Reviewerly/exHarmony huggingface dataset:
Balancing Assisted Reproductive Technology Dataset for Improving the Efficiency of Incremental Classifiers and Feature Selection Techniques

Assisted Reproductive Technology (ART) is a set of medical procedures primarily used to address infertility. Success Rate of ART is very low because it is affected by large number of variables. Machine Learning Techniques are now applied to predict ART outcome and to find strategies to improve success rate. For this, determining the best performing classifier for ART is very important. Previously, some classifiers are applied to ART with static data. But, in reality, the datasets are dynamic in nature and require dynamic setup which can be achieved with the help of Incremental Classifiers. Due to low success rate, the ART dataset contains less number of records for positive results that make the dataset imbalanced. This research work first finds the best evaluation metric for classification on imbalanced dataset and then balances the dataset using three different balancing techniques such as undersampling, oversampling and Synthetic Minority Oversampling Technique (SMOTE) and applies five different Incremental Classifiers, namely Stochastic Gradient Descent (SGD), Stochastic Primal Estimated sub-GrAdient SOlver for Support vector machine (SPegasos), Naïve Bayes Updatable, Instance Based (IBk), Averaged One Dependence Estimators (A1DE) Updatable and finds the best balancing technique and suitable classifier for ART outcome prediction. The result shows that for an imbalanced dataset Receiver Operating Characteristics (ROC) Area may be taken as a metric instead of the accuracy. It is found that SMOTE is best method for balancing the ART dataset and IB1 classifier performs well for the balanced data with the high prediction rate of 92.3 for ROC. Finally, various Feature Selection methods are applied to the top three best performing classifiers and suitable feature selection method for each classifier is identified.
Review of Fang Fang (2020). Wuhan Diary: Dispatches from a Quarantined City. Trans. M. Berry

In this introduction to the special issue on systematic reviews on emerging learning environments and technologies, we introduce best practices for conducting systematic reviews and meta-analysis and discuss the need for a systematic review on emerging learning environments and technologies We synthesize research on seven primary areas of emerging learning environments and technologies that include: (1) social media, (2) massive open online courses, (3) special education technology, (4) mobile learning, (5) game-based learning and gamification, (6) adaptive learning, and (7) learning analytics and introduce the thirteen articles that were included in this special issue This article also provides implications for the readers on using and conducting systematic reviews
An efficient method to compute simplified noise parameters of analog amplifiers using symbolic and evolutionary approach

Abstract Symbolic circuit analysis suffers as the symbolic terms increases by many folds with the increase in the size of the circuit. Hence simplification techniques are used for the analysis of the circuits which are larger in size. In this work, multi‐objective evolutionary optimisation algorithms for obtaining simplified symbolic expressions of analog amplifier circuits are presented which are not only very efficient in terms of speed and the amount of circuit reduction but also solve the accuracy problems. Initially, all MOSFETs are replaced with their nullor equivalent model. Then, the formulation of accurate fully‐symbolic noise expressions is carried out by the application of symbolic nodal analysis. The derived exact symbolic expressions are then simplified using an evolutionary approach. This method has been successfully coded in MATLAB. The numerical results obtained before and after simplification are compared with SPICE simulation to demonstrate the efficiency of the proposed methodology.
Experiencing Food, Designing Dialogues: Proceedings of the 1st International Conference on Food Design and Food Studies (EFOOD 2017), Ricardo Bonacho, Alcinda Pinheiro de Sousa, Cláudia Viegas, João Paulo Martins, Maria José Pires and Sara Velez Estêvão (eds) (2017)

Review of: Experiencing Food, Designing Dialogues: Proceedings of the 1st International Conference on Food Design and Food Studies (EFOOD 2017) , Ricardo Bonacho, Alcinda Pinheiro de Sousa, Cláudia Viegas, João Paulo Martins, Maria José Pires and Sara Velez Estêvão (eds) (2017) Leiden: CRC Press, 174 pp., ISBN 978-1-13857-538-7, h/bk, £120
Tourism Recommender System Utilising Property Graph Ontology as Knowledge Base

In recent years, the amount of Internet information and users has been increasing at a remarkable rate. Recommender System (RS) has emerged to solve the problem of drastic overloaded information over the Internet. RS automatically analyses all relevant information by integrating profiling tailored to specific user, as well as user rating to give more accurate and reliable recommendations. In this paper, we propose an ontology-based using Jaccard Index to define rules and interrelations between entities, thus, offering greater semantic relations within a particular domain.
A Support Vector Machine Classification-Based Signal Detection Method in Ultrahigh-Frequency Radio Frequency Identification Systems

Noise and distortion in real signals have posed challenges in signal detection in ultrahigh-frequency (UHF) radio frequency identification (RFID) systems, with the exception of large frequency deviation. In this article, signal detection in a UHF RFID system was treated as a time-series classification problem. A support vector machine-based detector was proposed for real-time signal detection to optimize the system performance. The improved system was able to achieve frame synchronization detection and time-series data detection with a frequency deviation of ± 22%. With the analysis of the frame format, the frame synchronization was based on the sequence detection with the most likely selection, and the data detection was performed using a symbol-by-symbol method with two-step adjustments in order to reduce time and hardware costs. The algorithm was mainly trained offline using the experimental data, and then efficiently implemented in a UHF RFID system based on a software-defined radio platform. The system was validated on real-time signals of commercial tags. The experimental results showed that the learned detector had displayed improvements in detection accuracy and stability when compared to the existing method based on a correlation algorithm.
Non-metric Propositional Similarity

Abstract The idea that sentences can be closer or further apart in meaning is highly intuitive. Not only that, it is also a pillar of logic, semantic theory and the philosophy of science, and follows from other commitments about similarity. The present paper proposes a novel way of comparing the ‘distance’ between two pairs of propositions. We define ‘ $$p_1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>p</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:math> is closer in meaning to $$p_2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>p</mml:mi> <mml:mn>2</mml:mn> </mml:msub> </mml:math> than $$p_3$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>p</mml:mi> <mml:mn>3</mml:mn> </mml:msub> </mml:math> is to $$p_4$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>p</mml:mi> <mml:mn>4</mml:mn> </mml:msub> </mml:math> ’ and thereby give a precise account of comparative propositional similarity facts. Notably, our definition eschews metric assumptions, which are unrealistic in most applications of interest.
Reinventing Library Research Support Services at Griffith University

Griffith University provides advice and services to researchers around research grants, ethics and integrity, research performance, and publications and outputs. A broad-based researcher education and development program helps inform and upskill researchers and research students. From 2019, Griffith University Library has developed new services to support data-driven, data-intensive research and assist researchers through the entire research lifecycle. This chapter describes the staffing structure and the ideas underpinning a new service catalogue based around key areas, such as data management, open scholarship, and data wrangling. Methods to achieve this included a collaboratively developed knowledge base, the development of new workshops, mapping of research environments and referral pathways, and developing support for researchers for whom there is no established tool for the kinds of research they want to do. A push to establish the library as a vital and valued partner in research projects was a key driver for change.
Basic Structure for Human Activity Recognition Systems: Preprocessing and Segmentation

Automatic recognition of human activities using sensor-based systems is commonly known as human activity recognition (HAR). It is required to follow a structural pipeline to recognize activity using a machine learning technique. This chapter represents the different stages of this structural pipeline in detail. Following this, the preprocessing steps have been analyzed to clean and remove noises from raw sensor data. The importance of segmentation and criterions to select the best windowing method have been also described based on previous research works. The challenges regarding the selection of window length, window type, choosing overlapping percentage, and the relation between window duration and performance have been also investigated in the end.
Making Frequent-Pattern Mining Scalable, Efficient, and Compact on Nonvolatile Memories

Frequent-pattern mining is a common means to reveal the hidden trends behind data. However, most frequent-pattern mining algorithms are designed for dynamic random-access memory (DRAM), instead of nonvolatile memories (NVMs) which are preferred by energy-limited systems. Due to the huge differences between the characteristics of NVMs and those of DRAM, existing frequent-pattern mining algorithms encounter the issues of write amplification and energy waste when they are run on NVMs. Moreover, the design complexity is exaggerated when parallel computing architecture is introduced to speedup the mining process. A scalable, time-efficient, and energy-economic solution to the frequent-pattern mining problem is thus urgently needed. Based on the well-known frequent-pattern tree (FP-tree) approach to frequent-pattern mining, this article proposes parallel EvFP-tree (PevFP-tree), a parallel frequent-pattern mining solution for NVMs. By considering the NVM characteristics, PevFP-tree accelerates the mining process and enhances the energy efficiency, as compared to a straightforward design of FP-trees on the parallel architecture. Moreover, PevFP-tree offers superior scalability in terms of the degrees of parallelism of the mining algorithm and the branching factor of its tree structure. Observing that keys are often sparsely distributed in FP-trees, we also propose a compression technique to PevFP-tree, namely, compressed PevFP-tree (CpevFP-tree), which further enhances the time and energy efficiencies of PevFP-tree. The proposed PevFP-tree and CpevFP-tree are evaluated by a series of experiments based on realistic datasets from diversified application scenarios, where CpevFP-tree achieves 88.73% of performance improvements over a straightforward design of FP-trees in the parallel architecture, and 79.47% of performance improvements over PevFP-tree, on average.
Docker based Intelligent Fall Detection using Edge-Fog Infrastructure

The uses of automation and sensors have become the order of the day which has led to a new paradigm of Internet of things. The computation of these data from the sensors has increased greatly with the need for real-time analytics of the generated data. To facilitate the integration of the Internet of Things( IoT) data and the analytics in real-time has brought the need for the use of dockers. Offloading latency-sensitive tasks to the edge devices can lead to great improvement in performance in terms of network utility, overall latency, and energy utilization. Certain tasks have to run on the end devices where the algorithms are deployed for real-time detection. The dockers are lightweight that containerizes the algorithms giving the ability to run on any platform. The fog devices are low configured devices with minimal communication and computation ability such that of a raspberry pi. Our proposed work deals with the fall detection with ensemble detection algorithm running on the edge devices containerized using dockers. The simulated results are presented in the paper along with the proposed fog architecture with the performance analysis in terms of accuracy and Bandwidth utility.
Sentimental Analysis on Twitter Data Using Hadoop with Spring Web MVC

This paper addresses the sentiment analysis on Twitter data, i.e., obtaining user sentiments by classifying tweets according to their opinions positive, negative or neutral. This paper primarily consists of live stream data generation, process and visualization through GUI based application for the results. Output will be visualized through Google Charts. Opinion tweet results will be displayed in the form of pie charts. Twitter is an online microblogging on social-networking platform which allows users to express their ideas. It is a rapidly expanding service with over 555 million users, generating millions of tweets per day. We can obtain public sentiment through this data, it is crucial for many applications such as predicting political elections suppose public opinion on the prime minister and for obtaining product reviews of firms. The aim of this project is to find sentiments from the Twitter live streaming data using Apache Flume, Hadoop, and visualize the results in the web application.
Differential Gaming approach with safety parameter for Mobile Robot to circumvent a Dynamic Obstacle

In a Mobile Robot system (MRS) where multitude of autonomous non-holonomic mobile robots move in afield with individual target goals, each robot is seen by another as a dynamic obstacle. In this research work, a differential game (DG) based approach is presented for two mobile robots to dodge each other in an optimal circumvention path. The conventional DG algorithm brings the robots in close encounter since it is completely driven by hard optimality considerations. In real world, the robot may be required to keep safe distance from the other robot for various operational and environmental reasons. To allow for this, the algorithm is modified by addition of a Safety Parameter (SP) which is adjusted to allow for customisation of the circumvention maneuver.Matlab simulation is done using two robots and results obtained with different scenarios are analysed.The results show that DG+SP algorithm delivers better results in terms of avoiding too close encounters of the robots while still giving an optimal circumvention route.
Dimension Reduction with Extraction Methods (Principal Component Analysis - Self Organizing Map - Isometric Mapping) in Indonesian Language Text Documents Clustering

Clustering algorithms such as k-Means, fail to function appropriately when used to analyze data with high dimensions. Therefore, in order to achieve a good clustering, a feature selection or a feature extraction dimensional reduction is needed. The Principal Component Analysis (PCA) algorithm often utilized the extraction methods, however, the reduction result is not too good, due to low quality of clustering and lengthy processing time. Therefore, it is necessary to study other algorithms methods to obtain alternatives to the PCA. This study therefore was conducted by comparing the results of Indonesian text document clustering, which had been reduced in dimensions by PCA, Self-Organizing Map (SOM), and Isometric Featuring Mapping (Isomap). The measurements were made on clustering quality parameters using the Davies Bouldin Index, computational time, and iterations. The results shows that SOM tend to improve cluster quality to 269.084% better than the k-Means, while, Isomap has the ability to speed up the clustering computing time by 190 times. In addition, the qualitative outcome determines the most appropriate algorithm extraction method capable of reducing clustering features of Indonesian language text documents.
Dual Channel Per-packet Load Balancing for Datacenters

Load balancing has become an important yet challenging performance optimization technology for datacenters with the immense proliferation of cloud based applications. The two dominant technical challenges when designing a datacenter load balancing mechanism are: determining the granularity of a data unit for scheduling, and determining the range of the load information to be collected. On one hand, the per-packet load balancing policies can occupy more paths for transmitting a flow, however they are also prone to cause packet reordering and degrade the performance. On the other hand, the global congestion-aware scheduling mechanisms outperform some local congestion-aware or some congestion-oblivious mechanisms by employing holistic views of the global load information; however the long duration control loops also limit the scalability of a global congestion-aware mechanism. Addressing the two issues, this paper presents a novel global congestion-aware per-packet load balancing scheduling mechanism, named SPLB (Stand-in Per-packet Load Balancing). Based on a dual channel architecture and the preview detection results, SPLB successfully maintains the arrival sequence of the data packets even in asymmetric datacenter topologies. Moreover, SPLB immediately determines the transmission route of each data packet based on the backhaul stand-in packets, without introducing any control loop durations. Extensive experiments validate the effectiveness of SPLB with selected datacenter topologies.
Texture Analysis of Mammogram Using Histogram of Oriented Gradients Method

Abstract The second foremost reason for dying ladies all across the world is breast cancer. The possibilities of survival can be raises when cancer detects earlier; therefore, the mortality reduction. The radiologist used mammograms to recognize breast tumors at an early level. Since the mammograms have little contrast, hence, it is unclear to the radiologist to distinguish small tumors. A computer-aided detection system contributes to explaining mammograms and helps the radiologist to indicate the appearance of breast mass and discriminate among normal and abnormal tissue. In this research, we introduce a histogram of oriented gradients as a method of feature extraction and identify mass regions in mammograms. The features extraction from this method classified by a support vector machine. To improve the diagnosis ability, contrast limited adaptive histogram equalization pre-processing system is utilized. Mini-MIAS database used to estimate this approach. The top accuracy, sensitivity, and specificity obtained are 90%, 69%, 100%, respectively.
Extension of Image Data Using Generative Adversarial Networks and Application to Identification of Aurora

In recent years, automatic auroral image classification has been actively investigated. The baseline method has relied on supervised learning. As this approach requires a large amount of labeled teacher data, it is necessary to collect the data manually and label them, which is a time-consuming task. In this study, we proposed a method to extend an image data set by inputting training images into a deep convolutional generative adversarial network (DCGAN) and generating images in this manner. The proposed approach implied using both generated and original images to train the classifier. It could reduce the number of labeling operations performed manually. As an evaluation experiment, we performed classifier learning on the data sets before and after extension and confirmed that the classification accuracy was improved because of training on the data set after the extension.
Harmonise and integrate heterogeneous areal data with the R package arealDB

Many relevant applications in the environmental and socioeconomic sciences use areal data, such as biodiversity checklists, agricultural statistics, or socioeconomic surveys. For applications that surpass the spatial, temporal or thematic scope of any single data source, data must be integrated from several heterogeneous sources. Inconsistent concepts, definitions, or messy data tables make this a tedious and error-prone process. To date, a dedicated tool to address these challenges is still lacking. Here, we introduce the R package arealDB that integrates heterogeneous areal data and associated geometries into a consistent database, in an easy-to-use workflow. It is useful for harmonising language and semantics of variables, relating data to geometries, and documenting metadata and provenance. We illustrate the functionality by integrating two disparate datasets (Brazil, USA) on the harvested area of soybean. arealDB promises quality-improvements to downstream scientific, monitoring, and management applications but also substantial time-savings to database collation efforts.
A Mixed-Method Analysis of Text and Audio Search Interfaces with Varying Task Complexity

Voice-based assistants have become a popular tool for conducting web search, particularly for factoid question answering. However, for more complex web searches, their functionality remains limited, as does our understanding of the ways in which users can best interact with audio-based search results. In this paper, we compare and contrast user behaviour through the representation of search results over two mediums: text and audio. We begin by conducting a crowdsourced study exposing the differences in user selection of search results when those are presented in text and audio formats. We further confirm these differences and investigate the reasons behind them through a mixed-methods laboratory study. Through a qualitative analysis of the collected data, we produce a list of guidelines for an audio-based presentation of search results.
Net Generation, Digital Natives and Learning Commons

In order to realize the transformation requirements of the “net generation” born after 2000 as the service objects of university libraries still better, this paper analyzes the characteristics of the net generation, and points out that the net generation has great differences from the traditional readers of libraries in terms of learning and borrowing habits, and has very great influences on the form of solid and virtual learning environments provided by university libraries. On this basis, this paper constructs the library learning commons service model facing the net generation, and discusses the principles for the reconstruction of the learning commons of university libraries from the four aspects “adaption to local conditions, demand orientation, real-virtual combination, and people foremost”, in order to achieve their sustainable development.