Reviewerly/exHarmony huggingface dataset:
Design and Implementation of Content-Based Image Retrieval on FPGA card

In recent years, real-time Image Processing (IP) has seen some applications and has posed a fast-growing field of programmable gate arrays (FPGA) implementation that has been the essential subject of the current engineering trend their simplicity and immense potential.One of the main problems of some IP is image extraction, which is that you can retrieve valuable knowledge about the quality of the images from the data effectively.We define the implementation of CBIR in FPGA image processing applications.The fundamental goal behind this initiative is to meet the shortest timeline by the optimum use of services.We are undertaking a review of this embedded approach's benefits and discussing its usefulness with a series of benchmark images.Implementation based on the FPGA indicates the viability of the suggested technique.The practical implementation of all processes using the FPGA Verilog code has been compiled into the ALTERA QUARTUS-II software method.The results obtained show qualitatively and quantitatively the feasibility of the proposed procedure.
Data de-duplication is a standout amongst the most explicit coagulation strategy for dispensing with indistinguishable duplicates of improved information in distributed storage to Defeat the measurement of the storage space and the recovery of the transmission ability. Information pressure performs coherent decrease of storage room by least hashing. To ensure the classification of delicate information while supporting de-duplication, the merged encryption procedure has been proposed to encode …

Data de-duplication is a standout amongst the most explicit coagulation strategy for dispensing with indistinguishable duplicates of improved information in distributed storage to Defeat the measurement of the storage space and the recovery of the transmission ability. Information pressure performs coherent decrease of storage room by least hashing. To ensure the classification of delicate information while supporting de-duplication, the merged encryption procedure has been proposed to encode the information before re-appropriating. To more readily ensure information security, it endeavors to formally address the issue of approved information de-duplication. Not quite the same as customary de-duplication, the benefits of client improved by upgrading their capacity limit and security examination .it likewise present a few new de-duplication developments supporting approved copy check in crossover cloud design. Security examination shows that are set up to keep away from unapproved get to. As proof of notion, we are updating the model of our proposed approved copy control system. and lead proving ground tests utilizing our model. We demonstrate that our proposed approved copy check conspire brings about negligible overhead contrasted with ordinary operations. Deduplication has demonstrated to accomplish high space and cost investment funds and many distributed storage suppliers are presently embracing it. Deduplication can diminish capacity needs by up to 90-95 percent for reinforcement.
Email Spam Detection using Ensemble Methods

The swiftly growth of spam email has escalated the need to upgrade the existing spam detection and filtration methods. There is the existence of several machine learning methods for the classification and detection of email spam but these lacks in some cases. In this research work ensemble methods are adapted to detect the email spam. The machine learning methods of Multinomial Naïve Bayes and J48 Decision Tree algorithms are considered and ensembled. The considered ensemble methods are bagging and boosting. The experimentation is conducted on the dataset of CSDMC2010 Spam corpus. The results for the considered dataset are evaluated using individual classifiers, bagging, and boosting ensemble approaches. The system performance is accessed in terms of precision, recall, f-measure, and accuracy. The experimental outcomes indicates the distinguish results for the detection of email spam using ensemble methods.
Developing Displayed Flipbook as Teaching Material for Assisting Teacher to Teach English in Online Learning for the Fourth Grade Elementary School Students

The lack of teaching materials that can facilitate students' independent learning has an impact on students' lack of understanding. In addition, the lack of teaching materials also has an impact on student learning outcomes. The purpose of this research is to create teaching materials with Flipbook. This type of research is developed using three stages, namely design, development, and evaluation. Data was collected through interviews and document analysis. There are two instruments used in this study, namely interview guidelines and expert assessment. The subject of this research is the English teacher. The technique used to analyze the data is descriptive qualitative and quantitative analysis. The results showed that the quality of the Flipbook assessed by the first expert got a score of 56. The quality assessment given by the second jury expert was 61, with a good category. The quality rating given by the user is 62, with a good category. Thus, flipbooks are categorized as suitable teaching materials. So it can be said that the flipped book teaching materials developed are feasible to be applied in the learning process. This research implies that the Flipbook developed can be used by students in an independent study.
Meningkatkan Keamanan Web Menggunakan Algoritma Advanced Encryption Standard (AES) terhadap Seragan Cross Site Scripting

In the millennial era, the internet has become a very basic need to support community activities in various fields, one of which is education. SMK Maritim Nusantara in supporting the learning process uses a web-based application called e-learning which is used by teachers and students. The school website has several documents in digital form that must be kept confidential, such as student data, teacher data, student grades. After scanning using the Acunetix WVS 10.5 application, information was obtained about the security holes found on the website https://www.e-learning.smkmn.sch.id, with the results of which there were 8 (eight) attacks with details, 2 (two). ) a hight category with the name Cross site scripting (XSS) attack, 4 (four) medium categories with the name HTML form attack without CSRF protection and 2 (two) low categories with the name Password type input attack with auto-complete enabled. The most dangerous attack category / hight is XSS. XSS attack is an attack that inserts malicious code in the form of javascript through an input form that aims to steal cookies and then uses the cookie to enter the web legally so that data can be manipulated and even deleted. For this reason, a strong system is needed to maintain security, confidentiality of school data, one way that can be used is by implementing the Standard Advance Encryption Algorithm (AES), this algorithm has a high level of security and uses little memory in its operation so that it does not burdensome to process and easy to implement. The results of research conducted by applying the AES Algorithm explain that previously there were 2 (two) high category vulnerabilities called XSS attacks, after the implementation of the AES Algorithm, the XSS attack vulnerability was no longer found. Based on the results obtained in the study, it can be concluded that the implementation of the AES Algorithm in tokens can improve the security of the https://www.e-learning.smkmn.sch.id website from XSS attacks.
Agric Financing and its Impact on Agricultural GDP: An ARDL Approach

This research investigated the impact of agricultural financing on agricultural sector contribution to GDP in Nigeria. The objective of the study was to determine whether agricultural financing had any significant impact on agricultural contribution to GDP in Nigeria. The dataset covered a 36year period from 1981-2016. Data was sourced from the CBN statistical Bulletin. The dependent variable was the Agricultural GDP (AGDP). The independent variables were government funding {represented by Government Capital Expenditure on Agriculture (GCAG) and Government Recurrent Expenditure on Agriculture (GRAG) }, Agric Credit Guarantee scheme Fund (ACGSF) and Commercial Banks’ Credit, Loans and Advances to the Agricultural Sector (CBCA). Standard analytical tests were used to determine the properties of the data. The Auto Regressive Distributed Lagged regression model (ARDL) was used to estimate the data. The study found that government funding to agriculture and Agric Credit Guarantee scheme Fund (ACGSF) had a non-significant impact on Agricultural Contribution to GDP (AGDP). On the other hand, the study found that Commercial Banks’ Credit, Loans and Advances to the Agricultural Sector (CBCA) had a positively significant impact on AGDP. The study recommended the sustenance of government policy that encouraged consistent injection of funds into agriculture. It advocated that a sizeable portion of Commercial Banks’ credit should be channelled to agricultural production.
Large-scale text classification with deeper and wider convolution neural network

The dominant approaches for most natural language processing (NLP) tasks like text classification are recurrent neural networks (RNNs) and convolutional neural networks (CNNs). These architectures are usually shallow and only have one or two layers, which cannot easily extract inner patterns in natural language. Different from the original feature of image pixels with regularity, words and phrases are highly abstracted from human knowledge without direct correlation. Shallow models only capture the surface relation between them while deep models cannot directly apply them. Therefore, a shuffle convolution neural network (SCNN) is proposed to address the shallow learning problem by introducing wider inception cell and deeper residual connection. In the paper, the difficulty of applying deep models to NLP problems is overcome by tricks of shuffling channel input and reshaping the output dimension in the first layer. The results of the experiments carried out in this research work demonstrate that the proposed SCNN makes a great improvement in accuracy and efficiency compared to shallow models.
Deduplication in Cloud Storage

Cloud Computing is well known today on account of enormous measure of data storage and quick access of information over the system. It gives an individual client boundless extra space, accessibility and openness of information whenever at anyplace. Cloud service provider can boost information storage by incorporating data deduplication into cloud storage, despite the fact that information deduplication removes excess information and reproduced information happens in cloud environment. This paper presents a literature survey alongside different deduplication procedures that have been based on cloud information storage. To all the more likely guarantee secure deduplication in cloud, this paper examines file level data deduplication and block level data deduplication.
Root Based Stemmer for Telugu Script

In this paper, a new stemmer has been proposed named as “Root based stemmer”. This stemmer is strictly based on Dravidian script. Stemming can be used to pick up the effectiveness of information retrieval. In proposed Root based stemming technique, each and every token is compared against with all the words of a valid root words dictionary until a match is found. Then extract the matched string or substring from a token and identified as valid root. The present work is aimed to build dictionary based stemmer to extract valid root words for Indian languages especially for Telugu and compare the results with existing stemmers.
An Extended Artificial Neural Network Assisted Hybrid Harris Hawks and Whale Optimizer to Find Optimal Solution for Engineering Design Problems

The algorithms that have been developed recently have decorous behavior to solve and find optimum solution to various optimization problems in search space. Withal such calculations stuck in issues nearby quest space for compelled engineering problems. In succession to achieve an optimal solution a hybrid algorithmic approach is proffered. Artificial Neural Network (ANN) is considered as better solution for the known outputs. A hybrid variant of applying ANN on Harris Hawks and Whale Optimization Algorithm (ANNHHOWOA) is proposed to achieve effective solution for engineering problems. The effectiveness of proposed algorithm is tested for various non-linear, non-convex and standard engineering problems and to approve consequences of proposed algorithm standard benchmarks and multidisciplinary design problems have been considered. The validation endorsed that the results shown by ANNHHOWOA showed much better results than individual ANN, HHO and WOA and its effectiveness on multidisciplinary engineering problems.
Word sense disambiguation in Tamil using Indo-WordNet and cross-language semantic similarity

Word sense disambiguation is the way to compute the correct sense of a word. It is considered as one of the important subtasks in natural language processing, machine translation and information retrieval. WSD found improving the overall performances of these systems. The job of WSD is to eliminate all senses of a word except the appropriate one as per the given context. The work in Tamil linguistics domain for information retrieval or natural language processing is very less. WSD can be performed in supervised and unsupervised manner. Here, we have proposed an unsupervised approach to disambiguate Tamil words in a given context using the context words and their dictionary gloss definitions. We have proposed two variants of our approach. The first approach uses the number of word overlapping between the glosses of context words whereas the second one uses the similarity between the glosses of context words with that of the ambiguous word. The second one found best among the two. For our approach, we have used Tamil Indo-WordNet, Oxford Tamil Dictionary and English WordNet dictionary glosses. Our method achieves better result in recognising correct senses in Tamil text.
Data Mining and Machine Learning: Design a Generalized Real Time Sentiment Analysis System on Tweeter Data Using Natural Language Processing

Sentiment analysis is a task, that is becoming recently important for numerous companies. Because the consigner subscriptions on social media like Facebook, twitter and other side get their product reviews. If the company wants to track tweets about their brand to command over the impact on time or many website analyze the comments on their articles. This will help them to track comments and impact. So the sentiment analysis is an automated system that collects and analyzes the content and generates the desired results. This paper proposes a sentiment analysis system for twitter posts. Proposed system will work on real time tweets. System is also designed in such a way that this can analyze data related to any topic. Python programming language is used to extract tweets form twitter feeds. Proposed system also calculates the level of sentiments. That how much negative or positive tweets are. This paper also presents some real time result analysis.
Variable Length K-SVD: A New Dictionary Learning Approach and Multi-Stage OMP Method for Sparse Representation

The Sparse representation research field and applications have been rapidly growing during the past 15 years. The use of overcomplete dictionaries in sparse representation has gathered extensive attraction. Sparse representation was followed by the concept of adapting dictionaries to the input data (dictionary learning). The K-SVD is a well-known dictionary learning approach and is widely used in different applications. In this thesis, a novel enhancement to the K-SVD algorithm is proposed which creates a learnt dictionary with a specific number of atoms adapted for the input data set. To increase the efficiency of the orthogonal matching pursuit (OMP) method, a new sparse representation method is proposed which applies a multi-stage strategy to reduce computational cost. A new phase included DCT (PI-DCT) dictionary is also proposed which significantly reduces the blocking artifact problem of using the conventional DCT. The accuracy and efficiency of the proposed methods are then compared with recent approaches that demonstrate the promising performance of the methods proposed in this thesis.
Homogeneous Intensity within the Rayleigh Length and Enhanced Depth of Focus for Gaussian Beams

The smallest width of a Gaussian beam at the focal plane is called waist and is only limited by the diffraction limit of the light.The waist determines the Rayleigh length which is the distance from the waist at which the width of the Gaussian beam increases by a factor of 2 1/2 or the intensity decreases by a factor of 2. With regard to laser material processing, these changes in spot size and intensity are typically undesirable.For example, variations in surface profiles or thickness of the processed material could require adjustment of focus.In this simulation study, we present a beam shaping method based on a development of the Fundamental Beam Mode Shaper (FBS) concept for enhanced depth of focus.Using this concept, an enhancement of Rayleigh length of 20% and a more homogeneous peak intensity around the focal plane is achieved.
Image Generation u sing Variational Autoencoders

The proposed system generates new images from the existing images using variational autoencoders. The autoencoder aims to map the input image to a multivariate normal distribution in the latent space. Variational autoencoder transforms input image into a remarkable output by reducing the reconstruction and KL divergence losses. The primary advantage of implementing variational autoencoder over the other autoencoders is that it follows a specific probability distribution called Gaussian distribution and results in generating high quality images.
An Efficient Radical Image Encryption Based on 3-D Lorenz Chaotic System

We propose a new encryption strategy based on the Lorenz chaotic system. Scrambling and diffusion techniques are carried out availing the chaotic sequences rendered by Lorenz system. The chaotic sequences tactically clutter the pixel positions and curtail the relationship between the original image and the encrypted image.Wittingly a high-dimensional system delivers a well robust cryptosystem bearing good efficiency and resistivity. Here we demonstrate through dilated measures and statistical analyses that the proposed system prominently improves security scales and is also potential in withstanding to various sort of attacks. Securing images in cyber space became vital in communication for instance, military affairs, national security, diplomatic affairs, medical database and so on expanding its attention broadly.
Entropy Measures for Data Analysis: Theory, Algorithms and Applications

Entropies and entropy-like quantities are playing an increasing role in modern non-linear data analysis and beyond [...]
Introduction to Volume 11, Issue 4 of topiCS

The October 2019 issue of topiCS encompasses two topics. We lead with Biette and Stone's topic, Remembering with Others and follow with a very different topic for topiCS that was sparked by a paper published in another journal titled, What Happened to Cognitive Science?. Lucas M. Bietti (Centre National de la Recherche Scientifique (CNRS), Télécom ParisTech, Interdisciplinary Institute for Innovation (i3), Paris, France) and Charles B. Stone (John Jay College of Criminal Justice, and The City University of New York, both in NY, USA) have organized a topic based around the idea that “remembering the past through conversations with others is a uniquely human endeavor”. To this end, they have recruited researchers to write 10 research papers and five commentaries. In addition, their introduction to this topic is written to communicate their excitement and passion to a broad range of Cognitive Scientists – not simply to others working in this specialty area. As always, the Editors' introduction to their topic can be freely downloaded from our Publisher's website by anyone interested in this topic. Our second topic, Commentaries on Rafael Núñez's article, “What happened to cognitive science?” was inspired by a paper published by Núñez and colleagues1 this past June in Nature Human Behavior. In that paper, Núñez et al., provide bibliotechnic analyses in support of their hypothesis that, as a discipline, Cognitive Science has not fulfilled its early promise. Exactly what that “early promise” was as well as the nature of the evidence that Núñez and colleagues cite is discussed by 10 Commentaries written by one or a team of Cognitive Scientists – including one paper written by an entire Department of Cognitive Science. These 10 short papers provide an interesting snapshot of the angst and optimism in our research community in 2019. topiCS encourages letters and commentaries on all topics, and proposals for new topics. Letters are typically 400–1,000 words (max two published pages) and will be published without an abstract or references (possibly 1–2 but usually none). Commentaries are often solicited by Topic Editors prior to the publication of their topic. However, commentaries after publication are also considered and should range between 1,000 to 2,000 words. Most commentaries would not have an abstract and would not include many references. The Executive Editor and the Senior Editorial Board (SEB) members are constantly searching for new and exciting topics for topiCS. Feel free to open communications with a short note to the Executive Editor ([email protected]) or an SEB member (SEB members are listed under the Editorial Board heading on the publisher's homepage for topiCS http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1756-8765/homepage/EditorialBoard.html).
Evaluating Image Quality of DIBR-synthesized Views in Texture and Depth Domains

Accurately predicting the quality of depth-image-based-rendering (DIBR) synthesized images is of great significance in promoting DIBR techniques. Recently, Many DIBR-synthesized image quality assessment (IQA) algorithms based on quantifying the distortion existed in texture images have been proposed. However, these methods ignore the damage of DIBR algorithms on the depth structure of DIBR-synthesized images and thus fail to evaluate the visual quality of DIBR-synthesized images accurately. To this end, we present a DIBR-synthesized image quality assessment metric with multi-modal information (i.e. integrating texture and depth information), dubbed as MMI. MMI predicts the quality of DIBR-synthesized images by jointly measuring the texture structure and depth structure of the synthesized image. The design principle of our MMI is that the local geometric distortion, introduced by DIBR techniques in the hole-filling process, destroys the texture structure and the depth structure of DIBR-synthesized images. Thus, we can accurately evaluate DIBR-synthesized image quality by a joint representation of texture structure and depth structure. Experiments show that our MMI is better than the competing state-of-the-art IQA algorithms in predicting DIBR-synthesized image quality.