Reviewerly/exHarmony huggingface dataset:
Machine Learning Tensor Flow Based Platform for Recognition of Hand Written Text

Handwritten text recognition in a computer is an automatic mode that transcribe documents. Mainly two types of approaches are used in handwritten text recognition are artificial neural network (ANN) and hidden markov model. Here handwritten text recognition system is based on artificial neural networks. Feature of input images are improved by pre-processing method. After that for classifier, problem is simplified. For increasing the size of data set, data augmentation and also contrast normalization methods are used. To improve the feature of input image, classifiers are used a convolutional neural network. Information are propagated by recurrent neural network. Probability distribution of the character on every position of image are contained by matrix of the recurrent neural network outputs. The function of connectionist temporal classification is decoded the matrix into the final text. In the decoded text, to spelling error in final text post processing accounts are also present, from this accuracy of handwritten text is improved, after that it can be used in different fields like security system etc. Almost all the institutes and governments having large amount of handwritten papers are created every day. This insistent use computers to interpret handwritten texts, also create that is searchable and editable. Therefore, handwritten recognition became extremely research matter with the huge number of applications. It is resourceful to break up complex problems and reduce in extent of human action by change over the handwritten text documents into digital form.
Analysing and extending privacy patterns with architectural context

Privacy is now an increasingly important software quality. Software architects and developers should consider privacy from the early stages of system design to prevent privacy breaches. Both industry and academia have proposed privacy patterns as reusable design solutions to address common privacy problems. However, from the system development perspective, the existing privacy patterns do not provide architectural context to assist software design for privacy. More specifically, the current privacy patterns lack proper analysis with regards to privacy properties - the well-established software traits relating to privacy (e.g., unlinkability, identifiability). Furthermore, the impacts of privacy patterns on other quality attributes such as performance are yet to be investigated. Our paper aims to provide guidance to software architects and developers for considering privacy patterns, by adding new perspectives to the existing privacy patterns. First, we provide a new structural and interaction view of the patterns by relating privacy regulation contexts. Then, we analyse the patterns in architectural contexts and map available privacy-preserving techniques for implementing each privacy pattern. We also give an analysis of privacy patterns with regard to their impact on privacy properties, and the trade-off between privacy and other quality attributes.
WITHDRAWN: A novel study of preventing the cyber security threats

The aim of the analysis is to determine the vulnerabilities that could cause hundreds of billions of dollars in companies to be lost due to a cyberattacks. The paper goes on to clarify how these challenges manifest themselves, their history, and how they have changed over time. Another article cited in this work provides two scientific case studies on cyber defense. Cybersecurity can be partially described as solving problems and partially mitigating risks. It can be described as an event which intends to injure, particularly in regard to sensitive information, or function as a danger, or as well as one which simply one which causes disturbances in any sort of advanced existence. In addition to network attacks, including PC viruses, knowledge breaks, Distributed Denial of Service (DDoS) and numerous other assault vectors. a fruitful hack that would try to gain unauthorized access to your PC, licensed goods, or collect sensitive information cyber protection may be achieved by expansion or obscurity depending on whether it originators (client or system) are close to the organization or far away. Cyber Security searches for real-time access to latest information in the field of information technology. With today's world so interdependent on electronic technology, preserving data has become an overwhelming issue. Because of its foremost thought is the threat of a colossally everyday online violence, cyber defense develops to be very involved. There are various steps being taken to deal with these computer crimes through administrations and organizations. While cyber security is an important issue to most, it has not yet gained sufficient interest from general population participants. The paper mostly deals with the topic of cyber security and its problems. Today's relevant discussions on cyber security revolve around how it is increasing importance and how it can affect the organization.
Human Following Robot

In this modern age, due to its abundant applications in everyday life and manufacturing, Human Following robots have been researched and actively developed over the decades. Different techniques such as robot control algorithm, human target detection, and obstacle avoidance are necessary for the human robot to execute. Several human-following robotic procedures have been suggested, such as the use of ultrasonic sensors, infrared sensors, laser range sensor, voice recognition sensor camera charging-coupled devices (CCD) and so on. These technologies can detect the particular location between a mobile robot and a human. In this paper, we present a new approach to detecting the location of a human robot using an ultrasonic & infrared camera, which is the fundamental technique in the robot's human follow-up. In our project, the robot is equipped with an ultrasonic sensor which captures and detects an individual. A simple application implemented in real time using a PI controller shows some advantages of the proposed method.
Rate-1 Key-Dependent Message Security via Reusable Homomorphic Extractor Against Correlated-Source Attacks

In this work, we first present general methods to construct information rate-1 PKE that is \(\mathsf {KDM}^{(n)}\)-secure with respect to block-affine functions for any unbounded polynomial n. To achieve this, we propose a new notion of extractor that satisfies reusability, homomorphic, and security against correlated-source attacks, and show how to use this extractor to improve the information rate of the \(\mathsf {KDM}\)-secure PKE of Brakerski et al. (Eurocrypt 18). Then, we show how to amplify \(\mathsf {KDM}\) security from block-affine function class into general bounded size circuits via a variant of the technique of Applebaum (Eurocrypt 11), achieving better efficiency. Furthermore, we show how to generalize these approaches to the IBE setting.Additionally, our PKE and IBE schemes are also leakage resilient, with leakage rates \(1-o(1)\) against a slightly smaller yet still general class – block leakage functions. We can instantiate the required building blocks from \(\mathsf {LWE}\) or \(\mathsf {DDH}\).
A Framework for Local Outlier Detection from Spatio-Temporal Trajectory Datasets

As one of the primary tasks in data mining, outlier detection serves a significant role in data quality enhancement for the scientific model prediction and revealing the abnormal hidden patterns from large scale trajectory datasets. In this paper, we introduce a versatile framework for detecting local trajectory outliers using spatial and temporal features of moving objects. Our local outlier detection consists of three phases. First, we divide the raw trajectory into trajectory segments by using a time-based partition strategy and extracting trajectory features from spatial attributes for each trajectory segment. Second, we create template trajectory segments based on a clustering schema. Finally, we compute the abnormal score, which measures the dissimilarity among the query and template trajectory segments, and thus determine the outlying trajectory segments according to the overall distribution of the abnormal score. To show the effectiveness of our approach, we conduct two case studies on the real-life solar active region and Coronal Mass Ejection (CME) trajectory datasets. Our results show that our local outlier detection method can successfully detect the reporting errors and anomalous phenomenon in both of our case studies.
Detection and Segmentation of Breast Masses Based on Multi-Layer Feature Fusion

In breast mass detection, there are many different sizes of masses in the image. However, when the existing target detection model is directly used to detect the breast mass, it is easy to appear the phenomenon of misdetection and missed detection. Therefore, in order to improve the detection accuracy of breast masses, this paper proposed a target detection model D-Mask R-CNN based on Mask R-CNN, which is suitable for breast masses detection. Firstly, this paper improved the internal structure of FPN, and modified the lateral connection mode in the original FPN structure to dense connection. Secondly, modified the size of the anchor of RPN to improve the location accuracy of breast masses. Finally, Soft-NMS was used to replace the NMS in the original model to reduce the possibility that the correct prediction results may be eliminated during the NMS process. This paper used the CBIS-DDSM dataset for all experiments. The results showed that the mAP value of the improved model for detecting breast masses reached 0.66 in the test set, which was 0.05 higher than that of the original Mask R-CNN.
Improving Current Glycated Hemoglobin Prediction in Adults: Use of Machine Learning Algorithms with Electronic Health Records (Preprint)

Background Predicting the risk of glycated hemoglobin (HbA1c) elevation can help identify patients with the potential for developing serious chronic health problems, such as diabetes. Early preventive interventions based upon advanced predictive models using electronic health records data for identifying such patients can ultimately help provide better health outcomes. Objective Our study investigated the performance of predictive models to forecast HbA1c elevation levels by employing several machine learning models. We also examined the use of patient electronic health record longitudinal data in the performance of the predictive models. Explainable methods were employed to interpret the decisions made by the black box models. Methods This study employed multiple logistic regression, random forest, support vector machine, and logistic regression models, as well as a deep learning model (multilayer perceptron) to classify patients with normal (&lt;5.7%) and elevated (≥5.7%) levels of HbA1c. We also integrated current visit data with historical (longitudinal) data from previous visits. Explainable machine learning methods were used to interrogate the models and provide an understanding of the reasons behind the decisions made by the models. All models were trained and tested using a large data set from Saudi Arabia with 18,844 unique patient records. Results The machine learning models achieved promising results for predicting current HbA1c elevation risk. When coupled with longitudinal data, the machine learning models outperformed the multiple logistic regression model used in the comparative study. The multilayer perceptron model achieved an accuracy of 83.22% for the area under receiver operating characteristic curve when used with historical data. All models showed a close level of agreement on the contribution of random blood sugar and age variables with and without longitudinal data. Conclusions This study shows that machine learning models can provide promising results for the task of predicting current HbA1c levels (≥5.7% or less). Using patients’ longitudinal data improved the performance and affected the relative importance for the predictors used. The models showed results that are consistent with comparable studies.
Emulation practices for software preservation in libraries, archives, and museums

Abstract Emulation practices are computational, technical processes that allow for one system to reproduce the functions and results of another. This article reports on findings from research following three small teams of information professionals as they implemented emulation practices into their digital preservation programs at a technology museum, a university research library, and a university research archive and technology lab. Results suggest that the distributed teams in this cohort of preservationists have developed different emulation practices for particular kinds of “emulation encounters” in supporting different types of access. I discuss the implications of these findings for digital preservation research and emulation initiatives providing access to software or software‐dependent objects, showing how implications of these findings have significance for those developing software preservation workflows and building emulation capacities. These findings suggest that different emulation practices for preservation, research access, and exhibition undertaken in libraries, archives, and museums result in different forms of access to preserved software—accessing information and experiential access. In examining particular types of access, this research calls into question software emulation as a single, static preservation strategy for information institutions and challenges researchers to examine new forms of access and descriptive representation emerging from these digital preservation strategies.
Is it worth to hold bitcoin?

This study examines if holding Bitcoin provides any benefit other than potential price appreciation. An estimation using the convenience yield term in the Cost-of-Carry model shows that the Bitcoin spot contract has a 5.4% premium compared to the futures contract, indicating a holding benefit. Short-selling restrictions in Bitcoin spot markets and voting rights are the sources of the premium. The short-selling restrictions make spot prices be downward sticky. The value of voting rights becomes more observable whenever a ballot is likely. When an important proposal is made to the Bitcoin network, the holding benefit increases by an additional 5.6%.
An Unsupervised Normalization Algorithm for Noisy Text: A Case Study for Information Retrieval and Stance Detection

A large fraction of textual data available today contains various types of “noise,” such as OCR noise in digitized documents, noise due to informal writing style of users on microblogging sites, and so on. To enable tasks such as search/retrieval and classification over all the available data, we need robust algorithms for text normalization, i.e., for cleaning different kinds of noise in the text. There have been several efforts towards cleaning or normalizing noisy text; however, many of the existing text normalization methods are supervised and require language-dependent resources or large amounts of training data that is difficult to obtain. We propose an unsupervised algorithm for text normalization that does not need any training data/human intervention. The proposed algorithm is applicable to text over different languages and can handle both machine-generated and human-generated noise. Experiments over several standard datasets show that text normalization through the proposed algorithm enables better retrieval and stance detection, as compared to that using several baseline text normalization methods.
Realizing the Effective Detection of Tumor in Magnetic Resonance Imaging using Cluster-Sparse Assisted Super-Resolution

Recently, significant research has been done in Super-Resolution (SR) methods for augmenting the spatial resolution of the Magnetic Resonance (MR) images, which aids the physician in improved disease diagnoses. Single SR methods have drawbacks; they fail to capture self-similarity in non-local patches and are not robust to noise. To exploit the non-local self-similarity and intrinsic sparsity in MR images, this paper proposes the use of Cluster-Sparse Assisted Super-Resolution. This SR method effectively captures similarity in non-locally positioned patches by training on clusters of patches using a self-adaptive dictionary. This method of training also leads to better edge and texture detection. Experiments show that using Cluster-Sparse Assisted Super-Resolution for brain MR images results in enhanced detection of lesions leading to better diagnosis.
A Unified Generative Adversarial Learning Framework for Improvement of Skip-Gram Network Representation Learning Methods

Network Representation Learning (NRL), which aims to embed nodes into a latent, low-dimensional vector space while preserving some network properties, facilitates the network analysis tasks. The goal of most NRL methods is to make similar nodes represented similarly in the embedding space. Many methods adopt the skip-gram model to achieve such goal by maximizing the predictive probability among the context nodes for each center node. The context nodes are usually determined based on the concept of \textit{proximity} which is defined based on some explicit network features. However, these proximities may result in a loss of training samples and have limited discriminative power. We propose a general and unified generative adversarial learning framework to address the problems. The proposed framework can handle almost all kinds of networks in a unified way, including homogeneous plain networks, attribute augmented networks and heterogeneous networks. It can improve the performances of the most of the state-of-the-art skip-gram based NRL methods. Moreover, another unified and general NRL method is extended from the framework. It can learn the network representation independently. Extensive experiments on proximity preserving evaluation and two network analysis tasks, i.e., link prediction and node classifications, demonstrate the superiority and versatility of our framework.
Gradient estimation of information measures in deep learning

Information measures including entropy and mutual information (MI) have been widely applied in deep learning. Despite the successes, exiting estimation methods suffer from either high variance or high bias. This may lead to unstable training or poor performance in deep learning. Since estimating information measures in themselves is very difficult, we explore an alternative appealing strategy, by directly estimating the gradients of information measures with respect to model parameters. We propose a general gradient estimation method for information measures based on the score estimation. In detail, we establish the Entropy Gradient Estimator (EGE) and the Mutual Information Gradient Estimator (MIGE) to estimate the gradient of entropy and mutual information with respect to model parameters, respectively. For dealing with the optimization of entropy and mutual information, we can directly plug in their gradient approximation with relevant parameters to enable stochastic backpropagation for stability and efficiency. Our proposed method exhibits higher accuracy and lower variance for gradient estimation of information measures. Extensive experiments on various deep learning tasks have demonstrated the superiority of our method.
Semi-automated Ontology Creation and Upgradation for Rail-Road Incidents: A Case of a Steel Plant in India

Most of the earlier studies for ontology creation and upgradation are carried out using structured data. Very few of them focused on unstructured texts for this purpose, which are restricted due to huge human involvement. In order to address this issue, in this study, we have proposed a methodology for ontology creation and upgradation using both unstructured (text) and structured data of rail-road incidents obtained from a steel plant. Features are predicted from the texts using machine learning (ML) techniques and added to structured data, which is finally used for ontology upgradation. This is performed in a semi-automated way, thereby substantially reducing level of human efforts. Ontology is created using Web Ontology Language (OWL) language in Protégé software. Two ML techniques, Random Forest and Support Vector Machine are used to see how accurately the logical relationships are established between classes/sub-classes used in this ontology. Hence, the study proves the effectiveness of the methodology for ontology creation and upgradation.
Adaptive Distillation for Decentralized Learning from Heterogeneous Clients

This paper addresses the problem of decentralized learning to achieve a high-performance global model by asking a group of clients to share local models pre-trained with their own data resources. We are particularly interested in a specific case where both the client model architectures and data distributions are diverse, which makes it nontrivial to adopt conventional approaches such as Federated Learning and network co-distillation. To this end, we propose a new decentralized learning method called Decentralized Learning via Adaptive Distillation (DLAD). Given a collection of client models and a large number of unlabeled distillation samples, the proposed DLAD 1) aggregates the outputs of the client models while adaptively emphasizing those with higher confidence in given distillation samples and 2) trains the global model to imitate the aggregated outputs. Our extensive experimental evaluation on multiple public datasets (MNIST, CIFAR-10, and CINIC-10) demonstrates the effectiveness of the proposed method.