Reviewerly/exHarmony huggingface dataset:
Automatic segmentation and fitting of image edge contours based on Douglas algorithm

Image edge extraction and recognition has great significance to measurement accuracy of various workpieces and parts. In order to solve the distortion problem caused by camera shooting, calculate the internal and external parameter distortion coefficients of the camera for the given three dot array images through MATLAB, and then establish an appropriate mathematical model to analyze the mapping relationship between the pixel distance and the actual distance, so as to calculate that the total length of the image edge contour in Annex 2 is 460.447mm. In order to automatically segment the edge contour with straight line segment, arc segment and elliptical arc segment, this paper uses the Ramer Douglas Peucker algorithm to establish the mathematical model, and uses Halcon software to calculate and verify the effectiveness of the model. Finally, the length of each segment of the two edge contours is calculated, as shown in Annex 2.
UBERT22: Unsupervised Pre-training of BERT for Low Resource Urdu Language

Natural Language Understanding (NLU) tools have enabled the development of sophisticated and powerful Natural Language Processing (NLP) models. However, this progress is limited to English and European languages and low resource languages lack such tools due paucity of resources. In this paper, we develop UBERT22- an unsupervised pre-trained BERT model for Urdu language. For this purpose, first, we develop a dataset of ‘Zakheera’ containing high-quality content of 1.16 million Urdu language news and blog articles posted on top 37 Urdu websites. Next, we pre-process the text and tokenize content in 21.8 million Urdu sentences. Finally, we extract the word-piece vocabulary of 30,000 tokens and pre-train the BERT model for Masked Language Modelling (MLM) and Next Sentence Prediction (NSP) tasks. We compare the performance of UBERT22 with existing multilingual and small-Urdu BERT for various downstream tasks. We notice that UBERT22 outperforms multilingual and small-Urdu BERT for fake news identification, propaganda classification, topic categorization, and sentiment analysis tasks. Overall, UBERT22 achieves 2-19% higher accu-racy compared to baseline results and competitive BERT models. We believe that the public availability of our pre-trained model and upstream dataset will enable the development of state-of-the-art NLP models of Urdu language such as chatbots, question answering systems, sentiment analyzers, virtual assistants, speech recognizers, and machine translators.
Word-level Language Identification and Localization in Code-Mixed Urdu-English Text

Language Identification is significant for most Natural Language Processing (NLP) tasks to work precisely. Language Identification is still very challenging because of the range of dialects. The major challenge in Language Identification (LID) task is the lack of availability of tools for understanding the context of multiple languages. We proposed a deep learning neural network Bi-LSTM CNN for word-level classification for Language Identification (LID) and localization of Roman Urdu and English in the code-switch text in this paper. We utilized the dataset of code-switch text having variant spellings of the same Roman Urdu words, generated from different social media platforms as they are a rich source of code-switch languages. We used GoogleNews Word2Vec Vectorizer for word embeddings. The embedding layer is followed by the Bidirectional long-short term memory (Bi-LSTM) layers along with the Convolutional Neural Network (CNN). We experimented with the dataset on different variations of LSTM and CNN to achieve the best possible results. We achieved 90.40% accuracy and a 90.39% F1 score.
HSMR: A Head-Shoulder Mask Aided ResNet to Guide Focus of Re-Identification Implemented on Tour-Guide Robot

For service robots, person re-identification (ReID) and multi-pedestrian tracking (MPT) are vital to get a person's location and link identities across frames. Though their accuracy keeps improving, most work lacks consideration of the application scenario, haunted by limited space, constrained power supply, and demands of the real-time response during human-robot interaction. Some ReID models learn trivial or unrelated features, inhibiting the downstream tasks. To solve these issues, the efficient and light-weighted Head-Shoulder Mask aided ResNet (HSMR) is proposed. This model applies multi-task learning to enhance the feature extraction performance in the training stage without extra computational load during inference. The auxiliary task fully uses head-shoulder information to guide the network and focuses on the head region, which contains the identity information. In experiments on the Tour-Guide Robot Data Base (TGRDB), HSMR earned results better than ResNet-18 on the ReID task and was superior to the recent two-stream method on the MPT task. On the mobile hardware, inference reaches an average of 15.2 FPS, three times faster than the two-stream method. The code is released at https://github.com/ZhYLin99/HSMR.
An object detection method from 3D point clouds based on seed filtering

3D object detection based on point clouds has important theoretical and application values. As an innova-tive algorithm, VoteNet takes raw point clouds as input and uses a Hough vote module to shift the points scattering on surfaces of potential objects towards their centers as vote points, then achieves efficient 3D object detection. However, in-depth analysis has revealed that in the proposal prediction stage, vote points from walls, grounds, and adjacent objects are inevitable noise that seriously affects the object detection performance. To solve this problem, we propose a two-step proposal prediction to explicitly denoise the negative vote points. First, step I predicts an initial 3D bounding box (3D-BBox) for each proposal. According to the initial 3D- BBox, we define a mask that identifies negative vote points. Then, step II uses the mask as a filter to eliminate the negative vote points for each proposal, thereby reducing the effect of the negative vote points in proposal prediction. Experiments show that our proposed optimization scheme can effectively improve the object detection performance, achieving a significant performance improvement on the public SUN RGB-D dataset, (58.2% in terms of mAP@0.25 and 33.7% in terms of mAP@0.5).
A Blockchain Distributed Computing Resource Scheduling Balancing Method Based on Variance Ratio

In this paper, we propose a variance ratio-based blockchain distributed computing resource scheduling and balancing method, in which the tasks processed by each distributed computing engine are clustered and sampled, and the variance ratio of the changes of the distributed computing engine is generated based on the results of the clustered sampling, so that the computing resources can be adjusted to different nodes based on the variance ratio and the post-adjustment verification. It can better ensure that each node has sufficient computing power and minimize the waste of computing resources, thus helping to balance the scheduling of blockchain distributed computing resources.
Owl Ontology for Liver Diseases Using Protégé &amp; WEB-V-OWL

Science and health care systems are working hand in hand to cater and support each other in the current era. The liver is one of the important body parts that need to work appropriately for a human body. But sometimes the most hazardous reasons for liver problems are infections or diseases like Hepatitis because they remain for an extended time and direct to dangerous difficulties (liver swelling, liver cancer, etc.). Liver hepatitis contains a diversity of different types of viruses: hepatitis A, B, C, and D. This study presents the design and implementation of liver hepatitis ontology. Protege-Owl and WEB-V-OWL is used for implementing and its visualization. The proposed ontology is portable and can be edited for further addition of concepts in the future. Web ontology language is used to implement this proposed ontology. By developing the Hepatitis ontology, both Intelligent health care systems and physicians can share, reason, and exploit this knowledge in different ways.
Real-time Underwater 3D Reconstruction Based on Monocular Image

Real-time 3D reconstruction is essential for unmanned autonomous robot operation. In this work, we present a real-time reconstruction algorithm for underwater environments based on monocular image. First, the depth of the sparse features is calculated by performing feature optical flow tracking. Then, Delaunay triangulation is performed based on these features and the depth image is densified using triangular interpolation with the assumption that the seabed is close to the plane. Moreover, the densified depth image is used as the initial value, and the matching cost of the pixels in the current image and the neighboring images are computed according to the depth information. The SGM (semi-global matching) algorithm is used for cost aggregation to smooth and optimize the depth image, which can help to improve the accuracy of the depth image; finally, the current depth image is fused with the previous depth image using depth filter to further improve the accuracy and remove outliers. The experimental study shows that the algorithm can obtain better visualization on real-world underwater datasets. By leveraging the GPU, the algorithm can get real-time processing speed on the laptop.
RDNet: A reinforced-deformable convolutional network for crowd counting

Crowd counting is still a challenging task in crowded scenario due to heavy occlusions, appearance variations and perspective distortions. Thanks to the development of deep convolutional neural networks, especially the deformable convo-lution neurral networks, great progress has been made for crowd counting. However, existing deformable convolution networks cannot get satisfactory performance due to limited receptive field that is not conducive to crowded object detection. In this paper, a novel end-to-end Reinforced-Deformable convolutional neural Network (RDNet) is proposed. First, a multi-scale context information fusion strategy with skipping connection and shortcut connection is utilized to obtain rich semantic features and local details of the crowd distribution. Then a reinforced-deformable convolution module is designed as the back-end of the network, which can sample with adaptive offsets and scalars to enlarge the receptive field for more accurate edge information and crowd distribution localization. Finally, an impulse function convolved with cuphead Gaussian kernel is applied to generate density map for counting. Extensive experimental results on four popular datasets, namely, Shangha-itech, UCF _CC_50, WorldEXPO'10, and UCF-QNRF, show that our proposed approach outperforms other state-of-the-art methods.
A music recommendation system based on collaborative filtering and SVD

With the development of the Internet and the advent of music-streaming platforms, a large amount of music data available for selection is now greater than ever on the Internet. In addition to searching expected music objects for users, it becomes necessary to develop a recommendation service. A music recommendation system (MRS) relieves users from sorting through the various options by automatically recommending music based on their historical behaviors like the play count of each song. Recommender systems have aroused a lot of awareness in the past decade. Although algorithms including content-based, collaborative, singular value decomposition, and other techniques are used in the recommendation system, there does not exist any perfect recommendation system that can give completely precise feedback on what users actually want. To figure out which algorithm does a better job, the paper proposes a music recommendation system based on two algorithms, item-based collaborative filtering, and singular value decomposition, that are used in the music recommendation system and compares the two methods to find out which one can make a more precise recommendation. Item similarity between the songs listened by the user and other users is used to predict which songs are preferred by the user. In this paper, D-Recall is regarded as an evaluation indicator between the two algorithms. And the performance of SVD is better than item-based collaborative filtering on the recommendation.
Robot Planning based on Behavior Tree and Knowledge Graph

Task planning refers to ordering executive actions to ensure the efficient and successful completion of the robot task. Unlike previous methods, we propose new ideas, based on Knowledge, that can make robot strategy, in addition to automatically enhancing behavior tree building. Various knowledge bases are applied to our robot brain system, serving as robot planning and used for lab exhibitions. According to And Or Graph and graphing database, a special knowledge graph is designed to simplify the robot's planned routes. We analyze the proposed approach and validate our ideas through experiments on human guide robots.
Structure-Aware Policy to Improve Generalization among Various Robots and Environments

Recently, Deep Reinforcement Learning (DRL) has been used to solve complex robot control tasks with outstanding success. However, previous DRL methods still exist some shortcomings, such as poor generalization performance, which makes policy performance quite sensitive to small vari-ations of the task settings. Besides, it is quite time-consuming and computationally expensive to retrain a new policy from scratch for new tasks, hence restricts the applications of DRL-based methods in the real world. In this work, we propose a novel DRL generalization method called GNN-embedding, which incorporates the robot hardware and the environment simultaneously with GNN-based policy network and learnable embedding vectors of tasks. Thus, it can learn a unified policy for different robots under different environment conditions, which improves the generalization performance of existing DRL robot policies. Multiple experiments on the Hopper-v2 robot are conducted. The experimental results demonstrate the effectiveness and efficiency of GNN-embedding on generalization, including multi-task learning and transfer learning problems.
A Benchmark of Planning-based Exploration Methods in Photo-Realistic 3D Simulator

Autonomous exploration is an essential ability of mobile robots and has been widely investigated. A reliable evaluation is required to compare exploration methods in different aspects. However, existing benchmarks are mostly implemented on gird-based simulators which are lack of real-world environmental factors. Besides, most benchmarks only focus on single-agent exploration or multi-agent exploration, lacking comprehensive analysis on both of them. In this paper, we select representative planning-based exploration techniques in both single-agent and multi-agent settings and evaluate them on large and highly cluttered maps. The experiment is conducted on a photo-realistic 3D simulator, Habitat, and the results indicate the performance of these methods in aspects of map size, cooperation, efficiency and agent's team size.
Precise subpixel edge localization based on partial area effect

With the development of science and technology, whether in the industrial field or in other fields, the requirements for the accuracy of image measurement are getting higher and higher. A sub-pixel is a virtual pixel defined between two physical pixels of an image acquisition sensor, and its data type is a floating-point number. If sub-pixel technology is used to increase the accuracy to 0.1 pixel, it is equivalent to increase the resolution of the image system analysis by a factor of 10. For the model shown, this paper uses accurate sub-pixel edge localization based on partial area effect, and uses Gaussian low-pass filtering for filtering to deal with edge glitches and edge shadows. At the same time, for other models, this paper uses Hough transform to fit separately to solve the problem of sub-pixels.
Natural interactive control method for unmanned swarm systems based on gesture recognition

Interactive control is very important for unmanned swarm systems to understand human intentions and execute tasks. However, traditional interactive control usually uses a keyboard, mouse, or remote controller, which is not intuitive and convenient enough. This paper proposes a natural interaction control method based on gesture recognition for unmanned swarm systems. First, considering the real-time requirement of interactive control, the YOLOv5 algorithm is improved by replacing the original GIoU loss function with the DIoU loss function to improve the performance of the algorithm. Then, according to the interactive control requirements of unmanned swarm systems, the corresponding gesture database is established. Finally, the AirSim platform is used to build the simulation system, and the interactive control based on gesture recognition is tested and verified. The experimental results show that the recognition accuracy of the control intention is 97% and the response time is approximately 0.027 s, which can realize the interactive control of unmanned swarm systems.
Business Data Clustering Analysis of Power Internet of Things Platform Based on Improved K-Means

The construction of smart IoT platform to support the digital power grid pushes the company's smart IoT system construction to a higher level. It is urgent to carry out the research on the application and management mechanism of smart IoT platform construction, to provide normative guidance for the company's smart IoT system construction. In the power distribution area, device management data connected to the IoT management platform, and service data distributed to the main power distribution station after connected to the IoT management platform. However, there are many kinds of data. In order to better classify the data in forwarding, this paper proposes a data clustering analysis algorithm based on Canopy improved K-Means. Experimental results show that this method can classify the number of services well and achieve fast forwarding.
Constrained Path Planning on Pipeline Surface for Wall Climbing Robots

Pipeline structures exist widely in industrial sce-narios and regular maintenance is an essential for sustainability. Manual maintenance suffers from high costs and risks and low efficiency and reliability. Thus, wall climbing robots (WCRs) play an increasingly important role in the field of pipeline non-destructive testing and repairing. Although many commercial wall climbing robots have been put on the market, they rely heavily on manual remote control and are difficult to operate autonomously. In order to improve their automation efficiency, we develop a path planning method based on the characteristics of the scenarios. In this paper, the pipeline surface is be reconstructed as a triangular mesh, thereby transforming the path planning problem into a graph search problem. The step size and the maneuverability and stability of the robot are considered as factors of cost function. By analyzing the attitude transformation of the robot from one vertex of the meshes to another, we then propose a specific mathematical form of the cost function and verify the effect of each term. Given the special topological morphology of the pipeline surface, the geodesic distance to the target point is used as a heuristic function to reduce unnecessary expansion and improve the efficiency of the search. Simulations are carried out to verify the proposed method.
A Novel Hybrid Path Planning Method for Mobile Robots

In practical applications, an appropriate path planning algorithm needs to be selected according to the actual situation. Using the Cartographer algorithm based on graph optimization to build the map, and using AMCL positioning at the same time, the problem of low accuracy caused by its own defects has been successfully solved. A* algorithm original path trajectory inflection point, node redundancy phenomenon is obvious, longer running time, and even difficult to plan the optimal path An improved A* algorithm is proposed, which uses adding weight coefficient w, adaptively adjusting the step size algorithm and cubic Bezier curve make up for the lack of more turning points, larger turning angles and longer running time in the algorithm search. The hybrid path planning algorithm combining the improved A* algorithm and the dynamic window method solves the problem that the A* algorithm cannot avoid dynamic obstacles in complex environments, and prevents the mobile robot from falling into local optimum. Experiments show that the improved algorithm can effectively solve the problems of more turning points, larger turning angles and longer running time encountered by the mobile robot in the search.
A Hand Gesture Detection Algorithm and Quantization Implementation

Using deep learning to recognize the hand gestures of deaf-mutes quickly and to translate coherent sign language efficiently is of great social significance to solve the communication barriers between deaf-mutes and normal people. However, the detection network based on deep learning cannot be deployed to mobile terminals with limited memory and computing power due to its huge model and computation. To solve this problem, an efficient gesture detection algorithm based on pose estimation is proposed, that is, after judging whether people raise their hands to make gestures by pose estimation, detection network quickly recognizes hand gestures. Firstly, a small dataset of high frequency isolated hand gestures is constructed to train and test the network.Secondly, the structure of the original pose detection network OpenPose is optimized from backbone, initial stage and refinement stage to make the network more lightweight. Then, a pruning algorithm combining channel and layer is carried out to compress the network. After pruning, the size of the model was only 7MB, and the inference speed was significantly increased to 106fps, and the accuracy sacrifice was only 1%. After the fine-pruning, the accuracy rises and slightly more than the original. Finally, the network is deployed to the chip AR9301, which is accelerated by hardware accelerator quantization to further improve the performance.
Hyper-net: A Dual-Path Convolutional Network for Thyroid Fine Needle Aspiration Cell Segmentation

In recent years, hundreds of thousands of people around the world have been diagnosed with thyroid cancer, and this number is increasing year by year. Thyroid fine needle aspiration is the most sensitive and specific method to determine the nature of nodules before operation. The diagnosis of thyroid FNA needs to judge the benign and malignant cells on the smear, which largely depends on the doctor’s experience and is quite challenging and time-consuming. Although some methods have good performance at present, it is not reliable to analyze FNA only by morphological classification. This paper collected the first hyperspectral pathological image data set of thyroid papillary carcinoma, which contains 117 hyperspectral images with pixel level labels of benign and malignant cells. It can be used as a benchmark for hyperspectral image analysis of thyroid papillary carcinoma. Irregularity and overlap are the prominent features of pathological images of papillary thyroid carcinoma, which brings challenges to accurate segmentation. Based on the collected data set, we designed a dual path network structure (Hyper-net) with attention mechanism for cell segmentation. The segmentation network combines the attention mechanism, spatial and spectral information to effectively complete the segmentation task in thyroid FNA smears. The global class attention module can extract the key features of each class in the whole data set and enhance the global perception of the network. Compared with the benchmark network and other dual path networks, our hyper-net with attention mechanism achieves much better performance for thyroid fine needle aspiration cell segmentation.