Reviewerly/exHarmony huggingface dataset:
If You Cannot Measure It, You Cannot Manage It: Assessing the Quality of Cybersecurity Risk Disclosure through Textual Imagification

Cybersecurity threats can exert significant adverse effects on a company’s performance, necessitating that stakeholders make an effort to understand the risks related to the companies with which they conduct business. However, since a company’s relative exposure to cybersecurity risks is related to both identified potential cybersecurity risks as well as previous cyberattacks (and efforts to mitigate future attacks), an accurate assessment of the level of a company’s cybersecurity risk can pose a daunting challenge. In this paper, we suggest a methodology to measure a company’s cybersecurity risks by focusing on its disclosed cybersecurity risks, aggregating said risks and ultimately assessing them in a comprehensive manner. Specifically, we use text analytics to examine the cybersecurity risk disclosures of companies. Then, by applying Textual Imagification (TI) and a new approach derived from machine learning techniques, we develop a measurement mechanism for cybersecurity risks for individual companies. By providing measures of cybersecurity risks across companies, we can facilitate the decision-making processes of stakeholders by allowing them to access and compare cybersecurity risks, thereby improving the social welfare of market participants.
User Interface Evaluation Methods for Elderly: A Systematic Review

Virtual reality (VR) solutions are increasing and establishing in society, considering the current use of nonconventional interaction devices in entertainment applications, serious games, and social networks. In this context, the elderly public already knows or is familiar with this type of technology, from rehabilitation processes that stimulate the practice of physical and cognitive activities. VR systems' usability needs to be evaluated to ensure user safety, user satisfaction, and health intervention efficiency for the olders. Evaluation processes also must consider the analysis of specific aspects of each interface, such as immersion and presence for 3D user interfaces. With this in mind, this study presents a systematic review of the literature to identify methods or approaches that have been used to evaluate VR applications for elderly. As a result, 28 papers were selected considering eligibility criteria. We did not find an accepted methodology to evaluate interfaces projected for elderly, once there was not a consensus in tools used from results. Our results highlight the lack of a specific evaluation methodology for the elderly, especially focused on 3D user interfaces. Evaluation tools for this audience may be relevant to suit validate an VR application, considering sensory feedback aspects and relationships between the interaction process and effects of aging.
Blockchain and Cryptocurrency

This chapter focuses on the evolution and interactions between Blockchain and Cryptocurrency from theoretical, technological, and practical aspects. Following a concise look at the Cryptocurrency market and its main players, we introduce the reader to the technicalities underlying Blockchain-ed Cryptocurrency, which enabled its prosperous developments over recent years. In particular, we discuss the intense competitions of Transaction Per Second (TPS) in comparison with traditional means of transactions; the upgrade and developments of Cryptocurrencies that reinforced its distinct products; the diverse functions and medium of exchanges Cryptocurrency brought to the market by those springing up Blockchain-ed DApp platforms; and the advancements of consensus mechanism which fundamentally enabled the booming progression of Blockchain-ed Cryptocurrency.
Engaging in Dialogue about an Agent’s Norms and Behaviors

We present a set of capabilities allowing an agent planning with moral and social norms represented in temporal logic to respond to queries about its norms and behaviors in natural language, and for the human user to add and remove norms directly in natural language.The user may also pose hypothetical modifications to the agent's norms and inquire about their effects.
Visual exploration of rating datasets and user groups

The increasing availability of rating datasets (i.e., datasets containing user evaluations on items such as products and services) constitutes a new opportunity in various applications ranging from behavioral analytics to recommendations. In this paper, we describe the design of VugA, a visual enabler for the exploration of rating data and user groups. VugA helps analysts, be they novice analysts or domain experts, acquire an understanding of their data through a seamless integration between exploring users and exploring their collective behavior via group analysis. VugA is data-driven and does not require analysts to know the value distributions in their data. While automated systems can identify and suggest potentially interesting groups, they can do that for well-specified needs (e.g., through SQL QUERIES or constrained mining). VugA helps analysts filter and refine their exploration as they discover what lies in the data. VugA enables analysts to easily acquire statistics about their data, form groups, and find similar and dissimilar groups. While most visual analytics systems are data-dependent, VugA relies on a data model that captures user data in such a way that a variety of group formation and exploration approaches can be used. We describe the architecture of VugA and illustrate its use via tasks and a user study. We conclude with a discussion on future work enabled by VugA.
An Inductive Bias for Distances: Neural Nets that Respect the Triangle Inequality

Distances are pervasive in machine learning. They serve as similarity measures, loss functions, and learning targets; it is said that a good distance measure solves a task. When defining distances, the triangle inequality has proven to be a useful constraint, both theoretically---to prove convergence and optimality guarantees---and empirically---as an inductive bias. Deep metric learning architectures that respect the triangle inequality rely, almost exclusively, on Euclidean distance in the latent space. Though effective, this fails to model two broad classes of subadditive distances, common in graphs and reinforcement learning: asymmetric metrics, and metrics that cannot be embedded into Euclidean space. To address these problems, we introduce novel architectures that are guaranteed to satisfy the triangle inequality. We prove our architectures universally approximate norm-induced metrics on $\mathbb{R}^n$, and present a similar result for modified Input Convex Neural Networks. We show that our architectures outperform existing metric approaches when modeling graph distances and have a better inductive bias than non-metric approaches when training data is limited in the multi-goal reinforcement learning setting.
Ontology-Based Naive Bayes Short Text Classification Method for a Small Dataset

Content less than two hundred words like comments or review statements is known as a short text. Short text classification is useful for automatically categorizing sentence into predefined group. There are several traditional short text classification methods by using bag-of-words with k nearest neighbors (k-NN), Naïve Bayes, Maximum entropy, support vector machines (SVMs), and an algorithm based on statistics and rules. The deep learning method is outperformed other methods on classification of short text with normal size of dataset. Some researches classify requirements into functional and nonfunctional requirements. There is no research on multi-classification of functional requirements with a small dataset particularly for an accounting field. This paper presents an approach to classify short text for a small dataset into multiple categories of functional requirements on the accounting domain. The proposed approach uses an ontology to construct bag-of-words and uses Naive Bayes to classify for small dataset. The experiment is conducted using four hundred of datasets with 5folds and 10-folds cross-validation. The result shows that the method can correctly classify more than 80%. Additionally, comparisons between the ontology-based Naive Bayes method and other methods are investigated.
Validation of Forecasting Performance of Two-Stage Probabilistic Solar Irradiation and Solar Power Forecasting Algorithm using XGBoost

We propose the novel solar power forecasting algorithm by using the Extreme Gradient Boosting (XGBoost) machine based on the 2-stage forecasting structure. Our algorithm is implemented to solve three problems. First, the solar power is linearly proportional to the solar irradiation on a target solar panel, but it is hard to obtain the target solar irradiation. Therefore, in the first stage, we predict the target solar irradiation by using the XGBoost based on numerical weather prediction, which is measured on a different location but modified for the target location. Second, the forecasting errors on the predicted solar irradiation can be transferred to the second stage when the predicted solar irradiation is used to predict the solar power. We forecast the conditional error distribution of predicted irradiation by collecting forecasting errors, and we sample solar irradiation scenarios, which are converted to the solar power scenarios. Then, the final point forecast of solar power is estimated by calculating the median of scenarios so that we can improve the forecasting accuracy. Third, in this process, the quality of numerical weather prediction deteriorates as the target hour is farther. Therefore, we build forecasting models for each target hour in parallel to minimize the forecasting accuracy deterioration from the quality deterioration. Finally, we verify our proposed algorithm by participating in the solar power forecasting competition hosted by KPX.
A Survey on Video Classification Methods Based on Deep Learning

With the development of multimedia and communication technology, the amount of multimedia data, for example, video data has been rapidly emerging. How to process these data accurately and efficiently has attracted more and more researchers' attention. Video classification is an important part of video data processing. After deep learning method successfully applied in image and audio fields, the study of video classification has gradually shifted to deep learning direction. This paper summarized the video classification methods based on deep learning and analyzed the differences in performance of typical algorithms, meanwhile, summarized the commonly used video classification data sets.
Dealing with Label Scarcity in Computational Pathology: A Use Case in Prostate Cancer Classification

Large amounts of unlabelled data are commonplace for many applications in computational pathology, whereas labelled data is often expensive, both in time and cost, to acquire. We investigate the performance of unsupervised and supervised deep learning methods when few labelled data are available. Three methods are compared: clustering autoencoder latent vectors (unsupervised), a single layer classifier combined with a pre-trained autoencoder (semi-supervised), and a supervised CNN. We apply these methods on hematoxylin and eosin (H&E) stained prostatectomy images to classify tumour versus non-tumour tissue. Results show that semi-/unsupervised methods have an advantage over supervised learning when few labels are available. Additionally, we show that incorporating immunohistochemistry (IHC) stained data provides an increase in performance over only using H&E.
Collaborating Externally and Training Internally to Support Research Data Services

The ASU Library is actively building relationships around and increasing its expertise in research data services. We have established a collaboration with our university’s research administration in order to coordinate our distinct areas of expertise in research data services so that both entities can better support researchers all the way through the research data lifecycle. The Library embedded itself into research administration’s learning management system and works with their research advancement officers to engage with researchers and staff we have not traditionally reached. Forging this new collaboration increased expectations that the Library will expand existing research data services to more investigators, so we have grown Library professionals’ internal competencies by providing research data management training opportunities to meet these demands. In addition, the Library’s Research Services Working Group established data competencies, workflows, and trainings so more librarians gain skills necessary to answer and assist patrons with data needs. Greater expertise throughout the Library enables us to authentically and confidently scale our research data services and form new collaborations. <em>The substance of this article is based upon a lightning talk given at RDAP Summit 2019.</em>
Development of a Peristaltic-Movement Duct-Cleaning Robot for Application to Actual Environment - Examination of Brush Type and Installation Method to Improve Cleaning Efficiency –

This paper describes a method to increase both the cleaning performance and speed of a peristaltic duct-cleaning robot, besides the cleaning of a real house duct. Duct piping ventilation is an important component for safeguarding indoor human health. However, dust accumulates inside such ducts during long-term use of ventilation systems. This dust leads to the generation of bacteria, dispersal of which can cause serious human health problems. Therefore, it is necessary to clean such ducts. The ducts used in factories, for example, have a large cross-sectional area and so are easy to clean by conventional duct-cleaning methods. However, as housing ducts have a small cross-sectional area and many curves, they are difficult to clean via the passive method of inserting a cleaning tool through the duct ports. For this reason, the authors attempted to develop a method of duct cleaning using a robot that imitates the peristaltic movement of earthworms. Herein, the authors examine the type and mounting position of the cleaning brush that produces the optimum cleaning efficiency. From this, we confirmed the duct cleanability of the peristaltic robot.
An Improved Reversible Data Hiding for Block Truncation Coding Compressed Images

Data hiding in the compression domain is a very common method. Nowadays, the images to be transferred are usually compressed first. Therefore, data hiding in the compression domain becomes more and more important. Joint-neighbor coding uses the pixel that wants to be compressed, and its nearby pixels are used to embed the secret information. Absolute Moment Block Truncation Coding (AMBTC) and the joint-neighbor coding are combined to embed the secret information in the compression domain. Compared with Block Truncation Coding (BTC), AMBTC has lower computation and less distortion. The experiment result shows that our proposed method is effective.
On Flying Backwards: Preventing Run-away of Small, Low-speed, Fixed-wing UAVs in Strong Winds

Small, low-speed fixed-wing Unmanned Aerial Vehicles (UAVs) operating autonomously, beyond-visual-line-of-sight (BVLOS) will inevitably encounter winds rising to levels near or exceeding the vehicles' nominal airspeed. In this paper, we develop a nonlinear lateral-directional path following guidance law with explicit consideration of online wind estimates. Energy efficient airspeed reference compensation logic is developed for excess wind scenarios (i.e. when the wind speed rises above the airspeed), enabling either mitigation, prevention, or over-powering of excess wind induced run-away from a given path. The developed guidance law is demonstrated on a representative small, low-speed test UAV in two flight experiments conducted in mountainous regions of Switzerland with strong, turbulent wind conditions, gusts reaching up to 13 meters per second. We demonstrate track-keeping errors of less than 1 meter consistently maintained during a representative duration of gusting, excess winds and a mean ground speed undershoot of 0.5 meters per second from the commanded minimum forward ground speed demonstrated in over 5 minutes of the showcased flight results.
Are We Coming Together? The Archival Descriptive Landscape and the Roles of Archivist and Cataloger

Traditionally, archival description remained distinct from bibliographic description due to differences in material format, usage, and professional traditions. However, archival descriptive standards and practice have undergone numerous changes in recent years. This evolution is in part due to the advent of MARC and its adoption by the academic archives community. How much influence has the use of MARC and overall bibliographic description had on academic archival description as well as on the collaboration between traditional catalogers and archivists? To address this question, this article presents the findings of a landscape survey of the Association of Research Libraries members' descriptive practices surrounding MARC records, linked and embedded metadata, and authority records. Survey responses indicate that archival descriptive work remains concentrated in the archival domain, with archivists creating description as one component of job responsibilities at most institutions. Descriptive work—including MARC record creation—has not been passed off to cataloging colleagues despite their longer professional experience with the standard even though the OPAC is the most commonly cited archival information system available to respondents. Decisions about appropriate levels of description, standards to be employed, workflows, and other factors related to archival description do not appear to rely on external buy-in or approval in most repositories, and descriptive practices employ a mix of standards from both the archival and bibliographic traditions. These and other findings provide a baseline understanding of current archival descriptive practices and workflows, enhancing our ability to improve archival description and therefore findability and access to archival materials.
Design of High Performance Manipulator for Small EOD Robot System

In this paper, we designed and manufactured a new manipulator (less than 15 kg) to make the total weight of SCOBOT-200 (EOD robot: its platform weight is 35 kg) commercialized by FIRSTEC Co., Ltd. Link1 and Link2 of the manipulator were designed and fabricated using CFRP (Carbon Fiber Reinforced Plastics) material, and the other components were made of AL6061 material. The fabricated manipulator has 5-DOF, and the opening width of the gripper is more than 1520 mm. As a result of the characteristic test, the weight of manipulator is 14.5 kg, the length of the manipulator is 1500 mm, the payload when the manipulator extended is 8 kg, when folded is 20 kg. Thus, the manipulator manufactured can be used as a manipulator for a small EOD (Explosive Ordnance Disposal) robot.
A Conceptual Redesign of a Modelling Language for Cyber Resiliency of Healthcare Systems

Security constraints that enforce security requirements characterize healthcare systems. These constraints have a substantial impact on the resiliency of the final system. Security requirements modelling approaches allow the prevention of cyber incidents; however, the focus to date has been on prevention rather than resiliency. Resiliency extends into the detection, mitigation and recovery after security violations. In this paper, we propose an enhanced at a conceptual level that attempts to align cybersecurity with resiliency. It does so by extending the Secure Tropos cybersecurity modelling language to include resiliency. The proposed conceptual model examines resiliency from three viewpoints, namely the security requirements, the healthcare context and its implementational capability. We present an overview of our conceptual model of a cyber resiliency language and discuss a case study to attest the healthcare context in our approach.
BIBLIOMETRICS AS A ROBUST ANALYTICAL TOOL FOR RESEARCH IN HIGHER EDUCATION

Appears in: ICERI2019 Proceedings Publication year: 2019Pages: 1108-1118ISBN: 978-84-09-14755-7ISSN: 2340-1095doi: 10.21125/iceri.2019.0339Conference name: 12th annual International Conference of Education, Research and InnovationDates: 11-13 November, 2019Location: Seville, Spain
Eye Monitored Wheelchair Control

Quadriplegia is a paralysis cause by illness or injury that result in the partial or total loss of use of four limbs and torso. Researchers suggested that in United State of America, there are 11000 new cases of Quadriplegia occur every year. The great scientist like Stephen Hawking has been suffering from such kind of disease. The aim of our work is to make lives of such people suffering from this disease easy, simpler and self-reliant. The idea is to create an eye monitored wheelchair control which allows the movement of the wheelchair depending on the patient's eye movements. We know that a person having Quadriplegia can partially move his eyes and tilt his head. So we want to design such kind of device where a patient sitting on the wheelchair and his/her eyes continuously monitored by camera fitted on a stand in front of patient. When the patient want to move some where he just look in that direction. The camera is capturing the signal across that direction and sending it towards the controller which is place on wheelchair. The controller process the script on OpenCV and instruct the motors to move in that particular direction.
Predictive Proof of Metrics – a New Blockchain Consensus Protocol

We present a new consensus protocol for Blockchain ecosystems - PPoM - Predictive Proof of Metrics. First, we describe the motivation for PPoM - why we need it. Then, we outline its architecture, components, and operation. As part of this, we detail our reputation and reward based approach to bring about consensus in the Blockchain. We also address security and scalability for a PPoM based Blockchain, and discuss potential improvements for future work. Finally, we present measurements for our short term Provider Prediction engine.
Research on the text detection and recognition in natural scene images

This paper aims to study the problem of text recognition in complex natural scenes. Now the rapid development of technology makes most of the industry need fast and efficient text recognition tools, such as the logistics industry and publishers. Although traditional OCR technology can solve most simple background text recognition problems, the performance of complex scenes is somewhat unsatisfactory. In this paper, the focus is to extract the text in complex scenes accurately. The algorithm of stroke width transform and Convolution Neural Network is proposed. Using the stroke width transform algorithm for text positioning; And then separate the text line, end to the classifier to identify, through the recognition of the correct rate determine the effectiveness of the algorithm. We evaluate the proposed approach on a widely used dataset. Results show that our method achieves desired result compared with the state-of-the-art methods of the participant team which clearly demonstrate its competency.