Reviewerly/exHarmony huggingface dataset:
White Blood Cells Classification Using Built-in Customizable Trained Convolutional Neural Network

This paper presents a new method of applying Convolutional Neural Network (CNN) on microscopic blood smear images to classify leukocytes or commonly known as white blood cells (WBCs). The proposed classification method leverages an existing novel segmentation algorithm to extract the template or sample WBC images from a given blood smear image. These template images are used to train the built-in customizable CNN, which is later simulated to classify each sample image as one of the five existing types of leukocytes. A total of ten blood smear images, each with more than one type of WBC, were used in our experiments. Furthermore, we increased the recognition rate of our proposed approach incrementally by training five unique CNNs. We were able to identify an optimum trained CNN out of the five trained CNNs and achieved an average recognition rate of 84%. Using our built-in custom trained CNN, we have been able to not only achieve a competitive recognition rate with similar existing methods, but also identify more than one type of leukocyte in a single blood smear image that contains multiple leukocyte types. To the best of our knowledge, this has been done for the first time and unlike other competitive methods, it does not limit the classification processes to only recognize a single leukocyte type in any given blood sample image.
Gaussian Process Based Disturbance Compensation for an Inverted Pendulum

This paper considers an inverted pendulum with velocity input to demonstrate the validity of a new method for estimating unknown disturbances such as nonlinear friction and damping. With only one run of a given swing-up strategy, the input and output data of the system are collected. They are the basis for a Gaussian process modelling that is used to estimate the unknown disturbance term. The learned Gaussian process model can be used subsequently to predict this disturbance and serve for an online disturbance compensation. Simulation results and a comparison with a classical observer-based disturbance compensation indicate the benefits of the proposed approach.
Discrete Cosine Transformation Based Approach for Offline Handwritten Character and Numeral Recognition

Abstract Handwritten Character Recognition (HCR) of Indic scripts is one of the most challenging as compared other scripts. Recognition of handwritten character is more difficult one as compares with the printed one due to their similar structure and orientation. To have a proper recognition system for handwritten characters various researchers have adapted various methods and still yet to have more feasibility and high recognition rate to achieve. Here in this proposed work we have completely focused on handwritten characters and numerals of various scripts of different regions of India such as Bengali, Odia scripts. To have a proper solution for the ambiguities that arises in handwritten one and which has been resolved using the Discrete Cosine Transformation (DCT) as feature extraction part. This transformed based featured is used to calculated GLCM values of each images of handwritten numerals and characters. To maintain the novelty of the work in simulation part we have listed up variance nature of the individual’s images. Subsequently we have also harnessed the SVM, Neural Network, and Quadratic Classifier over dataset to report the recognition rate. After validating the proposed work, we can conclude that adopted model is quite helpful in building the automatic recognition system for both handwritten character and numerals to have solution for real time problems.
Public Accusation on the Internet

Purpose – In a digital environment, a simple accusation has the potential to permanently attach to a person's identity. Our purpose here is to identify several types of accusations that persist in the internet environment: person to person accusations, media documented accusations, and accusations by the state. Approach – Using a typology of cases and legal analyses, the authors trace how accusations proliferate and persist across the internet and offer a set of social and legal explanations for the salience of public accusation online. Findings – The authors ultimately find that in contemporary society, the act of accusing increasingly replaces the desire or need for a fair and just outcome. The authors close by discussing implications for the accused and potential avenues for remedy. Originality – Our contribution bridges sociological and legal perspectives on the intersection of free speech, defamation, and digital media.
Analog/Mixed-Signal Integrated Circuits for Quantum Computing

Future error-corrected quantum computers will require analog/mixed-signal control systems capable of providing a million or more high-precision analog waveforms. In this article, we begin by introducing the reader to superconducting transmon qubit technology and then explain how the state of a quantum processor implemented in this technology is controlled. By example, we then show how one can implement one part of the quantum control system as a cryogenic integrated circuit and review experimental results from the characterization of such an IC. The article concludes with a discussion of analog/mixed-signal design challenges that must be overcome in order to build the system required to control a million-qubit quantum computer.
BNNet: Towards A Comprehensive Bangla WordNet

Advanced Natural Language Processing applications are highly dependant on the handiness of linguistic assets such as WordNet. Despite being a popular language in the world, Bangla has an inadequacy of many linguistic resources. Currently available resources are either inadequate for useful operation or not easily accessible for the research community. Constructing a Bangla WordNet again is a significant hurdle as it requires the critical integration of bilingual English-Bangla resources. In this paper, we take the hurdle to combine freely available online materials to build comprehensive English-Bangla and Bangla-English bi-lingual resources. We further build a Bangla WordNet, BNNet, using these resources and map it with the Princeton English WordNet. We again provide useful user interfaces and RESTful API to access and manipulate the Bangla WordNet as well as develop an English-Bangla similarity checker using the proposed system, BNNet.
A machine learning research template for binary classification problems and shapley values integration

<h2>Abstract</h2> This paper documents published code which can help facilitate researchers with binary classification problems and interpret the results from a number of Machine Learning models. The original paper was published in Expert Systems with Applications and this paper documents the code and work-flow with a special interest being paid to Shapley values as a means to interpret Machine Learning predictions. The Machine Learning models used are, Naive Bayes, Logistic Regression, Random Forest, adaBoost, Classification Tree, Light GBM and XGBoost.
Phishing Website Prediction: A Machine Learning Approach

Phishing is an act of stealing our precious, personal, sensitive data such as credentials that which we use for accessing the resources and services, available across the cyberspace. Seeing these rapidly growing phishing attacks and their adverse effect on the businesses including individual users, it has now become a need for the organizations and individuals worldwide to be able to effectively predict the phishing website and differentiate them from legitimate ones. The aim of this research paper is to efficiently predict the phishing websites so that users may be benefitted from this study and prevent them from getting trapped. In this paper, machine learning techniques are used for prediction. Data mining is used worldwide by almost every face of the society viz. business organizations, govt. organizations, and other kind of data collectors to extract knowledge from the collected data. On the other hand, machine learning is a data mining technique that is used to analyze, classify the data, and efficiently predict the results for the estimation and planning by all of the organizations all around the globe. Classification algorithms, namely logistic regression, decision tree, and random forest classification, are used to predict the fake websites and presented their comparison of their predictions achieved. The results have been presented in numeric format as well as graphically with the help of chart. The data used is taken from UCI machine learning online repository. The seed value is changed and analyzed, and results achieved are least accuracy of 95.93, 97.96, and 98.78% of accuracy as the highest as well. Some future study and applying some good practices may help in designing a better and more accurate solution for the prediction of the phishing website, just by examining the URL and its features.
Unsupervised foveal vision neural architecture with top-down attention

Deep learning architectures are an extremely powerful tool for recognizing and classifying images. However, they require supervised learning and normally work on vectors of the size of image pixels and produce the best results when trained on millions of object images. To help mitigate these issues, we propose an end-to-end architecture that fuses bottom-up saliency and top-down attention with an object recognition module to focus on relevant data and learn important features that can later be fine-tuned for a specific task, employing only unsupervised learning. In addition, by utilizing a virtual fovea that focuses on relevant portions of the data, the training speed can be greatly improved. We test the performance of the proposed Gamma saliency technique on the Toronto and CAT 2000 databases, and the foveated vision in the large Street View House Numbers (SVHN) database. The results with foveated vision show that Gamma saliency performs at the same level as the best alternative algorithms while being computationally faster. The results in SVHN show that our unsupervised cognitive architecture is comparable to fully supervised methods and that saliency also improves CNN performance if desired. Finally, we develop and test a top-down attention mechanism based on the Gamma saliency applied to the top layer of CNNs to facilitate scene understanding in multi-object cluttered images. We show that the extra information from top-down saliency is capable of speeding up the extraction of digits in the cluttered multidigit MNIST data set, corroborating the important role of top down attention.
Automatic Facial Forgery Detection Using Deep Neural Networks

The recent and rapid growth in image manipulation techniques raises grave concerns with respect to document forgery. It also diminishes trust in the data available publicly and can cause harm by spreading false information. In this paper, the problem of facial forgery by training a neural network to distinguish between real and fake faces has been addressed. The convolutional neural network has been trained with the help of transfer learning, which allows us to utilize computer vision models like ResNet and AlexNet, which were pre-trained on massive datasets. The network was evaluated on a dataset of 2041 images provided by the computational Intelligence and Photography Lab at the Department of Computer Science, Yonsei University. Results show that ResNet-152 is able to detect facial forgery with an accuracy of up to 76.79%.
Aggregated Capability Assessment (AgCA) For CAIQ Enabled Cross-cloud Federation

Cross-Cloud Federation (CCF) enables resource exchange among multiple, heterogeneous Cloud Service Providers (CSPs) to support the composition of services (workflow) hosted by different providers. CCF participation can either be fixed, or the types of services that can be used are limited to reduce the potential risk of service failure or secure access. Although many service selection approaches have been proposed in literature for cloud computing, their applicability to CCF i.e., cloud-to-cloud interaction, has not been adequately investigated. A key component of this cloud-to-cloud paradigm involves assessing the combined capability of contributing participants within a federation and their connectivity. A novel Aggregated Capability Assessment ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AgCA</i> ) approach based on using the Consensus Assessment Initiative Questionnaire from Cloud Security Alliance is proposed for CCF. The proposed mechanism is implemented as a component of a centralized broker to enhance the quality of the selection process for participants within a federation. Our experimental results show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AgCA</i> is a useful tool for partner selection in a dynamic, heterogeneous and multilevel cloud federation.
IOT Based Smart and Secure Surveillance System Using Video Summarization

Nowadays, security is important for every commercial property to prevent robberies and thefts and to ensure secure safe business operations. In CCTV (Closed-circuit television) systems, the data is non-intelligently recorded which produces huge volumes. It makes it difficult to search for the desired content from the big data. It is found from the literature that limited work is done in the field of a secure surveillance system using real-time videos. Therefore, there is a need for video summarization, classification (action recognition), and encryption. This paper aims to make decisions about abnormal events like suspicious activity detection in surveillance applications incorporating the above-said techniques. This IoT (Internet of Things) based smart secure surveillance system allows for reduced storage of unwanted data and helps to protect the confidential data to be sent to the user by cryptographic methods.
Dual-View Distilled BERT for Sentence Embedding

Recently, BERT realized significant progress for sentence matching via word-level cross sentence attention. However, the performance significantly drops when using siamese BERT-networks to derive two sentence embeddings, which fall short in capturing the global semantic since the word-level attention between two sentences is absent. In this paper, we propose a Dual-view distilled BERT~(DvBERT) for sentence matching with sentence embeddings. Our method deals with a sentence pair from two distinct views, i.e., Siamese View and Interaction View. Siamese View is the backbone where we generate sentence embeddings. Interaction View integrates the cross sentence interaction as multiple teachers to boost the representation ability of sentence embeddings. Experiments on six STS tasks show that our method outperforms the state-of-the-art sentence embedding methods.
Analysis of Encryption Techniques for Secure Communication

The process of digitization observed among various industries has led to significant amount of collection, processing, sharing and alteration of data. The rapid continuous increase in exchange of data over networks and cloud has encouraged activities such as unauthorized access, illegal usage, alteration of transmitted and stored data. Data security issues are now becoming important as society is moving towards digital age. To get over this problem encryption techniques plays vital role. Modern encryption techniques provides solution to these problems. This paper provides an overview of encryption techniques through which data security can be enhanced. In this paper analysis of security features and steps involved in designing of most broadly used symmetric encryption algorithms like DES, 3DES, AES is done. Furthermore analysis and comparison of these encryption process according to entropy, floating frequency and histogram is done.
Imputation of Missing Values for Solar Irradiance Data under Different Weathers using Univariate Methods

Abstract Significant investment risks of large photovoltaic (PV) systems are uncertainties of energy yield predictions from a PV power plant. Unfortunately, solar irradiance weather datasets often have missing values due to operational issues; hence techniques of imputing or recovering the missing values become essential. This paper examined five statistical imputation methods that are frequently adopted in missing values analysis for daily solar irradiance series based on two different weather conditions in the tropical climate with 10% to 50% of missing values and the methods were evaluated using performance indicators such as normalised root-mean-square at standard test conditions (nRMSE STC ). The results show that during Sunny weather, the Bezier curve and Stineman interpolation give the lowest nRMSE STC for 10% and 20% to 50% of daily missing values, respectively. Meanwhile, during Largely Cloudy weather, three methods share the best estimations, which are Linear interpolation for 10% missing values, Stineman interpolation for 20%, 30% and 50% missing values, and Spline interpolation for 40% of missing values.
UUV path planning method based on QPSO

Path planning is the basic requirement for unmanned underwater vehicle (UUV) to complete tasks. According to the requirements of UUV path planning in working environment, a path planning method for UUV based on QPSO algorithm is proposed in this paper. Firstly, the obstacles in the environment are divided into known obstacles and unknown obstacles. For static obstacles in the known environment, an annular space model is established and the global path planning method based on QPSO is designed. According to the fitness value of each particle in the iterative process of QPSO algorithm, an optimization method of particle classification evolution is proposed. Then, based on the realtime information of unknown obstacles detected by UUV forward-looking sonar, a local collision avoidance method is proposed based on the rolling window method and QPSO algorithm. Finally, a simulation platform is built to verify the proposed path planning method, and the feasibility of the method is verified by experimental simulation.
The J-Orthogonal Square-Root MATLAB-Based Continuous-Discrete Unscented Kalman Filtering Method

The paper suggests a general solution to the square-rooting problem existed for the Unscented Kalman Filter (UKF) since its appearance in the late 1990s. As properly noted in engineering literature, the previously suggested Cholesky-based UKF implementations are, in fact, the 'pseudo' square-root versions. Their key feature is the utilization of one-rank Cholesky update procedure required at each filtering step because of the possibly negative sigma points' weights. In a finite precision arithmetic, the resulting downdated matrix might be not a positive definite matrix. This yields a failure of the UKF estimator in practice. We resolve this problem by suggesting a novel square-root approach based on the J-orthogonal matrix utilization for updating the required Cholesky factors. Additionally, we explain how the MATLAB language with built-in numerical integration schemes developed for solving ordinary differential equations can be easily and effectively used for accurate calculations when implementing the continuous-discrete UKF time update stage.
Auxiliary-Filter-Free Incompressible Particle Flow Filtering Using Direct Estimation of the Log-Density Gradient with Target Tracking Examples

This paper presents an incompressible particle flow filtering method that does not require an auxiliary filter by estimating log-density gradients directly from particles. Particle flow filter (PFF) is likely to avoid particle impoverishment and degeneracy problems that occur in particle filters because particles themselves move toward desired density to perform measurement updates. There are various implementation forms for PFF depending on the assumptions made about the flow. This paper deals with PFF using incompressible flow. Incompressible PFF requires the log-density gradient to calculate the flow. The well-known gradient estimation method for incompressible PFF is a finite difference method collaborating with k nearest neighbors(kNN) method. Since this method requires the prior knowledge about the prior density value in each particle, it is necessary to use an auxiliary filter or a density estimation technique. As a result, the performance of an auxiliary filter or a density estimation technique can directly affect the PFF performance, and the finite difference method is more likely to be inaccurate than directly estimating the log-density gradient. Therefore, this paper presents a PFF structure applying least-squares log-density gradient (LSLDG) method that estimates the log-density gradient directly from particles. In order to verify the performance of the presented structure, this paper performs both single and multiple target tracking simulations. Simulation results demonstrate that the presented structure has a relatively good estimation performance and works more robustly for various situations.
BiLSTM-Attention-CRF model for entity extraction in internet recruitment data

With the development of the Internet, online recruitment has gradually become the mainstream. Analyzing the recruitment data and exploring the internal rules of the data can help enterprises and job seekers realize human-career matching. An extremely important step in analyzing recruitment data is to extract structured information from unstructured data. Named entity recognition can effectively extract entity information from unstructured data. In recent years, a lot of work has focused on this task, but no related work has been applied named entity recognition to Internet recruitment information. Therefore, this paper proposes the BiLSTM-Attention-CRF model for Internet recruitment information, which can be used to extract skill entities in job description information. This model introduces the BiLSTM and Attention mechanism to improve the effect of entity recognition. To verify the performance of the model, the paper used crawler technology to capture the real Internet recruitment data and annotate it and obtain a real data set. In this paper, a series of experiments are conducted on this data set, and the experimental results show that the proposed model achieves the best performance.
Cascaded Finite-State Chunk Parsing for Hindi Language

The Cascaded Finite-state Chunk Parsing for Hindi Language (CFC) is a deterministic bottom-up version based on an LR parser that employs a best-first search. It uses finite-state cascade as a sequence of strata. Each stratum is defined by a category and a set of regular-expression patterns for recognizing phrases. Phrases at one level are built on phrases at the previous level. The final chunk structure is a multi-level sequence to span the entire text as single parse tree. Our research is presented with a design, implementation steps and result analysis. Though, we present a case study for an Indian language i.e. Hindi, it is language independent approach and extendible for further scope.