Reviewerly/exHarmony huggingface dataset:
VIS4ML: An Ontology for Visual Analytics Assisted Machine Learning

While many VA workflows make use of machine-learned models to support analytical tasks, VA workflows have become increasingly important in understanding and improving Machine Learning (ML) processes. In this paper, we propose an ontology (VIS4ML) for a subarea of VA, namely "VA-assisted ML". The purpose of VIS4ML is to describe and understand existing VA workflows used in ML as well as to detect gaps in ML processes and the potential of introducing advanced VA techniques to such processes. Ontologies have been widely used to map out the scope of a topic in biology, medicine, and many other disciplines. We adopt the scholarly methodologies for constructing VIS4ML, including the specification, conceptualization, formalization, implementation, and validation of ontologies. In particular, we reinterpret the traditional VA pipeline to encompass model-development workflows. We introduce necessary definitions, rules, syntaxes, and visual notations for formulating VIS4ML and make use of semantic web technologies for implementing it in the Web Ontology Language (OWL). VIS4ML captures the high-level knowledge about previous workflows where VA is used to assist in ML. It is consistent with the established VA concepts and will continue to evolve along with the future developments in VA and ML. While this ontology is an effort for building the theoretical foundation of VA, it can be used by practitioners in real-world applications to optimize model-development workflows by systematically examining the potential benefits that can be brought about by either machine or human capabilities. Meanwhile, VIS4ML is intended to be extensible and will continue to be updated to reflect future advancements in using VA for building high-quality data-analytical models or for building such models rapidly.
An evaluative study of a mobile application for middle school students struggling with English vocabulary learning

This study evaluated the effectiveness of a mobile application (app) designed within the framework of cognitive apprenticeship, in order to facilitate students struggling with learning English as a foreign language (EFL). The participants were ten seventh-graders, who were performing significantly below grade level in their EFL classroom. The research questions targeted changes in learning outcomes with the aid of the app, attitudes the participants had towards the learning experience, and student perceptions regarding the design of the app. Data were collected by interviews, observation, surveys, and exams. The results indicated use of the app enhanced EFL learning. Not only did the app provide a feasible path for the students moving from individual learning to cooperative learning, it also bolstered their confidence in learning, ultimately promoting positive attitudes toward EFL learning. Finally, the design of the app was confirmed as an effective tool for mobile instruction, yet student perceptions of the interface design indicated a need for modification to be more adaptive. The findings confirm the legitimacy of the app's instructional design and may provide evidence-based learning materials to scaffold EFL learning.
Software product lines and variability modeling: A tertiary study

Context: A software product line is a means to develop a set of products in which variability is a central phenomenon captured in variability models. The field of SPLs and variability have been topics of extensive research over the few past decades. Objective: This research characterizes systematic reviews (SRs) in the field, studies how SRs analyze and use evidence-based results, and identifies how variability is modeled. Method: We conducted a tertiary study as a form of systematic review. Results: 86 SRs were included. SRs have become a widely adopted methodology covering the field broadly otherwise except for variability realization. Numerous variability models exist that cover different development artifacts, but the evidence is insufficient in quantity and immature, and we argue for better evidence. SRs perform well in searching and selecting studies and presenting data. However, their analysis and use of the quality of and evidence in the primary studies often remains shallow, merely presenting of what kinds of evidence exist. Conclusions: There is a need for actionable, context-sensitive, and evaluated solutions rather than novel ones. Different kinds of SRs (SLRs and Maps) need to be better distinguished, and evidence and quality need to be better used in the resulting syntheses.
On the performance improvement of elephant herding optimization algorithm

Thanks to fewer numbers of control parameters and easier implementation, the Elephant Herding Optimization (EHO) has been gaining research interest during the past decade. In our paper, to understand the impact of the control parameters, a parametric study of the EHO is carried out using a standard test bench, engineering problems, and real-world problems. On top of that, the main aim of this paper is to propose different approaches to enhance the performance of the original EHO, i.e., cultural-based, alpha-tuning, and biased initialization EHO. Acomparative study has been made between these EHO variants and the state-of-the-art swarm optimization methods. Case studies ranging from the recent test bench problems of CEC 2016 to the popular engineering problems of gear train, welded beam, three-bar truss design problem, continuous stirred tank reactor, and fed-batch fermentor are used to validate and test the performances of the proposed EHOs against the existing techniques. Numerical results show that the performances of the three new EHOs are better than or competitive with the population-based optimization algorithms.
Enhancing Cloud-Based IoT Security Through Trustworthy Cloud Service: An Integration of Security and Reputation Approach

The Internet of Things (IoT) provides a new paradigm for the development of heterogeneous and distributed systems, and it has increasingly become a ubiquitous computing service platform. However, due to the lack of sufficient computing and storage resources dedicated to the processing and storage of huge volumes of the IoT data, it tends to adopt a cloud-based architecture to address the issues of resource constraints. Hence, a series of challenging security and trust concerns have arisen in the cloud-based IoT context. To this end, a novel trust assessment framework for the security and reputation of cloud services is proposed. This framework enables the trust evaluation of cloud services in order to ensure the security of the cloud-based IoT context via integrating security- and reputation-based trust assessment methods. The security-based trust assessment method employs the cloud-specific security metrics to evaluate the security of a cloud service. Furthermore, the feedback ratings on the quality of cloud service are exploited in the reputation-based trust assessment method in order to evaluate the reputation of a cloud service. The experiments conducted using a synthesized dataset of security metrics and a real-world web service dataset show that our proposed trust assessment framework can efficiently and effectively assess the trustworthiness of a cloud service while outperforming other trust assessment methods.
Introduction to Kalman Filter and Its Applications

We provide a tutorial-like description of Kalman filter and extended Kalman filter.This chapter aims for those who need to teach Kalman filters to others, or for those who do not have a strong background in estimation theory.Following a problem definition of state estimation, filtering algorithms will be presented with supporting examples to help readers easily grasp how the Kalman filters work.Implementations on INS/GNSS navigation, target tracking, and terrain-referenced navigation (TRN) are given.In each example, we discuss how to choose, implement, tune, and modify the algorithms for real world practices.Source codes for implementing the examples are also provided.In conclusion, this chapter will become a prerequisite for other contents in the book.
Research on gesture recognition of smart data fusion features in the IoT

With the rapid development of Internet of things technology, the interaction between people and things has become increasingly frequent. Using simple gestures instead of complex operations to interact with the machine, the fusion of smart data feature information and so on has gradually become a research hotspot. Considering that the depth image of the Kinect sensor lacks color information and is susceptible to depth thresholds, this paper proposes a gesture segmentation method based on the fusion of color information and depth information; in order to ensure the complete information of the segmentation image, a gesture feature extraction method based on Hu invariant moment and HOG feature fusion is proposed; and by determining the optimal weight parameters, the global and local features are effectively fused. Finally, the SVM classifier is used to classify and identify gestures. The experimental results show that the proposed fusion features method has a higher gesture recognition rate and better robustness than the traditional method.
Real-Time Vehicle Make and Model Recognition with the Residual SqueezeNet Architecture

Make and model recognition (MMR) of vehicles plays an important role in automatic vision-based systems. This paper proposes a novel deep learning approach for MMR using the SqueezeNet architecture. The frontal views of vehicle images are first extracted and fed into a deep network for training and testing. The SqueezeNet architecture with bypass connections between the Fire modules, a variant of the vanilla SqueezeNet, is employed for this study, which makes our MMR system more efficient. The experimental results on our collected large-scale vehicle datasets indicate that the proposed model achieves 96.3% recognition rate at the rank-1 level with an economical time slice of 108.8 ms. For inference tasks, the deployed deep model requires less than 5 MB of space and thus has a great viability in real-time applications.
The implementation of univariable scheme-based air temperature for solar radiation prediction: New development of dynamic evolving neural-fuzzy inference system model

New development of dynamic evolving neural-fuzzy inference system model was proposed for modeling solar radiation based on univariable air temperature scheme. The proposed predictive model was validated against several robust heuristic regression models including multivariate adaptive regression spline, M5 model tree and least square support vector regression. Historical data of solar radiation and air temperature for two meteorological stations: Adana and Antakya located in Turkey, were investigated. The prediction results were evaluated based on several statistical metrics. The modeling is conducted based on different data division scenarios (training/testing) phases. The attained prediction results evidenced the potential of the dynamic evolving neural-fuzzy inference system over the comparable models and for all the investigated data division scenarios. In quantitative terms, dynamic evolving neural-fuzzy inference system was enhanced the solar radiation prediction capability over the multivariate adaptive regression spline, M5 model tree and least square support vector regression models (Adana-Antakya) by 20–42%, 29–47% and 19–43% based on the root mean square errors metric. The applied predictive models were compared with the field measured average monthly solar radiation values and it was found that the proposed model estimates accurately. However, the comparable models were exhibited a considerable overestimation of the monthly averaged solar radiation values and for both inspected stations specifically in the summer months (June, July and August).
A Novel K-medoids clustering recommendation algorithm based on probability distribution for collaborative filtering

Data sparsity is a widespread problem of collaborative filtering (CF) recommendation algorithms. However, some common CF methods cannot adequately utilize all user rating information; they are only able to use a small part of the rating data, depending on the co-rated items, which leads to low prediction accuracy. To alleviate this problem, a novel K-medoids clustering recommendation algorithm based on probability distribution for CF is proposed. The proposed scheme makes full use of all rating information based on Kullback–Leibler (KL) divergence from the perspective of item rating probability distribution, and distinguishes different items efficiently when selecting the cluster centers. Meanwhile, the distance model breaks the symmetric mode of classic geometric distance methods (such as Euclidean distance) and considers the effects of different rating numbers between items to emphasize their asymmetric relationship. Experimental results on different datasets show that the proposed clustering algorithm outperforms other compared methods in various evaluation metrics; this approach enhances the prediction accuracy and effectively deals with the sparsity problem.
Few-Shot Deep Adversarial Learning for Video-Based Person Re-Identification

Video-based person re-identification (re-ID) refers to matching people across camera views from arbitrary unaligned video footages. Existing methods rely on supervision signals to optimise a projected space under which the distances between inter/intra-videos are maximised/minimised. However, this demands exhaustively labelling people across camera views, rendering them unable to be scaled in large networked cameras. Also, it is noticed that learning effective video representations with view invariance is not explicitly addressed for which features exhibit different distributions otherwise. Thus, matching videos for person re-ID demands flexible models to capture the dynamics in time-series observations and learn view-invariant representations with access to limited labeled training samples. In this paper, we propose a novel few-shot deep learning approach to videobased person re-ID, to learn comparable representations that are discriminative and view-invariant. The proposed method is developed on the variational recurrent neural networks (VRNNs) and trained adversarially to produce latent variables with temporal dependencies that are highly discriminative yet view-invariant in matching persons. Through extensive experiments conducted on three benchmark datasets, we empirically show the capability of our method in creating view-invariant temporal features and state-of-the-art performance achieved by our method.
Embodied Question Answering in Photorealistic Environments With Point Cloud Perception

To help bridge the gap between internet vision-style problems and the goal of vision for embodied perception we instantiate a large-scale navigation task -- Embodied Question Answering [1] in photo-realistic environments (Matterport 3D). We thoroughly study navigation policies that utilize 3D point clouds, RGB images, or their combination. Our analysis of these models reveals several key findings. We find that two seemingly naive navigation baselines, forward-only and random, are strong navigators and challenging to outperform, due to the specific choice of the evaluation setting presented by [1]. We find a novel loss-weighting scheme we call Inflection Weighting to be important when training recurrent models for navigation with behavior cloning and are able to out perform the baselines with this technique. We find that point clouds provide a richer signal than RGB images for learning obstacle avoidance, motivating the use (and continued study) of 3D deep learning models for embodied navigation.
Osmotic Bio-Inspired Load Balancing Algorithm in Cloud Computing

Cloud computing is increasing rapidly as a successful paradigm presenting on-demand infrastructure, platform, and software services to clients.Load balancing is one of the important issues in cloud computing to distribute the dynamic workload equally among all the nodes to avoid the status that some nodes are overloaded while others are underloaded.Many algorithms have been suggested to perform this task.Recently, worldview is turning into a new paradigm for optimization search by applying the osmosis theory from chemistry science to form osmotic computing.Osmotic computing is aimed to achieve balance in highly distributed environments.The main goal of this paper is to propose a hybrid metaheuristics technique which combines the osmotic behavior with bio-inspired load balancing algorithms.The osmotic behavior enables the automatic deployment of virtual machines (VMs) that are migrated through cloud infrastructures.Since the hybrid artificial bee colony and ant colony optimization proved its efficiency in the dynamic environment in cloud computing, the paper then exploits the advantages of these bio-inspired algorithms to form an osmotic hybrid artificial bee and ant colony (OH_BAC) optimization load balancing algorithm.It overcomes the drawbacks of the existing bio-inspired algorithms in achieving load balancing between physical machines.The simulation results show that OH_BAC decreases energy consumption, the number of VMs migrations and the number of shutdown hosts compared to existing algorithms.In addition, it enhances the quality of services (QoSs) which is measured by service level agreement violation (SLAV) and performance degradation due to migrations (PDMs).
Success factors influencing requirements change management process in global software development

Planning and managing requirements changes in Global Software Development (GSD) is a challenging task. While requirements change has received much attention from researchers, Requirements Change Management (RCM) process is still an emerging area in GSD. The objective of this paper is to identify the success factors of RCM in the GSD environment. We applied the Systematic Literature Review (SLR) approach and identified 23 success factors that influence RCM in GSD projects. The findings of the SLR indicate that change impact analysis, change understanding, management support, RCM process awareness, standard for RCM, progress measure, updated requirements and minimize project failure risk are key factors that influence RCM in a GSD project. We present a comparison of success factors identified in client and vendor organizations. Moreover, we present a framework to classify the identified success factors for RCM process implementation. We believe GSD organizations can use the framework to better manage requirements change in GSD projects.
Distilled Person Re-Identification: Towards a More Scalable System

Person re-identification (Re-ID), for matching pedestrians across non-overlapping camera views, has made great progress in supervised learning with abundant labelled data. However, the scalability problem is the bottleneck for applications in large-scale systems. We consider the scalability problem of Re-ID from three aspects: (1) low labelling cost by reducing label amount, (2) low extension cost by reusing existing knowledge and (3) low testing computation cost by using lightweight models. The requirements render scalable Re-ID a challenging problem. To solve these problems in a unified system, we propose a Multi-teacher Adaptive Similarity Distillation Framework, which requires only a few labelled identities of target domain to transfer knowledge from multiple teacher models to a user-specified lightweight student model without accessing source domain data. We propose the Log-Euclidean Similarity Distillation Loss for Re-ID and further integrate the Adaptive Knowledge Aggregator to select effective teacher models to transfer target-adaptive knowledge. Extensive evaluations show that our method can extend with high scalability and the performance is comparable to the state-of-the-art unsupervised and semi-supervised Re-ID methods.
A machine learning approach for the identification of the deceptive reviews in the hospitality sector using unique attributes and sentiment orientation

The popularity of online reviews is causing a huge impact on consumers’ purchase intentions for goods and services. However, and hidden by the anonymity of the Internet, fraudsters can try to manipulate other consumers by posting fake reviews. Maintaining trust in online reviews require the development of automatic tools using machine learning approaches because of the huge volume of online opinions generated every day. This paper is focused on the hospitality sector and follows a content analysis approach based on a set of unique attributes and the sentiment orientation of reviews. The main contributions of the paper are i) a set of polarity-oriented unique attributes able to distinguish positive and negative deceptive and non-deceptive reviews and ii) the main topics associated to positive and negative deceptive and non-deceptive reviews. Findings reveal that positive and negative unique attributes lead to non-biased classifiers and that experience based reviews tend to be non-deceptive.
A geographical information system-based multi-criteria method for the evaluation of solar farms locations: A case study in Souss-Massa area, southern Morocco

It is now well established that solar energy is a reliable alternative way to reduce greenhouse gas emissions and to mitigate climate change effects. In Morocco, solar energy investments are rapidly rising since 2009 and are expected to grow dramatically in the next decade. Site selection is a critical issue when large-scale solar plants are being planned. In this paper, the multi-criteria evaluation method is used to identify suitable sites to host Photovoltaic projects in southern Morocco. For this purpose, geographical information systems and analytic hierarchy process methods were combined to represent a high-resolution map that compares the potential of the studied region to host large-scale Photovoltaic projects. HOMER software was then used for modeling and simulating a 10 MW grid-connected PV system. The optimum scheme is analyzed to assess the economic feasibility in the most suitable site in the Souss-Massa region. The findings from this study confirm that 24.08% of the studied area offers suitable sites to host the Photovoltaic farms. The HOMER simulation has clearly shown that the grid-connected Photovoltaic system is beneficial in the long run, although its initial capital is higher than the grid supply only. Also, the cost of electricity generated from Photovoltaic panels is affordable.
Measuring the impact of online personalisation: Past, present and future

Research on understanding, developing and assessing personalisation systems is spread over multiple disciplines and builds on methodologies and findings from several different research fields and traditions, such as Artificial Intelligence (AI), Machine Learning (ML), Human–Computer Interaction (HCI), and User Modelling based on (applied) social and cognitive psychology. The fields of AI and ML primarily focus on the optimisation of personalisation applications, and concentrate on creating ever more accurate algorithmic decision makers and prediction models. In the fields of HCI and Information Systems, scholars are primarily interested in the phenomena around the use and interaction with personalisation systems, while Cognitive Science (partly) delivers the theoretical underpinnings for the observed effects. The aim and contribution of this work is to put together the pieces about the impact of personalisation and recommendation systems from these different backgrounds in order to formulate a research agenda and provide a perspective on future developments.