Reviewerly/exHarmony huggingface dataset:
A Fast Obstacle Detection Method by Fusion of Double-Layer Region Growing Algorithm and Grid-SECOND Detector

Adjacent obstacles are difficult to be distinguished, and remote obstacles are detected easily to split. Besides, limited deep learning samples easily result in missed detection of obstacles in urban environment. In view of this, a fast and robust detection method is proposed by fusing Double-Layer Region Growth algorithm and Grid-SECOND detector. At first, SECOND detector is improved by replacing voxel grids with 2D grids and adopting multi-dimensional features to detect obstacles, which can reduce the time consumption and ensure the accurate detection of remote obstacles. Then, the first Region Growing algorithm is used to cluster the undetected and non-empty grids, which can detect obstacles outside the training set. At last, the second Region Growing algorithm is used to refine the detection results of obstacles with larger volume and multi-obstacles grids, and complete the obstacle detection. Through testing in our extracted urban dataset and KITTI dataset, it is verified that the proposed method outperforms state-of-the-art methods and can accurately achieve obstacle detection. The average duration of the entire process is about 50ms.
(Invited) A Novel Self-Localization Method for USV Based on GPS and Visual-Map

With the vigorous development of computer technology, information technology and intelligent transportation, their combination in the maritime field is becoming more and more extensive. At present, unmanned surface vehicle (USV) has been widely applied in the water transportation environment, especially for dangerous and time-consuming tasks. However, the intellectualization of USV is still far from actually being “unmanned”. Among them, USV uses its own sensors to perceive the surrounding environment and obtain high-precision position estimation is a prerequisite for its intelligence. In response to the demand, a novel two-step approach for USV self-localization is proposed in this paper. In the first step, the coarse-to-fine strategy is used to obtain the nearest node. Firstly, coarse image node is got by matching the current GPS information and the DGPS information of the visual-map. Then, the fine image node is obtained by the multi-vector fusion algorithm. In the second step, the pose of the current image is gained with respect to the nearest node by the epipolar geometry constraint, so as to achieve high-precision self-localization for USV. This method only needs an ordinary camera and GPS module in positioning, which can reduce the cost of high-precision positioning to the greatest extent. Pool experiments show that this method can achieve centimeter-level positioning accuracy.
Evaluation of geographical component of students’ scientific literacy using the principle of regionalization

Abstract The article discusses new approaches and methods, particularly the solution of situation problems aimed at generating and achieving high results of students in forming functional literacy. The use of the regional geographical material by the example of the Krasnoyarsk Territory is most effective in developing training problems that have a pronounced practice-oriented nature. Implementation of the regionalization principle in training involves the students’ perception of the processes of regional reality. Situation problems are proposed, the solution of which allows evaluating the maturity of scientific literacy of the students.
Innovation and Research on Practical Activities of Extensive Courses——Taking xx Primary School as an Example

With the introduction and wide application of extensive courses in recent years, their theoretical research and practical application have received more and more attention. In this paper, the current application and practice of the extensive courses at the elementary school level is studied through the integration of the theoretical concepts of extensive course and taking the practicals in the extensive courses of xx primary school as an example.
Probability Flow for Classifying Crystallographic Space Groups

We present a machine learning approach to classify convergent beam electron diffraction (CBED) patterns and a novel ensemble technique of combining binary classifiers to address label imbalance in the dataset. We train a primary classifier on the full dataset and additionally train $$n \atopwithdelims ()2$$ binary classifiers on pairs of labels using a weighted loss function that corrects for class imbalance. At test time, we combine the predictions of the primary classifier and binary classifiers using a method we call Probability Flow, leading to a 60.79% top-1 accuracy and a 76.95% top-5 accuracy. All of the source code is available at https://github.com/jinpan/smc_challenge2 .
Android-Based Application for Intangible Cultural Heritage in Balai Pelestarian Nilai Budaya Jawa Barat

Abstract The main purpose of this study is to help Balai Pelestarian Nilai Budaya (BPNB) Jawa Barat in preserving the Intangible Cultural Heritage (ICH) in Indonesia. BPNB Jawa Barat manages four provinces in Indonesia namely Banten, DKI Jakarta, Jawa Barat, and Lampung. This Android application aims to disseminate information regarding ICH aspects which includes history, location, maestro, and the distribution of ICH in the four provinces. The method used in application development is the Unified Process which is carried out in several phases, namely Inception, Elaboration, Construction, and Transition. The design of an Android application uses Unified Modeling Language (UML) as an application modeling. The application uses Google Maps API and coordinates stored in the database to display the locations of ICH on the map. The application has been tested using Black Box and User Acceptance Test (UAT) method, and based on the test results it can be concluded that the application can help the user to obtain information about aspects of the Intangible Cultural Heritage.
Exploration of Gold Standard, Credit Currency and Digital Currency

&lt;p&gt;In the perspective of present monetary system, the author proposes that we should analyze the comprehensive effectiveness with the combination of the former gold standard system and diversified monetary systems, such as the credit currency and the digital currency, which are highlighted in this new era, and confirm the more complete digital currency policy according to present development status so as to promote the healthier and more reasonable and effective development of the monetary funds. In this paper, the author launches research and exploration with the combination of gold standard, credit currency and digital currency.&lt;/p&gt;
Reconstruction-free fast depth segmentation via temporal orthogonal frequency fringe patterns

In many machine vision techniques, depth segmentation is a central process to segment targeted objects with abruptly changing depth into different regions for 3D data analysis. However, current techniques are limited by data-acquisition speed, computational cost, and the precision of segmentation. To overcome these challenges, this paper proposes a dynamic depth-segmentation technique that allows the segmentation of target objects into isolated depth regions without requiring three-dimensional reconstruction and background removal. Taking full advantage of the merits of the phase-shifting algorithm, singular-points theory about depth segmentation is summarized and applied to extract the segmenting lines as the markers for depth segmentation. Meanwhile, by using the proposed temporal orthogonal frequency scheme, the dynamic performance and the high-frequency components of the measured objects are both guaranteed during the computing process. Finally, a low-cost morphology-based optimization is created to solve the over-segmentation problem and enlarge the measurable dynamic range. The simulation result demonstrates the segmentation accuracy of this technique. Experimental results on objects of differing status show that the proposed approach provides satisfactory omnidirectional depth-segmentation results with high accuracy and fidelity.
Recursive LSTM with Shift Embedding for Online User-Item Interaction Prediction

Online businesses are ubiquitous nowadays. Accurately predicting the online user-item interactions such as buying, browsing, and price comparison is critically important to many e-commerce applications including recommendation, user behavior analysis and sales forecasting. In this paper, we propose RLSTM-SE, a recursive LSTM with shift embedding model, to learn the continuously evolving embeddings of users and items for more accurately predict their dynamic interactions. We conduct preliminary evaluation on two real datasets. The results show the superior performance of the proposal over several baseline models.
PARLA Experimental Method for Developing Play Activity of Preschool Children

The problem of stimulating play activity of preschoolers has been considered in the context of transforming a basic goal of play activity into subgoals, more meaningful for a child. There is a correlation between the ability of a preschooler to identify subgoals and the ability to perform conscious and purposeful behavior. The paper presents a step by step technique of stimulating play activity of preschoolers which corresponds to the components of PARLA method and includes: encouraging the diversity of contrived behavior - transforming a basic goal of play activity into subgoals – display of cognitive dissonance – continuing a game using cognitive activity of subjects of a play activity. The findings show that stimulating preschoolers’ activity through due reflection of purposeful behavior in game situations leads to practical skills of application of conscious behavior.
Floating Neighbourhood to anticipate Jakarta Flooding

Abstract The purpose of this paper is to offer alternative design solutions for the Kampung Muara Baru residence in Jakarta. Rapid groundwater extraction, population growth, infrastructure and construction development is causing land subsidence at an alarming rate. Thus, the capital city of Indonesia is on constant alert of potential flooding. Jakarta municipal government set policies under the Jakarta Coastal Defence Strategy to mitigate flood and urban planning. One of the major acts is the construction of the giant sea wall along the northern coast of Jakarta. However, the already built sea wall structure is showing signs of failing and cracking because the subsidence continues. In the meantime, report is saying the residence of Muara Baru still continue to experience flooding. The residents still have to raise the structure of their houses anyway possible to cope with the raising of the flood level. The finding suggests that vernacular, under the umbrella of sustainable and environmentally friendly (green design) should be considered as a better strategy towards the flooding disaster as it embraces nature to solve environmental issues and degradation.
Photovoltaic power generation data filling model based on tensor decomposition

Abstract With the development of new energy and the increase in demand, photovoltaic power generation has received more and more attention. However, due to the susceptibility of photovoltaic power generation to the influence of weather factors, especially the influence of solar irradiation, it is intermittent and fluctuating. Understanding the power generation data of a PV plant has a great effect on the subsequent photovoltaic power generation planning and photovoltaic power plant situation assessment. However, these data are often incompletely recorded due to frequent communication problems or equipment failures. Most current studies focus on photovoltaic power generation predictions, it is still a challenge to speculate the missing data around photovoltaic power plants. In this paper, a context-sensitive tensor decomposition model is proposed to intelligently fill the missing power generation entries. The experimental results show that our model can effectively fill missing data and obtain good results.
Reliability Growth Method and Application Based on Operation Fault Analysis

The operational reliability of civil aircraft is one of the core indexes in the reliability design of civil aircraft. This article establishes a reliability growth process based on fault analysis during the operational phase, analyzes in detail the collection and analysis methods of operation faults and reliability growth methods, and takes SPC as an example to give the practical application of this method, provide a theoretical basis and technical approach for the reliability growth work in the operational phase.
Performance characterization of Pauli channels assisted by indefinite causal order and post-measurement

Indefinite causal order has introduced disruptive procedures to improve the fidelity of quantum communication by introducing the superposition of { orders} on a set of quantum channels. It has been applied to several well characterized quantum channels as depolarizing, dephasing and teleportation. This work analyses the behavior of a parametric quantum channel for single qubits expressed in the form of Pauli channels. Combinatorics lets to obtain affordable formulas for the analysis of the output state of the channel when it goes through a certain imperfect quantum communication channel when it is deployed as a redundant application of it under indefinite causal order. In addition, the process exploits post-measurement on the associated control to select certain components of transmission. Then, the fidelity of such outputs is analysed to characterize the generic channel in terms of its parameters. As a result, we get notable enhancement in the transmission of information for well characterized channels due to the combined process: indefinite causal order plus post-measurement.
PROSES KOGNITIF DALAM LEGENDA JOKO TINGKIR Cognitive Process in Jaka Tingkir Legend

Tujuan penelitian ini adalah merumuskan proses kognitif dalam legenda Joko Tingkir. Pendekatan yang digunakan dalam penelitian ini adalah kualitatif. Jenis penelitian ini adalah grounded theory . Data penelitian ini adalah kata, kalimat, dan wacana yang menunjukkan proses kognitif di dalam legenda Joko Tingkir. Sumber data penelitian ini adalah legenda Joko Tingkir. Data diperoleh dengan cara wawancara. Data penelitian dianalisis dengan beberapa langkah. 1) Tokoh dan alur dalam legenda Joko Tingkir diklasifikasikan berdasarkan domain kognitif. 2) Hasil klasifikasi akan menunjukkan dominasi level berpikir. 3) Menetapkan kesesuaian proses kognitif dalam legenda Joko Tingkir terhadap level berpikir siswa. Penelitian ini menghasilkan pemetaan proses kognitif dalam legenda Joko Tingkir, yang meliputi domain memahami, mengaplikasikan, menganalisis, mengevaluasi, dan mencipta . Legenda Joko Tingkir didominasi oleh proses kognitif memahami . Bacaan ini lebih tepat mulai diberikan pada anak yang perkembangan kognitifnya telah mencapai tahap memahami . Apabila dikonversi dengan teori perkembangan kognitif yang dikemukakan oleh Piaget, proses kognitif memahami berada pada akhir tahap operasional konkret. Artinya, secara proses kognitif, legenda Joko Tingkir baru bisa diberikan kepada anak usia 10-11 tahun. v> The purpose of this research is formulate a cognitive process in the legend of Jaka Tingkir.The approach used in this research is qualitative. This research approach is qualitative. Thedata of this research are words, sentences, and discourses that show the cognitive processesin the legend of Jaka Tingkir. The primary data source of this research is the legend of Jaka Tingkir which comes from an interview with the caretaker of the Tomb of Need, the secondarydata source is a story book entitled Jaka Tingkir. The data collection technique was carriedout by interviewing the caretaker of the Tomb of Need in Plupuh, Sragen on October 16,2016 and intensive reading. The research data were analyzed by several steps. First, acomparative study was carried out between the Jaka Tingkir legend from primary andsecondary data sources. Second, the characters and plot in the Jaka Tingkir legend are classified based on the cognitive domain. Third, the classification results will show thedominance of the level of thinking. Fourth, determine the cognitive processes that aredominant in the level of thinking. This research resulted in the mapping of cognitive processesin the Jaka Tingkir legend, which includes the domains of understanding, applying, analyzing,evaluating, and creating. The Jaka Tingkir legend is dominated by the cognitive process of understanding. This reading is more appropriate starting to be given to children whosecognitive development has reached the stage of  understanding. When converted to the theoryof cognitive development put forward by Piaget, the cognitive process of understanding is atthe end of the concrete operational stage.
Data-to-Text Generation with Iterative Text Editing.

We present a novel approach to data-to-text generation based on iterative text editing. Our approach maximizes the completeness and semantic accuracy of the output text while leveraging the abilities of recent pre-trained models for text editing (LaserTagger) and language modeling (GPT-2) to improve the text fluency. To this end, we first transform data items to text using trivial templates, and then we iteratively improve the resulting text by a neural model trained for the sentence fusion task. The output of the model is filtered by a simple heuristic and reranked with an off-the-shelf pre-trained language model. We evaluate our approach on two major data-to-text datasets (WebNLG, Cleaned E2E) and analyze its caveats and benefits. Furthermore, we show that our formulation of data-to-text generation opens up the possibility for zero-shot domain adaptation using a general-domain dataset for sentence fusion.
Sample Complexity of Adversarially Robust Linear Classification on Separated Data.

We consider the sample complexity of learning with adversarial robustness. Most prior theoretical results for this problem have considered a setting where different classes in the data are close together or overlapping. Motivated by some real applications, we consider, in contrast, the well-separated case where there exists a classifier with perfect accuracy and robustness, and show that the sample complexity narrates an entirely different story. Specifically, for linear classifiers, we show a large class of well-separated distributions where the expected robust loss of any algorithm is at least $\Omega(\frac{d}{n})$, whereas the max margin algorithm has expected standard loss $O(\frac{1}{n})$. This shows a gap in the standard and robust losses that cannot be obtained via prior techniques. Additionally, we present an algorithm that, given an instance where the robustness radius is much smaller than the gap between the classes, gives a solution with expected robust loss is $O(\frac{1}{n})$. This shows that for very well-separated data, convergence rates of $O(\frac{1}{n})$ are achievable, which is not the case otherwise. Our results apply to robustness measured in any $\ell_p$ norm with $p > 1$ (including $p = \infty$).
Learning Sparsity and Block Diagonal Structure in Multi-View Mixture Models

Scientific studies increasingly collect multiple modalities of data to investigate a phenomenon from several perspectives. In integrative data analysis it is important to understand how information is heterogeneously spread across these different data sources. To this end, we consider a parametric clustering model for the subjects in a multi-view data set (i.e. multiple sources of data from the same set of subjects) where each view marginally follows a mixture model. In the case of two views, the dependence between them is captured by a cluster membership matrix parameter and we aim to learn the structure of this matrix (e.g. the zero pattern). First, we develop a penalized likelihood approach to estimate the sparsity pattern of the cluster membership matrix. For the specific case of block diagonal structures, we develop a constrained likelihood formulation where this matrix is constrained to be block diagonal up to permutations of the rows and columns. To enforce block diagonal constraints we propose a novel optimization approach based on the symmetric graph Laplacian. We demonstrate the performance of these methods through both simulations and applications to data sets from cancer genetics and neuroscience. Both methods naturally extend to multiple views.
A Comprehensive Comparison of Hybrid Mobile Application Development Frameworks

The number of users of smart mobile devices is growing every day. Because of the popularity of using mobile devices, it is important for business stakeholders to develop mobile applications targeting all mobile platforms in order to ensure that the number of users is as large as possible. One possible solution is the creation of hybrid mobile applications. These are applications that combine the properties of web and native mobile applications, and their main advantage is compatibility with multiple mobile operating systems. This paper presents the results of very comprehensive experiments that involved the use of various hybrid mobile development frameworks that were tested under different scenarios. Experiments revealed that the performance of hybrid applications in different scenarios varies considerably, although the results of these applications were comparable to those that were achieved in the experiment with the native application.
Construction and Application of Precise Teaching Mode Based on Cloud Classroom in the Context of Blended Teaching

In the context of the new curriculum reform driven by information technology, the hybrid teaching based on cloud classroom has achieved unprecedented development with its unique advantages. In order to evaluate the hybrid teaching effect based on cloud classroom and improve the quality of teaching, the article constructs a precise teaching mode based on cloud classroom based on constructivist theory and mastering learning theory. Then, taking the teaching of “Computer Basics and Information Literacy” as an example, the model was tested by group comparison experiment. Finally, the implementation effect of the model is explained by quantitative analysis, qualitative analysis and questionnaire survey of the experimental results.
Single image haze removal using variable fog-weight

Abstract In this paper, a new Image Haze Removal technique is proposed using variable fog-weight. The objective of the proposed technique is to improve the visibility, to remove the impact of weather factors and make image more usable. To achieve this, it is required to estimate the transmission map as well as atmospheric light by using Dark Channel Prior. The main contribution in this work, is to vary the fog-weight during transmission estimation. It is varied according to the density of haze in the input image. If the density of the haze in the input image is low, then lower fog-weight value will be applied and if the haze density in the input image is higher, than higher fog-weight value will be applied, according to the haze concentration. A guided filter is used as a refiner of transmission map to avoid the halo artifacts at the boundary of the edge. It provides smoothness to the image and build-up the visibility of the image, so that we restore a superior haze free image. At last, the performance of the proposed method is compared with some of the existing methods in terms of visibility, computational time and avoidance of halo effect.