Reviewerly/exHarmony huggingface dataset:
Improved RRT Path Planning Algorithm for Humanoid Robotic Arm

With the aging of the population and the increase in the employment cost of the elderly service industry, the elderly service robot has become an important development field. However, the working environment of the service robot is complex, and the robot arm may collide with the environmental obstacles, which will cause damage not only to the obstacles but also to the robot arm and even the served object. In this paper, an improved rapidly exploring random tree algorithm is proposed for path planning of humanoid robotic arm. First, the threshold of random probability sampling is set as the selection criterion of the extension mode. Then the random sampling value and the probability sampling threshold are compared, and the target point is determined as a random point if the random sampling value is within the set threshold value. Otherwise, the new node will be expanded under the joint action of the target gravity and the random point gravity. This is the extension of classification based on random probability. Finally, the breadth-first search is used to smooth the initial exploration path. The simulation results show that the proposed method can effectively reduce the path planning time and path length.
Depicting Negative Information in Photographs, Videos, and Comics: A Preliminary Analysis

It is often claimed that pictures are not well suited to representing negative information. Against this widely held view, we argue that a close look at how ordinary people use visual representations will show that negative information can be depicted in various interesting ways. We focus on three types of representations, namely, photographs, videos, and comics, and discuss design varieties for depicting negation.
Cyber-Physical-Social Systems for Big Data

The Cyber-Physical-Social space integrates different data originating from physical, cyber and social spaces. The large quantities of data obtained during the capturing of physical phenomena through distributed sensor networks can be subjected to analysis using various techniques. This, in turn, can give opportunity for realization of intelligent applications and services. Such applications rely on the effective monitoring of physical infrastructure and environment and combine the collected data via intelligent cybernetic processes for providing of better services. This is an endeavor to summarize the schematic image of negative and unclear effects of new technology usage consolidated in the Industry 4.0. Deeper understanding of dimensions, application and impacts of technologies under Intelligent Agriculture could be extracted from the results and be used as a framework or starting point.
Thermal Infrared Tracking using Multi-stages Deep Features Fusion

Thermal infrared (TIR) tracking can be utilized to track the target in the images generated by thermal infrared sensors due to the weak influence by illumination changes. However, there are still some challenges to do thermal infrared tracking when suffering drastic appearance variation, heavy occlusion and background clutters. The absence of RGB patterns and low resolution also constrain the tracking performance in complex scenarios. The deep convolutional features are widely utilized to solve visual tracking problems which successfully extracted the spatial and semantic information though object representation. Motivated by these methods, we firstly propose to combine multi-stages cascaded Siamese networks to achieve deep features fusion in three stages, then achieve the tracking procedure by candidates matching strategy. The final results are obtained by non-maximum suppression and scale penalty. The proposed method can inherit the advantages by fusing multi-stages deep features and achieve end-to-end learning simultaneously. The experiments are evaluated with state-of-the-art methods on VOT-TIR2016 benchmark and attributes based comparison. The tracking results demonstrate that our proposed method outperforms the compared methods in terms of accuracy and robustness.
The fast clustering algorithm for the big data based on K-means

As a powerful unsupervised learning technique, clustering is the fundamental task of big data analysis. However, many traditional clustering algorithms for big data that is a collection of high dimension, sparse and noise data do not perform well both in terms of computational efficiency and clustering accuracy. To alleviate these problems, this paper presents Feature K-means clustering model on the feature space of big data and introduces its fast algorithm based on Alternating Direction Multiplier Method (ADMM). We show the equivalence of the Feature K-means model in the original space and the feature space and prove the convergence of its iterative algorithm. Computationally, we compare the Feature K-means with Spherical K-means and Kernel K-means on several benchmark data sets, including artificial data and four face databases. Experiments show that the proposed approach is comparable to the state-of-the-art algorithm in big data clustering.
Counting Mathematical Diagrams with Machine Learning

The role and use of diagrams in mathematical research has recently attracted increasing attention within the philosophy of mathematics, leading to a number of in-depth case studies of how diagrams are used in mathematical practice. Though highly interesting, the study of diagrams still largely lack quantitative investigations which can provide vital background information regarding variations e.g. in the frequency or type of diagrams used in mathematics publication over time. A first attempt at providing such quantitative background information has recently been conducted [9], making it clear that the manual labour required to identify and code diagrams constitutes a major limiting factor in large-scale investigations of diagram-use in mathematics. In order to overcome this limiting factor, we have developed a machine learning tool that is able to identify and count mathematical diagrams in large corpora of mathematics texts. In this paper we report on our experiences with this first attempt to bring machine learning tools to the aid of philosophy of mathematics. We describe how we developed the tool, the choices we made along the way, and how reliable the tool is in identifying mathematical diagrams in corpora outside of its training set. On the basis of these experiences we discuss how machine learning tools can be used to inform philosophical discussions, and we provide some ideas to new and valuable research questions that these novel tools may help answer.
Depth Image Vibration Filtering and Shadow Detection Based on Fusion and Fractional Differential

The depth image generated by Kinect sensor always contains vibration and shadow noises which limit the related usage. In this research, a method based on image fusion and fractional differential is proposed for the vibration filtering and shadow detection. First, an image fusion method based on pixel level is put forward to filter the vibration noises. This method can achieve the best quality of every pixel according to the depth images sequence. Second, an improved operator based on fractional differential is studied to extract the shadow noises, which can enhance the boundaries of shadow regions significantly to accomplish the shadow detection effectively. Finally, a comparison is made with other traditional and state-of-the-art methods and our experimental results indicate that the proposed method can filter out the vibration and shadow noises effectively based on the [Formula: see text]-measure system.
Low-cost experimental apparatus for motion tracking based on image processing and camera calibration techniques

The present work focuses on the use of a low-cost equipment to monitor static and dynamic tests as an alternative to expensive commercial devices. First, we describe a procedure for cameras calibration and image processing in order to establish a less invasive (than other common instrumentation, such as strain gauges) and much cheaper motion capture method (than commercial tracking systems). Following, two cameras and one personal computer (with an image acquisition board) are used as the monitoring apparatus for tracking the movements of a flexible cable under harmonic excitation at its top end. The experimental results are then compared with numerical simulations, showing a fairly good agreement and the same level of precision as that obtained with one of the most used motion capture system in laboratories. The proposed experimental methodology correctly identifies the displacements, frequencies, and dynamic geometric behavior of the flexible model in all directions. Although commercial solutions are faster, since data processing and cameras calibration take place in real time, the acquisition cost of the suggested equipment is most affordable for small industries, educational institutions and projects with restrict budget on kinesiology, physiology, dentistry, facial recognition and mechanics, among others.
Multi-target vehicle detection and tracking based on video

In order to realize the vehicle tracking on the traffic road, a multi-target vehicle detecting and tracking method K-YOLOv3 based on video tracking is proposed in combination with experiments. The algorithm is composed of vehicle detection, tracking, trajectory generation part. The focus of this paper is vehicle detection and tracking. K-YOLOv3 is an improvement on YOLOv3, which is combined with KCF to detect and track the target vehicle at the same time, and then the vehicle trajectory set is established to form the vehicle trajectory. Experimental results show that the detection accuracy of k-yolov3 algorithm is slightly higher than YOLOv3, while the detection speed is much faster than YOLOv3.
Autonomous Indoor Vehicle Navigation Using Modified Steering Velocity Obstacles

Wheeled mobile robot as an autonomous vehicle that operate in indoor environment must be equipped with the ability to navigate from the initial position to the target. In the workspace with narrow passage such as office, capability to plan and track the path have to be completed with precise reaction to avoid other objects. This paper presents the development of navigation approach of an autonomous indoor vehicle in office environment. For the global path planning, Probabilistic Road Map is used to generate waypoints. Pure Pursuit is devised to track these points. For the local path planning, we propose to modify steering property of Velocity Obstacles based on distances read by LIDAR sensor. For evaluating our approach, the proposed indoor navigation is implemented in simulations for a two-wheeled differential-steering mobile robot. The results shows that our approach was able to avoid static obstacles when navigating the robot to cover a distance of 13.04 meters from the initial position to reach the target position in an environment with a limited area within 3.65 minutes using an average angular velocity of 0.02 rad/s.
A drift aware adaptive method based on minimum uncertainty for anomaly detection in social networking

The social attack is an example of the anomaly that often changed their behavior, increased data volumes, and should be detected as early as possible to minimize damage. Data streaming mining is one of the solutions, which can handle the social attacks, and adapt to the change in the anomaly data stream. In this paper, we propose Online Fusion of Experts based on a minimum uncertainty to predict the concept drift in a data stream of social network-attack. Online learning algorithms such as Linear-order algorithms and Gaussian-order algorithms employ as an expert to identify the change in the anomaly data stream. First, online learning algorithms determine the error value for each data sample when data stream enter individually. Second, OFE utilizes a maximum-posterior estimation of the error rate of online learning algorithms to generate a new input data stream. Third, the Uncertainty Error Correlation Matrix (UECM) of input data applies to real-time behavior change detection of a data stream. Performance of OFE is evaluated by related data streaming algorithms using a benchmark, and real dataset from UCI repository (NSL-KDD, ISCX, and etc.), and malicious web pages, respectively.
Shellproof: More Efficient Zero-Knowledge Proofs for Confidential Transactions in Blockchain

Blockchain-based cryptocurrencies enable decentralization in transaction systems. The transaction details of traditional cryptocurrencies, e.g. Bitcoin, are public to everyone, which can be a problem in confidential scenarios. To deal with the confidential problem, some current cryptocurrencies adopt confidential transactions to hide the details in proofs. Among all confidential transaction implementations, Bulletproof is the most efficient approach without a trusted setup. In this paper, we analyze Bulletproof's design and show that the computation cost can be further reduced. Based on our analysis, we propose Shellproof, which is only half of the computation cost of Bulletproof. Experimental results show Shellproof can preserve the advantages of Bulletproof and is much more efficient than Bulletproof.
E-Initiative for Food Security: Design of Mobile Crowdfunding Platform to Reduce Food Insecurity in Indonesia

Indonesia is a country with the fourth-largest economy power in the world. However, Indonesia is threatened by a severe problem, namely food insecurity. The aim of this research is to produce a mobile design of the e-initiative mobile application as a crowdfunding platform that can help overcome food insecurity and realize food security in Indonesia. The mobile apps designed using The Wheel method which focuses on the user experience of the application. This method is in the form of a cycle phase that starts from the analysis phase of user needs with the interview, questionnaire distribution, and competitive analysis. The design phase is by persona, sketch, storyboard, and wireframes. Then the results of the design phase are implemented in the form of a medium-fidelity prototype. At the evaluation stage of prototype medium-fidelity, usability testing is using the think-aloud method, and the results of the performance task with a success rate of 85.32% are obtained.
Anomaly detection of power grid dispatching platform based on Isolation Forest and K-means fusion algorithm

Abstract It is very important to detect abnormal Access IP of power grid dispatching platform accurately and rapidly to ensure the safety of power production. An Isolation Forest and K-means fusion algorithm is proposed, which not only solves the deficiency of Isolation Forest only being able to detect anomalies in binary classification, but also solves the defect of Isolation Forest threshold setting based on artificial experience or prior assumptions by designing a threshold setting strategy for Isolation Forest anomalies. The Access IP data of the southern power grid dispatching platform is taken as an example to evaluate the proposed algorithm and model. Through control experiments, the effectiveness and advancedness of the algorithm are illustrated in terms of AUC-ROC curve, accuracy, recall, and F1 score.
A Semantic and Syntactic Similarity Measure for Political Tweets

Measurement of the semantic and syntactic similarity of human utterances is essential in allowing machines to understand dialogue with users. However, human language is complex, and the semantic meaning of an utterance is usually dependent upon the context at a given time and learnt experience of the meaning of the words that are used. This is particularly challenging when automatically understanding the meaning of social media, such as tweets, which can contain non-standard language. Short Text Semantic Similarity measures can be adapted to measure the degree of similarity of a pair of tweets. This work presents a new Semantic and Syntactic Similarity Measure (TSSSM) for political tweets. The approach uses word embeddings to determine semantic similarity and extracts syntactic features to overcome the limitations of current measures which may miss identical sequences of words. A large dataset of tweets focusing on the political domain were collected, pre-processed and used to train the word embedding model, with various experiments performed to determine the optimal model and parameters. A selection of tweet pairs were evaluated by humans for semantic equivalence and correlated against the measure. The new measure can be used in a variety of applications, including for identifying and analyzing political narratives. Experiments on three diverse human-labelled test datasets demonstrate that the measure outperforms an existing measure, performs well on tweets from the political domain and may also generalize outside the political domain.
Image Retrieval using Multi Texton Co-occurrence Descriptor and Discrete Wavelet Transform

This paper describes an efficient algorithm for Content-Based Image Retrieval (CBIR) based on Multi Texton Co-Occurrence Descriptor (MTCD) and Haar (Wavelet) namely MTCD-H. The problems from the previous research were the computational speed and the low value of precision. The data that was used consisted of 10000 Corel images and 300 batik images. MTCD used the RGB colour feature, the Sobel edge detection, and global feature using Gray Level Co-Occurrence Descriptor (GLCM). Wavelet was considered an approach that could increase the precision value as well as reduce the features. This paper combined the MTCD with Haar for the image extraction process in order to increase the computational speed and the precision value. The contribution of this paper was aiming for extracting the Wavelet feature on a grayscale image before extracting the feature using GLCM. The results showed an increase of precision value pointed at 3.36 for batik images and 5.11 for the Corel images. In addition, the computational speed for batik images was performed 84. 35s faster as for the Corel images 2988 faster. Based on the specified results, it could be concluded that the MTCD-H were effective in reducing the computational speed as well as increase the precision.
Chinese Text Classification via Bidirectional Lattice LSTM

In this paper, we investigate a bidirectional lattice LSTM (Bi-Lattice) network for Chinese text classification. The new network is different from the standard LSTM in adding shortcut paths which link the start and end characters of words, to control the information flow. Character-level features can flow into word-level by an extra gate, and word-level features are integrated into character-level via a weighted manner by another gate. Previous models take as input embeddings pre-trained by Skip-Gram model, we utilize word sememes in HowNet to further improve the word representation learning in our proposal. Our experiments show that Bi-Lattice gives better results compared with the state-of-the-art methods on two Chinese text classification benchmarks. Detailed analyses are conducted to show the success of our model in feature fusion, and the contribution of each component.
A Privacy protection scheme for carpooling service using fog computing

Abstract As the traffic environment becomes more and more crowded, an increasing number of people are willing to carpool. Carpooling services provide convenience for drivers and passengers to share their journeys. But carpooling requests are uploaded to cloud server for processing and analysis in most existing schemes, resulting in communication delays and leakage of user privacy. To solve this problem, we propose a privacy protection scheme for carpooling service using fog computing. First, identity-based digital signature technology is used to achieve anonymous authentication. Second, location tags, bloom filters and fuzzy extractors are utilized to match passengers and drivers without disclosing the users’ location; Consortium blockchain technology is adopted to store carpooling records. Finally, security analysis and experiments results show that our scheme offers privacy preservation and security, while maintaining low computational cost.
Lattice Blind Signatures with Forward Security

Blind signatures play an important role in both electronic cash and electronic voting systems. Blind signatures should be secure against various attacks (such as signature forgeries). The work puts a special attention to secret key exposure attacks, which totally break digital signatures. Signatures that resist secret key exposure attacks are called forward secure in the sense that disclosure of a current secret key does not compromise past secret keys. This means that forward-secure signatures must include a mechanism for secret-key evolution over time periods. This paper gives a construction of the first blind signature that is forward secure. The construction is based on the SIS assumption in the lattice setting. The core techniques applied are the binary tree data structure for the time periods and the trapdoor delegation for the key-evolution mechanism.
Proposal of a Perimeter Line Management Method for Fog and Edge Computing with SDP Concept

In recent years, with the development of the Internet of Things (IoT), there are more and more situations where real-time performance is required, such as automated driving and surveillance cameras. However, when processing these data in the cloud, it is sometimes difficult to maintain the real-time performance of the data. In addition, a number of people are concerned about processing video data in the cloud from the viewpoint of privacy protection. As a response to these issues, fog and edge computing are attracting attention. On the other hand, the vulnerability of existing perimeter defenses has been pointed out and the importance of the zero-trust model is increasing. In this model, a new “perimeter” has been proposed: the Software Defined Perimeter (SDP). Considering these backgrounds, in this paper, we propose a new perimeter line management method using the concept of SDP as a security measure for fog and edge computing.
Diagnosis of diabetes in pregnant woman using a Chaotic-Jaya hybridized extreme learning machine model

Abstract As stated by World Health Organization (WHO) report, 246 million individuals have suffered with diabetes disease over worldwide and it is anticipated that by 2025 this estimation can cross 380 million. So, the proper and quick diagnosis of this disease is turned into a significant challenge for the machine learning researchers. This paper aims to design a robust model for diagnosis of diabetes using a hybrid approach of Chaotic-Jaya (CJaya) algorithm with Extreme Learning Machine (ELM), which is named as CJaya-ELM. In this paper, Jaya algorithm with Chaotic learning approach is used to optimize the random parameters of ELM classifier. Here, to assess the efficacy of the designed model, Pima Indian diabetes dataset is considered. Here, the designed model CJaya-ELM, has been compared with basic ELM, Teaching Learning Based Optimization algorithm (TLBO) optimized ELM (TLBO-ELM), Multi-Layer Perceptron (MLP), Jaya algorithm optimized MLP (Jaya-MLP), TLBO algorithm optimized MLP (TLBO-MLP) and CJaya algorithm optimized MLP models. CJaya-ELM model resulted in the highest testing accuracy of 0.9687, sensitivity of 1, specificity of 0.9688 with 0.9782 area under curve (AUC) value. Results reveal that CJaya-ELM model effectively classifies both the positive and negative samples of Pima and outperforms the competitors.