Reviewerly/exHarmony huggingface dataset:
Detection of Real Time Objects Using TensorFlow and OpenCV

Detecting and recognizing objects in unstructured aswell as structured environments is one of the most challengingtasks in computer vision and artificial intelligence research.This paper introduces a new computer vision-based obstacledetection method for mobile technology and its applications.Each individual image pixel is classified as belonging either toan obstacle based on its appearance. The method uses a singlelens webcam camera that performs in real-time, and alsoprovides a binary obstacle image at high resolution. In theadaptive mode, the system keeps learning the appearance ofthe obstacle during operation. The system has been testedsuccessfully in a variety of environments, indoors as well asoutdoors, making it suitable for all kinds of hurdles. It alsotells us the type of obstacle which has been detected by thesystem.
3D Automated Breast Ultrasound System: Comparison of Interpretation Time of Senior Versus Junior Radiologist

Objective: This study aimed to compare the automated breast ultrasound system (ABUS) reading time of breast radiologist to a radiology resident independent of the clinical outcomes. Materials and Methods:One hundred women who underwent screening ABUS between July and August 2017 were reviewed retrospectively.Each study was examined sequentially by a breast radiologist who has more than 20 years of experience in breast radiology and third year resident who has 6 months of experience in breast radiology.Data were analyzed with Spearman' correlation, Wilcoxon Signed Ranks Test and Kruskal-Wallis Test and was recorded. Results:The mean age of patients was 42.02±11.423years (age range16-66).The average time for senior radiologist was 223.36±84.334seconds (min 118 max 500 seconds).The average time for junior radiologist was 269.48±82.895seconds (min 150 max 628 seconds).There was a significant difference between the mean time of two radiologists (p=0.00001).There was a significant difference regarding the decrease in the reading time throughout study with the increase of number of cases read by the breast radiologist (p<0.05);but not with the resident radiologist (p=0.687).There was a correlation between BI-RADS category and reading time for both the breast radiologist and the resident (p=0.002,p=0.00043 respectively) indicating that patients who had findings caused longer reading times. Conclusion:ABUS reading time may differ according to the experience of the user, however the times of an experienced and non-experienced user is comparable.
Query-by-sketch image retrieval using homogeneous painting style characterization

As an effective and efficient way to graphically portray ideas and concepts, free-hand sketches are playing an increasingly significant role in today’s image retrieval systems. There are many factors that contribute to a successful sketch-based image retrieval (SBIR) system, but perhaps the most essential ones are the design of powerful image feature descriptors and effective perceptual similarity metrics in the feature space. However, the painting experiences and styles of users vary hugely, which poses a great challenge for consistently accurate feature representation and matching. We introduce an approach for SBIR by characterizing homogeneous sketch drawing styles. Specifically, given a sketch query, the proposed method first infers the user’s painting style from it by analyzing contour features. Then, top K-nearest sketches of historical users with similar drawing characters are identified from the database. Finally, the matched images of the query can be obtained by reranking those of the K-nearest sketches stored in the historical recordings. To evaluate the effectiveness of the proposed method, we conducted quantitative comparisons with several peer SBIR methods. Experimental results indicate that the proposed method is 15% higher than state-of-the-art in terms of mean average precision.
The Harvest Coach Architecture: Embedding Deviation-Tolerance in a Harvest Logistic Solution

We introduce a deviation-tolerance software architecture, which is devised for a prototype of a cloud-based harvest operation optimisation system issuing harvest plans. The deviation-tolerance architecture adapts the fault tolerance notions originating in the area of systems engineering to the harvest domain and embeds them into the Vienna developed method (VDM) model at the core of our harvest logistics system prototype. The fault tolerance supervision/execution level architecture is framed under the notion of an “harvest coach” which diagnoses deviations to the planned operations using “harvest deviation monitors” and deploys a novel “plan” (controller) that mitigates the encountered “deviation” (fault). The architecture enabled the early start of field experiments of the harvest logistics system prototype, which lead to the validation/refutation of early design stage assumptions on the diverse system components behaviours and capabilities. For instance, we casually found discrepancies in the arithmetic precision of open-source libraries used in the conversion of vehicle positioning coordinates, we assessed the maturity of the frameworks used to develop the field user interfaces, and we calibrated the level of system-operator interactivity when deviations occurs. The obtained results indicate that the architecture may have a positive impact in the context of developing systems featuring intrinsic human-driven deviations which require mitigation.
Paralympic VR Game

In the last few years, the interest in virtual reality has been increasing partially due to the emergence of cheaper and more accessible hardware, and the increase in content available. One of the possible applications for virtual reality is to lead people into seeing situations from a different perspective, which can help change beliefs. The work reported in this paper uses virtual reality to help people better understand paralympic sports by allowing them to experience the sports' world from the athletes' perspective. For the creation of the virtual environment, both computer-generated elements and 360 video are used. This work focused on wheelchair basketball, and a simulator of this sport was created resorting to the use of a game engine (Unity 3D). For the development of this simulator, computer-generated elements were built, and the interaction with them implemented. User studies were conducted to evaluate the sense of presence, motion sickness and usability of the system developed. The results were positive although there are still some aspects that should be improved.
THE EFFECTIVENESS OF FLASHCARDS ON THE MOTIVATION TO INCREASE ENGLISH VOCABULARY AMONG THE FOURTH ELEMENTARY SCHOOL

The primary objective of this study is to find out whether the illustrated flashcards have positive effect on the motivation to increase English vocabulary among elementary school fourth grade students. The problem of this study is why students of elementary school fourth-grade still find difficulties in understanding and comprehending the English material and especially that for vocabulary. The result of the analysis shows that there is a significance difference between the motivation to learn English vocabulary among the elementary school fourth-grade students before the implementation of flashcards and that after the implementation (76,6%), and there is a significant difference between English vocabulary mastery of elementary school fourth-grade students taught by using flashcards and that of those taught without using flashcards(t=7,74).
Stereo Matching Confidence Learning Based on Multi-modal Convolution Neural Networks

In stereo matching, the correctness of stereo pairs matches, also called confidence, is used to improve the dense disparity estimation result. In this paper, we propose a multi-modal deep learning approach for stereo matching confidence estimation. To predict the confidence, we designed a Convolutional Neural Network (CNN), which is trained on image patches from multi-modal data, namely the source image pairs and initial disparity maps. To the best of our knowledge, this is the first approach reported in the literature combining multiple modality and patch based deep learning to predict the confidence. Furthermore, we explore and compare the confidence prediction ability of multiple modality data. Finally, we evaluate our network architecture on KITTI data sets. The experiments demonstrate that our multi-modal confidence network can achieve competitive results while compared with the state-of-the-art methods.
Comparative Analysis of Illumination Normalizations on Principal Component Analysis Based Feature Extraction for Face Recognition

Principle Component Analysis (PCA) is an appearance-based technique for extraction of feature extraction that is commonly used in computer vision and image processing. This technique suffers from illumination variations, thus knowing which illumination control method to be used in PCA-based face recognition system is very important. This paper applies three selected normalization techniques; Discrete Cosine Transform (DCT), Adaptive Histogram Equalization (AHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) to normalize face images. PCA was further used to extract features from the normalized face images. Euclidean distance was used to classify extracted features. The best recognition accuracy of 91.84% was obtained in DCT for ORL Database, while the best accuracy of 76% was achieved in DCT for FERET Database. The highest FAR of 0.9 was achieved in DCT for ORL Database, while the highest FAR of 0.5 was obtained in DCT and AHE for FERET Database. The highest FRR of 0.2821 was achieved in CLAHE for ORL Database, while 0.3000 was obtained in AHE for FERET Database. It was concluded that illumination control approaches have predominant effect on PCA–based facial recognition system. Keywords— Adaptive Histogram Equalization, Contrast Adaptive Histogram Equalization, Discrete Cosine Transform Illumination Normalization, Principal Component Analysis
Effectiveness Management of Qualitative Research in Writing Scientific Papers

Good scientific work must have data that is not surprising or real facts. In managing this work, significant research methods are needed. However, some management uses in research are still not maximal and do not know the limitations of qualitative research. For example, are there researchers who do not yet know clearly what forms of management will be qualitative. Qualitative forms are often referred to as in accordance with quality. In this study will discuss the management of qualitative, qualitative, qualitative, and qualitative research. The existence of this study is expected to be used in research as well as using qualitative research procedures.&#x0D; Keywords: management, qualitative research, perspective.
Dynamic Immersive Visualisation Environments: Enhancing Pedagogical Techniques

As the speed of technical and scientific innovation accelerates past the speed at which humans can learn, the need for innovative pedagogical environments and techniques, such as immersive visualization environments, becomes essential and apparent. The primary purpose of this research is to explore the effectiveness immersive visualization environments may provide in combination with rich resources available on the Internet. An immersive visualization environment was designed and developed, and then an experimental research model was considered and conducted accordingly. Collected data were subjected to appropriate statistical analysis. The results showed a statistically significant difference in pedagogical outcomes when using an immersive visualization environment compared with traditional educational techniques. Immersive visualization environment models and experiments showed significant improvement in the effectiveness and efficiency of pedagogical techniques, enhancing the learning and teaching of abstract and complex computing concepts.
Electronic Health Record System using Blockchain

This paper deals with the Electronic Health Records for storing information of the patient which consist of the medical reports. Electronic Health Records (EHRs) are entirely controlled by Hospitals instead of patients, which complicates seeking medical advices from different hospitals. In the existing system of storing details of the patients are very dependent on the servers of the organization. In the proposed all the information of the patient are stored in the blockchain by using the Metamask and these details are stored in the block chain as a blocks of data. Each block consists of the data which is encrypted data. Electronic Health Record (EHR) systems record health-related information on an individual so that it can be consulted by clinicians or staff for patient care. The data is encrypted by the algorithm known as SHA-256 which is used to encrypt all the data of the patients into a single line 256 bit encrypted text which will be stored in the block at etherscan. These records for not only useful for the consultation but also for creation of historic family health information tree that keeps track of genetic health issues and diseases it can also be used for any health service with the authorization from both the patient and medical organization.
H-ORAM

Oblivious RAM (ORAM) is an effective security primitive to prevent access pattern leakage. By adding redundant memory accesses, ORAM prevents attackers from revealing the patterns in the access sequences. However, ORAM tends to introduce a huge degradation on the performance. With growing address space to be protected, ORAM has to store the majority of data in the lower level storage, which further degrades the system performance.
Lossy Source Coding via Deep Learning

Motivated by a recent work of learning rate distortion approaching posterior via Restricted Boltzmann Machines, we generalize the result to Deep Belief Networks and propose a deep learning based lossy compression for stationary ergodic sources. The compression algorithm consists of two stages, a training stage, which is to learn the posterior with the training data of the same class as the source, and a compression/reproduction stage, which consists of a lossless compression and a lossless reproduction. The theoretical result shows that our algorithm asymptotically achieves the optimum rate-distortion function for stationary ergodic sources, and the experimental results outperform the reported best results.
Adversarial learning for multi-view network embedding on incomplete graphs

Network embedding, as a promising way of node representation learning, is capable of supporting various downstream network mining tasks, and has attracted growing research interests recently. Existing approaches mostly focus on learning the low-dimensional node representations by preserving the local or global topology information of a static network. It is difficult for such methods to learn desirable features for the nodes in an incomplete graph whose topology information is sparse or the new nodes in a dynamic graph. It is also challenging for them to deeply incorporate node attributes as the complementary information to improve the network embedding performance. To this end, in this paper we propose a Multi-View Adversarial learning based Network Embedding model named MVANE to deeply fuse the network topology information and node attributes to better perform network embedding on incomplete graphs. The insight is that the network topology and the node attributes are treated as two correlated views. The learned embedding vector of a node should be able to reveal its unique characteristics in both views. Specifically, the adversarial autoencoder is introduced as the basic model of MVANE. Autoencoder can learn a projection function to directly map the input feature vectors into the latent space, which ensures the MVANE learn embeddings for the new nodes through features projection without the need of retraining the model. Meanwhile, the adversarial learning strategy is also applied to better capture the cross-view correlations. The idea is that the learned embeddings in one view can not only reconstruct the inputs in this view, but also generate the features in another view. Under a unified learning framework, the latent representations in different views are fused and jointly reinforced by the proposed self/cross-view learning model. Empirically, we evaluate MVANE over multiple network datasets, and the results demonstrate the superiority of our proposal.
Performance Prediction of GPU-based Deep Learning Applications

Recent years saw an increasing success in the application of deep learning methods across various domains and for tackling different problems, ranging from image recognition and classification to text processing and speech recognition. In this paper we propose and validate an approach to model the execution time for training convolutional neural networks (CNNs) deployed on GPGPUs. We demonstrate that our approach is generally applicable to a variety of CNN models and different types of G PG PU s with high accuracy, aiming at the preliminary design phases for system sizing.
English for Ecotourism and Its Sustainability with Augmented Reality Technology

When it comes to traveling, more and more people are becoming interested in having profound experiences to the places they are visiting and the local inhabitants. However, there currently very few tour guides in Taiwan who are well equipped to promote preservation of the environment and sustainable development. In addition, Taiwanese students interested in the field have been found to rarely talk with foreigners about topics related to ecology or environmental protection even though they are popular issues around the world. This study centres on English for ecotourism that is supplemented by comprehensive project-based learning (CPBL) and augmented reality (AR) technology to explore how teaching such a course using AR technology impacts English for specific purposes (ESP) learning and sustainable development. Two classes of ninety-nine college students in total participated in the study. The research also involved a survey, comprised of three sets of questionnaires concerning student satisfaction with AR application, CPBL, and ESP learning. An independent t-test and an analysis of variance were completed to examine the variables of gender, class, and English proficiency level to understand the significance of student satisfaction. The results found that more tourists chose the AR versions of the brochures than the general versions. Satisfaction between both foreign language classes regarding CPBL and ESP learning was significant. Across the three different English proficiency levels, the mean level of student satisfaction for all three variables was highest in the high proficiency group. This study reveals that adopting an AR approach for CPBL and ESP learning could better achieve the goals of ESP teaching and sustainable development than the traditional English teaching model based on in-class lectures.
Deep coupling neural network for robust facial landmark detection

Facial landmark detection aims at locating a sparse set of fiducial facial key-points. Two significant issues (i.e., Intra-Dataset Variation and Inter-Dataset Variation) remain in datasets which dramatically lead to performance degradation. Specifically, dataset variations will lead to severe over-fitting easily and perform poor generalization in recent in-the-wild datasets which severely harm the robustness of facial landmark detection algorithm. In this study, we show that model robustness can be significantly improved by leveraging rich variations within and between different datasets. This is non-trivial because of the serious data bias within one certain dataset and inconsistent landmark definitions between different datasets, which make it an extraordinarily tough task. To address the mentioned problems, we proposed a novel Deep Coupling Neural Network (DCNN), which consists of two strong coupling sub-networks, e.g., Dataset-Across Network (DA-Net) and Candidate-Decision Network (CD-Net). In particular, DA-Net takes advantage of different characteristics and distributions across different datasets, while CD-Net makes a final decision on candidate hypotheses given by DA-Net to leverage variations within one certain dataset. Extensive evaluations show that our approach dramatically outperforms state-of-the-art methods on the challenging 300-W and WFLW dataset.
Fuzzy Localization of Steganographic Flipped Bits via Modification Map

Adaptive steganography has become unprecedentedly prevalent compared with non-adaptive ones due to its remarkable performance when resisting modern steganalysis.It prefers hiding bits into pixels from texture regions as such modification is considerably difficult to detect.Current steganalysis capable of locating steganographic payload has only been investigated in the non-adaptive domain, while the works of locating hidden bits modified by adaptive steganographic algorithms have not been studied yet.In this paper, we propose a novel algorithm to locate flipped pixels modified by adaptive steganography in the spatial domain.By re-embedding randomly generated messages upon one single image, we observe that adaptive steganographic methods are prone to modify pixels in the same region, namely texture region.Such property straightforward inspires us to re-embed a random message at the same relative payload into the stego image to obtain the modification map.Then, we extend the modification map with a given margin to locate the modified pixels.The extensive experiments have verified the effectiveness of our designed algorithm in locating flipped pixels modified by the adaptive steganography in the spatial domain.
Quantum Approximate Optimization with a Trapped-Ion Quantum Simulator

Quantum computers and simulators may offer significant advantages over theirclassical counterparts, providing insights into quantum many-body systems andpossibly solving exponentially hard problems, such as optimization andsatisfiability. Here we report the first implementation of a shallow-depthQuantum Approximate Optimization Algorithm (QAOA) using an analog quantumsimulator to estimate the ground state energy of the transverse field Isingmodel with tunable long-range interactions. First, we exhaustively search thevariational control parameters to approximate the ground state energy with upto 40 trapped-ion qubits. We then interface the quantum simulator with aclassical algorithm to more efficiently find the optimal set of parameters thatminimizes the resulting energy of the system. We finally sample from the fullprobability distribution of the QAOA output with single-shot and efficientmeasurements of every qubit.
Machine Translation Evaluation Metric Based on Dependency Parsing Model

Most of the syntax-based metrics obtain the similarity by comparing the sub-structures extracted from the trees of hypothesis and reference. These sub-structures cannot represent all the information in the trees because their lengths are limited. To sufficiently use the reference syntax information, a new automatic evaluation metric is proposed based on the dependency parsing model. First, a dependency parsing model is trained using the reference dependency tree for each sentence. Then, the hypothesis is parsed by this dependency parsing model and the corresponding hypothesis dependency tree is generated. The quality of hypothesis can be judged by the quality of the hypothesis dependency tree. Unigram F-score is included in the new metric so that lexicon similarity is obtained. According to experimental results, the proposed metric can perform better than METEOR and BLEU on system level and get comparable results with METEOR on sentence level. To further improve the performance, we also propose a combined metric which gets the best performance on the sentence level and on the system level.