Reviewerly/exHarmony huggingface dataset:
What comes next: understanding BIBFRAME

Purpose Bibliographic framework initiative (BIBFRAME) is a data model created by the Library of Congress to with the long-term goal of replacing Machine Readable Cataloging (MARC). The purpose of this paper is to inform catalogers and other library professionals why MARC is lacking in the needs of current users, and how BIBFRAME works better to meet these needs. It will also explain linked data and the principles of Resource Description Framework, so catalogers will have a better understanding of BIBFRAME’s basic goals. Design/methodology/approach The review of recent literature in print and online, as well as using the BIBFRAME editor to create a BIBFRAME record, was the basis for this paper. Findings The paper concludes the user experience with the library catalog has changed and requires more in-depth search capabilities using linked data and that BIBFRAME is a first step in meeting the user needs of the future. Originality/value The paper gives the reader an entry point into the complicated future catalogers and other professionals may feel trepidation about. With a systematic walkthrough of the creation of a BIBFRAME record, the reader should feel more informed where the future of cataloging is going.
Working responsibly across boundaries? Some practical and theoretical lessons

This chapter examines some of the tensions between the ideals and the operationalization of responsible research and innovation (RRI). We are interested in those kinds of integrations that take place as social scientists, humanities and legal scholars seek closer collaborations with scientists, innovators, engineers, industrialists and policy-makers. Our aim is to address some aspects of what actually happens, as collaborations are worked out (or not) across boundaries, including some of the frictions and problems that arise in practice and theory. We propose a concept of epistemic networks to describe and articulate contemporary networked-based problem-solving to innovate and address societal issues. Situated within the broader horizon of an empirical investigation into integration of assessments in digital and smart technology domains, we recount three (practical and theoretical) lessons for RRI. Our lessons stem from three persistent problems: the framing of research questions, interdisciplinarity in practice and the role of law in interdisciplinary collaborations. We argue that such problems, being simultaneously epistemic and normative, should not be seen as mere obstacles to be done away with in order for innovation to run its course. Instead, they are pointers towards critical sites and sources for the deepening of RRI in theory and in practice.
Assessing Cyber Power

To understand how dangerous cyber power could be, we can start by measuring it.
Deep Tree Transductions - A Short Survey

The paper surveys recent extensions of the Long-Short Term Memory networks to handle tree structures from the perspective of learning non-trivial forms of isomorph structured transductions. It provides a discussion of modern TreeLSTM models, showing the effect of the bias induced by the direction of tree processing. An empirical analysis is performed on real-world benchmarks, highlighting how there is no single model adequate to effectively approach all transduction problems.
Query answering over uncertain RDF knowledge bases: explain and obviate unsuccessful query results

Several large uncertain knowledge bases (KBs) are available on the Web where facts are associated with a certainty degree. When querying these uncertain KBs, users seek high-quality results, i.e., results that have a certainty degree greater than a given threshold $$\alpha $$ . However, as they usually have only a partial knowledge of the KB contents, their queries may be failing i.e., they return no result for the desired certainty level. To prevent this frustrating situation, instead of returning an empty set of answers, our approach explains the reasons of the failure with a set of $$\alpha $$ minimal failing subqueries ( $$\alpha $$ MFSs) and computes alternative relaxed queries, called $$\alpha $$ maXimal succeeding subqueries ( $$\alpha $$ XSSs), that are as close as possible to the initial failing query. Moreover, as the user may not always be able to provide an appropriate threshold $$\alpha $$ , we propose three algorithms to compute the $$\alpha $$ MFSs and $$\alpha $$ XSSs for other thresholds, which also constitutes a relevant feedback for the user. Multiple experiments with the WatDiv benchmark show the relevance of our algorithms compared to a baseline method.
Theory and experiment in gravitational physics, 2nd edition

The recent years have seen world-wide celebrations of one hundred years of general relativity as brought forward by Albert Einstein in 1915/16, as well as the discovery of gravitational waves by th...
Real-Time Embedded Computer Vision on UAVs

In this paper we present an overview of the contributed work presented at the UAVision2018 ECCV workshop. This workshop focused on real-time image processing on-board of Unmanned Aerial Vehicles (UAVs). For such applications the computational complexity of state-of-the-art computer vision algorithms often conflicts with the need for real-time operation and the extreme resource limitations of the hardware. Apart from a summary of the accepted workshop papers, this work also aims to identify common challenges and concerns which were addressed by multiple authors during the workshop, and their proposed solutions.
Making high school students aware of optimization through games and puzzles

Optimization is among the oldest mathematical applications in history: human nature tends to obtain the best results with the minimum effort, in many sectors. Nevertheless, optimization and operations research as mathematical disciplines struggle to find their place in high school syllabi and it is usually very hard to explain what they are about to students and common people. This could be due to a lack of “optimization culture” that prevents one to see that many problems that we tackle everyday are actually optimization problems, or a lack of mathematical culture in a broader sense. With this paper we report on a small experience carried out in an Italian high school where we attempted to close this gap. Rather than using a conventional approach, we introduced a set of games, puzzles and challenges based on optimization problems that stimulated students’ intuition and creativity. Indeed the aim of this experiment was not teaching specific optimization methods, but starting the construction of an “optimization awareness” in the students.
SIMULATION MODEL FOR RATIONAL UNIFIED PROCESS (RUP) SOFTWARE DEVELOPMENT LIFE CYCLE

In software development there are several types of methodologies that can be used, one of which is Rational Unified Process (RUP). RUP is of software development process used in various software companies. There are various types of projects in RUP software development, small scale project, large-scale projects and re-engineering projects. Each type of project has different needs, especially in the use of each role. In RUP there are several important roles that cannot be done by an individual because they have to work at the same time. Basically the purpose of the RUP is to be able to produce high quality software. This cannot be achieved if the inappropriate role is used. This study proposes the number of roles needed for software development using RUP by creating simulation models. The purpose of simulation model is to determine the appropriate role based on each type of project. The results of the simulation model are in the form of a percentage of each role needed, the project manager has the largest percentage compared to other roles, which is 70% in small-scale projects and 65% in large-scale projects.
Design and Implementation of a Dual Mode Autonomous Gas Leakage Detecting Robot

Gas leakage can cause catastrophic damage and may stand as a big issue if not addressed properly at an exact time. Now-a-days flammable gas leakage has become a common phenomenon for explosions in industries and factories. It can also occur in some other places like small tunnels, vents, pipelines where human can hardly make access to detect the leakage of gas. Usually detection is possible by human being in two ways either by hearing the sound of leakage or by smelling the gas. But unfortunately none of the approaches are suitable and can be extremely dangerous and life threatening. This paper proposes an alternative way of detecting gas leakage by introducing a small autonomous robot. The robot can operate on both obstacle avoiding mode using ultrasonic sensor and line following mode using infrared sensor and can detect leakage by navigating through small areas. After detection it will send a wireless signal of the intensity to the receiving module which consists of a LCD display to monitor the intensity of gas leakage in real time.
Flexible Access Control and Confidentiality over Encrypted Data for Document-based Database

In this paper, we present a SDDB scheme regarding document-based store that satisfies three security requirements: confidentiality, flexible access control, and querying over encrypted data. The scheme is inspired by PIRATTE and CryptDB concepts. PIRATTE is a proxy for sharing encrypted files through a social network between the data owner and the number of users and the files are decrypted on user side with the proxy key, whereas in CryptDB, it is proxy between a database and one user to encrypt or decrypt data based on user’s queries. The scheme also improves CryptDB security and provides the possibility of sharing data with multi-users through PIRATTE concept which is used to verify authentication on the proxy side.
Telemedicine Across Time: Integrated Health System of the Future—A Prelude

Telemedicine and e-HealthVol. 26, No. 2 PerspectiveTelemedicine Across Time: Integrated Health System of the Future—A PreludeRashid L. Bashshur, Elizabeth A. Krupinski, Charles R. Doarn, Ronald C. Merrell, James O. Woolliscroft, and Julio FrenkRashid L. BashshurAddress correspondence to: Rashid L. Bashshur, PhD, Michigan Medicine, University of Michigan, 1383 Esch Court, Ann Arbor, MI 48104 E-mail Address: bashshur@med.umich.eduMichigan Medicine, University of Michigan, Ann Arbor, Michigan.Search for more papers by this author, Elizabeth A. KrupinskiDepartment of Radiology and Imaging Sciences, Emory University, Atlanta, Georgia.Search for more papers by this author, Charles R. DoarnDepartment of Family and Community Medicine, College of Medicine, University of Cincinnati, Cincinnati, Ohio.Search for more papers by this author, Ronald C. MerrellDepartment of Surgery, Virginia Commonwealth University, Richmond, Virginia.Search for more papers by this author, James O. WoolliscroftMichigan Medicine, University of Michigan, Ann Arbor, Michigan.Search for more papers by this author, and Julio FrenkUniversity of Miami, Miami, Florida.Search for more papers by this authorPublished Online:5 Feb 2020https://doi.org/10.1089/tmj.2019.0025AboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetailsCited byArtificial Intelligence in Health: Enhancing a Return to Patient-Centered Communication Bree Holtz, Victoria Nelson, and Ronald K. Poropatich16 November 2022 | Telemedicine and e-Health, Vol. 0, No. 0Evaluation of Tele-Dentistry and Face-to-Face Appointments during the Provision of Dental Services in Poland3 October 2022 | Journal of Personalized Medicine, Vol. 12, No. 10Advancing Telehealth to Improve Access to Health in Rural America25 November 2020 Volume 26Issue 2Feb 2020 InformationCopyright 2020, Mary Ann Liebert, Inc., publishersTo cite this article:Rashid L. Bashshur, Elizabeth A. Krupinski, Charles R. Doarn, Ronald C. Merrell, James O. Woolliscroft, and Julio Frenk.Telemedicine Across Time: Integrated Health System of the Future—A Prelude.Telemedicine and e-Health.Feb 2020.128-130.http://doi.org/10.1089/tmj.2019.0025Published in Volume: 26 Issue 2: February 5, 2020Online Ahead of Print:March 11, 2019Keywordstelemedicinehealth systemsinnovationtechnologyfuture health carePDF download
Dialectic Synthesis: Application to Human Trafficking

We place the concept of dialectic synthesis in a fuzzy logic setting. Our purpose is to use the triplet thesis–antithesis–synthesis in applications to human trafficking and modern slavery. We are particularly interested in a country’s government’s response to its vulnerability to these situations.
Dynamic turbulence mitigation for long-range imaging in the presence of large moving objects

Long-range imaging with visible or infrared observation systems is typically hampered by atmospheric turbulence. Software-based turbulence mitigation methods aim to stabilize and sharpen such recorded image sequences based on the image data only. Although successful restoration has been achieved on static scenes in the past, a significant challenge remains in accounting for moving objects such that they remain visible as moving objects in the output. Here, we investigate a new approach for turbulence mitigation on background as well as large moving objects under moderate turbulence conditions. In our method, we apply and compare different optical flow algorithms to locally estimate both the apparent and true object motion in image sequences and subsequently apply dynamic super-resolution, image sharpening, and newly developed local stabilization methods to the aligned images. We assess the use of these stabilization methods as well as a new method for occlusion compensation for these conditions. The proposed methods are qualitatively evaluated on several visible light recordings of real-world scenes. We demonstrate that our methods achieve a similar image quality on background elements as our prior methods for static scenes, but at the same time obtain a substantial improvement in image quality and reduction in image artifacts on moving objects. In addition, we show that our stabilization and occlusion compensation methods can be robustly used for turbulence mitigation in imagery featuring complex backgrounds and occlusion effects, without compromising the performance in less challenging conditions.
Disrupting dark web supply chains to protect precious data

In our digital-centric, always-on, cloud-powered world, data is your organisation's biggest asset. Whether it's sensitive customer information, intellectual property (IP) or closely guarded financial and other corporate data, it might already be up for sale on the dark web. Cybercrime and online fraud are now easier than ever thanks to these sophisticated underground markets. Data is your organisation's biggest asset. But whether it's customer information, intellectual property or financial and other corporate data, it might already be up for sale on the dark web.
The Impact of Artificial Intelligence on Rules, Standards, and Judicial Discretion

Artificial intelligence (AI), and machine learning in particular, promises lawmakers greater specificity and fewer errors. Algorithmic lawmaking and judging will leverage models built from large stores of data that permit the creation and application of finely tuned rules. AI is therefore regarded as something that will bring about a movement from standards to rules. Drawing on contemporary data science, this Article shows that machine learning is less impressive when the past is unlike the future, as it is whenever new variables appear over time. In the absence of regularities, machine learning loses its advantage and, as a result, looser standards can become superior to rules. We apply this insight to bail and sentencing decisions, as well as familiar corporate and contract law rules. More generally, we show that a Human-AI combination can be superior to AI acting alone. Just as today’s judges overrule errors and outmoded precedent, tomorrow’s lawmakers will sensibly overrule AI in legal domains where the challenges of measurement are present. When measurement is straightforward and prediction is accurate, rules will prevail. When empirical limitations such as overfit, Simpson’s Paradox, and omitted variables make measurement difficult, AI should be trusted less and law should give way to standards.
Fusion of GPS and Accelerometer Information for Anomalous Trajectories Detection

Nowadays the rise of Internet of Things (IoT) has led the use of a wide variety of sensor types able to collect multiple types of data. In order to manage this heterogeneous data and gather knowledge, it is useful to combine different sources of information. In this paper, a methodology to detect outliers combining several sources of information at similarity level is proposed. First, a similarity measure is defined for each source of information. These measures are combined using clustering results to produce a general similarity matrix. Then, this matrix is used to train a One-class Support Vector classifier in order to detect outliers. Experiments on the cattle domain have been achieved in order to illustrate the viability of the proposal. The geospatial information collected from a GPS system is combined with accelerometer information. The method detects anomalous animal trajectories and calving events in outfield environments.
Comparative study of hybrid fuzzy logic methods for mobile robot navigation in unknown environments

The navigation of non-holonomic mobile robot in unknown environments is one of the most important challenges in robotic. In order to accomplish that task of navigation, many techniques are used like fuzzy logic control, neural networks, etc. In this work, fuzzy logic controller is used and optimised by two soft computer techniques: genetic algorithm, and Particle Swarm Optimization (PSO). These methods are used to adjust the inputs and outputs of fuzzy logic controller in order to improve the mobile robot navigation. In this work, three methods have been presented: manually constructed fuzzy logic controller (M-Fuzzy), fuzzy logic controller optimised by genetic algorithm (GA-Fuzzy), and fuzzy logic controller optimized by PSO (PSO- Fuzzy). Simulation results are presented to compare the performances of these approaches. The results obtained prove that the evolutionary methods give more efficient mobile robot navigation in terms of distance travelled and/ or traveling time.
Using subject face brightness assessment to detect ‘deep fakes’ (Conference Presentation)

A technology to create videos that purport to be authentic recordings of a subject has been developed and, based on its use of deep learning neural networks, is commonly known as ‘deep fakes.’ The videos produced by this technology are visually indistinguishable from real recordings, in at least some cases. These videos can, prospectively, be used to defame celebrities, politicians and business leaders, to make false statements appear authentic and potentially even change the course of national and regional elections. Clearly, techniques are needed to ascertain the veracity of video recordings. Techniques have been proposed which measure blinking and other characteristics of the subject. However, these techniques which rely on minor flaws in the recreation video algorithm may disappear quickly, as developers improve their software. This paper presents work related to a search for a more systematic way to detect artificially constructed videos. Specifically, the work presented herein analyzes the brightness of the subject’s face, comparing algorithmically constructed / reconstructed videos to actual recordings made under similar circumstances. Brightness is compared and averaged across the image. Additionally, pixel-to-adjacent-pixels and regional differences are compared and contrasted. Comparative results for constructed and original recordings are presented. Also discussed are the differences in results from using different individuals as the basis for the subject’s reconstruction, including the subject him or herself. Based on the results presented and the analysis discussed, the paper discusses the pros and cons of using subject face brightness as a technique for detecting fakes, across several application areas.
Model Loss and Distribution Analysis of Regression Problems in Machine Learning

The machine learning regression model is based on the assumption of normal distribution. In this paper, we mainly study the probability distribution of the machine learning model and the effect of the convergence values of different loss functions on the probability distribution model. Based on the idea of robust regression and the assumption of homogeneous variance of the model, we solved the statistical solution of two-dimensional regression problem by using least square method. The maximum likelihood estimation parameters of the probabilistic model are obtained by using the maximum likelihood estimation method. In order to compare the solving parameters of the two methods, the convergence values of L1 loss function and L2 loss function are used for the regression verification. Through the mathematical and statistical rigorous derivation, obtained two important conclusions; First, under the condition that the data satisfies normal distribution and is based on the assumption of homogeneous variance, the probability model conforms to the multivariate gaussian distribution. Secondly, the model satisfying the multi-gaussian distribution has little influence on the parameter estimation under the condition of the large number theorem, that is, the multi-gaussian distribution model has good tolerance to the loss function.
A Dynamic Weight Grasshopper Optimization Algorithm with Random Jumping

Grasshopper optimization algorithm (GOA) is a novel meta-heuristic algorithm for solving single-objective numeric optimization problems. While it has a simple principle and it is easy to implement, grasshopper optimization algorithm performs badly in some aspects. GOA cannot make full utilization of every iteration, and it is not good at getting rid of local optima. To solve these problems and improve the performance of GOA, this paper proposed an improved grasshopper optimization algorithm based on dynamic weight mechanism and random jumping strategy (DJGOA). The dynamic weight mechanism promoted the utilization of the iterations of the algorithm. The random jumping strategy was introduced to help the algorithm jumping out of the local optima. Several experiments relating to 13 benchmark functions and 4 algorithms were conducted to demonstrate the performance of DJGOA. The results of the experiments demonstrated that DJGOA performed better than GOA and the other algorithms.
The vostars project: a new wearable hybrid video and optical see-through augmented reality surgical system for maxillofacial surgery

Background: Augmented Reality (AR) appears to be a significant development in the context of Image-Guided Surgery. This technology allows to merge virtual and real images into one single scene, allowing synchronized observation of medical imaging and surgical field.
Review of studies on target acquisition in virtual reality based on the crossing paradigm

Crossing is a fundamental paradigm for target selection in human-computer interaction systems. This paradigm was first introduced to virtual reality (VR) interactions by Tu et al., who investigated its performance in comparison to pointing, and concluded that crossing is generally no less effective than pointing and has unique advantages. However, owing to the characteristics of VR interactions, there are still many factors to consider when applying crossing to a VR environment. Thus, this review summarizes the main techniques for object selection in VR and crossing-related studies. Then, factors that may affect crossing interactions are analyzed from the perspectives of the input space and visual space. The aim of this study is to provide a reference for future studies on target selection based on the crossing paradigm in virtual reality.