Reviewerly/exHarmony huggingface dataset:
Deep Generative Recommendation with Maximizing Reciprocal Rank

Variational autoencoders (VAEs) have proven to be successful in the field of recommender systems. The advantage of this non-linear probabilistic generative model is that it can break through the limited modeling capabilities of linear models which dominate collaborative filtering research to a large extend. In this paper, we propose a deep generative recommendation model by enforcing a list-wise ranking strategy to VAE with the aid of multinomial likelihood. This model has ability to simultaneously generate the point-wise implicit feedback data and create the list-wise ranking list for each user. To seamlessly combine ranking loss with VAE loss, the Reciprocal Rank (RR) is adopted here and approximated with a smoothed function. A series of experiments on two real-world datasets (MovieLens-100k and XuetangX) have been conducted. We show that maximizing the ranking loss will cause as many relevant items appearing at the top of the predicted recommendation list as possible. The experimental results demonstrated that the proposed method outperforms several state-of-the-art methods in ranking estimation task.
Modeling and Formal Verification of Interlocking System Based on UML and HCPN

Aiming at the difficulties of modeling and verification of interlocking system and the easy explosion problem of interlocking system state space, a modeling method of interlocking system is proposed based on UML and Hierarchical Colored Petri Nets (HCPN). Firstly, UML is used to realize the semi-formal modeling of the interlocking system, which can reduce the difficulties of modeling interlocking system. Secondly, the UML-CPN transformational rules are used to establish the HCPN layered model of the interlocking system to alleviate the explosion problem of the system state space. Finally, the formal verification of the HCPN model is realized by using CPN Tools. The interlocking modeling method based on UML and HCPN can provide a new idea for the formal modeling and verification of computer interlocking system.
Graph Embedding Based on Characteristic of Rooted Subgraph Structure

Given the problem that currently distributed graph embedding models have not yet been effectively modeled of substructure similarity, biased-graph2vec, a graph embedding model based on structural characteristics of rooted subgraphs is proposed in this paper. This model, based on the distributed representation model of the graph, has modified its original random walk process and converted it to a random walk with weight bias based on structural similarity. The appropriate context is generated for all substructures. Based on preserving the tag features of the nodes and edges in the substructure, the representation of the substructure in the feature space depends more on the structural similarity itself. Biased-graph2vec calculates the graph representations with unsupervised algorithm and could build the model for both graphs and substructures via universal models, leaving complex feature engineering behind and has functional mobility. Meanwhile, this method models similar information among substructures, solving the problem that typical random walk strategies could not capture similarities of substructures with long distance. The experiments of graph classification are carried out on six open benchmark datasets. The comparison among our method, the graph kernel method, and the baseline method without considering the structural similarity of long-distance ions is made. Experiments show that the method this paper proposed has varying degrees inordinately improved the accuracy of classification tasks.
Fractal Batik Motifs Generation Using Variations of Parameters in Julia Set Function

The fractal concept is the generation of images graphically with self-similarity properties produced by recursive or iterative algorithms to produce a new image form. Fractal structure is a common tool for describing the visual effects of one or more objects. In this research, we will design a system that can design batik fractal motifs using the Julia set. Therefore, a programmable tool to generate computerized batik motifs is needed. In this research, Julia set was used as the basis for generating batik motifs. In this system, the process of generating fractal batik images consists of 3 (three) steps: (1) determining the shape of the Julia set function for some batik motifs; (2) visualizing the results of the first step using a Pythonbased program; and (3) designing the fractal batik motifs using Julia set obtained in the second step. From experiments conducted in generating batik fractal, some conclusions were obtained as follows. First, for the greater number of iterations, the resulting image will be more detailed and the number of colors will increase. Second, the result image is influenced by the value of the complex c and the number of colors or gray-levels indicated by the number of iterations, i.e., the greater the number of iterations, the more colors are generated. Third, several variations of input parameters produce images that approach traditional batik motifs. In this research, traditional motifs that can be approached by Julia-set functions are: Parangkusumo, Nitik, Batik Liong, Mega Mendung, and Ceplok motifs. Fourth, changes in the number of iterations and the value of c applied to Julia set are better able to generate the variations of motifs. Thus, the Julia set function can help the batik designer in making fractal batik motifs.
Performance of Different Graph Neural Networks on Graph Classification

Abstract In recent years, machine learning has become a common approach for solving problems of artificial intelligence, which is developing at a high speed. Convolutional Neural Network (CNN) is popular on solving image problems for its believable accuracy. However, it does not mean that other neural networks are useless on these problems. In this paper, performance of Convolutional Neural Network (CNN), Multilayer Perceptron (MLP) and Recurrent Neural Network (RNN) in the same database are presented. Performance of different types of networks are compared, and a conclusion is given that recurrent neural network might be able to classify pure black and white images at high accuracy, and that convolutional neural network performs better than multilayer perceptron on graph classification problems.
A Human-Centered Approach to Data Driven Iterative Course Improvement

In this paper we show how we can utilize human-guided machine learning techniques coupled with a learning science practitioner interface (DataShop) to identify potential improvements to existing educational technology. Specifically, we provide an interface for the classification of underlying Knowledge Components (KCs) to better model student learning. The configurable interface allows users to quickly and accurately identify areas of improvement based on the analysis of learning curves. We present two cases where the interface and accompanying methods have been applied in the domains of geometry and psychology to improve upon existing student models. Both cases present outcomes of better models that more closely model student learning. We reflect on how to iterate upon the educational technology used for the respective courses based on these better models and further opportunities for utilizing the system to other domains, such as computing principles.
Knowledge Graphs Meet Geometry for Semi-supervised Monocular Depth Estimation

Depth estimation from a single image plays an important role in computer vision. Using semantic information for depth estimation becomes a research hotspot. The traditional neural network-based semantic method only divides the image according to the features, and cannot understand the deep background knowledge about the real world. In recent years, the knowledge graph is proposed and used for model semantic knowledge. In this paper, we enhance the traditional depth prediction method by analyzing the semantic information of the image through the knowledge graph. Background knowledge from the knowledge graph is used to enhance the results of semantic segmentation, and further improve the depth estimation results. We conducted experiments on the KITTI driving dataset, and the results showed that our method outperformed the previous unsupervised learning methods and supervised learning methods. The result of the Apollo dataset demonstrates that our method can perform in the common case.
Mutual Constraint Learning for Weakly Supervised Object Detection

The abundance of image-level labels and the lack of large scale bounding boxes detailed annotations promotes the expansion of Weakly-Supervised techniques for Object Detection (WSOD). In this work, we propose a novel mutual constraint learning for convolutional neural networks applied to detect bounding boxes only with global image-level supervision. The essence of our architecture is two new differentiable modules, Determination Network, and Parameterised Spatial Division, which explicitly allows the spatial division of the feature map within the network. These learnable modules give neural networks the ability to constructively generate shadow activation maps, dependent on the class activation maps. To demonstrate the effectiveness of our model for WSOD, we conduct extensive experiments on the multi-MNIST dataset. Experimental results show that mutual constraint learning can (i) help improve the accuracy of multi-category tasks, (ii) implement in an end-to-end way only with the image-level annotations, and (iii) output accurate bounding box labels, thereby achieving object detection.
Predicted Viewer System of Road State Based on Crowd IoT Sensing Toward Autonomous EV Driving

In order to realize automotive electric car system in snow country, various road state information including dry, wet, slush, snowy, icy states on the road are determined in realtime using various environmental sensors. These state information is not only exchanged directly between EVs through V2X, but also collected into cloud servers on Internet and organized as wide area road state information platform for ordinal users to present as a viewer system of the road state using smartphone and tablet terminals. In this paper, the basic consideration of general architecture of road state information platform, EV autonomous control system, predicted viewer system, the predicted crowd sensing and viewing system and prototype system are precisely discussed.
A Data Science Approach to Analysis of Tweets Based on Cyclone Fani

The advent of social media has contributed to faster as well as wider propagation of information and emotions of people. In the time of emergencies and natural disasters, social media becomes an important tool for communication, spreading of alerts and knowing the needs and feelings of people in crisis. In this paper, analysis of the tweets of people based on cyclone Fani, which struck the Eastern region of India and adjoining regions in the month of May 2019, has been presented. The study has been divided into three phases—onset of cyclone Fani, during cyclone Fani and aftermath of cyclone Fani. As part of the primary analysis, Word Cloud representations have been used to depict the most frequent words in the tweets during all three phases of cyclone Fani. After that, Word Embedding using Word2Vec has been carried out using both Skip-Gram and Continuous-Bag-of-Words approaches. Using Principal Component Analysis, the results have been presented as bubble plots. Then, Sentiment Analysis using Naive Bayes Classifier has been performed and the tweets were classified based on both polarity and subjectivity. The results have been presented using graphical plots, and the accuracy of the results has been analyzed. Finally, an analysis of tweet and retweet counts belonging to credible Twitter handles has been showcased.
Deep Learning based Multiple Sensors Monitoring and Abnormal Discovery for Satellite Power System

The power system is a vital sub-system for satellite operated successfully. With test and working environment limitations, the telemetry data from sensors and actuators is the only message to communicate with the ground about the status of satellites. In this term, an efficient and accuracy anomaly detection method for satellite power system could promote a powerful manner for identifying fault and trend that decrease safe margins. However, mostly anomaly detectors have to seriously rely on the prior expert knowledge and a nonlinear dimension reduction on telemetry data as the preliminary to reduce the computation scale and complexity. In this paper, a deep learning-based multiple sensors monitoring and abnormal discovery method for satellite power system is proposed to alleviate the limitations mentioned above. Firstly, an overview of the abnormal discovery method for satellite telemetry data is described. Then, a LSTMs-based prediction model and anomaly detection method for satellite power system are established. The data of multi sensors are monitored in one-time-step prediction model simultaneously, and are detected with an unsupervised method to alleviate the dependency of experts’ knowledge. Finally, the experiments are performed with the telemetry data from a simulated satellite power system. With the experiments, the proposed method shows great performance on the anomaly detection in a different type of faults with a high precision rate.
Real-Time Strategy for Obstacle Avoidance in Redundant Manipulators

A robot working in a space shared with humans and obstacles needs an obstacle avoidance strategy. In this work, a kinematic control algorithm of a redundant robotic system for real-time obstacle avoidance is presented. The workspace is populated by fixed and moving obstacles. To plan a trajectory in real time, an artificial potential field and a distance algorithm are introduced: an artificial force set, composed by an attractive force towards the goal and repulsive forces from the obstacles, drives the robot end-effector in the workspace; at the same time repulsive velocities act on control points along the links of the robot, avoiding contacts with obstacles all over the serial kinematic chain of the manipulator. The algorithm has been implemented in a 2D environment for a 3R planar manipulator in order to assess the algorithm and present preliminary results. The strategy presented in this study will be the starting point for a more complex problem in 3D space with a 7-DOF redundant manipulator.
Asymptotically-Optimal Topological Nearest-Neighbor Filtering

Nearest-neighbor finding is a major bottleneck for sampling-based motion planning algorithms.The cost of finding nearest neighbors grows with the size of the roadmap, leading to a significant computational bottleneck for problems which require many configurations to find a solution.In this letter, we develop a method of mapping configurations of a jointed robot to neighborhoods in the workspace that supports fast search for configurations in nearby neighborhoods.This expedites nearest-neighbor search by locating a small set of the most likely candidates for connecting to the query with a local plan.We show that this filtering technique can preserve asymptotically-optimal guarantees with modest requirements on the distance metric.We demonstrate the method's efficacy in planning problems for rigid bodies and both fixed and mobile-base manipulators.
A Machine Learning Model for Review Rating Inconsistency in E-commerce Websites

Online consumer reviews and ratings are two important paradigms of any e-commerce business. Based on the posted reviews and ratings, customers decide the product's reliability and make their purchase decisions. Usually, reviews and ratings come together. In a review, the user writes all the pros and cons of the product, whereas the rating is a cumulative representation of the review text given on a scale of 1–5. If any review is positive, its associated ratings are 4 and 5, and if the review is negative, it comes with ratings 1 and 2. Sometimes, the rating does not represent the review text correctly, for example, a negative review gives a rating 4 or 5, or positive review gives a rating 1 or 2. This creates an inconsistency between reviews and ratings. This paper develops a machine learning-based model for identifying such inconsistency and prompting users for their posts. A Long Short-Term Memory-based model is developed to classify each review into either positive or negative polarity. The predicted polarity is then checked with the user's rating for consistency.
NovEA: A Novel Model of Entity Alignment Using Attribute Triples and Relation Triples

Entity alignment is the foundation of knowledge fusion, which can find the alignment relationships between entities in heterogeneous knowledge graphs. However, traditional methods rely on external information and need to construct data features manually. Meanwhile, embedding models do not fully utilize the pertinent information of attributes in the knowledge graphs, which limit the role of attribute information in entity alignment. Considering the shortcomings of existing methods, this paper proposes a novel model named NovEA that using attribute triples and entity triples in the knowledge graphs to complete the entity alignment task together. Besides, for attribute triples, we propose a method that can automatically generate the optimal attribute according to the data characteristics to constrain the result of attribute triples alignment and improve the accuracy of entities in alignment. Finally, we use a binary regression method to measure the similarity of the combination results of structure and attribute. Our research on real datasets shows that the NovEA model has a significant improvement in entity alignment compared with the most advanced methods.
One Year Experimental Evaluation of the Electrical Gain by Solar Tracking a 12 KW Photovoltaic System Installed on a Building Rooftop

To fulfil the requirements of the European Directive 31/2010, by January 2021, all new buildings and those undergoing major renovation must have renewable energy systems implemented on or near the building targeting the nearly Zero Energy Building standard. In areas with high density of buildings the available surfaces to install solar energy conversion systems are limited. A solution to tackle this issue consists of implementing solar tracking systems to increase the amount of received solar energy and thus, the thermal or electrical energy output of the solar energy conversion system. A solar tracking mechanism, with 2 DOF and mono-axial diurnal movement implemented in the Renewable Energy Systems and Recycling R&D Centre of the Transilvania University of Brasov, Romania is presented in the paper. This mechanism simultaneously rotates eight photovoltaic strings (each string consisting of six photovoltaic modules) around North-South oriented horizontal axes. Experimental results obtained during 2018 are comparatively presented in the paper for the solar tracking photovoltaic system kept in a fixed position for the first six months of 2018 and with active solar tracking system during the last six months of 2018. Significant solar tracking gain of 59.4% resulted for the sunniest clear sky days in the monitoring period. When considering the entire monitoring period of six months with active solar tracking system vs. the six months in fixed position, a solar tracking gain of 24.89% resulted.
Filter Pruning via Structural Similarity Index for Deep Convolutional Neural Networks Acceleration

Pruning can reduce the size of the model without reducing its performance. After pruning, the model can run in a small terminal flexibly. This paper proposes a new filter pruning method that uses soft filter pruning via a structural similarity index(FPSSI) to compress and prune the network. FPSSI uses the structural similarity index to measuring the difference between different filters, the filters with similar structures are pruned to achieve the purpose of compressing the Deep Convolutional Neural Networks(DNN) model. Compared to the norm-based approach to remove ”relatively low” importance filters, the proposed method takes into account the structure between the filters. When applied to the different classification benchmarks, our method validates its usefulness and advantages. In CIFAR10, the ResNet network uses the SFP-SSIM method to reduce 52% of FLOPs and has better accuracy.
Intelligent System Tutorial for Distance Learning the Computer Science Engineering Career

With the advance of technology, there are new ways of teaching that break the barrier of time and place; distance education can be considered as the most advanced model at this time, however you may bring difficulties if you need support an advisory and/or guardian by the student at a given time and this is not available. To solve this problem a technological tool that behaves like a teacher in both the teaching strategy and mastery of subject teaching is required, guiding the student on how to apply this knowledge in their individual characteristics. Intelligent tutoring systems simulate the behavior of a human tutor, using Artificial Intelligence techniques in order to provide the student the required cognitive support for learning.In this work, the creation of an Intelligent Tutorial System for the teaching of Engineering in Computer Science and distance many students at once using Artificial Intelligence techniques is proposed.
Relational characterisations of paths

Binary relations are one of the standard ways to encode, characterise and reason about graphs. Relation algebras provide equational axioms for a large fragment of the calculus of binary relations. Although relations are standard tools in many areas of mathematics and computing, researchers usually fall back to point-wise reasoning when it comes to arguments about paths in a graph. We present a purely algebraic way to specify different kinds of paths in Kleene relation algebras, which are relation algebras equipped with an operation for reflexive transitive closure. We study the relationship between paths with a designated root vertex and paths without such a vertex. Since we stay in first-order logic this development helps with mechanising proofs. To demonstrate the applicability of the algebraic framework we verify the correctness of three basic graph algorithms. All results of this paper are formally verified using the interactive proof assistant Isabelle/HOL.
Cinematography as One of the Methods of Forming the Information Space of Aerospace Industry Organizations in Russia

The authors consider the peculiarities of using cinematographic genres in communication activities by organizations of aerospace industry. As organizations form an information space around them, it is important that the information that is present in it is positive and allows them to effectively influence the key audience. In today’s environment, information broadcast by traditional media is perceived with less interest. Therefore, organizations have to look for and develop new communication channels such as social networks, blogs, etc. Cinematography is of particular importance. It allows to realize such functions as entertainment, information, education, organization of social community, education. In the aerospace field, the implementation of an effective impact on the target audience has its own peculiarities, as this industry is strategically important for the country, potentially crisis-prone, and it is constantly riveted on public and media attention. In addition, the dissemination of certain information may be restricted by classifying data as part of a government, commercial or other secret specifically protected by law. Cinematographic works as a means of forming the information space of aerospace industry organizations in Russia are effective. They are used to form a positive image of the industry, organizations, professions, to promote products and services. Full-length and short films created in fiction and non-fiction genres allow transforming ways of interaction between communicator and audience, expanding communicative possibilities of space, accelerating information exchange.