Reviewerly/exHarmony huggingface dataset:
Efficient Features Selection Based Breast Tumors Classification with Machine Learning

A tumor is the most dangerous word in health sciences worldwide. Hundreds of people are dying due to tumors every day. Breast tumor is one of the highest death factors among all types of tumors, especially in women. According to the literature, 40k people have died from breast tumors in a single year. This death percentage is relatively high in the last or fourth stage as compared to the early stages. That is why early stages detection using different machine learning techniques is required. The main objective is diagnosing and predicting breast tumors at early stages to overcome death rates. This research focuses on the early detection of breast tumors with the help of Machine Learning (ML) models and features selection. Moreover, this research compares the performances of the selected ML model, i.e., Gaussian Naïve Bayes, Linear Regression Classifier, AdaBoost Ensemble, and Neural Network. The experiments are done on a benchmark dataset, Wisconsin breast cancer (diagnostic). Our results show that one can easily classify and detect whether the tumor is in an early stage or not with 97.66%±1.12 accuracy.
Toward Symptom Assessment Guided Symptom Investigation and Disease Diagnosis

Automatic Disease Diagnosis (ADD) has gained immense popularity and demand over the past few years, and it is emerging as an effective diagnostic assistant to doctors. Diagnosis assistants assist clinicians in conducting a thorough symptom investigation and identifying possible diseases. Doctors correctly diagnose patients by observing only a few symptoms in most cases, even though the diagnosed disease has numerous symptoms. Also, some common symptoms, such as fever and headache, usually emerge due to other symptoms, which do not play a major role in identifying suffering diseases. In this work, we investigate the role of symptom importance in disease diagnosis through several feature engineering techniques and propose a novel symptom assessment incorporated symptom investigation and disease diagnosis (SA-SIDD) assistant using hierarchical reinforcement learning. The proposed SA-SIDD assistant first collects an adequate set of symptoms/sign information through conversing with users and then diagnoses a disease based on the extracted symptoms. We incorporated a symptom assessment module with the diagnosis framework that evaluates the relevance of current inspected symptom at each turn and reinforces the assistant to investigate distinctive and context-aligned symptoms using an assessment critic. The proposed methodology outperforms the state-of-the-art method, HRL, on two publicly available datasets, which firmly establishes the crucial role of symptom importance in disease diagnosis and the need for the proposed symptom assessment incorporated disease diagnosis framework. Furthermore, we have also conducted a human evaluation, revealing that the diagnosis method greatly enhances end-user satisfaction because of context-aligned relevant and minimal symptom investigation <xref ref-type="fn" rid="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><sup>1</sup></xref> <fn id="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><label><sup>1</sup></label> The code and data are available at <uri>https://github.com/NLP-RL/SA-SIDD</uri> </fn> .
Grounding Scene Graphs on Natural Images via Visio-Lingual Message Passing

This paper presents a framework for jointly grounding objects that follow certain semantic relationship constraints given in a scene graph. A typical natural scene contains several objects, often exhibiting visual relationships of varied complexities between them. These inter-object relationships provide strong contextual cues towards improving grounding performance compared to a traditional object query-only-based localization task. A scene graph is an efficient and structured way to represent all the objects and their semantic relationships in the image. In an attempt towards bridging these two modalities representing scenes and utilizing contextual information for improving object localization, we rigorously study the problem of grounding scene graphs on natural images. To this end, we propose a novel graph neural network-based approach referred to as Visio-Lingual Message Passing Graph Neural Network (VL-MPAG Net). In VL-MPAG Net, we first construct a directed graph with object proposals as nodes and an edge between a pair of nodes representing a plausible relation between them. Then a three-step inter-graph and intra-graph message passing is performed to learn the context- dependent representation of the proposals and query objects. These object representations are used to score the proposals to generate object localization. The proposed method significantly outperforms the baselines on four public datasets.
Impact of Clustering technique in enhancing the Blockchain network performance

Blockchain technology facilitates transparency, de-centralization, immutability, and security for each transaction. The decentralization characteristics enable the block chain network to have different nodes as the Certificate Authority (CA) for a different transactions. Choosing a different validator for every individual transaction may increase the security perspective of the transaction, but it can lead to one major concern such as the network overhead increase. There can be several numbers of transactions for a single network and selecting a validator every time can cause a block propagation delay which also decreases the network efficiency. To avoid such kind of issue clustering of network nodes can be an emerging solution. In the current research work, the entire network participating nodes are considered for making different clusters based on the response time factor. Every cluster has the average response time as the threshold value. The node that wants to initiate a transaction needs to raise a minimum response time requirement and the cluster selection procedure will be executed based on the received response time value. In the current research work, the K-Means clustering with the Elbow method as an internal validation method is considered to decide the number of clusters. The computational time for both cases is compared to measure the effectiveness of the clustering process on the blockchain network.
DSTNet: Dynamic-Static TransFormer Style Network for Cross-Resolution Vehicle ReIdentification

Vehicle ReIdentification (ReID) can be applied to multi-temporal remote sensing target-matching tasks in different locations. However, due to the uncertainty of UAV height and maneuvering target motion, a huge resolution mismatch can be expected. In the traditional cross-resolution ReID method, the Super-Resolution (SR) method is generally used. However, there is still a large data difference between the Super-Resolution Recovered (SR-Recovered) image and the High-Resolution (HR) image, which leads to a decrease in matching efficiency. Therefore, a dynamic-static TransFormer style network is proposed, which is named DSTNet. DSTNet is designed to reduce the difference between the SR-Recovered image and the HR image and to obtain the identity invariant representation of the SR-Recovered image and the HR image. Firstly, CNN and TransFormer are used to extract context information statically and dynamically, respectively, to enhance the representation of the target identity. Secondly, in order to obtain the invariant information between the SR-Recovered image and the HR image, different normalization strategies are designed in different depths of the DSTNet. Finally, to obtain a consistent representation of the SR-Recovered image and the HR image, the High-Resolution Constraint (HRC) input method is applied to the network. To the experimental results, the performance of rank-5 and mAP is improved by 3% and 3.6% respectively on datasets with large resolution differences by our method.
Multi-platform Framework for Pattern and Image Processing

This article describes the creation and development of a Web Framework for the processing of Images and Patterns based on the power of the HTML5 language and the object-oriented capabilities of the Javascript language, as well as the importance of its functionality on any operating system, thereby improving and simplifying its use. The object-oriented implementation will make it easier to access image variables and characteristics (width, height, color depth and bitmap), the calculation algorithms and matrix processing have been modified to perform convolution and color discretization processes, as well as the use of Gaussian filters and the Sobel, Cannis, Roberts Operator. Leaving the data ready to search for patterns and generate color histograms. As a result, a clear, compact implementation was obtained. to be able to derive and run on any browser that supports HTML5 technology and, more importantly, on any operating system. We provide greater flexibility and portability than other CGI frameworks based on C++ and Python.
Location-Aware Heterogeneous Graph Neural Network for Region Recommendation

With the diversification of human activity and travel demand in urban space, recommending ROIs (region-of-interest) to users is important for both satisfying commercial demands and better understanding user urban lifestyles. Current researches mainly resort to the traditional POI-level (point-of-interest) or neural network-based recommendation methods for ROI recommendation, in disregard of the rich heterogeneous graph information, such as user-region-user, region-category-region, just to name a few. In this work, we employ the heterogeneous graph to address this issue, considering heterogeneous graph contains more comprehensive information and rich semantics. We propose a novel meta-path based graph attention network for ROI recommendation, called MRec. MRec is a newly devised heterogeneous graph neural network, which is equipped with both node-level and semantic-level attentions. Specially, the node-level attention aims to learn the importance between a node and its meta-path based neighbors, while the semantic-level attention is to learn the importance of different meta-paths. This mechanism contributes to effectively embedding users and ROIs in a hierarchical manner of fully considering both node and semantic-level component information. An extensive experiment on two real-world datasets demonstrates the effectiveness of the proposed framework.
Comparing overlapping data distributions using visualization

We present results from a preregistered and crowdsourced user study where we asked members of the general population to determine whether two samples represented using different forms of data visualizations are drawn from the same or different populations. Such a task reduces to assessing whether the overlap between the two visualized samples is large enough to suggest similar or different origins. When using idealized normal curves fitted on the samples, it is essentially a graphical formulation of the classic Student’s t-test. However, we speculate that using more sophisticated visual representations, such as bar histograms, Wilkinson dot plots, strip plots, or Tukey boxplots will both allow people to be more accurate at this task as well as better understand its meaning. In other words, the purpose of our study is to explore which visualization best scaffolds novices in making graphical inferences about data. However, our results indicate that the more abstracted idealized bell curve representation of the task yields more accuracy.
First Steps Towards a Runtime Analysis of Neuroevolution

We consider a simple setting in neuroevolution where an evolutionary algorithm optimizes the weights and activation functions of a simple artificial neural network. We then define simple example functions to be learned by the network and conduct rigorous runtime analyses for networks with a single neuron and for a more advanced structure with several neurons and two layers. Our results show that the proposed algorithm is generally efficient on two example problems designed for one neuron and efficient with at least constant probability on the example problem for a two-layer network. In particular, the so-called harmonic mutation operator choosing steps of size $j$ with probability proportional to $1/j$ turns out as a good choice for the underlying search space. However, for the case of one neuron, we also identify situations with hard-to-overcome local optima. Experimental investigations of our neuroevolutionary algorithm and a state-of-the-art CMA-ES support the theoretical findings.
The Study on the Effect of Financial Instability on Income Inequality

Although recently the research on the relationship between financial instability and income inequality got deep interest, there is little study. This paper analyses the relationship between financial instability and income inequality. The Financial Stability Index (FSI) and the Financial Vulnerability Index (FVI) are used as the measure of financial instability and the income quintile share ratio is used to measure income inequality. As the result of empirical studies, the deterioration in FCI results in an increase in income inequality whreas the worsening in FVI appears not to have an impact on income inequality contemporaneously. This result is similar in the case of the GMM estimation which considers endoneous issues due to reverse causality. In the robust analysis where sufficient lags are incorporated into the FSI and the FVI, both variables appear to increase income ineqauity. We interpret that this result is because the FSI reflects current financial instability while the FVI takes into future inancial instability in terms of mid- and long-term. This result implies that the macro-prudential policy or the policy for financial stability can be useful for improving income inequality
Unmanned ground vehicle as a docking element of a ground-aerial robotic system

Using semiautonomous and autonomous vehicles to perform various missions can lead to increased safety and efficiency. With all risks and limitations included, great potential exists in the integration of unmanned aerial and ground vehicles into heterogeneous robotic systems. Considering the great advances that have been made in terms of path planning, localization, control, coordinated motion, cooperative exploration, and others, such heterogeneous systems are suitable for a very wide range of tasks. In this research, the architecture that includes the ground robot as a base and the aerial robot as an extension to 3D space is examined. Such an architecture is scalable, it can be used for a wide range of missions from data collection to smart spraying. The ground robot system has been prototyped with a tracked differential drive configuration. Preliminary tests will serve as guidelines for further steps in the system development.
Detecting Scattered and Tangled Quality Concerns in Source Code to Aid Maintenance and Evolution Tasks

Quality concerns, such as reliability, security, usability concerns, among others, are typically well-defined and prioritized at the requirement level with the set goal of achieving high quality, robust, user-friendly, and trustworthy systems. However, quality concerns are challenging to address at the implementation level. Often they are scattered across multiple modules in the codebase. In other instances, they are tangled with functional ones within a single module. Reasoning about quality concerns and their interactions with functional ones while being hindered by the effects of scattered and tangled code can only yield to more unseen problems. For example, developers can inadvertently retrofit new bugs or wrongly implement new features that deviate from original system requirement specifications. The goal of this thesis is twofold. First, we aim to detect quality concerns implemented at code level to differentiate them from functional ones when they are scattered across the codebase. Second, we aim to untangle quality concerns from unrelated changes to gain a detailed knowledge about the history of specific quality changes. This knowledge is crucial to support consistency between the requirements-and-design and to verify architecture conformance. From the practical stance, developers could gain a breadth of understanding about quality concerns and their relations with other artifacts. Thus, with more confidence, they could perform code modifications, improve module traceability, and provide a better holistic assessment of change impact analysis.
Comparative Analysis of Cognitive Services in Popular Cloud Platforms

Cognitive services aid humans in analyzing complex data and providing useful and intelligent information. The leading cloud providers Google, Amazon, and Microsoft and specialized services such as IBM Watson produce cognitive APIs with a competitive edge in pricing and features. An analysis of the various features of the major cognitive services, speech, language, vision, and decision are presented. Case studies are examined to understand the socio-economic impact of cognitive APIs. The current challenges in using cognitive services for developers and organizations are considered. The need for adapting to tap into the enriching customer experience is surmised.
Technological Model Based on Blockchain Technology for Genetic Information Protection in the Health Sector

In the health industry, the transparency of product data registration in the supply chain is a critical aspect in determining the source of genetic data. Various upcoming technologies, such as blockchain, can help with this challenge. Blockchain is a shared and immutable database that makes recording transactions and tracking assets in a commercial network easier. Currently, genetic information is regarded as a vital asset in the health sector, as more precise diagnostic samples in medical genomics enable improved treatments for patients suffering from a variety of disorders. Since actions connected to the storage or management of data might have several areas of vulnerability, this paper describes the development of a technological model employing Blockchain as technology to assure the protection of genetic information in the private health sector. Furthermore, unauthorized activities such as registration and access control in the exchange of genetic information have been carried out, primarily through entities that manage the eligibility of users with genomics information and grant access to specific data sets, demonstrating a lack of harmonization in access policies between the owners of genetic information and the health entities that manage it. A proof-of-concept was carried out to evaluate the model’s capabilities and ensure that a larger-scale deployment could be carried out. Experts agreed with our proposal, and consumers would be prepared to employ proof of concept to assure traceability and security of their data, according to the evaluation.
Text Region Localization and Recognition for ID Card Identification using Deep Learning Approaches

Deep Learning approaches are currently successful in the application areas of object detection and recognition. In Optical Character Recognition (OCR) applications, there are still challenges in the problems of text region localization and segmentation. Various conventional methods that used in OCR systems still couldn't get high accuracy and still to do to be more effective for real time OCR applications. This paper presented recognition and identification of the specified identity document (ID) to solve those problems by applying deep learning techniques. For the process of text region extraction, the Regional Proposal Networks (RPN) such as Faster R-CNN is used to identify which areas are the most probable of text in the ID card. For text sequence learning, Recurrent Neural Network (RNN) learns timestamp-based text sequences from images to avoid segmentation problems and recognize a given text. According to the experimental results, the deep learning techniques give a satisfied accuracy and loss rates in text region identification and recognition for ID cards.
A Security model with efficient AES and Security Performance Trade-off Analysis of Cryptography Systems with Cloud Computing

Cloud computing is awesome progress the information technology. It allows users to store a large amount of data on a remote storage location and access files, software, and services through their internet connection on the personal computer based on the pay-per-use model. End-users mostly use the cloud for storing information on the cloud, sharing files, and data backup. In fact, according to the flex era 2021 state of cloud report, roughly 90 % of enterprises anticipate cloud users have been expanded because of COVID-19. Data security and privacy are basic concerns because of the nature of storing unencrypted cloud computing. Though many encryption standards are being used to make crucial information safe, single encryption is not able to cope with fast computing systems. To circumvent the threats, this paper uses a triple encryption approach before porting on a cloud with a hash value to deliver the data integrity, confidentiality, and non-repudiation. This solution is designed to offer complete data security to the data throughout the data life cycle, such as to data in transit, and data in storage. Subsequently, Improved AES achieves better optimization over AES. Moreover, the security and performance analysis of the proposed approach with the existing approaches assist to find the best security performance trade-off.
Thermal Noise Removal of Magnetic Resonance Images: A Deep Learning Approach Based on an Attentive Residue Multi-Dilated Network with Adaptive Filtering and Discrete Cosine Transform

Magnetic resonance imaging (MRI) has been applied in various fields, especially for the medical purposes. However, fine details and smooth areas of some critical patterns in an MR image polluted by common thermal noise will interfere with the diagnosis of doctors. Thermal noise in an MR image obeys Rician distribution, which is hard for conventional denoising methods based on shift invariant spatial filtering approaches to dispose. Besides, the fine detail and edge information will be inevitably damaged when smoothing the noise, which is unacceptable for medical images. In this paper, we propose two corresponding solutions. First, we design a convolutional neural network (CNN) to learn a mask that aims to directly eliminate the thermal noise in the background region, and make the noise in the MR image obey almost the same distribution. Second, we propose several improvements in terms of existing deep learning approaches for thermal noise removal. Specifically, we establish a dual-branch neural network, a frequency-domain-optimizable discrete cosine transform (DCT) module, and adopt other effective structures such as residue blocks, convolutional block attention modules (CBAM), and parallel multi-dilated GoogLeNet inception based convolutional blocks to form an attentive residue multi-dilated network (ARM-Net). We evaluate our method over the BraTS 2018 dataset at noise levels ranging from 2% to 20%. Experimental results reveal that our method achieves state-of-the-art (SOTA) performance compared with the most recent works.