Reviewerly/exHarmony huggingface dataset:
Unsupervised Multi-class Cosegmentation

Cosegmentation aims to segment out similar objects from a set of images with minimum additional information. Most of the existing cosegmentation algorithms assume that the foreground objects should appear in all images of the image set. But under some conditions, if the foreground objects only appear in a few images, the segmentation results are possible to be wrong. The paper proposes a new cosegmentation algorithm which can segment and classify the foreground of different objects even if they do not appear in all images. In our work, an image is considered to contain several kinds of objects. Each object is composed of several object elements; therefore, each image can be expressed in terms of the combination of several object elements. Object elements with similar features could be grouped into one object-element cluster by using a density-clustering algorithm. Moreover, the density-clustering algorithm excludes a few object elements which do not have a sufficient number of similar object elements. During the segmentation process, we de-project the sub-object classes back to images. Observing the distribution of each sub-object classes, we select the appropriate classes as the segmented results through the selection criteria. In the work, an unsupervised multiple-object class framework is proposed, and the segmentation rate is enhanced by introducing the concept of independent object elements. A selection criterion is presented to relax the similar object constraint.
Recipe for secret housing

Method for bringing out the secret, hidden, shameful aspects of co-creation and collaboration and of the topic of work.
Application in Insulator Infrared Image Denoising with Dual-Tree Complex Wavelet Transform

In order to obtain a clear image and keep the details of the image as complete as possible, it has given an adaptive noise reduction method of insulator infrared image by using dual tree complex wavelet transform(DT-CWT) and Maximum likelihood estimation method based on local adaptive window(LAWML) method in this paper. At the same time, five other denoising methods are used to process the insulator thermal image with noise. The method has a good noise reduction effect on insulator infrared thermal image with low signal-to-noise ratio. It ensures the integrity of image information at the same time of noise reduction. It lays a foundation for the accuracy of the subsequent insulator pollution level identification.
Research on Image Reconstruction Algorithm Based on Complex Analysis Method

In order to improve the recognition ability of fuzzy sparse scattered feature distribution image, it is necessary to carry out spatial vision distributed reconstruction and image virtual reconstruction. A virtual reconstruction method of sparse scattered feature distribution image based on complex analysis method is proposed. The grid distribution model of sparse scattered feature distribution image is constructed, and the spatial information feature distributed reconstruction model of sparse scattered feature distribution image is carried out by using image sparse feature point extraction method. Combined with block feature detection and image segmentation method, the boundary region detection of sparse scattered feature distribution image is carried out, and the spatial visual distributed reconstruction model of sparse scattered feature distribution image is established. Combined with fuzzy structure reconstruction method, the image filtering and information fusion of sparse scattered feature distribution image are carried out. according to the texture and detail area of sparse scattered feature distribution image, the 3D texture structure and sparse evacuation scrambling point reconstruction of image are carried out, and the 3D reconstruction of image is carried out by complex analysis method, and the gray histogram of sparse scattered feature distribution image is reconstructed. Virtual reconstruction of sparse scattered feature distribution image is realized based on spatial vision distributed reconstruction. The simulation results show that the visual effect of virtual reconstruction of sparse scattered feature distribution image is better, and the quality of 3D image virtual reconstruction is higher.
PEX: Privacy-Preserved, Multi-Tier Exchange Framework for Cross Platform Virtual Assets Trading

In traditional virtual asset trading market, several risks, e.g. scams, cheating users, and market reach, have been pushed to users (sellers/buyers). Users need to decide who to trust; otherwise, no business. This fact impedes the growth of virtual asset trading market. In the past few years, several virtual asset marketplaces have embraced blockchain and smart contract technology to alleviate such risks, while trying to address privacy and scalability issues. To attain both speed and non-repudiation property for all transactions, existing blockchain-based exchange systems still cannot fully accomplish. In real-life trading, users use traditional contract to provide non-repudiation to achieve accountability in all committed transactions, so-called thorough non-repudiation. This is essential when dispute happens. To achieve similar thorough non-repudiation as well as privacy and scalability, we propose PEX, Privacy-preserved, multi-tier EXchange framework for cross platform virtual assets trading. PEX creates a smart contract for each virtual asset trading request. The key to address the challenges is to devise two-level distributed ledgers with two different types of quorums where one is for public knowledge in a global ledger and the other is for confidential information in a private ledger. A private quorum is formed to process individual smart contract and record the transactions in a private distributed ledger in order to maintain privacy. Smart contract execution checkpoints will be continuously written in a global ledger to strengthen thorough non-repudiation. PEX smart contract can be executed in parallel to promote scalability. PEX is also equipped with our reputation-based network to track contribution and discourage malicious behavior nodes or users, building healthy virtual asset ecosystem.
Research on motion capture of upper limb rehabilitation robot

Aiming at the safety and wearable coupling problems existing between the exoskeleton upper limb rehabilitation robot and the patient, especially the difference between the patient's arm posture and the exoskeleton rehabilitation robot's movement, a method based on MEMS sensor information fusion to monitor the arm movement posture was proposed. In order to express the position and attitude of the arm in space, the coordinate system model of the arm joint is constructed, and quaternions are used as the expression method of attitude solution. The hardware system uses the IIC bus to connect the sensor nodes and SPI bus to connect the sensor and microcontroller. Mahony algorithm is used to solve the attitude in the software system. Experimental results show that this algorithm can solve the pose information well, and the safety and motion coupling of the upper limb rehabilitation robot can be improved by monitoring the arm joint model.
Text Recommendation Based on Generalization Level of Objects

A reader sometimes requires a text containing more specific or general content based on current understanding on a certain field or topic. Traditional text recommendation systems barely meet the requirement because they recommend texts according to the relevance of semantics or topics. This paper proposes a text recommendation method that can provide texts with more specific or general content based on a user's currentunderstanding. Experiments show that the method is effective and useful in the kind of text recommendation.
Hierarchical Sparse Reconstruction Based Multi-feature Saliency for Target Detection in SAR Images

The utilization of clutter information is useful for target detection. However, the clutter statistical modeling, used in conventional constant false alarm rate (CFAR) methods, is usually difficult for a real synthetic aperture radar (SAR) image. And though intensity feature is basic for target detection, only using intensity feature is insufficient considering the speckle noise. To address these problems, we propose a novel hierarchical sparse reconstruction based multi-feature saliency method to detect targets in SAR images. The proposed method hierarchically generates saliency maps using sparse reconstruction based on clutter templates on instance feature and structural feature separately. The clutter templates sampled from the pure clutter SAR images contains the clutter information. Though the sparse reconstruction based on clutter templates, we can fully utilize clutter information without clutter statistical modeling. Moreover, the proposed method jointly uses the instance feature and the structure feature, making the saliency detection more robust to speckle noise. We evaluate the proposed method on the miniSAR real data. The experimental results demonstrate that the proposed method outperforms conventional SAR target detection methods in terms of the precision, recall and F1-score.
HDR Video System Based on Multi-Stage Motion Estimation Using Dual Exposure Camera

In this paper, we provide a new HDR video system using a dual exposure camera. In order to prevent ghost artifacts caused by misalignment of motions, multi-stage convolutional neural network (CNN)-based motion estimation is proposed. In the proposed method, true motions and occlusion maps are estimated, and then an HDR video is synthesized with motion compensated images. It provides not only robustness against exposure levels but also flexibility to extend easily search ranges by stacking stages with same CNN coefficients. Experimental results indicate that the proposed method is effective to estimate true motions so that ghost artifacts are eliminated well.
Complete Fix-Free Codes for the Statistical Dictionary-Based String Matching Problem

In the dictionary based string matching problem, the positions in which a short sequence of symbols, query, occurs in a longer sequence, source, have to be retrieved. To improve the query search efficiency, the source can be pre-processed to build an inverted index, that is a table storing positions of occurrences of certain substrings in the text. Given an inverted index, various algorithms can be utilized to retrieve the position of the query in the source. In the literature, motivated by text retrieval and web search applications, the small v/s small (SvS) algorithm is often considered for the case in which the substrings stored are k-gram codes, i.e. all possible substrings of length k. In [9] we have proposed a novel approach to the string matching problem by letting the strings generating the inverted index be a variable length code, i.e. posting code, to be optimally design for a given performance metric. Posting codes [9] generalize classic k-gram codes and enable new query retrieval algorithms with potentially lower complexity than the SvS algorithm. In this paper, we propose a new query retrieval algorithm, the forward-backward SvS (FB-SvS) algorithm which can be applied when the posting code is a complete fix-free code. The complexity of the FB-SvS algorithm for various fix-free codes is investigated and numerical experiments using the human DNA sequence as the source are presented.
“I See Myself, Therefore I Purchase”: Factors Influencing Consumer Attitudes Towards m-Commerce AR Apps

Mobile commerce (m-commerce) is starting to represent a significant share of e-commerce. The use of Augmented Reality (AR) by brands to convey information about their products—within the store and mainly as mobile apps—makes it possible for researchers and managers to understand consumer reactions. Although attitudes towards AR have been studied, the overall effect of distinct aspects such as the influence of others, the imagery, projection and perceived presence, has not been tackled as far as we know. Therefore, we conducted a study on 218 undergraduate students, using a pre-test post-test experimental design to address the following questions: (1) Do AR media characteristics affect consumer attitudes towards the medium in a mobile shopping context? Also, (2) Do the opinion and physical presence of people influence the attitude towards an m-commerce AR app? It found that AR characteristics such as projection and imagery positively influence attitudes towards m-commerce AR apps, whereas social variables did not have any influence.
Automatic Image Annotation using Conceptual Hierarchical Convolutional Neural Network

This paper explores the application of visual object detection by conceptual hierarchical convolutional neural network in automatic image annotation. To solve the image annotation due to the similarity of the object features cannot be classified perfectly, hypernyms tree with the semantic concept is used to enrich the recall rate. The development of CNN-based with conceptual object detect techniques provides another source about ISA relation with complementary concepts to ensure that the annotations are correct. The research method is divided into the two parts. The first part mainly introduces the multimodal learning neural network, which consist of visual model and hypernyms tree model. The visual model mainly carries out image content annotation using the R-CNN. We classify according to the features of the objects and propose a hierarchical labeling method. In order to make the system more effective in learning the image content, the system can detect the objects and understand the relationship between the objects. PASCAL VOC dataset is used to evaluate the proposed approach. According to the observation about the experimental results, the proposed approach outperforms traditional R-CNN and DPM approaches.
Using A New Approach in Deep Dictionary Learning to Handwriting Number Classification

Dictionary learning and sparse representation is a successful mathematical model for data representation that achieves state-of-the-art performance in various fields such as pattern recognition, machine learning, computer vision. The study aims to improve the classification performance of state-of-the-art methods by using a multi- layer framework. This paper presents the new idea of "multi- layered K-singular value decomposition (MLK-SVD)" dictionary learning as a multi-layer method of classification and is evaluated on MNIST dataset. The new idea is a multi- layer dictionary learning method for classification tasks. In order to learn better features, the frame uses a tag consistent in the K-SVD algorithm to learn a discriminative dictionary for sparse coding. We also include information on labels in addition to class labels for training data.
Sequence Data Enhancement Method Based on Knowledge Graph

To solve the problem of low recommendation accuracy caused by too little user behavior information in the current behavior recommendation system, an algorithm based on end-to-end data enhancement was proposed. In this paper, knowledge graph is constructed by learning and integrating structured knowledge network. Moreover, the characteristics of users with high preference similarity can be propagated through the inter-entity relations mapped by the knowledge map to reconstruct the preference vector of users. Through comparative experiments on open data sets, the AUC of RNN model, CNN model, RNN attention model and ATRank were improved by 3.28%, 2.35%, 2.89% and 1.30%, respectively.
Why Protocols Fail to Transition to Mobile Domains

Applying protocols to new settings for which they were not originally designed can result in failures and vulnerabilities. This problem becomes particularly severe when security protocols are concerned. As more and more novel technologies such as IoT are being put into practical use, the study of the above mentioned problems gains an increasing importance. This paper investigates the issues which arise when transitioning the OAuth protocol to the mobile phone setting. First, we informally discuss the issues arising during such transitioning. Then, we revist the formal analysis of the OAuth protocol presented by Fett et al. at ACM CCS 2017 [1], pinpointing the issues that may lead to vulnerabilities in the mobile scenario.
An Approach of Face Recognition Applied for Smarthome Using System – on – Chip Technology

The following topics are dealt with: learning (artificial intelligence); image classification; text analysis; support vector machines; feature extraction; convolutional neural nets; data mining; traffic engineering computing; Internet of Things; cryptography.
FIST-Toy: A Benchmark Dataset for Toy Component Classification

As a comprehensive technology of machinery, electronics and computer vision, industrial automation technology has been attracting extensive attention and developed rapidly recently years. The refinement of target classification plays an important role in the industrial automation, therefore, many computer vision based target classification methods have been proposed. However, there is few real industrial dataset can be used to evaluating the target classification algorithms. In this paper, we construct a novel toys dataset, named FIST-Toy, including 7411 images of 31 categories. Base on the proposed dataset, we conduct a comprehensive study of the state-of-the-art pretraining target classification models qualitatively and quantitatively. In addition, we propose a weight voting based classification neural network. By comparing with AlexNET, VggNet, Inception and ResNet, the proposed model outperforms in fine-grading. We evaluate the FIST-Toy dataset using various deep learning models. And the experimental results show that the FIST-Toy dataset provides a benchmark to evaluate the performance of different methods for target classification.
PENGARUH TINGKAT KEHADIRAN MAHASISWA TERHADAP HASIL BELAJAR KALKULUS MAHASISWA STIKOM TUNAS BANGSA

Abstract The purpose of this research was to determine the relationship and influence of student attendance rates on student calculus learning outcomes at STIKOM Tunas Bangsa Pematangsiantar. This type of research is quantitative research. The sampling technique in this research used a judgmental sampling technique. The sample in this research was the first semester students majoring in Information Engineering class 19T01, amounting to 46 students. In collecting data used instrument documentation and questionnaires. To find out the influence of it is used simple regression analysis. Regression analysis results obtained significance value 0.05 or 0.578 <0.05, which means that the data is linear patterned, with the regression formula is Y = 2.779 + 0.690X. Keyword s: attendance rate, learning outcome
Strategic Management of Emirates Airlines

This paper is primarily centered on the assessment of Emirates Airlines strategic management plan. In its scope, the paper looks into the various fundamental spheres that are vital for the successful formulation and implementation of Emirates Airlines strategic management. Also to provide an analysis and evaluation of the strategic management of Emirates Airlines and how this young company started with limited resources until reach in a position that became a major threat for the world's top airliners in the aviation sector. Emirates airlines build up a brand, and the travelers become loyal to this brand. The publication provides the present epoch outlook of the Airline as well as its chronological track to the current position. Apropos strategic management is, of course, composed of critical strategies. Hence, the publication does not shy away from illustrating the vital strategies employed by Emirates Airlines. The road to successful strategic management cannot be entirely smooth, and thus, the various barriers along the way are presented as the challenges that stood along the Emirates Airways course. Nevertheless, the research methodology that was used together with its primary tents and aspects is extensively epitomized for clarity and accuracy of the findings. More so, the research methodology employs the use of the Five Porter's Forces Model to analyze and understand the various factors of competition behind the development of Emirates Airlines. Also, SWOT is an analysis tool used to assess the internal environment, the external environment, and business performance. Types of strategic management are also another critical aspect of the publication.
Viewpoint-Free Photography for Virtual Reality

Viewpoint-free photography, i.e., interactively controlling the viewpoint of a photograph after capture, is a central challenge for real virtual reality (VR) experiences. In this chapter, we present algorithms that enable viewpoint-free photography from casual capture, i.e., footage easily captured with consumer cameras. We build on extensive work in image-based rendering, which often focuses on full or near-interpolation, where output viewpoints lie directly between captured images, or nearby. However, for 6-DOF VR experiences, it is essential to create viewpoint-free photos with a wide field-of-view and sufficient positional freedom to cover the range of motion a user might experience. We focus on two VR experiences: Overall, we provide evidence that viewpoint-free photography is feasible from casual capture—for both seated and room-scale VR experiences.
A Multi-scale Wavelet CNN for Scanning Electron Microscopy Nerve Image Super Resolution

Efficient acquisition of high-resolution SEM nerve images is an important part of brain science research. Because of the characteristics of SEM nerve image, the SR images obtained by current methods are too smooth and lack detailed information. We first analyze the multi-scale wavelet coefficients of SEM nerve images and find that when decomposed to the third scale, there is almost no structural information at high frequencies and the horizontal components are more obvious than the vertical ones. Based on these characteristics, an end-to-end full convolution neural network based on multi-scale wavelet is proposed. Firstly, the main structure of the network is constructed, which is divided into two modules: encoding and decoding. Then, the actual output of the network is changed to the prediction of multi-scale wavelet coefficients, and the final SR image is obtained by inverse transformation. Finally, multi-objective form is used to set hyper-parameters based on the wavelet characteristics of SEM nerve image. In terms of loss function, two-stage losses (gray-level loss and multi-scale wavelet coefficients loss) are used to emphasize the overall texture and the high-frequency details respectively. In addition, the inverse transformation can replace the deconvolution to reduce some network parameters. In the experiment, the relationship between SR effect and wavelet scale is analyzed. With the increase of wavelet scale, the performance of the model is not linearly enhanced, and the optimal wavelet scale and upscaling factor of the model are determined. Experiments show that, compared with other state-of-the-art methods, our method is more prominent and more realistic in the restoration of nerve structure texture.