Reviewerly/exHarmony huggingface dataset:
Job Type Extraction for Service Businesses

Google My Business (GMB) is a platform that hosts business profiles, which will be displayed when a user issues a relevant query on Google Search or Google Maps. GMB businesses provide a wide variety of services, from home cleaning and repair, to legal consultation. However, the exact details of the service provided (a.k.a. job types), are often missing in business profiles. This places the burden of finding these details on the users. To alleviate this burden, we built a pipeline to automatically extract the job types from business websites. We share the various challenges we faced while developing this pipeline, and how we effectively addressed these challenges by (1) utilizing structured content to tackle the cold start problem for dataset collection; (2) exploiting context information to improve model performance without hurting scalability; and (3) formulating the extraction problem as a retrieval task to improve both generalizability, efficiency, and coverage. The pipeline has been deployed for over a year and is scalable enough to be periodically refreshed. The extracted job types are serving users of Google Search and Google Maps, with significant improvements in both precision and coverage.
Performance Evaluation of State-of-the-Art Texture Feature Extraction Techniques on Medical Imagery Tasks

Interpreting medical images is certainly a complex task which requires extensive knowledge [1]. According to Computer Aided Diagnosis (CAD) serves as a second opinion that will help radiologists in diagnosis and on the other hand Content-based Image Retrieval uses visual content to help users browse, search and retrieve similar medical images from a database based on the user’s interest [2-4]. The competency of the CBMIR system depends on feature extraction methods [5]. The textural features are very important to determine the content of a medical image. Textural features provide scenic depth, the spatial distribution of tonal variation, and surface orientation [6]. Therefore, this study seeks to compare and evaluate some of the hand-crafted texture feature extraction techniques in CBMIR. This is to help those concerned in enhancing CBIR systems to make informed decisions concerning the selection of the best textural feature extraction techniques. Since there is no clear indication of which of the various texture feature extraction techniques is best suited for a given performance metric when considering which of the techniques to choose for a particular study in CBMIR systems. The objective of this work, therefore, is to comparatively evaluate the performance of the following texture feature extraction techniques; Local Binary Pattern (LBP), Gabor Filter, Gray-Level Co-occurrence Matrix (GLCM), Haralick Descriptor, Features from Accelerated Segment Test (FAST) and Features from Accelerated Segment Test and Binary Robust Independent Elementary Features (FAST &amp;BRIEF) using the metrics; precision, recall, f1-score, mean squared error (MSE), accuracy and time. These techniques are coupled with specific similarity measure to obtain results. The results showed that LBP, Haralick Descriptor, FAST, and GLCM had the best results in terms of (Precision and Accuracy), Time, F1-Score, and Recall respectively. LBP had 82.05% and 88.23% scores for precision and accuracy respectively. The following scores represent the performance of the Haralick descriptor, FAST, and GLCM models respectively; 0.88s, 38.7%, and 44.82%. These test scores are obtained from datasets ranging from 1k-10.5k. Aside from LBP outperforming the other 5 models mentioned, it still outperformed the following proposed models ' [7]’, ‘Tamura texture feature and wavelet transform combined with Hausdorff distance - [8]’, ‘ [9]’ in terms of (precision, accuracy, and recall) and (precision and recall) respectively and probably f1-score (since f1-score is the weighted average of precision and recall). It is believed that an ensemble of LBP, Haralick descriptors, and Support Vector Machine (SVM) can represent a robust system for both medical image retrieval and classification.
Edge-Preserved Low-Rank Representation via Multi-Level Knowledge Incorporation for Remote Sensing Image Denoising

The low-rank models have gained remarkable performance in the field of remote sensing image denoising. Nonetheless, the existing low-rank-based methods view residues as noise and simply discard them. This causes denoised results to lose many important details, especially the edges. In this paper, we propose a new denoising method named EPLRR-RSID, which focuses on edge preservation to improve the image quality of the details. Specifically, we considered the low-rank residues as a combination of useful edges and noisy components. In order to better learn the edge information from the low-rank representation (LRR), we designed multi-level knowledge to further distinguish the edge part and the noise part from the residues. Furthermore, a manifold learning framework was introduced in our proposed model to better obtain the edge information, as it can find the structural similarity of the edge part while suppressing the influence of the non-structural noise part. In this way, not only the low-rank part is better learned, but also the edge part is precisely preserved. Extensive experiments on synthetic and several real remote sensing datasets showed that EPLRR-RSID has superior advantages over the compared state-of-the-art (SOTA) approaches, with the mean edge protect index (MEPI) values reaching at least 0.9 and the best values in the no-reference index BRISQUE, which represents that our method improved the image quality by edge preserving.
The anomaly behavior detection algorithm with video-packet attention in transportation surveillance videos

This paper proposes an end-to-end abnormal behavior detection network to detect strenuous movements in slow moving crowds, such as running, bicycling in transportation surveillance videos. The algorithm forms continuous video frames into a video packet and use the video packet feature extractor to obtain the spatio-temporal information. The implicit vector-based attention mechanism will work on the extracted video packet features to highlight the important features. We use fully connected layers to transform the space and reduce the computation. Finally, the packet-pooling maps the processed video packet features to the abnormal scores. The network input is flexible to cope with the form of video streams, and the network output is the abnormal score. The designed compound loss function will help the model improve the classification performance. This paper arranges several commonly used anomaly detection datasets and tests the algorithms on the integrated dataset. The experiment results show that the proposed algorithm has significant advantages in many objective metrics comparing with other anomaly detection algorithms.
Research on distributed sensor network faulty recovery method based on attention mechanism

Faulty recognition and recovery are extremely essential components for existing sensor networks. However, current investigations concentrate on the recognition process and develop abundant advanced identification methods and existing recovery method is almost based on the multiple layers of neural network to achieve the recovery of sensor network, which would result in vast computation costs. Attention mechanism is proposed for neural network to focus some important neural nodes and perform high accuracy for training process with insignificant computation cost. In this article, we propose a novel neural network to recover the fault sensor network with attention mechanism. Thus, the developed mechanism can achieve rapid recovery of fault sensors network with reasonable computation costs and acceptable performance. From extensive experimental result, we can conclude that our proposed method can strictly process the fault sensor network and compared with existing fault sensor network recovery methods to illustrate the effective performance on recovery accuracy with reasonable costs of our proposed method.
Multi-classification recognition of blood cell images based on transfer learning

In this paper, three convolutional neural network models are used to achieve end-to-end recognition of blood cell images. The network model parameters are initialized by transfer learning from the pre-trained model on ImageNet, and then the blood cell images are input into the model, and the network model training is completed by back-propagation to continuously update the parameters. For small-scale datasets, the number of blood cell images is expanded using data increments to improve the generalization ability of the model. Experimental results on the BCCD dataset show that the best result MobileNetV2 achieves an accuracy and precision of 0.894 and 0.916, respectively.
Research on aerobics action pose recognition based on deep learning

Taking aerobics as an example, the human movement can be regarded as a series of posture data that changes over time. Compared with other methods, the special kinematic feature model of human skeleton has great advantages in describing the posture change state. In order to achieve the accurate capture of dynamic posture of aerobics, so as to complete the recognition and analysis of motion posture data in a short time, this paper proposes a 3D human dynamic posture recognition method based on Long Short-Term Memory (LSTM) network. First, the first frame model of the 3D human action sequence is selected as the template of the sequence, and the shape difference of the subsequent models of the action sequence is calculated by the shape difference operator relative to the template, which is represented as a low-dimensional shape difference information tensor. Then, the spatial and temporal dimensional features are extracted from the shape difference information tensor by combining two-dimensional convolutional neural network and LSTM to achieve the recognition of human dynamic posture. The above methods were evaluated by the dynamic pose datasets HumanEva, MoSh, SFU, SSM and Transitions; The classification accuracies were 98.4%, 99.7%, 100%, 99.4% and 100%, respectively.
Does gender affect the processing of object classification in natural scenes

Humans can quickly and efficiently extract information from a complex natural scene. Rapid detection of animals is such an example, which is fast and accurate. We can see that animals have gender differences, and human beings also have gender differences, and they all appear in our real life. Therefore, we will use a two-alternative forced-choice paradigm (2AFC) to investigate the gender differences between the two targets. In our experiment, we balanced the various factors that could be taken into account and subjected the images to histogram equalization. We analyzed the reaction time of the subjects to stimuli of the target gender (male or female). We report two main findings. First, when the type of target (human or animal) was not considered, subjects had faster reaction times to male targets than to female targets. Second, gender differences were only significant for animals when the kind of object (human or animal) was considered.
Retinex-SIE: self-supervised low-light image enhancement method based on Retinex and homomorphic filtering transformation

Low-light images suffer from low visibility, much noise, uneven illumination distribution, etc. Many existing methods have problems such as over enhancement or insufficient detail enhancement when dealing with low-light images with uneven illumination distribution. To remedy the above shortcomings, we propose a Retinex-based self-supervised low-light image enhancement model (Retinex-SIE), which is mainly composed of three parts: Retinex-based self-supervised image decomposition network (Retinex-DNet), nonlinear conditional illumination enhancement function (NCIEF), and Image Reconstruction (IR). First, a uniform illumination image of the same scene with the low-light image is generated by homomorphic filtering transformation, and the low-light image and the uniform illumination image are input into Retinex-DNet for decomposition to obtain reflectivity, noise and illumination. Then, NCIEF is used to enhance the illumination after decomposition. Finally, the final enhanced image is obtained by multiplying the decomposed reflectance and the enhanced illumination. Experiments on severa challenging low-light image datasets show that Retinex-SIE proposed in this paper can better handle low-light images with uneven illumination distribution and avoid problems such as excessive enhancement or insufficient detail enhancement.
Using biologically hierarchical modular architecture for explainable, tunable, generalizable, spatial AI

Explainable artificial intelligence (XAI) is an area of ongoing research for a variety of machine learning applications that aims to describe decision making employed by many artificial neural networks (ANNs) in an intuitive manner for humans. Another seemingly unsolved topic generating research interest is AI conceptual understanding, (such as in large language models) which falls within a more general problem of AI model generalizability and integrability. How might computational problems of that difficulty be solved, and more importantly, which approach will result in genuine advancement in the fields of neuroscience and/or artificial intelligence? An oftenused strategy in machine learning is to use knowledge about biological brains to inspire machine learning model design. That strategy can continue to be useful and novel because both neuroscience and artificial intelligence are rapidly growing fields of research; the same unification strategy between both fields can yield significantly different models as neuroscience knowledge and machine learning capabilities both grow and develop. In this paper, we focus on the usefulness of using current neuroscience knowledge to design biologically hierarchical and modular architecture (BHMA) models of the brain that can solve spatial learning tasks. We discuss the potential implications of biologically constrained hierarchical neural networks for the future of human-computer interaction, computational neuroscience, and machine learning given their potential for generating more explainable models with verifiable aspects of conceptual understanding.
Sensor-based multi-waypoint autonomous robot navigation with graph-based models

Multi-waypoint navigation for autonomous robot is in high demand in real-world robotics applications including search and rescue, disaster response, and environment exploration. In this paper, a sensor-based methodology is proposed for validation of autonomous robot multi-waypoint navigation utilizing graph-based models with adjacent node selection. In addition to time and distance efficiency, the proposed graph-based models incorporate safety in relation to the robot’s environment as a driving feature. These models provide path planning for autonomous robot experiments. This methodology is implemented in a real-world environment with simulated dynamic obstacles utilizing a Clearpath Jackal unmanned ground vehicle (UGV) featuring four-wheel drive, GPS, odometry, and a Velodyne VLP-16 LiDAR as the experimental robot configuration. The LiDAR unit utilized has a one hundred meter range with a thirty degree vertical field of view, as the primary navigation sensor. It is utilized to map the environment prior to performing autonomous navigation experiments. This robot configuration is also presented in a simulated environment for more thorough evaluation. The environments to be examined are controlled with obstacles and environment features within the expected view of the LiDAR. The proposed graphbased model is evaluated in terms of planning efficiency from simulated to real-world environments including dynamic obstacle avoidance.
Two cloud computing optimization models for dealing with burst traffic: resource demand prediction model and resource feedback model

Aiming at the burst traffic, this paper studies how to perform elastic capacity expansion before the burst traffic based on the current access traffic value in a period of time when there is little or no historical data.In this paper, a resource demand prediction model is constructed to improve the degree of fitting between the predicted and actual burst traffic.A resource feedback model is proposed. It can evaluate the resource utilization of the service node at the current moment through the resource performance indicator detection value obtained from the cloud data center in real time, and then calculate the feedback factor. Finally, the dynamic mapping between the predicted value of load demand and resource scaling volume is realized. Experiment shows that the accuracy of the proposed method is higher than that of the traditional methods.
Bayes Factors for Forensic Decision Analyses with R

In the book review, column editor Christian Robert reviews three books: Casanova’s Lottery by Stephen Stigler, Bayes Factors for Forensic Decision Analyses with R, and Bayesian Probability for Babies by Chris Ferrie and Dr. Sarah Kaiser. Casanova’s Lottery tells the “history of a revolutionary game of chance.” Bayes Factors for Forensic Decision Analyses with R provides a sort of blueprint for using Bayes factors in forensics for both investigative and evaluative purposes, including R code and free access to the whole material. The final book tells story follows two young girls competing for dinosaur expertise and being rewarded by cookies, in proportion to the probability of providing the correct answer to two dinosaur questions.
Bayesian Probability for Babies

In the book review, column editor Christian Robert reviews three books: Casanova’s Lottery by Stephen Stigler, Bayes Factors for Forensic Decision Analyses with R, and Bayesian Probability for Babies by Chris Ferrie and Dr. Sarah Kaiser. Casanova’s Lottery tells the “history of a revolutionary game of chance.” Bayes Factors for Forensic Decision Analyses with R provides a sort of blueprint for using Bayes factors in forensics for both investigative and evaluative purposes, including R code and free access to the whole material. The final book tells story follows two young girls competing for dinosaur expertise and being rewarded by cookies, in proportion to the probability of providing the correct answer to two dinosaur questions.
The Web Data Commons Schema.org Data Set Series

Millions of websites have started to annotate structured data within their HTML pages using the schema.org vocabulary. Popular entity types annotated with schema.org terms are products, local businesses, events, and job postings. The Web Data Commons project has been extracting schema.org data from the Common Crawl every year since 2013 and offers the extracted data for public download in the form of the schema.org data set series. The latest release in the series consists of 106 billion RDF quads describing 3.1 billion entities. The entity descriptions originate from 12.8 million different websites. From a Web Science perspective, the data set series lays the foundation for analyzing the adoption process of schema.org annotations on the Web over the past decade. From a machine learning perspective, the annotations provide a large pool of training data for tasks such as product matching, product or job categorization, information extraction, or question answering. This poster gives an overview of the content of the Web Data Commons schema.org data set series. It highlights trends in the adoption of schema.org annotations on the Web and discusses how the annotations are being used as training data for machine learning applications.
Blackbox Testing on Virtual Reality Gamelan Saron Using Equivalence Partition Method

Pengujian Blackbox Pada Virtual Reality Gamelan Saron Menggunakan Metode Equivalence Partition. Dalam pengembangan sebuah aplikasi, testing pada aplikasi sangat penting sebelum aplikasi dirilis. Testing berdasarkan kualitas dengan menggunakan Black Box mengutamakan pengujian fitur-fitur yang terdapat pada aplikasi, sehingga dapat menemukan permasalahan yang terjadi di aplikasi Gamelan VR Saron ini. Pada metode Black Box berbasis Equivalence Partition secara menyeluruh menguji dalam aspek penggunaan aplikasi Gamelan VR Saron. Dalam pengujian ini membutuhkan seleksi penggunaan berdasarkan test case, kemudian memastikan kualitas dari fitur-fitur yang tersedia serta menemukan error function yang bisa terjadi dalam aplikasi. Sehingga ujicoba pada aplikasi ini bisa menilai apakah telah sesuai dengan harapan dan direncanakan. Berdasarkan hasil pengujian yang telah dilakukan, dapat disimpulkan bahwa aplikasi ini dapat berjalan dengan baik tanpa adanya error sesuai dengan yang telah direncanakan. Hasil pengujian ini dapat dijadikan sebagai dokumentasi serta evaluasi untuk pengembangan aplikasi kedepannya.Kata Kunci: black box, equivalence partition, test case, error function, user Blackbox Testing on Virtual Reality Gamelan Saron Using the Equivalence Partition Method. Testing is essential in application development because it helps identify and eliminate defects. One of the most used testing methods is Black Box testing, which involves deeply examining the application’s functionality without knowing its internal workings. The Equivalence Partition method is frequently used in Black Box testing to divide input values into groups and select test cases from each group. Potential errors can be identified by testing the available features with appropriate test cases, and future improvements can be made to ensure seamless application performance. In addition, testing results also serve as documentation and research for future development. By using this method, the developers of the VR Gamelan Saron application can ensure that its quality meets user expectations to improve its quality to provide an optimal user experience. In summary, proper testing is crucial in application development, and the Equivalence Partition method is an effective tool foridentifying and eliminating potential issues.Keywords: black box, equivalence partition, test case, error function, user
Sports Science Movement model based on fractional differential equation

Abstract In order to construct the morphological model of Chinese excellent calisthenics athletes, overcome the shortage of evaluation of single and multiple indexes in the study of the morphology of calisthenics athletes in the past, the author proposes a sports science model research based on fractional differential equation. Sports biomechanics, as an independent discipline within sports science, the general task of the study of motion biomechanics is to evaluate the effect of force on perfectly achieving a given goal in the process of interaction between biological system and external environment, the author takes the outstanding male calisthenics athletes of Chinese college students as the research object, and adopts the method of literature and mathematical statistics, the morphological indexes were analyzed and studied, and the morphological model was established, through factor analysis, the morphology of Chinese outstanding male college student aerobics athletes is divided into four factors: Body fullness factor, limb scale factor, body width factor, body circumference factor, the weights of the four factors are 0.36, 0.31, 0.17 and 0.15, respectively. Chinese outstanding male college student aerobics athletes have the morphological characteristics of medium height and well-developed upper arm and lower limb muscles.