Reviewerly/exHarmony huggingface dataset:
E-books: yes or no? A case study of undergraduate students at the University of Namibia

Purpose The purpose of the paper is to determine how undergraduate students at the University of Namibia perceive and use e-books. This paper aims to report on the result of a study that investigated the adoption of, views about and use of e-books at the University of Namibia. Design/methodology/approach The study used a mixed-method approach. It used three methods, namely, focus group interviews, observation combined with the think aloud and a survey to investigate how undergraduate students use e-books. Findings Major findings of the study indicated that students use and prefer e-books for course and research purposes. But they mainly use non-library search engines such as Google, Yahoo and commercial sites. Lack of searching skills, slow/unreliable internet and limited or lack of relevant content of e-book collections were the major hindrances affecting e-book use. Originality/value The findings of the study could be used to understand the use of e-books at the University of Namibia and at academic institutions with similar context to Namibia. The study contributes to the knowledge base of library and information science (LIS) by providing a detailed analysis on the views and use of e-books at the University of Namibia. The recommendations of this study can be adopted by libraries in other countries with similar socio-economic conditions like Namibia.
Hardware Implementation of KLT Tracker for Real-Time Intruder Detection and Tracking Using On-board Camera

This paper presents the hardware implementation of a new hybrid KLT (Kanade–Lucas–Tomasi) algorithm for the sake of real-time intruder detection and tracking. The proposed algorithm takes advantage of BRISK sampling scheme to improve the feature detection and tracking performance. The RTL level hardware logics for feature detection and tracking are designed to implement the hybrid KLT tracker using on-board SoC FPGA. In particular, the hybrid KLT tracker is realized via a five-stage pipelining architecture to increase the throughput rate but also the availability of continuous output. The RTL hardware logics are written in VHDL codes and are executed on an Intel's Cyclone V SoC development board while the video frames of VGA resolution are obtained from the on-board camera at 60 fps. The performance of the hybrid KLT tracker algorithm has been tested and validated via the software simulation based on the reference codes written in C/C++. The speed acceleration due to the hardware implementation has been verified so that the computational throughput increases by 24.46 times faster than the software implementation, while the object tracking performance remains the same. Consequently, it has been shown that the hardware implementation of KLT tracker for intruder detection and tracking can operate in real-time while providing the supplement surveillance information using on-board camera for mid-air collision avoidance of UAV.
Real‐time recovery and recognition of motion blurry QR code image based on fractional order deblurring method

Most image deblurring methods require large algorithm computational cost because multi-scale blind deconvolution is used for estimating kernel. Furthermore, a moving quick response (QR) code image is regarded as a type of classical blurry image and requires real-time processing in practical applications. Therefore, this study proposes a new framework of motion blurry QR code image restoration in real-time based on the fractional-order deblurring method. The authors perform a trade-off between algorithm computational cost and quality of the deblurring image. First, a black frame is added around the traditional QR code, which is used for locating QR code and reducing the computational cost. Next, a new image deblurring method is proposed using fractional differential order and is used for improving the quality of the deblurring image. Furthermore, an average grey-level method is presented to reconstruct the standard QR code images. Comparisons with the existing algorithms demonstrate that the proposed method can achieve favourable deblurring quality and acceptable computational cost. Finally, their framework is validated in a practical platform of an actual conveyor belt system with a low-cost industrial camera. Experimental results indicate that their framework performs favourably with processing motion blurry QR code images.
Radar for Autonomous Driving – Paradigm Shift from Mere Detection to Semantic Environment Understanding

The challenges for sensors and their correlated perception algorithms for driverless vehicles are tremendous. They have to provide more comprehensively than ever before a model of the complete static and dynamic surroundings of the ego-vehicle to understand the correlation of both with reference to the ego-vehicle’s movement. For dynamic objects, this means that radar has to provide the dimension and complete motion state as well as the class information, in highway, rural, and inner city scenarios. For the static world, new algorithm schemes have to be developed to enhance the shape representation of an object by image like semantics. In order to generate the necessary information, radar networking for 360° coverage have to be reinvented. Radar data processing toolchains have to be revolutionized by applying artificial intelligence and advanced signal processing in a synergetic manner.
Lossless compression of hyperspectral imagery using a fast adaptive-length-prediction RLS filter

Recursive Least Square (RLS) filter has been applied to real-time lossless compression of hyperspectral imagery and been proved a high performance onboard algorithm. Recent research has revealed that the RLS filter with Adaptive-Length-Prediction (ALP) can significantly improve the compression performance. However, the prediction procedure with numerous bands slows down the run-time and is nearly impossible to be applied onboard. In this letter, we proposed a fast RLS algorithm which can accelerate the ALP stage by exploiting the feature of the projection matrix of the RLS algorithm. The experiment results illustrated that with the same compression ratio, the proposed algorithm is 100 times faster than the traditional RLS algorithm with ALP.
Keyphrase Extraction via an Attentive Model

Keyphrase extraction is a task of crucial importance for digital libraries. When performing automatically a task of this, the context in which a specific word is located seems to hold a substantial role. To exploit this context, in this paper we propose an architecture based on an Attentive Model: a neural network designed to focus on the most relevant parts of data. A preliminary experimental evaluation on the widely used INSPEC dataset confirms the validity of the approach and shows our approach achieves higher performance than the state of the art.
Multitarget prediction—A new approach using sphere complex fuzzy sets

This paper proposes a multitarget prediction approach using a new type of sphere complex fuzzy sets. The proposed model can adjust the number of outputs depending on the application. Moreover, a corresponding feature selection algorithm based on influence information and selection gain is proposed to solve the problem of cross-target feature selection in multitarget prediction. The proposed approach is tested using three experiments. The experimental results reveal that the model can predict at least four targets simultaneously and that the proposed feature selection algorithm can, in most cases, quickly determine a favorable feature combination.
Trends in Agile: From Operational to Strategic Agility [Practitioners' Digest]

Reports on meetings and events that were part of the 2018 Agile Conference hat took place August 6-10 in San Diego, CA.
Exploring “Company Personas” for Informing Design for Sustainability Implementation in Companies

The need for understanding the context of the case company during Design for Sustainability (DfS) implementation has been a long identified need among the researchers in the field. Yet, studies on company context have primarily focused on studying, enlisting, and prescribing standardized solutions for companies or clustering companies based on similarities. Such approaches have not been able to overcome the organizational “soft side” challenges that have been long addressed in DfS literature. This explorative paper takes insights from 20 case interviews conducted in Norwegian and Danish manufacturing companies and with sustainability experts and uses the concept of persona from design studies to explore the potential of defining “company personas” to better define the context of the company. The interview analysis produced 14 dimensions, including both hitherto identified factual needs of companies and soft-side elements required to create a company persona, thereby informing practitioners and researchers to take a DfS implementation approach tailored to the company context.
Prolegomena to any theory of proof simplicity

By looking at concrete examples from elementary geometry, we analyse the manner in which the simplicity of proofs could be defined. We first find that, when presented with two proofs coming from mutually incompatible sets of assumptions, the decision regarding which one is simplest can be made, if at all, only on the basis of reasoning outside of the formal aspects of the axiom systems involved. We then show that, if the axiom system is fixed, a measure of proof simplicity can be defined based on the number of uses of axioms deemed to be deep or valuable, and prove a number of new results regarding the need to use at least three times some axioms in the proof of others. One such major example is Pappus implies Desargues, which is shown to require three uses of Pappus. A similar situation is encountered with Veblen's proof that the outer form of the Pasch axiom implies the inner form thereof. The outer form needs to be used at least three times in any such proof. We also mention the likely conflicting requirements of directness of a proof and the length of a proof. This article is part of the theme issue 'The notion of 'simple proof' - Hilbert's 24th problem'.
Combining Machine Learning and Metaheuristics Algorithms for Classification Method PROAFTN

The supervised learning classification algorithms are one of the most well known successful techniques for ambient assisted living environments. However the usual supervised learning classification approaches face issues that limit their application especially in dealing with the knowledge interpretation and with very large unbalanced labeled data set. To address these issues fuzzy classification method PROAFTN was proposed. PROAFTN is part of learning algorithms and enables to determine the fuzzy resemblance measures by generalizing the concordance and discordance indexes used in outranking methods. The main goal of this chapter is to show how the combined meta-heuristics with inductive learning techniques can improve performances of the PROAFTN classifier. The improved PROAFTN classifier is described and compared to well known classifiers, in terms of their learning methodology and classification accuracy. Through this chapter we have shown the ability of the metaheuristics when embedded to PROAFTN method to solve efficiency the classification problems.
Additional Reviewer Assignment by Means of Weighted Association Rules

The assessment of the academic papers submitted to scientific journals and conferences is mostly based on peer reviewing. The reviewers assigned to each paper are usually shortlisted from a program committee by matching paper topics with reviewers' expertise. However, reviewer assignments often need to be reconsidered at a later time, because reviewers can be temporarily unavailable or some of the assignments turn out to be critical due to the presence of conflicts. Therefore, there is a need for automated tools that support experts in seeking additional reviewers external to the program committee. In this paper, we propose an association rule-based methodology to recommend additional external reviewers. Weighted Association Rules (WARs), which represent strong associations between paper co-authors, are first extracted from a benchmark publication dataset. Then, they are exploited to discover potential conflicts of interest between co-reviewers as well as between reviewers and authors. Finally, reviewer assignments for papers with conflicts or with low-quality assignments are reconsidered. To tackle this issue, a subset of selected WARs provide promptly usable additional reviewer recommendations, as they highlight external experts who have profitably collaborated with the shortlisted reviewers. The effectiveness of the proposed methodology were validated on both journal and conference datasets.
A Conditional Sentiment Analysis Model for the Embedding Patient Self-report Experiences on Social Media

Getting accurate, honest, reliable and credible minute insight is the most crucial objective of conducting medical and pharmaceutical research on social media. Nowadays, healthcare manufacturing companies use Sentiment Analysis (SA) to identifying the misleading of patients self-report experiences and shared medical information on social media. As a target level of analysis, a set of medical components in each document (post, message, tweet, etc.) have a semantic formalism which, similar to a dependency parse in the whole space of analysis regarding the time axes. However, Time property is been substantially very important allowing more real-time personalization to efficiently detect patient emotional state and what may be suffering from. Specially, when an irregular sentiment towards drugs or set of events may cover. In this paper, we aim at defining a conditional Sentiment Analysis model which summarizes sentiment information looking at the historical data towards dependent entities for yielding short or long-term predictions based on quantifying exactly what change is. This model hybrid an unsupervised biomedical concept extraction with autoregressive time series modelling. This hybridization aims at online updating the model by smoothing and extracting new relevant target features when deals specifically with newly emerged diseases, medical events, Drug issues and potential side effects. The evaluation results on a real pharmaceutical industry and healthcare tweets show that our proposed oriented-context method performs better than existing models.
A thermal‐aware VM consolidation mechanism with outage avoidance

Summary Efficient energy and temperature management techniques are essential elements for operators of cloud data centers. Dynamic virtual machine (VM) consolidation using live migration techniques presents a great opportunity for cloud service providers to adaptively reduce energy consumption and optimize their resource utilization. In recent studies, power consumption readings of individual physical hosts were chosen as the main monitoring parameters in their allocation policies, whereas very few have considered host temperature, which has shown to have a negative impact on server reliability, as a migration criterion. In this work, a thermal‐aware VM consolidation mechanism is proposed for resource allocation optimization and server reliability assurance. We consider the variability in host temperature as a migration criterion to avoid outage incidents via having better VM consolidations. Extensive simulation results obtained from CloudSim show the promising performance of the proposed mechanism in energy saving while reducing the number of server outage incidents due to fluctuations in host temperature.
Efficient quantum secret sharing based on polarization and orbital angular momentum

Huawang Qin*a & Raylin Tsoba School of Automatization, Nanjing University of Science and Technology, Nanjing, Chinab Department of Computer Science, National Chengchi University, Taipei, Taiwan
The Conceptual Niche of Seamless Learning: An Invitation to Dialogue

In this chapter, we seek conceptual clarity on the notion of seamless learning towards understanding its unique contributions towards learning. We will compare the salient characteristics of seamless learning with the definitions and framing of other relevant learning notions or approaches. We postulate that putting together related notions, theorisations or practices of different-named notions of learning together in juxtaposition, can create new sparkles that help us derive new perspectives and insights into learning theory and design. Our initial expositions in this chapter are intended to inform cross-fertilisation between seamless learning and other learning notions to inspire and guide the advancement of further relevant research and practice.
Creating a richly annotated corpus of papyrological Greek: The possibilities of natural language processing approaches to a highly inflected historical language

This article describes a first attempt to annotate the full Greek papyrus corpus automatically for linguistic information. It gives an overview of existing work on Ancient Greek and analyzes the typical problems one encounters when using natural language processing techniques on (1) a historical corpus of (2) a highly inflectional language (as opposed to the more analytic present-day English) and offers solutions to them, testing several different approaches. The focus is on part-of-speech/morphological tagging and lemmatization; some syntactic parsing experiments are also briefly discussed. The conclusion discusses the strengths and shortcomings of the examined techniques and suggests possible ways to further improve tagging and parsing accuracy.
Do Openness and Democracy Deepen Banking Sector in Sub-Saharan Africa?

This article examines whether openness and democracy deepen the banking sector in sub-Saharan Africa, using a sample of 33 countries over the period 1991–2015. After controlling for the potential effects of some country characteristics, this article shows that simultaneous opening of trade and capital borders and higher levels of democratic governance deepen the banking sector. It also shows that increasing financial openness without increasing trade openness would result in a shallower banking sector but increasing trade openness alone is adequate to deepen the banking sector. This article provides evidence to support the simultaneous openness hypothesis and political economy theory of financial development.
A multi-agent security framework for cloud data storage

In the last years, cloud computing has emerged as a new information technology (IT) model. Which provides an easy way for data processing and storage remotely through a network. Nowadays, cloud storage has become an attractive storage scheme for users to store their data. When users store their fil es remotely in a cloud storage system, they still worried about the security of their files and the guarantee of the confidentiality and the integrity of them. In this work, we propose a multi-agent framework to provide cloud data storage security. The proposed multi-agent framework enables the security of data in cloud storage by providing both integrity and confidentiality of data. For this purpose, we proposed a client-side encryption method, which is based on the use of both AES and RSA algorithms, it provides the confidentiality and privacy of data besides the performance. Proof of retrievability based on the use of hash function is adopted in our multi-agent framework, to enable data integrity check.
Improved distortion correction method and applications for large aperture infrared tracking cameras

Accurate distortion correction is especially crucial for applications involving quantitative measurements such as on-orbit geometric positioning of remoting sensing cameras, depth measurement from stereoscopy, and target tracking and recognition from images. However, the conventional complicated correction methods and the unsatisfied results greatly limit their application prospects, especially in the aerospace field. In this paper, we propose an improved distortion correction method (IDCM) for large aperture infrared tracking cameras, addressing the distortion model and the principle point and distance with high-precision measurement points in the focal plane. Experimental results showed that the distortion errors corrected by the proposed method were 0.26 pixels in a near-infrared image and 0.19 pixels in a short-wavelength infrared image. These corrected distortion errors are better than that corrected by the two-dimensional Lagrange interpolation method (TDLIM) of 0.36 pixels in a near-infrared image and 0.29 pixels in a short-wavelength infrared image, and the conventional distortion model (CDM) of 0.54 pixels in a near-infrared image and 0.64 pixels in a short-wavelength infrared image, respectively.
Steerable Fourier number transform with application to image encryption

In this paper, we introduce the steerable Fourier number transform (SFNT). It corresponds to a generalization of the well-known number-theoretic transform (NTT), obtained by applying a finite field rotation to pairs of vectors taken from the original Fourier-like NTT basis. We provide illustrative examples regarding the SFNT construction and propose an image encryption scheme based on the new transform; the principle of the method is to use the angles of the referred rotations as secret parameters. We demonstrate that the proposed scheme is resistant against the main cryptographic attacks, although it is simpler than other transform-based image encryption techniques.
MGPV: A novel and efficient scheme for secure data sharing among mobile users in the public cloud

With the significant popularity and utility, the web services have uniquely emerged as a new paradigm shift to many enterprises such as banking, government applications, telecom sectors and other solution providers. When web services are integrated with cloud services, web services achieve more flexibility and performance. Hence, through a web service, a mobile phone user can upload sensitive documents to cloud and share them with employees and customers, but the security in the cloud is yet to be completely resolved. Recently, the authors Zhu and Jiang have securely shared group keys among cloud users without secure communication channels. But, we have recently proved that, the existing method is susceptible to man-in-the-middle attack and message modification attack. A new protocol termed as MGPV has been proposed in this research work which averts all the possible attacks. It minimizes the computation complexity and ensures that the documents are accessible only by valid group users. It ensures that even the group manager and the cloud cannot access the documents stored in the cloud. The experiments conducted on the mobile cloud environments reveal that this protocol is worthy of implementation in the real world scenarios.
Revealing household characteristics using connected home products

Recent technological advances have helped make our homes more intelligent and responsive to our needs. In this context, analyzing data from smart home products to gain insights is becoming increasingly important. This paper proposes an analytical framework to reveal the characteristics of households using event logs from smart door lock systems. The analytical framework uses constraint satisfaction problems to enable streaming event log analysis to solve the problems of overlapping classes and a lack of information concerning the truth class. The proposed method was applied to two datasets: one consisting of door-lock log data from 40 households and the other of time-use survey data from more than 10,000 households in South Korea. The results verify its effectiveness in terms of estimating the number of occupants in a household. The performance of the proposed method was compared with that of a naïve clustering approach in terms of mean squared error.