. As a result, the high level knowledge from the source domain would be transferred to the wrong layer of target domain. Based on this observation, “where to transfer” proposed in this paper might be a novel research area. We propose a new mathematic model named DT-LET to solve this heterogeneous transfer learning problem. In order to select the best matching of layers to transfer knowledge, we define specific loss function to estimate the corresponding relationship between high-level features of data in the source domain and the target domain. To verify this proposed cross-layer model, experiments for two cross-domain recognition/classification tasks are conducted, and the achieved superior results demonstrate the necessity of layer correspondence searching.
Interpretation of buried basement in the southwestern Athabasca Basin, Canada, from integrated geophysical and geological datasets

Recent discoveries of basement-hosted uranium deposits in the Patterson Lake corridor in the southwestern Athabasca Basin of Canada have brought vigorous exploration interest to the region. New lithostratigraphic constraints, geochronology and airborne geophysical surveys have dramatically improved the understanding of the host basement geology, warranting a re-examination of the remote predictive mapping and geophysical responses of the buried basement rocks. This study took a two-step approach to examine the regional basement geology and architecture. First, a mosaic of the long-wavelength response of potential field (gravity and magnetic) datasets was examined to divide the basement into regional domains based on bulk physical property variations. The interpretive geological model was then refined using textural and lineament analysis of new airborne gravity and magnetic datasets, geological drill hole logs and magnetic susceptibility measurements. The new basement map identifies and updates major features including a crustal-scale structure that separates the southern Tantato Domain from the newly defined eastern Taltson Domain. This structure may have played a role in localizing fluid flow in the Patterson Lake corridor, defining the spatial extents of structurally controlled buried felsic intrusions, and redefines the boundaries of the Taltson, Clearwater and Tantato Domains. In addition, the potential field enhancements delineated significant regional faults that controlled the geometry of Paleoproterozoic cover sequences and have implications for understanding the crustal architecture of the southern Rae Province. These new interpretations shed light on the tectonic history of the region to support on-going exploration activities and delineate regionally prospective areas in this understudied area of the Canadian Shield. Thematic collection: This article is part of the Uranium Fluid Pathways collection available at: https://www.lyellcollection.org/cc/uranium-fluid-pathways
Identifying Geochemical Anomalies Associated with Gold Mineralization Using Factor Analysis and Spectrum–Area Multifractal Model in Laowan District, Qinling-Dabie Metallogenic Belt, Central China

The Laowan deposit is a typical gold deposit in the Qinling-Dabie metallogenic belt, which produces the most gold resources in Central China. After being explored for decades, follow-up exploration requires additional theoretical support. In this study, the factor analysis (FA) and spectrum–area (S–A) multifractal model were used to process multi-element geochemical data from 369 samples collected in the study area for identifying the geochemical anomalies associated with gold mineralization. The results showed that: (1) the mean Au content in this region is up to 1000 times higher than the Au background values of the upper crust of the South Qinling unit; (2) the factor analysis revealed that Au, Ag, Cu, As, Sb, and S can be used as direct ore-prospecting criteria; (3) the observed elemental zonation is consistent with the zonation of metallic elements in the magmatic–hydrothermal system. This supports the magmatic–hydrothermal origin of the Laowan deposit; (4) the spectrum–area fractal model can help to decompose the geochemical patterns in a complex geological setting. The decomposed geochemical anomaly map obtained by the S–A multifractal model indicated that highly anomalous areas have a great relationship with the Au occurrence and can be a guidance for further exploration in the study area.
Optimizing NMR quantum information processing via generalized transitionless quantum driving

High performance quantum information processing requires efficient control of undesired decohering effects, which are present in realistic quantum dynamics. To deal with this issue, a powerful strategy is to employ transitionless quantum driving (TQD), where additional fields are added to speed up the evolution of the quantum system, achieving a desired state in a short time in comparison with the natural decoherence time scales. In this paper, we provide an experimental investigation of the performance of a generalized approach for TQD to implement shortcuts to adiabaticity in nuclear magnetic resonance (NMR). As a first discussion, we consider a single nuclear spin-$\frac{1}{2}$ system in a time-dependent rotating magnetic field. While the adiabatic dynamics is violated at a resonance situation, the TQD Hamiltonian is shown to be robust against resonance, allowing us to mimic the adiabatic behavior in a fast evolution even under the resonant configurations of the original (adiabatic) Hamiltonian. Moreover, we show that the generalized TQD theory requires less energy resources, with the strength of the magnetic field less than that required by standard TQD. As a second discussion, we analyze the experimental implementation of shortcuts to single-qubit adiabatic gates. By adopting generalized TQD, we can provide feasible time-independent driving Hamiltonians, which are optimized in terms of the number of pulses used to implement the quantum dynamics. The robustness of adiabatic and generalized TQD evolutions against typical decoherence processes in NMR is also analyzed.
Multi-UAV Cooperative Mission Assignment Algorithm Based on ACO method

Taking multiple unmanned aerial vehicles (UAVs) cooperative combat field as background, a systemic research on multiple UAVs cooperative mission assignment algorithm based on ant colony optimization method is given in this paper. Multi-UAV cooperative mission assignment is an important part of multi-UAV cooperative combat field, which is also the security guarantee for multi-UAV system to accomplish cooperative suppression of enemy air defences. The essence of multi-UAV cooperative mission assignment is a kind of complicatedly combined optimization problem that has the characteristics as multiple objectives, multiple constraints, non-linear, strong coupling, etc. Thus, as a multi-parameter, multi-constrained NP-hard problem, multi-UAV cooperative mission assignment area is similar to task assignment field and resource allocation category, which can be validly solved by intelligent optimization methods. To address this problem, we introduce ant colony optimization approach in this paper to establish a multi-UAV cooperative mission assignment strategy to meet the cooperative combat task requirements of multiUAV system. In the simulation part, using the example that multiple UAVs cooperatively attack multiple objectives as the application background, we give a comprehensively experimental analyses of multi-UAV cooperative mission assignment based on ant colony optimization method. Finally, the rationality and effectiveness of the proposed multi-UAV cooperative mission assignment algorithm are verified. Therefore, the certain significance of this paper has been proved.
Encryption-Then-Compression-Based Copyright Protection Scheme for E-Governance

This article discusses a joint encryption-then-compression-based watermarking scheme for copyright protection and content verification in variety of applications. In this scheme, redundant discrete wavelet transform is first performed to decompose the nonsubsampled contourlet transform host image, and then, the singular value decomposition is applied on transformed coefficients of the host image. Furthermore, same procedure is applied for both watermarks (image and PAN No.). The PAN number is encoded by the quick response (QR) code before hiding into the host image. Finally, 2D hyperchaotic encryption is used to encrypt the watermarked image to make our method more secure against attacks. After this, the encrypted image is then compressed via Huffman compression to reduce the size of the image at an acceptable quality of the reconstructed image. Furthermore, performance outcomes confirm better robustness and security of the method while preserving the image quality.
A Learning Framework for <i>n</i>-Bit Quantized Neural Networks Toward FPGAs

The quantized neural network (QNN) is an efficient approach for network compression and can be widely used in the implementation of FPGAs. This paper proposes a novel learning framework for n-bit QNNs, whose weights are constrained to the power of two. To solve the gradient vanishing problem, we propose a reconstructed gradient function for QNNs in back-propagation algorithm that can directly get the real gradient rather than estimating an approximate gradient of the expected loss. We also propose a novel QNN structure named n-BQ-NN, which uses shift operation to replace the multiply operation and is more suitable for the inference on FPGAs. Furthermore, we also design a shift vector processing element (SVPE) array to replace all 16-bit multiplications with SHIFT operations in convolution operation on FPGAs. We also carry out comparable experiments to evaluate our framework. The experimental results show that the quantized models of ResNet, DenseNet and AlexNet through our learning framework can achieve almost the same accuracies with the original full-precision models. Moreover, when using our learning framework to train our n-BQ-NN from scratch, it can achieve state-of-the-art results compared with typical low-precision QNNs. Experiments on Xilinx ZCU102 platform show that our n-BQ-NN with our SVPE can execute 2.9 times faster than with the vector processing element (VPE) in inference. As the SHIFT operation in our SVPE array will not consume Digital Signal Processings (DSPs) resources on FPGAs, the experiments have shown that the use of SVPE array also reduces average energy consumption to 68.7% of the VPE array with 16-bit.
Privacy and Energy Co-Aware Data Aggregation Computation Offloading for Fog-Assisted IoT Networks

With the exponential growth of the data generated by Internet of Things (IoT) devices, computation offloading becomes a promising method to alleviate the computation burden of local IoT device and improve processing latency.In order to address the bottleneck problem of limited resources in IoT device more efficiently and provide security guarantee in data processing and forwarding process, in this paper, we propose a privacy and energy co-aware data aggregation computation offloading for fogassisted IoT networks.Specifically, a fog-assisted three-layer security computing architecture is developed to counteract security threats and enable the aggregation operation can be performed in ciphertext.Meanwhile, a momentum gradient descent based energy-efficient offloading decision algorithm is developed to minimize the total energy consumption of computation tasks, which can achieve the optimal value with fast convergence rate.Finally, the security and performance evaluations reveal that the developed data aggregation offloading scheme is a secure data processing scheme and achieves significant performance advantage in energy consumption.For example, the total energy consumption can be reduced by an average of 23.1% compared with benchmark PGCO solution.
The fiber optic gyro 'adventure' at Photonetics, iXsea and now iXblue

Based on the experience acquired early from pioneering work at Stanford University and Thomson-CSF starting in the mid 70s, fiber optic gyro (FOG) R&amp;D began at Photonetics in the late 80s to yield OCTANS, a FOG-based inertial strapdown system providing attitude and gyro compassing, at the end of the 90s. This FOG activity was spun out from Photonetics in October 2000 to create iXsea with only 16 people. The product line was rapidly expanded with PHINS, an inertial-grade INS (Inertial Navigation System) and later with MARINS, a strategic-grade INS, as well as with ASTRIX systems developed for satellites in cooperation with EADS-Astrium (today Airbus Defence &amp; Space). In 2010, iXsea merged with several subsidiaries of its parent company, iXcore, to create iXblue. Among these subsidiaries were iXfiber, a maker of specialty fibers, and Photline, producing lithium-niobate integrated optics, hence allowing iXblue to fully master the key FOG components supply chain. Ten years later, the ‘adventure' is continuing and the former start-up is now quite a significant player in the inertial world, especially for high-grade applications. The cumulated number of high-performance 3-axis systems in service has grown to over 8,000, i.e. more than 25,000 FOG axes, with a bias stability ranging from 30 mdeg/h down to 15 &mu;deg/h, and an angular random walk (ARW) performance ranging from 8 mdeg/&radic;h down to 40 &mu;deg/&radic;h depending on the size of their sensing coils (3 m<sup>2</sup> to 1000 m<sup>2</sup>) and on the application!
A Quaternion-Embedded Capsule Network Model for Knowledge Graph Completion

Knowledge graphs are collections of factual triples. Link prediction aims to predict lost factual triples in knowledge graphs. In this paper, we present a novel capsule network method for link prediction taking advantages of quaternion. More specifically, we explore two methods, including a relational rotation model called QuaR and a deep capsule neural model called CapS-QuaR to encode semantics of factual triples. QuaR model defines each relation as a rotation from the head entity to the tail entity in the hyper-complex vector space, which could be used to infer and model diverse relation patterns, including: symmetry/anti-symmetry, reversal and combination. Based on these characteristics of quaternions, we use the embeddings of entities and relations trained from QuaR as the input to CapS-QuaR model. Experimental results on multiple benchmark knowledge graphs show that the proposed method is not only scalable, but also able to predict the correctness of triples in knowledge graphs and significantly outperform the existing state-of-the-art models for link prediction. Finally, the evaluation of a real dataset for search personalization task is conducted to prove the effectiveness of our model.