Reviewerly/exHarmony huggingface dataset:
Face-Based Age and Gender Classification Using Deep Learning Model

Age and gender classification of human's face is an important research focus, having many application areas. Recently, Convolutional Neural Networks (CNNs) model has proven to be the most suitable method for the classification task, especially of unconstrained real-world faces. This could be as a result of its expertise in feature extraction and classification of face images. Availability of both high-end computers and large training data also contributed to its usage. In this paper, we, therefore, propose a novel CNN-based model to extract discriminative features from unconstrained real-life face images and classify those images into age and gender. We approach the large variations attributed to those unconstrained real-life faces with a robust image preprocessing algorithm and a pretraining on a large IMDb-WIKI dataset containing noisy and unfiltered age and genders labels. We also adopted a dropout and data augmentation regularization method to overcome the risk of overfitting and allow our model generalize on the test images. We show that well-designed network architecture and properly tuned training hyperparameters, give better results. The experimental results on OIU-Adience dataset confirm that our model outperforms other studies on the same dataset, showing significant performance in terms of classification accuracy. The proposed method achieves classification accuracy values of 84.8% on age group and classification accuracy of 89.7% on gender.
An Expert System To Diagnose Herpes Zoster Disease Using Bayes Theorem

Computers are often used as a tool that can give conclusions to a disease so that they can help the community or health practitioners. The expert system is a field of science that can process the knowledge of an expert into a computer-based system that can be used by many people. Expert systems use multiple search models to diagnose a disease. Forward Chaining is a search model that can be used to perform a symptom search of a disease. One of the diseases that can be diagnosed by using computer media is Herpes Zoster. Herpes Zoster is a disease that attacks the skin and mucosa. To provide a degree of certainty to the diagnosis can be used several methods, one of which Bayes Theorem. Bayes' Theorem is a method of calculating certainty that can be used to provide a degree of certainty of diagnosis of a disease by calculating each probability of each of its symptoms. The result of this study is a system that has the knowledge, such as a doctor to be able to diagnose herpes zoster disease. The diagnostic results obtained from the system will be reinforced by the level of certainty calculated using the Bayes theorem.
Web-scale Knowledge Collection

How do we surface the large amount of information present in HTML documents on the Web, from news articles to scientific papers to Rotten Tomatoes pages to tables of sports scores? Such information can enable a variety of applications including knowledge base construction, question answering, recommendation, and more. In this tutorial, we present approaches for Information Extraction (IE) from Web data that can be differentiated along two key dimensions: 1) the diversity in data modality that is leveraged, e.g. text, visual, XML/HTML, and 2) the thrust to develop scalable approaches with zero to limited human supervision. We cover the key ideas and intuition behind existing approaches to emphasize their applicability and potential in various settings.
DDNet: A Novel Network for Cerebral Artery Segmentation from MRA Images

Brain vascular segmentation is a very important step in the diagnosis and treatment of brain diseases. Since manual segmentation of the vascular system of the brain is laborious and has problems with accuracy, automatic or semi-automatic cerebrovascular segmentation is of great significance for the study of brain diseases. Compared to hand-defined features, deep learning methods are widely used in medical image processing due to their superior feature expression capabilities. Many of the existing deep learning-based blood vessel segmentation methods do not consider multi-scale information of blood vessels, and the extracted context information has a small receptive field. Besides, the network parameters are large and difficult to train. In this paper, we proposed a network named Dense-Dilated Neural Network (DDNet) for the segmentation of cerebral artery in TOF-MRA images. The backbone of DDNet is U-Net, consisting of an encoding path, a decoding path and the skip connections between the two paths. The network is able to obtain a multi-scale representation of the brain arterial network, and has a lager respective field. In addition, the final brain arterial network segmentation results can be obtained with less computational effort. Experiments show that the network proposed in this paper is superior to UNet, Vnet and Uception.
VISION-KG: Topic-centric Visualization System for Summarizing Knowledge Graph

Large scale knowledge graph (KG) has attracted wide attentions in both academia and industry recently. However, due to the complexity of SPARQL syntax and massive volume of real KG, it remains difficult for ordinary users to access KG. In this demo, we present VISION-KG, a topic-centric visualization system to help users navigate KG easily via entity summarization and entity clustering. Given a query entity v0, VISION-KG summarizes the induced subgraph of v0's neighbor nodes via our proposed facts ranking method that measures importance, relatedness and diversity. Moreover, to achieve conciseness, we split the summarized graph into several topic-centric summarized subgraph according to semantic and structural similarities among entities. We will demonstrate how VISION-KG provides a user-friendly visualization interface for navigating KG.
Static Handwritten Signature Verification Using Convolution Neural Network

Biometrics are very essential tools for the purpose of identification of the person may contain the basic identification like iris and gesture recognition whereas signature verification is also widely used for the purpose of identification. In this paper, we propose a method of offline signature verification in which convolution is applied to address the maximum accuracy and we present how the problem was being handled in the past few decades. The experimented result reveals the efficiency of algorithm. In this paper we have worked on multiple layers so that good accuracy may obtain in terms of originality of the input images.
False memories for scenes using the DRM paradigm

• Humans are surprisingly good at detecting visual scenes in odd-one-out task. • We can select distractors with CNNs and systematically manipulate recognition. • Humans make more recognition false alarms for scenes closer to visual prototype. People are remarkably good at remembering photographs. To further investigate the nature of the stored representations and the fidelity of human memories, it would be useful to evaluate the visual similarity of stimuli presented in experiments. Here, we explored the possible use of convolutional neural networks (CNN) as a measure of perceptual or representational similarity of visual scenes with respect to visual memory research. In Experiment 1, we presented participants with sets of nine images from the same scene category and tested whether they were able to detect the most distant scene in the image space defined by CNN. Experiment 2 was a visual variant of the Deese-Roediger-McDermott paradigm. We asked participants to remember a set of photographs from the same scene category. The photographs were preselected based on their distance to a particular visual prototype (defined as centroid of the image space). In the recognition test, we observed higher false alarm rates for scenes closer to this visual prototype. Our findings show that the similarity measured by CNN is reflected in human behavior: people can detect odd-one-out scenes or be lured to false alarms with similar stimuli. This method can be used for further studies regarding visual memory for complex scenes.
Research on Covert Communication Security Based on Screen Content Coding

Covert transmission technology is a new direction in the field of information security technology. It can protect copyright and authentication sources and integrity in an open network environment, and ensure the security of digital media content through information hiding technology. Some unique features of screen content video have promoted the further development of video coding technology, and the HEVC-SCC standard has emerged as the times require. It is of great significance and value to study the information security issues of screen content coding standards. Therefore, based on the unique characteristics of the SCC stream, this paper studies the information steganography technology, and uses the video information hiding technology to transmit private data. A video steganography scheme based on chaotic mapping in DCT domain is proposed where the proposed method applies to HEVC screen content coding. The mutation frame of the screen content sequence is judged according to the threshold value, and the location of the secret data embedding is shuffled and scrambled based on the value of the chaotic logistic map digital chaotic sequence, thereby improving the security.
A complementary regression network for accurate face alignment

This paper proposes a complementary regression network (CRN) that combines global and local regression methods to align faces. A global regression network (GRN) generates the coordinates of facial landmark points directly such that all facial feature points are fitted to the input face on the whole and a local regression network (LRN) generates the heatmap of facial landmark points such that each channel localizes the detail of its facial landmark point well. The CRN converts the GRN's coordinates to another heatmap, then uses with the LRN's heatmap to get the final facial landmark points. The CRN works complementarily such that the GRN's overall fitting tendency compensates for the LRN's poor alignment caused by missing local information, whereas the LRN's detailed representation compensates for the GRN's poor alignment caused by global miss-fitting. We conducted several experiments on the 300-W public dataset, the 300-W private dataset, and the Menpo dataset and the proposed CRN achieved 3.14%, 3.74%, and 1.996% the-state-of-art face alignment accuracy in terms of percentage of normalized mean error, respectively.
Remote Sensing Image Colorization Based on Multiscale SEnet GAN

Image colorization technique is to colorize the grayscale images or single-channel images. In the research of image colorization, the coloring of remote sensing images is a challenging problem. This paper proposes a new method of remote sensing image colorization method based on Deep Convolution Generative Adversarial Network (DCGAN). We combine multi-scale convolution with Squeeze-and-Excitation Networks (SEnet) to propose a new model that is applied to the generator of DCGAN. Therefore, the generator not only retains the largest image features in the process of the generating images, but also can adjust the channel weights in the training process. We have compared the proposed method with other image colorization methods, and the results show that the proposed method has a good performance on both human vision and image evaluation indicators on the colorization of remote sensing images.
Underwater Image Enhancement via L2 based Laplacian Pyramid Fusion

Underwater images are known to suffer from color shift and poor visibility because light is absorbed and scattered as it travels through the water. In this paper, we present an efficient fusion-based framework to enhance underwater images. Our approach has three main steps. First, color correction is used to eliminate color cast and preliminary results were obtained. Then, we processed the lumimance channel through enhancing contrast, and performe Laplacian pyramid decomposition for it and the lumimance channel after color correction respectively. Finally, we use proposed fusion rules, optimal balance based on L2 norm, to merge layer by layer the two pyramids obtained in the second step separately. Experiments show the effectiveness of the proposed method than others in restoring the original color and enhancing underwater image contrast and visibility.
Single Image Dehazing Algorithm Based on Sky Segmentation

Bad weather reduces the imaging quality of the intelligent vision system, such as haze and fog. Thus, haze removal has received wide attention from researchers. Most algorithms often suffer from color distortion and edge loss when dealing with the images containing large areas of sky. In this paper, we propose an effective dehazing method. The iterative threshold segmentation is used to segment the sky region out from the image, and then the brightness of the sky region is adjusted to increase clarity. The improved dark channel priori is used to process the rest regions. The transmission map is estimated by fast bilateral filtering. Finally, the two regions are merged together to get the haze removal result. Our algorithm achieves a clear and natural haze-free image, and has a faster processing speed. Meanwhile, it is universal and real-time in practical application.
Left Ventricle Quantification Using Direct Regression with Segmentation Regularization and Ensembles of Pretrained 2D and 3D CNNs

Cardiac left ventricle (LV) quantification provides a tool for diagnosing cardiac diseases. Automatic calculation of all relevant LV indices from cardiac MR images is an intricate task due to large variations among patients and deformation during the cardiac cycle. Typical methods are based on segmentation of the myocardium or direct regression from MR images. To consider cardiac motion and deformation, recurrent neural networks and spatio-temporal convolutional neural networks (CNNs) have been proposed. We study an approach combining state-of-the-art models and emphasizing transfer learning to account for the small dataset provided for the LVQuan19 challenge. We compare 2D spatial and 3D spatio-temporal CNNs for LV indices regression and cardiac phase classification. To incorporate segmentation information, we propose an architecture-independent segmentation-based regularization. To improve the robustness further, we employ a search scheme that identifies the optimal ensemble from a set of architecture variants. Evaluating on the LVQuan19 Challenge training dataset with 5-fold cross-validation, we achieve mean absolute errors of $$111 \pm 76\,\mathrm{mm}^2$$ , $$1.84 \pm 0.90\,\mathrm{mm}$$ and $$1.22\,\pm \,0.60\,\mathrm{mm}$$ for area, dimension and regional wall thickness regression, respectively. The error rate for cardiac phase classification is $${6.7\,\mathrm{\%}}$$ .
A joint guidance-enhanced perceptual encoder and atrous separable pyramid-convolutions for image inpainting

Satisfactory image inpainting requires visually-exquisite details and semantically-plausible structures, where encoder-decoder networks have shown their potentials but bear undesired local and global inconsistencies, such as blurry textures. To address this issue, we incorporate a perception operation in the encoder, which extracts features from known areas of the input image, to improve textured details in missing areas. We also propose an iterative guidance loss for the perception operation to guide perceptual encoding features approaching to ground-truth encoding features. The guidance-enhanced perceptual encoding features are transferred to the decoder through skip connections, mutually reinforcing the entire encoder–decoder performance. Since the inpainting task involves different levels of feature representations, we further apply atrous separable parallel-convolutions (i.e., atrous separable pyramid-convolutions or ASPC) with different receptive fields in the last guidance-enhanced perceptual encoding feature, which is used to learn high-level semantic features with multi-scale information. Experiments on public databases show that the proposed method achieves promising results in terms of visual details and semantic structures.
Preserving Privacy in Personal Data Processing

Nowadays, the spread of information systems and the operation of service sectors through digital applications such as data mining, data analysis, recommendation systems, and digital marketing have brought the need to store and process large amounts of personal data in the systems of organizations. Although it is beneficial to process these data within the scope of the services provided, it may cause victimization and loss of rights if unauthorized persons take over. Therefore, the protection methods to be applied to these personal data have become a necessity for all organizations. Ensuring privacy during the processing, storage and publishing (sharing) of data is an important concern. In recent years, privacy measures have gained more importance due to national and international regulations for the protection of personal data as well. Consequently, there are certain rules that organizations should follow in their information systems, where large volumes of personal data are processed, stored and shared. In this work, some of the techniques, which are used for preserving privacy while processing personal data, are reviewed.
FastDRC: Fast and Scalable Genome Compression Based on Distributed and Parallel Processing

With the advent of next-generation sequencing technology, sequencing costs have fallen sharply compared to the previous sequencing technologies. Genomic big data has become the significant big data application. In the face of growing genomic data, its storage and migration face enormous challenges. Therefore, researchers have proposed a variety of genome compression algorithms, but these algorithms cannot meet the processing requirements for large amount of biological data and high processing speed. This manuscript proposes a parallel and distributed referential genome compression algorithm-Fast Distributed Referential Compression (FastDRC). This algorithm compresses a large number of genomic sequences in parallel under the Apache Hadoop distributed computing framework. Experiments show that the compression efficiency of the FastDRC is greatly improved when it compresses large quantities of genomic data. Moreover, FastDRC leads to the only distributed computing method known to us in the field of genome compression. The source code for FastDRC can be obtained from this link: https://github.com/GhostCCCatHenry/FastDRC .
Who Can We Trust: A New Approach for Fraudulent Rater Detection in Reputation Systems

ABSTRACT Reputation systems are widely applied in the electronic marketplace to provide reference information for consumers and alleviate their transactional risk. Despite the convenience of reputation systems, they are vulnerable to malicious ratings injected by fraudulent raters. Although several rating fraud detection methods have been proposed in previous research, their performance has limitations in relatively complicated attack scenarios. In this study, we have used real‐world rating datasets from Expedia.com, TripAdvisor.com, and Amazon.com to explore features of collaborative rating fraud. Leveraging these features, this study proposes a two‐phase approach for fraudulent rater detection. It first examines the rating series of each entity and identifies the suspicious entities. For raters of all suspicious entities, a clustering technique is then adopted to discriminate fraudulent raters. We have performed a series of empirical evaluations to compare the effectiveness of the proposed method with three benchmark methods using another real‐world dataset, in terms of fraudulent rater detection accuracy or rating difference. Our results show the advantages of the proposed method in various rating fraud scenarios.
An infinite-resolution grid snapping technique based on fuzzy theory

In ordinary CAD systems with pointing devices, users input geometric objects by inputting their feature points one by one through pointing and dragging operations. Thus, each of the feature points is snapped into place interactively, and the geometric objects are aligned on the specified grid as a result. In sketch-based CAD systems with pen input devices, users simply draw a freehand stroke that the system recognizes as a geometric object, and then the system must automatically snap all of the feature points to the grid by a batch process. In this case, it is not easy for the user to set up the grid resolution in advance, because the appropriate resolution changes according to the drawing. Therefore, a multi-resolution fuzzy grid snapping (MFGS) technique has been proposed. In MFGS, the appropriate snapping resolution is dynamically selected in a multi-resolution grid system according to the roughness of the drawing manner. However, the multiplicity of the grid resolutions in MFGS is limited to a finite number, and grid snapping outside this range of resolutions cannot be appropriately handled. In this paper, we propose infinite-resolution fuzzy grid snapping (IFGS), in which there is an infinite number of grid resolutions, and experimentally demonstrate that IFGS effectively resolves the problems of MFGS.
Deep Feature Guided Image Retargeting

Image retargeting is the technique to display images via devices with various aspect ratios and sizes. Traditional content-aware retargeting methods rely on low-level features to predict pixel-wise importance and can hardly preserve both the structure lines and salient regions of the source image. To address this problem, we propose a novel adaptive image warping approach which integrates with deep convolutional neural network. In the proposed method, a visual importance map and a foreground mask map are generated by a pre-trained network. The two maps and other constraints guide the warping process to yield retargeted results with less distortions. Extensive experiments in terms of visual quality and a user study are carried out on the widely used RetargetMe dataset. Experimental results show that our method outperforms current state-of-art image retargeting methods.
Development of the Means of Engagement (MOE) Model in the Context of Enterprise Resource Planning (ERP) for User Acceptance Level

Enterprise Resource Planning, a system that carries out business process automation for companies. To have successfully implemented ERP, the company is required to consider the business requirement and strategy aligned horizontally in order to improve company efficiency and performance at the optimized level. Therefore, the success of implementing an ERP system is not easy to establish because it is required additional factors such as strategic leadership, learning curve, continuous development, personnel desired and many relevant operational process or execution to run smoothly and accordingly. It is necessary to take attention to the various factors that influence the acceptance of an ERP system for the company in order to be ready. However, the existence of engagement often determines the connection between expectation and realization as it is expected to be able to determine the direction that the company should take in order to allow minimizing errors and mitigating risks. This study takes into the consideration from a theory of diffusion of innovation by Rogers [1] by considering that the changes of trends, intangibility of product, vast amount of information, accessibility to capital, disruptive innovation of startup and pressure of competition has been shifted the users preferences and eliminating the order of marketing phases. This study wants to explore the possibility to develop a new approach in understanding the differences in terms of acceptance, approval, adoption, and agreement of users in implementing ERP as the solution for the automation, integration and coordination issues within the company.