Reviewerly/exHarmony huggingface dataset:
Performance Comparison of Julia Distributed Implementations of Dirichlet Process Mixture Models

The Dirichlet process mixture model (DPMM), one of the nonparametric Bayesian mixture models, is receiving more and more attentions from the statistical learning community. It has been demonstrated its great potentials in clustering analysis. When computational complexity increases as numbers of observations and features grow, the serial algorithms of DPMM need long processing time and cannot handle large volume of data on a single machine. To improve the computational efficiency, several parallel methods and implementations were proposed and implemented with C++ and Julia programming languages by different authors and publicly available on GitHub or published as a Julia package for users to download. However, the scalability of multi-cores and multi-node has not been thoroughly evaluated and compared among different implementations, even for multiple implementations of the same proposed distributed PPMM method. We selected two recent Julia implementations of parallel sampler via sub-cluster splits method proposed by Change and Fisher and performed a scalability comparison on supercomputer clusters. This paper presents some insights on the applicability of both implementations in terms of increasing number of dimensions of the feature space and provides some potential improvement strategies on multi-node scalability.
Data Replication for Reducing Computing Time in Distributed Systems with Stragglers

In distributed computing systems with stragglers, various forms of redundancy can improve the average delay performance. We study the optimal replication of data in systems where the job execution time is a stochastically decreasing and convex random variable. We show that in such systems, the optimum assignment policy is the balanced replication of disjoint batches of data. Furthermore, for Exponential and Shifted-Exponential service times, we derive the optimum redundancy levels for minimizing both expected value and the variance of the job completion time. Our analysis shows that, the optimum redundancy level may not be the same for the two metrics, thus there is a trade-off between reducing the expected value of the completion time and reducing its variance.
Neutrophil Scattering Data Driven Pre-Microscopic Flagging of Acute Leukemic Cases

The hematology analyzer, Sysmex XN-1000, generates white blood cell count with varying scattering intensities during a complete blood count (CBC) analysis.The objectives of the study were to study the predictive role of median and coefficient of variation of neutrophil scattering items in blood samples for differentiation of leukemic subjects.We evaluated six neutrophil scattering parameters: neutrophil side scatter mean intensity, neutrophil side fluorescence light (SFL) mean intensity, neutrophil forward scatter mean intensity, neutrophil side scatter area distribution width (NE-WX), neutrophil SFL area distribution width (NE-WY), and neutrophil forward scatter area distribution width (NE-WZ), measured in white blood cell differential scattergram generated by the hematology analyzer (Sysmex XN-1000) at an academic medical center.We collected 433 blood samples from acute myeloid leukemia (AML) and acute lymphoid leukemia (ALL) cases and normal controls. AML group showed highly significant differences in the mean values compared with the control group. Out of six neutrophil scattering items, NE-WX, NE-WY, and NE-WZ showed high efficiency, with area under the curve (AUC) values of 0.764, 0.748, and 0.757, respectively, to differentiate AML from ALL cases and control groups. When comparing combined acute leukemia cases (AML plus ALL) with the control group, NE-WX, NE-WY, and NE-WZ generated highly significant AUC values (0.840, 0.884, and 0.801, respectively).The neutrophil scattering parameters generated during CBC analysis provide a new tool for the prediction of acute leukemia and its lineage.
RAINWATER HARVESTING FOR FLOOD PEAK REDUCTION IN WAY AWI CATCHMENT, INDONESIA

This study is motivated by floodings which occur frequently in Jagabaya village, Way Awi catchment, Bandar Lampung city, Indonesia. In addition to the intensity of rainfall, this area is considered as an urban area with intense housing and insufficient drainage capacity during heavy rain. This study aims to analyze flood peak reduction by simulating the application of rain barrels for each house. The volume of rain barrels used in the simulation is 0.5 to 5 m3. An Intensity-Duration-Frequency (IDF) curve is generated usingshort-duration rainfall data (i.e. 5, 10, 15, 30, 60, 120, 180, 360 and 720 minutes) recorded from Panjang Meteorological Bureau, Lampung. The rational method, a simple rainfall-runoff model, is used to calculate runoff generated both from catchment and rooftop. The result shows that the application of rain barrels may reduce flood peak up to 28% for 2 year return period of flood, while for 100 year return period of flood, the reduction may reach 13.64%. From this study, it can be concluded that rainwater harvesting utilizing rain barrel is significant in reducing flood peak. Furthermore, this result can be used to persuade both the society as well as the local government to do such flood mitigation in this densely populated urban area
The value of personal data is the Data Protection and Privacy preliminary condition

The EU Regulation 2016/679 (GDPR) is a landmark, and we describe the value of personal data as the main starting point to address any possible issue. Once clarified the meaning of that value as a top-level point from which it not possible prescind and the strict relationship with ethics, we present one of the risks, that is the 'synthetic human profile' and can be realised by the use of faces images available on the web. In conclusion, we propose a blockchain system to guarantee the data subjects identity.
Visual Tracking With Online Assessment and Improved Sampling Strategy

The kernelized correlation filter (KCF) is one of the most successful trackers in computer vision today. However its performance may be significantly degraded in a wide range of challenging conditions such as occlusion and out of view. For many applications, particularly safety critical applications (e.g. autonomous driving), it is of profound importance to have consistent and reliable performance during all the operation conditions. This paper addresses this issue of the KCF based trackers by the introduction of two novel modules, namely online assessment of response map, and a strategy of combining cyclically shifted sampling with random sampling in deep feature space. A method of online assessment of response map is proposed to evaluate the tracking performance by constructing a 2-D Gaussian estimation model. Then a strategy of combining cyclically shifted sampling with random sampling in deep feature space is presented to improve the tracking performance when the tracking performance is assessed to be unreliable based on the response map. Therefore, the module of online assessment can be regarded as the trigger for the second module. Experiments verify the tracking performance is significantly improved particularly in challenging conditions as demonstrated by both quantitative and qualitative comparisons of the proposed tracking algorithm with the state-of-the-art tracking algorithms on OTB-2013 and OTB-2015 datasets.
Group-Teaching: Learning Robust CNNs From Extremely Noisy Labels

Deep convolutional neural networks have achieved tremendous success in a variety of applications across many disciplines. However, their superior performance relies on correctly annotated large-scale datasets. It is very expensive and time-consuming to get the annotated large-scale datasets, especially in the medical field. While collecting a large amount of data is relatively easy, given the amount of data available on the web, but these data are highly unreliable, and they often include a massive amount of noisy labels. The past research works have shown that these noisy labels could significantly affect the performance of the deep convolutional neural networks on image classification. However, training a robust deep convolutional neural network with extremely noisy labels is a very challenging task. Inspired by the co-teaching concept, this paper proposes a novel method for training a robust convolutional neural network with extremely noisy labels, which is called group-teaching. Specifically, we train a group of convolutional neural networks simultaneously, and let them teach each other by selecting possibly clean samples for each network in each mini-batch. Each network back propagates the samples selected by other networks except itself and then it updates itself. The empirical results on noisy versions of CIFAR-10 and CIFAR-100 datasets demonstrate that our method is superior to the state-of-the-art methods in the robustness for noisy labels. Particularly, to verify the efficacy of our group-teaching in real-world noisy labels distribution, we have also validated the effectiveness of our method on the real-world noisy WebVision1000-100 dataset. The results show that our method has achieved higher performance than the state-of-the-art methods.
Enhancing precision of phase estimation by homodyne-mediated feedback and classical driving

A two-level atom interacting with a single-mode cavity is considered. The atom-cavity system is driven by a classical field with the homodyne-mediated feedback. The influences of the driving field and two types of the feedback on the precision of the phase estimation are investigated. The results show the quantum Fisher information (QFI) can be preserved by the homodyne-mediated feedback and the classical driving when the system parameters are suitable. In addition, the initial value of the relative phase greatly effects the QFI. Therefore, the phase estimation precision of the two-level atom can be enhanced, and the extent of the enhancement is decided by not only the driving frequency and the type of quantum feedback, but also the initial parameters of the two-level system.
A Proposal for Evaluating the Clearness Index Based on Extra Terrestrial Radiations at a Given Site

Forecasting of intermittent power produced due to renewable energy resources is highly desirable to accurately determine its future values to be utilized for various purposes like scheduling, contingency solutions by the grid operators. Among the renewable resources, solar energy is the most widely used that has grown leaps and bounds over the past due to technology advancements and it is anticipated that it is going to dominate the future as well. This power is often used independently or integrated into the grid. Such integration of solar generation is demanding the reliable forecast of the same. Clearness index determination is very important for accurately forecasting the solar power. This paper presents a methodology to determine the clearness index values which is of paramount importance to handle the forecasting issue to provide better forecast of the Photovoltaic (PV) power. This method includes finding out the clearness index by estimating the Extra-terrestrial Radiations at a given location and using the Global Solar radiation past data available at a given site. This methodology has been implemented successfully for district Rajouri of Jammu & Kashmir state and clearness index values have been estimated for the same. Moreover the correlation between Extra-terrestrial radiation and the Global solar radiation from the past data for the chosen site was found around 0.95 which is quite high.
Grey Wolf algorithm based on S-function and particle swarm optimization

Abstract Based on the analysis of the shortcomings of the grey wolf optimization algorithm, an improved grey wolf optimization algorithm (SGWO) is proposed. The algorithm uses the convergence factor based on S-function change to balance the global search and local search ability of the algorithm. At the same time, the proportion weight based on Euclidean distance of step size and the individual optimal position of the particle swarm optimization algorithm are introduced to update the grey wolf position, thus speeding up the convergence speed of the algorithm to 8. The simulation results of three classical test functions show that the SGWO algorithm has higher accuracy and better stability.Introduction
Compressed Video Restoration Using a Generative Adversarial Network for Subjective Quality Enhancement

High Efficiency Video Coding (HEVC) is a widely used video compression standard that minimizes the sacrifice in visual quality. Convolutional neural networks (CNNs) are being used as a post-processing tool for video restoration degraded by compression. Improving on CNN-based video restoration, this paper attempts a new generative adversarial network (GAN)-based video restoration called a compressed video restoration generative adversarial network (CVRGAN). Although a GAN is widely used for perceptual image enhancement in super-resolution and noise reduction, it is not yet used for compressed video restoration. The proposed CVRGAN is the first attempt to utilize a GAN to create the texture of a degraded image, and consequently, to generate detailed textures that were lost due to compression. In order to avoid the side effect of a GAN boosting the blocking and ringing artifacts incurred by compression, the CVRGAN employs a new content loss that is a combination of VGG feature difference, which represents a perceptual loss, and an objective loss, such as mean squared error (MSE) or mean absolute error (MAE). The new loss function is effective in the enhancement of subjective image quality while suppressing artifact boosting. An extensive mean opinion score (MOS) test shows that the CVRGAN achieves an improvement in perceptual quality over previous CNN-based video restoration.
Content Based Image Retrieval using Adaptive Semantic Signature

Semantic gap optimization is the major objective of Content Based Image Retrieval(CBIR) system. It is challenging to derive possible query objectives in case of an image having multiple objects or complex background. The query requirements are defined as the Bag of Words(BoW) and used for retrieval of the similar images. The saliency based segmentation is popularly used to eliminate the background objects that works well when the image is focused on the foreground objects. In case saliency-map fail to extract the foreground object, the feature vectors are equally biased by the background scene and that imposter the actual query objectiveThis paper proposes a two phase framework CBIR system. In the first phase we compute the Local Binary Pattern and the RGB Colour information(LBPC) features of all the images in the large image database. Then the pertinent images are clustered and the image on the cluster centre is assumed as ideal indexed image for the same class. In the second phase, each input image is divided into nine blocks having common central sub-block. The LBPC feature is extracted for all the blocks and only the feature of the block that best matches with the indexed image is stored in the database. The selection of the best block image feature minimizes the effect of the background scene. This approach helps select the potential saliency block of the image with respect to the indexed image. The result section shows the performance of the proposed framework while BoW is defined by the cluster indexed image.
Using Bayesian Methods for Predicting the Development of Children Autism

In this paper, we propose a methodology for using static Bayesian networks (BN) in the tasks of predicting the development of the children’s disease “autism” aged 4 to 11 years. Methods of constructing the structure of a static BN, parametric learning, validation are considered, a sensitivity analysis and scenario analysis of “What-if’ are carried out. The model was designed in collaboration with pediatricians, as well as subject matter experts in the selection and quantification of input and output variables.
A Qualitative Analysis of the Effects of Task Complexity on the Functional Role of Information

An important question in interactive information retrieval (IIR) is: How do task characteristics influence users' needs? In this paper, we investigate the effects of cognitive task complexity on the types of information considered useful for a task. We characterize information types from two perspectives. From one perspective, we classify task-related information items based on inherent characteristics (referred to as info-types): factual statements, concepts/definitions, opinionated statements, and insights---tips/advice related to the task domain. From a second perspective, we used Byströ m and J\"a rvelin's framework~\citebystrom1995 to define information types based on how the information might be used to complete the task (referred to as functional roles): (1) to help the task doer understand the task requirements (problem information); (2) to help the task doer strategize on how to approach the task (problem-solving information); and (3) to help the task doer learn about the task domain (domain information). Our results suggest that: (1) cognitive task complexity influences the functional roles of information items deemed useful for the task (RQ1); (2) certain info-types are more (or less) likely to play certain functional roles (RQ2); and task complexity influences the variety of functional roles played by info-types (RQ3).
A Prototype of Service Oriented Architecture for Precision Agriculture

Precision Agriculture (PA) takes advantage of digital technologies to improve agricultural production and its economic and environmental sustainability. The main issues in the implementation and spread of PA include (a) harmonization and (b) interpretation of heterogeneous data collected from different sources; (c) interoperability of systems and data; (d) implementation of new algorithms and methodologies coming from research projects; (e) semantic enablement of meta-data. To help solve these issues we propose a software infrastructure prototype developed in the framework of the SATFARMING project, and based on Service Oriented Architecture (SOA) concept, free open-source software (FOSS), interoperability of data and web services through the accomplishment of international standards, semantic enablement of data description.
Initial Validation of the Cybersecurity Concept Inventory: Pilot Testing and Expert Review

We analyze expert review and student performance data to evaluate the validity of the Cybersecurity Concept Inventory (CCI) for assessing student knowledge of core cybersecurity concepts after a first course on the topic. A panel of 12 experts in cybersecurity reviewed the CCI, and 142 students from six different institutions took the CCI as a pilot test. The panel reviewed each item of the CCI and the overwhelming majority rated every item as measuring appropriate cybersecurity knowledge. We administered the CCI to students taking a first cybersecurity course either online or proctored by the course instructor. We applied classical test theory to evaluate the quality of the CCI. This evaluation showed that the CCI is sufficiently reliable for measuring student knowledge of cybersecurity and that the CCI may be too difficult as a whole. We describe the results of the expert review and the pilot test and provide recommendations for the continued improvement of the CCI.
A Comprehensive Study of Data Mining-based Financial Fraud Detection Research

Financial Fraud Detection is a subject concerning many industries viz. banking, insurance, government agencies, law enforcement, Etcetera. A vast amount of financial exchanges and transactions happen daily and fraud cases are on a constant ascent. This makes the traditional methods of fraud detection very tedious to utilize and implement. Auditing the transactions manually is no longer feasible; and, therefore, such practices must constantly adapt to the increasing load. Use of data mining helps predict and, thus, quickly detect fraud, so that an organization can take immediate action to minimize effective costs. This study reviews the implementation and efficiency of Data Mining Methods- Decision Trees, Artificial Neural Networks, Logistic Model, and Bayesian Belief Networks, which offer fundamental solutions to predicaments in detecting Financial Frauds. This paper subsequently also addresses the challenges faced by the application of Data Mining Methods in Financial Fraud Detection (FFD) and the additional organisational requirements for its implementation.
Blood transfusion prediction using restricted Boltzmann machines

The availability of blood transfusion has been a recurrent concern for medical institutions and patients. Efficient management of this resource represents an important challenge for many hospitals. Likewise, rapid reaction during transfusion decisions and planning is a critical factor to maximize patient care. This paper proposes a novel strategy for predicting the blood transfusion need, based on available information, by means of Restricted Boltzmann Machines (RBM). By extracting and analyzing high-level features from 4831 patient records, RBM can deal with complex patterns recognition, helping supervised classifiers in the task of automatic identification of blood transfusion requirements. Results show that a successfully classification is obtained (96.85%), based only on available information from the patient records.
Analytics over RDF Graphs

The continuous accumulation of multi-dimensional data and the development of Semantic Web and Linked Data published in RDF bring new requirements for data analytics tools. Such tools should take into account the special features of RDF graphs, exploit the semantics of RDF and support flexible aggregate queries. In this paper, we present an approach for applying analytics to RDF data, based on a high-level functional query language called HIFUN. According to that language, each analytical query is considered as a well-formed expression of a functional algebra and its definition is independent of the nature and structure of the data. In this work, we detail the required transformations, as well as the translation of HIFUN queries to SPARQL and we introduce the primary implementation of a tool, developed for these purposes.
Optimized Approach (SPCA) for Load Balancing in Distributed HDFS Cluster

Hadoop is an open-source utility which allows users to provide massive input in terms of data and facilitates the computation. Role of Hadoop in load balancing is enormous which allows the user to configure the network of nodes having master/slave nodes. Hadoop’s typical architecture takes into consideration the default configuration for the machine as homogeneous, but many of the real-time application or clusters of nodes will have the homogeneous configurations. Thus, an effort is made in this paper to consider the homogeneity of the nodes in clusters and build an efficient algorithm which does load balancing in an efficient way when compared with the default balancer of Hadoop which works well only on homogeneous nodes.