Reviewerly/exHarmony huggingface dataset:
Is convenient secure? Exploring the impact of metacognitive beliefs in password selection

Author(s): Choudhary, Mukund; Srivatsa, K V Aditya; Upadhyay, Ishan Sanjeev; Srivastava, Priyanka | Abstract: Recently, there has been research on what factors influence a user’s password setting practices, which include various types of emotions such as anger, risk-taking tendencies, etc. However, research has shown that factors such as memorability and perceived memorability have a greater influence on password choice. Some recent research has shown a negative correlation between the perceived memorability and the perceived security of passwords, particularly passphrases (that are technically more secure). However, it is unclear whether this effect can be extended to groups with good experiences with digital spaces (IT professionals, entrepreneurs, etc.). Furthermore, it has not been determined whether random, uncommonly-worded, or complex structure passphrases would also maintain the correlation, as opposed to relatively less secure, common/simple passphrases. This study examines this problem using a diverse demographic and different categories of passphrases.
On the topic of interdisciplinary collaboration between art and music

This improvised performance drew from a shared body of knowledge in which the aesthetic outcome (as “product”) is considered as of lesser significance than the underlying artistic and creative processes (the real-time “making” aspect) underpinning the venture. This session explored processes of cooperative and circulatory reciprocity in a triangular encounter between musician, artist, and audience.
Identifying Untrustworthy Predictions in Neural Networks by Geometric Gradient Analysis

The susceptibility of deep neural networks to untrustworthy predictions, including out-of-distribution (OOD) data and adversarial examples, still prevent their widespread use in safety-critical applications. Most existing methods either require a retraining of a given model to achieve robust identification of adversarial attacks or are limited to out-of-distribution sample detection only. In this work, we propose a geometric gradient analysis (GGA) to improve the identification of untrustworthy predictions without retraining of a given model. GGA analyzes the geometry of the loss landscape of neural networks based on the saliency maps of their respective input. We observe considerable differences between the input gradient geometry of trustworthy and untrustworthy predictions. Using these differences, GGA outperforms prior approaches in detecting OOD data and adversarial attacks, including state-of-the-art and adaptive attacks. Code is available at: https://github.com/mad-lab-fau/GGA-Identifying-Untrustworthy-Predictions
Can computers tell a story? Discourse Structure in Computer-generated Text and Humans

Author(s): Wey, Alex; Hemmatian, Babak; Avram, Rachel; Feucht, Sheridan; Spitalnic, Kate; Garg, Muskaan; Eickhoff, Carsten; Pavlick, Ellie; Sandstede, Bjorn; Sloman, Steven | Abstract: Text-generation algorithms like GPT-2 (Radford et al., 2019) and GPT-3 (Brown et al., 2020) produce documents which resemble coherent human writing. But no study has compared the discourse linguistics features of the artificial text with that of comparable human content. We used a sample of Reddit and news discourse as prompts to generate artificial text using fine-tuned GPT-2 (Grover; Zellers et al., 2019). Blind annotators identified clause-level discourse features (e.g., states and events; Smith, 2003), and coherence relations (e.g., contrast; Wolf and Gibson, 2005) in prompts and generated text. Comparing the g20000 clauses, Grover recreates human word co-occurrence patterns and clause types across discourse modes. However, its coherence relations have shorter length and lower quality, with many nonsensical instances. Therefore, annotators could perfectly guess the human/algorithmic source of documents. Using a corresponding GPT-3 sample, we discuss aspects of generation that have and have not improved since Grover.
Fast semantic segmentation for images with complex backgrounds based on reuse of the feature map

In complex detection scenarios in machine vision, identification of the pixel object label (comprising the detection region and detection pattern information) and the pixel part label (comprising the detection part type and location information) are often essential. Subsequent analyses of the detection region and pattern information are also carried out in such cases. In this context, a method to reuse the feature map has been studied in this work. First, the utility in reusing the feature map in the fast execution of semantic segmentation at the object-level and part-level for images with complex backgrounds is discussed, which operates by striking a balance between the degree of feature reuse and corresponding segmentation accuracy. Secondly, a semantic segmentation network, based on two independent and identical encoderdecoder pairs in parallel, is proposed. In this parallel network, feature maps with identical hidden layers are merged together for reuse in order to reduce the computational complexity of associated processes, and thereby optimize segmentation time. Finally, simulation experiments are conducted to evaluate the proposed technique. The results demonstrate that the method exhibits 100% detection accuracy, and reduces the detection time by 20.3% — from 602 ms to 480 ms — when applied to chassis assembly detection. Further, the effect of segmentation time optimization on the detection process is evaluated.
Realistic Occlusion of Virtual Objects Using Three-Dimensional Hand Model

We develop a method for predicting virtual object occlusions during hand-object interaction in mixed reality. We reconstruct a 3D hand model from a monocular RGB camera frame and use it to predict occlusions. The quality of these occlusions, evaluated using dice coefficient, is at the same level as reported for other depth sensor and hand model based methods. Our model runs in real-time on off-the-shelf smartphone and gives a plausible experience of grabbing, rotating and translating virtual objects, which we confirmed through a dedicated user study. Volume penetration is the main reason for occlusion errors. However, the suitability of the object to the user scenario and its appealing look can compensate for the occlusion errors, and make a positive mixed reality experience.
A Survey on Impulse Noise Removal from Color Image

A broad survey on various filters for removing impulse noise from color images is presented in this paper. Color images are multivariate vector value signals which are non-stationary in nature. Various nonlinear filters are suggested in the literature. The filters are categorized into 11 classes and discussed in details.
Blockchain and antitrust law: A roadmap

The chapter sets the scene for the correct application of competition law to blockchain technologies. In particular, it provides an antitrust-based roadmap to be applied - on a case-by-case basis - to blockchain scenarios. To this end, the chapter first lays the foundations, providing the reader with an oriented definition of the blockchain and framing the categories of antitrust law functional to understand its dynamics. Building on these definitions, the possible interactions between the blockchain technology and competition law are analysed, with the aim of understanding the potential relevance of such a technology from an antitrust perspective and to provide the reader with a toolkit to address current blockchain scenarios through the lens of competition law. In particular, the chapter develops on two lines of reasoning: the first that considers the blockchain as a technology, i.e. a technological output. The second that frames blockchain as a tool, i.e. as a place (or places) where other products and services can be distributed. The analysis carried out in the chapter allows to conclude that current competition law is already well equipped to deal with the blockchain - and for competition authorities to be effective, the burden should fall more on the technical tools at their disposal than on increasing their prerogatives.
Towards Fair Principles for Research Information: Report on a Series of Workshops

This is a summary report of the series of workshops on FAIR research information in open infrastructures that was jointly organised by the State Scientific and Technical Library of Ukraine (SSTL) and Leibniz Information Centre for Science and Technology (TIB) which have been collaborating under the framework of Joint German-Ukrainian project supported by the Federal Ministry of Education and Research of Germany and the Ministry of Science and Education of Ukraine. The workshops successfully harnessed the enthusiasm and experience of librarians, researchers, software providers, public funding body representatives, content providers, scientometricians and information specialists in an attempt to shed light and define criteria which assist discovery and reuse of research information by third-parties and make it FAIR. The series of workshops consisted of four separate workshops which addressed single aspects of FAIR– findability, accessibility, interoperability and reuse concerning research information. Due to Covid-19 travel restrictions workshops were held online between September 2020 and January 2021.
Hierarchical Learning of Hidden Markov Models with Clustering Regularization

Hierarchical learning of generative models is useful for representing and interpreting complex data. For instance, one application is to learn an HMM to represent an individual's eye fixations on a stimuli, and then cluster individuals' HMMs to discover common eye gaze strategies. However, learning the individual representation models from observations and clustering individual models to group models are often considered as two separate tasks.In this paper, we propose a novel tree structure variational Bayesian method to learn the individual model and group model simultaneously by treating the group models as the parents of individual models, so that the individual model is learned from observations and regularized by its parents, and conversely, the parent model will be optimized to best represent its children. Due to the regularization process, our method has advantages when the number of training samples decreases. Experimental results on the synthetic datasets demonstrate the effectiveness of the proposed method.
Library & Cultural Services: Open Research: Negotiations between Elsevier and UK Universities

Introduces the open research movement, and goes into more detail about different aspects of this movement, including open access.
Hyperconnectivity and (Im)Mobility: Uber and Surveillance Capitalism by the Global South

Apart from governments’ increased opportunities to monitor citizens, businesses, civil servants, and services, companies are mobilizing personal data to build profitable, algorithmically based business models with profound ramifications. With companies that have rapidly become giants in this sector, such as Uber, the phenomenon is spreading to various services at the same overwhelming speed as many companies bet on what is known as Uberization. In this paper, we aim to use one example of such a phenomenon from the Global South to show how a potential hyper-connected society is, in fact, creating the possibility for expanded patterns of immobilization for certain groups. We aim to show how highly indirect corporate surveillance involved in businesses such as Uber can run in parallel with a specific direct form of worker surveillance that, without any legal or social safeguards, increases the vulnerability of the weakest link in this chain.
Respuesta académica a desafíos de la pandemia de COVID-19: Unidades de Aislamiento Epidemiológico Portátil

The article presents a project of emergency epidemiological isolation units developed in the context of the COVID-19 pandemic from pneumatic structures of icosahedral geodesic domes. This academic and research project was developed as part of the Architecture Program of the University of La Salle within the Lasallian Laboratory for the Construction of the Colombian Habitat (LAB-LAHC). The proposed isolation system currently benefits various regions of the country, providing, from the experimental architecture, solutions that today are used for vaccination in remote areas, and the care of other emergencies. This article shows details of the research and the development stages of the project after ten months of work. The experience is emerging as a university extension action to be replicated in the future for its contribution to solving real problems in society.
Causality: A Portable Protocol for Rapid Development of Applications for Social Interactions

This paper proposes Causality, a portable protocol for quickly developing HCI applications for social interactions based on the context through cause and effect, and a Windows application assisting social and emotional behavior targeted to help people with autism spectrum disorder. For application’s interactivity and convenient post-analysis, we added some enhancements to our protocol. For example, we implemented speech synthesis and recognition modules to promote users’ participation and natural interactions. Our application generates a summary when the user finishes the application for the post-analysis of the user’s interactions for further refinement. We expect our Causality protocol can help future researchers as a starting point to make their own social and sentimental assistance application without requiring extensive programming skills.
Cybersecurity threat intelligence knowledge exchange based on blockchain

Although cyber threat intelligence (CTI) exchange is a theoretically useful technique for improving security of a society, the potential participants are often reluctant to share their CTI and prefer to consume only, at least in voluntary based approaches. Such behavior destroys the idea of information exchange. On the other hand, governments are forcing specific entities and operators to report them specific incidents depending on their impact, otherwise there could be sanctions to those operators which are not reporting them on time. Obligations and sanctions are usually discouraging participants to share information voluntarily which will just share and report what is strictly required. We propose a paradigm shift of cybersecurity information exchange by introducing a new way to encourage all participants involved, at all levels, to share relevant information dynamically. It will also contribute to the support and deployment of Dynamic Risk Management (DRM) frameworks to keep risks under an acceptance level along the time. Participants will have new and specific incentives to share, invest and consume threat intelligence and risk intelligence information depending on their different roles (producers, consumers, investors, donors and owner). Our proposal leverages from standards like Structured Threat Information Exchange (STIX), as well as W3C semantic web standards to enable a workspace of knowledge related to behavioral threat intelligence patterning to characterize tactics, techniques and procedures (TTP). At the same time, we propose an Ethereum Blockchain Smart contract Marketplace to better incentivize the sharing of that knowledge between all parties involved as well as creating a standard CTI token as a digital asset with a promising value in the market. Simulations and an experimentation were performed to demonstrate its benefits and incentives, but also its potential limits with regard to storage and cost of transactions.
New Commitment Schemes Based on Conjugacy Problems over Rubik’s Groups

Commitment schemes are important tools in cryptography and used as building blocks in many cryptographic protocols. We propose two commitment schemes by using Rubik’s groups. Our proposals do not lay the security on the taken-for-granted hardness of the word problem over Rubik’s groups. Instead, our first proposal is based on a symmetric encryption algorithm that is secure based on the hardness of the conjugacy search problem over Rubik’s groups, while our second proposal is based on the hardness of a newly derived problem—the functional towering conjugacy search problem over Rubik’s groups. The former is proved secure in the sense of both computational hiding and binding, while the latter is proved even secure in the sense of perfect hiding and computational binding. Furthermore, the proposed schemes have a remarkable performance advantage: a linear commitment/opening speed. We also evaluate the efficiency of the commitment schemes and show that they are considerably fast.
To Tweet or Not to Tweet? The Role of Social Media Boycotts on Stock Market Reactions

The ability of boycotts to affect firm strategies and outcomes has long held the interest of organizational scholars. However, with the rise of social media in the last decade, our understanding of how social movements influence firms is limited as social movement research typically emphasizes the role of traditional media. The advent of social media makes it easier for informal activist organizations and even individuals to drive social movements, as well as reducing the control that traditional media outlets have over information. As a result, social movements may not be as reliant on traditional media as they have been in the past. In this paper, we develop and test theory about how social media tenor surrounding the announcement of a boycott impacts stock market reaction. We find that the stock market does react to information provided over social media, suggesting that our understanding of how social movements influence stock price returns may be limited in the social media age.
Implementasi Sistem Informasi Pemasaran Rumah Dengan Pendekatan Sistem Berorientasi Objek Pada Developer Property

Marketing in this century has developed quite rapidly and is in demand by business actors because it has promising opportunities, including in big cities such as Tangerang. Property developer is a property developer engaged in housing. Until now, the marketing system for property developers is how to sell as much housing as possible in one month. The problem that occurs in the marketing system at property developers is that it is not yet computerized or web-based because the home marketing process is still using the technique of distributing brochures and data processing so far it is still conventional and the list of booking lists and pricelists of house unit prices are not yet detailed. The purpose of the research is to build a housing unit marketing system using conceptual analysis and to find out what supporting and inhibiting factors are on property developers. The research method used is descriptive qualitative. Data collection techniques were carried out by means of observation, interviews and literature study. The results of this study are in the form of software, namely a web-based housing marketing system that can display detailed housing information including housing building specifications, price lists, housing locations, stock units, location maps and payment details
Model-Driven Software Engineering: A Bibliometric Analysis

Model-Driven Software Engineering (MDSE) is a software development methodology that reduces the cost and production time of the final product by concentratingon a higher level of abstraction. The main focus of MDSE is to generate automated code by applying different types of transformations to high-level models. The effectiveness of MDSE has been proved in different domains for solving various types of problems.This research provides the bibliometric analysis of research in the field of MDSE. The population of the study consists of all MDSE articles indexed in the Web of Science database in ten years from 2010 to 2019. The work aims to identify the volume of scientific production, the most influential countries, universities, authors, and journals, the cooperation network among the countries, universities, and authors, keyword ranking, and Co-word analysis of keywords and titles of the articles. Keyword analysis revealed that Model Transformation and Model Checking are two very important clusters and topics of interest to researchers in this field. The results provide valuable insights that can be used as a guideline by both fresh and experienced researchers for the current state and future trend of MDSE research in different scientific disciplines to establish a baseline beforeinitiating an MDSE research project in the future.
PENGARUH SELFT REGULATED LEARNING DAN LITERASI DIGITAL TERHADAP MOTIVASI BERPRESTASI SISWA

The context of learning during the Covid-19 emergency requires learning to be carried out from home or distance learning. Distance learning requires students to be able to manage their own learning, in addition, digital literacy is needed for students to be able to access the learning process and information and select the information needed. The ability to organize them in learning and good digital literacy will increase student achievement motivation. This study aims to determine: 1) The Effect of Self- Regulated Learning on Achievement Motivation of Class VII Students of SMP Negeri 9 Ambon in the Distance Learning System, 2) The Effect of Digital Literacy Ability on Achievement Motivation of Class VII Students of SMP Negeri 9 Ambon in Distance Learning System 3) The Influence of Self-Regulated Learning and Digital Literacy Ability on Achievement Motivation of Class VII Students of SMP Negeri 9 Ambon in Distance Learning Systems. The method used is a quantitative method. The data analysis technique used is multiple linear regression analysis. Data processing used the SPSS version 21 program. The results show that: 1) there is an effect of self-regulated learning on achievement motivation, 2) there is an effect of digital literacy on student achievement motivation, and 3) there is an effect of simultaneous self-regulated learning and digital literacy towards the students’motivation of SMP Negeri 9 in distance learning system.
Questions in Cognitive Mimetics

Human thinking advances through questions and answers. Any field of human endeavor is permeated by the presence of questions, answers and presuppositions. Questions have a kind of universality, whereby one can place the question marks on anything, including questions themselves. The process of asking the right questions about the right things and in the right way are key for the explication of an approach. Recently, we have begun thinking about an approach to the design of intelligent technology: Cognitive mimetics. In brief, the idea is to take inspiration of empirical human thinking in specific contexts to develop AI solutions. The purpose of this article is to question this approach from various angles to take steps towards specifying it as a methodology. Some of the questions are like the chips a sculptor would make on a rough unfinished piece of marble. They and their answers uncover a particular shape immanent in the broad idea we have presented. Other questions are like the tools the sculptor needs to turn an idea into reality, these are the questions which will have permanent applicability within the methodology itself. Finally, some questions are promissory notes, they concern issues that will need to be responded to as the method construction proceeds further. The purpose is to establish a waypoint of where our thinking stands at the moment and an idea of where it may lead. The way we present and answer some of the questions may be of broader interest for researchers involved in fundamental and practical questions in AI design.