Reviewerly/exHarmony huggingface dataset:
Neural Network Based on Work Piece Recognition and Robot Intelligent Capture in Complex Environments

In today’s rapid development of science and technology, the manual work in the factory has been gradually replaced by machines, and the process of industrial intelligence has been further deepened. Workpiece recognition is the use of machine learning, computer vision and other technologies to identify the target workpiece, and the robot intelligent capture is a higher level of operation of the workpiece recognition, which is the key to realize the intelligentization of industrial robots. Due to the complex environmental factors and the diversity of the shape and size of the objects to be grasped, the accuracy and efficiency of the workpiece recognition are not ideal at this stage, and intelligent crawling is even more difficult to talk about. Aiming at the above problems, this paper builds a crawling planning model based on the convolutional neural network and the grasping pose mapping rules. Based on the established crawling planning model, the sampling candidate grabbing algorithm is designed and the migration learning method is adopted. The pretrained convolutional neural network for image recognition on the ImageNet dataset was migrated to the capture detection task of the Carnegie Mellon dataset. Experiments show that the network model proposed in this paper performs well, and its correct crawl rate is as high as 81.27%. This is to achieve a more stable and reliable identification and intelligent crawling work.
Dynamic Vehicle Path-Planning in the Presence of Traffic Events

Advanced Driver Assistance Systems require a tremendous amount of sensor information to support the driver's comfort and safety. In particular, systems that provide (good) route options to a vehicle rely on information, such as traffic jams and road blockages, which is sensed by other (possibly distant) vehicles and distributed by a central server. This information is clearly dynamic and may be invalid by the time the vehicle arrives at the affected location. In this work, we develop an innovative approach to determine optimal routes (minimizing the costs like travel-time to their destination) for vehicles whose original route is adversely impacted by a (severe) road event. A set of recursive equations is developed that yields the optimal decision for each vehicle at each decision-point. Simulations show that our approach adapts to the considered event and finds routes of similar quality as a full-knowledge approach with limited communication overhead.
Proactive Security Auditing in Clouds

In this chapter, we present an automated learning-based proactive auditing system, namely LeaPS, which automatically learns probabilistic dependencies, and hence, addresses the inefficiencies of existing solutions. To this end, we describe a log processor, which processes (as discussed later) real-world cloud logs and prepares them for different learning techniques (e.g., Bayesian network and sequence pattern mining) to allow capturing dependency relationships. Unlike most learning-based security solutions, since we are not relying on learning techniques to detect abnormal behaviors, we avoid the well-known limitations of high false positive rates; any inaccuracy in the learning phase would only affect the efficiency, as will be demonstrated through experiments later in this chapter. We believe this idea of leveraging learning for efficiency, instead of security, may be adapted to benefit other security solutions. As demonstrated by our implementation and experimental results, LeaPS provides an automated, efficient, and scalable solution for different cloud platforms to increase their transparency and accountability to tenants.
Analytical approach of spam and sarcasm detection

Emotion analysis is extensively used, mostly as part of social media analysis for multiple domains like business, a recently released movie or a product launch, to understand its acknowledgement by the people and what they think based on their opinions or in other words, their sentiment!
Study on Eye Movement Behavior of Interface Complexity

Eye movement tracking technology is widely used in user research and user experience testing. In order to further study the feasibility of using the first gaze time to evaluate the interface complexity, this paper designed experiments to explore the relationship between the first gaze time and the interface complexity. In this study, interfaces with three levels of complexity were designed, and an appropriate number of users were selected to carry out visual search tasks, and the rule of the first fixation duration changing with the interface complexity was analyzed. The results show that the complexity of the interface is negatively correlated with the duration of the first gaze, which can be used to evaluate the complexity of the interface.
Composite reduced-kernel weighted extreme learning machine for imbalanced data classification

Abstract In order to solving the problem that the weighted extreme learning machine based on the ensemble learning method enhances the classification performance while increasing the running time of the algorithm, starting from the perspective of multi-core learning, a weighted extreme learning machine based on composite kernel functions and reduced-kernel technique is proposed. The composite kernel function based on Gaussian kernel and Polynomial kernel weighted combination is designed, which effectively improves the classification performance of weighted extreme learning machine. Meanwhile, based on the sample distribution characteristics of the imbalanced dataset, the balanced input sub-matrix is designed to reduce the computational cost of the composite kernel method. The eight binary classification imbalanced datasets of KEEL dataset repository were used for testing. The experimental results show that compared with the original weighted extreme learning machine algorithm, the G- mean and AUC classification performance indicators of the composite reduced-kernel weighted extreme learning machine algorithm are improved in each dataset, and the computation cost is effectively reduced.
Mono-camera based calibration method for two-axes LRF measurement system

A mono-camera based calibration method for laser ranger finder (LRF) combined with a two-axes turntable measurement system is proposed in this paper. The LRF rotates around the two axes and two parallel calibration planes are used to intercept a laser beam in order to calculate the manually-set output point of the LRF. The space coordinate of the two laser beam spots is computed using monocular vision method. Then the manually-set output point of LRF is calculated based on the principle of light propagating linearly. At last multiple manually-set output points are collected to fit the axis parameters. The space coordinates of targets can be calculated by LRF measurement system through accurately calibrating the two-axes of the turntable. A quantitative experiments show that the measuring precision of the system is within 0.5mm. Our calibration method is simple, effective can be extended in other measuring system with a single, two and more axes turntable.
Conclusion: Henri Rousseau and Synthetic Naïveté

Beginning with a detailed description of the Fauvist movement, the conclusion illustrates the ways in which the artist as animal myth entered the twentieth century. This chapter highlights the stakes of the project. The notion that we may have something to learn from animals threatens our political and philosophical investment in being human, and it undermines our notions of creative achievement. After all, who is willing to place Degas’ ballerinas on the same instinctual level as a spider’s web? That said, animals and art have been inextricably linked in the age of the Anthropocene. Given our time of environmental crises, this chapter emphasizes the continued role of art and literature today to replace the memory of flora and fauna during a time of its disappearance as well as bridge the divide between human and animal.
Speech-Based Automatic Assessment of Question Making Skill in L2 Language

In this paper, we present a spoken educational system to automatically assess Arabic-native children’s skill in forming English questions for different presented prompts. These prompts consist of images with a sentence that includes the answer to the required question. The answer key is colored to indicate what to ask. The main methodology of the proposed system is to record the spoken response of the child and pass it through state-of-the-art ASR to convert it into text. The output transcription is passed through three pipelined subsystems; Wh-question word checker, English grammar checker, which returns the number of grammar errors in the given question, and machine learning based grammar/language checker. The student response is accepted only if it is accepted by the three subsystems. The system was trained on 650 recorded responses made by 60 students (5th to 8th grades) as response to 75 different prompts. The number of grammar errors produced by the English grammar checker, best cosine similarity, best edit distance and best Jaccard distance between student response and the corresponding reference possible responses, are used to train KNN and SVM models with different parameters. The best precision, recall, f-measure and accuracy were achieved by SVM with linear kernel and degree of 2, 91%, 88%, 89% and 89%, respectively.
Research on All Terrain Tracking Platform Based on Inertial Navigation

Abstract In order to solve the dangerous situation that search and rescue personnel are not suitable for direct internal search and rescue in harsh closed environment without GPS signals, an all-terrain trajectory tracking platform based on inertial navigation is designed and studied. An autonomous navigation system is composed of low cost, low power consumption, small size MEMS inertial elements, which do not depend on external information and radiate energy. Kalman filter is used to fuse the data of accelerometer, gyroscope and magnetometer and output stable and reliable azimuth angle. By combining the Hexapod joint structure with the roller track structure, the multi-environment and all-terrain machine traveling is realized. The experimental results show that the three-dimensional and two-dimensional space trajectories can be clearly drawn in outdoor complex environment. In indoor environment, the platform achieves positioning error within 5 meters, deviation is not more than 10 cm, and draws a map that conforms to the platform trajectory. The development of the platform provides a powerful help for exploration work.
Feasibility Study on Quality Standards for Patrol and Inspection Transmission Lines by Helicopter

Abstract The framework for the basic content of quality standards for patrol and inspection by helicopter in power industry is designed through the analysis carried out to the current status of patrol and inspection business of the State Grid Corporation of China, especially the problems in aspects such as false alarms of defect, lack of standardization in descriptions of defective photos, abandoning of aviation and carryover lines, untimely submission of products, and incomplete submission of products, etc., and on the basis of the method of correlation analysis and analysis of factors of influence. And based on that, it formed the inner quality standard of the State Grid Corporation of China are formed.
Data processing of a local seismological network for West Texas seismicity characterization

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2019Data processing of a local seismological network for West Texas seismicity characterizationAuthors: Dmitrii MerzlikinAlexandros SavvaidisStefanie WhittakerIbinabo BestmannDmitrii MerzlikinThe University of Texas at AustinSearch for more papers by this author, Alexandros SavvaidisThe University of Texas at AustinSearch for more papers by this author, Stefanie WhittakerThe University of Texas at AustinSearch for more papers by this author, and Ibinabo BestmannUniversity of AlbertaSearch for more papers by this authorhttps://doi.org/10.1190/segam2019-3215062.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractTexas Seismological Network (TexNet) has been established to investigate patterns of induced seismicity across Texas. We improve detection sensitivity of TexNet to locatable events in West Texas using template matching. We use three earthquakes from three clusters as our templates. Template matching is applied to each station independently. Obtained picks are then fed into an associator, which groups activations following moveouts between stations consistent with a velocity model into seismic events. In comparison to STA/LTA detection workflow a number of “new”/previously unseen events more than doubled. We then de-noise the detected waveforms using soft-thresholding in the S-transform domain and unravel P-wave arrivals thus allowing for absolute event location.Presentation Date: Wednesday, September 18, 2019Session Start Time: 1:50 PMPresentation Time: 2:15 PMLocation: 302BPresentation Type: OralKeywords: earthquake, crosscorrelation, induced seismicity, signal processing, filteringPermalink: https://doi.org/10.1190/segam2019-3215062.1FiguresReferencesRelatedDetails SEG Technical Program Expanded Abstracts 2019ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2019 Pages: 5407 publication data© 2019 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 10 Aug 2019 CITATION INFORMATION Dmitrii Merzlikin, Alexandros Savvaidis, Stefanie Whittaker, and Ibinabo Bestmann, (2019), "Data processing of a local seismological network for West Texas seismicity characterization," SEG Technical Program Expanded Abstracts : 2084-2088. https://doi.org/10.1190/segam2019-3215062.1 Plain-Language Summary Keywordsearthquakecrosscorrelationinduced seismicitysignal processingfilteringPDF DownloadLoading ...
The Comparison of Semantic Suffix Tree Clustering and Suffix Tree Clustering Algorithm Influence on the Accuracy Rate of an Indonesian Question Answering System

Abstract This research analyses the comparison between Semantic Suffix Tree Clustering (SSTC) algorithm and Suffix Tree Clustering (STC) algorithm in clustering documents on Indonesian Question Answering System. The fundamental difference between the two algorithms is that SSTC considers the meaning of the words to generate clusters and readable labels rather than relying only on string matching like STC. As such, SSTC is able to return a more specific and accurate clusters, and has potential to increase the accuracy rate of a question answering system. However, contrary to the hypothesis, comparison results shows that the accuracy rate of Indonesian Question Answering System after the documents is clustered by SSTC is lower than by STC. The accuracy rate degradation occurred in almost every question category, except Definition category. In average, the accuracy rate obtained by Indonesian Question Answering System with SSTC is only 23.31%, while Indonesian Question Answering System with STC is able to obtain 83% accuracy rate. This significant difference indicates that Semantic Suffix Tree Clustering algorithm is not suitable in the context of document clustering on Indonesian Question Answering System.
A Subjective Assessment Method of Aerospace Image Quality Based on Human Visual System

Aiming at the problems of blur, noise and contrast distortion in aerospace images, a subjective image quality assessment method based on human visual characteristics is proposed in this paper. First, the aerospace image database with different blur, noise and contrast distortions is simulated by 3D modelling software. Second, the subjects are required to give three image quality scores based on the indexes above, respectively. Third, the correlations of three indexes’ scores are calculated by the Pearson formula and then different weights are given to the indexes based on the calculated correlation coefficient above. Finally, the modified subjective image assessment scores can be obtained by the multi-factor model. The proposed subjective evaluation model takes into account the influence of multiple factors that can show the characteristics of images comprehensively. Many experiment results have verified the correctness of the proposed method.
Quantum Search on Networks

Abstract We find a lower bound on the computational complexity of Grover’s quantum search algorithms in low-dimensional networks using the renormalization group (RG). It highlights the competition between Grover’s abstract algorithm, i.e., a rotation in Hilbert space, and quantum transport in an actual geometry. It can be characterized in terms of the quantum walk dimension <?CDATA ${d}_{w}^{Q}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msubsup> <mml:mi>d</mml:mi> <mml:mi>w</mml:mi> <mml:mi>Q</mml:mi> </mml:msubsup> </mml:mrow> </mml:math> and the spatial (fractal) dimension d f or, alternatively, the spectral dimension of the network, d s , even when translational invariance is broken.
Allocation of an Exponentially Modulated Harmonic from a Short Nonstationary Time Series by the SSA Method

The method of dividing a short nonstationary time series into trend and exponentially modulated harmonic components is proposed. It is based on the SSA (Singular Spectrum Analysis) method. A feature of the method is the transformation of the original series into a new series. This is achieved by removing the trend from the original series and then double-differentiating the remainder. This procedure provides a new series of more contrasting manifestation of the harmonic component and its subsequent selection. The method is tested with polynomial trends of the second and third order. It showed high resolution, including in the presence of weak normal noise, and can be recommended for use in various physical and engineering applications for the study of harmonic processes with small coefficients of attenuation or growth.
Study of higher order squeezing and subpoissonian photon statistics in generalised binomial state of light

Higher order nonclassicality in terms of squeezing and subpoissonian photon statistics is explored in Generalized Binomial State (GBS) of light. It is shown that this state satisfy the both criteria in different limits of depending parameters of state. Depth of nonclassicality and witness of nonclassicality depends on various parameters related to GBS. Mutual relation between different signatures of nonclassicality in GBS is also provided and further observed that the state may show nonclassicality for subpoissonian statistics criterion in the absence of squeezing. Earlier our group shown that some nonlinear excited states show squeezing in the absence of subpoissonian photon statistics. Finally this study claims that Photon statistics and squeezing of a particular order are independent phenomenon.
Comparison of training methods for the binarized neural object detection network

Binarized neural networks are of great interest because they can dramatically reduce the amount of computation and memory, but they are still in the early stages of research and are still challenging. In this paper, we investigate three training methods of a binarized object detection network and measure their training speeds and detection accuracies based on Darknet platform.
Feel-It

Web browsing is a tedious exploratory process for blind people who have to primarily rely on the screen reader's keyboard shortcuts or gestures and the serial audio readout of the textual content to navigate web pages. In contrast, sighted users can review the 2D structure of the page and use the mouse or touch he screen to focus on any part of the page. In this paper, we describe the Feel-It system for audio-tactile web browsing. Feel-It uses a combination of hardware and software to provide a usable personalized web browsing experience for blind computer users.
Invariance to Affine-Permutation Distortions

An object imaged from various viewpoints appears very different. Hence, effective shape representation of objects becomes central in many applications of computer vision. We consider affine and permutation distortions. We derive the affine-permutation shape space that extends, to include permutation distortions, the affine only shape space (the Grassmannian). We compute the affine-permutation shape space metric, the sample mean of multiple shapes, the geodesic defined by two shapes, and a canonical representative for a shape equivalence class. We illustrate our approach in several applications including clustering and morphing of shapes of different objects along a geodesic path. The experimental results on key benchmark datasets demonstrate the effectiveness of our framework.
Research on Opinion Leaders Recognition Based on TOPSIS in open source design community

In order to identify the opinion leaders in open source design community, a comprehensive evaluation method of node importance based on TOPSIS is proposed in this paper. Firstly, a user collaboration network model of community organization is established by using complex social network technology. Secondly, in order to improve the accuracy of opinion leader identification, the benefit indicators and cost indicators of each node are measured. Degree centrality, median centrality and near centrality are used as the benefit indicators, meanwhile structural hole is used as the cost indicator. Then the comprehensive evaluation index of nodes is taken as the attribute of decision evaluation scheme to calculate synthetically to determine its importance in the network. On this basis, this paper takes the Local Motors community as the research object and establishes a user cooperative directed weighted network. The static topology index of the network is analyzed. According to the results, the opinion leader node of the user cooperative network is identified based on TOPSIS method. Experiments have shown that more accurate evaluation results can be obtained than using a single indicator.
Research and Application of Laser Ranging Method in Form and Position Tolerance of Precision Machining Products

Abstract Precision measurement of shape and position tolerance plays an important role and significance in manufacturing industry. It is an important link to measure and guarantee the quality of manufacturi ng. Especially for large-scale precision parts, the repeated use of measuring tools easily leads to measurement errors. Aiming at the problems of low detection efficiency, complex process and high cost of high precision three-coordinate measuring instrument for large-size parts, the characteristics of contact measurement and non-contact measurement for large-size parts are studied based on the characteristics of large-size parts. A measurement method combining laser ranging with reflective target is proposed, and a rotating shaft for fixing laser launcher is designed. A complete spherical coordinate measuring system is constructed, which is applied to the measurement of straightness, flatness, parallelism, verticality and height difference of large-scale precision machined parts. The corresponding measurement results are obtained in a constant greenhouse. On this basis, under the same temperature conditions, the same shape and position tolerance dimensions of the same part are measured by a three-coordinate measuring instrument. Comparing the results of measurement, it can be seen that the measurement results obtained by the two methods are the same in the range of 0.001 mm, and the measurement efficiency and cost of the laser ranging method have obvious advantages. This paper also gives the method of measuring the shape and position tolerance of large-scale precision parts.