Reviewerly/exHarmony huggingface dataset:
Guided Learning for Efficient Optical Flow Estimation

Current optical flow estimation models require a large number of computing resources, resulting in excessive runtimes to perform inference for mobile devices. Considerable efforts have been exerted to compress deep neural networks, but these approaches have resulted in reduced accuracy. Comparing with other network compression methods, guided learning achieves competitive performance without the need of modifying or retraining the original network as network pruning or specific hardware environment support as network quantization. Guided learning has demonstrated excellent improvements in classification and detection. However, there is still leak of research on guided learning for optical flow estimation. In this work, we propose a novel framework to learn a compact and fast optical flow estimation network, and it can achieve competitive performance through guided learning. We train the small network through additional supervision from high-level feature maps in original optical flow estimation networks to effectively learn robust and efficient features. The overall compression framework is implemented by distilling knowledge from the feature maps of the decoder in original network with a proposed feature guiding loss to transfer knowledge to small network. Lastly, we conduct a comprehensive empirical evaluation over three datasets. Results show consistent improvement in accuracy-speed tradeoffs for the optical flow estimation network.
Reinforcement-Based Divide-and-Conquer Strategy for Side-Channel Attacks

Previous works have proven that power consumption side-channel attacks, such as the Template Attack and the Stochastic Model, are effective for small secrets, such as those with 8 or 16 bits. However, directly applying those side-channel attacks on systems with large secrets, for example AES 128, is computationally intractable. Attackers usually apply a divide-and-conquer strategy to partition the secret in order to scale to larger numbers of bits. In the case of AES, divide-and-conquer strategy based side-channel attacks are usually launched on either the first round or the last round of the AES encryption. In this paper, we propose an efficient and pragmatic attack strategy that exploits the samples from multiple rounds, which significantly improves the key recovery compared to standard divide-and-conquer strategies.
A re-engineering approach for extension of the Tourist Guide Knowledge Base

The paper presents an extension of the knowledge-base of the Tourist Guide with the rich information about Bulgarian cultural, historical and natural sites, available in the databases created under the BECC project. To accomplish this task, the architecture of Tourist Guide that was created as the reference architecture of Virtual-Physical Space (ViPS) is presented, and the restructuring process of the components in this architecture is described. In order to use the created databases in BECC project, we had to re-engineered them on the basis of standards for the presentation of cultural and historical sites such as UNESCO and CCO (Cataloging Cultural Objects).
WITHDRAWN: Interfacing advanced NoSQL database with Python for internet of things and Big data Analytics

Nowadays the worldwide industry, both corporate and government, is making open source technology known. A variety of fields use Open Source products with various uses in the tech industry. NoSQL databases, big data processing and online resources are top of the line, and are used for a variety of purposes. In social media systems and large data management portals, NoSQL databases have been used to manage massive, heterogeneous and unstructured information formats. NoSQL Databases are used to easily view the large data set records in the background. The implementation of AADHAAR Card in India took place through the NoSQL database since a lot of information, including text data, photographs, thumb prints and the iris detection, is correlated with this information. A classical database system cannot manage the various types of datasets simultaneously (such as image, text, video, audios, recordings, thumb impressions, iris sample).
Dimensionality-reduced Secure Outlier Detection on Union of Subspaces

In the problem of outlier detection (OD) on a union of subspaces (UoS), inliers are assumed to lie around a union of low-dimensional subspaces, and the goal is to detect the outliers that are not close to any of these subspaces. Among various algorithms, sparse self-representation-based ones have attracted much attention because of their theoretical performance guarantee. However, these algorithms need direct access to all raw data, and thus have poor data security and privacy protection capability. To solve this problem, in this paper we propose a new algorithm called dimensionality-reduced secure outlier detection (DrSOD), which uses random projection as a preprocessing step to avoid direct access to the raw data. We theoretically prove that DrSOD can correctly detect outliers with overwhelming probability under connectivity assumptions. In addition, the random projection step improves the computational efficiency of the algorithm. Experiments on synthetic and real-world datasets also demonstrate the effectiveness and efficiency of DrSOD.
Establishing Robust Feature Point Detection and Tracking Methods for Face Orientation

Recently, there has been an increasing need of a face alignment or facial feature point tracking for applying face recognition, facial expression estimation, face attributes prediction, and clinical face observation etc. This study proposes a robust facial feature tracking method against camera shake and face orientation changes. The method applies a cascaded composed learning (CCL) based facial feature point tracking method by incorporating optical flow for improving tracking accuracy and robustness. Experiments are conducted to show that efficient tracking is achieved by performing CCL combined with initial shape estimation via the optical flow.
Temporal Action Detection based on Temporal Deformable Proposal Generation

Temporal action detection is a challenging task in video understanding. Locating the activities more accurately on fuzzy temporal action boundary is a difficulty. So it is crucial to generate temporal proposals with precise boundaries and high-quality. To better solve this issue, we propose a novel temporal action detection method based on Temporal Deformable Proposal Generation (TDPG). In TDPG, modified C3D network is used to extract robust features. Deformable Proposals Generation module adaptively expands the receptive field through temporal deformable convolution to generate deformable proposals with more precise temporal boundaries. And then selected deformable proposal segments are used to predict action classification scores and refine temporal boundary. Our method is trained end to end with jointly optimized classification and regression loss. Experimental results show that our method achieves better performance than state-of-the-art method on two public datasets THUMOS'14 and Charades.
Approaches for Cognitive Assistance in Industry 4.0 Assembly

The high degree of customization and small lot sizes necessitate a considerable amount of manual assembly work in industry 4.0 scenarios. Cognitive assistance systems could enable workers with limited experience to fulfill these roles. Approaches for designing such systems are illustrated by ADAMAAS-I (“Adaptive and Mobile Action Assistance for Industry”), an advanced cognitive assistance system using smart glasses in the context of industry 4.0 production scenarios. The aim of ADAMAAS-I is to move from stationary systems to a mobile and adaptive action support and monitoring system that reacts dynamically to human actions and prevent mistakes by giving appropriate assistance. For this purpose, ADAMAAS-I uses 3D in-situ instructions via augmented reality and state-of-the-art machine learning techniques for action recognition. Furthermore, ADAMAAS-I incorporates a systematic and largely automatized method from cognitive science for analyzing users’ action representations in long-term memory. This information about individual memory structures is used to anticipate human errors and to improve the accuracy of action recognition. By these means, ADAMAAS-I provides individualized, user- and situation-specific assistance, e.g. for manual assembly tasks in an industrial context.
Improving Robustness and Time Efficiency with Weight Function in Occlusion Face Recognition

The need for occlusion face recognition in daily life, professional fields like criminal investigation and the recent epidemic is increasing, so it is still a hot issue in the current research and many researchers are pursuing the technical excellence for the face recognition of random occlusion. In the previous research, RRC model is proposed for the excellent performance in the occlusion face recognition and the IR <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> C algorithm is adopted to solve this model. Among them, the weight function in this algorithm plays a big role, which will directly affect the recognition rate and running time of the experiment. In this paper, three weight functions: Inverse trigonometric function, Hyperbolic tangent function and Log function are proposed for the purpose of improving the robustness and time efficiency of IR <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> C algorithm. Applying these functions on the comparable types and levels of random occlusion and then comparing them with the previous research, the experimental results indicate that the inverse trigonometric function is superior to other functions with better recognition rates and shorter running time.
Argumentation Based Ontology Alignment Extraction

The ontology matching technique aims at establishing the correspondence between two ontologies’ entities to solve their heterogeneity problem. In the matching process, the quality of the single matcher’s alignment can not be ensured. Usually, multiple matches are required to work together to increase the evidence of potential matches or mismatches. Selecting, combining, and adjusting these ontology matches to obtain high-quality ontology comparisons is one of the main challenges in ontology matching. This paper proposes an Argumentation Framework (A.F.) based on the ontology extracting technique to address this challenge. First, the proposed approach obtains the alignments obtained by different ontology matches. It extracts the final alignment by debating the disputed entity correspondences with A.F. The experiment is conducted on the Bibliographic track provided by Ontology Alignment Evaluation Initiative (OAEI), and the statistical comparison with the state-of-the-art ontology matching techniques shows the effectiveness of the proposed approach.
Method for Evaluating and Correcting Power Grid Operating Data Quality Based on Spark Framework

Aiming at the difficulty of quantifying the importance of each evaluation index in power grid operation data quality evaluation, this paper proposes a key index weight calculation model based on analytic hierarchy process and entropy weight method, which distinguishes the importance of different indexes by weight. Aiming at the most prominent data abnormally problem in the evaluation of power grid operation data quality, the paper designs an abnormal data correction model based on RBF neural network under the Spark framework to realize the correction of abnormal data. Finally, the effectiveness of the method is verified by using SCADA data from a provincial dispatch center. The results show that the proposed method can effectively deal with abnormal grid operation data and has practical application value.
Correlation of Surface Binary Segmentation and Object Contour Sizing Threshold in Clustering Image Processing

Abstract Image segmentation and contouring plays a significant role in computer vision. It aims at extracting meaningful objects contained in an image. Obtaining an appropriate threshold value yield a higher accuracy in identifying the specified object of interest. A min-max threshold value was examined for surface binary segmentation and object contour sizing. The binary segmentation threshold (T) suggested to be in the range of 155 to 250 in order to get an appropriate result and the acceptable range for frame divider is between 150 to 250. These results can assist researchers to focus on the applicable threshold value in image processing especially for video analysis.
Exploration of University Informatization Construction Based on Mathematical Modeling Thought

Applying modern information management to teaching management in colleges and universities is not only the need of colleges and universities' own development, but also the trend of social development. As the basic language of the basic discipline, mathematics coexists with diversity and interest, and its application in various disciplines has infinite potential, and the transformation way to realize this process is the idea of mathematical modeling. With the rapid development of big data technology, universities can integrate resources, analyze their advantages and make their undertakings flourish. Big data technology is mainly used to collect, process and statistically analyze university construction data, predict the results after mathematical modeling, let the data express the truest and most objective affairs truth, and provide data support for the efficient and rapid development of universities. Combining mathematical modeling technology for data analysis and data mining, refining valuable data and establishing a perfect data security protection system can contribute to teaching, scientific research, management, decision-making and other aspects, thus promoting the all-round development of colleges and universities.
Intelligent and Interactive Video Annotation for Instance Segmentation Using Siamese Neural Networks

Training machine learning models in a supervised manner requires vast amounts of labeled data. These labels are typically provided by humans manually annotating samples using a variety of tools. In this work, we propose an intelligent annotation tool to combine the fast and efficient labeling capabilities of modern machine learning models with the reliable and accurate, but slow, correction capabilities of human annotators. We present our approach to interactively condition a model on previously predicted and manually annotated or corrected instances and explore an iterative workflow combining the advantages of the intelligent model and the human annotator for the task of instance segmentation in videos. Thereby, the intelligent model conducts the bulk of the work, performing instance detection, tracking, and segmentation, and enables the human annotator to correct individual frames and instances selectively. The proposed approach avoids the computational cost of online retraining by being based on the one-shot learning paradigm. For this purpose, we use Siamese neural networks to transfer annotations from one video frame to another. Multiple interaction options regarding the choice of the additional input data to the neural network, e.g., model predictions or manual corrections, are explored to refine the given model’s labeling performance and speed up the annotation process.
Interdisciplinary Research Towards Creating a Design Framework for Adaptable Virtual Learning Environments

This work-in-progress paper describes an interdisciplinary research project which relies on innovation in computer science to enhance pedagogy for second language learning and teaching. A design framework for adaptation was applied to a virtual reality teaching environment aimed at teaching prepositions to learners of English as a foreign/second language. The design was informed by Cognitive Learning Theory [1], [2], [3], Cognitive Linguistics [4]-[6], and Enaction Theory [7]; more precisely, the virtual reality teaching environment presents physical tasks specifically designed to teach the different senses of the preposition `over' based on Tyler and Evan's theoretical framework [8]. The dynamic environment provides real-time data on the actions and progress of the students allowing for empirical analysis of the effects of changes on learning outcomes.
Research on Vehicle Re-Identition based on Image Feature fusion

In order to improve the accuracy of vehicle Re-Identition, a method combining global features and local features was proposed to solve the problem. Vehicle appearance blur, pedestrian and motor vehicle block, and vegetation block could effect the accuracy of the algorithm. This study proposed a new method. First of all, the siamese network was used to match the shape of the vehicle's head, vehicle's tail and the whole vehicle. Then, a license plate recognition network based on YOLOv3 was used to identify the characters. The experimental results showed that the algorithm could achieve better vehicle Re-Identition effect in the complex road environment, and the accuracy of Re-Identition reached 95.82%, which was 7.89, 4.02 and 1.81 percentage points higher than the three classic Re-Identition models of DRDL, OIFE and RAM, respectively.
A Virtual Menu Using Gesture Recognition for 3D Object Manipulation in Mixed Reality

For years, the study on human-computer interaction in the mixed reality has been exploring in more natural and efficient interacting ways. As a common interacting component, the traditional menu on tangible user interface is not well suited to virtual interactive scenes in mixed reality and virtual menu represents a new category of efficient interacting methods in mixed reality. Recently, the combination of gesture recognition with the virtual menu has attracted little attention from researchers. To implement a natural, intuitive, and efficient interaction, in this work, a virtual menu using gesture recognition is proposed for the 3D object manipulation in mixed reality. In particular, several gesture states with respect to the different stages of menu interaction are first defined; then, the gestures on the screen are mapped to 3D virtual objects through coordinate transformation; and finally, the style and interaction logic of the menu is introduced to. In the experiment, the user’s interaction results using different menus are evaluated by comparing the time needed and the result’s accuracy with the same tasks. The experimental results show that our proposal can effectively reduce the number of interacting operations, and improve the efficiency of interacting process.
Elaborate Lexicon Extended Language with a Lot of Conceptual Information

The use of model such as LEL (Lexicon Extended Language) in natural language is very interesting in Requirements Engineering. But LEL, even if it is derived from the Universe of Discourse (UofD) does not provide further details on the concepts it describes. However, we believe that the elements inherent in the conceptual level of a system are already defined in the Universe of Discourse. Therefore, in this work we propose a more elaborate natural language model called eLEL. It is a model that describes the concepts in a domain in more detail than the conventional LEL. We also propose a modeling process of a domain using an eLEL model.
WITHDRAWN: A framework enhancement method of deep web data extraction

The solutions suggested for data extraction issue depends on the HTML DOM trees and response pages’ tags being analyzed. Although these solutions can achieve excellent outcomes, they are strongly dependent on HTML specifics. Therefore, to solve this issue this paper proposes a framework of two stages, for proficiently disclosure profound web data. The primary organizes, the proposed system performs “normal crawling” to get significant pages related to the user’s text query. To choose up significant web pages, a strategy is proposed based on the moved forward weighting work (ITF-IDF) is received by the crawler. In the second stage, “data region extraction “is performed to obtain data records. The proposed data extractor exploits the visual features of blocks to extract visual blocks. The strategy is proposed to cluster the visual blocks in a comparable format based on format tree and appearance likeness. Within the cluster with the most elevated weight, the visual blocks are chosen to be extricated as information records. The test comes about the outline that the system proposed is superior to past information extraction works.
WITHDRAWN: Detecting the outliers in high dimensional datasets using poisson regression

Detecting an Outlier has been a critical issue or problem in the field of machine learning. The issue is about identifying the patterns distinctly different from normal behaviors. Several algorithms have been projected to conquer the challenges as well as explorations in the field of outlier mining, but these methods unable to yields potentially higher accuracy results in such environments. Now a days, developing an efficient method for detecting the outliers in a huge database is a crucial task. In this research article, Poisson Regression technique is projected for outlier’s detections in high dimensional datasets. The proposed methodology is realized in the open source software called Rapid miner. Here, the factors like Average Precision 0.327, Average R-Precision 0.355, MSE 0.154, RMSE 0.2745 and Maximum f_measure 0.452 are calculated using iris dataset. And also the Root relative squared error is 91.48% and relative absolute error 85.09% and Outcomes from experimental analysis illustrate that Poisson method identifies the outliers with potentially higher precision in high dimensional datasets.
Fraudulent Detection in Healthcare Insurance

Our paper provides an extensive study of detecting fraudulent claims in healthcare insurance by leveraging machine learning algorithms. By using the publicly available medicare dataset, we are able to classify as fraud and non-fraud providers. Moreover, synthetically minority oversampling technique is used to avoid the class imbalance problem. Furthermore, a hybrid approach is used which is based on clustering and classification. Additionally, we have used other machine learning algorithms to check the efficiency of the best-suited algorithm.
An Efficient CTC Decoding Strategy with Lattice Compression

Connectionist Temporal Classification (CTC) has been widely used for acoustic modeling task in state-of-the-art automatic speech recognition (ASR) systems. To complement the acoustic model, several streams of works have been proposed to decode CTC output and incorporate language model: WFST decoder, Seq2Seq decoder and RNN decoder. Among these candidates, RNN decoder is the most suitable for CTC since it does not require any explicit lexicon dictionary for decoding, and thus it is able to produce open vocabulary output. However, the state-of-the-art RNN decoder is often slower during decoding process, and this severely limits their usage for realtime speech recognition.
Joint sparsity-biased variational graph autoencoders

To bring the full benefits of machine learning to defense modeling and simulation, it is essential to first learn useful representations for sparse graphs consisting of both key entities (vertices) and their relationships (edges). Here, we present a new model, the Joint Sparsity-Biased Variational Graph AutoEncoder (JSBVGAE), capable of learning embedded representations of nodes from which both sparse network topologies and node features can be jointly and accurately reconstructed. We show that our model outperforms the previous state of the art on standard link-prediction and node-classification tasks, and achieves significantly higher whole-network reconstruction accuracy, while reducing the number of trained parameters.