Reviewerly/exHarmony huggingface dataset:
Analysis of an Ultra-Short True Time Delay Line Optical Reservoir Computer

This article presents the experimental demonstration of a true-time delay line optical reservoir computer (ORC) using an incoherent light chaos oscillator. In contrast to other benchtop ORC systems, no external fiber spool was employed, enabling a characteristic delay of 28.4 ns, one of the fastest reported optical reservoirs to date. Comparable error metrics were obtained for standard benchmark tasks despite the reduced time scale. Practical experimental techniques, namely preamble functions and fading memory capacity measures, are introduced in this article. A mathematical model of optimized virtual nodes for the best performance of the RC was established. The fast ORC was also applied for two real world applications: respiratory motion prediction used in radiotherapy and perovskite compound property prediction used in photovoltaic material discovery. The respiratory motion prediction was compared with long short-term memory (LSTM) machine learning algorithms, the former attaining compatible results with orders of magnitude faster training speed. The ORC results for the perovskite compound classification task were compared with random forest approach, where the former demonstrated slightly better computation predication but again with much faster computing speed.
Patient-level Classification on Clinical Note Sequences Guided by Attributed Hierarchical Attention

In spite of clinical notes in Electronic Health Records (EHR) providing abundant information about patient health, effective modeling of clinical notes remains in its infancy. A patient's clinical notes correspond to a sequence of free-form texts generated by health care professionals over time; with each note in turn containing a sequence of words. Additionally, notes are accompanied by external attributes at multiple layers such as the time at which each note was created (note level) or the demographics of the patient (patient level). Thus, EHR notes correspond to a nested structure of text sequences augmented with external multi-layer attributes. To model this complex problem, we propose an Attributed Hierarchical Attention model, named HAC-RNN, that integrates multiple RNN layers that encode nested sequential notes with contextual and temporal attention layers that are conditioned on the external attributes. While the bottom layer of HAC-RNN is responsible for contextual summarization of the note content, the top layer combs through the entire timeline of notes to focus on those which are most relevant. These attention layers, which are each conditioned on layer-specific hierarchical attributes, allow personalized predictions through inferring patient profiles.We evaluate HAC-RNN using three real-world medical tasks, detecting in-hospital acquired infections and predicting patient mortality using critical care database MIMIC-III. Our results demonstrate that our model significantly outperforms state-of-the-art techniques for all tasks.
Unified non-uniform scale adaptive sampling model for quality assessment of natural scene and screen content images

Among many multimedia scenarios, digital images are mainly composed of natural scene images (NSIs) and screen content images (SCIs). NSIs are always captured by cameras from the real world while SCIs are typically the mixture of pictures and text. Traditional full-reference image quality assessment (IQA) metrics are designed for natural scene images, and they cannot work well on SCIs, because they do not take account of characteristics of different visual content/regions between NSIs and SCIs. Inspired by early eye movement study that human visual system samples the world non-completely or non-uniformly, we observe that human brain perceive different image content at different scales. Given these discoveries, we propose a non-uniform scale adaptive sampling model (NUSM) which dedicates to preprocess the input images for IQA methods, enabling traditional NSI IQA models to predict the quality of SCIs effectively. The proposed sampling model applies different sample scales into different regions to generate pre-sampled image pairs, then send to existing IQAs to evaluate the quality of images. The experimental results demonstrate that our model has achieved a promising performance on both NSIs and SCIs.
Collaborative Filtering based Recommender System using Regression and Grey Wolf Optimization Algorithm for Sparse Data

In this epoch of technology, where recommendations are the backbone of E-commerce and Media-services, one of the extensively used approach for implementing recommender systems is Collaborative Filtering. This approach recommends items by identifying users who share similarity in rating the items by observing the user-item ratings matrix. In the real-world, users only rate a limited number of items from the catalogue leading to high sparsity in user-item ratings, which makes it difficult to identify similar users and recommend items accurately. To handle sparsity in user-item matrix, which is one of the major challenges encountered while implementing recommender systems, the paper proposes a technique of Collaborative Filtering using Regression to predict the ratings of unrated items on the basis of the user's history of ratings and similarity to other users, combined with - a nature-inspired algorithm called - Grey Wolf Optimization (GWO) algorithm applied to optimize the loss function. Personalised recommendations can be provided to the users for those highest predicted ratings. The proposed technique is observed to perform well in highly sparse data scenarios in terms of accuracy, user coverage and different variants of hit rate.
Robonomics as a Blockchain-based Platform for Unmanned Traffic Management of Mobile Vehicles

The article describes the concept of a decentralized architecture of a traffic management system for mobile vehicles and is a continuation of the results presented in the previous article “Blockchain-based protocol of autonomous business activity for multi-agent systems consisting of UAVs.. Robonomic protocol is the basis for the system architecture - a combination of the decentralized Ethereum computer, the IPFS distributed file system, Robot Operating System and market mechanisms. In particular, its focused on the principle of communication between nodes of the traffic management system and the stages of the unmanned mission. As a proof of concept, two experiments on the integration of the proposed architecture are presented: air quality measurements using unmanned aerial systems (UAS) and water quality measurements using unmanned surface vessels (USV). Our work demonstrates that distributed ledger and smart contracts technologies are applicable to the traffic management system and increases the transparency and immutability of the data.
Dynamic Computation Off-loading and Control based on Occlusion Detection in Drone Video Analytics

Unmanned Aerial Vehicles (UAVs) or drones equipped with cameras are extensively used in different scenarios such as surveillance of hazardous locations, disaster response and crime fighting. The related video streaming/analytics requires real-time drone-to-Ground Control Station (GCS) communication and computation co-ordination for desired user Quality of Experience (QoE). In situations where the quality of the video can be affected by occlusions (e.g., image distortion, frame stalling) due to network bottlenecks, there is a need to dynamically make decisions on the computation offloading and networking protocols in order to properly handle the video data for real world application purposes. In this paper, we propose a novel function-centric computing approach that helps a user to perform drone video analytics to assess a wide-area scene to chart a plan of action. Our approach involves handling network impairments affecting the switching between high resolution/low resolution video capture, or change of camera direction for assessment of the scene effectively. It also features a novel video quality enhancing algorithm based on occlusion-detection that adapts to video impairments related to image distortion and frame stalling. Our experiment results from a realistic testbed show that our approach can efficiently choose the suitable networking protocols (i.e., TCP/HTTP, UDP/RTP, QUIC) and orchestrate both the camera control on the drone, and the computation off-loading of the video analytics over limited edge computing resources. The performance improvements for computation off-loading involving our video quality enhancing algorithm are shown for different network conditions in terms of occlusion rate and processing times.
WJEC’s Paris Syndicate Program: Discussions Advance Global Journalism Education

The World Journalism Education Congress (WJEC) holds global conferences every 3 years offering a unique opportunity for attendees: access to its Syndicate Program. The program gives participants the chance to meet in small groups with international counterparts to take part in research-based discussions focused on the most important journalism topics of the day. These discussions conclude with global recommendations to help advance journalism education pedagogy, practices, and research. This special section of Journalism &amp; Mass Communication Educator features WJEC’s Paris (2019) syndicate results.
Immigrants and refugees: two sides of the same problem. A linguistic analysis through newspapers and social network in Spain 2006 and 2015

The phenomenon of immigration has been studied from diverse perspectives. It is important to note that immigration is common during times of crisis. The reason for the avalanche of immigrants to the Canary Islands (Spain) is because it is the gateway to Europe, and therefore, immigrants wanted to enter from this point. This research is based on the need to linguistically determine the treatment of the phenomenon of immigration in the Spanish press. This analysis is relevant given the arrival of thousands of foreign citizens to the coast of the Canary Islands, first in 2006 and almost a decade later, in 2015 to the south of Europe in another great population movement. This research attempts to study four Spanish newspapers, two from the Canary Islands (El Día and Canarias 7) and two Spanish mainland national newspapers (El País and ABC), the online version of the newspapers and their websites were analyzed beside one of the most important social networks, Twitter. The role of incorporating social networks to immigration discourse is also analyzed. To support the data analysis of this research, the Iramuteq software was used.
Augmented visual feature modeling for matching in low-visibility based on cycle-labeling of Superpixel Flow

There are many automation systems that are required to work under poor visual conditions, such as auto-navigation in foggy or underwater environment. The low-visibility poses challenges for traditional feature modeling methods, which commonly contribute as a key component to such autonomous systems. It can thus negatively impact the performance of those computing systems. For example, the matching precision (matching quality) and the successfully identified matches (matching quantity) can both drop dramatically in low-visibility. On the other hand, human vision system can robustly identify visual features correctly despite the variations in lighting conditions. Inspired by human knowledge of perceiving visual features, this paper presents a novel feature modeling solution under poor visual conditions. Based on a color constancy enhanced illumination alignment, a new concept called Superpixel Flow (SPF) is proposed to model the visual features in images. SPF is generated considering the content motions across frame pairs, which make it easier to track across frames compared with classic Superpixels. The matching is achieved by a cycle-labeling strategy using Markov Random Field (MRF) with energy functions composed according to human knowledge of compare visual features. An outlier removal follows to further improve the matching accuracy. Competitive performance is demonstrated in the experiments compared with state-of-the-art approaches.
User Authentication through Keystroke Dynamics by means of Model Checking: A Proposal

The current authentication systems based on password and pin code are not enough to guarantee attacks from malicious users. For this reason, in the last years, several studies are proposed with the aim to identify the users basing on their typing dynamics. In this paper, we propose the adoption of formal methods to discriminate between different users by exploiting a set of keystroke features. The idea behind the proposed method is to identify the users silently and continuously during their typing on a monitored system. To perform such user identification effectively, we consider a feature vector able to capture the typing style that is specific to each given user. By considering this feature model, in detail we propose to consider model checking with logic temporal properties to discriminate between different users using a set of keystroke features.
Publishing with Open Journal Systems (OJS): A Librarian’s Perspective

The genesis of this paper is a workshop presentation on publishing with Open Journal Systems (OJS) that was delivered during the Kenya Library and Information Services Consortium Pre-Annual General Meeting held on May 30, 2019, at the Masinde Muliro University of Science and Technology, Kenya. The article lays the context of online hosting of university sponsored journals in Kenya and gives a background of Online Journals Systems. The paper further describes potential roles that librarians can play in institutional journal publishing. The article concludes that librarians possess the requisite skills for participating in scholarly journal publishing and recommends that including publishing literacy skills in information literacy sessions provides an excellent opportunity to showcase librarians’ value in the scholarly journal publishing process.
Tablet identification using support vector machine based text recognition and error correction by enhanced<i>n</i>‐grams algorithm

Unidentified and/or misidentified tablets always present challenges to both patients and health care professionals alike. Consumption of these misidentified tablets often results in adverse drug reaction and sometimes may even cause ill health leading to death. Thus, identification of unknown tablets is an important task in the medical industry. This study proposes an algorithm that uses the text imprinted on the tablet images to identify unknown images. Text imprinted on pills often contains important information that can be used to identify tablets. In this study, multiple feature sets were extracted from tablet images. The proposed work first identifies the text region, from which the text is recognised using support vector machine classifier. A post processing algorithm based on the confusion model combined with n-grams is used to correct the substitution, insertion and deletion errors in the text recognised. Finally, the corrected text is matched with a template tablet database to identify similar tablets. Experimental results showed that the proposed system is efficient with respect to accuracy and can be safely used by both common public and health care professionals to identify tablets.
Deep Convolutional Network Based on Interleaved Fusion Group

It is known that the classification accuracy of the deep convolutional network can be remarkably improved by increasing its depth and width. However, as the network size increases, the number of network parameters will increase significantly, which results in network redundancy and performance degradation. In order to reduce network redundancy and improve classification accuracy by means of reducing the number of parameters, a highly modularized and lightweight deep interleaved fusion group convolutional network is proposed. The main idea is to design a more efficient “network computing manner,” so as to simplify the network model, reduce redundancy, and improve computational efficiency without the loss of classification accuracy. First, a template block is constructed by using the stacking and split-transform-merge strategies, which simplifies the network model by reducing the number of parameters. Then, the introduced group convolution and structured sparse convolution further simplify the network model and improve the computational efficiency. Experiments on the standard image recognition tasks have shown that the proposed convolutional network achieves better classification performance and has superior generalization ability compared with state-of-the-art deep networks.
MongoDB Indexing for Performance Improvement

For any digital application, database positions at the heart of that application. Today with the big data requirement, databases are roaming from traditional relational databases towards NoSQL databases. The diverse numbers of database options are available under the NoSQL category. As per the database engine survey, MongoDB is the preferred NoSQL database among other databases. Due to numerous features available in MongoDB, this database is widely used in different applications. This database is fulfilling the needed requirements for upcoming applications. This paper is a study of indexing, which is one of the important artifacts of the database. Indexing is one of the special forms of the data structure. It plays an important role in performance improvement by saving execution time in terms of document scan. Use of indexing and its effect on query result is highlighted here. Another reason for selecting this artifact is indexing study is also important from a database forensics perspective. Database forensics is a detailed analysis of the database to find the origin of the problem.
Sentiment Analysis Platform of Customer Product Reviews

Opinions and reviews are very important data coming from the customer for the companies to improve their product or services. The study focuses on creating a platform that would be able to capture the data in the form of comments or reviews. The platform was able to create and generate quantitative and text data based on the reviews and comments. The researchers used the Scrum Agile methodology. This study utilized the descriptive research method with the use of the questionnaire as the instrument testing the acceptability of the system's application. It was evaluated by 100 respondents. The mean value was determined and compared using analysis of variance to determine if there is a significant difference. ANOVA was also used to test for critical value of the null hypothesis. Based on the results, there is a marked significant difference among the evaluation of the 4 groups of respondents as indicated in the F-value of 6.48972. The respondents gave an acceptable rating and all agreed that the proposed product sentiment analysis application is a useful tool in evaluating company's products.
Copula Guided Parallel Gibbs Sampling for Nonparametric and Coherent Topic Discovery

Hierarchical Dirichlet Process (HDP) has attracted much attention in the research community of natural language processing. Given a corpus, HDP is able to determine the number of topics automatically, possessing an important feature dubbed nonparametric that overcomes the challenging issue of manually specifying a suitable topic number in parametric topic models, such as Latent Dirichlet Allocation (LDA). Nevertheless, HDP requires a much higher computational cost than LDA for parameter estimation. By taking the advantage of multi-threading, a parallel Gibbs sampling algorithm is proposed to estimate parameters for HDP based on the equivalence between HDP and Gamma-Gamma Poisson Process (G2PP) in terms of the generative process. Unfortunately, the above parallel Gibbs sampling algorithm requires to apply the finite approximation on the number of topics manually (i.e., predefine the topic number), thus can not retain the nonparametric feature of HDP. Another drawback of the above models is the lack of capturing the semantic dependencies between words, because the topic assignment of words is independent with each other. Although some works have been done in phrase-based topic modelling, these existing methods are still limited by either enforcing the entire phrase to share a common topic or requiring much complex and time-consuming phrase mining methods. In this paper, we aim to develop a copula guided parallel Gibbs sampling algorithm for HDP which can adjust the number of topics dynamically and capture the latent semantic dependencies between words that compose a coherent segment. Extensive experiments on real-world datasets indicate that our method achieves low perplexities and high topic coherence scores with a small time cost. In addition, we validate the effectiveness of our method on the modelling of word semantic dependencies by comparing the extracted topical phrases with those learned by state-of-the-art phrase-based baselines.
Detection and Classification of Voltage Sag Causes Based on S-Transform and Extreme Learning Machine

This manuscript presents a method based on S-transform (ST) and extreme learning machine (ELM) to identify the causes of voltage sag. Exact recognition of voltage sag causes (VSCs) can help decrease the problems initiated due to voltage dip in electric power system. ST is a well-known time–frequency analysis technique. Initially, the extracted voltage signals are pre-processed through ST and several statistical features are extracted, which are later applied as inputs to ELM classifier for voltage sag cause detection. Here, three significant causes are simulated for voltage sag in MATLAB/Simulink, like (i) single-phase and three-phase fault, (ii) starting of induction motor and (iii) energization of transformer. The performance of the proposed technique is compared with other existing voltage sag cause classification techniques. The classification results indicate the ability of the proposed technique for detection and classification of VSCs more accurately.
Explainable Recommendation Using Review Text and a Knowledge Graph

Recommender systems using a knowledge graph can comprehensively organize users and items and their attributes and thereby improve recommendation performance. In addition, the relationship between users and items can be easily interpreted on the basis of entities and relations, thus giving explanations to recommendations. The algorithms and knowledge graphs used for generating explanations have not utilized review text. We have developed a recommendation method for predicting interactions between users and items using a knowledge graph and review text. The underlying user-item relationships are reflected and explanations are generated by predicting user-item interactions from the paths between a user and an item. The modeling is done using a recurrent neural network or a factorization machine. Items' aspects that interest users are extracted from review text and leveraged using an attention-like mechanism. Since the path between a user and an item can be easily interpreted, and the important aspects between a user and an item can be interpreted by observing the attention weight, the proposed model can generate a reasonable recommendation explanation. Testing using a real-world dataset demonstrated that the proposed model can explain the recommendations.