Reviewerly/exHarmony huggingface dataset:
AIR-IA: an analogous image removal approach using the intelligent archive

Deduplication is maturing as a standard attribute on backup and archives, whereby the aim is to free the storage space by removing the duplicates. Considering the storage room demand and justifiable deletion, this paper proposes a multi-container intelligent deduplication image archive system; where analogous images are disposed from the system based on the similarity approach. Similarity-aware image deduplication is achieved by calculating image fingerprints and the images are deleted when their hamming distance matches the predefined threshold. A probability model is addressed for the overall probability of getting similar images on the respective containers based on their relative storage and similarity scores of the images. In addition, the linear optimisation model is formulated to target data minimisation and storage space maximisation; which is further verified with the dataset. We perform experimentation of our work on the existing as well as synthesised datasets and various accuracy metrics are calculated in terms of precision, recall, f-score and deduplication ratio. It is observed that binary hashes used in our system give the fair contribution in removing similar images.
Self embedding digital watermark using hybrid method against compression attack

&lt;p&gt;In the modern time interacting with digital world become standard life activity, human need a way to protect properties as individuals or corporals, and we do that by embedding a digital mark to the target, and this technique call digital watermarking. But there still is a chance to manipulate or even remove this marks we embed for protection with various attacks like adding noises, compression-decompression or bits manipulations, and that why companions, individuals, laboratories are still developing new methods to embed this marks and make them more robust and more hard to detect for others. There are so many methods for digital watermarking, so we chose the least significant bits watermarking (LSB-watermarking) to provide an invisible digital watermarking, and on top of that we proceed with the blind LSB-watermarking method so that we don't get bind to the original image, and for our attack we chose compression joint photographic experts group (JPEG) compression because it’s the most used method for image and videos compression along with singular value decomposition (SVD) to make our mark as robust as possible. And the results we gain from our method are promising and it did give as high quality digital watermarking.&lt;/p&gt;
RankGPES: learning to rank for information retrieval using a hybrid genetic programming with evolutionary strategies

In recent years, Learning to Rank has not only shown effectiveness and better suitability for modern Web Era needs, but also has proved that it outperforms traditional ranking in terms of accuracy and efficiency. Evolutionary approach to Learning to Rank such as RankGP [37] and RankDE [3] have shown further improvement over non-evolutionary algorithms. However when Evolutionary algorithms have been applied to a large volume of data, often they showed they required so much computational efforts that they were not worth applying to industrial applications. In this thesis, we present RankGPES: a Learning to Rank algorithm based on a hybrid approach combining Genetic Programming with Evolution Strategies. Our results not only showed that it outperformed both RankGP [37] by 20% and RankDE [3] by 6% in terms of accuracy but also it showed it required significant less amount of time to converge to a near-optimal result.
Do Healthcare Metadata Models Designed for Web Publishing Meet the Accreditation Standards? A Case Study in the Healthcare and Medical Education

This article reports comparison results from studying the alignment of selected metadata models, used to manage E‑Learning Materials (ELM) in the medicine and healthcare education, against the accreditation standards of the European Accreditation Council for Continuing Medical Education (EACCME). The study aims to (1) illustrate the variety of application profiles developed as metadata models in that field, (2) identify the alignment of the metadata models with the criteria set by the EACCME. This study com¬pared several application profiles, developed based on established metadata standards, using an evaluation matrix created using the accreditation criteria set by the EACCME. Further technical criteria were added to compare between the metadata models and their alignment with the new techniques used for publishing data online. Such techniques aid in the searchabililty and discovery of online content via the internet. Therefore, the use of XML and RDF technologies combined with the use of ontologies and controlled vocabulary was emphasized in the implementation of many metadata models. One limitation faced during conducting this research was the poor documentation of the methodology for designing the metadata models. Despite this, it is apparent that the EACCME criteria can work as best practices guidelines for describing the content of digital libraries with medical and healthcare content in order to be accredited later on. The study showed that the medicine and healthcare educational content requires specialized metadata models that consider having specific properties such as clinical history and expiry time of its material shown in some of the application profiles. Further research should be conducted to evaluate the completeness and conformity of applying the application profiles in digital libraries and online repositories to the standards’ guidelines.
Recognition of Human Emotion Detection and Annotation, using Local Descriptor and Support Vector Machine

In multimedia data analysis, video tagging is the most challenging and active research area. In which finding or detecting the object with the dynamic environment is most challenging. Object detection and its validation are an essential functional step in video annotation. Considering the above challenges, the proposed system designed to presents the people detection module from a complex background. Detected persons are validated for further annotation process. Using publically available dataset for module design, Viola-Jones object detection algorithm is used for person detection. Support Vector Machine (SVM) authenticate the detected object/person based on it local features using Local Binary Pattern (LBP). The performance of the proposed system presents given architecture is effectively annotating the detected people emotion.
A Web Based MATLAB Solution for Classifying Micro-Calcification on Mammograms

In the aeon of deep learning, CNN outperform significant part in medical image analysis. CADx(“Computer Aided Detection and Diagnosis “) for Mammography utilizes significant features to detect and diagnose breast malignancy. Now a day CNNs based CADx are worth popular due to automatic relevant features extraction. CNNs can be trained from ground up for medical images but due to finite number of medical images transfer learning and data augmentations are used for training. And also performance of CADx can be decreased due to some factors like appearance of noise, artifacts, low contrast in both CC and MLO views of Mammogram and pectoral muscles which appears in MLO view of Mammogram. Mammograms can contain different types of abnormality like Micro-Calcification, Masses, Architectural distortion in case of breast cancer. In this work we developed a Web Based MATLAB Solution for the classification of Micro-Calcification malignancy either benign or malignant. This web based solution performs different steps to remove artifact, to enhance contrast, to segment pectoral muscle and to extract breast profile. At the final step proposed system classify mammograms either into benign or malignant. It has been examined on mammographic images containing both views from CBIS-DDSM database.
Novel Supervised Learning Scheme for Optimizing the Classification Performance of Breast Cancer MRI

Usage of machine learning has been always proven potential in identifying the best solution from the set of complex variables with the highly inter-twined relationship of problems. Similarly, supervised learning approach is one essential operation under machine learning that has always contributed in the area of healthcare and diagnostics. However, there are still some problems associated with the detection and classification of complex disease condition that is yet to be solved. The proposed system introduces a novel supervised learning approach along with a novel feature extraction scheme which is more progressive and less iterative. The proposed system considers a case study to perform classification of breast cancer using Magnetic Resonance Imaging (MRI) where it is subjected to normalization first followed by a novel segmentation process that compliments the classification operation too. The study outcome shows that the proposed system offers better classification performance in contrast to existing supervised approaches.
VM Consolidation Plan for Improving the Energy Efficiency of Cloud

Abstract Achieving energy-efficiency with minimal Service Level Agreement (SLA) violation constraint is a major challenge in cloud datacenters owing to financial and environmental concerns. The static consolidation of Virtual Machines (VMs) is not much significant in recent time and has become outdated because of the unpredicted workload of cloud users. In this paper, a dynamic consolidation plan is proposed to optimize the energy consumption of the cloud datacenter. The proposed plan encompasses algorithms for VM selection and VM placement. The VM selection algorithm estimates power consumption of each VM to select the required VMs for migration from the overloaded Physical Machine (PM). The proposed VM allocation algorithm estimates the net increase in Imbalance Utilization Value (IUV) and power consumption of a PM, in advance before allocating the VM. The analysis of simulation results suggests that the proposed dynamic consolidation plan outperforms other state of arts.
Earthquake information products and tools from the Advanced National Seismic System (ANSS)

First posted September 9, 2020 For additional information, contact: Director, Geologic Hazards Science CenterU.S. Geological SurveyBox 25046, MS-966Denver, CO 80225-0046 This Fact Sheet describes post-earthquake products and tools provided by the Advanced National Seismic System (ANSS) through the U.S. Geological Survey Earthquake Hazards Program. The focus is on products that provide situational awareness immediately after significant earthquakes.
Triplet Contents based Medical Image Retrieval System for Lung Nodules CT Images Retrieval and Recognition Application

Content Based Medical Image Retrieval (CBMIR) has found its relevance in medical diagnosis by processing massive medical databases based on visual and semantic features and user preferences. In this paper we address two issues such as retrieval and recognition. We present a novel method called Triplet-CBMIR for lung nodules CT images retrieval and recognition application. A Triplet CBMIR is a combination of three properties: Visual Features (Shape and Texture), Semantic Features and Relevance Feedback. Dataset training is done using: Preprocessing, Feature Extraction, Selection, Nodules Sign Detection and Clustering. In preprocessing we perform image scaling, denoising and normalization. In feature extraction, two methods are presented such as Hybrid Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT), Bounding-Box based Convolutional Network (CNN) for visual and semantic features extraction. Then optimum set of feature vectors are selected using Mutual Information based Neighborhood Entropy (MINε). Based on selected features, lung nodule sign is detected using K-nearest Neighbor (KNN) algorithm in which Hassanat Distance used and similar images are grouped using Multi-Self organizing Map (SOM). For similarity measurement, d_1 distance metric is used. Benchmark dataset such as LISS and LIDC are used for the study. Performance matrices such as Average Precision Rate (APR), Average Retrieval Rate (ARR), Average Recognition Rate (ArR), Running Time found in the simulation results are compared with some other already present state-of-the-art works. The proposed method shows a significant improvement as compared to other existing methods.
A Hybrid Set of Handwriting Features for Handwritten Recognition

Handwriting of each person is unique since each person has their own unique and different style of handwriting. Handwriting verification can be performed in two ways, dynamic and static. The dynamic verification process is the writer dependent whereas the static verification process is the writer independent procedure. The features can be spatial, structural, statistical, geometrical, graphological, and from other feature extraction techniques. In this work, we are considering the combination of multilevel feature set for writer recognition and identification purpose. A dataset of different handwriting samples collected from 100 different writers is used for this experiment. A decision tree classifier with random forest implementation is used for recognition and identification of writer with 98.2% accuracy.
Accelerating Information Security in Cloud Computing using a Novel Holomorphic Scheme

In the past decades, many security algorithms are invented and used in many applications for encryption and decryption of information or data. All the secure algorithms were uses the security key of size 8bit, 16bit, 32bit, 64bit and 128bits for encryption and decryption but literature survey says that higher the key size, higher the security. But the higher key size has number more bits and its requires more memory for storage and also to perform each and every bit through computational operations leads to more delay. To address these issues, the key and plain text are of 128bits and converted into integer. The converted integer values of key and plaintext are encrypted and decrypted using Holomorphic through Advanced Encryption Standard (AES). The research method is sophisticated and more protected through meaning fully lesser key in size and is accomplished for encryption in terms integer key and plaintext moderately than binary bits, therefore larger size of key and plaintext can be minimized and reduced the computational complexities. Finally the cipher text is uploaded into cloud through Real Time Transport Protocol (RTP) and Real Time Transport Control Protocol (RTCP) for storage. The main problem with continuous sharing of information into cloud is security attacks so in this research work, three different multimedia signals such as ECG, EEG and biomedical images are converted into integer and encrypted using AES. The stored data can access by the authorized users and can decode the information after decryption using AES.
Privacy Preservation of Healthcare Data in Hybrid Cloud using a Hybrid Meta-Heuristics Based Sanitization Technique

Over the recent years, the expansion of cloud computing services enable hospitals and institutions to transit their healthcare data to the cloud, thus it provides the worldwide data access and on-demand high quality services at a cheaper rate. Despite the benefits of healthcare cloud services, the associated privacy issues are widely concerned by individuals and governments. Privacy risks rise when outsourcing personal healthcare records to cloud due to the sensitive nature of health information and the social and legal implications for its disclosure. Over the recent years, a privacy-preserving data mining (PPDM) technique has become a critical issue for the problems. Our goal is to design a privacy-preserving outsourcing framework under the hybrid cloud model. In this work we propose a Hybrid Ant Colony Optimization and Gravitational Search Algorithm (ACOGSA) to express the problem of hiding sensitive data through transaction deletion. Thus, it reduces the side effects of the hybrid cloud. Substantive experiments will be carried to compare the performance of the designed algorithm with the state-of-the-art approaches in terms of the side effects and database similarity (integrity). Over the past to sanitize the databases used for hiding sensitive information, a few heuristic approaches have been proposed. The method used for the comparison involves GA, PSO, ACO, and Firefly framework.
DEVELOPMENT OF TELEMEDICINEPROCESSSUPPORT VISUAL TOOLKIT

Thediagnosis and treatment processesof diseases, the effectiveness of different programming approaches in the automation of the treatment process were studied.A telemedicine process support visual toolkithas been suggested, which will allow the doctorstobuild the treatment schemesusingsimple blocks, depending on the patient's inputdata.The visual blocks that are planned to haveintheIDE canbe divided tothe following groups:dataFlow, arithmetic and logical operationsblocks, Artificial intelligenceblocksthat will be implemented on the basis ofvarious machine learning models, Blocks intended for read data and configure the devices of theIOT network, and custom blocks.The toolkit will translate the represented treatment visual schemes to python code, which will call the corresponding functions of represented treatment regimen’s blocks, which will be developed by us.
Analisis IT Governance Menggunakan Framework Cobit 5 Domain DSS, MEA dan BAI

Information Technology Governance is a process for overcoming problems, and making decisions and frameworks that must be accounted for. Information Technology is good, able to increase the effectiveness and efficiency in human activities. Evaluation of information technology governance is needed to determine the success of IT. In overcoming problems, a framework is needed. One of them is COBIT 5 (Control Objective for Information Related Technology version 5). The Cobit 5 framework has selectable domains, namely the DSS (Deliver, Service and Support), MEA (Monitor, Evaluate and Asses), and BAI (Build, Acquire and Implement) domains. This research method uses several methods, namely literature method, method of conceptual model, and analysis. The purpose of this study is to improve the audit program, overcome problems and decision making by using COBIT 5 by using the DSS, MEA, and BAI domains. From this research it can be concluded that in overcoming problems and decisionmaking can be done using the COBIT 5 framework with domains DSS, MEA, and BAI.
Spam detection in twitter using machine learning Algorithms

Twitter is being one of the most generally utilized interpersonal organizations on the planet which has been a key objective for interlopers. In this work, Identifying spammers in twitter System is to be proposed which isolate the spammers tweets among specialists tweets by distinguishing and recognizing twitter messages. Here the emphasis depends on the tweet level spammer recognition. This work is a methodology for recognizing spammer tweets among specialists tweets utilizing three classifiers, for example, Best First Decision Tree, K Nearest Neighbor. This thusly prompts better tweet characterization. The thing to be considered is the preparation information is created naturally as master tweets and spammers tweets. It is finished by investigating the tweets and extricating watchwords. The HITS calculation is utilized to rank the spammers. Three classifiers here are utilized to characterize the tweets and casting a ballot strategy is utilized to mark the most extreme estimations of the tweets which has been arranged by the classifiers.
Artificial Intelligence Design Suggestions for Apparel Retail Counters

Artificial Intelligence is the requirement felt widely in retail industry support system, especially in Apparel Retail counters. On different occasions, functions, parties and celebrations, the shopping is done using these Apparel retail counters. Artificial Intelligence is an area of computer science which simulates the human intelligence and human sensory abilities. So, Artificial Intelligence is becoming an imperative tool for the retail counters to enhance the quality, improve customer’s experience, automation, and to lower the operating costs. This paper will discuss how Artificial Intelligence is influencing the retail counters by automation of cash management, improving customer’s experience and providing other supports for adjustment of prices and prediction of prices considering seasonal as well as occasional effects.
Corporate Communication Strategy and Sustainable Community Relations of Indorama Eleme Petrochemical Limited

This study examined corporate communication strategy and sustainable community relations of Indorama ElemePetrochemical Limited.To achieve this, four objectives were set with equivalent four research questions.Stakeholders' theory and corporate community theory guided the study.The population of this study is 74,240 which is the sum of the respective populations of the six host communities of Indorama Elem Petrochemical Limited with survey as the research design.The researcher employed mixed method using questionnaire and interview as data gathering instruments.For the quantitative data, a sample of 382 was arrived at using Krejcie and Morgan sample determinant table and for the qualitative data; a sample size of 9 was used comprising 3 staff of corporate communication department of Indorama Eleme Petrochemical Limited and the 6 members of Project Advisory Committee (PAC).Respondents for the interview were purposively selected based on their knowledge of the subject matter and the fact that they can offer the needed answer to the study questions.The findings of the study show that corporate communication strategies of the company proved to be major contributory factor that enhanced sustainable community relations and ensured mutual understanding and harmony that currently exist between the company and host communities.The study therefore recommends that the Indorama Eleme Petrochemical Limited model of corporate communication strategies and sustainable community relations be adopted by companies operating not only in Rivers State but in Nigeria especially the 7.5% equity share to host communities.This is to ensure mutual understanding between companies and host communities.
Neural Network and Cuckoo Optimization Algorithm for Remote Sensing Image Classification

Since Remote sensing images are used for a variety of applications, classification of such images is essential for extracting information. However, due to the lack of sufficient training examples, classification of remote sensing images is more complex. Also, in traditional method of classification, the statistical procedure uses only the gray value of the digital images to extract information. Neural network (NN) has a better impact in this domain. The proposed work uses an integrated NN and cuckoo Optimisation Algorithm (COA) for classification. The NN picks, organise and constructs the data to be trained into a network. The network is then trained and tested. The COA is combined with NN to aid the task of classification and to calculate the cost function. The optimization algorithm provides an appropriate feature suitable for the neural network classifier to produce final output. This hybridized technique presents the effective advantages of the neural network and further investigate the classification of the multispectral remote sensible images. The performance and error rate of the system is better when compared with other classical methods. The observed results illustrate the effectiveness of the COA to optimize the cost function. As training parameters in Neural network is found to be NP hard, utilization of COA involve its responsibility in optimizing the learning rate of NN.