Reviewerly/exHarmony huggingface dataset:
Prognosis of Breast Cancer by Implementing Machine Learning Algorithms Using Modified Bootstrap Aggregating

Breast cancer has been known to be the most prevalent cancers found in women all over the globe. Early stage diagnosis can save a lot of lives, but in most cases, it is an arduous task for doctors, as the symptoms are not essentially salient. The use of artificial intelligence for the prognosis of breast cancer has proved to be an asset for the biomedical and healthcare stratum. In this paper, a hybrid predictive model is proposed for the prediction of breast cancer. The proposed hybrid model is based on the use of decision trees for the bifurcation and preprocessing of data, followed by the application of any machine learning algorithm. We have used the proposed system for deep learning (DL) and support vector machine (SVM). The proposed model has been found to achieve up to 99.3% of accuracy, exceeding the accuracy obtained by any other approach used so far.
Exploiting Ensemble Classification Schemes to Improve Prognosis Process for Large for Gestational Age Fetus Classification

Large for gestational (LGA) means the fetus having an abnormal birth weight. It adheres severe complications during and after the maternal period. Therefore, this research presents an ensemble classification scheme using Chinese National Pre-Pregnancy Examination Program dataset to classify a fetus as an LGA or non-LGA based on provided Chinese LGA classification guidelines. Moreover, the proposed scheme is comprised of data cleansing and ensemble classification schemes that have drastically improved the LGA classification process with improved performance results compared to present published studies. Therefore, the recommended scheme can be utilized by healthcare professionals to build an enhanced and reliable LGA classification system.
Quantum Abstraction Interface: Facilitating Integration of QKD Devices in SDN Networks

This work presents and implements Quantum Abstraction Interface (QuAI). QuAI is integrated in an SDN agent to enable the vendor agnostic configuration of QKD devices. The work demonstrates the framework to provide a flexible procedure to integrate QKD devices in production networks.
Developing sonication-based method as a preliminary qualitative study to analyze methamphetamine in the hair of the user at Datuk Etham rehabilitation center

Determination of methamphetamine in the user can be done by analyzing their urine, blood, and hair. In this study, the analysis has been performed using the user's hair based on the advantage that the analysis still can be performed, even after 27 h. The effect of solvent to perform the qualitative study has been conducted at certain day, i.e. 0-12th, 30th, 60th, and 90th day. The reference methamphetamine was obtained from Laboratory of the Technical Service Unit Technical Services Testing and Field Identification Agency (BPIB). The analysis was begun with decontamination, destruction, sonication and Marquis test for qualitative data. Solvent that used in the sonication treatment showed a significant role, especially by using a mixture of methanol: acetone: ammonia.
Evaluation of Parameter Update Effects in Deep Semi-Supervised Learning Algorithms

Semi-Supervised Machine Learning (SSML) algorithms are combinations of unsupervised and supervised learning algorithms. This combination enables SSML algorithms to learn from both labelled and unlabelled data. One of the challenges is identifying the key contributing factors from both kinds of algorithms to the learning performance, in terms of training time, training loss, and accuracy. Previously, researchers have adopted Deep Neural Networks (DNNs) to construct the core learning models of SSML algorithms with improved accuracy. However, there is still a lacks of a systematic study to understand the key contributing factors and their effects. In this paper, we generalize the common components of SSML algorithms from state-of-the-art models (- Model, Temporal Ensembling and Mean-Teacher). We form a conceptual Semi-Supervised Computation Graph (SSCG) to inject different kinds of DNNs to the network classifier component in the computation graph. Such a combination illustrates two major aspects to investigate the effects: (1) parameter updates during the training across labelled and unlabelled data; (2) the ratio of labelled and unlabelled data. We performed 27 experiments with 3 SSML algorithms, 3 DNNs and 3 different ratios of labelled and unlabelled data. Our experimental results demonstrate that parameter updates are a dominating factor to the training loss and the learning precision. The experiments show that training loss is lowered by 6% and precision is increased by 4.21% using shake-shake26 as the network classifier in the SSML algorithm of Mean-Teacher, compared to all other combinations. We also observed a positive correlation with an R score value of 0.69 and the p-value of 0.03887 between the training time and the ratio of labelled to unlabelled data. Introducing more labelled data leads to longer training time, which triggers more parameter updates in back-and forward-propagations.
A New Embedded Chien Search Block for Reed- Solomon (RS) Codes based on Factorization of Error Locator Polynomial

Coding corrector of errors is one of the most vital and emerged system in telecommunication field. In this paper, we propose an efficient new embedded Chien Search block for RS decoder. However, it adopts a factorization of error locator polynomial. The proposed design is developed by using hardware description language (HDL), after that, simulated and verified Quartus development software. To test the performances of the new developed design, minimization rate (Number of minimized logic gates) and BER criteria were used. Simulation results show the effectiveness of the proposed design than the basic. First, because it minimizes the number of logic gates and hardware resources. Therefore, a reduction of the power consumption with a minimization's rate that can attain approximately 40 % compared to the basic algorithm. Further, because it reaches a lower bit error rate.
Novel Deep Learning Architectures: Feature Extractor and Radial Basis Function Neural Network

It is well-known that Radial Basis Function Neural Networks (RBFNN) work as a classifier by converting non linearly separable data into linearly separable patterns. In this research paper, the essential idea is to utilize an architecture in which trained feature extractor is cascaded with a Radial Basis Function Neural Network i.e. trained convolutional/pooling layers output is fed to RBFNN (in place of fully connected layers). In a similar spirit, trained Convolutional auto-encoder output (feature vector) is fed to an RBFNN. After training the network Feature extractor cascaded with Radial Basis Function is giving good accuracy compared to pure Radial Basis Function Neural Network.
Stack-Based Genetic Improvement

Genetic improvement (GI) uses automated search to find improved versions of existing software. If originally GI directly evolved populations of software, most GI work nowadays use a solution representation based on a list of mutations. This representation however has some limitations, notably in how genetic material can be re-combined. We introduce a novel stack-based representation and discuss its possible benefits.
Feasibility of Adoption of Blockchain Technology in Banking and Financial Sector of India

The purpose of this research paper is to analyze the feasibility of adoption of blockchain technology as a viable, transparent, and traceable solution for storing data and transactions in firms belonging to the banking and financial sector of India. This study examines the role of four key variables, namely technical knowledge, availability of resources, collaborations among firms, and usability; for successful adoption of the technology in financial functions. This research is based on the analysis of data acquired from these factors which influence the feasibility of blockchain technology as per the in-depth study of 30 firms belonging to the banking and financial sector of India. This study investigates the current penetration of technical knowledge, research, and development of use cases with available resources, preparedness level of firms for collaborations, and the challenges faced in the usability of implementing blockchain technology for financial applications.
Design and Implementation of Distributed Fault-tolerant Computer Communication Software of Deterministic Communication Based on TTE

The distributed fault-tolerant computer technology of deterministic communication takes the needs of a new generation of IVMS as background in this paper, the system architecture design and software architecture of distributed fault-tolerant computer are studied, and one kind of communication software based on VxWorks653 partition operating system is designed and implemented, moreover, it has been practically applied in engineering, and it can meet the communication needs of big data and high-performance in avionics applications after function and performance verification. This communication protocol software is implemented with layered architecture, which is conducive to cross-platform migration, software reuse, and subsequent hardware upgrades.
Follow-Up Control and Image Recognition of Neck Level for Standard Metal Gauge

An image recognition technique is proposed for determining optimal neck levels for standard metal gauges, in the process of validating pipe provers. A camera-level follow-up control system was designed to achieve automated tracking of fluid levels by a camera, thereby preventing errors from inclined viewing angles. An orange background plate was placed behind the tube to reduce background interference, and highlight scale numbers/lines and concave meniscus. A segmentation algorithm, based on edge detection and K-means clustering, was used to segment indicator tubes and scales in the acquired images. The concave meniscus reconstruction algorithm and curve-fitting algorithm were proposed to better identify the lowest point of the meniscus. A characteristic edge detection model was used to identify centimeter-scale lines corresponding to the meniscus. A binary tree multiclass support vector machine (MCSVM) classifier was then used to identify scale numbers corresponding to scale lines and determine the optimal neck level for standard metal gauges. Experimental results showed that measurement errors were within ±0.1 mm compared to a ground truth acquired manually using Vernier calipers. The recognition time, including follow-up control, was less than 10 s, which is much lower than the switching time required between measuring individual tanks. This automated measurement approach for gauge neck levels can effectively reduce measurement times, decrease manmade errors in liquid level readings, and improve the efficiency of pipe prover validation.
Direct Sampling of Multiview Line Drawings for Document Retrieval

Engineering drawings, scientific data, and governmental document repositories rely on degraded two-dimensional images to represent physical three-dimensional objects. The collection of two-dimensional multiview images are generated from a set of known camera positions that are aimed directly at the target object. These images provide a convenient method for representing the original physical object but significantly degrades the interpretability of the object. The multiview images from the document repositories may be integrated to reconstruct an approximation of the original physical object as a point cloud. We show that retrieval methods for documents are improved by directly sampling point clouds from the multiview image set to reconstruct the original physical object. We compare the retrieval results from direct image retrieval, multiview convolutional neural networks (MVCNN), and point clouds reconstructed from sampled images. To evaluate these models, we trained them on line drawings generated from models in the ShapeNet Core data set. We show retrieval of the reconstructed object is more accurate than single image retrieval or the multiview image set retrieval.
BiGCNN: Bidirectional Gated Convolutional Neural Network for Chinese Named Entity Recognition

Recent advances on Chinese named entity recognition (NER) are mostly based on the recurrent neural network (RNN). Since RNNs are limited in parallel processing, some works apply the convolutional neural network (CNN) to perform NER. However, existing CNN-based models fail to explicitly distinguish the preceding and subsequent contexts, so they are difficult to handle cases that are sensitive to the location of the contexts. Moreover, they pay equal attention to the context within a convolution kernel, while not all the information is useful for semantic understanding. In this paper, we propose a novel CNN-based model, Bidirectional Gated Convolutional Neural Network (BiGCNN), to differentiate the entity-related information between preceding and subsequent contexts and filter out the convolution information adaptively. By incorporating automatic segmentation and glyph information, BiGCNN outperforms state-of-the-art models on four Chinese NER datasets. Additionally, benefiting from the parallelism processing, the proposed method enjoys higher training and testing efficiency, e.g., 12.04 times faster than RNN-based models, while with better performance.
The Winning Combo

Online financial systems depend exclusively on third parties for handling transactions. While the framework performs well for most businesses, it has the disadvantages and shortcomings of traditional trust-based models. Cryptocurrency presents a new perspective of monetary exchange. It is digital money in which cryptography, in combination with blockchain, is used to control transactions and create additional units. Though the evolution of the concept of electronic cryptocurrency dates back to 1983, it became popular with the introduction of bitcoin in 2009. Blockchain is the threaded arrangement of permanent records of data information that encompasses a number of transactions in an encrypted form. These transactions form a distributed ledger in which each block is chained to the previous block by a hash value. “Miners” update the blockchain and are paid for their services in the form of the newly generated cryptocurrency. They process exchanges solving a complex problem along these lines, making the square and sending subtleties of the exchanges to different hubs, making the chain. Excavators utilize the idea of confirmation of work to process and approve exchanges on the system. Everyone on the network can see what each one has and also determine whether a particular transaction is valid or invalid. There are a number of sites that aid in cryptocurrency transactions. These sites are similar to stock markets and give the trend of each currency, the conversion rates, portals for exchange, etc. A cryptocurrency wallet stores the cryptographic keys and facilitates spending or receiving cryptocurrencies, monitoring the balance, and managing transaction details. Cryptocurrencies entering the market is on a steep rise and is attracting potential investors. In addition to offering a solution to the double-spending problem, decentralization, immutability, and security are some of the features that make cryptocurrency attractive. Like any other digital currency, it has its share of disadvantages: the prominent lack of knowledge, the complexity involved in usage, delayed acknowledgments, and strong volatility. All cryptocurrencies originate from unregulated companies, some of which are likely to be susceptible to fraud. Even with encryption to protect cryptocurrency, transactions have been hacked, resulting in substantial loss.
An empirical analysis of graph‐based linear dimensionality reduction techniques

Summary Many emerging applications such as social networks have prompted remarkable attention in graph data analysis. Graph data is typically high‐dimensional in nature, and dimensionality reduction is critical regarding storage, analysis, and querying of such data efficiently. Although there are many dimensionality reduction methods, it is not clear to what extent the performances of the various dimensionality reduction techniques differ. In this article, we review some of the well‐known linear dimensionality reduction methods and perform an empirical analysis of these approaches using large multidimensional graph datasets. Our results show that in linear unsupervised learning methods, the principal component analysis, singular value decomposition, and neighborhood preserving embedding methods achieve better retrieval data performance than other methods of the statistical information category, dictionary methods, and embedding methods, respectively. Regarding supervised learning methods, the experimental results demonstrate that linear discriminant analysis and partial least squares presented almost similar results.
Heterogeneous Replicas for Multi-dimensional Data Management

Multi-dimensional data is widely used in different scenarios, such as cluster monitoring and user behavior analysis for web services. The data is usually managed by distributed databases with a replication strategy, which enhances the availability, fault-tolerance, and I/O throughput. Normally, these replicas share the same physical layout on the disk, which is designed by database administrators according to the target workload. However, it is critical to derive an optimal layout that benefits as many queries as possible, because a layout that accommodates only some queries can negatively impact the others. To tackle this limitation, we propose heterogeneous replicas for multi-dimensional data that provide a higher query throughput without additional disk occupation and without slowing down the writing speed, while still ensuring high availability and load balance. The proposed replication method allows different replicas to be logically identical while having different physical data layouts on the disk. We verified the efficiency of our method in a NoSQL system, Cassandra, with the TPC-H dataset and with a synthetically generated dataset. The results show that our method outperforms state-of-the-art solutions.
The Paradox of Help Seeking in the Entropy Mastermind Game

Research on Bayesian reasoning suggests that humans make good use of available information. Similarly, research on human information acquisition suggests that Optimal Experimental Design models predict human queries well. This perspective contrasts starkly with educational research on help seeking, which suggests that many students wait excessively long to ask for help, or even decline help when it is offered. We bring these two lines of work together, exploring when people seek help as a function of problem state in the Entropy Mastermind code breaking game. The Entropy Mastermind game is a probabilistic version of the classic code breaking game, involving inductive, deductive and scientific reasoning. Whether help in the form of a hint was available was manipulated within subjects. Results showed that participants tended to ask for help late in the game play, often when they already had all the necessary information needed to crack the code. These results pose a challenge for the Bayesian and Optimal Experimental Design frameworks and highlight the importance of considering whether other forms of help could be more effective.
Two Improved Methods of Generating Adversarial Examples against Faster R-CNNs for Tram Environment Perception Systems

Trams have increasingly deployed object detectors to perceive running conditions, and deep learning networks have been widely adopted by those detectors. Growing neural networks have incurred severe attacks such as adversarial example attacks, imposing threats to tram safety. Only if adversarial attacks are studied thoroughly, researchers can come up with better defence methods against them. However, most existing methods of generating adversarial examples have been devoted to classification, and none of them target tram environment perception systems. In this paper, we propose an improved projected gradient descent (PGD) algorithm and an improved Carlini and Wagner (C&amp;W) algorithm to generate adversarial examples against Faster R-CNN object detectors. Experiments verify that both algorithms can successfully conduct nontargeted and targeted white-box digital attacks when trams are running. We also compare the performance of the two methods, including attack effects, similarity to clean images, and the generating time. The results show that both algorithms can generate adversarial examples within 220 seconds, a much shorter time, without decrease of the success rate.
Extract and Merge: Superpixel Segmentation with Regional Attributes

For a certain object in an image, the relationship between its central region and the peripheral region is not well utilized in existing superpixel segmentation methods. In this work, we propose the concept of regional attribute, which indicates the location of a certain region in the object. Based on the regional attributes, we propose a novel superpixel method called Extract and Merge (EAM). In the extracting stage, we design square windows with a side length of a power of two, named power-window, to extract regional attributes by calculating boundary clearness of objects in the window. The larger windows are for the central regions and the smaller ones correspond to the peripheral regions. In the merging stage, power-windows are merged according to the defined attraction between them. Specifically, we build a graph model and propose an efficient method to make the large windows merge the small ones strategically, regarding power-windows as vertices and the adjacencies between them as edges. We demonstrate that our superpixels have fine boundaries and are superior to the respective state-of-the-art algorithms on multiple benchmarks.
Towards a semantic recommender system for cultural objects: Case study Draa-Tafilalet region

In the Big data era, a large number of functionalities and applications are created. The immediate consequence is the loss of time for the user due to the difficulty of accessing relevant information, and therefore a questioning of the usefulness of the services offered. Recommender system (RS) aims to help potential users by recommending the most suitable offers according to their profiles and preferences, RSs based on collaborative filtering, or those based on content or even hybrid filtering have shown interesting results to be explored for the resolution of the problems encountered. But some limits remain unresolved which are mainly related to the ability of these techniques to build a robust and complete system capable of forming a complete idea of the user profile and then recommend them the most suitable offers. Hence, the advantage of using semantic RSs based on data web and semantic web technologies, specifically the ontologies. This paper offers a comparative study of existing semantic RSs in the field of cultural heritage in order to extract a complete vision of a RS for the scientific cultural heritage of the region of Drâa-Tafilalet in Morocco.