Reviewerly/exHarmony huggingface dataset:
Object-Oriented Dynamics Learning through Multi-Level Abstraction

Object-based approaches for learning action-conditioned dynamics has demonstrated promise for generalization and interpretability. However, existing approaches suffer from structural limitations and optimization difficulties for common environments with multiple dynamic objects. In this paper, we present a novel self-supervised learning framework, called Multi-level Abstraction Object-oriented Predictor (MAOP), which employs a three-level learning architecture that enables efficient object-based dynamics learning from raw visual observations. We also design a spatial-temporal relational reasoning mechanism for MAOP to support instance-level dynamics learning and handle partial observability. Our results show that MAOP significantly outperforms previous methods in terms of sample efficiency and generalization over novel environments for learning environment models. We also demonstrate that learned dynamics models enable efficient planning in unseen environments, comparable to true environment models. In addition, MAOP learns semantically and visually interpretable disentangled representations.
Scientific Collaboration, Citation and Topic Analysis of International Conference on Agile Software Development Papers

The International Conference on Agile Software Development (XP) was established almost sixteen years ago. Based on data from Scopus database, a total of 789 papers have been published in between years of 2002 and 2018. We employed bibliometrics analysis and topic modeling with R/RStudio to analyze these published papers from various dimensions, including the most active authors, collaboration of authorship, most cited papers, used keywords and trends of probable topics from the titles and abstracts of those papers. The results show that the first five years of XP conference cover nearly 40% of the papers published until now and almost 62% of the XP papers have been cited at least once. Mining of XP conference paper titles and abstracts result in these hot research topics: “Coordination”, “Technical Debt”, “Teamwork”, “Startups” and “Agile Practices”, thus strongly focusing on practical issues and problems faced by the practitioners in the industry. The results highlight the most influential researchers and institutions, and the collaboration between the authors in the conference papers. The approach applied in this study can be extended to other software engineering venues and can be applied to large-scale studies.
Occluded Facial Recognition with 2DPCA based Convolutional Neural Network

Face occlusions with glasses or scarf are quite common in the real-world scenes, or more seriously, terrorists often cover their faces with sunglasses or a mask to hide themselves from the cameras. Occluded facial recognition is, therefore, an important problem in surveillance & defense department. A system that can recognize faces with occlusions may need to be trained by a huge set of facial databases. To reduce the complexity of an occluded facial recognition system, this paper investigates the effects of the two-dimensional principal component analysis (2DPCA) in the initialization phase on image classification by the convolutional neural network (CNN). Our experiments show that 2DPCA can reduce the image dimension for training while keeping the accuracy rate comparing to using the whole images. Our results, at 0.001 learning rate, showed 81.91% accuracy with 120 eigenvectors for the AR database, and 99.95 % accuracy rate with 190 eigenvectors for the GTAV database.
A Robust SLIC Segmentation Based Zero Watermarking Scheme Using DWT And Partial Pivoting LU Decomposition

This article presents a novel simple linear iterative clustering (SLIC) superpixels and partial pivoting lower triangular upper triangular (PPLU) factorization based zero-watermarking (ZWM) scheme is proposed. The novelty of this article is that the use of SLIC superpixels and PPLU decomposition for ZWM. The original image is subjected to SLIC segmentation and non-overlapping high entropy blocks are selected. On the selected blocks discrete wavelet transform (DWT) is applied and those blocks are undergone PPLU factorization to get three matrices <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L, U$</tex> , and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$P$</tex> . The product matrix <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L\times U$</tex> is used to construct zero-watermark. This work is validated with BOWS, and SIPI datasets, and shown robust against the attacks. The test results demonstrate that this work has better performance than the recent ZWM schemes.
Multi-Dimensional Processing for Big Data with Noise

We are in the era of information explosion, and everyday a large amount of data are generated from all kinds of sources such as social media, biology, online purchase, etc. It is crucial that we acquire data mining tools to efficiently and effectively analyze the data and gather useful information. There are various aspects of data mining approaches such as clustering, outlier detection and dimension reduction. In this paper we propose an approach to selecting dimensions based on the information of outliers on different dimensions, along with the information of clusters. We also conduct experiments to demonstrate the performance of our algorithm.
Flood-vulnerability area analysis in Karawang regency as an impact of Jakarta-Bandung mega-urban region formation using weighted overlay approach

Jabodetabek and Greater Bandung areas experiencing the phenomenon of urban expansion which has caused both metropolitan areas to be more connected and become a mega-urban area by a corridor through a conurbation process. Karawang regency is part of the region in the Jakarta-Bandung mega-urban corridor. Vegetated lands in Karawang Regency are threatened by land conversion as a result of the Jakarta-Bandung conurbation process which tends to trigger the formation of the Jakarta-Bandung Mega-Urban Region. The increasing population in Karawang Regency has increasingly triggered the conversion of vegetated lands into built-up lands so that it can cause the region to be prone to anthropogenic disasters such as flood because of water catchment areas that reduced. The aims of this study are to analyze the land cover changes that occured in Karawang Regency in 2005–2015 and 2015–2025 and mapping the floodprone areas in Karawang Regency. Land cover changes analysis is carried out by overlapping Landsat Imagery of land cover in the 2005 and 2015 and 2015 and 2025 based on projection results using CA-Markov method. Mapping the floodprone areas is carried out by a Weighted Overlay process on variables or factors that are considered influential on the occurrence of floods. Karawang Regency has experienced an increase in land use change, especially changes in vegetated lands to built-up lands from year to year. Flood-prone areas analysis result in Karawang Regency show that most of the total area of Karawang Regency is classified as high and moderate classification flood-prone areas.
Análisis de las herramientas de gamificación online Kahoot y Quizizz en el proceso de retroalimentación de aprendizajes de los estudiantes

21st century students demand that learning sessions be more challenging and exciting, and education has become the resource humanity needs to grow as a society, supported by tools that innovate learning sessions. The purpose of this research is to evaluate the influence of online gamification tools such as Kahoot and Quizizz in the process of feedback of student learning from the secondary level of regular basic education, the reasons that justify the research are: the educational strategies for optimal learning are constantly changing and propose a series of challenging activities seeking to transcend the learning sessions to become part of the student's life for which first took a test of previous knowledge about the issues of Educational Technology, where most of the students involved have basic knowledge by placing them on a scale of process, a second assessment is applied with respect to the knowledge in the process and the final test in which students present significant progress using the platforms, demonstrating their effectiveness for learning feedback; also despite the similarity of the platforms, through Quizizz students present a greater response to feedback of learning compared to Kahoot demonstrated through aspects of software, hinders optimal feedback of learning.
Tour Miner: Mining System of Tour Plans from SNS: Extraction of Travel Records from Check-in Information

We have been researching and developing the system called “Tour Miner” which creates a tour plan suitable for individual preferences or interests from the information accumulated in SNS. In this paper, we proposed a method to extract travel records from check-in information accumulated in SNS sharing location information “Foursquare” and constructed a database of travel records. In addition, we proposed a method to select travel records matching personal preferences and interests from the database. As a result of applying the proposed method to the eight examples, it was possible to obtain a sufficient number of travel records that can support the preparation of the tour plan.
A Blockchain-Based Mobile IOT Network Interconnection Security Trusted Protocol Model

With the continuous development of information technology and internet of things, technology of the mobile Internet of Things (mobile IOT) has been widely used in medical and health, smart home, transportation, logistics and other industries. At the same time, the network security issues of mobile IOT have become increasingly prominent. What’s more serious is that the destructive power of these cyber threats has grown geometrically by taking the advantage of cloud computing and big data. This threat can be summarized as a non-trusted model threat under a “centralized” network architecture. By analyzing such security threats through real-world cases, this paper proposes a model to solve such security problems. Firstly, a variety of smart home and intelligent voice-controlled robots in the market are selected as research objects, and then the Man-in-the-Middle Attacks such as intelligence mining and data packet hijacking are used to detect the security vulnerabilities of such mobile IOT schemes. Through illustrating the huge network security threat faced by mobile IOT under the new situation, the example summarizes the current security risks in such mobile IOT solutions, and propose a secure and trusted mobile IOT network interconnection security model based on blockchain technology to remedy this problem.
State Analysis in Navigation of Multiple Autonomous Mobile Robots by Local interaction

In this reserch, a method of navigation by local interaction of oscillators depending on sensor outputs is proposed. Under an environment where communication difficult for robots, we need a communication method with law cost. On the other hand, true slime mold uses phase for information transmission. However, previous studies needed special robots capable of an ordered formation. In this paper, we propose a method of swarm navigation of multiple autonomous robots that does not require special robots by local interaction of oscillators and show the result of navigation simulation of the system on a 2-dimensional plane. Furthermore, we analyze the convergence state of the system.
A Method to Enhance Homogeneous Distribution of Matched Features for Image Matching

Features are distinctive landmarks of an image. There are various feature detection and description algorithms. Many computer vision algorithms require matching of features from two images. Large number of correct matches with homogeneous distribution in the images is needed for robustness of the image matching. The matches are generally obtained using a feature distance threshold and ambiguous matches are rejected using a ratio test. This paper proposes a method that can be added to image matching pipeline for enhancing homogeneous distribution and increasing the number of matched feature points. After successfully matching an image pair, spatially close feature points go through an elimination process which aims to decrease ambiguity at the second matching step. Then, a coarse geometric transformation between two images is calculated, through which the detected feature points in one image (i.e. the moving image) are projected to the other image (i.e. the fixed image). Then, feature points from the moving image are matched to neighboring feature points of the fixed image within a pre-determined spatial distance. This narrows down the possible candidates and enables less correct matches being rejected because of the ratio test. The effectiveness and feasibility of our method is demonstrated with experiments on images acquired from a drone camera during flight.
A Deep Learning Approach for Motion Segmentation Using An Optical Flow Technique

There is a severe need of a constant human surveillance of the real-time security footage. Computer Vision is a novel way to reduce human involvement for the said task. Motion segmentation is a crucial step in analyzing video data. The challenges present in motion segmentation, such as illumination changes, dynamic background, and camouflage negatively affect the performance of existing motion segmentation algorithms. In this paper a method of using Convolutional Neural network with optical flow is proposed to improve performance and segment required motion properly. The proposed method is compared with the Lucas-Kanade optical flow method in terms of F1 score. The dataset used is wallflower video dataset. This contains different challenges of motion segmentation viz., illumination changes, dynamic background and clutter.
Research on Cloud Computing Security Problems and Protection Countermeasures

In recent years, the Chinese government has attached great importance to the development and application of cloud computing industry, and issued a series of policies to encourage and regulate the development of cloud computing. The widespread use of cloud computing brings us both opportunities and challenges. This article will from the overview of cloud computing, the narrative model and characteristics, combined with the development of cloud computing and the basic structure, analysis of the main safety problems of cloud computing, it puts forward the model of elastic cloud security system, and puts forward some protective measures, so as to improve the security of cloud computing, cloud computing security applications, facilitating the orderly development of the role, in response to a cloud computing security issues and risks emerge in endlessly.
Tree Radial Growth Measurement System Based on Line Structured Light Vision

In order to achieve accurate measurement of the radial growth of tree, this paper proposes a tree trunk diameter measurement algorithm based on line structured light, and constructs a tree radial growth measurement system based on line structured light vision. The original image is acquired by using an infrared camera and the original image is preprocessed. The image is segmented by the target contour extraction function findContours and the contour minimum bounding rectangle function boundingRect in OpenCV to extract the area where the tree is located. Line structured light is extracted by using a Hough transform. The distance between the camera and the tree is obtained in real time by using the TOF module, and the diameter of the tree trunk is obtained based on the principle of line structured light stereo measurement. The test results show that the proposed method can quickly and accurately measure the diameter of tree and achieve the automatic monitoring of the radial growth of tree.
Chinese Character CAPTCHA Recognition Based on Convolutional Neural Network

The goal of this paper is to achieve effective recognition of Chinese character CAPTCHA, we propose a convolutional neural network model with reference to LeNet-5, the number of convolution kernels is increased to enable more efficient extraction of features, while adding dropout layers to prevent overfitting and adding normalized layers to prevent gradient explosions. The model takes the grayscale, binarization, and segmented CAPTCHA pictures as input, and outputs the vector of 3,500 dimensions which indicate the probability of each Chinese character. After training, the model can achieve a recognition rate of 99.6%. The experiment also compares the model with existing model, the results show that the model can identify Chinese character CAPTCHA more effectively.
Sentiment Extraction From Text Using Emotion Tagged Corpus

Emotion tagging aims to tag words with appropriate emotion that a word expresses in a document or a sentence. In this paper, authors introduced an approach to tag words automatically and then analyzed in sentence and document level. The evaluation of tagged words described and the performance based on changing the tone of a document has been explained. The experiment showed that the approach could be effective if the tag words are not constant or static. Application of this approach in a contextual level analysis is briefly concluded and proposed tentative emotional tree by using emotion tagging in word level.
Design of Active Visual Marker for High Speed Object Tracking by Frame Subsampling

The trajectory of a moving object may be extracted from video using image processing algorithms. However, the quality of the extracted information largely depends on the frame rate and exposure time of the camera, thus it is difficult to capture fast movement using slower and less expensive cameras. To this end, we propose to use an active modulated light source for object tracking, interacting with exposure times and subsampling existing frames. A prototype of a multi-functional active visual marker is presented in this paper. The system is based on the ESP-WROOM-32 microcontroller, which is configured to use various communication protocols, namely WiFi 802.11, RF 2.4 GHz GFSK and RS485. The microcontroller controls the RGB LED, which is used as light source. In addition, the system can be synchronized with the external real-time clock. The experimental results have illustrated the advantages and disadvantages of the designed active markers and pointed out the directions for future work and development.
Penerapan Metode HOTS (Higher Order Thinking Skill) dalam Pembelajaran Bahasa Jepang di SMA

( Application of HOTS (Higher Order Thinking Skills) Method in Japanese Language Learning in Senior High School This study aims to find out how the application and inhibiting factors of the HOTS method in learning Japanese at the high school level. The scope of this research is the planning and learning process in one of the KD (Basic Competency) Japanese XI grade at SMAN 6 Malang. The process of making an evaluation, and the evaluation process itself does not belong to the scope of this study. The method used in this research was a descriptive qualitative method using Classroom Research design. Data collection techniques used non-participant observation, documentation, and interviews. The data in this study were dialogues, monologues, and activities related to the application of the HOTS method in learning Japanese. The results of this study indicated that the learning used by teachers for the application of HOTS was following the Problem Based Learning model in which there were learning processes in analyzing (C4), evaluating (C5), and creating (C6). Inhibiting factors that arose in the process of applying the HOTS method consisted of teacher doubt in planning the learning tools, differences in the understanding of each teacher in interpreting HOTS-based teaching materials, limitations of instructional media that are compatible with learning materials, inadequate school facilities, and students who tend to be passive.
Design and Experiment of Dynamic Measurement Method for Bulk Material of Large Volume Belt Conveyor Based on Laser Triangulation Method

Abstract In order to improve the measurement accuracy and efficiency of bulk material transported by large-capacity belt conveyor, an online dynamic non-contact metering system based on laser triangulation is designed on the belt conveyor platform. The system includes: laser Scanners, industrial camera, signal processing software, mechanical assemblies. High-precision basic measurement data is an important guarantee for the accuracy of bulk flow measurement. The laser scanner is used as a line source, and the scanning frequency is set to 300HZ, which is installed on the belt conveyor. Industrial camera accepts images produced by a line source projected onto the bulk surface. The signal processing software system analyzes the images, and uses the triangulation method directly calculate the three-dimensional contour information and volume flow of the bulk material. The flow filtering algorithm filters the instantaneous flow, smoothing the fluctuation of the flow data to reflect the real changes of the dry and wet degree of the bulk material on the belt, the degrees of surface cracking and shapes. Establish a mathematical model based on neural network and apply the belt shaking elimination algorithm to correct the previously calculated flow data to reduce the measurement error. In order to restore the actual belt conveyor bulk transportation conditions, a test belt with length 20m and speed 2m/s was produced according to the actual size. The physical simulation test of bulk material for potassium salt was carried out, the results show that the on-line measurement method based on laser triangular method has high efficiency and stable operation, and eliminates the influence of bulk density. The measurement error is ≤3.
Visualising Image Data through Image Retrieval Concept using a Hybrid Technique: Songket Motif’s

It has been proven that the massive dataset is strictly complex in Content Based image Retrieval (CBIR) because the present strategies in CBIR might have faced difficulties in feature extraction of the images.Moreover, technological constraints encountered in the analysis and extraction of the image arrays are how the system customizes the primitive geometric structures known as polygonal approximations structure.Hence, this study has discovered that image feature extraction is utilized by applying the Principal Component Analysis (PCA) technique, which is primarily based on the matrix of image representation that will enlarge the similarity of detection.The PCA approach needs to be enhanced resulting from the lack of the extraction of features in songket motives images.Therefore, this study proposes a new hybrid model that will integrate PCA with geometric techniques for image feature extraction to increase the recall and precision result.This paper employs the use of a qualitative experimental design model that involves three phases of activities.First, the analysis and design phase, secondly is a development phase, and lastly is the testing and evaluation phase.This paper focuses on those two phases in terms of design and development phases.The outcome process of the empirical phase is followed by designing the algorithm and model based on the result of literature review.This study has found that the hybrid between the principal component analysis model and the geometry technique will help to reduce the problems faced by the basic engineering technique model, which is the constraint in analysing and extracting the image features to customize the geometric primitive structure.