Reviewerly/exHarmony huggingface dataset:
Improving Clustering and Cluster Validation with Missing Data Using Distance Estimation Methods

Missing data introduces a challenge in the field of unsupervised learning. In clustering, when the form and the number of clusters are to be determined, one needs to deal with the missing values both in the clustering process and in the cluster validation. In the previous research, the clustering algorithm has been treated using robust clustering methods and available data strategy, and the cluster validation indices have been computed with the partial distance approximation. However, lately special methods for distance estimation with missing values have been proposed and this work is the first one where these methods are systematically applied and tested in clustering and cluster validation. More precisely, we propose, implement, and analyze the use of distance estimation methods to improve the discrimination power of clustering and cluster validation indices. A novel, robust prototype-based clustering process in two stages is suggested. Our results and conclusions confirm the usefulness of the distance estimation methods in clustering but, surprisingly, not in cluster validation.
Minimally Invasive Surgery for Sparse Neural Networks in Contrastive Manner

With the development of deep learning, neural networks tend to be deeper and larger to achieve good performance. Trained models are more compute-intensive and memory-intensive, which lead to the big challenges on memory band-width, storage, latency, and throughput. In this paper, we propose the neural network compression method named minimally invasive surgery. Different from traditional model compression and knowledge distillation methods, the proposed method refers to the minimally invasive surgery principle. It learns the principal features from a pair of dense and compressed models in a contrastive manner. It also optimizes the neural networks to meet the specific hardware acceleration requirements. Through qualitative, quantitative, and ablation experiments, the proposed method shows a compelling performance, acceleration, and generalization in various tasks.
On the Reversibility of Adversarial Attacks

Adversarial attacks modify images with perturbations that change the prediction of classifiers. These modified images, known as adversarial examples, expose the vulnerabilities of deep neural network classifiers. In this paper, we investigate the predictability of the mapping between the classes predicted for original images and for their corresponding adversarial examples. This predictability relates to the possibility of retrieving the original predictions and hence reversing the induced misclassification. We refer to this property as the reversibility of an adversarial attack, and quantify reversibility as the accuracy in retrieving the original class or the true class of an adversarial example. We present an approach that reverses the effect of an adversarial attack on a classifier using a prior set of classification results. We analyse the reversibility of state-of-the-art adversarial attacks on benchmark classifiers and discuss the factors that affect the reversibility.
Mark Us: A Social Experience Built Using Augmented Reality

MarkUs - a social experience built using the immersive power of Augmented Reality. There has been a plethora of Social Networking Applications around they provide some of the best Social Experiences. However, the territory of Augmented Reality in Social Networking remains an uncharted region. There has been extensive use of face filters by various social networking sites, but we believe that AR has a lot to offer in the social sector. MarkUs tries to extract the best AR experiences for its users by using various technologies which include real-time cloudless atlas and positioning systems. It takes advantage of AR technologies available for mobile phones and makes sure that the heavy computation required for AR does not fall on these devices. MarkUs facilitates various features which include AR journey Books, Various Community Sections dedicated especially to a particular field of interest. AR Live Location Posts and flawless communication with friends. MarkUs uses Mapbox SDK that provides cloudless atlas and positioning required to create Live Location Posts and 3D Map of the world. The proposed system selected React Native to build the foundation. React Native is one of the most popular options to create mobile apps and is extremely lightweight and provides higher flexibility to create flawless UI and their corresponding functionalities. In this system, MongoDB is used to store user data along with their Live Location so that they can reap the benefits of our exclusive features. Also use highly-scalable NodeJs Micro-Services for fast, smooth and efficient data flow.
Deadline based Energy Efficient Scheduling Algorithm in Cloud Computing Environment

Cloud computing provides the on demand services to cloud users over the internet. Lots of datacenters have been present across the web to fulfill the needs and the services of the users on demand, so there is need to properly schedule the user&#x2019;s tasks to the Virtual machines (VM) used in the datacenters. Scheduling algorithm must be such that it minimizes the consumption of energy, makespan time and also increase overall performance. This paper describes a Deadline based Energy Efficient Scheduling Algorithm (DEESA) that has been proposed for task and VM scheduling. Proposed work in this paper aims that task of all the users must be completed within their deadlines. All the tasks and Virtual machines have been classified into three dynamic queues to increase the scheduling efficiency. Experimental work has been done in the Cloudsim simulator by setting resource parameters. Results of proposed algorithm indicate that it can effectively minimize the consumption of energy and makespan time as compared with existing scheduling algorithms.
Chinese Entity Relation Classification via Fusing Granularity Information and Gated Recurrent Mechanism

Entity relation classification is a foundational natural language processing task, which plays an important role in text analysis. At present, most of the methods for Chinese entity relation classification have the problem of segmentation errors, which will cause the accumulation and propagation of errors in the subsequent steps. For this problem, we propose a deep learning framework that integrates word-lever information and character-lever information, which utilizes the efficient fusion of multi-granularity features and gated recurrent mechanism (FGGRM) to dynamically learn semantic information with less effect of segmentation errors. Through the comparison of multiple methods on two datasets, our model shows excellent performance and ability to learn features.
Square network on a word

Left-aligned last occurrences of squares (double-square) have provided profound insights into the question of how many distinct squares can be packed on a word ω of length n and brought the best known upper bound 11n/6. Other types of alignments such as being center-aligned as well as long distance relations such as being the occurrences of the same square incorporate the squares on ω into a bipartite graph of the squares and positions {1,2,…,n}, which we call the square network. Packing more and more squares in a limited space certainly induces specific kinds of subgraphs once the square density exceeds a threshold, and if one of the subgraphs is known to be forbidden, the threshold provides an upper bound on the number of distinct squares. In this paper, we designate a specific position of double-squares as P1 and obtain certain induced subgraphs as well as forbidden ones guaranteed by the presence of P1-aligned double squares; an induced subgraph of particular interest expresses the global uniqueness of squares involved.
Efficient fuzzy keyword search scheme over encrypted data in cloud computing based on Bed-tree index structure

In cloud computing, enabling search directly over encrypted data is an important technique to effectively utilize encrypted data. Most of the existing techniques are focusing on fuzzy keyword search as it helps achieve more robust search performance by tolerating misspelling or typos of data users. Existing works always build index without classifying keywords in advance. They suffer from efficiency issue. Furthermore, Euclidean distance or Hamming distance is often chosen to evaluate strings’ similarity, ignoring prefixes matching and the influence of strings’ length on the accuracy. We propose an efficient fuzzy keyword search scheme with lower computation cost and higher accuracy to address the aforementioned problems. We employ the sub-dictionaries technique and the Bed-tree structure to build an index with three layers for achieving better search efficiency. With this index structure, the server could locate the keyword and could narrow the search scope quickly. The Jaro-Winkler distance is introduced to qualify the strings’ similarity by considering the prefixes matching and string length. The secure privacy mechanism is incorporated into the design of our work. Security analysis and performance evaluation demonstrate our scheme is more efficient compared to the existing one while guaranteeing security.
Key Problems of Complex Topics in Mathematics as the Basis of Teaching Methods in the Conditions of Self-Education

Ensuring a high quality of teaching mathematics to students is inextricably linked with teaching how to solve creative mathematical problems. These problems traditionally include tasks of high educational, developmental and diagnostic value. Teaching how to solve such problems allows training graduates with the highest level of the subject knowledge. The problem under study is relevant due to the need to form students’ ability to solve problems with parameters in order to achieve high results in mathematics, intellectual and personal growth. The problem has a new meaning during temporary distance learning, emphasizing the need to create educational and methodological materials that allow students to organize self-education on complex topics in mathematics. The purpose of this research is to develop and describe a teaching methodology for solving problems with parameters based on the allocation of basic (key) problems. The authors have developed a methodology for constructing a system of tasks based on systematizing the theoretical and task material, highlighting students’ basic knowledge and skills, describing the intra- and inter-subject connections of the topic “Equation of a circle in tasks with parameters”. This methodology includes a typology of problems with parameters containing the equation of a circle, a substantiated description of the system of basic (key) problems, the role and content of the propaedeutic stage of teaching. The materials have passed multi-stage approbation and shown their consistency in achieving high results in mathematics. They can help teachers to prepare lessons and extracurricular activities, authors to provide teaching aids for students and teachers, and serve as the basis for a special course for students of pedagogical universities.
Multi-Scale Graph Convolutional Interaction Network For Salient Object Detection

Remarkable progress has been achieved for salient object detection based on deep learning. However, most of the previous works have the issues of how to extract more effective information from scale-varying data and how to improve the boundary quality. In this paper, we propose the multi-scale graph convolutional interaction network (MGCINet), which consists of the feature interaction module (FIM), the feature aggregation module (FAM), and the residual refinement module (RRM). FIMs fuse interactive features from neighboring scales. Based on two-layers graph convolutional network, FAMs aggregate scale-specific information by graph nodes interaction. RRMs optimize the coarse saliency maps with blurred boundaries by U-net residual blocks. In addition, we propose multi-scale weighted structural loss to assign different weights to pixels while focusing on image structure at various scales. Experiments show that our method outperforms the state-of-the-arts on five benchmark datasets under different evaluation metrics.
Novel Technology for Obtaining Dynamic Load Information of Intelligent Sensing Wheels Based on WEIS

Aiming at the issues that the traditional TPMS method for monitoring wheel dynamic load parameter (DLP) is limited to measure two parameters of the tire real-time pressure and temperature, and is unable to measure wheel dynamic load parameters directly, this paper proposes a novel technology for obtaining dynamic load information of intelligent sensing wheels based on WEIS. Through installing an intelligent sensing unit on each wheel hub, in virtue of MAP information of motor vehicles and the information of intelligent three-dimensional acceleration sensing unit installed on each suspension system, and with the help of wheel DLP monitoring model, it is able to calculate all the dynamic load information of wheels and vehicle load information, and at the same time the integrated temperature and pressure sensor on the wheel hub can get the wheel's real-time pressure and temperature. The experimental results indicate that it is able to measure the vertical load change of wheel dynamically, truly and accurately under the experimental conditions. This new technology can measure 4 of the 27 condition monitoring parameters for safe operation of motor vehicles with good measurement accuracy of performance indicators, reaching the expected results.
The Integrated Probabilistic Data Association Filter Adapted to Lie Groups

The Integrated Probabilistic Data Association Filter (IPDAF) is a target tracking algorithm based on the Probabilistic Data Association Filter that calculates a statistical measure that indicates if an estimated representation of the target properly represents the target or is generated from non-target-originated measurements. The main contribution of this paper is to adapt the IPDAF to constant velocity target models that evolve on connected, unimodular Lie groups, and where the measurements are also defined on a Lie group. We present an example where the methods developed in the paper are applied to the problem of tracking a ground vehicle on the special Euclidean group SE2.
Rating Prediction for Mobile Applications via Collective Matrix Factorization Considering App Categories

Abstract The rapid development of Internet and mobile communication, smart phone, tablet computer and other mobile devices become widespread, they have become an indispensable part of life, changing the human lifestyle, way of working and learning style, it brings great convenience and fun to us. This paper mainly discusses the status of personalized recommendation system, mobile application and analyse the main problems of recommendation system. This research proposes a rising mobile application domain, which uses web crawling technique to get explicit feedback from a real-world mobile application market. The explicit feedback is the users’ ratings data on Apps and the corresponding category information. To establish a mobile application recommendation model based on user interest and categorization information and propose a novel collective matrix factorization algorithm. Through the crawled data, the improved algorithm, and the traditional collaborative. The traditional mobile application recommendation systems are mostly based on keyword search, popularity, download usage and categories, thus they do not make personalized recommendation to users, and invalid to find applications that users are interested in. Through the user’s historical behaviour, a personalized recommendation system can be established to recommend the applications that fit user’s interests effectively. This paper is based on the collective matrix factorization using user interest such as user’s ratings and category information, designs a personalized recommendation system for mobile applications by using the relationship between them.
Precipitation forecast based on the Bayesian Network

Abstract The purpose of the article is to consider an alternative approach to the forecasting meteorological events by using expert systems based on the Bayesian network (belief network). Also there is an example of building a Bayesian network capable to predict a type and a probability value of precipitation, with implementation it in the HUGIN software, considered in the article. The conclusion considers the positive and negative aspects of the presented approach.
A comprehensive study on deep learning approach for CBIR

The content analysis of multimedia is used in computer applications. The multimedia data mostly contain digital images. Over the years, the multimedia contents have become more complex. Especially the images, have shown exponential jump. Twitter, Facebook, Instagram and other different archives are flooded with more than millions of images every day. Searching for the right image from the archive is a difficult research task for the computer vision technology. Over the past two decades, there has been an increase in the research area of content-based image retrieval (CBIR), analysis and image classification using deep learning methods. The deep learning methods proved to be an alternative for manual feature engineering where hand-crafted features were created depending on visual contents like shape, color, texture and were adopted in early days. Deep learning is capable of learning the features from the data automatically. Research done in the field of CBIR has shown that, there remained a remarkable gap between the features presented and the visual perception of humans. Researchers focused on reducing this semantic gap to enhance the efficiency of CBIR. This paper shows a decade review of the recent development in CBIR using deep learning methods is done. We will do the performance analysis using the state-of- the-art methods. It will help in future development and research growth in deep learning image retrieval systems.
Bayesian Learning for Regression Using Dirichlet Prior Distributions of Varying Localization

When taking a Bayesian approach to machine learning applications, the performance of the learned function strongly depends on how well the prior distribution selected by the designer matches the true data-generating model. Dirichlet priors have a number of desirable properties – they result in closed-form posterior distributions given independent training data, have full support over the space of data probability distributions, and can be maximally informative or non-informative depending on their localization parameter. This paper assumes a Dirichlet prior and details the predictive distributions that characterize unobservable random quantities given observed data. The results are then applied to the most common loss function for regression, the squared error loss. The optimal Bayes estimator and the resultant risk trends are presented for different prior localizations, demonstrating a bias/variance trade-off.
360° Video for Immersive Learning Experiences in Science Education

The availability of affordable, consumer-oriented video cameras capable of recording high-quality, full-motion video in 360° in every direction has enabled a rapid proliferation of what is commonly referred to as VR360 video across social and entertainment media, especially in travel, adventure sports and virtual location tours, and particularly as a trade tool for the real-estate industry. This chapter examines the uses, affordances, challenges, needs, and potential of VR360 and stereoscopic 180° (VR180) media for effective learning of science through the development and application of immersive learning experiences (ILE). ILEs are especially suited to support out-of-classroom (OOC) learning and/or where the use of first-person view (FPV) perspective may be beneficial, such as field trips and laboratory-based practical work. Essentially, these types of learning activities are viewed as important aspects of science education by engendering the embodied sense of "being there" and "doing". ILEs based on VR360 video have the potential to provide a sense of this embodied presence and engagement and thus can serve primarily as adjunct and supporting resources to traditional OOC and laboratory activities, and if necessary, replace these activities.
Table-structure recognition method using neural networks for implicit ruled line estimation and cell estimation

Tables are often used to summarize accurate values in academic papers, while graphs are used to show them visually. Automatic graph generation from a table is therefore a topic of research interest. Given that the way tables are written varies depending on the author, in earlier work we proposed a cell-detection-based table-structure recognition method. Our method achieved fair performance in experiments using the ICDAR 2013 table competition dataset, but could not outperform the top-ranked participant in the competition. This paper proposes an improved method using two neural networks: one estimates implicit ruled lines that are necessary to separate cells but are undrawn, and the other estimates cells by merging detected tokens in a table. We demonstrated the effectiveness of the proposed method by experiments using the same ICDAR 2013 dataset. It achieved an F-measure of 0.955, thereby outperforming the other methods including the top-ranked participant.
Multimedia Technology Can be used to Enrich Teaching Resources of Aerobics

research-article Multimedia Technology Can be used to Enrich Teaching Resources of Aerobics Share on Authors: Yanjuan Dun Beijing Film Academy Modern Creative Media Academ Beijing Film Academy Modern Creative Media AcademView Profile , Haoyuan Xu Beijing Film Academy Modern Creative Media Academ Beijing Film Academy Modern Creative Media AcademView Profile , Rui Kong Beijing Film Academy Modern Creative Media Academ Beijing Film Academy Modern Creative Media AcademView Profile , Hui Yu Beijing Film Academy Modern Creative Media Academ Beijing Film Academy Modern Creative Media AcademView Profile , Chao Dong Beijing Film Academy Modern Creative Media Academ Beijing Film Academy Modern Creative Media AcademView Profile , Xiao Gong Beijing Film Academy Modern Creative Media Academ Beijing Film Academy Modern Creative Media AcademView Profile Authors Info & Claims IPEC2021: 2021 2nd Asia-Pacific Conference on Image Processing, Electronics and ComputersApril 2021 Pages 662–665https://doi.org/10.1145/3452446.3452607Online:25 April 2021Publication History 0citation17DownloadsMetricsTotal Citations0Total Downloads17Last 12 Months17Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access