Reviewerly/exHarmony huggingface dataset:
Cybersecurity Vulnerability Mitigation Framework Through Empirical Paradigm (CyFEr): Prioritized Gap Analysis

Cybersecurity vulnerability assessment tools, frameworks, methodologies, and processes are commonly used to understand the cybersecurity maturity and posture of a system or a facility. Although those tools are strictly developed based on standards defined by organizations such as the National Institute of Standards and Technology (NIST) and the U.S. Department of Energy, the majority of these tools and frameworks do not provide a platform to prioritize the requirements to reach a desired cybersecurity maturity. To address that challenge, we have been developing a framework and a software application called the cybersecurity vulnerability mitigation framework through empirical paradigm (CyFEr). The efficacy of CyFEr was evaluated by implementing it on the NIST cybersecurity framework (CSF). This paper provides a detailed architecture of CyFEr and demonstrates its application to CSF by testing against a real-world cyberattack that targeted industrial control systems in a critical infrastructure facility.
Registration methods to enable augmented reality-assisted 3D image-guided interventions

Augmented reality (AR) can be used to visualize virtual 3D models of medical imaging in actual 3D physical space. Accurate registration of these models onto patients will be essential for AR-assisted image-guided interventions. In this study, registration methods were developed, and registration times for aligning a virtual 3D anatomic model of patient imaging onto a CT grid commonly used in CT-guided interventions were compared. The described methodology enabled automated and accurate registration within seconds using computer vision detection of the CT grid as compared to minutes using user-interactive registration methods. Simple, accurate, and near instantaneous registration of virtual 3D models onto CT grids will facilitate the use of AR for real-time procedural guidance and combined virtual/actual 3D navigation during image-guided interventions.
Use of electronic resources by students in a premier postgraduate theological university in Ghana

Background: Most electronic resource (e-resource) studies have been undertaken in universities because e-resources are crucial in enhancing students’ research and learning activities. However, there is a paucity of e-resources research in postgraduate theological universities, particularly in Ghana. This study, therefore, investigated students’ level of use of e-resources at the Akrofi-Christaller Institute of Theology, Mission and Culture. Objectives: The study’s specific objectives were to identify the types of e-resources available for use, discover the extent of awareness of e-resources, find out how e-resources were accessed and used, ascertain the purposes of using e-resources and identify the benefits of and problems in using e-resources. Method: Using the survey research design, copies of the questionnaire were administered to 33 postgraduate students by accidental sampling. Data analysed using Microsoft Excel 2013 were mainly presented as frequency and percentage distributions. Results: The study revealed high awareness levels, adequate computer competencies, derivation of multiple benefits, use of e-resources for academic purposes and inadequate search skills of most respondents because of the dearth of training. Constraints to e-resources usage were access problems, search and retrieval problems and staff-related problems. Conclusion: The study informs university administrators and libraries about the need to ensure students leverage e-resources in research and studies. The study proposed, among other recommendations, the provision of regular and mandatory information literacy training for students, the need for the institute’s library staff to provide personalised search support to students and the provision of off-campus access to the institute’s e-resource subscriptions.
Real-Time Traffic Light Control System Based on Background Updating and Edge Detection

Traffic congestion at intersections is one of the most problems encountered by the population in large cities. Shutdowns and delays in traffic light consume fuel and increase the vehicle's travel time and greenhouse gas emissions. Therefore, an effective traffic control system can minimize shutdowns and delays at traffic lights. In this paper, an image processing system in real time has been proposed to measure the density of traffic to adjust the timing of the traffic light. The method proposed here is based on applying the combination of background updating and edge detection to separate the edges of vehicles from the edges of the objects of the background (e.g., marks on the road or shadows of trees or building). The algorithm is applied to videos obtained by stationary cameras.
Monocular gaze depth estimation using the vestibulo-ocular reflex

Gaze depth estimation presents a challenge for eye tracking in 3D. This work investigates a novel approach to the problem based on eye movement mediated by the vestibulo-ocular reflex (VOR). VOR stabilises gaze on a target during head movement, with eye movement in the opposite direction, and the VOR gain increases the closer the fixated target is to the viewer. We present a theoretical analysis of the relationship between VOR gain and depth which we investigate with empirical data collected in a user study (N=10). We show that VOR gain can be captured using pupil centres, and propose and evaluate a practical method for gaze depth estimation based on a generic function of VOR gain and two-point depth calibration. The results show that VOR gain is comparable with vergence in capturing depth while only requiring one eye, and provide insight into open challenges in harnessing VOR gain as a robust measure.
An Unsupervised Deep Learning Framework via Integrated Optimization of Representation Learning and GMM-Based Modeling

While supervised deep learning has achieved great success in a range of applications, relatively little work has studied the discovery of knowledge from unlabeled data. In this paper, we propose an unsupervised deep learning framework to provide a potential solution for the problem that existing deep learning techniques require large labeled data sets for completing the training process. Our proposed introduces a new principle of joint learning on both deep representations and GMM (Gaussian Mixture Model)-based deep modeling, and thus an integrated objective function is proposed to facilitate the principle. In comparison with the existing work in similar areas, our objective function has two learning targets, which are created to be jointly optimized to achieve the best possible unsupervised learning and knowledge discovery from unlabeled data sets. While maximizing the first target enables the GMM to achieve the best possible modeling of the data representations and each Gaussian component corresponds to a compact cluster, maximizing the second term will enhance the separability of the Gaussian components and hence the inter-cluster distances. As a result, the compactness of clusters is significantly enhanced by reducing the intra-cluster distances, and the separability is improved by increasing the inter-cluster distances. Extensive experimental results show that the propose method can improve the clustering performance compared with benchmark methods.
Evaluating Role Playing Efficiency to Teach Requirements Engineering

Weak requirements engineering can lead to the failure of software projects, unstable software products and unclear designation of responsibilities in software projects. Requirements engineering education is essential for preparing students for the real world of software engineering, especially the proper handling of requirements. Students should be able to elicit, analyze, specify, and validate known and unknown requirements from stakeholders with different backgrounds. Role playing has been suggested by many researchers as a pedagogical technique for teaching requirements engineering efficiently. However, there is hardly any empirical evaluation demonstrating its efficiency. The objective of this study is to assess the use of role playing and role reversal in an intensive requirements engineering course. This paper reports our experience teaching requirements engineering courses using role playing for four sessions of two hours each. The results demonstrate the efficiency of role playing as a tool for teaching requirements engineering over short durations, and for creating student engagement during the course.
78‐2: Measurement and Evaluation of Subpixel Brightness for Demura

The luminance measurement accuracy of the demura system is a key factor that affects the performance of the demura. To objectively evaluate the performance of a demura system, we proposed a new evaluation method using white noise as test pattern. A test pattern was generated by adding a white noise to the gray level test pattern, the demura camera captures both the gray level pattern and the new pattern with the white noise. By calculating the subpixel brightness difference from the two captures, the subpixel brightness of this white noise pattern can be derived. The accuracy of the subpixel brightness measurement system can be evaluated with the autocorrelation of the derived subpixel brightness and the originally added random noise. The autocorrelation function characterizes both the effect of noise and spatial blur. If the display gamma is measured or known, the measured brightness can be converted to display digital counts and the root measure square error (RMSE) can be used as a metric for demura accuracy.
Model Architecture for Cloud Computing-Based Library Management

The purpose of this article is to provide a cloud computing system-based architecture for library automation services. In this article, a consortium-based library automation system has been conceptualized and a model architecture has been proposed. The article describes the benefit of virtualization (Virtual Machine as a library) of cloud computing toward libraries and why/how a librarian can shift from an on-premise based library system to cloud-based system to overcome infrastructure requirement, staff requirements for administration, system maintenance, costs for backup, and so forth. The article outlines the traditional system of library automation. The proposed model provides cloud platform and application virtualization solutions for deploying and managing Service Oriented Architecture (SOA) for libraries. The model illustrates how a group of libraries, participating into a consortia, could collaborately obtain access to LMS software application through the virtual platform. This article is one of the first of its kind to propose such architecture for library automation.
Post Training Weight Compression with Distribution-based Filter-wise Quantization Step

Quantization of models with lower bit precision is a promising method to develop lower-power and smaller-area neural network hardware. However, 4- or lower bit quantization usually requires additional retraining with labeled dataset for backpropagation to improve test accuracy. In this paper, we propose a quantization scheme with distribution-based filter-wise quantization step without labeled dataset. ResNet-50 model with 8-bit activation and 3.04-bit weight precision quantized with the proposed techniques achieves top-1 inference accuracy of 74.3% on ImageNet.
An outlook at Agile methodologies for the independent games developer

This paper explores the Agile methodologies, Scrum and Kanban, to define their advantages and limitations of use. The methods are compared for their use in software development, specifying which would be a suitable option for a game development process focusing on maximizing productivity and quality of the software. Exploration of use cases and methodology guides are discussed, defining the benefits of choosing Kanban or Scrum for a software development team. Findings show Scrum to be a method that focuses on the verbal communication through sprint meetings and reviews. Kanban is described as a visually driven framework that utilizes a Kanban board and cards for communication. Scrum is a strict method can only implement change after a sprint, while Kanban is more flexible and can make changes at any point in the development process. A hybrid of the two methods, Scrumban, is also investigated and found to be an alternative for users who find aspects of both methodologies to be appropriate for their team. There is a great deal of consideration required for independent game developers as they need to reflect on their team dynamics and situation before determining which methodology is best suited for them.
Virtual Reality Models for Promoting Learners Engagement in Construction Studies

Virtual Reality (VR) models have been used in tertiary education to provide unreachable real-life experiences to learners. Often construction class facilitators find it is very challenging to engage in experiential learning activities (e.g., site visits) due to hectic project environment and its health and safety risks. VR models can, therefore, be used as an alternative way of conducting virtual construction site visits, as part of an effective curriculum delivery process. This study aims to evaluate the effectiveness of the use of VR models for promoting learners engagement in construction studies. We created a virtual reality model of sustainable building features (earth building techniques, rainwater harvesting tank, dry toilet, solar panels, and green wall) to stimulate sustainability in construction studies. We showcased the project at Auckland Build Expo 2018 (ABE2018) for the public. Construction students evaluated the effectiveness of the VR model through an anonymous questionnaire survey. The results show that VR models used could provide an intuitive and safe way to engage construction students. It was evident through student evaluation survey results, some visitors who commented on our VR model during ABE2018 and student assessment results. Further, 71.5% of learners involved in this project believed that VR models are useful in construction studies due to their convenience and easy access and found it an asset to their learning. VR models help understand earth building techniques and sustainable features which are rarely available to access. They could help class facilitators engage with experiential learning activities to improve their teaching quality and to make the learning process more enjoyable.
The linear hidden subset problem for the (1 + 1) EA with scheduled and adaptive mutation rates

We study unbiased (1+1) evolutionary algorithms on linear functions with an unknown number n of bits with non-zero weight. Static algorithms achieve an optimal runtime of O(n(ln⁡n)2+ε), however, it remained unclear whether more dynamic parameter policies could yield better runtime guarantees. We consider two setups: one where the mutation rate follows a fixed schedule, and one where it may be adapted depending on the history of the run. For the first setup, we give a schedule that achieves a runtime of (1±o(1))βnln⁡n, where β≈3.552, which is an asymptotic improvement over the runtime of the static setup. Moreover, we show that no schedule admits a better runtime guarantee and that the optimal schedule is essentially unique. For the second setup, we show that the runtime can be further improved to (1±o(1))enln⁡n, which matches the performance of algorithms that know n in advance. Finally, we study the related model of initial segment uncertainty with static position-dependent mutation rates, and derive asymptotically optimal lower bounds. This answers a question by Doerr, Doerr, and Kötzing.
Efficient Small Object Detection with an Improved Region Proposal Networks

Abstract Although the state-of-the-art object detection methods, which depend on region proposal algorithms to hypothesize object locations, have achieved high detection accuracy, it still struggles in small-size object detection. In this paper, we present a novel method with a multi-scale and multi-tasking region proposal method to effectively detect small object. In the proposed method, multi-scale features and high-level features are employed to locate object position and identify object category, respectively. The main contributions of the proposed approach are two-fold: (1) A simpler way is used to improve the accuracy performance of small object detection, instead of the complex image pyramids and the complex combination framework, and make the object detection task more flexible. (2) Based on multi-scale and multi-tasking approaches, object location information in low layers and object semantic information in deep layers are made fully advantage respectively. The experimental results on the PASCAL VOC dataset show that the proposed method achieves the state-of-the art object detection accuracy.
Enhancing pedestrian safety through in-situ projections

This paper presents a conceptual application of drone-based in-situ projections to support people when crossing busy roads that lack dedicated pedestrian crossings. To overcome technical, legal and risk challenges of evaluating these kinds of speculative pervasive display applications, we introduce the concept of hyperreal prototypes. We describe the layers that contribute to a hyperreal prototype and how such prototypes allow the evaluation of HCI solutions in high-risk scenarios in a virtual-reality environment.
A Skywatch on the Challenging Gradual Progression of Scheduling in Cloud Computing

The continuing innovation across new and existing wireless technologies presents challenges in designing and testing of features, modules, and devices that makes the connected world possible. In computing system, the end users have to follow the basic idea of cloud computing, i.e., pay and use. The most vital continues to be at the forefront offering a full portfolio of test and designing ways that defines the overall process of the computing and its simulation. According to research and development teams, the overall view is verified and issued with all the computing service level agreements. The concept of scheduling is being achieved by the high-performance computing and efficient throughput. Computing system is a heterogeneous view, and the increasing speed of the computer systems and services is increasing rapidly. Cloud computing is the best viral technology which is increasing drastically. The best effective working of cloud computing for the job scheduling is to provide maximum profit to cloud service providers. This algorithm is the future proof, making it easier to evolve with changing technologies and stay on the leading edge. This algorithm efficiency design and test are available to advise, assist, and augment cloud computing all around the world. Fundamentally, the arrangement of standards works for the extraordinary bolstered booking between cloud benefit bearers and cloud clients. The cloud brokers are betting a basic part inside the offerings outfitted by methods for the CSPs.
Directives of Communicability: Towards Better Communication through Software Models

In the development process, software models are commonly used to support communication among members of the development team. The risks of miscommunication through software models relate to the model content and to how content is expressed by the modeler. Communication failures mediated by software models may generate inconsistencies in software behavior, generating rework to correct them later. Aiming to improve model communication quality between their producers and consumers, we developed the Directives of Communicability (DCs). The aim of this paper is to present the DCs and an empirical study. The empirical study indicated that the directives had the intended potential to reduce miscommunication of what software models meant to those who produced them. From the results of this study, we notice that DCs can improve communication quality between model producers and consumers. Our results also show that DCs helped the producers' reflections on their models' content.
Hyperion

Indexes are essential in data management systems to increase the speed of data retrievals. Widespread data structures to provide fast and memory-efficient indexes are prefix tries. Implementations like Judy, ART, or HOT optimize their internal alignments for cache and vector unit efficiency. While these measures usually improve the performance substantially, they can have a negative impact on memory efficiency. In this paper we present Hyperion, a trie-based main-memory key-value store achieving extreme space efficiency. In contrast to other data structures, Hyperion does not depend on CPU vector units, but scans the data structure linearly. Combined with a custom memory allocator, Hyperion accomplishes a remarkable data density while achieving a competitive point query and an exceptional range query performance. Hyperion can significantly reduce the index memory footprint and its performance-to-memory ratio is more than two times better than the best implemented alternative strategy for randomized string data sets.
Gradually focused fine-grained sketch-based image retrieval

This paper focuses on fine-grained image retrieval based on sketches. Sketches capture detailed information, but their highly abstract nature makes visual comparisons with images more difficult. In spite of the fact that the existing models take into account the fine-grained details, they can not accurately highlight the distinctive local features and ignore the correlation between features. To solve this problem, we design a gradually focused bilinear attention model to extract detailed information more effectively. Specifically, the attention model is to accurately focus on representative local positions, and then use the weighted bilinear coding to find more discriminative feature representations. Finally, the global triplet loss function is used to avoid oversampling or undersampling. The experimental results show that the proposed method outperforms the state-of-the-art sketch-based image retrieval methods.
Generalizations of Aggregation Functions for Face Recognition

The problem of aggregation of the classification results is one of the most important task in image recognition or decision-making theory. There are many approaches to solve this problem as well as many operators and algorithms proposed such as voting, scoring, averages, and more advanced ones. In this paper, we examine the well-known existing and recently introduced to the practice of classification tasks generalizations of aggregation functions. Moreover, we introduce a few operators which have not been presented in the literature of the subject. The findings of this paper can shed a new light onto the theory of face recognition and help build advanced ensembles of classifiers.