Reviewerly/exHarmony huggingface dataset:
EDNAS: An Efficient Neural Architecture Design based on Distribution Estimation

Neural architecture search (NAS) is the process of automatically searching for the best performing neural model on a given task. Designing a neural model requires a lot of time for experts, NAS's automated process effectively solves this problem and makes neural networks easier to promote. Although NAS has achieved excellent performance, its search process is still very time consuming. In this paper, we propose a neural architecture design method based on distribution estimation method called EDNAS, a fast and economical solution to design neural architecture automatically. In EDNAS, we assume that the best performing architecture obeys a certain probability distribution in search space. Therefore, NAS can be transformed to learning this probability distribution. We construct a probability model on the search space, and search for this probability distribution by iterating the probability model. Finally, an architecture that maximizes the performance on a validation set is generated from this probability distribution. Experiment shows the efficiency of our method. On CIFAR-10 dataset, EDNAS discovers a novel architecture in just 4 hours with 2.89% test error, which shows efficent and strong performance.
A Deep Learning-Based Framework for Accurate Facial Ethnicity Classification and Efficient Query Retrieval

Facial images carry important demographic information such as ethnicity and gender. Ethnicity is an essential part of human identity and serves as a useful identifier for numerous applications ranging from biometric recognition, targeted advertising to social media profiling. Recent years have seen a huge spike in the use of convolutional neural networks (CNNs) for various visual, face recognition problems. The ability of the CNN to take advantage of the hierarchical pattern in data makes it a suitable model for facial ethnicity classification. As facial datasets lack ethnicity information it becomes extremely difficult to classify images. In this chapter a deep learning framework is proposed that classifies the individual into their respective ethnicities which are Asian, African, Latino, and White. The performances of various deep learning techniques are documented and compared for accuracy of classification. Also, a simple efficient face retrieval model is built which retrieves similar faces. The aim of this model is to reduce the search time by 1/3 of the original retrieval model.
Atom—Field Correlations in the Weak-Excitation Limit of Absorptive Optical Bistability

We calculate the steady-state and first-order time varying atom—field correlation functions in the weak-excitation limit of absorptive optical bistability from a linearized theory of quantum fluctuations. We formulate a Fokker—Planck equation in the positive P representation following the phase-space analysis in [H. J. Carmichael, Phys. Rev. A 33, 3262 (1986)], which is suitable for the determination of cross-correlations as it does not resort to adiabatic elimination. Special emphasis is placed on the limit of collective strong coupling as attained from a vanishing photon-loss rate. We compare to the cavity-transmission spectrum with reference to experimental results obtained for macroscopic dissipative systems, discussing the role of anomalous correlations arising as distinct nonclassical features.
An Image Watermarking Scheme Robust to Geometric Distortion Based on SIFT Feature

A geometrically robust image watermarking scheme based on scale invariant feature transform (SIFT) and pseudo-Zernike moment is proposed. Firstly, the steady feature points are extracted from the host image by using SIFT; then the size and orientation of each local feature region are ascertained adaptively according to its feature scale and orientation; Lastly, some un-overlapped local feature regions with large-scale are selected for embedding watermarking, and the same digital watermarking is embedded into each local feature region by quantizing the magnitudes of the pseudo-Zernike moments. Simulation results show that the proposed watermarking scheme is invisible, and robust against common signal processing and geometric attacks.
Domain‐invariant adversarial learning with conditional distribution alignment for unsupervised domain adaptation

Unsupervised domain adaption aims to reduce the divergence between the source domain and the target domain. The final objective is to learn domain‐invariant features from both domains that get the minimised expected error on the target domain. The divergence between domains which is also called domain shift is mainly between the distributions of domains' samples. Additionally, the label shift is also a tricky challenge in domain adaptation. In this study, domain‐invariant adversarial learning with conditional distribution alignment is proposed to alleviate the effect of domain shift with label shift. To obtain the domain‐invariant features, the proposed method modifies adversarial auto‐encoder architecture and performs semi‐supervised learning to enlarge the inter‐class discrepancy. The marginal distribution is aligned in the adversarial learning process of extracting domain‐invariant features. Meanwhile, the label information is incorporated in this way to align the conditional distribution. The proposed work also theoretically analyses the generalisation bound of the proposed model. Finally, the proposed method is evaluated based on several domain adaptation tasks, including digit classification and object recognition, and achieves state‐of‐the‐art performance.
Automated Classification and Detection of Malaria Cell Using Computer Vision

With the advancement of technology, its involvement in medical science is inevitable. Rapid progress has been made in the fields of automated disease detection and diagnosis. The same concept has been extended to the field of malaria cell analysis in this research. From the image of a cell, this model is capable of determining whether it is uninfected or parasitized with malaria infection. The method discussed here is lightweighted and easily implementable in terms of time and space complexity. Also, it has a training accuracy of 97.98% and a validation accuracy of 97.6%. A new method called “Information Padding” has been used and discussed in the methodology section.
Arduino-Based Plastic İdentification and Picking Robot

In recent times, every state and every part of India are facing the most disastrous situation due to plastic usage. All the parts of the roads as well as on the sides of the dustbin, the plastic is thrown. In order to eradicate this vacant non-degradable plastic on ground, we come up with an idea of developing an Arduino-based plastic identification and collecting robot. Nowadays robotic arm is becoming the most common element for the industrial purpose as well as the domestic purpose, and they are commercially available due to their repeatability, excellent accuracy, and reducing human efforts. The robotic arm discussed here is a prototype for a project used generally for a domestic purpose where it helps to pick the plastic objects by using proximity sensors and places it in a bin.
WITHDRAWN: Low light image contrast enhancement using advanced perfusion technique

The images captured tend to degrade due to the scattering and absorption of light. This may be due to the diminished colors, low light and undistinguishable objects in the image. For the improvement of quality of such degraded images, Advanced Perfusion technique is employed to enhance the contrast of the low light Image. In this technique, the input image in RGB color space is converted to LAB color spaces. Two versions of input image are considered, in which, the first image is processed for brightness contrast enhancement and the second image is processed for color contrast enhancement. For the enhancement of color contrast, the second image is passed through a Recursive Separated and Weighted Histogram Equalization, (RSWHE) followed by power law transformation. Two main features are extracted from both the images, namely, Laplacian weight map and Saliency weight map. These two features of the two LAB images are fused together to produce a final enhanced image, which has improved brightness contrast and color contrast. The proposed image enhancement technique is evaluated with various performance parameters such as Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Lightness Order Error (LOE). The performance of the proposed method is then compared with the existing methods for validation.
Towards the Development of an Online Coverage Path Planner for UUV-based Seafloor Survey using an Interferometric Sonar

Seafloor survey is an important practice for both defense and scientific purposes. Often time, we could refer the seafloor survey mission to as a coverage path planning (CPP) problem where the robot has to explore all the points of an area. In this paper, we propose a new online CPP algorithm for a UUV equipped with an interferometric sonar that has inherent problem of nadir gap and changing swath width. The proposed algorithm will account for the above two factors, and schedule new waypoints for the UUV intermittently. The way-points are determined based on an objective function quantifying the potential measurement accuracy and information gain. We present the algorithm validation in a simulated environment with a 6DOF REMUS AUV model with an actual bathymetric map of Narragansett Bay, Rhode Island. The proposed algorithm is compared to boustrophedon coverage path at different inter-distances. In the result, we found advantages of the proposed algorithm in terms of total path length to fully explore a 200 by 500 meters rectangular workspace.
Simulation of solar irradiation assessment for power generation

Abstract Solar Energy forms the perennial source to all living processes existing on Earth, directly or indirectly owing to its widespread usage to support the primary energy producers of the eco-system. The solar energy has been an important driver to all activities, supporting the civilizations as it has been used in different applications ranging from, drying of hide to sophisticated application of powering a satellite. The world today has found relief, through use of eco-friendly sources, of which solar energy has been primary form. The study has identified typical solar system design for household application through computational tool. The solar power module performance and variation in incident solar intensity on both horizontal and inclined surfaces are compared with load variation. The energy gain by integrating equations for Hubballi city was evolved, along with its comparison with locations like Delhi and Hamburg (Germany). It was observed that existing fixed SPV device was inadequate to meet the anticipated energy demand. The four different types of solar tracking modes were investigated using Empirical equations on MATLAB computational tool as a measure to enhance energy gain at the location. It was observed that operating in sun-tracking mode, SPV module programmed on basis of the objective function to minimize incidence angle, yielded higher solar energy throughout the year.
Using a Context Based Knowledge for Software Product User Support

The aim of the research is to create a framework for a smart user Support Assistant LAVA - an automated service desk solution to support a user of a software product. The solution is based on learning and search in a context-sensitive two-level distributed knowledge store.
Deep Automated Text Scoring Model Based on Memory Network

Automated text scoring system provides an effective alternative to manual scoring because of its advantages in speed and integrity of scoring. Currently in most online examination software, the scoring is only focused on the writing abilities in grammar and styles rather than the content details. To assess the content of the text answer, an auto-grading system is developed for short answered questions. It combines word weights in different aspects and uses the generated weight matrix for system to concentrate on the important words and phrases. Then it stores the knowledge base in the memory and use neural network for information fusion of the student's answer and the weighted facts. The final score is predicted based on the fusion result. The system is tested on the dataset of online technology interview question and answers. Experiment results show that it has good generalization ability and gets accuracy rate of 83.37%, which can be used in assisting human grading while saving substantial human resources.
Categorization of Data Mining Systems and Data Secrecy Towards Wireless Sensor Networks

The reliable database administration systems have actually been very vital assets for monitoring of a large corpus of data as well as particularly for reliable as well as efficient access of particular information from a huge collection whenever required. The expansion of data source monitoring systems has actually also contributed to recent huge gathering of all kind of info. This paper deals with the classification of data mining systems and also issues available in Data Mining.
A Tax Evasion Detection Method Based on Positive and Unlabeled Learning with Network Embedding Features

Tax evasion detection has a crucial role in addressing tax revenue loss. In the real world, an accessed tax dataset only contains a small number of labeled taxpayers who evade tax (positive samples) and a large number of unlabeled taxpayers who either evade tax or do not evade tax. It is difficult to address this issue due to this nontraditional dataset. In addition, the basic features of taxpayers designed according to tax experts' domain knowledge and experience are very limited to determining whether taxpayers evade tax. These limitations motivate the contribution of this work. In this paper, we argue that the tax evasion detection task in the real world should be formalized as a positive unlabeled (PU) learning problem. We propose a novel tax evasion detection method based on PU learning with Network Embedding features (PUNE). PUNE effectively detects tax evasion based on basic features and transaction network features that are extracted by a network embedding algorithm. Moreover, PUNE can work well even under label noise. To evaluate the effectiveness of PUNE, we conduct experimental tests on a real-world tax dataset. The results demonstrate that PUNE can significantly improve the performance of tax evasion detection.
Sentiment analysis of commodity reviews based on ALBERT-LSTM

Abstract Sentiment analysis of product reviews has now become one of the important research directions in the field of NLP, which is of great significance in helping merchants better understand user preferences and providing decision-making basis for other users to purchase products. Existing product review sentiment analysis models based on deep learning mostly use traditional word vector models. It is difficult to obtain the contextual semantic information of words, and there is a problem of “polysemous one word”. A product review sentiment analysis model combining ALBERT and LSTM is proposed. First, we use the ALBERT pre-training model to get the word vector containing sequence and semantic information. Then we use the LSTM model that can obtain long-distance semantic features for training. Finally, the emotional polarity of product reviews is classified and output through the emotional polarity discrimination layer. The experimental results on the data set of digital products of JD Mall show that the F1 value of the ALBERT-LSTM model is improved compared with the ALBERT and LSTM models.
Improving Instance Segmentation using Synthetic Data with Artificial Distractors

Despite the advances in deep learning, training instance segmentation models like convolutional neural networks still tend to depend on enormous training data that are expensive and require labor to annotation. To avoid labor-intensive procedure, synthetic data can be an alternative because it is easy to generate and automatically segmented. However, it is challenging to train instance segmentation model that perform well at real world using only synthetic data because of domain gap. It is wrong direction to put a lot of effort into solving these problems by making synthetic data more photorealistic. In this paper, we suggest how to learn the instance segmentation model using synthetic data with artificial distractors. The performance has been improved about 7% by adding flying distractors compared to original synthetic data.
Classifying and clustering malicious advertisement uniform resource locators using deep learning

Abstract Malicious online advertisement detection has attracted increasing attention in recent years in both academia and industry. The existing advertising blocking systems are vulnerable to the evolution of new attacks and can cause time latency issues by analyzing web content or querying remote servers. This article proposes a lightweight detection system for advertisement Uniform resource locators (URLs) detection, depending only on lexical‐based features. Deep learning algorithms are used for online advertising classification. After optimizing the deep neural network architecture, our proposed approach can achieve satisfactory results with false negative rate as low as 1.31%. We also design a novel unsupervised method for data clustering. With the implementation of AutoEncoder for feature preprocessing and t‐distributed stochastic neighbor embedding for clustering and visualization, our model outperforms other dimensionality reduction algorithms by generating clear clusterings for different URL families.
HTTPS Contribution in Web Application Security: A Systematic Literature Review

A Web application is one of the most used technology nowadays due to its flexibility in delivering services to society. It also plays a good portion in enhancing our daily life since it could provide almost any kind of services through an application served from the internet. Thus, many users' private information runs the risk of being exposed to an unauthorized party. Standard browser connection uses HTTPS protocol, while both TLS over HTTP and Web application are known for several of vulnerabilities. This paper presents the results of an SLR study on web application security of HTTPS implementation. The study selects 45 qualified papers related to the topic and analyzed 24 of the documents. The findings are categorized into three labels: threats, threats impact, and defense mechanisms. This work also classifies the attack and threats based on the impact produced. In this study, the lack of understanding about security-related mechanism in TLS, session management, and web application still become the culprit of most attack and vulnerability. Based on this work, a researcher could better prioritize and prepare security mechanism to overcome the threats.
Introducing Script as a Software Design Tool for Agile Software Development Methodology

Nowadays, agile software development methodology has acquitted attention in software development industry. Agile practices have been denoted as popular choice for software development teams due to its flexibility and ease of use. In software industry, there are many technical descriptions for applying agile practices in software development. Although these diversities could allow a company to tailor its own agile practices, diversities could lead to lack of standardization that could allow collaboration between development teams. In this paper, a well know technique for knowledge representation which is script has been introduced as a tool for implementing agile practices. Script has been used for long time in knowledge engineering domain for representing unstructured knowledge and transfer it to structure knowledge representation. Two illustrative examples have been used in this paper. First definition of script and its part has been mentioned. Second, the first example is used to describe the usage of script as knowledge representation tool. Third, the second example is used to explain how script could be a useful tool for agile practices in agile software development methodology. Finally, the benefits of the proposed tool and the future direction has been explained.
Approach to the Creation of a Digital Content Copyright Protection System

The purpose of this article is to present a project of a system for protecting digital copyright content based on elements of blockchain technology for trusted storage of the author's work and providing objective evidence in the check-author procedure. The scientific novelty lies in the original approach to the processing and secure storage of digital copyright information, as well as the study of the possibility of using elements of blockchain technology in the check-author procedure. Relevance: the presented solution can be used to protect copyright materials from plagiarism before publishing the entire work of authorship. Also, the solution can be a convenient tool for reviewing copyright works and controlling the implementation of work-stages. As a result, the approach allows the development of an information system for secure storage and processing of digital copyright content. The principle of sequential block binding is proposed to be applied. It is proposed to provide secure access to the digital copyright content and information support for the author verification procedure.
Camouflage Generative Adversarial Network: Coverless Full-image-to-image Hiding

Image hiding, one of the most important data hiding techniques, is widely used to enhance cybersecurity when transmitting multimedia data. In recent years, deep learning-based image hiding algorithms have been designed to improve the embedding capacity whilst maintaining sufficient imperceptibility to malicious eavesdroppers. These methods can hide a full-size secret image into a cover image, thus allowing full-image-to-image hiding. However, these methods suffer from a trade-off challenge to balance the possibility of detection from the container image against the recovery quality of secret image. In this paper, we propose Camouflage Generative Adversarial Network (Cam-GAN), a novel two-stage coverless full-image-to-image hiding method named, to tackle this problem. Our method offers a hiding solution through image synthesis to avoid using a modified cover image as the image hiding container and thus enhancing both image hiding imperceptibility and recovery quality of secret images. Our experimental results demonstrate that Cam-GAN outperforms state-of-the-art full-image-to-image hiding algorithms on both aspects.
MMSPP: Multimodal Social Media Popularity Prediction

People post and share news on social media in daily lives. Popular posts get attention and further influence the society. It's helpful to know in advance if certain posts will become popular. In order to achieve this, several challenges need to be resolved: (i) more posts become multi-modal - smartphones make it easy to capture and share images and videos with texts in posts, and (ii) the popularity of posts depends not only on the quality of the content, but also on the popularity of the post author themselves. To this end, we propose MMSPP, a multimodal social media popularity prediction system incorporating image, text and author social features of a post to predict its retweet counts and favorite counts in Twitter as the measurements for popularity. Specifically, two novel multimodal fusion methods are proposed. We evaluate MMSPP with the two fusion methods in a Tweeter dataset with 3448 image-text pairs of posts. MMSPP is able to beat three baselines by at least 12.50% and 7.69% MSE for retweet counts and favorite counts respectively.