Reviewerly/exHarmony huggingface dataset:
Moving Object Detection Through Image Bit-Planes Representation Without Thresholding

Background subtraction is an example of a moving object detection technique that uses machine vision systems. Conventional moving object detection methods need complicated thresholds for background modeling to address changes in illumination. This paper proposes a novel background modeling approach without thresholding based on a bit-planes method, which fully utilizes color characteristics through spatial and temporal-based improvement. The proposed idea is effective and efficiently solving for shadow disturbance and brightness changes. We evaluate our proposed method using several challenging indoor and outdoor sequences from the CDNET 2014 dataset. The experiments show that the proposed idea typically achieves a higher rate of detection accuracy than those of the current state-of-the-art approaches.
The Mental Image Revealed by Gaze Tracking

Humans involuntarily move their eyes when retrieving an image from memory. This motion is often similar to actually observing the image. We suggest to exploit this behavior as a new modality in human computer interaction, using the motion of the eyes as a descriptor of the image. Interaction requires the user's eyes to be tracked but no voluntary physical activity. We perform a controlled experiment and develop matching techniques using machine learning to investigate if images can be discriminated based on the gaze patterns recorded while users merely think about image. Our results indicate that image retrieval is possible with an accuracy significantly above chance. We also show that this result generalizes to images not used during training of the classifier and extends to uncontrolled settings in a realistic scenario.
New discrimination diagrams for basalts based on big data research

In recent days, discrimination diagrams have been widely used for tracing the tectonic settings and origins of basalts from orogenic belts. However, conventional discrimination diagrams are not accurate enough. Here, we reported six new discrimination diagrams obtained from the global database using data mining methods. For most individual diagrams, island arc basalt can be nearly 100% was identified, whereas ocean island basalt and mid-ocean ridge basalt can be discriminated from each other with less than 10% of overlap, under a confidence coefficient of 85%. Using the six new discrimination diagrams together, basalts of different origins can be efficiently identified.
Bi-Sequential Video Error Concealment Method Using Adaptive Homography-Based Registration

This paper proposes a novel video error concealment method for the reconstruction of all unknown regions in frames. The proposed method first performs temporal error concealment (TEC) sequentially in forward direction with the propagation of source information for the unknown regions of frames in group-of-frames (GOF). Then, it performs the TEC sequentially in backward direction for the remaining unknown regions in the GOF. For the reconstruction of unknown regions in each target frame in forward and backward TEC stages, we propose an adaptive homography-based registration of the reference frame. The proposed method selects adaptively global or local homography-based registration for the reconstruction of each unknown region. After the backward direction, the TEC is performed in each frame and the spatial error concealment is applied to reconstruct completely the remaining unknown regions with relatively small sizes. The experimental results show that the proposed method gives significantly improved objective and subjective video quality compared with the traditional reconstruction methods. The proposed method also gives comparably good reconstruction results for large size unknown regions and is feasible for practical real-time applications.
Interactive Learning Media Using Kvisoft Flipbook Maker for Mathematics Learning

This study aims to produce interactive learning media using Kvisoft Flipbook Maker for mathematics learning for 5th semester students in Mathematics Department of Universitas Ahmad Dahlan as well as knowing the feasibility of the learning media based on the assessment of media experts, material and learning experts and 70 students. The development model used was the Borg and Gall development model which are 1) needs analysis, 2) preparing media designs, and 3) making learning media in the form of e-modules arranged in the form of compact disks (CDs). Learning media that has been made is validated to expert lecturers. Subsequent input is used to revise the e-module. The final contents of this e-module are: Basic Competency and Competency Standards, Instructions for Use, Material, Video related to material and Evaluation. The developing learning media has validated by the material expert and media expert. The material expert result shows 3,389 score for the content aspect, 3,5 score for presentation with the learning approach, and 3,222 score for didactic requirements aspect. The final score 3,383 shows a very valid criteria. The resulting assessment of media expert of an interactive learning media using Kvisoft Flipbook Maker shows 3,556 score for the languages aspect and 3,5 score for the graphics aspect. The final score 3,524 shows very valid criteria. The questionnaire result of students response used to find out the practicality value. From the student response questionnaire data obtained a score of 3.24 with good criteria.
Text-to-picture tools, systems, and approaches: a survey

Text-to-picture systems attempt to facilitate high-level, user-friendly communication between humans and computers while promoting understanding of natural language. These systems interpret a natural language text and transform it into a visual format as pictures or images that are either static or dynamic. In this paper, we aim to identify current difficulties and the main problems faced by prior systems, and in particular, we seek to investigate the feasibility of automatic visualization of Arabic story text through multimedia. Hence, we analyzed a number of well-known text-to-picture systems, tools, and approaches. We showed their constituent steps, such as knowledge extraction, mapping, and image layout, as well as their performance and limitations. We also compared these systems based on a set of criteria, mainly natural language processing, natural language understanding, and input/output modalities. Our survey showed that currently emerging techniques in natural language processing tools and computer vision have made promising advances in analyzing general text and understanding images and videos. Furthermore, important remarks and findings have been deduced from these prior works, which would help in developing an effective text-to-picture system for learning and educational purposes.
Analyzing Value Discovery in Design Decisions Through Ethicography

HCI scholarship is increasingly concerned with the ethical impact of socio-technical systems. Current theoretically driven approaches that engage with ethics generally prescribe only abstract approaches by which designers might consider values in the design process. However, there is little guidance on methods that promote value discovery, which might lead to more specific examples of relevant values in specific design contexts. In this paper, we elaborate a method for value discovery, identifying how values impact the designer's decision making. We demonstrate the use of this method, called Ethicography, in describing value discovery and use throughout the design process. We present analysis of design activity by user experience (UX) design students in two lab protocol conditions, describing specific human values that designers considered for each task, and visualizing the interplay of these values. We identify opportunities for further research, using the Ethicograph method to illustrate value discovery and translation into design solutions.
A training image optimal selecting method based on composite correlation coefficient ranking for multiple-point geostatistics

A training image (TI) can be regarded as a low to high order spatial pattern database for multiple point geostatistical modelling. Geologists can construct a number of candidate training images (CTIs) containing different spatial patterns for a certain simulation domain based on their subjective criteria. Each of these CTIs has different compatibility with the simulation domain. Thus, it is essential to select one or a number of appropriate TIs for multiple point simulation. To date, a number of CTI optimal selecting methods have been proposed. However, the existing methods are all based on compatibility ranking between hard data and CTIs. The reliability of the ranking results may significantly reduce when hard data is really sparse. In a large fraction of simulation scenarios, the density of hard data is really low while a corresponding high quality seismic attribute volume (SAV) is available. Since the SAV is good enough for the spatial structures forecasting of the reservoir, it can be used for CTI ranking and selecting, too. In this paper, a training image optimal selecting method by ranking the composite correlation coefficients (CCCs) between a SAV and a set of CTIs was proposed. It allows to rank CTIs using a SAV instead of the very sparse hard data. The correlation coefficient (CC) is used to quantify the consistency of two data events. For every conditional data event (CDE) extracted from the SAV, all the training data events (TDEs) in a CTI were exhaustively extracted to calculate CC with the CDE. The TDE that possesses the maximum correlation coefficient (MCC) was selected as the most compatible one for the CDE and the MCC value was placed at the location of the CDE in the simulation domain. After calculating and placing the MCC values for all the CDEs extracted from the SAV, a MCC volume was constructed for every CTI. In the case where a set of CTIs is available, a set of MCC volumes can be constructed. A set of CCC values was calculated by taking the mean value and the standard deviation of the MCC volumes into consideration. CCC values quantified the compatibilities of these CTIs and indicated the appropriate CTI(s). The applicability of the proposed method is successfully evaluated in a synthetic 3D categorical example, a synthetic 3D continuous example and an actual 3D example at the Xihu Depression, East China Sea Shelf Basin.
I Don't Even Have to Bother Them!

The privacy guaranteed by secure messaging applications relies on users completing an authentication ceremony to verify they are using the proper encryption keys. We examine the feasibility of social authentication, which partially automates the ceremony using social media accounts. We implemented social authentication in Signal and conducted a within-subject user study with 42 participants to compare this with existing methods. To generalize our results, we conducted a Mechanical Turk survey involving 421 respondents. Our results show that users found social authentication to be convenient and fast. They particularly liked verifying keys asynchronously, and viewing social media profiles naturally coincided with how participants thought of verification. However, some participants reacted negatively to integrating social media with Signal, primarily because they distrust social media services. Overall, automating the authentication ceremony and distributing trust with additional service providers is promising, but this infrastructure needs to be more trusted than social media companies.
An Evaluation of Touch Input at the Edge of a Table

Tables, desks, and counters are often nearby, motivating their use as interactive surfaces. However, they are typically cluttered. As an alternative, we explore touch input along the 'edge' of table-like surfaces. The performance of tapping, crossing, and dragging is tested along the two ridges and front face of a table edge. Results show top ridge movement time is comparable to the top face when tapping or dragging. When crossing, both ridges are at least 11% faster than the top face. Effective width analysis is used to model performance and provide recommended target sizes. Based on observed user behaviour, variations of top and bottom ridge crossing are explored in a second study, and design recommendations with example applications are provided.
Development and Evaluation of a Novel Point-of-Use Quality Assurance Tool for Digital Pathology

Flexible working at diverse or remote sites is a major advantage when reporting using digital pathology, but currently there is no method to validate the clinical diagnostic setting within digital microscopy.To develop a preliminary Point-of-Use Quality Assurance (POUQA) tool designed specifically to validate the diagnostic setting for digital microscopy.We based the POUQA tool on the red, green, and blue (RGB) values of hematoxylin-eosin. The tool used 144 hematoxylin-eosin-colored, 5×5-cm patches with a superimposed random letter with subtly lighter RGB values from the background color, with differing levels of difficulty. We performed an initial evaluation across 3 phases within 2 pathology departments: 1 in the United Kingdom and 1 in Sweden.In total, 53 experiments were conducted across all phases resulting in 7632 test images viewed in all. Results indicated that the display, the user's visual system, and the environment each independently impacted performance. Performance was improved with reduction in natural light and through use of medical-grade displays.The use of a POUQA tool for digital microscopy is essential to afford flexible working while ensuring patient safety. The color-contrast test provides a standardized method of comparing diagnostic settings for digital microscopy. With further planned development, the color-contrast test may be used to create a "Verified Login" for diagnostic setting validation.
Integrated Workflows

As design thinking shifted away from conventional methods with the rapid adoption of computer-aided design and fabrication technologies, architects have been seeking ways to initiate a comprehensive dialogue between the virtual and the material realms. Current methodologies do not offer embodied workflows that utilize the feedback obtained through a subsequent transition process between physical and digital design. Therefore, narrowing the separation between these two platforms remains as a research problem. This literature review elaborates the divide between physical and digital design, testing and manufacturing techniques in the morphological process of architectural form. We first review the digital transformation in the architectural design discourse. Then, we proceed by introducing a variety of methods that are integrating digital and physical workflows and suggesting an alternative approach. Our work unveils that there is a need for empirical research with a focus on integrated approaches to create intuitively embodied experiences for architectural designers.
CCHAN: An End to End Model for Cross Domain Sentiment Classification

Cross domain sentiment classification (CDSC) aims to adopt a model trained by a source domain to a target domain. It has received considerable attention in recent years. Most existing models mainly focus on learning representations that are domain independent in both the source domain and the target domain. However, domain specific features, which should also be informative are ignored by these models. In this paper, we propose an end to end model. It can capture both the source domain and target domain features at the same time. This model includes two parts; one is a cloze task network (CTN), we use it as an auxiliary task to fine-tune words embedding in both domains. Another is a Convolutional hierarchical attention networks (CHAN), we use it for sentiment classification. The CHAN can capture important words and sentences concerning sentiment based on its two stages of attention mechanism. The CTN and CHAN conduct jointly learning we abbreviate this model as CCHAN. The experiments on the Amazon review datasets demonstrate that the proposed CCHAN can significantly outperform the state-of-the-art methods.
A Pilot Study to Test the Feasibility of a Home Mobility Monitoring System in Community-Dwelling Older Adults

Technology enables home-based personalized care through continuous, automated, real-time monitoring of a participant's health condition and remote communication between health care providers and participants. Technology has been implemented in a variety of nursing practices. However, little is known about the use of home mobility monitoring systems in visiting nursing practice. Therefore, the current study tested the feasibility of a home mobility monitoring system as a supportive tool for monitoring daily activities in community-dwelling older adults. Daily mobility data were collected for 15 months via home-based mobility monitoring sensors among eight older adults living alone. Indoor sensor outputs were categorized into sleeping, indoor activities, and going out. Atypical patterns were identified with reference to baseline activity. Daily indoor activities were clearly differentiated by sensor outputs and sensor outputs discriminated atypical activity patterns. During the year of monitoring, a health-related issue was identified in a participant. Our findings indicate the feasibility of a home mobility monitoring system for remote, continuous, and automated assessment of a participant's health-related mobility patterns. Such a system could be used as a supportive tool to detect and intervene in the case of problematic health issues.
Virtual Objects in the Physical World

As technology advances, people increasingly interact with virtual objects in settings such as augmented reality (AR) where the virtual layer is superimposed on top of the physical world. Similarly to interactions with physical objects, users may assign virtual objects with value, experience a sense of relatedness, and develop psychological ownership over these objects. The objective of this study is to understand how AR's unique characteristics influences the emergence of meaning and ownership perceptions amongst users. We conducted a study of users' interactions with a virtual dog over a three-week period, comparing AR and fully virtual settings. Our findings show that engagement with the application is a key determinant of the relation users develop with virtual objects. However, the effect of the background layer-whether physical or virtual-dominates the development of relatedness and ownership feelings, highlighting the importance of the "real" physical layer in shaping users' perceptions.
Superconductor Amoeba-Inspired Problem Solvers for Combinatorial Optimization

Among advanced paradigms in the physics of computing, adiabatic quantum-flux-parametron (AQFP) logic is interesting, in that it can easily introduce stochastic processes by exploiting naturally occurring thermal fluctuations. This study proposes using AQFP logic to implement stochastic-local-search solvers. Experiments show that AQFP circuits can find solutions to simple logical constraint-satisfaction problems when moderate thermal fluctuations are applied. This result indicates the possibility of superconducting digital circuits dedicated to solving combinatorial optimization problems.
Integrating an augmented reality sandbox challenge activity into a large-enrollment introductory geoscience lab for nonmajors produces no learning gains

Studies have consistently documented that introductory geoscience students struggle with visualizing features presented on topographic maps. This is a problem that has the potential to increase in a digital age when engagement with maps consists primarily of GPS navigation via smartphones. Since the first augmented reality (AR) sandbox in 2012, geoscience educators have been wondering how it might be used to improve students' ability to read topographic maps. This study examines one potential approach. Here we present the results of research that took place over a full academic year at a large, primarily undergraduate-serving, public university in 40 lab sections of introductory geology. This research assessed data from 730 comparison and experimental group participants to determine (a) students' ability to represent a 2D topographic map in 3D, (b) the impact of the AR sandbox on student engagement, and (c) the impact of the AR sandbox on student learning. The results of this study revealed that students were successful in representing a 2D map in 3D and reported increased engagement in the experiment group due to experience with the AR sandbox; however, there was no difference in student learning between the experimental and comparison groups.
Too many tags spoil the metadata: investigating the knowledge management of scientific research with semantic web technologies

Scientific research is increasingly characterised by the volume of documents and data that it produces, from experimental plans and raw data to reports and papers. Researchers frequently struggle to manage and curate these materials, both individually and collectively. Previous studies of Electronic Lab Notebooks (ELNs) in academia and industry have identified semantic web technologies as a means for organising scientific documents to improve current workflows and knowledge management practices. In this paper, we present a qualitative, user-centred study of researcher requirements and practices, based on a series of discipline-specific focus groups. We developed a prototype semantic ELN to serve as a discussion aid for these focus groups, and to help us explore the technical readiness of a range of semantic web technologies. While these technologies showed potential, existing tools for semantic annotation were not well-received by our focus groups, and need to be refined before they can be used to enhance current researcher practices. In addition, the seemingly simple notion of "tagging and searching" documents appears anything but; the researchers in our focus groups had extremely personal requirements for how they organise their work, so the successful incorporation of semantic web technologies into their practices must permit a significant degree of customisation and personalisation.
FCTcon: Dynamic Control of Flow Completion Time in Data Center Networks for Power Efficiency

In the era of cloud computing, data center network (DCN) can consume a significant amount of power (e.g., 10 to 20 percent) in large-scale data centers. To reduce the power consumption of DCN, traffic consolidation has been recently proposed as an effective approach to reduce the number of DCN devices in use. However, existing consolidation approaches do not sufficiently consider the flow completion time (FCT) requirement. On one hand, missing the FCT deadlines can cause serious violation of service-level agreement, especially for delay-sensitive networking services, such as web search and E-commerce. On the other hand, keeping all the devices on to make FCTs much shorter than the desired requirements is unnecessary because 1) users may not be able to perceive the difference, and 2) such a greedy strategy can lead to unnecessarily high DCN power consumption and thus more electricity costs. In this paper, we propose FCTcon, a dynamic FCT control strategy for DCN power optimization. FCTcon is designed rigorously based on control theory to dynamically control the FCT of delay-sensitive traffic flows exactly to the requirements, such that the desired FCT performance is guaranteed while the maximum amount of DCN power savings can be achieved. Results from both hardware experiments and simulation evaluation demonstrate that, compared to the state-of-the-art DCN power optimization schemes, FCTcon can improve the DCN FCT performance, while achieving nearly the same or even more power savings. Consequently, FCTcon can result in more than 22.0 to 62.2 percent extra net profits for a data center with 50K servers. In addition, we further propose two extended designs of FCTcon to handle the coflow abstraction recently proposed for DCN, which successfully reduce the coflow deadline miss ratio by 12 to 15 percent.