Reviewerly/exHarmony huggingface dataset:
Big Data, Little Data, or No Data? Why Human Interaction with Data is a Hard Problem

Enthusiasm for big data is obscuring the complexity and diversity of data in scholarship and the challenges of human interaction and retrieval. Data practices are local, varying from field to field, individual to individual, and country to country. As the number and variety of research partners expands, so do the difficulties of sharing, reusing, and sustaining access to data. Information retrieval is hindered by the lack of agreement on what are "data." Complexities of human interaction with data will be illustrated with empirical examples from environmental sciences, sensor networks, astronomy, biomedicine, and other fields. Unless larger questions of knowledge infrastructures and stewardship are addressed by research communities, "no data" often becomes the norm. Implications for policy and practice in the information sciences will be explored, drawing upon the presenter's book, Big Data, Little Data, No Data: Scholarship in the Networked World (MIT Press, 2015), and subsequent research.
A Methodology for Implementing the Formal Legal-GRL Framework: A Research Preview

[Context and motivation] Legal provisions create a distinct set of requirements for businesses to be compliant with. Capturing legal requirements and managing regulatory compliance is a challenging task in system development. [Question/problem] Part of this task involves modeling legal requirements, which is not trivial for requirements engineers as non-experts in law. The resultant legal requirements models also tend to be very complex and hard to understand. [Principal ideas/results] To facilitate the modeling process, we propose a formal framework for modeling legal requirements. This framework includes a methodology that helps to resolve complexities of legal requirements models. [Contribution] In this paper, we outline this methodology and present a procedure that reduces modal and conditional complexities of legal models and facilitates automation of the modeling process.
P-value Based Task Allocation in a Scrum Team: A Multi-Agent Simulation

Scrum is an agile framework within which people can address complex problems, while productively and creatively delivering products of the highest possible value. P-value is a relatively view on the agent and the task it works, which is the key factor for task allocation algorithm design, and most importantly, p-value is extensible, because p-value can include other real-world factors into modeling and simulation, to further evaluate the relation between agent and task.
Subtle Visual Attention Guidance in VR

The research field of visual attention guidance in virtual reality (VR) explores possibilities to help viewers finding their way through immersive environments. A specialized area within this field —subtle guidance— arises with the goal to achieve this with least possible distraction, to prevent misrepresentation of actual scene content as well as degradation of immersion and presence in VR. This chapter provides an introduction to the general topic, commonly used terminology, and coarsely introduces some exemplary approaches.
Projection‐based 3D/2D registration for prospective motion correction

Purpose To develop a registration method that is capable of estimating the full range of rigid body motion from three orthogonal collapsed images of the head. These images can be obtained using the collapsed FatNav, a previously introduced navigator for prospective motion correction. It combines a short duration with wide compatibility with different main sequences due to its robustness against spin history effects. Theory and Methods A projection‐based 3D/2D registration method is presented and then modified to take into account the peculiarities of the collapsed FatNav. Water/fat separated volumes were used in simulations to assess the accuracy of the proposed method at different resolutions by comparison with high‐resolution 3D registration. The sensitivity with respect to masking strategies and starting motion parameters was investigated. Finally, prospective experiments with a healthy volunteer were performed with different types of motion patterns. A PROPELLER main sequence was chosen to compare the prospective correction with PROPELLER’s own retrospective correction. Results In the simulations the proposed method has shown comparable performance to 3D registration. Furthermore, evidence of its robustness with respect to masking strategies and starting motion parameters was presented. The combination with collapsed FatNav has performed well in correcting most of the motion artifacts prospectively with improved image quality compared to only using PROPELLER’s retrospective motion correction. Conclusions The proposed 3D/2D registration together with collapsed FatNav is characterized by a good balance between navigator duration and estimate accuracy. Further work is needed to validate the method across a wider variety of subject anatomies.
Implementation of Augmented Reality in Manufacturing: A Case Study Exercise

Many companies are facing challenges implementing AR in various disciplines, including manufacturing. Against this background, there is a high demand to learn about major obstacles that come up during the implementation of AR in manufacturing, and to discuss strategies how to overcome those barriers. This current case study exercise resulted from an AREA-funded research project in collaboration with the xreality lab at the Bundeswehr University Munich. It presents a realistic situation of a fictitious yachting company that is confronted with multiple obstacles that come up with the implementation of AR in manufacturing. Trainers and educators can apply this case study. Participants of the case study should understand the challenges of implementing AR and discuss ideas of how to deal with these situations in real-life. Multiple approaches can be used to find answers to the proposed questions, such as (1) group discussions, (2) research in academic and industry publications (such as thearea.org blog), and/or (3) interviews or discussions with experts.
Ancestral Gumbel-Top-k Sampling for Sampling Without Replacement

We develop ancestral Gumbel-Top-k sampling: a generic and efficient method for sampling without replacement from discrete-valued Bayesian networks, which includes multivariate discrete distributions, Markov chains and sequence models. The method uses an extension of the Gumbel-Max trick to sample without replacement by finding the top k of perturbed log-probabilities among all possible configurations of a Bayesian network. Despite the exponentially large domain, the algorithm has a complexity linear in the number of variables and sample size k. Our algorithm allows to set the number of parallel processors m, to trade off the number of iterations versus the total cost (iterations times m) of running the algorithm. For m=1 the algorithm has minimum total cost, whereas for m=k the number of iterations is minimized, and the resulting algorithm is known as Stochastic Beam Search. We provide extensions of the algorithm and discuss a number of related algorithms. We analyze the properties of ancestral Gumbel-Top-k sampling and compare against alternatives on randomly generated Bayesian networks with different levels of connectivity. In the context of (deep) sequence models, we show its use as a method to generate diverse but high-quality translations and statistical estimates of translation quality and entropy.
CLASSIFICATION AND RANKING OF TRENDING TOPICS IN TWITTER USING TWEETS TEXT

Every day millions of twitter user tweet their views on various topics using short messages of 140 to 280 characters length. Here we monitor the public opinion in twitter thereby identifying trending topics. To identify the current trending topics on Twitter, tweet classification is done. Tweet classification is a process of classifying the tweets based on the topics using the keywords of the tweets as a feature. Tweets are extracted from twitter using Twitter API. In the existing system, trending topics in twitter can be identified by hash tags. This work is going to categorize the tweets into seventeen classes and identify the trending topics by using words. Dataset used in this work is the list of tweets taken for two to three days from Twitter. By classifying tweets based on sensitive words for a certain time, top k most trending words from a topic will be obtained that convey the dynamic trend
A Fast Filling Algorithm for Image Restoration Based on Contour Parity

Filling techniques are often used in the restoration of images. Yet the existing filling technique approaches either have high computational costs or present problems such as filling holes redundantly. This paper proposes a novel algorithm for filling holes and regions of the images. The proposed algorithm combines the advantages of both the parity-check filling approach and the region-growing inpainting technique. Pairing points of the region’s boundary are used to search and to fill the region. The scanning range of the filling method is within the target regions. The proposed method does not require additional working memory or assistant colors, and it can correctly fill any complex contours. Experimental results show that, compared to other approaches, the proposed algorithm fills regions faster and with lower computational cost.
Carry Forward and Access Control for Unused Resources in Multi Sharing System of Hybrid Cloud

Abstract The cloud is a collection of resources like hardwares and softwares etc., and these resources are shared between public and private cloud for better resource utilization. The entire idea behind our model is to concentrate on how to magnify the resource usage on Multi sharing system with reservation policy plans in hybrid cloud environment and how to provide access control to Carry Forward of Unused Resources. In existing system, the cloud offers various types of reservation plans and on-demand plans with restrictions. Scope for either under or over provisioning of reserved resources was not taken care of. It was very much restricted to sharing of resources with multiple users. This was leading to an over and under provisioning of resources and no option was provided to carry forward the unused resources and there was no access control for their contribution of resources. So, to overcome with this problem “Multi Sharing Resources in Hybrid Cloud (MSRHC)” model is proposed. The proposed model mainly aims to enable sharing of multiple resources by multiple users and providing access control to unused resources. The experimental results show that by using Carry Forward of Unused Resources (CFUR) and Access Control for Contributed User (ACCU) approach, the results have produced 100 percent of resource utilization and full fledge access only to contributor user which reduces the cost.
A Random Gossip BMUF Process for Neural Language Modeling

Neural network language model (NNLM) is an essential component of industrial ASR systems. One important challenge of training an NNLM is to leverage between scaling the learning process and handling big data. Conventional approaches such as block momentum provides a blockwise model update filtering (BMUF) process and achieves almost linear speedups with no performance degradation for speech recognition. However, it needs to calculate the model average from all computing nodes (e.g., GPUs) and when the number of computing nodes is large, the learning suffers from the severe communication latency. As a consequence, BMUF is not suitable under restricted network conditions. In this paper, we present a decentralized BMUF process, in which the model is split into different components, each of which is updated by communicating to some randomly chosen neighbor nodes with the same component, followed by a BMUF-like process. We apply this method to several LSTM language modeling tasks. Experimental results show that our approach achieves consistently better performance than conventional BMUF. In particular, we obtain a lower perplexity than the single-GPU baseline on the wiki-text-103 benchmark using 4 GPUs. In addition, no performance degradation is observed when scaling to 8 and 16 GPUs.
Patch-Level Selection and Breadth-First Prediction Strategy for Reversible Data Hiding

A core work in reversible data hiding is designing an embedding method enabling the hider to take advantages of smooth elements as many as possible while the detection procedure for marked elements is invertible to the receiver. It motivates us to introduce a novel patch-level selection and breadth-first prediction strategy for efficient reversible data hiding. However, different from conventional works, the proposed work allows us to preferentially and simultaneously use adjacent smooth elements as many as possible. Experiments show that it significantly outperforms a part of state-of-the-arts at relatively low embedding rates, demonstrating the superiority.
Adaptive Local Gray-Level Transformation Based on Variable S-Curve for Contrast Enhancement of Mammogram Images

El Malali, Hamid Assir, Abdelhadi Harmouchi, Mohammed Rattal, Mourad Lyazidi, Aissam Mouhsen, AzeddineMammogram image enhancement plays an important role in the accuracy of the diagnosis. Resulting images must allow a good contrast and better appearance of the limiting edges between different areas in the image, while preserving both information and brightness. In this paper, an adaptive local gray-level s-curve transformation is proposed. The main aim is to improve as much contrast of mammogram images as possible. The principle is to find all the parameters of the local gray-level transformation for each image which will allow for a better improvement using the genetic algorithm that is among global optimization methods. The evaluation of the results found is done based on image quality assessment (IQA) metrics and visual inspection in comparison with three existing techniques based on histogram equalization.
The Effects of Virtual Reality Space Game on Spatial Sense and Mathematical Affective Domain: Focused on the Sixth Graders' Building Block Activity

The purpose of this study is to investigate the effects of math lessons based on the Minecraft Education Edition, which is a virtual reality space game, on spatial sense and mathematical affective domain of sixth graders. For this purpose, we conducted a pre-test on spatial sense and mathematical affective domain with seven 6th-grade classes at S Elementary School in Eunpyeong-gu, Seoul and selected one class as the experiment group and one of the homogeneous classes as the comparison group. While the experiment group participated in six lessons on the unit ‘Building Blocks’ using the Minecraft Educational Edition, the comparison group did the lessons on the same unit with Building-Blocks. After the lessons, the post-tests on spatial sense and mathematical affective domain were conducted respectively, and the test results were analyzed by the independent-sample t-test using i-STATistics. Based on these results of analysis, we induced some conclusions and discussed about didactical implications.
Discover DaVinci – A Gamified Blockchain Learning App

Discover DaVinci is a novel augmented reality system that incorporates blockchain technology with experiential learning to engage participants in an interactive discovery of Leonardo da Vinci's ouvre. In the true spirit of this "Renaissance man", Discover DaVinci explores new ideas and technologies "ahead of their time".
How is Reasoning Ability in Learning Real Analysis?

Each Mathematics Education students take Real Analysis subject must have reasoning ability.However most of them have a low reasoning ability.Reasoning ability is a drawing conclusion process by linking facts in problem solving which includes deductive thinking.The reasoning ability is able to explore their ideas and knowledge in Real Analysis subject.This research aims to describe students' reasoning ability achievement in Real Analysis subject.This research uses qualitative data.The subjects of the research are students who take the Real Analysis subject chosen by using purposive sampling.The research result reveals most of the students answer incorrectly in solving Real Analysis questions which the error is a common mistake, however the other mistakes such as misconception, careless, or just write the question are also revealed in this research.
Data mining approach to internal fraud in a project-based organization

Data mining is an efficient methodology for uncovering and extracting information from large databases, which is widely used in different areas, e.g., customer relation management, financial fraud detection, healthcare management, and manufacturing. Data mining has been successfully used in various fraud detection and prevention areas, such as credit card fraud, taxation fraud, and fund transfer fraud. However, there are insufficient researches about the usage of data mining for fraud related to internal control. In order to increase awareness of data mining usefulness in internal control, we developed a case study in a project-based organization. We analyze the dataset about working-hour claims for projects, using two data mining techniques: chi-square automatic interaction detection (CHAID) decision tree and link analysis, in order to describe characteristics of fraudulent working-hour claims and to develop a model for automatic detection of potentially fraudulent ones. Results indicate that the following characteristics of the suspected working-hours claim were the most significant: sector of the customer, origin and level of expertise of the consultant, and cost of the consulting services. Our research contributes to the area of internal control supported by data mining, with the goal to prevent fraudulent working-hour claims in project-based organizations.
Visual Commonsense Graphs: Reasoning about the Dynamic Context of a Still Image.

Even from a single frame of a still image, people can reason about the dynamic story of the image before, after, and beyond the frame. For example, given an image of a man struggling to stay afloat in water, we can reason that the man fell into the water sometime in the past, the intent of that man at the moment is to stay alive, and he will need help in the near future or else he will get washed away. We propose VisualComet, the novel framework of visual commonsense reasoning tasks to predict events that might have happened before, events that might happen next, and the intents of the people at present. To support research toward visual commonsense reasoning, we introduce the first large-scale repository of Visual Commonsense Graphs that consists of over 1.4 million textual descriptions of visual commonsense inferences carefully annotated over a diverse set of 60,000 images, each paired with short video summaries of before and after. In addition, we provide person-grounding (i.e., co-reference links) between people appearing in the image and people mentioned in the textual commonsense descriptions, allowing for tighter integration between images and text. We establish strong baseline performances on this task and demonstrate that integration between visual and textual commonsense reasoning is the key and wins over non-integrative alternatives.
A pressure-based transfer matrix method and measurement technique for studying resonances in flutes and other open-input resonators

This paper provides an alternative formulation of a transfer matrix method (TMM) long used for modeling the passive resonances of wind instruments. In the conventional, impedance-based TMMs, the quantities being evaluated are the acoustic pressure P and the acoustic flow U; in the present pressure-based TMM (a variation of the Heavens matrix method developed for thin film optics), the quantities being evaluated are the amplitudes of the forward-going and backward-going pressure waves, P+ and P−. Power spectra (|P++P−|2 vs frequency) for several types of branched and non-branched open-input resonators are computed and then compared to experimental spectra obtained from a Fourier analysis of the acoustic pressure at a position inside the resonator recorded during excitation by a localized sound source placed just outside the resonator. Agreement is good for both one-end-closed polyvinyl chloride chimney pipes and a modern flute set up with a variety of lip, keywork, and open/closed tonehole configurations, although fits for very short straight tubes appeared to require unphysically high wall losses. Input impedance derived from the computed values of P+ and P− at the flute's embouchure hole appears consistent with directly measured input impedance data in the literature.
Use of free CAD software for 3D printing individualized face masks based on face scans

Aim To describe a method of digitally customizing 3D-printed face mask designs using 3D face scans and free software. Materials and methods The procedure of creating customized face masks initially involved importing and aligning STL files of face scans and mask components in free CAD software. The imported mask described in this article is composed of three different STL files (body, filter structure, and grid). The body of the mask was then edited to fit precisely into the face scan STL by using the software's offset tool, followed by adjustments and smoothening of the surfaces of the edges. The resulting customized body of the mask plus the filter and grid STL files were exported and 3D printed with polylactic acid (PLA) filament using a fused deposition modeling (FDM) 3D printer. For the purposes of comparison, a conventional 3D-printed mask (from the original STL files, without being customized for the face scan) was also 3D printed from the original STL files. Both face masks were tested on the same two volunteers. Results The customized 3D-printed face mask presented a higher adaptation compared with the conventional face mask. The area of facial contact matched the one digitally designed in the software. The 3D-printed grid could clip exactly into the filter, which in turn could be precisely screwed into the body of the face mask. Conclusion Within the limitations of this technical report, the present findings suggest that customized 3D-printed face masks with enhanced adaptation can be digitally designed using face scans and free CAD software.
Worse in real life: An eye-tracking examination of the cost of CAD at low prevalence.

Computer-aided detection (CAD) is applied during screening mammography for millions of women each year. Despite its popularity, several large studies have observed no benefit in breast cancer detection for practices that use CAD. This lack of benefit may be driven by how CAD information is conveyed to the radiologist. In the current study, we examined this possibility in an artificial task modeled after screening mammography. Prior work at high (50%) target prevalence suggested that CAD marks might disrupt visual attention: Targets that are missed by the CAD system are more likely to be missed by the user. However, targets are much less common in screening mammography. Moreover, the prior work on this topic has focused on simple binary CAD systems that place marks on likely locations, but some modern CAD systems employ interactive CAD (iCAD) systems that may mitigate the previously observed costs. Here, we examined the effects of target prevalence and CAD system. We found that the costs of binary CAD were exacerbated at low prevalence. Meanwhile, iCAD did not lead to a cost on unmarked targets, which suggests that this sort of CAD implementation may be superior to more traditional binary CAD implementations when targets occur infrequently. (PsycInfo Database Record (c) 2020 APA, all rights reserved).