Reviewerly/exHarmony huggingface dataset:
Performance Analysis Image Watermarking using Discrete Cosine Transforms

Digital images, video, and audio have been revolutionized in the way they can be captured, stored, transmitted, and manipulated. Therefore, the idea of using a digital watermark to detect and trace copyright violations has stimulated signifacantly. In this research will evaluate the robustness of watermark embedding with some attacks and extracting. Several functions were used to qualify the robustness of watermark algorithm, such as Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM). The measurements involve 3 types of watermark image, as follows 16 x 16 pixels, 32 × 32 pixels, and 64 × 64 pixels with the value of scaling factor (SF) and quality factor (K) of 10, 30, and 50. The simulation shows that in the condition of SF and K for 50, the watermark image of 64 × 64 have the highest PSNR 51.868 dB and the highest SSIM 0.983. From these results prove that the size of the watermark image could provide the better resistance to attack.
Scalable Data Reporting Platform for A/B Tests

A/B Test data platform enables users, such as a Product Manager or Product Owner, to answer important questions such as "How will this new product feature benefit ?" or "Does this new page layout improve user engagement and increase GMV (Gross Merchandise Value)?" Testing allows business units to conceptually explore new ideas with respect to page content/style, search algorithms, product features, etc. which can vary from subtle to radical variations. Such test variations can be easily targeted to segments of the total customer population based on desired percentage/ramp up and contextual criteria (geographic, system or app specific) providing a level of assurance before launching to a broader audience. A/B Test data platform runs hundreds of live experiments that are responsible for processing over hundreds of terabytes of reporting data contained in millions of files in Hadoop data platform and data warehouse tables using Hadoop infrastructure, consuming thousands of computing resources. The entire report generation process generates around ~300 metrics of different categories for each experiment. Report generation process takes around 10-12 hours daily to enable our millions of customers to experience small and large innovations to enable them to buy and sell products in various countries in different currencies and using different payment mechanisms in a better way every day. This paper describes the architecture and components of this scalable reporting infrastructure.
Faster Unsupervised Semantic Inpainting: A GAN Based Approach

In this paper, we propose to improve the inference speed and visual quality of contemporary baseline of Generative Adversarial Networks (GAN) based unsupervised semantic inpainting. This is made possible with better initialization of the core iterative optimization involved in the framework. To our best knowledge, this is also the first attempt of GAN based video inpainting with consideration to temporal cues. On single image inpainting, we achieve about 4.5-5× speedup and 80 × on videos compared to baseline. Simultaneously, our method has better spatial and temporal reconstruction qualities as found on three image and one video dataset.
Impact of Factors Affecting Pre-copy Virtual Machine Migration Technique for Cloud Computing

Cloud managers trigger Virtual Machine (VM) migration to achieve the challenges of server consolidation, load balancing, resource management, and server fault situation. However, the performance of VM migration is affected by VM size, running application’s dirty rate and the number of migration iterations. Therefore, to measure VM migration performance by considering all these parameters becomes a challenging issue. The main contribution of this study is to evaluate the performance impact of these migration control parameters. Simulation results demonstrate that: (i) Migration time and downtime increases of two orders of magnitude while increasing the VM size, (ii) As the application dirty rate increases the migration time and downtime also increases, (iii) Half iterations of pre-copy migration are enough to reduce the migration time and downtime. In particular, the results obtained can help cloud service providers to find the performance impact of these factors on VM migration technique and meet the required goals of efficient deployment of cloud services.
Structure-constrained discriminative dictionary learning based on Schatten p-norm for face recognition

Dictionary learning based on low-rank representations has been widely studied in recent years and has achieved impressive performance in face recognition. However, studies on the case where both the training and testing data are polluted with large noise are still in their infancy. In this paper, to capture the essential structure hidden in noisy data and to learn a discriminative dictionary, we propose a novel model of structure-constrained discriminative dictionary learning based on the Schatten p-norm (SDDLSp), in which (1) the nonconvex Schatten p-norm (0<p<1) is used to approximate the rank minimization problem, (2) a weighted representation regularization term is constructed to generate a coefficient matrix with a class-wise block-diagonal structure, and (3) a linear classifier based on the coefficient is simultaneously optimized with the dictionary and representation update. Thus, the learned dictionary and representation perform well in face recognition problems, particularly with training and testing images that are both polluted by large noises. An iterative algorithm to solve the proposed SDDLSp is also presented in this paper. Experiments on four face databases demonstrate the advantages of the proposed method over previous dictionary-learning methods.
Multilayer Active Learning for Efficient Learning and Resource Usage in Distributed IoT Architectures

The use of machine learning modeling techniques enables smart IoT applications in geo-distributed infrastructures such as in the areas of Industry 4.0, smart cities, autonomous driving, and telemedicine. The data for these models is continuously emitted by sensor-equipped devices. It is usually unlabeled and commonly has dynamically-changing data distribution, which impedes the learning process. However, many critical applications such as telemedicine require highly accurate models and human supervision. Therefore, online supervised learning is often utilized, but its application remains challenging as it requires continuous labeling by experts, which is expensive. To reduce the cost, active learning (AL) strategies are used for efficient data selection and labeling. In this paper we propose a novel AL framework for IoT applications, which employs data selection strategies throughout the multiple layers of distributed IoT architectures. This enables an improved utilization of the available resources and reduces costs. The results from the evaluation using classification and regression tasks and synthetic as well as real-world datasets in multiple settings show that the use of multilayer AL can significantly reduce communication, expert costs, and energy, without a loss in model performance. We believe that this study motivates the development of new techniques that employ selective sampling strategies on data streams to optimize the resource usage in IoT architectures.
Depth-Assisted Demosaicing for Light Field Data in Layered Object Space

Light field technology, which emerged as a solution to the increasing demands of visually immersive experience, has shown its extraordinary potential for scene content representation and reconstruction. Unlike conventional photography that maps the 3D scenery onto a 2D plane by a projective transformation, light field preserves both the spatial and angular information, enabling further processing steps such as computational refocusing and image-based rendering. However, there are still gaps that have been barely studied, such as the light field demosaicing process. In this paper, we propose a depth-assisted demosaicing method for light field data. First, we exploit the sampling geometry of the light field data with respect to the scene content using the ray-tracing technique and develop a sampling model of light field capture. Then we carry out the demosaicing process in a layered object space with object-space sampling adjacencies rather than pixel placement. Finally, we compare our results with state-of-art approaches and discuss about the potential research directions of the proposed sampling model to show the significance of our approach.
Secure Stern Signatures in Quantum Random Oracle Model

The Stern signatures are a class of lattice-based signatures constructed from Stern protocols, a special class of sigma protocols, admitting diverse functionalities with good asymptotic efficiency. However, the post-quantum security of existing Stern signatures is unclear, since they are built via the Fiat-Shamir transformation, which has not been proved to be secure in the quantum random oracle model (QROM). The goal of this paper is to find an alternative transformation for constructing post-quantum secure Stern signatures. The Unruh transformation (Eurocrypt 2015) is an alternative that can build secure signatures in QROM from post-quantum secure sigma protocols. Unfortunately, its proof relies on the 2-special soundness of the underlying sigma protocol, while Stern protocols are 3-special sound. We fill this gap by providing an extended proof for the Unruh transformation. Specifically, we prove that it is still secure in the QROM even if the underlying sigma protocols are k-special sound, where $$k>2$$ could be an arbitrary integer. Observing that Stern protocols are post-quantum secure sigma protocols with 3-special soundness, our proof implies a generic method to obtain secure Stern signatures in the QROM.
Comparative Analysis of CPU Scheduling Algorithms and Their Optimal Solutions

Scheduling is one of the primary functions of an operating system (OS). In multiprogramming system, multiple processes in the main memory exist concurrently. The goal of Central Processing Unit (CPU) scheduling is to allocate processes to be executed by the processor over time. In this paper, we have discussed about various advantages and disadvantages of CPU scheduling algorithms and an optimal solution for each of them.
Three-dimensional Local Path Planning of Robot Based on AR-ANT Algorithm and B-spline Curve

In order to ensure the safety of robot, the problem of robot path planning is studied. First by rasterize approach to 3 dimension modeling, the search space and at a safe distance, the execution time and cost as the objective function, an improved pheromone update rule of ant colony algorithm, then combined with simulated annealing algorithm is effective and good convergence and optimization results, the three dimensional line path planning by using the B spline interpolation method and curve fitting, generating a smooth path, conducive to the stable robots. At last, the feasibility and local adaptability of the algorithm are verified by MATLAB simulation and physical experiment. The results show that compared with the traditional algorithm, the path planned by the algorithm is safer and more optimal, and the quality of robot path planning is effectively improved.
Sentiment Analysis Using SVM and Deep Neural Network

With the incremental growth of Web based platforms, Automated Review sentiment analysis has become main focus, with the very primary purpose is to get the implicit information by the reviews for the better acknowledgement of the customers. Conventional methods have made tremendous growth on the basic level and are not very much reliable including the sentiment analysis, inspite the rapid growth of data scale and processing high dimension of data faster, effectively and accurately becomes the biggest drawbacks. The primary focus of the paper is the use of Support vector machines, embedding & deep neural networks which are suitable for the high dimensional data analysis, and proposes the structure of sentiment analysis using deep learning. This will act as the generalized automated review system for various industries Such as E-Commerce platform, YouTube comments, movie review rating etc. with more high precision and has potential to replace orthodox star-based rating System. Experiments which were carried on different data scales and various domains reveals that the proposed method can be used to solve the problem with better performance and higher Accuracy.
Deep Multi-Stage Learning for HDR With Large Object Motions

High Dynamic Range (HDR) imaging provides a methodology to capture a wide luminance range in a single image which traditional imaging techniques fail to capture. State of the art deep learning methods in multi-frame HDR imaging follow an end-to-end learning approach and often fail to generate realistic details in large occluded regions. In this paper, we propose to split the HDR problem into multiple stages and tackle them using separate Convolutional Neural Networks (CNNs) rather than attempting an end-to-end learning. First, in the Exposure Alignment Stage, we propose to generate virtual exposure images which are similar in structure to a chosen reference input using deep CNNs. This is followed by an HDR Merge stage, where another CNN learns to generate the HDR output from the virtual exposure images. We perform extensive comparative studies to show that the proposed method generates artifact-free outputs with plausible details in occluded regions.
Data Text Mining Based on Swarm Intelligence Techniques

Due to the great growth of data on the web, mining to extract the most informative data as a conceptual brief would be beneficial for certain users. Therefore, there is great enthusiasm concerning the developing automatic text summary approaches. In this chapter, the authors highlight using the swarm intelligence (SI) optimization techniques for the first time in solving the problem of text summary. In addition, a convincing justification of why nature-heuristic algorithms, especially ant colony optimization (ACO), are the best algorithms for solving complicated optimization tasks is introduced. Moreover, it has been perceived that the problem of text summary had not been formalized as a multi-objective optimization (MOO) task before, despite there are many contradictory objectives in needing to be achieved. The SI has not been employed before to support the real-time tasks. Therefore, a novel framework of short text summary has been proposed to fulfill this issue. Ultimately, this chapter will enthuse researchers for further consideration for SI algorithms in solving summary tasks.
Impression Estimation for Deformed Portraits With a Landmark-Based Ranking Network

In recent years, it has become a trend for people to manipulate their own portraits before posting them on a social networking service. However, it is difficult to get a desired portrait after manipulation without sufficient experience or skill. To obtain a simpler and more effective portrait manipulation technique, we consider an automated portrait manipulation method based on the five impression words: clear, sweet, elegant, modern, and dynamic. In the first part of this work, we analyzed the relationship between deformed portrait (only facial features) and the five impression words over a huge data which we collected by crowd-sourcing. In the next part, we leveraged the knowledge from the results of the analysis to develop a ranking network trained on the collected data for estimating the impression associated with a portrait. Our network demonstrates superior performance in impression estimation when compared to other state-of-the-art methods.
A Comparative Study on Parsing in Natural Language Processing

The main focus of this paper is to deduce which parsing technique is better among the natural language processing techniques. A brief study of LR parsing, its construction and implementation, providing limitations (drawback) on different types of parsing. Comparing LR parsing, through proven results and usage in the field of `Natural Language Processing' and comparing different methods of parsing. Showing drawbacks of LL parsing and coming to the conclusion that LR is more efficient than other parsing methods.
A Systematic Cloud Workload Clustering Technique in Large Scale Data Centers

In large scale data centers Virtual Machines and Tasks (VMs/tasks) scheduling, VMs allocation, workload predictions and monitoring are a vital concern in any cloud-based data center. In all these fields, clustering algorithms are very useful to a group of workload components that have similar behaviors characteristics. Effective clustering is to select an appropriate clustering technique for a specific application. This choice is very necessary, especially when there are wide choices that provide different results. To address such issue, this paper proposes a novel systematic framework to select the suitable VMs/tasks clustering method in large-scale data centers based on clustering purpose, validation indices and results comparison.
Learning to Render Better Image Previews

A rapidly increasing portion of Internet traffic is dominated by requests from mobile devices with limited and metered bandwidth constraints. To satisfy these requests, it has become standard practice for websites to transmit small and extremely compressed image previews as part of the initial page-load process. Recent work, based on an adaptive triangulation of the target image, has performed well at extreme compression rates: 200 bytes or less. Gains have been shown, in terms of PSNR and SSIM, over both JPEG and WebP standards. However, qualitative assessments and preservation of semantic content can be less favorable. We present a novel method to significantly improve the reconstruction quality of the original image that requires no changes to the encoded information. Our neural-based decoding triples the amount of semantic-level content preservation while also improving both SSIM and PSNR scores. In addition, by keeping the same encoding stream, our solution is completely inter-operable with the original, and remains suitable for small-device deployment.
Comparative Analysis of Segmentation Techniques using Histopathological Images of Breast Cancer

Breast cancer is one of the most common disease from which most of the females are suffering. Histopathological images play remarkable notch in the medical domain. Segmentation of breast cancer images for cell analysis is the utmost thought-provoking task because of uncertainties present in these images. Identifying cancerous cells effectively in histopathological images may help in early diagnosis of breast cancer. In this paper, comparative analysis of different state-of-art segmentation techniques have been carried out to extract cancerous cells in histopathological images using Triple Negative Breast Cancer (TNBC) dataset. The experimental results of segmentation techniques have been analysed with respect to Accuracy, False Positive Rate (FPR), and True Positive Rate (TPR). Experiments using histopathological images validates Spatial Fuzzy C-Means with Level Set finds the cancerous breast cells effectively.
Cooperative Object Classification for Driving Applications

3D object classification can be realised by rendering views of the same object from different angles and aggregating all the views to build a classifier. Although this approach has been previously proposed for general objects classification, most existing works did not consider visual impairments. In contrast, this paper considers the problem of 3D object classification for driving applications under impairments (e.g. occlusion and sensor noise) by generating an application-specific dataset. We present a cooperative object classification method where multiple images of the same object seen from different perspectives (agents) are exploited to generate more accurate classification. We consider model generalisation capability and its resilience to impairments. We introduce an occlusion model with higher resemblance to real-world occlusion and use a simplified sensor noise model. The experimental results show that the cooperative model, relying on multiple views, significantly outperforms single-view methods and is effective in mitigating the effects of occlusion and sensor noise.
How to Evaluate Object-of-Fixation Detection

One research aspect of the growing field of driver monitoring is the estimation of the driver's focus of attention and the detection of seen or missed surrounding objects. For this purpose, wearable head eye trackers, also called eye tracking glasses, are commonly used in research. For new Advanced Driver Assistance Systems, however, only remote gaze tracking systems in combination with series surround sensors (e. g. radar, camera) are a reasonable and suitable configuration. Remote eye trackers, though, do not reach the precision of wearable devices. Moreover, the use of inside-outside looking systems is affected by calibration errors. Both aspects lead to traffic participants classified as missed although they have been perceived by the driver and vice versa. Insight in the performance not only of the eye tracker itself but also on the model performance of an object of fixation detection algorithm, i. e. how a model handles those shortcomings, would be useful. In this work, we propose an approach to evaluate different models. For this, we combine the remote eye tracker with a wearable device, compare different models based on the newly obtained reference, and discuss the encountered problems.
Recommending Energy-Efficient Data Mining Services with Data as Contextual Factors

Energy-efficiency is considered an important aspect when consuming web services. For data mining services, input dataset can have an impact on the energy consumption level as well as other QoS values of a service. For instance, a service not good at dealing with high-dimensional data may consume more energy than another service offering the same functionality. Therefore, when recommending such services, it is beneficial to consider dataset information in the recommendation process. However, in the past research on service recommender systems, usually, only the historical data on how services are consumed by different users is taken as the main source for making recommendations. Sometimes, contextual factors such as time and location (when and where the services are consumed) are also included. In this paper, we propose to consider dataset information as contextual factors in the recommendation model. We apply contextual modeling approach to incorporate them into our service recommender system. We explore different ways of representing dataset information as contextual factors and investigate their impacts on recommendation accuracy. Experimental results show that our introduced approach outperforms the baseline model by 11.7-22.6% on various evaluation metrics measuring recommendation accuracy.
Automatic Text Summarization: A New Hybrid Model Based on Vector Space Modelling, Fuzzy Logic and Rhetorical Structure Analysis

In this paper, we present a new hybrid system for automatic text summarization. First, vector space modelling is used to compute two original metrics of coverage and fidelity. The latter metrics are combined onto a unified Fidelity-Coverage (F-C) score using fuzzy logic theory. Then, a rhetorical analysis is performed on top of sentences having the highest F-C scores in order to achieve coherence. Conducted experiments on the Timeline17 dataset show that the proposed system outperforms state of the art extractive summarization models. Also, generated abstracts generally satisfy the three criteria of a good summary, namely coverage, fidelity and coherence.