Reviewerly/exHarmony huggingface dataset:
Implementing Teachers’ Training Technologies at a Federal University: E-portfolio, Digital Laboratory, PROLog Module System

Students' training quality directly depends on the modern digital environment resources in the federal university. Rich digital environment of the university provides effective learning and allows students to use innovative learning technologies (e-portfolio, digital laboratories, MOOC, etc.) in professional activities for projecting and realization of individual educational paths. E-learning, distant learning technologies implementation and other digital environment resources for potential teachers’ training at the federal university make the learning process more effective. The article describes the practice at Institute of Education, Psychology and Sociology of Siberian Federal University in creating modern digital environment for teachers’ training by the means of e-portfolio technologies, digital laboratory, PROLog module system of experiments, learning management system (LMS) Moodle.
A BP Neural Network Recommendation Algorithm Based on Cloud Model

Rating prediction is one of the key studies in the recommendation system. The traditional rating prediction algorithms only utilize user's rating data to predict unknown ratings. In fact, however, we need to deal with the randomness and fuzziness of user ratings. At the same time, the sparsity of the data limits the performance of these algorithms. Therefore, a backpropagation neural network recommendation algorithm based on cloud model is proposed. First, the algorithm uses cloud model qualitative and quantitative transformation method to deal with the user ratings, which generates multiple cloud prediction values for users, and these values constitute the cloud layer. Then, the cloud layer joins the neural network, which can improve the accuracy of rating prediction. The missing value of rating matrix is filled in and recommendation list for the target user is generated. Finally, we perform experiments on the real-world data set, finding out that the proposed method achieves better results compared with the traditional recommendation methods in term of recall, precision, and F1.
Adaptive security architectural model for protecting identity federation in service oriented computing

With the tremendous growth of Internet and its related technologies, the Service Oriented Architecture (SOA) became a dominant paradigm shift for enterprise computing. In SOA, business functionalities are offered by many different Service Providers as services. In order to get served by different service providers, the client has to authenticate with those service providers at multiple times. Single Sign On (SSO) mechanism provides the client to login only one time so that access to different services is made possible without needing to re-authenticate. Here, the identity of the logged-in client is federated among the enterprise computing nodes. This is one of the simplest forms of federated identity. The goal of identity federation is to benefit ease of use, flexibility, productivity and reduced cost of the authentication process, but trust and security is a major concern in this situation. Major threats on federated identity management are due to identity misuse, identity theft, and trust deficit between identity providers and services providers. As of now, the Security Assertion Markup Language (SAML), Open Authorization (OAuth) and OpenID are the three important federated identity management standards in the industry. However, none of them is equipped by itself to provide comprehensive security protection for identity federation even within a single enterprise computing environment. In fact, these federated solutions result in additional security vulnerabilities due to their openness of identity federation. The security threats are becoming severe when federated identity is spanned into the inter-organizational and intra-organizational computing environment. This paper analyses the vulnerabilities and security gaps in the existing federated identity solutions. To overcome these gaps, an adaptive security architectural model is proposed for identity federation at inter and intra-organizational level using public key infrastructure that adheres to the SOA security standards and specifications. The proposed architecture is implemented and tested in a large-scale federated identity enterprise computing environment with security-centric financial data to acquire the desired results. A cross-sectional comparative analysis is done between existing and proposed solutions to validate the improvement in the protection of identity federation environment.
The speed-curvature power law in tongue movements of repetitive speech

The speed-curvature power law is a celebrated law of motor control expressing a relation between the kinematic property of speed and the geometric property of curvature. We aimed to assess whether speech movements obey this law just as movements from other domains do. We describe a metronome-driven speech elicitation paradigm designed to cover a wide range of speeds. We recorded via electromagnetic articulometry speech movements in sequences of the form /CV…/ from nine speakers (five German, four English) speaking at eight distinct rates. First, we demonstrate that the paradigm of metronome-driven manipulations results in speech movement data consistent with earlier reports on the kinematics of speech production. Second, analysis of our data in their full three-dimensions and using advanced numerical differentiation methods offers stronger evidence for the law than that reported in previous studies devoted to its assessment. Finally, we demonstrate the presence of a clear rate dependency of the power law's parameters. The robustness of the speed-curvature relation in our datasets lends further support to the hypothesis that the power law is a general feature of human movement. We place our results in the context of other work in movement control and consider implications for models of speech production.
Understanding software developers' cognition in agile requirements engineering

During agile requirements engineering, developers need to assimilate and transform the original requirements information into system functions in the form of user stories. Obviously, this is a challenging cognition-based process, in which developers' cognition plays a key role. However, prior research has not explored developers' cognition during the process. The purpose of this study is to investigate and understand developers' cognitive representation styles and interaction patterns in agile requirements. A classification of developers' cognitive representation styles and interaction patterns was first proposed based on literature review. Then, an empirical research was conducted in a capstone software engineering course. Students were playing the role of developers and engaging in agile software development during this course, so their conversation about splitting and defining user stories was recorded and analyzed to examine developers' cognition via a content analysis method. The results show that, even when facing requirements analysis, developers tend to exhibit a technology-oriented cognitive representation style. Additionally, developers have more cognitive difficulty in determining activity and granularity than the role and business value of a user story. Developers also exhibit a preference for cognitive interaction pattern; assertion, comment, and two question-patterns are the major four patterns in our paper rather than information sharing, which was considered a major pattern in previous research. This paper contributes to our understanding of developers' cognition, further predicting and guiding developers' behaviors toward achieving good quality requirements analysis.
Unifying Leakage Models on a Rényi Day

In the last decade, several works have focused on finding the best way to model the leakage in order to obtain provably secure implementations. One of the most realistic models is the noisy leakage model, introduced in [PR13, DDF14] together with secure constructions. These works suffer from various limitations, in particular the use of ideal leak-free gates in [PR13] and an important loss (in the size of the field) in the reduction in [DDF14]. In this work, we provide new strategies to prove the security of masked implementations and start by unifying the different noisiness metrics used in prior works by relating all of them to a standard notion in information theory: the pointwise mutual information. Based on this new interpretation, we define two new natural metrics and analyze the security of known compilers with respect to these metrics. In particular, we prove (1) a tighter bound for reducing the noisy leakage models to the probing model using our first new metric, (2) better bounds for amplification-based security proofs using the second metric. To support that the improvements we obtain are not only a consequence of the use of alternative metrics, we show that for concrete representation of leakage (e.g., “Hamming weight + Gaussian noise”), our approach significantly improves the parameters compared to prior works. Finally, using the Rényi divergence, we quantify concretely the advantage of an adversary in attacking a block cipher depending on the number of leakage acquisitions available to it.
A hybrid approach based on the BWM-VIKOR and GRA for ranking facility location in construction site layout for Mehr project in Tehran

This study presents a new hybrid framework based on the multi-criteria decision making in order to rank the potential site layout locations by consideration of the cost and safety criteria in the Mehr Construction Project in Tehran, Iran.To this end, all of the criteria in selecting suitable potential locations are extracted from the research literature and the most effective ones, which are matched with existing conditions in Tehran are considered based on the opinion of experts,.Then, the proper locations for site layout are determined as the potential alternatives and ranked by experts based on the structure.According to the data collected from the questionnaires, the weights of the selected criteria are calculated using Best Worst Method (BWM) and the final ranking of the locations is performed using two Gray Relational Analysis and VIKOR methods.The computational results indicate that both VIKOR and GRA methods yield the same ranking.However, a method with higher reliability should be used to select the best potential location of construction site layout.Therefore, the sensitivity analysis of final outputs on the parameters existing in VIKOR and GRA methods is used in order to rank the alternatives and select the best approach.According to the computational results, the GRA method provides higher robustness compared with the VIKOR method.Accordingly, the ranking obtained from the GRA method is employed as the final solution in implementing the case study..
Examining User-Developer Feedback Loops in the iOS App Store

Application Stores, such as the iTunes App Store, give developers access to their users' complaints and requests in the form of app reviews.However, little is known about how developers are responding to app reviews.Without such knowledge developers, users, App Stores, and researchers could build upon wrong foundations.To address this knowledge gap, in this study we focus on feedback loops, which occur when developers address a user concern.To conduct this study we use both supervised and unsupervised methods to automatically analyze a corpus of 1752 different apps from the iTunes App Store consisting of 30,875 release notes and 806,209 app reviews.We found that 18.7% of the apps in our corpus contain instances of feedback loops.In these feedback loops we observed interesting behaviors.For example, (i) feedback loops with feature requests and login issues were twice as likely as general bugs to be fixed by developers, (ii) users who reviewed with an even tone were most likely to have their concerns addressed, and (iii) the star rating of the app reviews did not influence the developers likelihood of completing a feedback loop.
New provable data transfer from provable data possession and deletion for secure cloud storage

With the continuous and rapid development of cloud computing, it becomes more popular for users to outsource large-scale data files to cloud servers for storage and computation. However, data outsourcing brings convenience to users while it also causes certain security problems. The integrity of outsourced data needs to be periodically checked by users to protect their data. Also, the secure transfer of cloud data can avoid data losses to users. Aiming at solving these problems in the data outsourcing process, a provable data transfer scheme based on provable data possession and deletion is recently proposed by Xue et al. However, we found a security flaw in Xue et al.’s scheme. Concretely, the block tags can be forged in their scheme. In this article, we first give a brief review of Xue et al.’s scheme and then a detailed attack is shown. To remove the security flaw, an improved scheme is proposed. Furthermore, we replace the integrity checking protocol of their proposal with a more efficient protocol to improve the efficiency of data integrity auditing.
Ultra-rapid object categorization in real-world scenes with top-down manipulations

Humans are able to achieve visual object recognition rapidly and effortlessly. Object categorization is commonly believed to be achieved by interaction between bottom-up and top-down cognitive processing. In the ultra-rapid categorization scenario where the stimuli appear briefly and response time is limited, it is assumed that a first sweep of feedforward information is sufficient to discriminate whether or not an object is present in a scene. However, whether and how feedback/top-down processing is involved in such a brief duration remains an open question. To this end, here, we would like to examine how different top-down manipulations, such as category level, category type and real-world size, interact in ultra-rapid categorization. We have constructed a dataset comprising real-world scene images with a built-in measurement of target object display size. Based on this set of images, we have measured ultra-rapid object categorization performance by human subjects. Standard feedforward computational models representing scene features and a state-of-the-art object detection model were employed for auxiliary investigation. The results showed the influences from 1) animacy (animal, vehicle, food), 2) level of abstraction (people, sport), and 3) real-world size (four target size levels) on ultra-rapid categorization processes. This had an impact to support the involvement of top-down processing when rapidly categorizing certain objects, such as sport at a fine grained level. Our work on human vs. model comparisons also shed light on possible collaboration and integration of the two that may be of interest to both experimental and computational vision researches. All the collected images and behavioral data as well as code and models are publicly available at https://osf.io/mqwjz/.
Understanding the Information Behaviors of Doctoral Students: An Exploratory Study

This exploratory study seeks to understand how the research experiences of humanities PhD students influence their information behavior. The authors interviewed 10 participants from two academic institutions multiple times over several months. The study used open-ended, unstructured interviewing techniques in an attempt to avoid the introduction of library bias. The authors found that the information behavior of PhD students is driven by their need to understand and follow a scholarly dialogue. Resource discovery was heavily influenced by people, and resource use was varied, distributed, and unpredictable.
LCC: Learning to Customize and Combine Neural Networks for Few-Shot Learning

Few-shot learning aims to train efficient predictive models with a few examples. The lack of training data leads to poor models that perform high-variance or low-confidence predictions. In this paper, we propose to meta-learn the of Bayes models (E3BM) to achieve robust predictions. Epoch-wise means that each training epoch has a Bayes model whose parameters are specifically learned and deployed. Empirical means that the hyperparameters, e.g., used for learning and ensembling the models, are generated by hyperprior learners conditional on task-specific data. We introduce four kinds of hyperprior learners by considering inductive vs. transductive, and epoch-dependent vs. epoch-independent, in the paradigm of meta-learning. We conduct extensive experiments for five-class few-shot tasks on three challenging benchmarks: miniImageNet, tieredImageNet, and FC100, and achieve top performance using the epoch-dependent transductive hyperprior learner, which captures the richest information. Our ablation study shows that both epoch-wise ensemble and empirical encourage high efficiency and robustness in the model performance.
How cars became mobile spatial media: A geographical political economy of on-board navigation

While cars, by definition and necessity, have always been (auto)mobile, they are not often considered, or studied, as media. In light of this, the present article seeks to elucidate the technological, political, and economic forces that have converged to transform cars into mobile spatial media. This article provides a framework to contextualize the nodal role of navigation in creating the conditions for a large-scale disruption in automobile technologies with potentially far-reaching impacts: autonomous navigation. The arguments at the core of this article bring together, and build on, recent theoretical developments in (a) locative media, (b) automobiles as mobile media, and (c) new mapping technologies, practices, and spatial media to provide a coherent perspective of cars as mobile spatial media. Informed by a geographical political economy of navigation, this perspective contributes to our understanding of cars in the context of digital and informational capitalism as these vehicles undergo qualitative transformations catalyzed by increased digital interconnections and comprehensive automation.
CGraph

Distributed graph processing platforms usually need to handle massive Concurrent iterative Graph Processing (CGP) jobs for different purposes. However, existing distributed systems face high ratio of data access cost to computation for the CGP jobs, which incurs low throughput. We observed that there are strong spatial and temporal correlations among the data accesses issued by different CGP jobs, because these concurrently running jobs usually need to repeatedly traverse the shared graph structure for the iterative processing of each vertex. Based on this observation, this article proposes a distributed storage and processing system CGraph for the CGP jobs to efficiently handle the underlying static/evolving graph for high throughput. It uses a data-centric load-trigger-pushing model, together with several optimizations, to enable the CGP jobs to efficiently share the graph structure data in the cache/memory and their accesses by fully exploiting such correlations, where the graph structure data is decoupled from the vertex state associated with each job. It can deliver much higher throughput for the CGP jobs by effectively reducing their average ratio of data access cost to computation. Experimental results show that CGraph improves the throughput of the CGP jobs by up to 3.47× in comparison with existing solutions on distributed platforms.
Context-Aware Systems for Chronic Disease Patients: Scoping Review

Background: Context-aware systems, also known as context-sensitive systems, are computing applications designed to capture, interpret, and use contextual information and provide adaptive services according to the current context of use.Context-aware systems have the potential to support patients with chronic conditions; however, little is known about how such systems have been utilized to facilitate patient work.Objective: This study aimed to characterize the different tasks and contexts in which context-aware systems for patient work were used as well as to assess any existing evidence about the impact of such systems on health-related process or outcome measures.Methods: A total of 6 databases (MEDLINE, EMBASE, CINAHL, ACM Digital, Web of Science, and Scopus) were scanned using a predefined search strategy.Studies were included in the review if they focused on patients with chronic conditions, involved the use of a context-aware system to support patients' health-related activities, and reported the evaluation of the systems by the users.Studies were screened by independent reviewers, and a narrative synthesis of included studies was conducted.Results: The database search retrieved 1478 citations; 6 papers were included, all published from 2009 onwards.The majority of the papers were quasi-experimental and involved pilot and usability testing with a small number of users; there were no randomized controlled trials (RCTs) to evaluate the efficacy of a context-aware system.In the included studies, context was captured using sensors or self-reports, sometimes involving both.Most studies used a combination of sensor technology and mobile apps to deliver personalized feedback.A total of 3 studies examined the impact of interventions on health-related measures, showing positive results. Conclusions:The use of context-aware systems to support patient work is an emerging area of research.RCTs are needed to evaluate the effectiveness of context-aware systems in improving patient work, self-management practices, and health outcomes in chronic disease patients.
Dependency-Based Local Attention Approach To Neural Machine Translation

Recently dependency information has been used in different ways to improve neural machine translation. For example, add dependency labels to the hidden states of source words. Or the contiguous information of a source word wo... | Find, read and cite all the research you need on Tech Science Press
Adaptive Lucas-Kanade tracking

Dense image alignment, when the displacement between the frames is large, can be a challenging task. This paper presents a novel dense image alignment algorithm, the Adaptive Forwards Additive Lucas-Kanade (AFA-LK) tracking algorithm, which considers the scale-space representation of the images, parametrized by a scale parameter, to estimate the geometric transformation between an input image and the corresponding template. The main result in this framework is the optimization of the scale parameter along with the transformation parameters, which permits to significantly increase the convergence domain of the proposed algorithm while keeping a high estimation precision. The performance of the proposed method was tested in various computer-based experiments, which reveal its interest in comparison with geometric as well as learning-based methods from the literature, both in terms of precision and convergence rate.
Deep Forest-Based Monocular Visual Sign Language Recognition

Sign language recognition (SLR) is a bridge linking the hearing impaired and the general public. Some SLR methods using wearable data gloves are not portable enough to provide daily sign language translation service, while visual SLR is more flexible to work with in most scenes. This paper introduces a monocular vision-based approach to SLR. Human skeleton action recognition is proposed to express semantic information, including the representation of signs’ gestures, using the regularization of body joint features and a deep-forest-based semantic classifier with a voting strategy. We test our approach on the public American Sign Language Lexicon Video Dataset (ASLLVD) and a private testing set. It proves to achieve a promising performance and shows a high generalization capability on the testing set.