Reviewerly/exHarmony huggingface dataset:
Optimal Secret Text Compression Technique for Steganographic Encoding by Dynamic Ranking Algorithm

Abstract In this research work, proposing an algorithm, which dynamically selects the best compression algorithms among several compression techniques for steganography encoding. Ranking and selection of best algorithm for each and every steganographic transaction is based on several factors like type of cover image being used for the transmission, length of the secret message, type of the message, compression ratio of the secret message being shared, encoding ratio of secret message over the medium etc., The proposed algorithm dynamically ranks and selects the right compression algorithm to be used for the given secret file to occupy lesser embedding space in stego-image.
Face Attributes Recognition Based on One-Way Inferential Correlation Between Attributes

Attributes recognition of face in the wild is getting increasingly attention with the rapid development of computer vision. Most prior work tend to apply separate model for the single attribute or attributes in the same region, which easily lost the information of correlation between attributes. Correlation (e.g., one-way inferential correlation) between face attributes, which is neglected by many researches, contributes to the better performance of face attributes recognition. In this paper, we propose a face attributes recognition model based on one-way inferential correlation (OIR) between face attributes (e.g., the inferential correlation from goatee to gender). Toward that end, we propose a method to find such correlation based on data imbalance of each attribute, and design an OIR-related attributes classifier using such correlation. Furthermore, we cut face region into multiple region parts according to the category of attributes, and use a novel approach of face feature extraction for all regional parts via transfer learning focusing on multiple neural layers. Experimental evaluations on the benchmark with multiple face attributes show the effectiveness on recognition accuracy and computational cost of our proposed model.
Artificial Neural Network Based Load Balancing in Cloud Environment

With heavy demand for cloud technology, it is important to balance the cloud load to deliver seamless Quality of Services to the different cloud users. To address such issues, a new hybridized technique Artificial Neural Network based Load Balancing (ANN-LB) is introduced to calculate an optimized Virtual Machine (VM) load in cloud systems. The Particle Swarm Optimization (PSO) technique is used to perform task scheduling. The performance of the proposed ANN-LB approach has been analyzed with the existing CM-eFCFS, Round Robin, MaxMin, and MinMin algorithms based on MakeSpan, Average Resource Utilization, and Transmission Time. Calculated values and plotted graphs illustrate that the presented work is efficient and effective for load balancing. Hybridization of ANN and iK-mean methods obtains a proper load balancing among VMs and results have been remarkable.
Robust Absolute and Relative Pose Estimation of a Central Camera System from 2D-3D Line Correspondences

We propose a new algorithm for estimating the absolute and relative pose of a camera system composed of general central projection cameras such as perspective and omnidirectional cameras. First, we derive a minimal solver for the minimal case of 3 line pairs per camera, which is used within a RANSAC algorithm for outlier filtering. Second, we also formulate a direct least squares solver which finds an optimal solution in case of noisy (but inlier) 2D-3D line pairs. Both solver relies on Grobner basis, hence they provide an accurate solution within a few milliseconds in Matlab. The algorithm has been validated on a large synthetic dataset as well as real data. Experimental results confirm the stable and real-time performance under realistic outlier ratio and noise on the line parameters. Comparative tests show that our method compares favorably to the latest state of the art algorithms.
Trust in digital evidence

When conducting digital forensic investigations, practitioners are concerned with understanding whether the digital artefacts they encounter are authentic and have not been the subject of tampering activity. This is one factor of investigations which could potentially impact of the reliability of any subsequent findings. Some research into this problem has already been undertaken, however there is currently very little understanding of how effective current technique are. In this paper, a Systematic Review (SR) of existing literature will be undertaken to identify the techniques that currently exist to authenticate digital artefacts. Furthermore, consideration will be given to understanding whether existing techniques are effective in solving the problem of digital artefact authentication and whether they are accessible by the practitioner community. The results of the SR will show that while research effort has been devoted to this problem, there are relatively few techniques which can be generally applied. Additionally, very little effort has been devoted to understanding the effectiveness of these techniques. Furthermore, the lack of standardised datasets for evaluation makes comparison between techniques impossible and none of the identified papers provided publicly available implementations. The shortcomings identified in this SR show that further research effort in this area could benefit the community in its aim to produce more reliable findings in forensic investigations.
Representation Learning in Graphs for Credit Card Fraud Detection

Representation learning in graphs has proven useful for many predictive tasks. In this paper we assess the feasibility of representation learning in a credit card fraud setting. Data analytics has been successful in predicting fraud in previous research. However, the research field has focused on techniques which require tedious and expensive hand-crafting of features. In addition, existing works often ignore information related to the network of transactions. Representation learning in graphs tackles both of these challenges. First, it provides the possibility to tap into the relational and structural aspects of the transaction network and leverage these in a predictive model. Second, it featurizes the graph without the need for manual feature engineering. This work contributes to the literature by being the first to explicitly and extensively show how fraud detection modeling can benefit from representation learning. We discern three different approaches in this paper: traditional network featurization, an inductive representation learning algorithm and a transductive representational learner. Through extensive experimental evaluation on a real-world dataset we show that state-of-the-art representation learning in graphs outperforms traditional graph featurization.
Using Deep Neural Networks to Address the Evolving Challenges of Concealed Threat Detection within Complex Electronic Items

INTRODUCTION'We identified vulnerabilities with TSA's screener performance, screening equipment, and associated procedures' 1 -report published by the Office of Inspector General (OIG), U.S. Department of Homeland Security on covert testing of Transportation Security Administration (TSA) screening checkpoint effectiveness (2017).Undercover investigators managed to conceal threat items, such as explosives, imitation guns, knives, etc. through the security checkpoints 70%∼80% of the time at varius US airports 2 .Although these statistics show an evident improvement compared to two years previously, the failure rate above 70% is cause for concern.
Full-field 3D displacement and strain analysis during low energy impact tests employing a single-camera system

Characterising materials behaviour during dynamics events such as impact require several features related to monitoring and instrumentation which find in full-field techniques one of the main responses due to high spatial and, nowadays, also temporal resolution. 3D-Digital Image Correlation has provided an interesting tool to evaluate the material response during impact testing. It contributes with displacement and strain fields at high frame rates, which represents enormous potential for subsequent analysis. However, 3D-DIC also presents some issues which may condition the experimentation. The first of them is the requirement of a stereoscopic optical system, which lead to important economic issues if high speed cameras are required. Secondly, certain impact arrangements have limited space which make complicated to dispose of a proper stereoscopic system observing an area of interest with a suitable angle that makes it possible r a successful processing. In this work, a recent procedure combining Fringe projection and 2D-DIC is presented as an alternative for impact testing analysis. As main characteristic, the optical arrangement is simplified to only one high speed camera and an additional simple LCD projector which makes it easier the experimentation, reducing significantly the setup cost. To demonstrate that capabilities of FP+2D-DIC as a simpler approach with results comparable to those obtained with 3D-DIC, in this work it was analysed 2, 3, 4, and 5 mm thickness aluminium plates under low speed impacts with different energy levels. Impacts were performed employing an impact tower with a hemispherical impactor, connected to a load cell. During the impact, displacement and strain maps were measured at the non-impacted face of the specimen, using a single camera system implementing the combination of FP and 2D-DIC techniques. Permanent displacement and strain maps were validated with those obtained with a 3D-DIC system, supporting the potential of this FP+2D-DIC approach for impact testing.
Digital Curriculum Importance for New Era Education

Education is the key to success for any society. The quality of education is directly linked with the curriculum it contains, the mode of delivery, how effective it is, as well as the volume of dispersion among the society. Digital curriculum (DC) changes the tradition of old teaching and bring higher results in different domains. This chapter examines how DC has played a main role in education in society at various levels and the importance of digital curriculum in education for teachers and students. The digital curriculum has been upgraded to enhance the status of education in a new era. Change provides a greater focus on building the era education. Moreover, the effect and the future of the DC will be illustrated. This research will further bring several ways of implementing DC in different domains, different benefits of it, and its impact on the economic growth as well as in digital governance.
DehazeGAN: Underwater Haze Image Restoration using Unpaired Image-to-image Translation

In this paper, we propose a Generative Adversarial Networks (GAN)-based image restoration method. Our method adopts an unpaired image-to-image translation network to learn the characteristics of underwater haze images. To enhance restoration, we propose multiple cyclic consistency losses that capture the detail of images and suppress distortion image translation. To prepare unpaired images of clean and degraded scenes, we collected images from Flickr and filter out false images using image characteristics. The proposed network is tested on public underwater images and shows promising results under severe image distortion.
Achieving Digital-Twin Through Advanced Analytics Support: A Novelty Detection Framework to Highlight Real-Time Anomalies in Time Series

As Industrial plants evolve towards massive digitalization and their "digital twin" architectures are constantly enriched with a wide range of advanced analytics solutions that are becoming part of the ordinary control operations, the quality of the input data becomes of paramount importance. But as the number of variables, KPIs and processes monitored in plants raise of several orders of magnitude, automated procedures are needed for an effective monitoring. We have developed a novelty detection framework with the objective of monitoring each and every variable in the plant, detecting anomalies in real time and therefore allowing the operators to investigate more in depth on the possible causes before any damage is made. In order to do so, we had to define the "normality" of a signal, which can heavily vary from one to the other. Therefore, we setup a learning procedure that integrates several steps. First, we need to distinguish the normal periods from the anomalous ones. While there are failures that are known to have impacted at certain times certain sections of the plant, three will definitely be more anomalies that have remained hidden so far. We hence labelled each timestamp of the series using an isolation forest algorithm. Using the obtained normal dataset, we then extracted for each sensor the features that we have identified to better characterize a time series in its normal operating conditions. First, we select the signals in the plant that are the most correlated with the one at hand and fit a Ridge Regression and estimate the residual distribution. Then, we extract statistics such as mean, standard deviation, mean length and frequency of frozen periods, outliers, NaNs, Fast Fourier Transform and Welch’s approach for spectral density estimation. Finally, we heuristically define a number of tests capable of distinguishing a normal from an anomalous time window. With this procedure, we are able to detect in real time whenever a signal is behaving differently from the way it is expected to. Depending on the operator’s experience, it would then be possible to understand whether it is a sensor malfunction or whether it indicates that something is wrong with the physical phenomenon, determining different actions, such as maintenance rescheduling. We have therefore conceived a dashboard that allows each operator to input its feedback, producing a refined dataset ready for continuous retraining. Ultimately, this anomaly detection framework will be used also to filter out the inputs to many advanced analytics solutions, guaranteeing the quality of their results.
Robust Learning of Multi-Label Classifiers under Label Noise

In this paper, we address the problem of robust learning of multi-label classifiers when the training data has label noise. We consider learning algorithms in the risk-minimization framework. We define what we call symmetric label noise in multi-label settings which is a useful noise model for many random errors in the labeling of data. We prove that risk minimization is robust to symmetric label noise if the loss function satisfies some conditions. We show that Hamming loss and a surrogate of Hamming loss satisfy these sufficient conditions and hence are robust. By learning feedforward neural networks on some benchmark multi-label datasets, we provide empirical evidence to illustrate our theoretical results on the robust learning of multi-label classifiers under label noise.
An Empirical Approach to Understanding Data Science and Engineering Education

As data science is an evolving field, existing definitions reflect this uncertainty with overloaded terms and inconsistency. As a result of the field's fluidity, there is often a mismatch between what data-related programs teach, what employers expect, and the actual tasks data scientists are performing. In addition, the tools available to data scientists are not necessarily the tools being taught; textbooks do not seem to meet curricular needs; and empirical evidence does not seem to support existing program design. Currently, the field appears to be bifurcating into data science (DS) and data engineering (DE), with specific but overlapping roles in the combined data science and engineering (DSE) lifecycle. However, curriculum design has not yet caught up to this evolution. This working group report shows an empirical and data-driven view of the data-related education landscape, and includes several recommendations for both academia and industry that are based on this analysis.
Towards a Māori Telepresence System

The A̅tea project is investigating how technology can be used to help Ma̅ori share and preserve knowledge, culture, and language. One aspect of this is the use of telepresence systems to connect members of an iwi (tribe) or ru̅nanga (council) with their marae (meeting place, communal centre) and community. We describe the range of interactions we can support through voxel-based telepresence, and give examples of their application in the context of the A̅tea project. We also discuss the issues around presence, as a defining element of virtual environments, that are important to this project, particularly the distinction between copresence and social presence. We show that it is possible to distinguish between these two using existing evaluation methods.
Learning hierarchical concepts based on higher-order fuzzy semantic cell models through the feed-upward mechanism and the self-organizing strategy

Concept representation and learning is a basic topic of artificial intelligence. The aim of this paper is to explore the representation issue and the learning issue of abstract concepts. In this paper, we first introduce higher-order fuzzy semantic cell models to represent abstract concepts, based on which we develop a hierarchical representation of concepts called abstract concept graphs. Then, we put forward an unsupervised algorithm to learn a second-order abstract concept graph from a given data set. This method combines the feed-upward mechanism and the self-organizing strategy. In addition, we provide an evaluation metric for this learning algorithm. A series of experiments is provided to demonstrate the feasibility and validity of the proposed method. We also conduct a preliminary exploration of the potential application of this method to image segmentation.
A progressive learning framework based on single-instance annotation for weakly supervised object detection

Fully-supervised object detection (FSOD) and weakly-supervised object detection (WSOD) are two extremes in the field of object detection. The former relies entirely on detailed bounding-box annotations while the later discards them completely. To balance these two extremes, we propose to make use of the so-called single-instance annotations, i.e., all images that contain only a single object are labeled with the corresponding bounding-boxes. By using such instance annotations of the simplest images, we propose a progressive learning framework that integrates image-level learning, single-instance learning, and multi-instance learning into an end-to-end network. Specifically, our framework is composed of three parallel streams that share a proposal feature extractor. The first stream is supervised by image-level annotations, which provides global information of all training data for the shared feature extractor. The second stream is supervised by single-instance annotations to bridge the features learning gap between the image level and instance level. To further learn from complex images, we propose an overlap-based instance mining algorithm to mine pseudo multi-instance annotations from the detection results of the second stream, and use them to supervise the third stream. Our method achieves a trade-off between the detection accuracy and annotation cost. Extensive experiments demonstrate the effectiveness of our proposed method on the PASCAL VOC and MS-COCO dataset, implying that a few single-instance annotations can improve the detection performance of WSOD significantly (more than 10%) and reduce the average annotation cost of FSOD greatly (more than 5 times).
ATuMm: Auto-tuning Memory Manager in Apache Spark

Apache Spark is an in-memory analytic framework that has been adopted in the industry and research fields. Two memory managers, Static and Unified, are available in Spark to allocate memory for caching Resilient Distributed Datasets (RDDs) and executing tasks. However, we found that the static memory manager (SMM) lacks flexibility, while the unified memory manager (UMM) puts heavy pressure on the garbage collection of JVM on which Spark resides. To address these issues, we design an auto-tuning memory manager (ATuMm) to support dynamic memory allocation with the consideration of both memory demands and latency introduced by garbage collection. We implement our new memory manager in Spark 2.2.0 and evaluate it by conducting experiments in a real Spark cluster. Our experimental results show that our auto-tuning memory manager can reduce the total garbage collection time and thus further improve the performance (i.e., reduced latency) of Spark applications, compared to the existing Spark memory management solutions.
Improving the performance of solar panels by the used of dual axis solar tracking system with mirror reflection

Abstract This work proposes the dual axis solar tracker with mirror reflection for optimum output of solar panel by using arduino unoR3 as the control unit. The objectives of this work are to track and optimize the maximum output power of the solar panel by designing and developing a dual axis solar tracker with mirror reflection. The system includes a 10 watt solar panel, an arduino unoR3 and a customized mechanical body to carry the solar panel. This system will track and detect the angle of the sun to locate the surface of solar panel at the position and the angle where it can get maximum amount of energy. The sensors will detect the position of the sun and servo motors act as free moving neck to make it easier to move freely depending on the angle detected. The Light Depending Resistor (LDR) will be used in tracking system. These LDR will detect the existence of sunlight and therefore the mechanical hardware will move horizontal and vertical axis depending on the value of LDR detected to follow the angular degree of sun in order to get maximum and best result of absorbing energy. The final result obtained from dual axis solar tracker showed that the output power has been maximized compared to stationary panel. Based on the experimental result, it show that the designed system successful improve the performance of the solar panel.
Approximate QoS Rule Derivation Based on Root Cause Analysis for Cloud Computing

Ensuring proper quality of service (QoS) is essential for cloud service providers and customers alike. To this end, cloud systems must rely as much as possible on automated and efficient methods of monitoring, introspection, and recovery. In particular, automated recovery is essential to ensure long-term reliability and availability because human intervention is too slow and not every situation can be anticipated. In turn, automated recovery requires both efficient monitoring and accurate identification of root causes to ensure that the same causes will not lead to failures in the future. Current cloud systems use an in-memory time-series database for dynamic analysis or aggregation purposes. When done at all, root cause analysis serves the convenience of reporting and does not need to be very accurate. As a result, recent studies lack details on how to accurately find root causes from time-series monitoring data. This study proposes a novel event-driven monitoring rule inference method based on dynamic case-based reasoning and shape-based root cause analysis. It is designed for autonomous recovery so as to guarantee long-term QoS of cloud systems. The accuracy and performance of the approach are evaluated using realistic monitoring data combining more than a decade of experience as a major cloud service provider (Yahoo). The results show that our approach makes effective use of monitoring data in improving overall QoS and hence opens interesting directions.