Reviewerly/exHarmony huggingface dataset:
Object Detection Based on Embedding Internal and External Knowledge

Object detection has great application value. It is frequently used in many fields such as industry, transportation, security, aerospace, etc. In the object detection task, the objects are inevitably occluded, blurred, etc. At the same time, the multi-object detection datasets inevitably have certain data bias and long-tailed distribution, which makes the model unable to extract complete visual features and seriously hinders the object detection. Therefore, we propose an object detection algorithm based on embedding internal and external knowledge (EIEK) into the detection network to enrich the feature representation and guide the model to get better detection performance. First, the basic object detection framework is built on Faster RCNN with Swin Transformer. And then, the internal and external knowledge embedding modules are presented to give the model spatial awareness and semantic understanding abilities. The experiments on multiple object detection datasets show the superiority of our EIEK and it achieves improvements of the mAP by 1.2%, 2.2%, and 3.1% on the PASCAL VOC 2007, MS COCO 2017, and ADE20k datasets, respectively.
Optimal Design of Digital FIR Band Pass Filter using Lightning Attachment Procedure Optimization Algorithm

The Lightning Attachment Procedure Optimization (LAPO) approach, a physical-based metaheuristic optimization tool, is used in this study to demonstrate the design of a digital finite impulse response (FIR) 20 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> order band pass filter (BPF). In order to achieve the required ideal band pass filter response, the design aims to find such optimal filter coefficients that minimize their magnitude and phase errors. The simultaneous minimizing of pass band ripples and stop band ripples is a key component of filter design's objective function. The extensive simulation results and Wilcoxon rank-sum test at 99% level of confidence show how well the LAPO algorithm works in finding the optimal coefficients of the digital FIR BPF in comparison to the Cuckoo Search Algorithm (CSA) and Particle Swarm Optimization (PSO). For this design issue, the LAPO method achieves faster convergence than the CSA and PSO algorithms since it has no parameter adjustment.
Coarse Error Elimination Method for Image Matching Based on Topological Structure and Adaptive Local Space Constraint

Feature-based image matching is the key technology of computer vision and image understanding. Local invariant feature point matching is a classical and popular method for image matching, and the coarse error elimination method is often used to improve the accuracy of matching results. However, complex spatial transformation representation relations existed between images in the multi-object scene. The traditional coarse error elimination method is difficult to effectively remove the matching error by constructing a single transformation matrix. Thus, this paper proposes a coarse error elimination algorithm for multi-object image matching based on adaptive local space constraints. The main idea of this paper is to construct the adaptive size local space with prior knowledge of rough matching, and mine the affine constraints of the local space to remove outliers. Experimental results show the effectiveness of the proposed method, compared with six main coarse error elimination methods (AdLAM, NBCS, LLT, VFC, GLOF, RANSAC). And the proposed method can improve the matching accuracy by about 32.6% in this experiment.
Low-Resource Speech Keyword Search Based on Residual Neural Network

Low-resource speech keyword search is usually based on DTW template matching. However, this method has low precision and is sensitive to speaker or recording environment changes. This paper proposes a low-resource speech keyword search method based on image recognition. Bottleneck features are extract through a pre-trained model based on multi-language to generate similarity matrix images. ResNet is used to classify similarity matrix images. According to the image classification result, it is determined whether the speech keyword appears. This model does not require prior language knowledge and abundant corpus resources, so it is suitable for low-resource speech keyword search. We validate the performance of the model on different datasets. On SWS 2013, the model recall, accuracy, and F1 increased by 20.83%, 17.59%, and 19.18%, respectively. On QUESST 2014, they increased by 24.44%, 24.96%, and 24.70%, respectively. Experimental results show that the propose method is effective.
Generative adversarial networks combined with deep feature interpolation for image style transfer

Image style transfer technique is a popular topic in computer vision. This paper proposes a reconstructed generator model based on Cycle Generative Adversarial Networks (CycleGAN) for image style transfer tasks. Firstly, deep feature interpolation is performed to extract image high-level semantic representations in deep feature space, and then it is combined with CycleGAN to construct a new deep network model. In this model, the generator network is organized by sampling layers and residual modules. According to the above process, a new deep feature generator is designed to realize the cross-region style transfer. Experiments are carried out on several standard datasets, horse2zebr, monet2photo and summer2winter. The model is evaluated through the objective evaluation indicators of PSNR and SSIM. Experimental results show a better performance improvement compared to the classical CycleGAN.
Progress and Prospects of Quantum Algorithms

Quantum computers are designed to outperform standard computers by running quantum algorithms. Fields in which quantum algorithms can be applied include cryptography, search and optimization, simulation of quantum systems and solving large systems of linear equations. Here we briefly survey some known quantum algorithms, with an emphasis on a broad overview of their applications rather than their technical details. We focus to introduce the development of entire field, and provide a comprehensive and accurate understanding concerning this field, which may be helpful for interested researchers in this area.
Quantitative Evaluation of Multi-Sensor Image Registraction Feature Descriptor

Multi-sensor image registration is a basic and important issue in the field of remote sensing applications. At present, many algorithms have not directly evaluated and analyzed the feature descriptor design of the algorithm. Taking the feature descriptors of RIFT, SIFT, SAR-SIFT and HAPCG as the analysis objects, this paper designs experiments to analyze their stability under gray distortion and local geometric distortion, gives a quantitative evaluation, and reveals the contribution of the feature descriptor of each multi-sensor image registration algorithm in the process of multi-sensor image registration.
Análisis de datos utilizando web scraping para repertorio otomí educativo en dispositivos móviles android

Around the world there are about six thousand languages, among the nations most threatened languages Mexico is one of the first places according to the Atlas of Endangered Languages in the World Organization of the United Nations Educational Scientific and Cultural Organization. In the State of Mexico, INEGI registered a total of 97,820 Otomi language speakers, most of whom live in the etnorregión. However one of the current social situations in the country and particularly in the State of Mexico is the loss of identity by new generations on their roots, customs, traditions and culture the interest of young people to preserve this language it is almost nil. On the other hand mobile technology it is becoming a revolution in our society. The use of scraping and data collection will provide the information that is available on the web, facilitating the search for words and integrating an application, resulting in a more efficient translation to support the use, teaching and learning of the Otomi language teens / young Otomi communities north of the State of Mexico.
Pilot DroneUI: An User Interface to Control UAV using Robot Operating System

This paper presents ongoing research work on developing a Graphical User Interface (GUI) to control drones using the Robot Operating System (ROS) and Qt framework from the ground station PC. Due to an increase in work complexity, robotic application development is becoming exhaustive and demanding to use multiple terminal tabs and need for the repeated multiple commands execution while conducting experiments. The combination of ROS and Qt frameworks allows an opportunity to develop a robust and simple-yet-effective GUI for controlling robots and autonomous vehicles. The novel pilot GUI transmits fundamental navigational commands to the drone for maneuvering in the 3-dimensional space. A user study was done to evaluate the pilot GUI's performance. The findings indicated that the developed pilot GUI is simple to use and operate the drone.
Singing Voice Synthesis with Vibrato Modeling and Latent Energy Representation

This paper proposes an expressive singing voice synthesis system by introducing explicit vibrato modeling and latent energy representation. Vibrato is essential to the naturalness of synthesized sound, due to the inherent characteristics of human singing. Hence, a deep learning-based vibrato model is introduced in this paper to control the vibrato's likeliness, rate, depth and phase in singing, where the vibrato likeliness represents the existence probability of vibrato and it would help improve the singing voice's naturalness. Actually, there is no annotated label about vibrato likeliness in existing singing corpus. We adopt a novel vibrato likeliness labeling method to label the vibrato likeliness automatically. Meanwhile, the power spectrogram of audio contains rich information that can improve the expressiveness of singing. An autoencoder-based latent energy bottleneck feature is proposed for expressive singing voice synthesis. Experimental results on the open dataset NUS48E show that both the vibrato modeling and the latent energy representation could significantly improve the expressiveness of singing voice. The audio samples are shown in the demo website <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://mango321321.github.io/ExpressiveSing/.
Neural Network Decision-Making Criteria Consistency Analysis via Inputs Sensitivity

Neural networks (NNs) have demonstrated exciting results on various tasks within the last decade. For example, the performance on image classification tasks has been improved dramatically. However, the performance evaluations are often based on a black-box performance, such as accuracy, while insightful analysis of the black-box, such as the prediction formation mechanism, is often missing. Empirically, a NN usually produces a stable overall performance on the same task across multiple training trials when treating it as a black-box. However, when unveiling the black-box, the performance is usually volatile. The decision-making criteria learned by the training trials are often significantly different, which is problematic in many ways. We believe achieving consistent criteria between different training trials is equally important to achieving high performance, if not more. This work, firstly, evaluates the decision-making criteria of NNs via inputs sensitivity using feature-attribution explanation methods in combination with computational analysis and clustering analysis. Through intensive experimentation, we find that decision-making criteria are easily distinguishable between training trials of the same architecture and task, suggesting the criteria learned between training trials are significantly inconsistent. To mitigate this inconsistency, we propose three general training schemes. Our demonstration result shows that the proposed methods effectively reduce the inconsistency of the decision-making criteria learned by different training trials while maintaining the overall performance.
Co-design with Cultural for Creative Design Industry

This paper is about co-design and the cultural and creative industry. It explains co-design and cultural and creative industries and discusses the current situation of global tourism under the Covid-19 pandemic. The research is conducted to find out and evaluate the current situation of integrating co-design with cultural and creative design, indicating some of the current drawbacks and discussing new opportunities and suggestions for it. This research is aiming for the young generation but also for the mass public to engage better interaction with cultural heritage, eventually can promote cultural influence.
Comparative Study of Classification Algorithms in Sign Language Recognition

Sign language can be used to converse with other people who are HSI. But there is the issue of many people not being able to properly understand due to the lack of people who know sign language. Therefore, image processing and different algorithms with machine learning can be used to recognize signs made by people. Even though sign language recognition has been possible there are many variations of sign language while most only focus on a single type of sign language while majority use ASL. In this article, two types of sign languages have been used to classify and train different types of machine learning models. The images have been preprocessed using background subtraction and then finding the edges using canny edge detection. This articles also goes through the different methods in which the image can be preprocessed. These have been performed using OpenCV library. Two different feature extraction algorithms have been used, namely ORB and SIFT, to perform feature extraction. K-Means Clustering has been used on the features and a histogram is obtained for each sign by using K-Means. Two different datasets, each for ISL and ASL have been split for training and testing in 80:20 ratio and then different machine learning models from sklearn library have been trained using this and the results compared.
Mitigating qubit leakage errors in quantum circuits with gadgets and post-selection

Leakage is a quantum error process in which a qubit ends up in a state outside of the computational subspace. In trapped-ion hyperfine qubits, leakage can occur as a result of spontaneous emission from an excited state during a two-qubit gate. This error can be detected by a circuit gadget that couples a data qubit to an ancilla qubit, followed by a measurement of the ancilla. The effects of leakage errors on the circuit can then be removed by post-selecting shots in which leakage was not detected. We first experimentally demonstrate improved two-qubit gate fidelity as measured by randomized benchmarking and cycle benchmarking. We then benchmark the use of leakage detection on system-level mirror benchmarking circuits. Finally, we present a protocol to measure the failure rate of the leakage detection gadget, defined as the probability that the gadget produces an error on the data qubit while not heralding the ancilla.
Monte Carlo Tree Search: A Survey of Theories and Applications

Monte Carlo Tree Search (MCTS) is a state-of-the-art family of algorithms that combine Monte Carlo evaluations with tree search. It is attracting more and more interest due to its great success in the Grand Challenge of computer Go, and it has been proven effective in various other decision-making scenarios. This paper is a survey of the literatures to date, aims to make a comprehensive overview of the background history, basic theory, and applications in various fields of MCTS. We outline variants and policy enhancement techniques that have been proposed, these make MCTS applicable to more kinds of scenarios. Finally we point out that the use of MCTS is still a great challenge in realtime scenarios, most of which have imperfect information.
Spatial-Attention and Demographic-Augmented Generative Adversarial Imputation Network for Population Health Data Reconstruction

As a fundamental component of the public health system, population health monitoring plays an important role in health policy-shaping. However, due to the high-cost nature of traditional data collection approaches, many sparse-sampling-completion algorithms are proposed to solve this problem. Existing data-completion methods are usually based on adjacent-spatial correlations, but this correlation isn't sufficient to ensure accurate inference when prevalence data for its neighboring areas are also missing due to cost constraints. To tackle this problem, we propose a novel deep-learning-based prevalence inference model called Spatial-attention and Demographic-augmented Generative Adversarial Imputation Network (SDA-GAIN). SDA-GAIN can improve accuracy by learning novel “health semantic space similarities” between cross-space areas. The key insight of SDA-GAIN is that we use the Transformer-based model to learn healthy semantic similarities between areas, and use the GAN-based model to make a high-accuracy completion. We further introduce demographic data to augment the model's ability to learn a better health semantic representation through using CNN. Extensive experiments show that SDA-GAIN outperforms other state-of-the-art approaches at low sampling rates (lower than 30%) which has a significant benefit on saving sampling costs. Also by visualizing the health semantic similarity learned by SDA-GAIN, the results are very similar to the real situation.
Animal Pose Estimation Algorithm Based on the Lightweight Stacked Hourglass Network

Pose estimation has been a hot topic in the field of machine vision in recent years.Animals exist widely in nature, and the analysis of their shape and movement is important in many fields and industries.In the pose estimation task, to improve the detection accuracy, the existing models often need to consume a lot of computing and memory resources.Therefore, it is a key problem for the pose estimation methods to carry out a lightweight model and reduce the computational overhead on the premise of ensuring model accuracy.In this paper, we focus on the structure of the convolutional neural network in animal pose estimation, construct a lightweight and efficient stacked hourglass network model oriented to optimize the balance of model computation and accuracy, and implement the application algorithm design based on it.Aiming at the problem of large parameters in depthwise convolutional neural networks, a lightweight residual module is proposed, that is, based on the lightweight efficient channel attention improved conditional channel-weighted method (ICCW-Bottle), thereby reducing the weight of the network and obtaining the feature information of different scales.Given the problem that a large amount of feature information is easily lost after the network pooling operation, a lightweight dual-branch fusion module is proposed that fully integrates high-level semantic information and low-level detailed features under the condition of a small number of parameters.Finally, the same as the CC-SSL method: the model is trained jointly using synthetic and real animal datasets, but the CC-SSL method does not take into account the computational power of the model, which consumes a lot of time and memory to run.Through experiments, it is known that compared with the CC-SSL method, the PCK @ 0.05 of this method is increased by 5.5 % on the TigDog dataset.The model in this paper reduces the number of parameters and calculations of the network while ensuring less information loss and model accuracy.The ablation experiment verifies the advancement and effectiveness of the overall network.
Method for Aircraft Path Planning Based on Triangle Inequality Secondary Optimization

Aiming at the problem of route point redundancy for heuristic intelligent algorithm planning, firstly, the dynamic programming model of aircraft path planning based on secondary optimization is established, the process and search principle of dynamic programming function is proposed; then, based on triangle inequality, the bidirectional dynamic programming algorithm is utilized to do secondary optimization of track under different 2-D planning environments and different step lengths, the optimal strategy and the process of algorithm are presented; finally, the applicability and the optimal performance of this method is verified by the simulation comparison experiment.
Managing the Root Causes of “Internal API Hell”: An Experience Report

When growing the software infrastructure for a large-scale scientific project (namely ALeRCE, Automatic Learning for the Rapid Classification of Events), we observed an “internal API hell” phenomenon in which numerous and various API issues coexist and are inextricably interwoven with each other. Driven by this observation, we conducted a set of investigations to help both understand and deal with this complicated and frustrating situation. Through individual interviews and group discussions, our investigation reveals two root causes of the “internal API hell” in ALeRCE, namely (1) an internal API explosion and (2) an increased “churn” of development teams. Given the nature of the system and the software project, each root cause is inherent and unavoidable. To demonstrate our ongoing work on tackling that “hell”, we discuss five API issues and their corresponding solutions, i.e., (1) using a multi-view catalog to help discover suitable APIs, (2) using a publish-subscribe channel to assist API versioning management and negotiation, (3) improving the quality of API adoption through example-driven delivery, (4) using operation serialisation to facilitate API development debugging and migration, and (5) enhancing the usability of long and sophisticated machine learning APIs by employing a graphical user interface for API instantiation. We also briefly consider the threats to validity of our project-specific study. On the other hand, we argue that the root causes and issues are likely to recur for other similar systems and projects. Thus, we urge collaborative efforts on addressing this emerging type of “hell” in software development.
Reactive Neural Path Planning with Dynamic Obstacle Avoidance in a Condensed Configuration Space

We present a biologically inspired approach for path planning with dynamic obstacle avoidance. Path plan-ning is performed in a condensed configuration space of a robot generated by self-organizing neural networks (SONN). The robot itself and static as well as dynamic obstacles are mapped from the Cartesian task to the configuration space by precomputed kinematics. The condensed space represents a cognitive map of the environment, which is inspired by place cells and the concept of cognitive maps in mammalian brains. Generation of training data as well as the evaluation are performed on a real industrial robot accompanied by simulations. To evaluate reactive collision-free online planning within a changing environment, a demonstrator was realized. Then, a comparative study regarding sample-based planners was carried out. The robot is able to operate in dynamically changing environments and re-plan its motion trajectories within impressing 0.02 seconds, which proofs the real-time capability of our concept.