Reviewerly/exHarmony huggingface dataset:
Object Detection Network Based on Module Stack and Attention Mechanism

Currently, visual computer applications based on convolutional neural networks are rapidly developing. However, several problems remain: (1) high-quality graphics processing equipment is needed, and (2) the trained network model has several unnecessary convolution operations. These problems result in a single-stage target detection network that often requires unnecessary computing power and is difficult to apply to equipment with insufficient computing resources. To solve these problems, based on YOLOv5, a YOLOv5-L (YOLOv5 Lightweight) network structure is proposed. This network is improved using YOLOv5. First, to enhance the inference speed of the detector on the CPU, the PP-LCNet (PaddlePaddle-Lightweight CPU Net) is employed as the backbone network. Second, the focus module is removed, and the end convolution module in the head network is replaced by a deep separable convolution module, which eliminates redundant operations and reduces the amount of computation. The experimental results show that YOLOv5-L enables a 48% reduction in model parameters and computation compared to YOLOv5, a 35% increase in operation speed, and a less than 2% reduction in accuracy, which is significant in the environment of low-performance computing equipment.
Concatenation Schemes for Topological Fault-tolerant Quantum Error Correction

We investigate a family of fault-tolerant quantum error correction schemes based on the concatenation of small error detection or error correction codes with the three-dimensional cluster state. We propose fault-tolerant state preparation and decoding schemes that effectively convert every circuit-level error into an erasure error, leveraging the cluster state's high threshold against such errors. We find a set of codes for which such a conversion is possible, and study their performance against the standard circuit-level depolarizing model. Our best performing scheme, which is based on a concatenation with a classical code, improves the threshold by <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>16.5</mml:mn><mml:mi mathvariant="normal">&amp;#x0025;</mml:mi></mml:math> and decreases the spacetime overhead by <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>32</mml:mn><mml:mi mathvariant="normal">&amp;#x0025;</mml:mi></mml:math> compared to the scheme without concatenation, with each scheme subject to a physical error rate of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mn>10</mml:mn><mml:mrow class="MJX-TeXAtom-ORD"><mml:mo>&amp;#x2212;</mml:mo><mml:mn>3</mml:mn></mml:mrow></mml:msup></mml:math> and achieving a logical error rate of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mn>10</mml:mn><mml:mrow class="MJX-TeXAtom-ORD"><mml:mo>&amp;#x2212;</mml:mo><mml:mn>6</mml:mn></mml:mrow></mml:msup></mml:math>.
Research on quality evaluation of online teaching platform based on RS-BP Neural Network

Online teaching platforms have developed into mainstream knowledge learning and exchange platform. The research on the quality evaluation of online teaching platforms and the construction of an applicable and scientific evaluation index system model can help explore the key factors affecting the quality of online teaching platforms and provide some references for evaluating online teaching platforms and improving online teaching quality. This study combines the rough set theory (RS) with the BP (Back Propagation) neural networks to build an RS-BP neural network model to evaluate the quality of online teaching platforms. Firstly, an initial online teaching platform quality evaluation index system is constructed based on knowledge transfer theory from four aspects: course content, knowledge transmitter, knowledge receiver and teaching platform. Then, 12 core evaluation indicators were generated by attribute reduction using rough set theory, and the weights of each core indication were determined. The normalized data input was then trained, validated, and tested to generate a rough set neural network quality evaluation model for online teaching platforms. After that, three representative online education platforms of content, interaction and compatibility are selected for empirical research. The accuracy of the model is first tested by the error between the simulated and output values, after which the core metric scores and the overall scores are calculated for the three types of platforms. The empirical results demonstrate that the model has certain advantages in terms of index simplification and adaptive training when evaluating online teaching platforms, as well as strong operability and practicality. The evaluation results show that the content online teaching platform has the highest comprehensive score, followed by the compatible and interactive online teaching platforms. According to the index scores, the quality of the course content, stage assessments, and contact between professors and students were identified as major elements influencing the quality of the online teaching platform. Finally, suggestions for optimization for each of the three types of online teaching platforms were made based on the core indicators and their weights, as well as the scores and characteristics of the three types of online teaching platforms, with the goal of improving the quality of online teaching platforms.
An Efficient Detection Framework for Aerial Imagery Based on Uniform Slicing Window

Drone object detection faces numerous challenges such as dense clusters with overlapping, scale diversity, and long-tail distributions. Utilizing tiling inference through uniform sliding window is an effective way of enlarging tiny objects and meanwhile efficient for real-world applications. However, merely partitioning input images may result in heavy truncation and an unexpected performance drop in large objects. Therefore, in this work, we strive to develop an improved tiling detection framework with both competitive performance and high efficiency. First, we formulate the tiling inference and training pipeline with a mixed data strategy. To avoid truncation and handle objects at all scales, we simultaneously perform global detection on the original image and local detection on corresponding sub-patches, employing appropriate patch settings. Correspondingly, the training data includes both original images and the patches generated by random online anchor-cropping, which can ensure the effectiveness of patches and enrich the image scenarios. Furthermore, a scale filtering mechanism is applied to assign objects at diverse scales to global and local detection tasks to keep the scale invariance of a detector and obtain optimal fused predictions. As most of the additional operations are performed in parallel, the tiling inference remains highly efficient. Additionally, we devise two augmentations customized for tiling detection to effectively increase valid annotations, which can generate more challenging drone scenarios and simulate the practical cluster with overlapping, especially for rare categories. Comprehensive experiments on both public drone benchmarks and our customized real-world images demonstrate that, in comparison to other drone detection frameworks, the proposed tiling framework can significantly improve the performance of general detectors in drone scenarios with lower additional computational costs.
DUA: A Domain-Unified Approach for Cross-Dataset 3D Human Pose Estimation

Human pose estimation is an important Computer Vision problem, whose goal is to estimate the human body through joints. Currently, methods that employ deep learning techniques excel in the task of 2D human pose estimation. However, the use of 3D poses can bring more accurate and robust results. Since 3D pose labels can only be acquired in restricted scenarios, fully convolutional methods tend to perform poorly on the task. One strategy to solve this problem is to use 2D pose estimators, to estimate 3D poses in two steps using 2D pose inputs. Due to database acquisition constraints, the performance improvement of this strategy can only be observed in controlled environments, therefore domain adaptation techniques can be used to increase the generalization capability of the system by inserting information from synthetic domains. In this work, we propose a novel method called Domain Unified approach, aimed at solving pose misalignment problems on a cross-dataset scenario, through a combination of three modules on top of the pose estimator: pose converter, uncertainty estimator, and domain classifier. Our method led to a 44.1mm (29.24%) error reduction, when training with the SURREAL synthetic dataset and evaluating with Human3.6M over a no-adaption scenario, achieving state-of-the-art performance.
New joint probabilistic data association algorithm based on variational Bayesian adaptive moment estimation

To improve the accuracy of multiple target tracking in the clutter environment, a new joint probabilistic data association (JPDA) algorithm based on variational Bayesian adaptive moment estimation is proposed. First, considering the existence of measurements, the posterior distribution of the target state in JPDA is composed of two parts of probability weighting, that is, the posterior distribution of the target state that the real measurement exists in the association gate and the posterior distribution of the target state that the real measurement does not exist in the association gate. By combining the conjugate properties of the prior and posterior distributions, the prior distributions of the target state in the two cases are classified to provide more accurate a priori information to filter, so as to improve the accuracy of data association. Second, considering the coupling effect between state estimation and data association process, combined with variational Bayesian inference, the problem of minimizing Kullback–Leibler divergence is transformed into the problem of maximizing the evidence lower bound, thereby effectively measuring the distance between the posterior distribution of target state estimation and the real posterior distribution, so as to improve the accuracy of data association again from the perspective of optimizing nonlinear filter. Finally, the adaptive momentum estimation strategy is introduced to iteratively solve the variable distribution that meets the maximization of the evidence lower bound, and the optimization of the posterior distribution of the target state is completed. Theoretical derivation and simulation experiments are conducted to verify the feasibility and effectiveness of the algorithm.
Translation from a Distinguishable to Indistinguishable Two-Photon State

Indistinguishability is a crucial element for quantum interference in scalable quantum information processing. Here, we demonstrate the ability to erase the distinguishability of a nondegenerate two-photon state using Bragg-scattering four-wave mixing (BS-FWM) by tuning the idler-photon frequency. We achieve a maximum translation efficiency of 93.2 ± 2.0% and verify the indistinguishability using a Hong–Ou–Mandel interferometer. The frequency anticorrelation of the input state is preserved as the two-photon states are translated from the distinguishable to indistinguishable state, as confirmed by the joint spectral intensity. Our results demonstrate precise control of indistinguishability using BS-FWM, which is a critical step toward large-scale quantum information processing including scalable linear quantum computing and long-distance quantum communications.
Hand gesture recognition method using FMCW radar based on multidomain fusion

Abstract Radar-based hand gesture recognition is a potential noncontact human–machine interaction technique. To enhance the recognition performance of hand gesture, a multidomain fusion-based recognition method using frequency-modulated continuous wave radar is proposed in this article. The received raw echo data of gestures is preprocessed to obtain the range–time matrix, Doppler–time matrix, and range–Doppler–frame tensor. The obtained three-domain radar data corresponding to each gesture are input into the three-channel convolutional neural network for feature extraction. In particular, the extracted features from three-domain data are fused with learnable weight matrices to obtain the final gesture classification results. The experimental results have shown that the classification accuracy of the proposed multidomain fusion network based on learning weight matrix-based fusion is 98.45%, which improves the classification performance compared with the classic average-based fusion and concatenation fusion.
Mapping natural language procedures descriptions to linear temporal logic templates: an application in the surgical robotic domain

Abstract Natural language annotations and manuals can provide useful procedural information and relations for the highly specialized scenario of autonomous robotic task planning. In this paper, we propose and publicly release AUTOMATE, a pipeline for automatic task knowledge extraction from expert-written domain texts. AUTOMATE integrates semantic sentence classification, semantic role labeling, and identification of procedural connectors, in order to extract templates of Linear Temporal Logic (LTL) relations that can be directly implemented in any sufficiently expressive logic programming formalism for autonomous reasoning, assuming some low-level commonsense and domain-independent knowledge is available. This is the first work that bridges natural language descriptions of complex LTL relations and the automation of full robotic tasks. Unlike most recent similar works that assume strict language constraints in substantially simplified domains, we test our pipeline on texts that reflect the expressiveness of natural language used in available textbooks and manuals. In fact, we test AUTOMATE in the surgical robotic scenario, defining realistic language constraints based on a publicly available dataset. In the context of two benchmark training tasks with texts constrained as above, we show that automatically extracted LTL templates, after translation to a suitable logic programming paradigm, achieve comparable planning success in reduced time, with respect to logic programs written by expert programmers.
Recipe Sharing

Today’s world is very much dependent on the web applications. "Recipe Sharing” website is a project that presents a web application for users to contribute their own recipes. The website features a user authentication system to allow registered users to access and manage their own recipes, as well as new functionality help users find specific recipes. In addition, the website provides the ability for users to add, edit, and delete their own recipes, making it a dynamic and interactive platform for food enthusiasts and home cooks. The project aims to create a useful resource for users to find and share new and interesting recipes, while also showcasing the skills and abilities of the student team in developing web applications. The website may utilize various technologies and frameworks, such as React, HTML, CSS, JavaScript, and server-side languages, to create a seamless and engaging user experience
Nonlinear feature selection using sparsity-promoted centroid-encoder

Abstract The contribution of our work is two-fold. First, we propose a novel feature selection technique, sparsity-promoted centroid-encoder (SCE). The model uses the nonlinear mapping of artificial neural networks to reconstruct a sample as its class centroid and, at the same time, apply a ℓ 1 -penalty to the weights of a sparsity promoting layer, placed between the input and first hidden layer, to select discriminative features from input data. Using the proposed method, we designed a feature selection framework that first ranks each feature and then, compiles the optimal set using validation samples. The second part of our study investigates the role of stochastic optimization, such as Adam, in minimizing ℓ 1 -norm. The empirical analysis shows that the hyper-parameters of Adam (mini-batch size, learning rate, etc.) play a crucial role in promoting feature sparsity by SCE. We apply our technique to numerous real-world data sets and find that it significantly outperforms other state-of-the-art methods, including LassoNet, stochastic gates (STG), feature selection networks (FsNet), supervised concrete autoencoder (CAE), deep feature selection (DFS), and random forest (RF).
Visual Representations to Stimulate New Musicking Strategies in Live Coding

In live coding, the code can be considered as an archetypal form of score that notates formal processes. We aimed at investigating the possibility of using graphic visuals as a complementary form of descriptive score by visualising sound events using different time representations. To this end, we devised two visualisation systems (Time_X and Time_Z). Time_X represents time along the x -axis, while in Time_Z the objects overlap along an imaginary z -axis. Based on our previous personal experience with the system, such forms of visual scores can help to develop new musicking strategies while live coding. In this article, we wanted to broaden such reflections, and we used them as probes in a study with three live coders. After tailoring the two systems to the usual practice of the three participants, we asked them to use the systems for three weeks and keep a diary. At the end, we interviewed them. Based on their comments, we present some reflections on the use of graphic forms of visualisation in live coding, on how they can support musicking process, and to what extent such visuals can be considered scores.
Pavement dynamic monitoring data processing based on wavelet decomposition and reconfiguration methods

Early damage to asphalt pavements generally occurs due to the increasing traffic flow and the loads of vehicles, coupled with alternating high- and low-temperature cycles, freeze–thaw cycles, ultraviolet radiation, and other harsh environments. Several types of distress, such as rutting, cracking, and other damage, deteriorate the serviceability of asphalt pavements and shorten the road service life. Thus, the long-term structural mechanical response of asphalt pavements under the influence of loads and the environment is crucial data for the road sector, which provides guidance about road maintenance. Effectively processing the pavement dynamic monitoring data is a prerequisite to obtain the dynamic response of asphalt pavement structures. However, the dynamic monitoring data of pavements are often characterized by transient weak signals with strong noises, making it challenging to extract their essential characteristics. In this study, wavelet decomposition and reconstruction methods were applied to reduce the noise of pavement dynamic response data. The parameters of the signal-to-noise ratio (SNR) and root mean square error (RMSE) were introduced to compare and analyze the effect of the decomposition of two different wavelet functions: the symlet (sym) wavelet function and the Daubechies (db) wavelet function. The results showed that both the sym and db wavelet functions can effectively obtain the average similarity information and the detailed information of the dynamic response signals of the pavement, the SNR after the sym wavelet fixed-threshold denoising process is relatively higher, and the RMSE is smaller than that of the db wavelet. Thus, wavelet transformation exhibits good localization properties in both the time and frequency domains for processing pavement dynamic monitoring data, making it a suitable approach for handling massive pavement dynamic monitoring data.
Empirical exploration of critical challenges of requirements implementation in global software development

Abstract Requirements collection is difficult and a critical phase of software development life cycle, in particular in a global software development (GSD) environment. In GSD, clients and vendors are physically separated such that there exist challenges such as lack of face‐to‐face communication, language differences, culture variation, and time zone differences. The objective of current research is to identify critical challenges of requirement engineering in GSD. There is no work done yet to empirically analyze all possible challenges via questionnaire survey from software industries. This research paper empirically investigates and analyze the identified challenges from systematic literature review (SLR) based on a questionnaire survey. For this purpose, 50 respondents from different countries are organized. From this research, 13 challenges during requirements implementation in the context of GSD have been identified previously via SLR. These challenges were then evaluated using an empirical approach of questionnaire survey. The results from respondents were analyzed based on type of respondents, level of experience of respondents, and from client–vendor perspective. Finally, the challenges were prioritized based on its frequency of occurrences from the SLR and the questionnaire survey. The relationships between the challenges and the survey results were evaluated using the Spearman's correlation coefficient. The results produced a 0.835 Spearman's correlation coefficient at significance level ρ = 0.000, which showed a strong positive correlation between the outcome of SLR and survey with no significant difference. The implication of this research work lies in both fundamental and practical perspective. The prioritized set of challenges was provided based on SLR, and questionnaire survey acts as a knowledge base for both researchers and industrial practitioners. This work will help researchers to identify challenges in GSD projects or other software engineering areas.
A New Background-Based Extraction Algorithm under Factor Space Theory

Under the factor space theory, this paper proposes a new background base extraction algorithm to address the background base extraction algorithm being sensitive to outliers, the excessive number of iterations of the background base extraction algorithm, and the calculation time-consuming problem being too long. Based on the isolated forest algorithm, the background-based anomaly detection algorithm can identify and delete the anomalous points that will interfere with the background base extraction results, and the background-based anomaly detection uses each factor as an attribute to segment the data space, which is suitable for high-dimensional factor space; combined with α-shape, a new background base extraction algorithm, the base point detection algorithm, was proposed, which has low time and space complexity.
Bone whistle modeling method based on robust scan point tracking

Several excavations at the Hemudu site in Zhejiang Province have led to the discovery of more than 100 pieces of bird bones that were likely used to create a kind of musical instrument. However, in contrast to the Jiahu bone flute, it is not clear for what the Hemudu bone whistle was used, although it was probably used for musical purposes. In this paper, the 7000-year-old Hemudu bone whistle is reproduced with 3D scanning and printing technology. Therefore, we propose a robust scan point detection and tracking method that includes three main stages. First, we conducted robust point detection and selection where noise and irrelevant points were removed. Then, we propose to track the key points and conduct 3D model optimization. With the above efforts, we obtained the 3D model of the bone whistle, and the audio of the bone whistle is replicated. The purpose of the bone whistles is inferred and evaluated through audio analyses. The experimental results show that the method is feasible.