Reviewerly/exHarmony huggingface dataset:
Multi-Instance Sketch to Image Synthesis With Progressive Generative Adversarial Networks

Real-world images usually contain multiple objects, as a result, generating an image from a multi-instance sketch is an attractive research topic. However, existing generative networks usually produce a similar texture on different instances for those methods focus on learning the distribution of the whole image. To address this problem, we propose a progressive instance texture reserved generative approach to generate more convincible images by decoupling the generation of the instances and the whole image. Specifically, we create an instance generator to synthesize the primitive color distribution and the detailed texture for each instance. Then, an image generator is designed to combine all of these instances to synthesize an image retaining texture and color. Besides, to generate more significant details, such as eyes, ears, and so on, we propose a novel technique called discriminative sketch augmentation, which can provide structural constraint by obtaining the sketch of the discriminative region. The extensive experiments demonstrate that our model not only generates convincing images but also achieves higher inception score and lower Fréchet Inception Distance on the MS-COCO dataset.
Using Fractional Latent Topic to Enhance Recurrent Neural Network in Text Similarity Modeling

Recurrent neural networks (RNNs) have been widely used in text similarity modeling for text semantic representation learning. However, referring to the classical topic models, a text contains many different latent topics, and the complete semantic information of the text is described by all the latent topics. Previous RNN based models usually learn the text representation with the separated words in the text instead of topics, which will bring noises and loss hierarchical structure information for text representation. In this paper, we proposed a novel fractional latent topic based RNN (FraLT-RNN) model, which focuses on the text representation in topic-level and largely preserve the whole semantic information of a text. To be specific, we first adopt the fractional calculus to generate latent topics for a text with the hidden states learned by a RNN model. Then, we propose a topic-wise attention gating mechanism and embed it into our model to generate the topic-level attentive vector for each topic. Finally, we reward the topic perspective with the topic-level attention for text representation. Experiments on four benchmark datasets, namely TREC-QA and WikiQA for answer selection, MSRP for paraphrase identification, and MultiNLI for textual entailment, show the great advantages of our proposed model.
An Introductive Training for Welding Workshop: A Biometric Evaluation Using Virtual Reality Scenes to Improve Practice

Welding is one of the most used process in manufacturing industry. Training in this process demands a long period of time and involves several resources such as specific facilities, metal pieces, energy, electrodes, gases, etc. Evaluations requires those training conditions, which every day are needed in welding industry. At the same time, this includes the safety understandings that welding training implies. Considering these situations, this work proposes a Virtual and Augmented Reality cabin, for evaluation and training in welding process. The method used includes understandings of safety, knowledge of workshop elements, welding tools identification and practical exercises to test several welding skills. Testing was applied to an experimental group who trained using the virtual welding tool, allowing the study of cognitive effects caused by virtual training before any previous real practice; there was also was a control group which trained only with real practices. By using the biometrical device EPOC of Emotiv, concentration and stress in both groups while doing the first welding real practice was tested. Experimental group showed less stress and more concentration than control group.
Machine Comprehension of Spoken Content: TOEFL Listening Test and Spoken SQuAD

A user can scan through a text easily, but it is not the case for spoken content, because they cannot be directly displayed on-screen. As a result, accessing large collections of spoken content is much more difficult and time-consuming than doing so for the text content. It would therefore be helpful to develop machines that understand spoken content. In this paper, we propose two new tasks for machine comprehension of spoken content. The first is a listening comprehension test for TOEFL, a challenging academic English examination for English learners who are not the native English speakers. We show that the proposed model outperforms the naive approaches and other neural network based models by exploiting the hierarchical structures of natural languages and the selective power of attention mechanism. For the second listening comprehension task - spoken SQuAD - we find that speech recognition errors severely impair machine comprehension; we propose the use of subword units to mitigate the impact of these errors.
A Bibliometric Perspective on Technology-Driven Innovation in the Gulf Cooperation Council (GCC) Countries in Relation to Its Transformative Impact on International Business

This chapter presents a novel scientific research landscape of the Gulf Cooperation Council (GCC) in order to access the research productivity, scholarly impact, and international collaborations across all GCC countries over the time period of 2008–2018, using the Scopus database. While we observe a significant increase in investing the resources in GCC world in order to build research infrastructure to make shift from only oil producing countries to knowledge-based economies, not enough efforts have been done to measure the outcomes of these investments. The chapter explores different thematic areas that have evolved over a decade related to TDI, top scientists, and top institutions in GCC. Further, the study investigates international collaboration networks within or outside GCC to better understand the venues of knowledge exchange with GCC countries. It is expected that the findings of this case study would provide an insight to the research landscape of the GCC and useful information to the scientific community as well as to the technology and innovation policymakers.
High-Order Generalized Orderless Pooling Networks for Synthetic-Aperture Radar Scene Classification

Fixed coding style in bag of visual words (BOVW) model and strong spatial information in convolutional neural network (CNN) feature representation make the feature vector less adaptable for scene classification. With the purpose of extracting the learnable orderless feature for SAR scene classification, the high-order generalized orderless pooling network trained by backpropagation is proposed for learning the high-order vector of locally aggregated descriptors (VLADs) and locality constrained affine subspace coding (LASC), compared with the first-order feature coding style, the proposed network could learn high-order coding features by outer product automatically. Subsequently, for making the feature representation more powerful, the matrix normalization (square root) whose gradients are computed via singular value decomposition (SVD) and elementwise normalization are introduced into the proposed network. Finally, experiments on the SAR scene classification data set from TerraSAR-X image show the proposed networks achieve better performance than the state-of-the-art approaches.
PERC-An Emotion Recognition Corpus for Cognitive Poems

There are several emotion detection systems to identify emotions from speech, gestures, and text (blogs, newspapers, stories and medical reports). Since such systems do not exist for poetry, we take the first step in building a system to recognize emotions in poetry by constructing a benchmark corpus, the PERC (Poem Emotion Recognition Corpus), of poems written by Indian poets in English. This novel corpus is based on the Navarasa (nine emotions), described in the Natya Shastra, and is annotated by experts. Experimental results obtained using the Fleiss kappa measurement and further analyzed by the central-limit theorem prove that the PERC is indeed a benchmark corpus revealing excellent inter-annotator reliability. This novel corpus is publicly available for the research in computational poetry.
Implementation of Augmented Reality Globe in Teaching-Learning Environment

The purpose of the present study is to investigate the effect of Augmented Reality emerging technology into teaching-learning process. Augmented Reality, a technology that delivers information in form of text, audio, visual and 3D animated data engages learner's visual, auditorily, and kinesthetic learning styles. In this study "AR Globe" project is proposed to reveal learners attitude towards AR content integration into existing learning environment that can enhance learner's VAK learning styles.
Efficient Incremental Training for Deep Convolutional Neural Networks

While the deep convolutional neural networks (DCNNs) have shown excellent performance in various applications, such as image classification, training a DCNN model from scratch is computationally expensive and time consuming. In recent years, a lot of studies have been done to accelerate the training of DCNNs, but most of them were performed in a one-time manner. Considering the learning patterns of the human beings, people typically feel more comfortable to learn things in an incremental way and may be overwhelmed when absorbing a large amount of new information at once. Therefore, we demonstrate a new training schema that splits the whole training process into several sub-training steps. In this study, we propose an efficient DCNN training framework where we learn the new classes of concepts incrementally. The experiments are conducted on CIFAR-100 with VGG-19 as the backbone network. Our proposed framework demonstrates a comparable accuracy compared with the model trained from scratch and has shown 1.42x faster training speed.
Secret Multiple Share Creation with Color Images using Visual Cryptography

Visual cryptography technique which allows the visual information to be confidential. Visual Cryptographic technique can be applied over those visual information or images before transmission. In presented method is plant for the share created, it is encrypted individually by using AES algorithms. The input image is improved to the RGB color code forms. The proposed method is also used to generate the share creation techniques. The proposed method is created to RGB images and then share1, share2 created using blowfish algorithm. The proposed method is RGB image, shares Encrypted and Decrypted to stacked image and then same as the original image. The testing results of the proposed system are compared to the share generation technique. The planned algorithm is designed and implemented with Matlab coding.
Automated Face Detection, Recognition and Gender Estimation Applied to Person Identification

Face detection, recognition and gender estimation are one of the most significant research areas in computer vision, not only because of the challenging nature of faces as an object but also due to the countless applications that require the application of face detection, tracking and recognition.Although many significant types of research on face detection, recognition and gender estimation problems have done in the last few years separately, there is no particular research on face detection, recognition and gender estimation together from a real-time video for person identification.So, we feel that these types of significant research are still needed to work.The main contributions of our paper are divided into three parts, namely face detection, recognition and gender estimation for person identification.In our research work, we use Local Binary pattern Histogram (LBPH) method and Convolution Neural Network (CNN) to extract the facial features of face images whose computational complexity is very low.By calculating the Local Binary Patterns Histogram (LBPH) neighborhood pixels and Convolution levels, we extract effective facial feature to realize face recognition and gender estimation.We show the experimental results using these methods to recognize face and gender for person identification.CNN increase the calculating speed of testing real-time video and also improve the recognition rate.By using LPBH, we get 63% accuracy on average where CNN gives 99.88% training accuracy for face recognition-1 and 96.88% accurate for gender estimation-1 and 100% training accuracy for face recognition-2 and 93.38% training accuracy for gender estimation-2.However, Convolution Neural Networks (CNN) learns fast and predict efficiently.
Boolean Combination of Circular Arcs using Orthogonal Spheres

An orthogonal sphere representation of arcs on spatial circles can be used to compactly perform Boolean combinations of such arcs. We formulate this using conformal geometric algebra, of which the oriented nature allows both minor and major arcs to be treated. Easily computable quantities discriminate the cases of relative positions. An application in the first stages of a problem in Discretizable Molecular Distance Geometry is included. We give a suggestion on how to extend this characterization by orthogonal spheres to the manifolds of arcs in the subsequent stages, using probabilistic eigenspheres of the distributions.
Augmented Reality Engine Applications: A Survey

Augmented reality is currently upgrading technology where the main problem is pose tracking. When the device is static, pose tracking is not required. But when working with mobile devices, pose tracking is required to track user movements. In pose tracking, an application has to identify features from the given real-time video frames and mapping of those features between consecutive frames. After that, the model is used to render virtual content for augmented reality. Feature extraction and model detection are difficult to apply in the mobile device where a user has low computing power as well as low battery backup. To overcome this problem, there are many algorithms available which can provide effective feature tracking for the mobile environment and also uses less CPU power. Therefore augmented reality is somehow possible in mobile devices. But still, there is an accuracy issue related to model detection in augmented reality. In this paper, we have analyzed different augmented reality engines, their drawbacks, and possible improvements.
DHGAN: Generative adversarial network with dark channel prior for single‐image dehazing

Summary Image dehazing technology has attracted much interest in the field of image processing. Most existing dehazing methods based on neural networks are inflexible and do not consider the loss in haze‐related feature space. They sacrificed texture details and perceptual characteristics in images. To overcome these weaknesses, we propose an image‐to‐image dehazing model based on generative adversarial networks (DHGAN) with dark channel prior. The DHGAN takes a hazy image as input and directly outputs a haze‐free image by applying a U‐net‐based generator. In addition to pixelwise loss and perceptual loss, we introduce dark‐channel‐minimizing loss to constrain the generated images to the manifold of natural images, thus leading to better texture details and perceptual properties. Comparative experiments on benchmark images with several state‐of‐the‐art dehazing methods demonstrate the effectiveness of the proposed DHGAN.
Passive Video Forgery Detection Considering Spatio-Temporal Consistency

This paper proposes a method for detecting forged objects in videos that include dynamic scenes such as dynamic background or non-stationary scenes. In order to adapt to dynamic scenes, we combine Convolutional Neural Network and Recurrent Neural Network. This enables us to consider spatio-temporal consistency of videos. We also construct new video forgery databases for object modification as well as object removal. Our proposed method using Convolutional Long Short-Term Memory achieved Area-Under-Curve (AUC) 0.977 and Equal-Error-Rate (EER) 0.061 on the object addition database. We also achieved AUC 0.872 and EER 0.219 on the object modification database.
Dual Watermarking in Tele-radiology using DWT for Data Authentication and Security

Technological development in the field of digital enhancement and computer access has echoed rapid change in simplifying medical imaging and maintaining hospital information system (HIS), including radiology information system (RIS). Digital watermarking is a promising technique which hides the information like images, video or audio on a digital support to provide authentication control, copyright protection, integrity verification of the owner. At earlier stage, cryptography, digital watermarking and steganography methods were used to secure the data transmission over Internet. Tele-radiology is one of the successful medical health services. Although it enabled effective practice of medical service, it has several security issues. In this paper, we intend to develop a novel and proficient implementation of an image watermarking system based on Discrete Wavelet Transform (DWT) for medical data. The approach includes Least Significant Bit (LSB) substitution for finger print encryption and LSB along with block cipher algorithm for medical data encryption. Verification of fingerprint eludes legal disputes like fake users, misuse of data, image retention etc. Compared to the conventional SVD-based watermarking method experimental results of the proposed method based on DWT validate secured transmission for multiple documents of medical data by providing privacy, integrity and authenticity. The proposed method shows better PSNR value and is robust. The proposed strategy is achieved using MATLAB software tool.
A Novel Shape Representation Method for Complex Trademark Image

Trademark orthogonal representation is an important research field in artificial intelligence technology and has extensive applications in trademark retrieval and recognition. However, the Gibbs phenomenon arises when complex trademark shapes are represented by using traditional methods, such as continuous orthogonal function and wavelet. In order to represent the complex trademark without Gibbs phenomenon, a novel method named hierarchical V-system (HV system) is proposed in this study, which is generated by V-system of multi-degree k (k = 0, 1, 2, 3). The hierarchical structure brings more detailed shape representation information. We show that the proposed method in this paper can represent the trademark shapes with fewer finite terms and obtain accurate representation result. This study also proposes the normalized descriptors of hierarchical V-system (HV descriptors). Furthermore, we demonstrate that the HV descriptors satisfy the invariance in rotation, translation, and scale transform. The experimental results show that the hierarchical V-system method can give reasonable descriptors for representing complex trademark without Gibbs phenomenon.
Solving Quantum Channel Discrimination Problem With Quantum Networks and Quantum Neural Networks

Distinguishing one quantum channel from another becomes a next natural question following the advent of quantum state discrimination.Here, we address the problem of quantum channel discrimination with the separable states and the entangled states input.Two schemes, based on the simulator for quantum networks and channels framework (SQUANCH) and the quantum machine learning toolbox (QMLT), are proposed to perform the quantum channel discrimination task.In the first scheme, we give an example of several qubit channels, verify that the performance metrics of channel discrimination can be improved by the entangled states.In the second scheme, we train a quantum circuit learning model to classify quantum channels using the Adam stochastic optimization algorithm.Although we consider the distinguishability in the case of only two possible channels, this model can be extended to any number of channels.This method can provide a good solution for distinguishing some complex quantum channels.
Heritage image annotation via collective knowledge

The automatic image annotation can provide semantic illustrations to understand image contents, and builds a foundation to develop algorithms that can search images within a large database. However, most current methods focus on solving the annotation problem by modeling the image visual content and tag semantic information, which overlooks the additional information, such as scene descriptions and locations. Moreover, the majority of current annotation datasets are visually consistent and only annotated by common visual objects and attributes, which makes the classic methods vulnerable to handle the more diverse image annotation. To address above issues, we propose to annotate images via collective knowledge, that is, we uncover relationships between the image and its neighbors by measuring similarities among metadata and conduct the metric learning to obtain the representations of image contents, we also generate semantic representations for images given collective semantic information from their neighbors. Two representations from different paradigms are embedded together to train an annotation model. We ground our model on the heritage image collection we collected from the library online open data. Annotations on the heritage image collection are not limited to common visual objects, and are highly relevant to historical events, and the diversity of the heritage image content is much larger than the current datasets, which makes it more suitable for this task. Comprehensive experimental results on the benchmark dataset indicate that the proposed model achieves the best performance compared to baselines and state-of-the-art methods.
Combining Meta-Graph and Attention for Recommendation over Heterogenous Information Network

Recently heterogeneous information network (HIN) has gained wide attention in recommender systems due to its flexibility in modeling rich objects and complex relationships. It's still challenging for HIN based recommenders to capture high-level structure and fuse the mined features of users and items effectively. In this paper, we propose an approach for the recommendation over HIN, called MGAR, which combines Meta-Graph and Attention to address the challenge. Informally speaking, meta-graph is applied to feature extraction, so as to capture more semantic information, while the attention mechanism is used to fuse the features arising from different meta-graphs. MGAR can be divided into two stages. In the first stage, we apply the matrix factorization technique to generate latent factors based on predefined meta-graphs. In the second stage, the embeddings of users and items are fused with the neural attention mechanism. And then the deep neural network is employed to make recommendations by modeling complicated interactions. Experiments over two real datasets indicate MGAR achieves state-of-the-art performance.