Reviewerly/exHarmony huggingface dataset:
Attitudes towards Social Robots in Education

While social robots bring new opportunities for education, they also come with moral challenges. Therefore, there is a need for moral guidelines for the responsible implementation of these robots. When developing such guidelines, it is important to include different stakeholder perspectives. Existing (qualitative) studies regarding these perspectives however mainly focus on single stakeholders. In this exploratory study, we examine and compare the attitudes of multiple stakeholders on the use of social robots in primary education, using a novel questionnaire that covers various aspects of moral issues mentioned in earlier studies. Furthermore, we also group the stakeholders based on similarities in attitudes and examine which socio-demographic characteristics influence these attitude types. Based on the results, we identify five distinct attitude profiles and show that the probability of belonging to a specific profile is affected by such characteristics as stakeholder type, age, education and income. Our results also indicate that social robots have the potential to be implemented in education in a morally responsible way that takes into account the attitudes of various stakeholders, although there are multiple moral issues that need to be addressed first. Finally, we present seven (practical) implications for a responsible application of social robots in education following from our results. These implications provide valuable insights into how social robots should be implemented
Library guides / Biblioteekgidse: Makerspace: Arduino

Provide a collaborative technologically focused environment for SU students and staff to invent, create and learn while sharing.
Big Tech doesn't censor conservatives and other myths

We've moved from 'conservatives should build their own platforms' to 'conservatives don't deserve their own platforms'.
빅데이터 현황 및 활용 방안 연구

This study examined the current status of big data and the application of big data technology in the future society. Firstly we analyzed the technology that supports data explosion, the settlement of social media and smartphones, and the spread of M2M(Machine to Machine) and sensor data as the appearant background of big data. In addition by investigating big data-related policies, we found that the level of public data openness in Korea is limited and identified the factors that do not form a virtuous cycle.In general big data processing is to collect and process large amounts of data to extract desired information, and analyze it. While examining the technical environment and processing techniques in each process, the representative analytical and expression techniques were discussed. The most basic analytic infrastructure is Apache Hadoop, and NoSQL(Not only SQL) technology is applied to process data flexibly and faster. Additionaly the future directions were discussed by analyzing the prediction of the big data market and the application plan of big data technology in the enterprise.In this study domestic policies, market situations, and utilization plans, focusing on the technology status of big data were generally examined. And based on the contents discussed in this paper, further study is needed to analyze the case study of each field to evaluate and verify the utility of big data technology in each field.
Online Learning of Graph Neural Networks: When Can Data Be Permanently Deleted

Online learning of graph neural networks (GNNs) faces the challenges of distribution shift and ever growing and changing training data, when temporal graphs evolve over time. This makes it inefficient to train over the complete graph whenever new data arrives. Deleting old data at some point in time may be preferable to maintain a good performance and to account for distribution shift. We systematically analyze these issues by incrementally training and evaluating GNNs in a sliding window over temporal graphs. We experiment with three representative GNN architectures and two scalable GNN techniques, on three new datasets. In our experiments, the GNNs face the challenge that new vertices, edges, and even classes appear and disappear over time. Our results show that no more than 50% of the GNN's receptive field is necessary to retain at least 95% accuracy compared to training over a full graph. In most cases, i.e., 14 out 18 experiments, we even observe that a temporal window of size 1 is sufficient to retain at least 90%.
Statistical Method of Space Distribution for Riding Based on R-Tree Under Differential Privacy

This paper outlines a proposal ,which was provided regional riding statistical data query, to address the high vacancy rate in taxis and the low matching rate between taxis and passengers. It proposed a statistical method of space distribution for riding based on R-tree under differential privacy (DBRTree-opt) combined with the information of reference dataset. First, several minimum bounding rectangles (MBRs) were generated through specific spatial distribution rules relating to regional statistic reference dataset as the basic unit of the R-tree index structure. Second, a spatial index structure of optimized R-tree was also designed to solve the problem of overlapping within the R-tree structure. Then, the optimized R-tree for information generation of reference data was applied to unpublished riding space data. Finally, combined with the publishing technology of differential privacy tree structure and consistency processing, the algorithm provided a statistical query related to riding region while ensuring privacy protection standards. The new approach was compared with traditional algorithms in assessing the accuracy of range counting queries in the release data. Experimental results showed that DBRTree-opt is more effective and feasible.
Preface to the Special Issue on Dependable Software Engineering: Theories, Tools and Applications (SETTA 2017)

Runtime verification (RV) is an approach to verification consisting in dynamically checking that the event traces generated by single runs of a system under scrutiny (SUS) are compliant with the formal specification of its expected correct behavior.RML (Runtime Monitoring Language) is a simple but powerful Domain Specific Language (DSL) for RV which is able to express non context-free properties. When designing RML, particular care has been taken to favor abstraction and simplicity, to better support reusability and portability of specifications and interoperability of the monitors generated from them; this is mainly achieved by decoupling the two problems of property specification and event generation, and by minimizing the available primitive constructs.The formalization and implementation of RML is based on a trace calculus with a fully deterministic rewriting semantics. The semantics of RML is defined by translation into such a calculus, which, in fact, is used as intermediate representation (IR) by the RML compiler.The effectiveness of RML and its methodological impact on RV are presented through interesting patterns that can be adapted to different contexts requiring verification of standard properties. A collection of tested examples is provided, together with benchmarks showing that the deterministic semantics and the performed dynamic optimizations based on the laws of the trace calculus significantly improve the performances of the generated monitors.
Genetic Algorithms to Generate Data for a Social Learning Recommendation Approach

The genetic algorithm is regarded as one of the foremost evolutionary algorithms, especially in optimization problems. In addition to optimization problems, genetic algorithms can contribute to solving other problems. In the context of social learning, particularly recommendation systems, researchers are usually faced with a problem of lack of data when testing their approaches. This is where the genetic algorithm can intervene to generate additional simulation data from an already existing database. In this article, we will outline how we can apply the genetic algorithm to address the lack of data, particularly in our context of recommendation systems within social learning.
Finding the problem : Improvements to increase efficiency and usability when troubleshooting

In a time where competition for software services is big and the time to market crucial, speed and productivity is the key to competing with other organizations. One of the competitors is Spotify, ...
Opportunistic Learning: Algorithms and Methods for Cost-Sensitive and Context-Aware Learning

Author(s): Kachuee, Mohammad | Advisor(s): Sarrafzadeh, Majid | Abstract: Classical approaches to machine learning sought to improve the efficiency and accuracy of prediction but often failed to account for the costs associated with the collection of data and expert labels. This shortcoming is particularly limiting in the smart health setting, where accurate classification often requires an invasive level of information querying. Furthermore, in domains such as medical diagnosis, appropriate data should be collected based on a scientific hypothesis, and ground-truth labels may only be provided by highly trained domain experts. Additionally, in many studies, informative features are not scientifically predetermined, and usually, there are many information sources that can be considered as hypothetical relevant features that including all of them is not practical.In order to address these issues, we suggest novel end-to-end solutions considering different aspects of a real-world learning system. Specifically, we consider feature acquisition, labeling, model training, and prediction at test-time as different aspects of a system that tries to achieve the goal of making accurate predictions efficiently. In this paradigm, information is acquired incrementally based on the value it provides and the cost that should be paid for acquiring it. In this thesis, we explore dynamic and context-aware information acquisition techniques to collect the right piece of information at the right time. Additionally, as inference using incomplete data is an inevitable part of such methods, we propose a novel approach to not only impute missing values but also to capture prediction uncertainties.
Neural Network Virtual Sensors for Fuel Injection Quantities with Provable Performance Specifications.

Recent work has shown that it is possible to learn neural networks with provable guarantees on the output of the model when subject to input perturbations, however these works have focused primarily on defending against adversarial examples for image classifiers. In this paper, we study how these provable guarantees can be naturally applied to other real world settings, namely getting performance specifications for robust virtual sensors measuring fuel injection quantities within an engine. We first demonstrate that, in this setting, even simple neural network models are highly susceptible to reasonable levels of adversarial sensor noise, which are capable of increasing the mean relative error of a standard neural network from 6.6% to 43.8%. We then leverage methods for learning provably robust networks and verifying robustness properties, resulting in a robust model which we can provably guarantee has at most 16.5% mean relative error under any sensor noise. Additionally, we show how specific intervals of fuel injection quantities can be targeted to maximize robustness for certain ranges, allowing us to train a virtual sensor for fuel injection which is provably guaranteed to have at most 10.69% relative error under noise while maintaining 3% relative error on non-adversarial data within normalized fuel injection ranges of 0.6 to 1.0.
Hybrid Quantum-Classical Stochastic Networks with Boltzmann Layers

Quantum Machine Learning (QML) has the potential to significantly advance the state-of-the-art in artificial intelligence, due to recent developments in quantum computing hardware and algorithm design. Particularly, an avenue opened up by these advances is the possibility of enhancing classical models through developing quantum analogues, which have greater representational power at no extra cost in terms of training and inference. Here, we investigate analogues of classical networks with stochastic layers, by introducing a class of hybrid stochastic networks that combine layers of several types, including stochastic quantum and classical layers and deterministic classical layers. Further, we introduce Quantum-Annealing (QA)-based sampling techniques that allow such models to be efficiently learned on current QA architectures, using variational and importance-sampling based approaches. Our framework provides benefits in training existing models, including Quantum Boltzmann Machines (QBMs) and Quantum Variational Autoencoders, by allowing local transverse field weights to be optimized jointly with other model parameters, and allows novel hierarchical hybrid models to be learned efficiently. We use classical simulations on synthetic and genomics data to test the impact of including quantum mechanical transverse field terms in such models relative to their classical counterparts. We show that hybrid models are able to achieve better predictive accuracy compared to classical models of matching architecture in these settings, and provide evidence that the local transverse terms can be interpreted as introducing tunable higher-order interactions by connecting genes belonging to common biological pathways.
Bitcoin Price Forecast Using LSTM and GRU Recurrent networks, and Hidden Markov Model

Author(s): Xu, Yike | Advisor(s): Wu, Yingnian | Abstract: Bitcoin, the first decentralized cryptocurrency, has become popular not only because a growing size of merchants accepts it in transactions, but also because people buy it as an investment. This study focuses on the Bitcoin price forecast using Hidden Markov Model and two machine learning methods, LSTM and GRU Recurrent networks. Evaluated by MAPE and RMSE, the results indicate that the Hidden Markov Model with the Gaussian Mixture Models has the best performance among all methods. The GRU model outperforms the LSTM model, though sometimes it might have a more extreme result. When the price remains constant or changes steadily, the predictions are more precise than the fluctuation period.
The Analysis of Knowledge Management Process on Thesis/Dissertation Management: A Systematic Review

Knowledge management is the process of creating, sharing and storing knowledge to be managed as a resource, which can increase values or encourage innovation. This systematic review aims to analyze the state of the art within a literature subject, which is knowledge management process in the management of thesis/dissertation. The systematic review uses the PRISMA (Preferred Reporting Item for Systematic Review and Meta-Analysis) guidelines. This review encompasses 24 literature from various publishers and knowledge management processes in the management of thesis/dissertation. The result reveals that there is a knowledge management process existing in the management of thesis/dissertation despite a little coverage of the three processes in the literature. This systematic review concludes that the management of thesis/dissertation can be identified using the knowledge management concept.
MENINGKATKAN SISTEM KEARSIPAN ELEKTRONIK SEKOLAH DENGAN BUDAYA MANUNGGU DURIAN

The background of this paper is an important document records management schools that are not organized and are not secured properly so that when the data is needed is hard to find . The cause is still conventional storage system making it less effective and less safe . Tupoksi, appropriate school superintendent, the author of innovating is to build an electronic records management system of schools using IT. The approach used is culture manunggu durian. In this approach , there are several steps that must be implemented . In addition , there are also the values that are beneficial in fostering a school management that the value of tenacity / kegihan, patience and discipline. Keywords: Managerial, mananjemen, manunggu durian culture and manunggu durian values
R-MONet: Region-Based Unsupervised Scene Decomposition and Representation via Consistency of Object Representations

Decomposing a complex scene into multiple objects is a natural instinct of an intelligent vision system. Recently, the interest in unsupervised scene representation learning emerged and many previous works tackle this by decomposing scenes into object representations either in the form of segmentation masks or position and scale latent variables (i.e. bounding boxes). We observe that these two types of representation both contain object geometric information and should be consistent with each other. Inspired by this observation, we provide an unsupervised generative framework called R-MONet that can generate objects geometric representation in the form of bounding boxes and segmentation masks simultaneously. While bounding boxes can represent the region of interest (ROI) for generating foreground segmentation masks, the foreground segmentation masks can also be used to supervise bounding boxes learning with the Multi-Otsu Thresholding method. Through the experiments on CLEVR and Multi-dSprites datasets, we show that ensuring the consistency of two types of representation can help the model to decompose the scene and learn better object geometric representations.
Link Buster Pack: Best Link Building Package with Niche Edits and Guest Posts

Looking to boost your website? Give your website an edge with a mix of powerful aged niche edit backlinks with high domain DA authority and uniquely written relevant referral content via guest posts.
LibGuides: Federal Register Library: Related HeinOnline Blog Posts

The Federal Register is the official journal of the federal government of the United States. The database is updated daily and its coverage is comprehensive, beginning from inception in 1936. Posts using Women and the Law on the HeinOnline Blog.
DARKNET DEVELOPMENT IN RUSSIA

At present, Russia has been exposed to such an Internet problem as DarkNet or shadow Internet. The danger is that it is a large platform for the underworld: drugs, false documents and much more. The Russian government has set itself the goal of changing legislation to reduce crime over the Internet.
A Reward-based Algorithm for Hyperparameter Optimization of Neural Networks

Machine learning and its wide range of applications is becoming increasingly prevalent in both academia and industry. This thesis will focus on the two machine learning methods convolutional neural ...
System of Standards on information, librarianship and publishing: Current state and prospects

The current state of the system of standards on sci-tech information, librarianship and publishing (SIBID) is reviewed. The landmarks of the national standardization system and SIBID are named. The role of the Technical Committee for Standardization (TC) 191 «Sci-tech Information, Librarianship and Publishing» in pursuing national and international standardization, TC standards collection updating, is emphasized. TC’s 191 key tasks and prospective vectors to maintain and develop SIBID are discussed.
Press publishers’ right and artificial intelligence

This Chapter examines the application of the press publishers’ related right to AI-based news production. It analyses the conditions under which AI-generated news content can be protected by the related right and the risk that infringement of the related right poses to AI-based news production. The Chapter concludes that there is no fundamental obstacle for protecting AI-generated news content but certain business models and extensive automatization of editorial activities may fail to meet certain key conditions of protection. Avoiding infringement of others’ related right may be challenging due to the low threshold for infringement and ubiquity of press publications but approaches for infringement assessment and application of copyright exceptions are identified that alleviate infringement risks in situations where the objectives of the right are not threatened. Overall, human oversight and involvement is required, as in many situations conditions of protection and copyright exceptions are difficult to meet with AI alone.
Online Instructional Modalities and Pragmatic Business Education: Evidence from transition to eLearning during COVID-19 Crisis

As the pedagogical landscape evolves in response to the emerging challenges posed by stringency and other social distancing regulations, remote learning appears to have successfully evolved, complemented by a considerable boost in pragmatism. The development and implementation of an alternative education strategy during the pandemic is inevitable to allow for the continuity of instruction and therefore, the sustainability of education outcomes. Amid the transition to non-conventional modes of educational platforms and pedagogical approaches, rather forced by the pandemic, this research aims to explore the potential post-crisis developments in the education systems with the relevant antecedents, processes, and outcomes.The findings of this research are developed from the survey conducted in the universities in Georgia. Results are based on both the qualitative as well as the quantitative analysis of the reflection/perception of instructors in the business disciplines to the pragmatic approach to teaching, to the success/failure, or administrative eagerness and preparedness to respond to the crisis, and the evaluation of the student outcomes. The results suggest that the transition to online education in response to COVID-19 has sufficiently contributed toward the goal of the pragmatic approach in business education.
Quality and Equity: The Effect of Private Sector Financing in Higher Education

Higher education is heavily dependent on sufficient funding in order to realize quality and equity. Access to higher education is undoubtedly an avenue to tapping into opportunities present in any economy and it is only morally and ethically right to provide inclusive and equitable access. Funding of higher education institutions has traditionally relied extensively on the public sector for several decades. However, over the recent past and with greater expansion of institutions of higher learning, an emerging trend of private sector funding to these institutions is rapidly increasing. The public sector still remains the dominant financier of higher education with the private sector coming in at a distance. The main challenge of many economies is the structured participation of the private sector in augmenting the role of the public sector in funding higher education. This would ensure equitable and quality learning which would promote sustainable development. This study reviewed the emerging trends of the private sector in funding higher education around the globe and its impact on creating equitable learning opportunities and enhancing quality in higher education. This study employed a descriptive desktop research approach. The study relied on secondary data available on the internet from international agencies and other sources. The private sector funding was reviewed on its effects on inclusivity and equity as per global standards on access to higher education. The study found that though there is a considerable contribution (over 30%) by the private sector in funding higher education, there is no evidence to support that this private funding in higher education contributes to better quality or improves equity in higher education.
Research Guides: Marijuana: Data | Statistics

Developed by Rutgers librarians with expertise in a variety of disciplines, this guide aims to help find resources on marijuana-related issues from Rutgers Libraries and beyond.
LibGuides: How to Research for a Debate Using Library Resources: Researching Pro and Con

Recommended resources for researching a debate topic A structured set of electronic data, containing data searchable by the subject, title and keyword. This is where you need to go to find arguments evidence , examples, statistics and reports.