Reviewerly/exHarmony huggingface dataset:
Multi Mobile Robot Path Planning Based on Rough-Fine Search Strategy

Multi-mobile robot path planning technology is the key technology in the field of intelligent warehousing and intelligent logistics. Solving the optimal solution for multi-mobile robot path planning will cause “dimension explosion”. Existing multi-mobile robot path planning algorithm could not get the solution with high quality fast, causes the low efficiency of multi-mobile robot systems. This paper proposes a multi-mobile robot path planning method based on rough-fine search strategy. Firstly, the environment map is discretized to two kinds of grid maps with different grid sizes. And then, the M* algorithm is used to obtain the rough optimal solution in the grid map with large size, in the next, the rough optimal solution was mapped to the fine grid map as the teaching path, Finally, in the fine grid map, select the Gaussian distribution band with teaching path as the mean value. In the Gaussian distribution band, the RRT* algorithm is used to optimize the teaching path. Through experimental verification, our algorithm can obtain a higher quality path and has a faster solution speed.
Offline Text-Independent Writer Identification Using Different Levels of Features

The effective extraction of feature information in handwriting identification has been the focus of researchers, especially the completion of a robust handwriting identification method is still an urgent technical problem. In this paper, we will combine two different levels of features: local directional chain-code feature (LDCF) and global improved texture feature (GITF). According to the advantages of each of these two features, it is applied to different matching processes. In the first stage, the LDCF is extracted and then roughly matched to obtain a handwriting image candidate sample set. The next stage is to refine the candidate sample set using the GITF. The experimental results evaluated on the database containing 203 writers of address images demonstrate the effectiveness of our method.
Web-based application design for agile stakeholder communication

Abstract An effective communication comes from good communication management. Agile service development has principles regarding to the communication management. The principles focus communications into a situation, thus the communications are well organized in operation so the communication will be effective and efficient. Aims of this research are to create communication management system which is appropriate for agile stakeholder to manage their communication during project development. The method used in this research is software development life cycle (SDLC). This research presents a web-based application design for Agile stakeholder communication support in order to help project developers in organizing and planning communication activities during project development based on Agile service development principles.
Texture Signature for Recognizing Human Emotions

In the last two chapters, we discussed how distance and shape information could be used efficiently as a descriptor for facial expression recognition. In this chapter, we propose a texture signature-based human facial expression recognition system. At the same time as done in the last two chapters, effective landmark detection of a human face offers a crucial role in facial expression tasks.
DATA SMART-CARD AS A SOURCE OF INFORMATION ABOUT TRANSPORTATION MOVEMENTS ON PUBLIC TRANSPORT

The new payment technologies for public transport are considered in article. A brief&#x0D; overview of foreign research in the field of obtaining raw data from smart cards is given.
General Coordinate Invariant Image Smoothing Using a Metric Tensor

Images are often deformed for various reasons such as viewpoint movement, lens distortion, etc. It is desirable that image processing is invariant for such deformation. This paper proposes a new image smoothing which is invariant under general coordinate transformations. General coordinate transformation is a continuous coordinate transformation defined by differentiable function. To realize general coordinate transformation invariance, we introduce the metric tensor which transforms appropriately for the general coordinate transformation. Using the metric tensor, we define a general coordinate invariant evaluation function. General coordinate invariant smoothing is achieved by a process of minimizing the evaluation function. Effectiveness of the proposed method is confirmed by computer experiments.
A Service Value Measurement Method Based on User Context Awareness

With the rapid development of the Internet, network service technology has been well developed. In order to further verify the satisfaction degree of building network service network to user value expectation, further research on service value measurement methods is needed. In the traditional service value measurement method, an urgent problem that needs to be solved is how to eliminate the interference of users' subjective factors to the value measurement results. Therefore, this paper designs a service value measurement method based on user context awareness. Firstly, the social network relationship of users is delineated, and the relationship strength of social network users is calculated, and the user group with strong relationship strength is identified. Then, it perceives and analyzes context information of users in the user group, calculates user similarity, selects n users with the highest degree of similarity to the target user to form a similar feature user group; Finally, it collects the comments of users in the user group of similar features, analyzes the emotional tendency contained in the comments, and finally realizes indirect and objective analysis of users' views on this online service. It is necessary to explore the influence mechanism of public opinion on service value realization and design the service value measurement method based on user context perception.
Hybrid Method of Altitude Estimation Under Low-angle Target Tracking Environment

In modern radar applications, the performance of target localization may be affected by the multipath reflections, especially in the complex terrain environment where the radar echoes are seriously perturbed by the reflections from the irregular surface. To address this problem, a practical multipath propagation signal model under general terrain environment is first presented, where the influence of the perturbed multipath reflected from the irregular terrain is taken into account. A novel sparse representation based DOA estimation algorithm, combined with low-rank decomposition technique, is then proposed, in which the nondeterminacy of the reflections can be effectively eliminated to acquire more accurate height estimation. Experiments on simulated data and real measured data validate the effectiveness of the proposed algorithm.
Learning How to Interpolate Fourier Data With Unknown Autoregressive Structure: An Ensemble-Based Approach

It has been previously shown that the Fourier samples acquired in magnetic resonance imaging (MRI) experiments possess shift-invariant autoregressive structure, which has led to the emergence of various autocalibrated convolution-based image reconstruction approaches. Such approaches, which include GRAPPA, AC-LORAKS, RAKI, and LO-RAKI, each have their own relative strengths and weaknesses. In this work, we propose a novel ensemble-based approach that uses all of these approaches simultaneously as parallel building blocks within a larger data-adaptive reconstruction network. Results with real data suggest that the ensemble-based approach can synergistically utilize the strengths of each method, providing robust reconstruction performance without the need for interactive parameter tuning.
Semantic Enhancement and Multi-level Label Embedding for Chinese News Headline Classification

News headline classification is a specific example of short text classification, which aims to extract semantic information from the short text and classify it accurately. It can provide a fast classification method for data of various kinds of news media, thus arousing the common concern of academia and industry. Most short text classification methods are based on the semantic expansion of external knowledge, which is unable to expansion dynamically in real time and make full use of label information. To overcome these problems, we propose a novel method which consists of three parts: semantic enhancement, multi-dimensional feature fusion network and multi-level label embedding. Firstly, the word-level semantic information are embedded into the character encoding from pre-train model to enhance semantic features. Secondly, both of Bi-GRU and multi-scale CNN are used to extract sequence and local features of text to enhance the semantic representation of the sentence. Furthermore, the multi-level label embedding is used to filter textual vector and assist classification in the word and sentence level respectively. Experimental results on NLPCC 2017 Chinese news headline classification task show that our model achieves 84.74% of accuracy and 84.75% of F1, improves over the best baseline model by 1.5% and 1.6%, respectively, and reaches the state-of-the-art performance.
A Fast and Accurate Purification Method of Image Feature Point Pairs Using Structural Consistency Constraints

Feature-based matching of images is a key method in pattern recognition, but due to noise, out-of-focus, similar texture, et. al., mismatched feature point pairs are usually generated, and easily result in the incorrect final results. Based on the linear transformation of image scenes, this paper proposes a fast and accurate purification method of matched feature point pairs using the structural consistency of the constructed matching Delaunay triangulations. The structural consistency is expressed and quantified from two aspects: distribution correspondence of the corresponding adjacent vertexes, and the similarity of the corresponding triangular units. By taking the two aspects as restraints, the mismatches are screened out and removed dynamically, which can improve the matching foundation and give better support for the further processing for images and targets. Representative images with different imaging platforms and environments are chosen for experimentation. Results show that compared with the existing classical methods, the proposed method has high accuracy, good robustness and fast computational speed for removing the mismatched feature point pairs.
TransNet: Linkage Recognition with Contextual Nexus for Hard Object

One of the major challenges in object recognition is to propose a structure with highly performance of overlapping, small and misclassification objects, called hard object recognition. There is a contextual nexus among the spatial location and geometric characteristics of objects, which we find is essential to the recognition efficacy. We propose regress connection, nexus calculation module and linkage loss, which are contained in an improving design TransNet framework. It can not only demonstrates the interactivity of explicit nexus between objects, but also achieve better results through the integration of two mainstream methods. Adjusting the training attention through linkage loss function, thus significantly enhance the performance of hard objects. The experiment results show that our method achieves remarkable performance on PASCAL VOC2012 and MS COCO data set. Also, the head network is capable of integrating with region-based methods and gains better performance.
Background perception for correlation filter tracker

Abstract Visual object tracking is one of the most fundamental tasks in the field of computer vision, and it has numerous applications in many realms such as public surveillance, human-computer interaction, robotics, etc. Recently, discriminative correlation filter (DCF)-based trackers have achieved promising results in short-term tracking problems. Most of them focus on extracting reliable features from the foreground of input images to construct a robust and informative description of the target. However, it is often ignored that the image background which contains the surrounding context of the target is often similar across consecutive frames and thus can be beneficial to locating the target. In this paper, we propose a background perception regulation term to additionally exploit useful background information of the target. Specifically, invalid description of the target can be avoided when either background or foreground information becomes unreliable by assigning similar importance to both of them. Moreover, a novel model update strategy is further proposed. Instead of updating the model by frame, we introduce an output evaluation score, which serves to supervise the tracking process and select high-confidence results for model update, thus paving a new way to avoid model corruption. Extensive experiments on OTB-100 dataset well demonstrate the effectiveness of the proposed method BPCF, which gets an AUC score of 0.689 and outperforms most of the state-of-the-art.
Delaunay Triangulation Based Thinning Algorithm for Alphabet Images

Image thinning is one of the fundamental pre-processing steps prior to the shape representation in the image analysis of printed and handwritten alphabet recognition applications. In this paper, an approach is introduced for the thinning of alphabet images using Delaunay Triangulation algorithms. Delaunay Triangulation is one of the most essential data structures in the field of Computational Geometry. Given a finite set of points that represent an image of an alphabet, the reconstruction of the alphabet using a simplified point set is studied. The performance of the proposed model evaluated by evaluating the Thinning Rate. The alphabet images of the Chara74K dataset were used to test the model and the average Thinning Rate was found to be 91%. It is demonstrated that the proposed model can be used for reconstruction of text images, broken alphabet images and Dot-Matrix alphabet images.
Probabilistic Three-Valued Argumentation Frameworks

Dung’ AF has been extended in many different directions. One particular direction is to allow uncertainty in AFs. Among others, probability and fuzzy theory are typical approaches used in this direction. In this paper, we argue that arguments can be both fuzzy and random. We thus introduce probabilistic-fuzzy argumentation frameworks in which probabilities and fuzzy values are combined to describe fuzzy and random arguments. We introduce an algorithm for revising probabilities. Based on this algorithm, we study semantics of probabilistic-fuzzy argumentation frameworks.
An Improved Image Steganography Scheme Based on RDWT and QR Decomposition

Abstract This paper demonstrates an improved image steganography that hides grayscale secret image into grayscale cover image using RDWT and QR decomposition. The proposed scheme made use of the human visual system in the information hiding process. Both cover and secret image are being divided into non-overlapping image blocks with identical block size. Entropy values generated from every image block will be sorted from the lowest value to the highest value. The embedding process starts by embedding the secret image block with lowest entropy value into the cover image block with lowest entropy value. The process goes on until all image blocks have been embedded. Embedding secret image into cover image according to the entropy values causes differences that HVS can less likely to detect because of the small changes on image texture. The proposed scheme gives higher PSNR values and better image quality.
Video Subtitle Location and Recognition Based on Edge Features

Subtitle location and recognition technology is a special case of natural scene text recognition, but there are sorne differences between them, The subtitles in video frame have fixed position and lots of edge features. This papertakes that as a starting point, extracting edge features with Sobel operatorto locate the subtitle, adjusting color scale and taking the method of binarization to get the text image with pure background, finally obtains the text inthe frame via OCR technology.
Temporal Action Detection Based on Hierarchical Object Detection Networks

This paper addresses the problem of temporal action detection from untrimmed videos. Considering that actions can be recognized by the occurrence of objects and the corresponding moving information in the video, a hierarchical model is proposed which consists of two object detection networks to do temporal action detection. The first network is used to detect objects in each frame, and the second one is for temporal action detection. We also proposed a method which converts the object detection results of the first network into a new type of frame so that it can be fed to the second network. The generated frame has six channels with spatiotemporal information beneficial to action detection. Quantitative results on THUMOS14 dataset demonstrate the superior of the proposed model with satisfactory performance gains over state-of-the-art action detection methods.
SmartNavi: A Smart Navigation Application with Main and Auxiliary Distinguishing Using Deep Learning Approach

Metropolitan road system is built with main and auxiliary roads to facilitate urban traffic. However, due to the same trajectory and the close distance of main and auxiliary roads, the limited localization system often can't distinguish between main and auxiliary roads correctly, resulting in navigation errors that confuse main and auxiliary roads. In this paper, we attempt to distinguish between main and auxiliary roads by built-in smartphone sensors. By collecting the data from smartphone sensors, it can detect driving events and tell the maneuver that change from main road to auxiliary road or from auxiliary road to main road through a deep learning approach which enhance the accuracy of the distinction. We further develop an Android application named SmartNavi. The implementation results show that the proposed approach can reach up to 89.5% of accuracy.
Median Filtering Detection of Small-Size Image Using AlexCaps-Network

Digital image forgers often use various image processing software to maliciously tamper with image contents, and then use some anti-forensics techniques such as median filtering to hide the obvious traces of these tampered images. Therefore, median filtering detection is one of the key technologies in the field of image forensics. Recently, with the rapid development of the deep learning, more and more researchers have proposed many image median filtering detection algorithms based on deep learning. Deep learning method can automatically extract the image median filtering features and unify them with classification steps in a deep learning model, which has better detection performance than traditional algorithms. However, existing methods based on deep learning still have the promotion space when facing small size or highly compressed images. To solve this problem, a median filtering detection method of small-size image using AlexCaps-network is proposed in this paper. AlexCaps-network is a joint network combining the classical network Alexnet and Capsule network. Firstly, in order to cope with the difficulty of extracting median filtering features caused by the low-resolution of small size and highly compressed image blocks, we add image preprocessing layer to the first layer of the network to enhance the trace of median filtering. Secondly, the general feature of median filtering in learning images is extracted by shallow ordinary convolutional neural network. The capsule network layer extracts the more complex spatial information in the median filtering image by dynamic routing algorithm and predicts the results. Finally, the experimental results show that the effective detection performance of our proposed method for small size and highly compressed images, even though the size is 16 × 16 image blocks and QF of compression is 70, is still good.
Perishable Digital Goods Trading Mechanism for Blockchain- Based Vehicular Network

Recently, Internet of Vehicles (IoV) equipped with autopilot technology show much concern in their quality of service (QoS), especially in how to ensure the quality of crowdsourcing data for QoS. It is an open issue to encourage high-quality data to be sold as digital goods. Although existing works manage to design incentive mechanisms in data trading for IoV, they fail to address the trust problem. The blockchain technology has been widely studied to establish trust among participants, however, little is currently known about the perishability in the data market, which leads to the failure in explaining the price difference of digital goods. In this paper, we propose a perishability-oriented pricing mechanism to support perishable digital goods trading among IoVs. We also introduce consortium blockchain that provides distributed hyper ledger to address the trust issue in the market. By employing Stackelberg game theory, we obtain the optimal response of selfish users and providers. And finally, we propose a distributed algorithm to simulate our mechanism. Our experiment results demonstrate the efficiency of our distributed algorithm and prove the correctness and consistency of our mechanism.
Automatic Recognition of Human Actions

Understanding and recognizing human actions automatically have several applications in computer vision and human-machine interaction. In this paper, we aim to identify human actions in a short video automatically on KTH Dataset. We represented videos involving human actions into feature vector, which is invariant to small changes in the video and thus makes classification easier. We developed our model under reasonable assumptions that performs well. Our method achieves an average accuracy of 91.67% on 8 subjects with varying actions and gaits. We also compared our results with various algorithms and achieved good accuracy.
Comparison of Metaheuristic Methods for Template Matching

In many image processing applications, the Template Matching (TM) technique plays an important role in recognizing and locating patterns or objects within a digital image. The main task of the TM is to seeks and find a position within an original image that resembles a predetermined sub-image (template) and a corresponding region of the original image as much as possible. TM involves two main aspects: similarity measurement and search strategy. The simplest method applied to TM involves a comprehensive calculation of the value of the Normalized Cross-Correlation (NCC) over all the pixel locations of the source image (search strategy). Unfortunately, the high computational cost that implies the evaluation of the NCC coefficient makes this approach restricted. We propose several TM methods based on evolutionary approaches as an alternative to reduce the number of search locations in the TM process. We have conducted a comparison of several evolutionary methods, to obtain which of these is the optimum to perform the TM task. Experimental results of this comparison show us which methods achieve the best balance between estimation and computational cost.
The Application of Multimedia Network Technology in Network Technology

Abstract This paper reviews the core technology system of machine translation under the condition of multimedia technology, and holds that the key links of machine translation are “sentence element” extraction, corpus selection, word selection, word order adjustment, sentence evaluation, etc., which can be realized by machine learning neural network. Corpus maintenance is not only the core work of MT, but also the key to determine the quality of Mt.
Prototyping of Electronic Musical Instrument “Photomin” as Physical Programming Learning Material for STEAM Education

I prototyped electronic musical instruments “Photomin” using microcomputer “Mbed” as physical programming learning materials for STEAM education with the aim of fostering human resources capable of carrying regional innovation. And it was confirmed that the instruments ran normally. The instruments would be effective for STEAM education for various people.