Reviewerly/exHarmony huggingface dataset:
Studying Continual Service Improvement and Monitoring the Quality of ITSM

Through Continual Service Improvement (CSI), IT service providers aim at identifying, documenting, evaluating and prioritising improvement areas in services and service processes. CSI plays a crucial role in managing service and process quality as well as transforming improvement suggestions and ideas into concrete actions and product features. Unfortunately, many companies lack the systematic CSI approach and tools. This may lead into lost business opportunities, long processing times in implementing customers’ change requests and finally losing key customers. Additionally, digital transformation creates a need for more agile and flexible CSI. Therefore, CSI is an actual and interesting research target from service quality perspective. In this paper, CSI practices of a Finnish IT service provider company are studied. The research problem is: How IT service provider organizations perform Continual Service Improvement methods as part of daily service operation management? The main contribution of this paper is to present findings from a case study with a ISO/IEC 20000 compliant service organization. We focus on exploring how the quality of CSI targets is monitored, how CSI is organized and deployed into practice and how service-related improvements are managed.
Toward more rapid and efficient peer review: A case study on publication in Ecological Research

Abstract The peer‐review system ensures the reliability of academic publication. However, authors may be unsatisfied with the duration of peer‐review and/or the reviewers' comments, whereas both reviewers and editors have many concerns over peer‐reviewing. To provide information to increase the efficiency of the peer‐review process for authors, reviewers and editors, we analyzed recent trends in publication and peer‐review processes in Ecological Research, and asked Editorial Board members to answer an online questionnaire. The acceptance rate of “Original Articles,” which accounted for 89.2% of all submissions, was 20.1%, whereas those of “Special Features,” “Data Papers,” and “Technical Reports” were 76.3, 73.7 and 38.3%, respectively. The median number of days to final decision was 168 for accepted manuscripts. The stage when handling editors invited reviewers to review was the longest stage during the peer‐review process. This stage was prolonged when potential reviewers declined the invitations to review. The results of the questionnaire revealed that both editors and reviewers felt burdened by the load of peer‐reviewing. Most editors encountered declining of peer‐review invitations. As the period of assigning reviewers is the most important step in determining the number of days to the final decision, the editorial office should support handling editors to locate reviewers. Motivating reviewers to review manuscripts is another way to speed up the process. Since many respondents indicated that improvements in the logical structure and English standard of manuscripts would facilitate review, authors could improve the process by carefully preparing their manuscripts with consideration to readers, including voluntary reviewers and editors.
Quantum Communication Based on the Photon Area Function as Basis States for Photon Qubit

In the field of communication either by classically or by quantumly light is the natural carrier of information through both material space and vacuum therefore it is essential to look upon the different aspects, here, the mensuration and the geometry chapters of photon were considered as this may aid in understanding of different features and applications in enhanced way. The proposed mensuration and geometry chapters of photon deal with photon area function to serve as basis states for photon qubit under peculiar conditions. Based on the photon area function for geometry of circular type in perpendicular to the line of travel as basis states for photon-based qubit to serve the purpose of secure quantum communication, the complex coefficient so establish in this paper are like any ordinary function to which the calculus approach of different, for instance, differentiation, integration, etc., can apply.
SLA-Aware and Deadline Constrained Profit Optimization for Cloud Resource Management in Big Data Analytics-as-a-Service Platforms

Discovering optimal data analytics solutions to extract value from data for better and faster decision making is essential for many application domains, especially in the big data era. Big data analytics typically requires a tremendous amount of computational resources to process large data volumes that can be very expensive and time consuming. Our research focuses on providing optimization solutions for Analytics-as-a-Service (AaaS) platforms that automatically and elastically provision cloud resources to execute queries guaranteeing Service Level Agreements (SLAs) across a range of Quality of Service (QoS) requirements. We propose admission control and resource scheduling algorithms for AaaS platforms to maximize profits while providing time-minimized query execution plans to meet user demands and expectations. To enable timely responses as required for many domains, the algorithms utilize data splitting-based query admission and resource scheduling offering parallel processing on the split datasets. Extensive experiments are conducted to evaluate the algorithm performance compared to state-of-the-art optimization algorithms. Experimental results show that our algorithms perform significantly better from a range of perspectives, including increasing query admission rates and creating higher profits, whilst supporting efficient resource configurations that are able to support big data processing demands under tight deadlines.
Classification of Human Driving Behaviour Images Using Convolutional Neural Network Architecture

Traffic safety is a problem that concerns the worldwide. Many traffic accidents occur. There are many situations that cause these accidents. However, when we look at the relevant statistics, it is seen that the traffic accident is caused by the behavior of the driver. Drivers who exhibit careless behavior, cause an accident. Preliminary detection of such actions may prevent the accident. In this study, it is possible to recognize the behavior of the state farm distracted driver detection data, which includes nine situations and one normal state image, which may cause an accident. The images are preprocessed with the LOG (Laplasian of Gaussian) filter. The feature extraction process is carried out with googlenet, which is the convolutional neural network architecture. As a result, the classification process resulted in 97.7% accuracy.
Multi-Objective Resource Mapping and Allocation for Volunteer Cloud Computing

Although Virtual Machine placement has been extensively researched in traditional Cloud Computing environments, it remains an open challenging problem for Volunteer Cloud Computing, which exhibits several divergent characteristics, including intermittent availability of nodes and unreliable infrastructure. In this paper, we model the Virtual Machine placement problem in Volunteer Cloud Computing as a bounded 0-1 multi-dimensional knapsack problem and develop three heuristic based algorithms to meet the objectives and constraints specific to Volunteer Cloud Computing. Empirical evidences on a real Volunteer Cloud Computing test-bed show the competitive performance results of these algorithms.
Nature Inspired Metaheuristics and Their Applications in Agriculture: A Short Review

Nature inspired metaheuristics algorithms have been the target of several studies in the most varied scientific areas due to their high efficiency in solving real world problems. This is also the case of agriculture. Among the most well-established nature inspired metaheuristics the ones selected to be addressed in this work are the following: genetic algorithms, differential evolution, simulated annealing, harmony search, particle swarm optimization, ant colony optimization, firefly algorithm and bat algorithm. For each of them, the mechanism that inspired it and a brief description of its operation is presented, followed by a review of their most relevant agricultural applications.
Prior Knowledge Guided Small Object Detection on High-Resolution Images

When applying common object detection algorithms to detect small objects on high-resolution images, the down-sampling operation of the input images is inevitable due to the limitation of GPU memory. Accordingly, the details for characterizing small objects are lost. To resolve this contradiction, a small object detection method in a coarse-to-fine manner is presented. Specifically, some rough regions of interest (ROI) are firstly computed from low-resolution images. The prior knowledge of the positions of objects is used to guide the generation of ROIs. Then the features of small ROIs are recomputed from high-resolution images, and the features of large ROIs are obtained from the feature maps used to generate ROIs. The proposed method is validated on two datasets. One is a plant phenotyping dataset and the other is a public traffic sign dataset. Experimental results convincingly show the effectiveness of the proposed method.
A Cryptographic Ensemble for secure third party data analysis: Collaborative data clustering without data owner participation

This paper introduces the twin concepts Cryptographic Ensembles and Global Encrypted Distance Matrices (GEDMs), designed to provide a solution to outsourced secure collaborative data clustering. The cryptographic ensemble comprises: Homomorphic Encryption (HE) to preserve raw data privacy, while supporting data analytics; and Multi-User Order Preserving Encryption (MUOPE) to preserve the privacy of the GEDM. Clustering can therefore be conducted over encrypted datasets without requiring decryption or the involvement of data owners once encryption has taken place, all with no loss of accuracy. The GEDM concept is applicable to large scale collaborative data mining applications that feature horizontal data partitioning. In the paper DBSCAN clustering is adopted for illustrative and evaluation purposes. The results demonstrate that the proposed solution is both efficient and accurate while maintaining data privacy.
Underwater Stereo Using Refraction-Free Image Synthesized From Light Field Camera

There is a strong demand on capturing underwater scenes without distortions caused by refraction. Since a light field camera can capture several light rays at each point of an image plane from various directions, if geometrically correct rays are chosen, it is possible to synthesize a refraction-free image. In this paper, we propose a novel technique to efficiently select such rays to synthesize a refraction-free image from an underwater image captured by a light field camera. In addition, we propose a stereo technique to reconstruct 3D shapes using a pair of our refraction-free images, which are central projection. In the experiment, we captured several underwater scenes by two light field cameras, synthesized refraction free images and applied stereo technique to reconstruct 3D shapes. The results are compared with previous techniques which are based on approximation, showing the strength of our method.
Scaling hypothesis of a spatial search on fractal lattices using a quantum walk

We investigate a quantum spatial search problem on fractal lattices, such as Sierpinski carpets and Menger sponges. In earlier numerical studies of the Sierpinski gasket, the Sierpinski tetrahedron, and the Sierpinski carpet, conjectures have been proposed for the scaling of a quantum spatial search problem finding a specific target, which is given in terms of the characteristic quantities of a fractal geometry. We find that our simulation results for extended Sierpinski carpets and Menger sponges support the conjecture for the ${\it optimal}$ number of the oracle calls, where the exponent is given by $1/2$ for $d_{\rm s} > 2$ and the inverse of the spectral dimension $d_{\rm s}$ for $d_{\rm s} < 2$. We also propose a scaling hypothesis for the ${\it effective}$ number of the oracle calls defined by the ratio of the ${\it optimal}$ number of oracle calls to a square root of the maximum finding probability. The form of the scaling hypothesis for extended Sierpinski carpets is very similar but slightly different from the earlier conjecture for the Sierpinski gasket, the Sierpinski tetrahedron, and the conventional Sierpinski carpet.
Blockchains in Supply Chains: Potential Research Directions

The blockchain is a distributed digital ledger technology that is maintained and governed by a consensus mechanism. While the technology took root in the financial services industry, supply chain and logistics industry are considered to be the next promising use case for this fast-evolving technology. This paper recaps, reviews, and discusses key research issues that needs to be explored pertinent to application of blockchains in supply chains and logistics.
Multitask defect prediction

Abstract Within‐project defect prediction assumes that we have sufficient labeled data from the same project, while cross‐project defect prediction assumes that we have plenty of labeled data from source projects. However, in practice, we might only have limited labeled data from both the source and target projects in some scenarios. In this paper, we want to apply multitask learning to investigate such a new scenario. To our best knowledge, this problem (ie, both the source project and the target project have limited labeled data) has not been thoroughly investigated, and we are the first to propose a novel multitask defect prediction approach mask . mask consists of a differential evolution optimization phase and a multitask learning phase. The former phase aims to find optimal weights for shared and nonshared information in related projects (ie, the target project and its related source projects), while the latter phase builds prediction models for each project simultaneously. To verify the effectiveness of mask , we perform experimental studies on 18 real‐world software projects and compare our approach with four state‐of‐the‐art baseline approaches: single‐task learning (STL), simple combined learning (SCL), Peters filter, and Burak filter. Experimental results show that mask can achieve F1 of 0.397 and AUC of 0.608 on average with a few labeled data (ie, 10% of data). Across the 18 projects, mask can outperform baseline methods significantly in terms of F1 and AUC. Therefore, by utilizing the relatedness among multiple projects, mask can perform significantly better than the state‐of‐the‐art methods. The results confirm that mask is promising for software defect prediction when the source and target projects both have limited training data.
Naming and epistemic necessity

Abstract Kripke (1980) hypothesizes a link between rigidity and scope: a singular term is rigid over a space S of possibilities just in case it is scopeless with respect to modals that quantify over S . Kripke's hypothesis works well when we consider the interaction of singular terms with metaphysical modals, but runs into trouble when we consider the interaction of singular terms with epistemic modals. After describing the trouble in detail, and considering one non‐solution to it, I develop a novel version of dynamic semantics that resolves the problem.
Eigenbasis of the evolution operator of 2-tessellable quantum walks

Staggered quantum walks on graphs are based on the concept of graph tessellation and generalize some well-known discrete-time quantum walk models. In this work, we address the class of 2-tessellable quantum walks with the goal of obtaining an eigenbasis of the evolution operator. By interpreting the evolution operator as a quantum Markov chain on an underlying multigraph, we define the concept of quantum detailed balance, which helps to obtain the eigenbasis. A subset of the eigenvectors is obtained from the eigenvectors of the double discriminant matrix of the quantum Markov chain. To obtain the remaining eigenvectors, we have to use the quantum detailed balance conditions. If the quantum Markov chain has a quantum detailed balance, there is an eigenvector for each fundamental cycle of the underlying multigraph. If the quantum Markov chain does not have a quantum detailed balance, we have to use two fundamental cycles linked by a path in order to find the remaining eigenvectors. We exemplify the process of obtaining the eigenbasis of the evolution operator using the kagome lattice (the line graph of the hexagonal lattice), which has symmetry properties that help in the calculation process.
KGEARSRG: Kernel Graph Embedding on Attributed Relational SIFT-Based Regions Graph

In real world applications, binary classification is often affected by imbalanced classes. In this paper, a new methodology to solve the class imbalance problem that occurs in image classification is proposed. A digital image is described through a novel vector-based representation called Kernel Graph Embedding on Attributed Relational Scale-Invariant Feature Transform-based Regions Graph (KGEARSRG). A classification stage using a procedure based on support vector machines (SVMs) is organized. Methodology is evaluated through a series of experiments performed on art painting dataset images, affected by varying imbalance percentages. Experimental results show that the proposed approach consistently outperforms the competitors.
What Do We Do When We Teach Software Engineering?

Many UK higher education institutions offer software engineering programmes, but the purpose and relevance of these programmes within computing science departments is not always obvious. The reality is that while advanced economies require many more skilled software engineers, universities are not delivering them. This is at least true in the context of the United Kingdom, where there are high numbers of software engineering vacancies and unemployed software engineering graduates. A possible explanation could be that curriculum content of software engineering programmes in universities needs to be reconsidered to meet the needs of industry. However, reconsidering curriculum content alone is unlikely to be transformative as there is little to be gained from changing to an emerging methodology, language or framework. Instead, an alternative direction could be to reconsider curriculum delivery and the identity of software engineering within computing science itself. In this paper, we contextualise the challenge by considering the history of software engineering education and some of its key developments. We then consider some of the alternative delivery approaches, before arguing cooperative programmes provide a opportunity for institutions to reconsider software engineering education.
No-Reference Image Sharpness Assessment Based on Rank Learning

To address the label shortage problem and the fixed-size input constraint of CNN models, we propose a no-reference image sharpness assessment method based on rank learning and effective patch extraction. First, we train a Siamese mobilenet network by learning quality ranks among the synthetically blurred and unsharpen seed images without any human label, which provides effective prior knowledge about the appropriate image sharpness. The extracted single branch finetuned on benchmark datasets is used to predict the subjective rating regarding image sharpness. While the performance of CNN based IQA metrics is compromised due to their fixed-size input constraint, we design a multi-scale gradient guided patch extraction method to increase the quality-sensitive input information and boost the performance. Extensive experimental results on the six public datasets demonstrate that our approach outperforms other state-of-the-art no-reference image sharpness assessment metrics.
Intra-day forecasting of building-integrated PV systems for power systems operation using ANN ensemble

Renewable energy sources (RES) like solar and wind, naturally present high daily and seasonal variability of power generation. This paper presents a real case study of solar photovoltaic (PV) power forecasting of a building-integrated PV plant as a tool for power systems operation. A new methodology composed of data processing strategy, Artificial Neural Networks (ANN) modelling, and error metrics definitions are proposed and validated for the intra-day PV power forecasting. Data were modelled in time steps of 15-minute averages and the forecasts obtained from 15 minutes to 120 minutes ahead. A persistence model is developed for the building-integrated PV systems (BIPVS) case. The forecast skills achieved range from 9.79% to 23.75%, which measures the ANN improvement in relation to the proposed benchmark persistence model.
Random Part Localization Model for Fine Grained Image Classification

Fine-grained recognition is challenging due to its subtle local inter-class differences versus large intra-class variations. Finding those subtle traits that fully characterize the object is not straightforward. In this paper, we present a novel random part localization model, which first extracts the foreground object using the saliency map, and then localizes the discriminative parts through a set of potential regions in a random way based on their contribution to classification. We train three convolutional neural networks to capture the features that belong to different levels and average their classification results as our final prediction score. Experiments show that our approach achieves competitive performance compared with state-of-the-art methods on three publicly available fine-grained recognition datasets (CUB200-2011, Stanford Cars and FGVC-Aircraft).
A No-Reference Autoencoder Video Quality Metric

In this work, we introduce the No-reference Autoencoder VidEo (NAVE) quality metric, which is based on a deep au-toencoder machine learning technique. The metric uses a set of spatial and temporal features to estimate the overall visual quality, taking advantage of the autoencoder ability to produce a better and more compact set of features. NAVE was tested on two databases: the UnB-AVQ database and the LiveNetflix-II database. Results show that the method is able to estimate the perceived video quality with a good correlation performance and a small error, when compared to currently available no-reference and full-reference video quality objective metrics.
Mining of data through various soft computing techniques

We experience a daily reality such that we can be overpowered with data; in this manner it has turned out to be progressively critical to separate pertinent data from the unstable measure of information for. Information Mining is the iterative and intuitive procedure of finding substantial, novel, valuable, and justifiable examples or models in gigantic databases. Information Mining implies looking for important data in extensive volumes of information, utilizing investigation and examination, via programmed or self-loader implies, of expansive amounts of information so as to find significant examples and standards. Soft Computing (SC) alludes to methods of figuring in which accuracy is exchanged for tractability, heartiness and simplicity of usage. For the most part, SC encompasses the technologies of fuzzy logic, genetic algorithms, and neural networks, and it has emerged as an effective tool for dealing with data mining, control, modeling, and decision problems in complex systems. This is a review of the role of various soft-computing tools for different data mining tasks.