Reviewerly/exHarmony huggingface dataset:
Human Rights and Technology: New Challenges for Justice and Accountability

This review surveys contemporary challenges in the field of technology and human rights. The increased use of artificial intelligence (AI) in decision making in the public and private sectors—e.g., in criminal justice, employment, public service, and financial contexts—poses significant threats to human rights. AI obscures and attenuates responsibility for harms in ways that undermine traditional mechanisms for holding wrongdoers accountable. Further, technologies that scholars and practitioners once thought would democratize human rights fact finding have been weaponized by state and non-state actors. They are now used to surveil and track citizens and spread disinformation that undermines public trust in knowledge. Addressing these challenges requires efforts to ensure that the development and implementation of new technologies respects and promotes human rights. Traditional distinctions between public and private must be updated to remain relevant in the face of deeply enmeshed state and corporate action in connection with technological innovation.
Research Guides: Fact Checking Web Sites: Home

A Guide to Online Sites that Provide Information to Fact Check Claims, Sources, and Stories
Research Guides: Data Visualization: Geographic Information

This guide will walk you through what data visualization to use based on your research need and data type
Mind the Box: $l_1$-APGD for Sparse Adversarial Attacks on Image Classifiers

We show that when taking into account also the image domain $[0,1]^d$, established $l_1$-projected gradient descent (PGD) attacks are suboptimal as they do not consider that the effective threat model is the intersection of the $l_1$-ball and $[0,1]^d$. We study the expected sparsity of the steepest descent step for this effective threat model and show that the exact projection onto this set is computationally feasible and yields better performance. Moreover, we propose an adaptive form of PGD which is highly effective even with a small budget of iterations. Our resulting $l_1$-APGD is a strong white box attack showing that prior work overestimated their $l_1$-robustness. Using $l_1$-APGD for adversarial training we get a robust classifier with SOTA $l_1$-robustness. Finally, we combine $l_1$-APGD and an adaptation of the Square Attack to $l_1$ into $l_1$-AutoAttack, an ensemble of attacks which reliably assesses adversarial robustness for the threat model of $l_1$-ball intersected with $[0,1]^d$.
LibGuides: COM 101 - Public Speaking: Citing Your Sources

Instruction in preparation and delivery of speeches to an audience. The student is graded on the choice and phrasing of thesubject, the selection and arrangement of the material, the adaptation to the audience and the delivery. Usually each studentgiv
Impact of Risk Determinants on the Perceived Performance of Software Projects in Emerging Economies

Effective control of risk factors ensures the performance of projects in any industry. Risk factors can influence software projects of any size and type. This research aims to identify and examine different risk factors associated with projects in the software industry. The relationship between Software Project Risks (SPRs) and Perceived Project Performance (PPP) measures is determined. This study is based on a survey approach, and a questionnaire is used to record opinions and responses from 199 software professionals working in the Pakistan software industry. The results showed that the association between SPRs and PPP measures is statistically significant, and there exist a positive relationship. It is concluded that an increase in understanding of SPRs can increase PPP measures used to evaluate the software project. The results will help researchers and professionals in understanding the impact of different risk factors on software projects' perceived performance.
The Relation between Augmented Reality and its Spatial Context Conceptualising the defining features of AR

Key decision-makers in Big Tech have raised the notion that Augmented Reality (AR), atechnology that enables the overlay of virtual elements onto the physical world, will emerge asan integral part for the way we interact with digital information in the nearby future. A growingbody of AR research has examined in what way AR’s technological particularities haveimplications for several areas of application, such as industrial, marketing, entertainment andcultural use-cases. What did not get as much attention was AR’s second defining feature that AR,by widely adopted definitions, is spatially embedded in three-dimensional space. Therefore, thepurpose of the present research is to address the complex relation between AR and the space inwhich it is deployed. To approach the research topic an exploratory qualitative research designwas applied, which gathered qualitative data through semi-structured, in-depth expert interviewswith a heterogenous group of AR practitioners and conducted a thematic content analysis toexamine the data and derive relevant results. In the course of the study AR’s mediacharacteristics and the role of space for AR are conceptualised in order to examine the spatialrelation. It was found that a purposeful application of AR profits from the interdependency withits immediate spatial environment and improves the user experience. Further, found implicationsof the relations between AR and space are that certain AR media characteristics, such asvisuality and interaction, drive AR’s ability to mediate a sense of place and even allow for placemaking, which describes the social construction of place. The study concludes with the proposalto adapt the definition of AR in a way that stronger accounts for the importance of a purposefulspatial interdependency between AR and the physical location of its deployment.
Crime Detection In Videos And Alerting System Using Artificial Intelligence

During the last few decades, surveillance cameras have been installed in different locations just to watch manually. Object detection and analysis of the information captured using these cameras can play effective roles in event prediction, monitoring video feeds and goal-driven analysis applications including anomalies or intrusion detection. Large number of Artificial Intelligence techniques has been used to detect anomalies; amongst them Convolutional Neural Networks (CNN) using deep learning techniques improved the detection accuracy significantly. The goal of this article is to propose a new method based on deep learning techniques for Crime detection in video surveillance cameras. The proposed method has been evaluated in the UCSD dataset, and showed an increase in the accuracy of the Crime detection
An Improved Secure Enabled Information Sharing on Hybrid Cloud

Distributed computing guarantees the adaptable conveyance of figuring administrations in a pay-more only as costs arise way. It permits clients to effectively scale their foundation and save money on the general expense of activity. Anyway Cloud administration contributions can possibly flourish if clients are happy with administration execution. Permitting immediate access and adaptable scaling while at the same time keeping up the help leaves and offering serious costs represents a critical test to Cloud registering suppliers. Moreover administrations will stay accessible over the long haul just if this business creates a steady income stream. To address these difficulties we present novel approach based assistance confirmation control models that target expanding the income of Cloud suppliers while considering educational vulnerability with respect to asset prerequisites. Our assessment shows that arrangement based methodologies measurably altogether outflank the early bird gets the worm draws near, which are still cutting edge. Moreover the outcomes give bits of knowledge in how and how much vulnerability contrarily affects income.
Student-led Development of Online Support Material (Pre-lab Technical Films and Moodle Quizzes) to Facilitate Transition into Year 1 Chemistry

My perceptions, as a student transitioning from school to undergraduate Chemistry, were that there was an increase in: expected background knowledge, complexity of lab environment, and extent of required independent thought. This cognitive overload was particularly stark during practical sessions. Feedback from my peers confirmed that I was not alone in feeling underprepared, and overwhelmed, by the amount of learning[i]. Evidence shows that many students encounter cognitive overload when beginning a degree course at university, irrespective of the subject area [ii].Pre-lab resources (in the form of online simulations) have been available to Year 1 students for some time, and results show that this approach improves student confidence and proficiency[iii]. However, simulations do not demonstrate the specific equipment that our students will encounter. Therefore, in an attempt to complement existing resources, we began a project to co-create tailored online support material. This involved designing and producing short pre-lab films, demonstrating key techniques encountered in our Year 1 labs. Videos have been made accessible using subtitling and text pop-ups [iv]. These attempt to highlight safety information and practical advice, as well as assist students to familiarise themselves with the labs that they will actually use in Year 1. In order to further build on the visual learning associated with videos, accompanying Moodle quizzes were also developed, to promote deeper learning[v]. We will measure the impact of the resources on student perceptions of their ability and preparedness. This project demonstrates innovative use of technology to build inclusivity, and co-creation of curriculum by students.[i] S De Meo, J. Chem. Educ. 2001, 78, 3, 373.[ii] B Eddaif, IOSR-JRME 2017, 7, 2, 33-37.[iii] RAR Blackburn, J. Chem. Educ. 2018, 96, 1, 153.[iv] A Ardisara, J. Chem. Educ. 2018, 95, 10, 1881.[v] DF Jolley, J. Chem. Educ. 2016, 93, 1855-1862.
Prevalencia, factores asociados y diferencias de género en el cumplimiento de la propiedad intelectual: Una revisión sistemática

espanolEl interes por el desarrollo de modelos efectivos para la proteccion de la propiedad intelectual esta trasladandose en la actualidad desde los tradicionales enfoques eminentemente normativos a las propuestas preventivas informadas desde las evidencias empiricas. Por ello, en el marco del proyecto de investigacion InfractXr, el presente estudio desarrolla una revision sistematica de la literatura cientifica de la ultima decada con el triple proposito de (1) detectar aquellas conductas de vulneracion de la propiedad intelectual mas estudiadas desde una perspectiva empirica, (2) establecer los factores mas relevantes en la explicacion del cumplimiento de la propiedad intelectual y (3) analizar diferencias de genero tanto en el cumplimiento normativo de la propiedad intelectual como en factores asociados. Con una muestra formada por 13 estudios, los resultados nos indican, en primer lugar, que la pirateria es la conducta mas estudiada desde una perspectiva empirica; en segundo lugar, que la teoria del aprendizaje social, las tecnicas de neutralizacion, la teoria del autocontrol, la influencia de la ley Hadopi, el modelo de la disuasion, el modelo de la legitimidad o la teoria de la influencia social ofrecen marcos explicativos prometedores del cumplimiento normativo y, finalmente, que las mujeres muestran menores niveles de incumplimiento que los hombres. EnglishThe interest in the development of effective models for the protection of intellectual property is currently shifting from the traditional eminently normative approaches to preventive proposals informed by empirical evidence. Therefore, within the framework of the InfractXr research project, the present study develops a systematic review of the scientific literature of the last decade with the triple purpose of (1) detecting the most studied behaviors of intellectual property infringement from an empirical perspective, (2) establishing the most relevant factors in the explanation of intellectual property compliance and (3) analysing gender differences both in the regulatory compliance of intellectual property and in associated factors. With a sample of 13 studies, the results indicate, firstly, that piracy is the most studied behavior from an empirical perspective; secondly, that the theory of social learning, the techniques of neutralization, the theory of self-control, the influence of the Hadopi law, the model of deterrence, the model of legitimacy or the theory of social influence offer promising explanatory frameworks for regulatory compliance and, finally, that women show lower levels of non-compliance than men.
LibGuides: Next Generation Integrated Library System: Alma and Primo VE: FAQs

This guide includes information about the new next-generation Integrated Library System that is now live!
Canny Edge Detection for Goldfish (Carrasius auratus) Identification

Goldfish (Carrasius auratus) is one of freshwater ornamental fishesthat has interested by community. The uniqueness of goldfish isbody shape, scale, and color. goldfish have similar morphology somany goldfish enthusiasts and many goldfish fans and marine andfisheries biologists find it difficult to identify goldfish species.Fantail, Oranda, dan Ranchu are three of 130 strains of goldfish thatare difficult to identify. Canny Edge Detection is a feature extractionmethod that detects the edges of the goldfish image object. Thisresearch using 225 images of goldfish with 180 images for trainingdata and 45 images for testing data. Distribution of datasets used 5-fold cross validation. Clasification of goldfish image using PNNwith the great greatest accuracy is obtained at the smoothing value(σ) = 1.5 reaching 99.11%.
Design and implementation of a conversational agent to stimulate teacher-students interactions in synchronous on-line lecture environments

Recently, many universities and schools have switched to online teaching due to the COVID-19 pandemic. Distance learning could limit social interaction between teachers and peer-learners. As this may demotivate learners in the long term, better social engagement providing solutions such as Virtual Reality (VR) can be used for teaching and learning. In the field of technology-enhanced learning, research has indicated that using conversational agents to engage learners in one-to-one (student-agent) tutorial dialogues can improve students' comprehension and foster students' engagement and motivation. Such agents try to simulate the behavior of a human instructor or tutor and engage in a discussion with a learner on a series of predefined topics. Research in the field of Computer-Supported Collaborative Learning has revealed that unsolicited conversational agent interventions can intensify the knowledge exchange among learning partners and increase students' explicit reasoning and participation levels. In this thesis, it was designed a virtual conversational agent (VCA) intended to act as a classmate to stimulate teacher-student interactions in an on-line learning environment during a synchronous lecture. The VCA was experimented in a controlled lecturing scenario. It was implemented to be plugged into Mozilla Hubs, which provides the virtual classroom in which a real teacher teaches to a real student and his/her virtual and active classmate (a Solar System lecture was used). The VCA exploits DialogFlow to interpret the meaning of conversations and interacts appropriately to increase the level of interaction and engagement during the lesson. Experiments were designed to evaluate the potential of conversational agents and co-learning with a virtual classmate (agent) to increase engagement, commitment and enjoyment of learning in VR.
Construction of Motivation Analysis Model for College Students’ Online Information Query

This paper proposes a modeling method of college students’ online information query motivation based on statistical feature information fusion. The statistical sequence model of university students’ online information query motivation distribution big data is constructed, and the big data prediction of university students’ online information query motivation is carried out by using the method of restructuring the regional structure of big data space. The association rule distribution feature set of university students’ online information query motivation distribution big data is established, and the fuzzy correlation constraint control method is combined to predict university students’ online information query motivation. The decision tree model is used for big data test and template matching of college students’ online information query motivation in complex network environment, and the statistical sequence model of college students’ online information query motivation distribution big data is constructed. Predicate logic reasoning is used to realize the optimization prediction of the development direction of college students’ online information query motivation, and the subsection test method is used to control the optimization of data clustering center to realize the analysis model of college students’ online information query motivation. The simulation results show that this method has higher accuracy and better reliability in analyzing the motivation of college students’ online information query.
Diagnosis and Treatment of Unbalance about Power Plant Rotating Auxiliary Equipment

This paper introduces the characteristics of unbalanced fault and the handling method of rigid rotor unbalanced fault, and expounds the handling process of large fan unbalanced fault through two balancing cases, which can be used for reference to the fault analysis and handling of the unbalanced fault of power plant rotating auxiliary equipment.
Cyber attacks and defenses: current capabilities and future trends

In the new cyber landscape, the legal rules apply only to defenders. Even nonprimary countries and companies may constitute a harmful adversarial scenario for politics, military, intelligence, and enterprises. Attackers can leverage physical distance from targets, different laws, anonymity, and almost impossible attribution, known and unknown software vulnerabilities, human weaknesses, and many freely available tools. Defenders need expensive security frameworks, cyber procedures and competent people guarding vulnerable surfaces with no defined perimeters. This asymmetric scenario generates a dangerous cyber arms race where national investments focus more on aggressive tools and attackers than on defense technologies. Two emerging factors – integration of cyber-attacks with artificial intelligence and the diffusion of smart devices and autonomous vehicles – are creating an even more risky battleground where cyber security will permeate social safety. This paper analyzes the main cyber capabilities and actors involved in the past, present and visible future cyber landscape.
LOS ESTADOS DE AGREGACIÓN DE LA MATERIA: UNA PROPUESTA PARA LAS AULAS DE SECUNDARIA BÁSICA

The current context of a pandemic faced physics and chemistry teachers with an unforeseen situation. The pedagogical strategies that in the past required laboratories with complex equipment and facilities, today are transformed into experiences that are done with low-cost materials that can be found in any home. Simple experimental activities return. They change, complement and enrich themselves with technological resources. The work presented relates a pedagogical experience in the context of basic technical secondary education. The use of technological resources was found to enrich learning and offer new formats for socialization, while virtual applications and laboratories allow spaces for intervention, testing and the design of new hypotheses, in quarantined homes, under a diversity of glances.
Learnability can be independent of set theory (invited paper)

A fundamental result in statistical learning theory is the equivalence of PAC learnability of a class with the finiteness of its Vapnik-Chervonenkis dimension. However, this clean result applies only to binary classification problems. In search for a similar combinatorial characterization of learnability in a more general setting, we discovered a surprising independence of set theory for some basic general notion of learnability. Consider the following statistical estimation problem: given a family F of real valued random variables over some domain X and an i.i.d. sample drawn from an unknown distribution P over X, find f in F such that its expectation w.r.t. P is close to the supremum expectation over all members of F. This Expectation Maximization (EMX) problem captures many well studied learning problems. Surprisingly, we show that the EMX learnability of some simple classes depends on the cardinality of the continuum and is therefore independent of the set theory ZFC axioms. Our results imply that that there exist no finitary combinatorial parameter that characterizes EMX learnability in a way similar to the VC-dimension characterization of binary classification learnability.
An Exact Aggregation-Disaggregation Algorithm for Mandatory Set Decomposable Markov Chains

Many Markov chain models have very large state spaces, making the computation of stationary probabilities very difficult. Often the structure and numerical properties of the Markov chain allows for more efficient computation through state aggregation and disaggregation. In this paper we develop an efficient exact single-pass aggregation-disaggregation algorithm that exploits structural properties of large finite irreducible mandatory set decomposable Markov chains. Mandatory set decomposable structure is a generalization of several other Markov chain structures for which exact aggregation-disaggregation algorithms exist.
LibGuides: Literature: More About the MLA International Bibliography

Tools you can use research topics in literature, along with resources to help you write effectively within the discipline.
LibGuides: Philosophy: Essential Resources

A research guide designed to introduce you to the field of philosophy. You'll find the materials, search tools, and other resources you need to start your research. If you need any assistance, please contact your librarian.
Virtual strings filter design revisited

Digital filters have been used for decades to simulate losses and dispersion in real-time vibrating-string models for musical sound synthesis. String-loop filters in digital-waveguide models have generally been very low-order due to CPU limitations on typical available devices. In recent years, CPU speeds have increased so dramatically that very realistic string models can easily run in real time. This presentation revisits filter design for real-time musical string modeling.