Reviewerly/exHarmony huggingface dataset:
Bagging Using Instance-Level Difficulty for Multi-Class Imbalanced Big Data Classification on Spark

Most machine learning methods work under the assumption that classes have a roughly balanced number of instances. However, in many real-life problems we may have some types of instances appearing predominantly more frequently than the others which causes a bias towards the majority class during classifier training. This becomes even more challenging when dealing with multiple classes, where relationships between them are not easily defined. Learning from multi-class imbalanced data has not been widely considered in the context of big data mining, despite the fact that this is a learning difficulty frequently appearing in this domain. In this paper, we address this challenge by proposing a comprehensive ensemble-based framework. We propose to analyze each class to extract instance-level characteristics describing their difficulty levels. We embed this information into the existing UnderBagging framework. Our ensemble samples instances with probabilities proportional to their difficulty levels. This allows us to focus the learning process on the most difficult instances, better capturing the properties of multi-class imbalanced problems. We implemented our framework on Apache Spark to allow for high-performance computing over big data sets. This experimental study shows that taking into account the instance-level difficulty leads to training of significantly more accurate ensembles.
Magnetic detaching system for Modular UAVs with perching capabilities in industrial environments

This article presents an automatic magnetic detaching system integrated in the perching mechanism of an Unmanned Aerial Vehicle. This system is prepared to be activated in emergencies, releasing the mechanism and letting the UAV to take off and fly away safely. This design has been particularly developed for using UAVs in the oil and gas industries. In these environments, emergencies are related to escapes of flammable gases. With the proposed design, the UAV can be aware of dangerous gas concentrations and escapes when it detects a rise in the level of these gases. All the power is held in the UAV, so the perching system remains mechanically stable but without dangerous power sources.
General-Purpose vs. Specialized Data Analytics Systems: A Game of ML &amp; SQL Thrones

Over the past decade, a plethora of systems have emerged to support data analytics in various domains such as SQL and machine learning, among others. In each of the data analysis domains, there are now many different specialized systems that leverage domain-specific optimizations to efficiently execute their workloads. An alternative approach is to build a general-purpose data analytics system that uses a common execution engine and programming model to support workloads in different domains. In this work, we choose representative systems of each class (Spark, TensorFlow, Presto and Hive) and benchmark their performance on a wide variety of machine learning and SQL workloads. We perform an extensive comparative analysis on the strengths and limitations of each system and highlight major areas for improvement for all systems. We believe that the major insights gained from this study will be useful for developers to improve the performance of these systems.
Integration of Blockchains with Management Information Systems

In the era of the fourth industrial revolution (In-dustry 4.0), many Management Information Systems (MIS) integrate real-time data collection and use technologies such as big data, machine learning, and cloud computing, to foster a wide range of creative innovations, business improvements, and new business models and processes. However, the integration of blockchain with MIS offers the blockchain trilemma of security, decentralisation and scalability. MIS are usually Web 2.0 client-server applications that include the front end web systems and back end databases; while blockchain systems are Web 3.0 decentralised applications. MIS are usually private systems that a single party controls and manages; while blockchain systems are usually public, and any party can join and participate. This paper clarifies the key concepts and illustrates with figures, the implementation of public, private and consortium blockchains on the Ethereum platform. Ultimately, the paper presents a framework for building a private blockchain system on the public Ethereum blockchain. Then, integrating the Web 2.0 client-server applications that are commonly used in MIS with Web 3.0 decentralised blockchain applications.
Architecture to improve the accuracy of automatic image annotation systems

Automatic image annotation (AIA) is an image retrieval mechanism to extract relative semantic tags from visual content. So far, the improvement of accuracy in newly developed such methods have been about 1 or 2% in the F1‐score and the architectures seem to have room for improvement. Therefore, the authors designed a more detailed architecture for AIA and suggested new algorithms for its main parts. The proposed architecture has three main parts: feature extraction, learning, and annotation. They designed a novel learning method using machine learning and probability bases. In the annotation part, they suggest a novel method that gains the maximum benefit from the learning part. The combination of the proposed architecture, algorithms, and novel ideas resulted in new accuracy milestones in F1‐score on most commonly used datasets. In their architecture, N + measure which shows the number of tags with non‐zero recalls showed that they could recall all tags for IAPRTC‐12 and ESP‐Games datasets.
Reduction of random variables in the Stochastic Harmonic Function representation via spectrum-relative dependent random frequencies

Two significant developments pertaining to the application of the Stochastic Harmonic Function representation of stochastic processes are presented. Together, they allow for Gaussian records to be simulated within the bounds of the representation with the fewest number of random variables. Specifically, independent random frequencies that form a staple component of the Stochastic Harmonic Function are replaced by dependent random frequencies, along with a specific scheme for choosing frequency interval widths. Numerical examples demonstrating spectrum reconstruction accuracy and estimated PDF convergence to the Gaussian are presented to support the work.
Disease Diagnosis Prediction of EMR Based on BiGRU-Att-CapsNetwork Model

Electronic Medical Records (EMR) carry a large number of diseases characteristics, history and other specific details of patients, which has great value for medical diagnosis. These data with diagnostic labels can help automated diagnostic assistant to predict disease diagnosis and provide a rapid diagnostic reference for doctors. In this study, we designed a BiGRU-Att-CapsNetwork model based on our proposed CMedBERT Chinese medical domain pre-trained language model to predict disease diagnosis in Chinese EMR. In the wide-ranging comparative experiments involving a real EMR dataset (SAHSU) and an academic evaluation task dataset (CCKS 2019), our model obtained competitive performance.
Cloud providers ranking and selection using quantitative and qualitative approach

Cloud computing is basically an internet based computing, whereby shared configurable resources are provided to cloud service consumers as services on demand. As an increasing growth of cloud computing, many enterprises provide different cloud services to cloud service consumers. From cloud service consumer’s perspective, it is difficult to choose an appropriate cloud service that satisfies their QoS requirements. As requirements of one cloud service consumer will vary from another, dynamic ranking has to be used to satisfy the requirements of different cloud service consumers. A simple model is needed to address the dynamic ranking of cloud services. The dynamic ranking and selection of cloud services is solved using Linear Programming (LP) model. This project considers quantifiable attributes such as processor speed, cost, etc. and some non-quantifiable attributes like feedback to rank various cloud services according to the requirements of cloud service consumer using Linear Programming (LP) technique.
Challenges beyond blockchain: scaling, oracles and privacy preserving

The blockchain technology is one of the most actively investigated areas in applied cryptography, but its widespread adoption is being held back by some technical drawbacks. In this paper, we make a review of the most significant issues and possible approaches to handle them.
A lightweight identity-based provable data possession supporting users’ identity privacy and traceability

Cloud storage auditing is considered as a significant service used to verify the integrity of data stored in public cloud. However, most existing auditing protocols suffer form complex certificate management/verification since they rely on expensive Public Key Infrastructure (PKI). On the other hand, most cloud users have constrained computational resources. The few existing ID-based storage auditing protocols in the literature, require resource-constrained users to perform costly operations for generating metadata on file blocks. In this paper, we propose a storage auditing protocol which benefits from ID-based structure and lightweight user computations, simultaneously. Our construction keeps user’s identity private from the server and TPA and also provides traceablity of misbehaving users. Our mechanism is also extended to support batch auditing of multiple tasks in multi-user setting. Performance evaluations demonstrate excellent efficiency of our scheme specially at the user’s side in comparison with the state of the art.
A Comparative Analysis on Filtering Techniques Used in Preprocessing of Mammogram Image

Breast cancer is one of the vital causes of an increase in the rate of mortality of women in developing countries. Detection of micro-calcifications in the breast tissue by the radiologists is a significant step in the process of identification of cancer in the breast. Preprocessing in mammogram has played a vital role in identifying such micro-calcifications, masses and architectural distortion in the breast. It is important in the process of breast cancer analysis as it helps in reducing the number of false positive. The digital mammogram has emerged as the popular screening approach for early detection of masses and other abnormalities in the breast. In this manuscript, we introduced four standard filtering methods, which improve the efficiency of the model used for early detection of breast cancer. To compare the performance of the studies filter methods the mean square error and peak signal to noise ratio are widely used performance measure being considered in our work.
The influence of semantic link network on the ability of question-answering system

Semantic Link Network plays an important role in representing and understanding text. This paper investigates the influence of semantic links on the basic abilities of a type of QA system that extracts answers from a range of texts (answer range). Research concerns how semantic links influence the answer range and the performance of this type of QA system. Research also concerns the ability to answering different types of questions and supporting different patterns of answering questions. Based on the semantic link network extracted from Wikipedia, an experimental QA system is developed to answer questions according to a range of pages in Wikipedia. Research reached the following results: (1) the answer range and the semantic link network influence each other: keeping a certain range of performance, increase one can decrease the request of the other; and, (2) the semantic link network can enhance the ability of QA system in answering questions and supporting patterns of answering questions covered by semantic link network.
Using Social Networks for English Teaching and Learning

This paper aims to use social networking and some information technology tools to support English teaching in order to improve students 'foreign language skills as well as their motivation outside of school time. The paper also addresses the self-study element of students based on the support of modern scientific and technological equipment today. Since then, the interest in improving the teaching and learning process towards the application of software is quite necessary.
A Case Study of a Cybersecurity Programme

Cybersecurity is an area of growing international importance. In response to global shortages of Cybersecurity skills, many universities have introduced degree programmes in Cybersecurity. These programmes aim to prepare students to become Cybersecurity practitioners with advanced skills in a timely manner. Several universities offer Cybersecurity degrees, but these have been developed ad hoc, as there is currently no internationally accepted Cybersecurity curriculum. Recently, an ITiCSE working group on global perspectives on Cybersecurity education developed a competency-based framework that aims to help institutions to implement Cybersecurity programmes. In this report, we present a case study of a Cybersecurity programme at the University of Auckland. We discuss how the curriculum and resource management of this programme evolved, and we present some challenges for the design and delivery of a Cybersecurity programme in the light of this competency-based framework.
Multitasking differential evolution with difference vector sharing mechanism

As a new emerging research topic in the field of evolutionary computation, evolutionary multitasking optimization (EMTO) is presented to solve multiple optimization tasks concurrently by transferring knowledge across them. However, the promising search directions found during the evolutionary process have not been shared and utilized effectively in most EMTO algorithms. Therefore, this paper puts forward a difference vector sharing mechanism (DVSM) for multitasking differential evolution (MDE), with the purpose of capturing, sharing and utilizing the useful knowledge across different tasks. The performance of the proposed algorithm, named MDE with DVSM (MDE-DVSM), is evaluated on a suite of single-objective multitasking benchmark problems. The experimental results have demonstrated the superiority of MDE-DVSM when compared with other competitive algorithms.
Non-local Attention Learning on Large Heterogeneous Information Networks

Heterogeneous information network (HIN) summarizes rich structural information in real-world datasets and plays an important role in many big data applications. Recently, graph neural networks have been extended to the representation learning of HIN. One very recent advancement is the hierarchical attention mechanism which incorporates both nodewise and semantic-wise attention. However, since HIN is more likely to be densely connected given its diverse types of edges, repeatedly applying graph convolutional layers can make the node embeddings indistinguishable very quickly. In order to avoid oversmoothness, existing graph neural networks targeting HIN generally suffer from a shallow structure. Consequently, those approaches ignore information beyond the local neighborhood. This design flaw violates the concept of non-local learning, which emphasizes the importance of capturing long-range dependencies. To properly address this limitation, we propose a novel framework of non-local attention in heterogeneous information networks (NLAH). Our framework utilizes a non-local attention structure to complement the hierarchical attention mechanism. In this way, it leverages both local and non-local information simultaneously. Moreover, a weighted sampling schema is designed for NLAH to reduce the computation cost for largescale datasets. Extensive experiments on three different realworld heterogeneous information networks illustrate that our framework exhibits extraordinary scalability and outperforms state-of-the-art baselines with significant margins.
Detection of Phishing websites using Generative Adversarial Network

Phishing is typically deployed as an attack vector in the initial stages of a hacking endeavour. Due to it low-risk rightreward nature it has seen a widespread adoption, and detecting it has become a challenge in recent times. This paper proposes a novel means of detecting phishing websites using a Generative Adversarial Network. Taking into account the internal structure and external metadata of a website, the proposed approach uses a generator network which generates both legitimate as well as synthetic phishing features to train a discriminator network. The latter then determines if the features are either normal or phishing websites, before improving its detection accuracy based on the classification error. The proposed approach is evaluated using two different phishing datasets and is found to achieve a detection accuracy of up to 94%.
Fast computation of Hahn moments on gray images using block representation

The moments constitute a well-known tool for image analysis and recognition tasks. The family of moments that has the most advantages is the discrete orthogonal moments. A set of these moments is the Hahn moments, as they have a great number of advantages in comparison with other sets of moments. The main disadvantage of moments, including Hahn moments, is the high computational cost, which is increased as higher order moments are involved in the computations, so the real-time analysis is hard to be done. We propose an effective approach for the computation of Hahn moments. The gray image is decomposed in a set of binary images that are named as bitplanes. The most significant bitplanes are represented using image block representation and their moments are computed fast using block techniques. The least significant binary images are substituted by a constant ideal image called “half-intensity” image, which has known Hahn moment values. The proposed method has low computational error, low computational complexity, and under certain conditions is able to achieve real-time processing rates.
From Object Detection to Room Categorization in Robotics

This article deals with the problem of room categorization, i.e. the classification of a room as being a bathroom, kitchen, living-room, bedroom, etc., by an autonomous robot operating in home environments. For that, we propose a room categorization system based on a Bayesian probabilistic framework that combines object detections and its semantics. For detecting objects we resort to a state-of-the-art CNN, Mask R-CNN, while the meaning or semantics of those detections is provided by an ontology. Such an ontology encodes the relations between object and room categories, that is, in which room types the different object categories are typically found (toilets in bathrooms, microwaves in kitchens, etc.). The Bayesian framework is in charge of fusing both sources of information and providing a probability distribution over the set of categories the room can belong to. The proposed system has been evaluated in houses from the [email protected] dataset, validating its effectiveness under real-world conditions.
Transparent E-Voting dApp Based on Waves Blockchain and RIDE Language

Since 2004, different research was handling the challenges in the centralized voting systems, e-voting protocols and recently the decentralized voting. Electronic voting puts forward some difficulties regarding the voter anonymity, the secure casting of the votes and to prevent the voting process from fraud. The decentralized property of the blockchain technology will bring new solutions for many of the challenges in the voting research area and it will bring a new security mechanism of safe and transparent voting. In this paper, a broad comparison between ongoing voting systems has studied by analyzing their structure and the drawbacks that should consider in future to improve the whole election process from keeping the privacy of the voter, casting a vote with the possibility to check if it was counted correctly to publishing the results. The result of the paper will give a new approach to extend the target of the election from small scale to large scale despite the fact of Ethereum limitation which can cast five votes per minute on an average. The primary challenge is to find an answer for this question: “how to balance between voter privacy and transparency without breaking the important rule where the voter can proof for a specific candidate that he voted for him in a bribe situation?”.
Deep learning based Customized Model for Features Extraction

In the last few years, deep learning is one of the powerful technique, used for image recognition. Among the various kinds of deep neural networks, Convolutional Neural Network (CNN) nowadays has been widely used for the purpose of image recognition. In this work, we present the customized model developed for feature extraction of medical packages. Now a days, it is difficult to distinguish an original medicine from counterfeit. The proposed model based on CNN can be useful in identifying the original medicine which forms the first step in the process to identify counterfeits. The medicine images are used as dataset for feature extraction and image classification. Medicine package shape, edges and colour are used for feature extraction of customized model. Classification is done to distinguish medicines which is of same colour and differs in their name, printed text, barcode and the company logo. One of the popular pre-trained CNN architecture model VGG-19 is used for comparing the results of developed customized model. Customized model consists of only five layers convolution layer, maxpooling layer, dropout layer, flatten layer and dense layer. In comparison to pre-trained VGG-19 model customized model reduces number of layers from 19 to 5. Number of layers are reduced to 5 because increasing the number of layers were not showing much improvement in the testing results for given dataset. Training accuracy of 93.17%, validation accuracy of 88.68% and testing accuracy of 76.67% is obtained for the customized model. The results can be made more precise and accurate by optimizing the number of layers in the model. In the proposed model we have used 5 layers. The optimization of number of layers is done based on the prediction accuracy. The accuracy of less than 50% was achieved using 3 layers and it is increased up to 76% with 5 layers. No further improvement in the prediction accuracy was observed by increasing number of layers to 6 or 7, so for the proposed model 5 layers are selected.
Evolving Energy Efficient Convolutional Neural Networks

As deep neural networks have been deployed in more and more applications over the past half decade and are finding their way into an ever increasing number of operational systems, their energy consumption becomes a concern whether running in the datacenter or on edge devices. Hyperparameter optimization and automated network design for deep learning is a quickly growing field, but much of the focus has remained only on optimizing for the performance of the machine learning task. In this work, we demonstrate that the best performing networks created through this automated network design process have radically different computational characteristics (e.g. energy usage, model size, inference time), presenting the opportunity to utilize this optimization process to make deep learning networks more energy efficient and deployable to smaller devices. Optimizing for these computational characteristics is critical as the number of applications of deep learning continues to expand.