Reviewerly/exHarmony huggingface dataset:
TriTag-NFPF: Knowledge Denoising for Chinese Encyclopedia based on Triple Tag-Constructed Potential Function

In this paper, a novel method is proposed for Chinese large-scale online encyclopedia knowledge denoising. Firstly, the initial similarity of the triples is acquired by the similarity computing method integrating the Edit-Distance and TongYiCiCiLin similarity algorithm. Secondly, a novel nuclear field-like potential function of the Infobox knowledge triples is constructed in virtue of Chinese encyclopedia entry semantic tag. Finally, large-scale knowledge triple clustering and denoising are performed by means of the improved potential function proposed in this paper for the purpose of minimizing the influence of massive repetition and ambiguity in the Chinese open encyclopedia Knowledge Base (KB). The proposed method has solved the problems of semantic duplication, ambiguity and inappropriate classification of knowledge triples arising from constructing Chinese KBs. The experimental results indicate that the open-domain oriented Chinese encyclopedia KBs constructed by the method proposed in this paper is outperformed than the state-of-the-art methods.
Securing Data with Selective Encryption Based DAC Scheme for MANET

MANETs consist of mobile nodes which can any time enter or exit from the network [16]. The no central admin set up of the MANET makes it more prone to attacks [5]. Sending much more secured data than any other networks is verily needed in MANETs since the mobile nodes consume a lot of energy for capturing signals and cryptanalysis of messages. Here we are presenting a security scheme that adorns selective encryption strategy and encrypts selective bit positions instead the entire data set. The scheme needs less computation capability while providing adequate security to the MANET nodes [15]. It provides better security compared to the existing selective encryption algorithms. Thus we manage safety as well energy conservation of mobile nodes.
Estimation of the Attractiveness of Food Photography Based on Image Features

We introduce a method to estimate the attractiveness of a food photo. It extracts image features focusing on the appearances of 1) the entire food, and 2) the main ingredients. To estimate the attractiveness of an arbitrary food photo, these features are integrated in a regression scheme. We also constructed and released a food image dataset composed of images of ten food categories taken from 36 angles and accompanied with attractiveness values. Evaluation results showed the effectiveness of integrating the two kinds of image features.
Facial Expression Recognition

The purpose of this project was to analyze which image pre-processing technique was most beneficial in improving the performance of Facial Expression Recognition through Deep Learning and High-Performance Computing. Contrary to our expectations, the results obtained in this work showed that deep learning does not significantly benefit from various commonly used image pre-processing techniques such as resizing, smoothing, or edge detection. The results confirm previous findings that an increase in accuracy is obtained by increasing the size of the training dataset. This study proceeds to show that the increase in training data size can easily be handled by the High-Performance Computing (HPC) cluster provided by the Pittsburg Supercomputing Center (PSC) through XSEDE.
Accelerating Clustering using Approximate Spanning Tree and Prime Number Based Filter

Motivation: Clustering genomic data, including those generated via high-throughput sequencing, is an important preliminary step for assembly and analysis. However, clustering a large number of sequences is time-consuming. Methods: In this paper, we discuss algorithmic performance improvements to our existing clustering system called PEACE via the following two new approaches: (1) using Approximate Spanning Tree (AST) that is computed much faster than the currently used Minimum Spanning Tree (MST) approach, and (2) a novel Prime Numbers based Heuristic (PNH) for generating features and comparing them to further reduce comparison overheads. Results: Experiments conducted using a variety of data sets show that the proposed method significantly improves performance for datasets with large clusters with only minimal degradation in clustering quality. We also compare our methods against wcd-kaboom, a state-of-the-art clustering software. Our experiments show that with AST and PNH underperform wcd-kaboom for datasets that have many small clusters. However, they significantly outperform wcd-kaboom for datasets with large clusters by a conspicuous ~550× with comparable clustering quality. The results indicate that the proposed methods hold considerable promise for accelerating clustering of genomic data with large clusters.
Towards Self-Organizing Cloud Polyglot Database Systems

The demand for database OPEX reduction emerging from strict and heterogeneous storage requirements of modern cloud applications motivates shifting the contemporary manual and semi-automated approaches into an autonomic database management framework. As a first step towards fulfilling that vision, we introduce the Self-organizing Polyglot Persistence Architecture encompassing the key elements required for modeling self-adaptive capabilities into distributed database environments. We then implement SPPA-derived infrastructure framework based on services provided by OpenStack private cloud platform. Our experimental studies comprising various database scaling scenarios in the context of diverse database technologies (MariaDB Galera Cluster, Cassandra) confirm that the proposed SPPA approach enables cloud operators with a modular, declarative and flexible end-to-end solution for introducing adaptive database behaviors in the cloud.
Automatic Exercise Recognition with Machine Learning

Although most individuals understand the importance of regular physical activity, many still lead mostly sedentary lives. The use of smartphones and fitness trackers has mitigated this trend some, as individuals are able to track their physical activity; however, these devices are still unable to reliably recognize many common exercises. To that end, we propose a system designed to recognize sit ups, bench presses, bicep curls, squats, and shoulder presses using accelerometer data from a smartwatch. Additionally, we evaluate the effectiveness of this recognition in a real-time setting by developing and testing a smartphone application built on top of this system. Our system recognized these activities with overall F-measures of 0.94 and 0.87 in a controlled environment and real-time setting respectively. Both users who were and who were not regularly physically active responded positively to our system, noting that our system would encourage them to continue or start exercising regularly.
Recommendation System in Business Intelligence Solutions for Grocery shops

With the surge in digital platforms and extension of e-commerce, the field of recommendation has been a topic of interest not only for the data scientist but deemed important by the business experts to enhance the user-centric services. A large number of retail & service-oriented companies such as Amazon, Netflix, Goodreads, and Spotify etc. use Business Intelligence (BI) and recommendation systems to provide users with various choices of products based on their interest. Evidently, such a customized user-experience not only provide them with a better service, but also enables the companies to understand customer behavior and enhance their business. The aim of this paper is to introduce a recommendation system in the business intelligence platform to a new-system where no user's previous interaction information is available. We present an exploratory study of implementing recommendation system in the project SmartEmma, a grocery shop application in Aachen, funded by EFRE.NRW, European Union and WIRTSCHAFT.NRW.
Depth Separations in Neural Networks: What is Actually Being Separated?

Existing depth separation results for constant-depth networks essentially show that certain radial functions in $$\mathbb {R}^d$$ , which can be easily approximated with depth 3 networks, cannot be approximated by depth 2 networks, even up to constant accuracy, unless their size is exponential in d. However, the functions used to demonstrate this are rapidly oscillating, with a Lipschitz parameter scaling polynomially with the dimension d (or equivalently, by scaling the function, the hardness result applies to $$\mathcal {O}(1)$$ -Lipschitz functions only when the target accuracy $$\epsilon $$ is at most $$\text {poly}(1/d)$$ ). In this paper, we study whether such depth separations might still hold in the natural setting of $$\mathcal {O}(1)$$ -Lipschitz radial functions, when $$\epsilon $$ does not scale with d. Perhaps surprisingly, we show that the answer is negative: In contrast with the intuition suggested by previous work, it is possible to approximate $$\mathcal {O}(1)$$ -Lipschitz radial functions with depth 2, size $$\text {poly}(d)$$ networks, for every constant $$\epsilon $$ . We complement it by showing that approximating such functions is also possible with depth 2, size $$\text {poly}(1/\epsilon )$$ networks, for every constant d. Finally, we show that it is not possible to have polynomial dependence in both $$d,1/\epsilon $$ simultaneously. Overall, our results indicate that in order to show depth separations for expressing $$\mathcal {O}(1)$$ -Lipschitz functions with constant accuracy—if at all possible—one would need fundamentally different techniques than existing ones in the literature.
3D Human Pose Dataset Augmentation Using Generative Adversarial Network

Methods for 3D human pose estimation from monocular images based on convolutional neural networks require a large number of training data of well annotated pose-image pairs. Although many 3D human pose datasets have been created, more training data with accurate 3D annotation is still in shortage for the training of neural networks. Recently in image generation area, techniques based on generative adversarial network show potential to generate realistic image data from user input pose data. In this paper, we propose a neural network-based method for 3D human pose dataset augmentation. In our method, we use an autoencoder to learn a latent representation of existing pose data and to produce new poses of similar style. Human pose data generated by autoencoder is input into a generative adversarial network to synthesize mask images with an actor performing the same style, which can be transformed to colored images at the end. For the evaluation of the proposed method, we augment it with a small amount of labeled data. The experimental analysis shows that our method can generate more valid labeled data from small labeled data, which can boost the training of pose estimation using neural networks.
Missing Data Imputation for Operation Data of Transformer Based on Functional Principal Component Analysis and Wavelet Transform

The operation data of transformer plays an important role in assessing the status of electric devices. However, the real operation data often have missing values, which will result in the unreliability of the following data analysis. In view of this, we propose a method based on functional principal component analysis (FPCA) and wavelet transform to impute the missing data. According to the characteristic of the operation data, we separate the daily data curve to low frequency part and high frequency part. We first investigate the fluctuation patterns over the historical data and use FPCA to estimate the low frequency part. We then estimate the residual function and use wavelet transform to estimate the high frequency part. Combining these two parts, we get the approximation to the original data and impute the missing values. Applications of the proposed method to the real operation data show that the method performs very well and it works well whenever the missing points are randomly distributed or in continuous form.
The Effectiveness of Mixed Reality Environment-Based Hand Gestures in Distributed Collaboration

Mixed reality (MR) technologies are widely used in distributed collaborative learning scenarios and have made learning and training more flexible and intuitive. However, there are many challenges in the use of MR due to the difficulty in creating a physical presence, particularly when a physical task is being performed collaboratively. We therefore developed a novel MR system to overcomes these limitations and enhance the distributed collaboration user experience. The primary objective of this paper is to explore the potential of a MR-based hand gestures system to enhance the conceptual architecture of MR in terms of both visualization and interaction in distributed collaboration. We propose a synchronous prototype named MRCollab as an immersive collaborative approach that allows two or more users to communicate with a peer based on the integration of several technologies such as video, audio, and hand gestures.
A Novel Methods Based on Clustering Algorithms as The Neural Network Preprocessing

This paper presents experiments in the preprocessing area for Radial Basic Function Neural Network (RBF NN). The main ideas of it are to find optimal pre-processing methods and algorithms, which can optimize input parameters and expedite the processing of neural network. The proposed methods are some novel experiments with flexible shape parameters and automated determination of the neural network initial parameters.
Recognizing Indonesian Sign Language Gestures Using Features Generated by Elliptical Model Tracking and Angular Projection

In this paper, we propose a method of feature extraction applied to the hands, fingers and arms, intended to improve the accuracy of Sistem Isyarat Bahasa Indonesia (SIBI) gesture recognition. Using common smartphone camera, we recorded multiple sequence of gestures used to sign for affixes and root words in SIBI then applied image processing and tracking methods to extract features from the shape and location of the hands. To extract the features, skin color segmentation was applied to separate hands and face blob from the background. Then the object would be registered to an ellipse model and tracked through the videos using elliptical model tracking. Finally, the video was then processed frame by frame, and each successfully tracked object is subjected to angular projection to generate the aforementioned features. The model that was used to recognize SIBI gestures is 2-layers Long Short-Term Memory (LSTM) neural network. Accuracy of the proposed method is measured by comparing the prediction with the actual gesture of the testing data. The highest level of accuracy achieved for the prefix, root and suffix datasets are 91.74%, 98.94%, and 97.71% respectively.
Detection and Identification of Gate Faults in Reversible Circuit

In recent time, efficient implementation of reversible logic circuits has come out as an important research area before the design industry. With the advancement in reversible logic synthesis, developing mechanism for identification of faults finds importance. Though there exist well-known testing techniques, but developing improved testing algorithms is the need of the hour. Aiming to develop efficient testing technique, here in this work, we show an improved testing scheme based on Boolean logic function. Two different testing approaches are presented here, where in the first work, by using Boolean difference method SMGFs in reversible circuit are tracked successfully, where a test vector generator is derived to find the faults. In the second work, a Reed–Muller (RM) form based testing approach is developed that not only detects the faults but also locates the exact position of the faulty area. A limitation for the second testing scheme is that it can only be employed over a specific type of reversible circuit known as Exclusive-Or Sum-Of-Product (ESOP) design. Both the testing techniques have been executed over different benchmark suites and a comparative study with state-of-the-art testing approaches have been included in the work.
Robust Label Prediction via Label Propagation and Geodesic &lt;i&gt;k&lt;/i&gt;-Nearest Neighbor in Online Semi-Supervised Learning

This paper proposes a computationally efficient offline semi-supervised algorithm that yields a more accurate prediction than the label propagation algorithm, which is commonly used in online graphbased semi-supervised learning (SSL).Our proposed method is an offline method that is intended to assist online graph-based SSL algorithms.The efficacy of the tool in creating new learning algorithms of this type is demonstrated in numerical experiments.
The Processing Methods of the Computer Format Information Flow in the Network English Translation System

The development of the network technology, and especially the web search engine, has brought great changes to the field of the English translation. Translators can acquire the background information of the translated texts by using the web search engine correctly, inquire about the correct translation methods of the rare professional terms, apply the fixed sentence patterns, and check the correctness of the translation, so as to improve the translation speed and quality.
Non-Convex Transfer Subspace Learning for Unsupervised Domain Adaptation

Transfer subspace learning aims to learn robust subspace for the target domain by leveraging knowledge from the source domain. The traditional methods often adopt the convex norm to approximate the original sparse and low-rank constraints, which make the optimization problem be easily solved. However, such relax approximation leads to the performance deviation of the original non-convex model. In this paper, we propose a novel Non-convex Transfer Subspace Learning~(NTSL) method to provide a tighter approximation to the original sparse and low-rank constraints. Specifically, we design an objective function that leverages the Schatten p-norm and ℓ_2, p-norm to preserve the structure between the source and target domains. With Schatten p-norm, the objective function better approximates the rank minimization problem than the nuclear norm and preserves the structure of domains. Besides, the ℓ_2, p-norm can reduce the effect of noise and improve the robustness to outliers. Meanwhile, we develop an efficient algorithm to solve the non-convex minimization problem. Extensive experimental results on cross-domain tasks show the effectiveness of our proposed method.
Touch Gesture Recognition System based on 1D Convolutional Neural Network with Two Touch Sensor Orientation Settings

Touch is regarded as an important channel in human-robot interaction. This paper presents a touch gesture recognition system that can be applied to hard-skinned robots. Related studies have been based on traditional machine learning methods with hand-crafted features that make it difficult for developers to access optimal features that they cannot imagine. To prevent this, our proposed touch gesture recognition system uses a 1D convolutional neural network (1D CNN) that can learn features from data directly. The recognition system classifies four touch patterns: hit, pat, push, and rub. The results show an average recognition rate of 90.5%, which is higher than one of the related studies. Additionally, we verify the effect of touch sensor orientation on recognition performance. Many studies achieved accuracy with a touch sensor installed in only one orientation. In this study, we experimentally confirm that a classifier trained with data from a vertically installed touch sensor shows degraded performance on test data from a horizontally installed touch sensor, and vice versa. To achieve high recognition accuracy for both orientations, the network is newly trained with data from both vertically and horizontally installed sensors. The results show an 88.5% and 89.1% accuracy rate for the vertical and horizontal test data, respectively. That is, the model achieves reliable performance in both orientations whereas classifiers trained with data from a certain orientation cannot show good performance on test data from the different orientation.
Educational guidance process integrating opinion mining

Abstract Opinion mining is a fast‐developing field of study with numerous applications. It raises economic and political interests, especially in decision‐making processes. In this paper, we are interested in educational opinion mining. We propose an educational guidance process that aims to improve university orientation after the secondary school cycle. Unlike the ordinary orientation processes, our process integrates the opinions of pupil, parents, and teachers, collected periodically. These opinions ​​are treated to sort out the studies’ domains in a positive polarity order. An orientation score is then calculated based on these opinions, the student's aptitudes and the continuous evaluation marks to guide pupils toward the most favourable discipline. The experimentations conducted on a sample of pupils (300) give a better convergence to the pupils’ explicit choices using our proposal with a κ coefficient of 0.71, against 0.29 using only marks.
Large-scale Multi-label Image Retrieval Using Residual Network with Hash Layer

In recent years, increasing deep hashing methods have been applied in large-scale multi-label image retrieval. However, in the existing deep network models, the extracted low-level features cannot effectively integrate the multi-level semantic information and the similarity ranking information of pairwise multi-label images into one hash coding learning scheme. Therefore, we cannot obtain an efficient and accurate index method. Motivated by this, in this paper, we proposed a novel approach adopting the cosine distance of pairwise multi-label images semantic vector to quantify existing multi-level similarity in a multi-label image. Meanwhile, we utilized the residual network to learn the final representation of multi-label images features. Finally, we constructed a deep hashing framework to extract features and generate binary codes simultaneously. On the one hand, the improved model uses a deeper network and more complex network structures to enhance the ability of low-level features extraction. On the other hand, the improved model was trained by a fine-tuning strategy, which can accelerate the convergence speed. Extensive experiments on two popular multi-label datasets demonstrate that the improved model outperforms the reference models regarding accuracy. The mean average precision is improved by 1.0432 and 1.1114 times on two datasets, respectively.
Adaptive Correlation Filtering Algorithm for Video Target Tracking based on Multi Feature Fusion

In recent years, the target tracking algorithm based on the correlation filter has become a hot topic in the field of target tracking with its excellent performance in tracking precision and tracking speed. Aiming at problem of the insufficient accuracy of complex scene image and video image data, a multi feature fusion adaptive correlation filtering algorithm for video image tracking is proposed. In this work, 36 groups of color video sequences in the tracking benchmark database (OTB-2013) are utilized as samples. Firstly, histogram of oriented gradient (HOG) and color name (CN) are used to extract the two complementary features from video sequences. Then these features are used to train correlation filters and according to the complementarity of features, the response graph of two correlation filters is weighted together to effectively for tracking the image targets. After that, the confidence level of response graph and the intra-frame variation rate are calculated to dynamically adjust the learning rate and update the parameters of two correlation filters. Finally, the scale adaptive estimation algorithm is introduced to achieve scale adaptive tracking of targets. The experimental results from OTB-2013 tracking datum database show that the multi feature fusion adaptive correlation filter is suitable for complex scene video image data and the accuracy and speed of automatic tracking is improved.