nils-herrmann/clean_scirepeval huggingface dataset:
A Scandinavian challenge, a US response: methodological assumptions in Scandinavian and US prototyping approaches

In the early 1980s, Scandinavian software designers who sought to make systems design more participatory and democratic turned to prototyping. The "Scandinavian challenge" of making computers more democratic inspired others who became interested in user-centered design; information designers on both sides of the Atlantic began to employ prototyping as a way to encourage user participation and feedback in various design approaches. But, as European and North American researchers have pointed out, prototyping is seen as meeting very different needs in Scandinavia and in the US. Thus design approaches that originate on either side of the Atlantic have implemented prototyping quite differently, have deployed it to meet quite different goals, and have tended to understand prototyping results in different ways.These differences are typically glossed over in technical communication research. Technical communicators have lately become quite excited about prototyping's potential to help design documentation, but the technical communication literature shows little critical awareness of the methodological differences between Scandinavian and US prototyping. In this presentation, I map out some of these differences by comparing prototyping in a variety of design approaches originating in Scandinavia and the US, such as mock-ups, cooperative prototyping, CARD, PICTIVE, and contextual design. Finally, I discuss implications for future technical communication research involving prototyping.
Viral Misinformation: The Role of Homophily and Polarization

The spreading of unsubstantiated rumors on online social networks (OSN) either unintentionally or intentionally (e.g., for political reasons or even trolling) can have serious consequences such as in the recent case of rumors about Ebola causing disruption to health-care workers. Here we show that indicators aimed at quantifying information consumption patterns might provide important insights about the virality of false claims. In particular, we address the driving forces behind the popularity of contents by analyzing a sample of 1.2M Facebook Italian users consuming different (and opposite) types of information (science and conspiracy news). We show that users’ engagement across different contents correlates with the number of friends having similar consumption patterns (homophily), indicating the area in the social network where certain types of contents are more likely to spread. Then, we test diffusion patterns on an external sample of 4,709 intentional satirical false claims showing that neither the presence of hubs (structural properties) nor the most active users (influencers) are prevalent in viral phenomena. Instead, we found out that in an environment where misinformation is pervasive, users’ aggregation around shared beliefs may make the usual exposure to conspiracy stories (polarization) a determinant for the virality of false information. ∗Corresponding author General Terms Misinformation, Virality, Attention Patterns
Minimalist Cryptography for Low-Cost RFID Tags

A radio-frequency identification (RFID) tag is a small, inexpensive microchip that emits an identifier in response to a query from a nearby reader. The price of these tags promises to drop to the range of $0.05 per unit in the next several years, offering a viable and powerful replacement for barcodes. The challenge in providing security for low-cost RFID tags is that they are computationally weak devices, unable to perform even basic symmetric-key cryptographic operations. Security researchers often therefore assume that good privacy protection in RFID tags is unattainable. In this paper, we explore a notion of minimalist cryptography suitable for RFID tags. We consider the type of security obtainable in RFID devices with a small amount of rewritable memory, but very limited computing capability. Our aim is to show that standard cryptography is not necessary as a starting point for improving security of very weak RFID devices. Our contribution is threefold: 1. We propose a new formal security model for authentication and privacy in RFID tags. This model takes into account the natural computational limitations and the likely attack scenarios for RFID tags in real-world settings. It represents a useful divergence from standard cryptographic security modeling, and thus a new view of practical formalization of minimal security requirements for low-cost RFID-tag security. 2. We describe protocol that provably achieves the properties of authentication and privacy in RFID tags in our proposed model, and in a good practical sense. Our proposed protocol involves no computationally intensive cryptographic operations, and relatively little storage. 3. Of particular practical interest, we describe some reduced-functionality variants of our protocol. We show, for instance, how static pseudonyms may considerably enhance security against eavesdropping in low-cost RFID tags. Our most basic static-pseudonym proposals require virtually no increase in existing RFID tag resources.
The Omnibus model: a new model of data fusion?

Over the last two decades there have been several process models proposed (and used) for data and information fusion. A common theme of these models is the existence of multiple levels of processing within the data fusion process. In the 1980’s three models were adopted: the intelligence cycle, the JDL model and the Boyd control. The 1990’s saw the introduction of the Dasarathy model and the Waterfall model. However, each of these models has particular advantages and disadvantages. A new model for data and information fusion is proposed. This is the Omnibus model, which draws together each of the previous models and their associated advantages whilst managing to overcome some of the disadvantages. Where possible the terminology used within the Omnibus model is aimed at a general user of data fusion technology to allow use by a distributed audience.
Cross-tenant trust models in cloud computing

Most cloud services are built with multi-tenancy which enables data and configuration segregation upon shared infrastructures. Each tenant essentially operates in an individual silo without interacting with other tenants. As cloud computing evolves we anticipate there will be increased need for tenants to collaborate across tenant boundaries. This will require cross-tenant trust models supported and enforced by the cloud service provider. Considering the on-demand self-service feature intrinsic to cloud computing, we propose a formal cross-tenant trust model (CTTM) and its role-based extension (RB-CTTM) integrating various types of trust relations into cross-tenant access control models which can be enforced by the multi-tenant authorization as a service (MTAaaS) platform in the cloud.
Spectrogram Image Feature for Sound Event Classification in Mismatched Conditions

In this letter, we present a novel feature extraction method for sound event classification, based on the visual signature extracted from the sound's time-frequency representation. The motivation stems from the fact that spectrograms form recognisable images, that can be identified by a human reader, with perception enhanced by pseudo-coloration of the image. The signal processing in our method is as follows. 1) The spectrogram is normalised into greyscale with a fixed range. 2) The dynamic range is quantized into regions, each of which is then mapped to form a monochrome image. 3) The monochrome images are partitioned into blocks, and the distribution statistics in each block are extracted to form the feature. The robustness of the proposed method comes from the fact that the noise is normally more diffuse than the signal and therefore the effect of the noise is limited to a particular quantization region, leaving the other regions less changed. The method is tested on a database of 60 sound classes containing a mixture of collision, action and characteristic sounds and shows a significant improvement over other methods in mismatched conditions, without the need for noise reduction.
An algorithm to increase speech intelligibility for hearing-impaired listeners in novel segments of the same noise type.

Machine learning algorithms to segregate speech from background noise hold considerable promise for alleviating limitations associated with hearing impairment. One of the most important considerations for implementing these algorithms into devices such as hearing aids and cochlear implants involves their ability to generalize to conditions not employed during the training stage. A major challenge involves the generalization to novel noise segments. In the current study, sentences were segregated from multi-talker babble and from cafeteria noise using an algorithm that employs deep neural networks to estimate the ideal ratio mask. Importantly, the algorithm was trained on segments of noise and tested using entirely novel segments of the same nonstationary noise type. Substantial sentence-intelligibility benefit was observed for hearing-impaired listeners in both noise types, despite the use of unseen noise segments during the test stage. Interestingly, normal-hearing listeners displayed benefit in babble but not in cafeteria noise. This result highlights the importance of evaluating these algorithms not only in human subjects, but in members of the actual target population.
Connectivity and thought: The influence of semantic network structure in a neurodynamical model of thinking

Understanding cognition has been a central focus for psychologists, neuroscientists and philosophers for thousands of years, but many of its most fundamental processes remain very poorly understood. Chief among these is the process of thought itself: the spontaneous emergence of specific ideas within the stream of consciousness. It is widely accepted that ideas, both familiar and novel, arise from the combination of existing concepts. From this perspective, thought is an emergent attribute of memory, arising from the intrinsic dynamics of the neural substrate in which information is embedded. An important issue in any understanding of this process is the relationship between the emergence of conceptual combinations and the dynamics of the underlying neural networks. Virtually all theories of ideation hypothesize that ideas arise during the thought process through association, each one triggering the next through some type of linkage, e.g., structural analogy, semantic similarity, polysemy, etc. In particular, it has been suggested that the creativity of ideation in individuals reflects the qualitative structure of conceptual associations in their minds. Interestingly, psycholinguistic studies have shown that semantic networks across many languages have a particular type of structure with small-world, scale free connectivity. So far, however, these related insights have not been brought together, in part because there has been no explicitly neural model for the dynamics of spontaneous thought. Recently, we have developed such a model. Though simplistic and abstract, this model attempts to capture the most basic aspects of the process hypothesized by theoretical models within a neurodynamical framework. It represents semantic memory as a recurrent semantic neural network with itinerant dynamics. Conceptual combinations arise through this dynamics as co-active groups of neural units, and either dissolve quickly or persist for a time as emergent metastable attractors and are recognized consciously as ideas. The work presented in this paper describes this model in detail, and uses it to systematically study the relationship between the structure of conceptual associations in the neural substrate and the ideas arising from this system's dynamics. In particular, we consider how the small-world and scale-free characteristics influence the effectiveness of the thought process under several metrics, and show that networks with both attributes indeed provide significant advantages in generating unique conceptual combinations.
GlobalFS: A Strongly Consistent Multi-site File System

This paper introduces GlobalFS, a POSIX-compliant geographically distributed file system. GlobalFS builds on two fundamental building blocks, an atomic multicast group communication abstraction and multiple instances of a single-site data store. We define four execution modes and show how all file system operations can be implemented with these modes while ensuring strong consistency and tolerating failures. We describe the GlobalFS prototype in detail and report on an extensive performance assessment. We have deployed GlobalFS across all EC2 regions and show that the system scales geographically, providing performance comparable to other state-of-the-art distributed file systems for local commands and allowing for strongly consistent operations over the whole system. The code of GlobalFS is available as open source.
A Triangle Bending Constraint Model for Position-Based Dynamics

We present a novel bending model and constraint creation method for position-based dynamics. Our new bending model is introduced as an alternative to the current state-of-the-art dihedral bending model. Our model is motivated by geometric principles and operates on virtual triangles. It has the same cheap computational cost as the stick constraint model but with higher simulation quality and faster convergence than the dihedral bending model. Along with the model a new global bending parameter is introduced to control the curvature deformation at high precision compared to the traditional stiffness constant. Further, we propose a new constraint creation method that we believe is well suited for the triangle bending model and less affected by the underlying mesh tessellation.
Do the Big Five Personality Factors affect knowledge sharing behaviour? A study of Malaysian universities

This study aims to develop an integrative understanding of the Big Five Personality (BFP) factors supporting or inhibiting individuals’ online entertainment knowledge sharing behaviours. Survey data are collected from 255 university students from two Malaysian universities. As hypothesised, structural equation modelling shows that extraversion and neuroticism are positively related to the attitude towards knowledge sharing. Openness to experience is found to have an inverse relationship with the attitude towards knowledge sharing. Subjective norm is positively related to the attitude towards knowledge sharing. Both attitude towards knowledge sharing and subjective norm are found to be independently and significantly related to the intention to share knowledge, which significantly influences the knowledge sharing behaviour. The research model proposed in the present study is useful to other researchers seeking to understand the personality factors that influence the knowledge sharing behaviour among the organisational communities. The results of this study provide empirical evidence for a new model that shows that the BFP factors are implicated in individuals’ knowledge sharing behaviour. This study and its findings have filled the research gap in the literature of the BFP factors and knowledge sharing behaviours. Furthermore, the inclusion of the BFP factors in the Theory of Reasoned Action framework is an important distinction that other studies have not established.
Open-Source Tools for Morphology, Lemmatization, POS Tagging and Named Entity Recognition

We present two recently released opensource taggers: NameTag is a free software for named entity recognition (NER) which achieves state-of-the-art performance on Czech; MorphoDiTa (Morphological Dictionary and Tagger) performs morphological analysis (with lemmatization), morphological generation, tagging and tokenization with state-of-the-art results for Czech and a throughput around 10-200K words per second. The taggers can be trained for any language for which annotated data exist, but they are specifically designed to be efficient for inflective languages, Both tools are free software under LGPL license and are distributed along with trained linguistic models which are free for non-commercial use under the CC BY-NC-SA license. The releases include standalone tools, C++ libraries with Java, Python and Perl bindings and web services.
Applications of transcranial direct current stimulation for understanding brain function

In recent years there has been an exponential rise in the number of studies employing transcranial direct current stimulation (tDCS) as a means of gaining a systems-level understanding of the cortical substrates underlying behaviour. These advances have allowed inferences to be made regarding the neural operations that shape perception, cognition, and action. Here we summarise how tDCS works, and show how research using this technique is expanding our understanding of the neural basis of cognitive and motor training. We also explain how oscillatory tDCS can elucidate the role of fluctuations in neural activity, in both frequency and phase, in perception, learning, and memory. Finally, we highlight some key methodological issues for tDCS and suggest how these can be addressed.
Online Display Advertising: Targeting and Obtrusiveness

We use data from a large-scale field experiment to explore what influences the effectiveness of online advertising. We find that matching an ad to website content and increasing an ad’s obtrusiveness independently increase purchase intent. However, in combination these two strategies are ineffective. Ads that match both website content and are obtrusive do worse at increasing purchase intent than ads that do only one or the other. This failure appears to be related to privacy concerns: The negative effect of combining targeting with obtrusiveness is strongest for people who refuse to give their income, and for categories where privacy matters most. Our results suggest a possible explanation for the growing bifurcation in internet advertising between highly targeted plain text ads and more visually striking but less targeted ads.
Cross-Modal Deep Variational Hand Pose Estimation

The human hand moves in complex and high-dimensional ways, making estimation of 3D hand pose configurations from images alone a challenging task. In this work we propose a method to learn a statistical hand model represented by a cross-modal trained latent space via a generative deep neural network. We derive an objective function from the variational lower bound of the VAE framework and jointly optimize the resulting cross-modal KL-divergence and the posterior reconstruction objective, naturally admitting a training regime that leads to a coherent latent space across multiple modalities such as RGB images, 2D keypoint detections or 3D hand configurations. Additionally, it grants a straightforward way of using semi-supervision. This latent space can be directly used to estimate 3D hand poses from RGB images, outperforming the state-of-the art in different settings. Furthermore, we show that our proposed method can be used without changes on depth images and performs comparably to specialized methods. Finally, the model is fully generative and can synthesize consistent pairs of hand configurations across modalities. We evaluate our method on both RGB and depth datasets and analyze the latent space qualitatively.
Concept inventories in computer science for the topic discrete mathematics

This report describes concept inventories, specialized assessment instruments that enable educational researchers to investigate student (mis)understandings of concepts in a particular domain. While students experience a concept inventory as a set of multiple-choice items taken as a test, this belies its purpose, its careful development, and its validation. A concept inventory is not intended to be a comprehensive instrument, but rather a tool that probes student comprehension of a carefully selected subset of concepts that give rise to the most common and pervasive mismodelings. The report explains how concept inventories have been developed and used in other STEM fields, then outlines a project to explore the feasibility of concept inventories in the computing field. We use the domain of discrete mathematics to illustrate a suggested plan of action.
Application of Higher Order Spectra for the Identification of Diabetes Retinopathy Stages

Diabetic retinopathy (DR) is a condition where the retina is damaged due to fluid leaking from the blood vessels into the retina. In extreme cases, the patient will become blind. Therefore, early detection of diabetic retinopathy is crucial to prevent blindness. Various image processing techniques have been used to identify the different stages of diabetes retinopathy. The application of non-linear features of the higher-order spectra (HOS) was found to be efficient as it is more suitable for the detection of shapes. The aim of this work is to automatically identify the normal, mild DR, moderate DR, severe DR and prolific DR. The parameters are extracted from the raw images using the HOS techniques and fed to the support vector machine (SVM) classifier. This paper presents classification of five kinds of eye classes using SVM classifier. Our protocol uses, 300 subjects consisting of five different kinds of eye disease conditions. We demonstrate a sensitivity of 82% for the classifier with the specificity of 88%.
Performance of cable suspended robots for upper limb rehabilitation

This work presents a general simulation tool to evaluate the performance of a set of cable suspended rehabilitation robots. Such a simulator is based on the mechanical model of the upper limb of a patient. The tool was employed to assess the performances of two cable-driven robots, the NeReBot and the MariBot, developed at the Robotics & Mechatronics Laboratories of the Department of Innovation in Mechanics and Management (DIMEG) of University of Padua, Italy. This comparison demonstrates that the second machine, which was conceived as an evolution of the first one, yields much better results in terms of patient's arm trajectories.
Phishing Websites Detection Based on Web Source Code and URL in the Webpage

Major security issues for banking and financial institutions are Phishing. Phishing is a webpage attack, it pretends a customer web services using tactics and mimics from unauthorized persons or organization. It is an illegitimate act to steals user personal information such as bank details, social security numbers and credit card details, by showcasing itself as a truthful object, in the public network. When users provide confidential information, they are not aware of the fact that the websites they are using are phishing websites. This paper presents a technique for detecting phishing website attacks and also spotting phishing websites by combines source code and URL in the webpage. Phishing, Website attacks, Source Code, URL.