Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis

Sentiment about a product or a service offered by a company is all too valuable in this era than ever before. Knowing whether their established customer base and potential customers are showing a positive or negative sentiment (as shown in Figure 7-1) toward their product or service can be game-changing for companies. However, extracting the true sentiment from a phrase written in English is a challenge, let alone in all the languages. That’s because human languages could be ambiguous, and we can be sarcastic at time and understanding sarcasm is a huge challenge for computers. Although we are getting better with each passing year, but it is still a long way to go.
Predicting the dynamical behaviors for chaotic semiconductor lasers by reservoir computing

We demonstrate the successful prediction of the continuous intensity time series and reproduction of the underlying dynamical behaviors for a chaotic semiconductor laser by reservoir computing. The laser subject to continuous-wave optical injection is considered using the rate-equation model. A reservoir network is constructed and trained using over 2 × 104 data points sampled every 1.19 ps from the simulated chaotic intensity time series. Upon careful optimization of the reservoir parameters, the future evolution of the continuous intensity time series can be accurately predicted for a time duration of longer than 0.6 ns, which is six times the reciprocal of the relaxation resonance frequency of the laser. Moreover, we demonstrate for the first time, to the best of our knowledge, that the predicted intensity time series allows for accurate reproduction of the chaotic dynamical behaviors, including the microwave power spectrum, probability density function, and the chaotic attractor. In general, the demonstrated approach offers a relatively high flexibility in the choice of reservoir parameters according to the simulation results, and it provides new insights into the learning and prediction of semiconductor laser dynamics based on measured intensity time series.
Dense coding capacity in correlated noisy channels with weak measurement*

Capacity of dense coding via correlated noisy channel is greater than that via uncorrelated noisy channel. It is shown that the weak measurement and reversal measurement need to further improve their quantum dense coding capacity in correlated amplitude damping channel, but this improvement is very small in correlated phase damping channel and correlated depolarizing channel.
Sentiment Analysis of Amazon Product Reviews Using Machine Learning and Deep Learning Models

Due to the expansion of social networks and e-commerce websites, sentiment analysis or opinion mining has become a more active study issue in recent years. The objective of sentiment analysis is to identify and categorize the positive and negative sentiment expressed in a piece of text. Consumers can submit reviews with a specified rating on e-commerce websites like Amazon.com. As a result, in our paper, we sought to construct sentiment analysis related to product ratings and text reviews utilizing Amazon&#x0027;s dataset. Linear Support Vector Ma-chine, Random Forest, Multinomial Naive Bayes, Bernoulli Naive Bayes, and Logistic Regression were among the machine learning algorithms used. We acquired accuracy with the Random Forest classifier (91.90&#x0025;). We also use RNN with LSTM as a deep learning approach in our paper and got maximum accuracy (97.52&#x0025;). For our model RNN-LSTM is ideal approach.
Pupillary measurement during an assembly task

We conducted an empirical study of 57 children using a printed Booklet and a digital Tablet instruction for LEGO® construction while they wore a head-mounted gaze tracker. Booklets caused a particularly strong pupil dilation when encountered as the first media. Subjective responses confirmed the booklet to be more difficult to use. The children who were least productive and asked for assistance more often had a significantly different pupil pattern than the rest. Our findings suggest that it is possible to collect pupil size data in unconstrained work scenarios, providing insight to task effort and difficulties.
Engaging Information Systems Students in a Practicum-Based Project

This article presents the research related to the work integrated learning (WIL) model. The objectives were to explore their characteristics and the techniques associated with them. The focus is models linked to the IT industry or IT department in non-IT industries. A systematic literature review (SLR) was applied as the methodology. The authors collected data reported from 2006 to 2016 from eight databases. There were 24 articles which matched their search criteria. The WIL models reported can be separated into four groups. The two key features are activities created by universities, and activities offered by industry. The model techniques are knowledge, methods, and tools for acquiring research problems and developing solutions. The authors found that the models offered are very broad and need to be narrowed down so that prospective careers and the needs of the digital workforce are prioritized. They suggest future trends in WIL models in the digital economy.
Knowledge Aware Conversation Generation with Reasoning on Augmented Graph.

Two types of knowledge, triples from knowledge graphs and texts from documents, have been studied for knowledge aware open-domain conversation generation, in which graph paths can narrow down vertex candidates for knowledge selection decision, and texts can provide rich information for response generation. Fusion of a knowledge graph and texts might yield mutually reinforcing advantages, but there is less study on that. To address this challenge, we propose a knowledge aware chatting machine with three components, an augmented knowledge graph with both triples and texts, knowledge selector, and knowledge aware response generator. For knowledge selection on the graph, we formulate it as a problem of multi-hop graph reasoning to effectively capture conversation flow, which is more explainable and flexible in comparison with previous work. To fully leverage long text information that differentiates our graph from others, we improve a state of the art reasoning algorithm with machine reading comprehension technology. We demonstrate the effectiveness of our system on two datasets in comparison with state-of-the-art models.
Visualization of Big Data Text Analytics in Financial Industry: A Case Study of Topic Extraction for Italian Banks

Textual data and analysis can derive new insights and bring valuable business insights. These insights can be further leveraged by making better future business decisions. Sources that are used for text analysis in financial industry vary from internal word documents, email to external sources like social media, websites or open data. The system described in this paper will utilize data from social media (Twitter) and tweets related to Italian banks, in Italian. This system is based on open source tools (R language) and topic extraction model was created to gather valuable information. This paper describes methods used for data ingestion, modelling, visualizations of results and insights.
Automated Classification of Oral Cancer Histopathology images using Convolutional Neural Network

In latest years, convolutional neural networks (CNNs) have accomplished state-of-the-art performance in many computer vision tasks such as classification of images, object detection, instance segmentation etc. CNN has a robust learning capacity and can enhance the use of datasets for feature extraction. Identifying the key visual features from the oral squamous cells are the significant and compulsory task for the clinicians to detect the different stages of oral cancer. The computer-aided instrument performing the same identifying job would provide clinicians with a vital guidance during diagnosis for evaluating histological images. In this study, we suggest the use of 4-layered (5X5X3) patches of convolutional neural networks (CNNs) for feature extraction and classification from oral cancer images. To prevent overfitting the images were augmented by rotating, inverting and flipping. The proposed model has achieved 96.77 % accuracy, with 10 fold cross validation, which is at par with the accuracy of cytotechnologists and pathologists. Therefore, this model is helpful in classifying oral cancer microscopic images.
Assessing books’ depth and breadth via multi-level mining on tables of contents

Compared with journal articles, books can provide broader, deeper and more comprehensive information, and often have higher expertise and academic depth. However, most researches on book assessment focus on measuring academic value of books (e.g. citations analysis) or identifying attitudes of readers (e.g. book review mining), depth and breadth reflected by book contents is neglected. Therefore, in this paper, we measure books’ depth and breadth by mining books’ tables of contents, so as to enrich resources and methods for book assessment research, help users understand book contents quickly and improve efficiency of book selection. Specifically, we measured books’ depth and breadth based on books’ tables of contents via two levels: topic level and feature level. Firstly, we obtained topic-level metrics by identifying topics expressed in tables of contents and calculating topic distributions. Then, we got feature-level results via feature extraction and feature distribution calculation. Finally, we compared depth and breadth metrics and other book assessment metrics. Experimental results reveal that, books’ depth and breadth at two levels are different, and substantial differences between disciplines and book types are obvious. In addition, books’ depth and breadth can provide alternative and supplementary information for assessing multi-dimensional values of books.
Polar coordinates and generalized h-type indices

This article highlights the importance of using polar coordinates when studying functions, in particular in relation to generalized h-type indices. Concretely, generalized h-type indices are essentially polar coordinates. This observation ties informetric ideas to standard mathematics. This article is essentially meant to provide tools for further studies.
A benchmarking framework using nonlinear manifold detection techniques for software defect prediction

Prediction of software defects in time improves quality and helps in locating the defect-prone areas accurately. Although earlier considerable methods were applied, actually none of those measures was found to be fool-proof and accurate. Hence, a newer framework includes nonlinear manifold detection model, and its algorithm originated for defect prediction using different techniques of nonlinear manifold detection (nonlinear MDs) along with 14 different machine learning techniques (MLTs) on eight defective software datasets. A critical analysis cum exhaustive comparative estimation revealed that nonlinear manifold detection model has a more accurate and effective impact on defect prediction as compared to feature selection techniques. The outcome of the experiment was statistically tested by Friedman and post hoc analysis using Nemenyi test, which validates that hidden Markov model (HMM) along with nonlinear manifold detection model outperforms and is significantly different from MLTs.
Aircraft detection in remote sensing images using centre-based proposal regions and invariant features

Aircraft detection in remote sensing imagery has drawn much attention in recent years, which plays an important role in various military and civil applications. While many advanced works have been developed with powerful learning algorithms in natural images, there still lacks an effective one to detect aircraft precisely in remote sensing images, especially in some complicated conditions. In this paper, a novel method is designed to detect aircraft precisely, named aircraft detection using Centre-based Proposal regions and Invariant Features (CPIF), which can handle some difficult image deformations, especially rotations. Our framework mainly contains three steps. Firstly, we propose an algorithm to extract proposal regions from remote sensing imagery. Secondly, an ensemble learning classifier with the rotation-invariant HOG is trained for aircraft classification. Lastly, we detect aircraft in remote sensing images by combining the products of the above steps. The proposed method is evaluated on a public dataset RSOD and the results are performed to demonstrate the superiority and effectiveness in comparison with the state-of-the-art methods.
Guideline for Reporting Standards of Eye-tracking Research in Decision Sciences

We develop a reporting guideline for eye-tracking research in the behavioral sciences. To this end, we coded 215 articles on behavioral decision-making published between 2009 and 2017 and extracted a list of reported items. The coded articles were from a broad range of disciplines linked to judgement and decision making, such as cognitive science, marketing, economics, developmental research, vision research, and human–computer interaction. We then had a panel of eye-tracking experts rate the necessity of each item for reproducing a reported study. From these two sources, we generated a guideline containing 31 items that are judged as 'necessary' by the majority of experts for reproducing an eye-tracking study. None of the 215 coded articles report all identified items and approximately 70 percent of the articles report less than 50 percent of the 'necessary' items. We provide the data and list of recommendations as a hands-on shiny app to allow for an easy adoption of the proposed reporting guideline to improve transparency and reproducibility in eye-tracking research.
Mass vs. Count: Where Do We Stand? Outline of a Theory of Semantic Variation

I will illustrate and discuss the extent to which grammars may vary vis-à-vis the mass/count distinction with three main phenomena and use this as a springboard for outlining a theory of semantic variation, with a universal logical basis. The first form of variation concerns the most widespread empirical test associated with the mass/count distinction, namely how noun phrases (NPs) combine with numerals. The second is that of the so called ‘fake’ mass nouns (like furniture). The third concerns alternations between mass vs. count interpretations of nouns like beer or chicken.
Next Generation Mobile Learning: Leveraging Message Design Considerations for Learning and Accessibility

Online machine translation (OMT) tools are not exclusively designed for language learners; however, these tools are popular among them. This quantitative study investigated the perceptions and attitudes of Turkish speaking EFL learners and instructors in a university English program regarding the use of OMT tools. Two online questionnaires were administered to 462 Turkish-speaking learners and 34 instructors. The results revealed that 94% of the learner participants reported using OMT tools for their language learning studies. The learners predominantly used these tools for single-word or phrase translations. Reading and writing assignments were the main areas where the learners most frequently referenced to OMT tools. The learner participants thought the accuracy of the tools was not high, and the ethicality of using them depended on how they were used. Three-quarters of the instructor participants reported using OMT tools, and their judgements concerning the accuracy of these tools were more positive than the learners’. The results also revealed a mismatch between learners’ and instructors’ perceptions and attitudes regarding OMT tools in foreign language learning. Accordingly, the instructors often overestimated how much learners use OMT tools, while learners underestimated the instructors’ interest in them. These findings suggest policies should be developed within language learning institutions to guide students’ use of OMT tools, as well as improve the mutual understanding between students and teachers in terms of their ethicality.
Phase sensitivity enhancement for the SU(1,1) interferometer using photon level operations

We propose to employ photon level operations (PLOs) to enhance the phase sensitivity of the SU(1,1) interferometer (SUI). Two types of PLOs are considered, including photon subtraction scheme (PSS) and photon addition scheme (PAS). By performing either PSS or PAS on the two arms inside the SUI, we demonstrate that the phase sensitivity of the SUI can be enhanced. More interestingly, we find that both PSS and PAS are able to increase the robustness of the SUI against the internal loss of the interferometer, which is one of the main imperfect factors that limit the performance of the SUI.
Aeromagnetic and Landsat-8 data interpretation for structural and hydrothermal alteration mapping along the Central and Southern Eastern Desert boundary, Egypt

Pan-African basement rocks cover the study area along the boundary between Central and Southern Eastern Desert with small exposed sedimentary sequences. Several vein-type gold mineralization with hydrothermal alteration zones have long been known in the study area. Aeromagnetic and remote sensing imagery data were integrated for mapping the structural elements and hydrothermal alteration zones related to gold mineralization. By aeromagnetic imagery inspection, the structural complexity and porphyry intrusions density maps have been effectively constructed by applying Enhanced Center for Exploration Targeting (CET) techniques. Major NW shear zones along with NNW-SSE, WNW-ESE, NS, and NE-SW are the main trends controlling gold mineralization. On the other hand, applying band ratios and constrained energy minimization (CEM) technique on Landsat-8 data pointed out alteration minerals associated with the hydrothermal fluids. Calcite, sericite, and chlorite are the common alteration minerals detected, while goethite and epidote are less common in alteration zones of the area under study. A combination of CET structural complexity, circular features, and alteration zones produced a potential mineralization map pointing out numerous prospective zones for mineralizing gold. The predictive map was validated by projecting the known gold prospects at the study area, reflecting an agreement between the known gold occurrences and the higher classes of the predictive zone. The discussed techniques in the current research can be adopted as a direct indicator for structural mapping and detecting the alteration zones related to gold mineralization elsewhere.
A Simple Federated Learning-Based Scheme for Security Enhancement Over Internet of Medical Things

Nowadays, Federated Learning (FL) over Internet of Medical Things (IoMT) devices has become a current research hotspot. As a new architecture, FL can well protect the data privacy of IoMT devices, but the security of neural network model transmission can not be guaranteed. On the other hand, the sizes of current popular neural network models are usually relatively extensive, and how to deploy them on the IoMT devices has become a challenge. One promising approach to these problems is to reduce the network scale by quantizing the parameters of the neural networks, which can greatly improve the security of data transmission and reduce the transmission cost. In the previous literature, the fixed-point quantizer with stochastic rounding has been shown to have better performance than other quantization methods. However, how to design such quantizer to achieve the minimum square quantization error is still unknown. In addition, how to apply this quantizer in the FL framework also needs investigation. To address these questions, in this paper, we propose <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedMSQE</small> – Federated Learning with Minimum Square Quantization Error, that achieves the smallest quantization error for each individual client in the FL setting. Through numerical experiments in both single-node and FL scenarios, we prove that our proposed algorithm can achieve higher accuracy and lower quantization error than other quantization methods.
Distributed Gibbs Sampling and LDA Modelling for Large Scale Big Data Management on PySpark

Big data management methods are paramount in the modern era as applications tend to create massive amounts of data that comes from various sources. Therefore, there is an urge to create adaptive, speedy and robust frameworks that can effectively handle massive datasets. Distributed environments such as Apache Spark are of note, as they can handle such data by creating clusters where a portion of the data is stored locally and then the results are returned with the use of Resilient Distributed Datasets (RDDs). In this paper a method for distributed marginal Gibbs sampling for widely used latent Dirichlet allocation (LDA) model is implemented on PySpark along with a Metropolis Hastings Random Walker. The Distributed LDA (DLDA) algorithm distributes a given dataset into P partitions and performs local LDA on each partition, for each document independently. Every n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> iteration, local LDA models, that were trained on distinct partitions, are combined to assure the model ability to converge. Experimental results are promising as the proposed system demonstrates comparable performance in the final model quality to the sequential LDA, and achieves significant speedup time-optimizations when utilized with massive datasets.
Detailed Human Activity Recognition based on Multiple HMM

A wide array of activities is performed by humans, everyday. In healthcare, precocious detection of movement changes in daily activities and their monitoring, are important contributors to assess the patient general well-being. Several previous studies are successful in activity recognition, but few of them provide a meticulous discrimination. Hereby, we created a novel framework specialized in detailed human activities, where signals from four sensors were used: accelerometer, gyroscope, magnetometer and microphone. A new dataset was created, with 10 complex activities, suchlike opening a door, brushing the teeth and typing on the keyboard. The classifier was based on multiple hidden Markov models, one per activity. The developed solution was evaluated in the offline context, where it achieved an accuracy of 84±4.8%. It also showed a solid performance in other performed tests, where it was tested with different detailed activities, and in simulations of real time recognition. This solution can be applied in elderly monitoring to access their well-being and also in the early detection of degenerative diseases.
The Advent of Human Life Data Economics

The recent bust of the Blockchain bubble was detrimental to many projects intended to return control and ownership of human life data to the hands of individuals. As the industry struggles to recover and rebrand, new applications of digital ledger technologies in traditional healthcare applications have started to emerge. The recent bust of the Blockchain bubble was detrimental to many projects intended to return control and ownership of human life data to the hands of individuals. As the industry struggles to recover and rebrand, new applications of digital ledger technologies in traditional healthcare applications have started to emerge. The Advent of Human Life Data Economics: (Trends in Molecular Medicine , 566–570; 2019)Zhavoronkov et al.Trends in Molecular MedicineAugust 6, 2019In BriefThe authors regret that the below disclaimer was missing in the original published article. Full-Text PDF