Reviewerly/exHarmony huggingface dataset:
Large-scale T-wise interaction sampling using YASA

Testing highly-configurable software systems (i.e., software product lines) is challenging due to their large configuration space. T-wise sampling is one method of finding a representative subset of configurations for a system, which can then be tested. However, for large-scale systems, such as Linux, existing t-wise sampling algorithms do not scale well. To this end, Pett et al. proposed the sampling challenge for large-scale systems at SPLC 2019. In this paper, we attempt to solve the proposed challenge using our sampling algorithm YASA. We report our experience for all three of the given systems FinancialServices01, Automotive02, and Linux. In addition, we present the results for computing samples for all versions of the system FinancialServices01.
Space identification of sexual harassment reports with text mining

Abstract Sexual harassment is an invisible problem that has been difficult to combat because victims are often reluctant to report. However, within the past years, the sheer volume of women who have spoken up about sexual harassment has brought the issue to the forefront. This change has been largely driven, in part, by Internet and social media technologies. Given the large size of data posted on these online technologies, it is impossible to manually analyze and organize it; therefore, there is a need to utilize data and text mining methods. In order to help the fight against sexual harassment, this study proposes a predictive framework to collect more than 14,000 sexual harassment reports on the everyday sexism project (ESP) website and identify the space (location) in the reports. Our framework achieves 85.33% accuracy for seven space classes including workplace, public space, home, public transport, school, university, and media. This paper also enriches experiments by merging similar classes (e.g., school and university) and applies a feature selection method to reduce the number of features for efficiency and effectiveness purposes. This enrichment process offers promising results for different sets of classes and features, ranging from 86% – 93% accuracy.
Heuristic Based Approach for Usability Evaluation of Mobile Games

The increasing popularity of mobile games has grasped the attention in both academia and industry. The game market has evolved to the extent that it has great impact in the economy of the worlds. As there are multiple areas of issues that affect the game quality, but game usability is one of the major problems that game designers are facing. Heuristic evaluation has been very popular method. But the lack of validation has weakened the effectiveness and confidence in the heuristics. This paper focuses on the issues related to usability of the mobile games. A survey was conducted to identify the issues of usability in a game directly from the game players. This survey covered one major genre of games. After identification and analysis, we proposed a new set of heuristics to enhance the usability of mobile games. We have also used these heuristics as guidelines to redesign a game. And the redesigned game has been evaluated by the players to validate our newly proposed heuristics. By validation of heuristics we can certainly increase the usability.
Modeling Socio-Emotional and Cognitive Processes from Multimodal Data in the Wild

Detecting, modeling, and making sense of multimodal data from human users in the wild still poses numerous challenges. Starting from aspects of data quality and reliability of our measurement instruments, the multidisciplinary endeavor of developing intelligent adaptive systems in human-computer or human-robot interaction (HCI, HRI) requires a broad range of expertise and more integrative efforts to make such systems reliable, engaging, and user-friendly. At the same time, the spectrum of applications for machine learning and modeling of multimodal data in the wild keeps expanding. From the classroom to the robot-assisted operation theatre, our workshop aims to support a vibrant exchange about current trends and methods in the field of modeling multimodal data in the wild.
Parametric Evaluation Of Collaborative Filtering Over Apache Spark

Recommender systems are mechanisms that filter information in order to predict the preference of a user for an item drawn from a finite collection. Prime examples include recommendation platforms for movies, games, travel destinations, and books. Such systems rely on identifying users with similar preferences, as indicated by an appropriately selected metric, to yield reliable preference estimations. Recently, distributed implementations of recommender systems have improved scalability and can be applied by Internet-based companies to obtain insight for their respective products through latent patterns, typically of higher order nature, with having considerable impact on their profits. One of the potent personalization technologies currently powering the adaptive Web is collaborative filtering. The latter brings together the opinions of large interconnected communities on the Web, supporting filtering of substantial quantities of data. An implementation over Apache Spark of a typical recommender system is presented here. The evaluation of its prediction correctness, based on the Web movies dataset from Amazon as a benchmark, is in terms of the mean absolute error, the mean square error, and the root mean square error. Based on the results, suggestions for improved ratings are given, mainly by increasing user engagement and immersion.
Description of research data in laboratory notebooks: Challenges and opportunities

Abstract In scientific research, laboratory (lab) notebooks have traditionally been used to record experiments and their results. Lab notebooks also act as an important record of the generation, processing and analysis of data over the research data lifecycle. As research data becomes increasingly digitized and voluminous, it becomes harder to record and describe research data, especially in paper lab notebooks (PLNs). This paper addresses the challenges of recording contemporary research data in lab notebooks and discusses the requirements in lab notebooks for describing research data. Two basic requirements of lab notebooks are the completeness of the data description and the ability to link the experiment records with their corresponding research datasets. Descriptions of research data should also document the provenance of the research data so that the original data can be retrieved. Guidelines for consistent file naming and systematic directory structures for saved research data can support efficient research data retrieval.
Water turbidity estimation in water sampled images

This paper tackles the problem of estimating water turbidity by analyzing images. This computer-vision solution avoids to resort to use specific laboratory instruments and, hence facilitates the water characterization in situ. Our contribution consists in designing a whole image processing chain composed of pre-processing, segmentation, feature extraction and classification modules. The second originality of our work relies on comparing two dual approaches for the segmentation and feature extraction: handcrafted and deep neural network based approaches. Finally, the lack of a publicly available dataset has motivated the building of an appropriate dataset. Experimental results indicate satisfactory performances of the proposed approaches.
X-ray tomography based numerical analysis of stress concentrations in non-crimp fabric reinforced composites - assessment of segmentation methods

Abstract In this study two automated segmentation methodologies of an X-ray computer tomography based numerical analysis are compared. These are then assessed based on their influence on the stress distribution results of finite element models of glass fibre reinforced composites made out of non-crimp fabrics. Non-crimp fabrics reinforced composites are commonly used for wind turbine blades due to their high stiffness to weight ratio for the dominating bending load. Finite element modelling based on X-ray computer tomography allows the reduction of the cost and can accelerate the development process of the key material parameters of wind turbine blades. Recent research progress in the last years has laid the basis for such a procedure. Those processes must be easy applicable, fast and accurate. The main challenge in current methodologies is the segmentation part. The segmentation methods applied for this study have overcome this issue by being automated. This allows for a comparatively fast transfer from X-ray computer tomographic data to finite element results.
Design and Improvement of e-Collab Classroom as Learning Support System on Intelligent System Subject Electrical Department, Universitas Negeri Malang

Practicum activity on Intelligent System requires a computer laboratory with have a Graphics Processing Unit (GPU) computer spesification to increase the speed of computing. A computer with a GPU is a high-tech and expensive device. The amount of practicum equipment needed adjusts the number of students. Especially in practicums held in parallel, the users will increase several times. In addition, this device requires assistance with training in operating procedures, and requiring longer study time. One way to overcome this problem is a cloud computing device as a practicum media. The students and lecturers interact online with practicum media, online compilers, and GPU server facilities for computing. Online classes that are collaborated with learning media are called e-Collab Classroom. The student ability of computer programming is very diverse so that it has the potential to obstruct practical activities. This is used to minimize this condition and unite the platforms used in each topic contained in the Smart Systems Course. This research improve e-Collab Classroom which can be accessed from computer browser to be easily access by students from one gate mobile application. Designing e-Collab based on mobile applications for Intelligent system subjects with the topic of practical activities, namely Convolutional Neural Network (CNN). The topic of CNN was chosen as a specific topic that represented the entire simulation of practical learning activities with cloud computing. The development of e-Collab is carried out using a prototyping model consisting of the identification of needs and objects, the identification of risks, prototype design, the experimental stage, the testing phase, and the evaluation stage. The results of the development research are the CNN jobsheet, online classroom system, and mobile application.
An improved hidden behavioral pattern mining approach to enhance the performance of recommendation system in a big data environment

The proposed work aims to solve data sparsity problem in the recommendation system. It handles two-level pre-processing techniques to reduce the data size at the item level. Additional resources like items genre, tag, and time are added to learn and analyse the behaviour of the user preferences in-depth. The advantage of the proposed method is to recommend the item, based on user interest pattern and avoid recommending the outdated items. User information are grouped based on similar item genre and tag feature. This effectively handle overlapping conditions that exist on item's genre, as it has more than one genre at initial level. Further, based on time, it analyses the user non-static interest. Overall it reduces the dimensions which is an initial way to prepare data, to analyse hidden pattern. To enhance the performance, the proposed method utilized Apache's spark Mllib FP-Growth and association rule mining approach in a distributed environment. To reduce the computation cost of constructing tree in FP-Growth, the candidate data set is stored in matrix form. The experiments were conducted using MovieLens data set. The observed results shows that the proposed method achieves 4% increase in accuracy when compared to earlier methods.
Reading Born-Digital Scholarship: A Study of Webtext User Experience

Born-digital scholarship follows ideals of multimodal, interactive design, embodied in the webtext, that open new possibilities for individual and collective reader experience. However, author-designers often fail to consult actual readers about their experiences using born-digital scholarship to assess whether the realities of webtext use measure up to the ideals inspiring webtext design. User-centered design approaches offer opportunities to examine webtext user experience and design for new rhetorical engagements between born-digital designers and users. Findings from think-aloud testing show that users engage webtexts through hyperreading practices of skimming, scanning, and close reading, and experience a sense of placelessness when navigating webtexts, unfamiliarity with webtext designs, and a freedom to read nonlinearly across fragmented texts that can unsettle readers accustomed to a single, linear narrative. These findings suggest implications for webtext designs that facilitate users' reading practices and suggest that user-centered design approaches can reveal and, in turn, expand the scope of rhetorical engagements possible in born-digital scholarship.
Research on recognition of ice and snow athletes based on feature extraction and cloud computing platform

A common method for the treatment of sports injuries for a long time to provide localized cooling in ice or snow is fomentation. This system will support a feature extraction cloud computing platform application of localized cooling which is applied to the shape of ice snow for treatment of sports injuries. It is a common method of treatment of sports injuries. Athletes usually utilizes the application of therapeutic hypothermia to return to athletic activities.. Two review authors have independently assessed the relevance and impact calculations involved in the trial, and the cloud worker permits the highlights removed legitimately from the scrambled picture. Awareness of disability sport, in particular para snowboard, may not be common or easily available, so it is important that these potential athletes know that the sport exists and where they can try the sport for the first time Some examination and test results show that, for protection trademark picture extraction, proposed technique is successful, safe, and it might be applied to the cloud picture secure figuring applications.
Palynostratigraphic analysis of a Permian section from southern Paraná Basin (Brazil), Western Gondwana

New palynological assemblages from the CBM-001-ST-RS borehole, drilled in the Santa Terezinha coalfield, Rio Grande do Sul State are presented. The studied section is composed of the Rio Bonito, Palermo, Irati and Serra Alta formations, Permian units of the Paraná Basin. According to the stratigraphic distribution of guide-species and the general composition of the palynological associations, two palynozones are recognized, in ascending order: the Vittatina costabilis (VcZ) and the Lueckisporites virkkiae (LvZ) zones. Differences in the stratigraphic range of specific taxa are observed when compared to those proposed by former palynostratigraphic schemes. Subdivision of the Vittatina costabilis Zone into two subzones is not verified. Multivariate cluster analysis based on the studied material and two other sections from the Rio Grande do Sul State does not allow precise recognition of the aforementioned intervals, corroborating previous related efforts. Considering radiometric datings available for some equivalent levels within the Paraná Basin, as well as correlations with other Gondwana palynological associations, an Asselian-Wordian age is suggested for the studied section.
A Novel and Secure Approach for Quantum Key Distribution in a Cloud Computing Environment

The vast majority of the clients worry about the security, privacy, and verification in the cloud computing framework. Cloud foundation requires better approaches for giving security. Public key infrastructure (PKI) is important critical arrangement. PKI depends on asymmetric key cryptography (AKC). The issue is accidentally losing the secure key might be hopeless. The recommended method is to make quantum key distribution (QKD) reasonable for a multi-party framework with quantum user authenticate plan utilizing a secured finite key. We use network control and acknowledgement of threat authentication plan to accomplish objective. The principle highlight of the proposed plan utilizes multi-party key and lower utilization of quantum transmission. In addition, the loss and other factors are taken care of utilizing this multi-party key and correspondence with various groups. The results shows the high security of quantum cryptography where malicious or eves dropper unable to retrieve the information in the future.
An Efficient Framework of Hybrid Recommendation System based on Multi Mode

Recommendation systems have been widely applied in many areas, such as E-commerce, and so on. However, in some complex systems such as missed sparse data, it will be increasingly difficult to build a model for user recommendations. In this research we develop a recommendation system on E-Commerce. This system will be able to adapt and provide the best recommendations for each user dynamically even in sparse environment. The system will be created in a web-based application to display the product recommendations to users. The recommendation system developed is expected to be able to solve cold-start problem when there is no other relevant data to be recommended for the new added product and also the sparsity problem. To overcome this problem, the system will implement multi-mode algorithm that uses more than one search algorithm for the closest characteristics in the recommendation system and can choose one of the best algorithms to use in accordance with the existing data and hybrid-filtering that can use a combination of Collaborative Filtering is to make recommendations based on information equations between users and Content-Based Filtering is to make recommendations based on information representation of a content. Thus the system will be able to provide product recommendations on any state of data on E-Commerce.
Design and Optimization of CNN for Lane Detection

The large push for smart transportation and self-driving cars has brought out an array of research problems and a pertinent one among them is lane detection. With Convolutional Neural Network (CNN) coming up as the leading algorithm for this purpose, a lot of research has gone into the optimization of its performance. This paper proposes the application of Dynamic Mode Decomposition (DMD) algorithm for reducing the complexity of CNN for lane detection. The work involves implementation of a CNN model for lane detection and optimization of its inference time by reducing the number of layers while maintaining accuracy of the model with the help of DMD. The model implementation was done using TensorFlow library and the optimized model achieved accuracy levels similar to that of the baseline model. Both models were tested and it was found that the proposed model performed faster than the baseline; due to the lesser number of computations it performs as a result of reduction in its layers. This work shows that DMD as a background foreground separation tool, can be used to optimize the layers in a deep neural network trained to identify sparse details in images.
Temporally Refined Graph U-Nets for Human Shape and Pose Estimation From Monocular Videos

This work addresses a challenging problem of estimating the full 3D human shape and pose from monocular videos. Since real-world 3D mesh-labeled datasets are limited, most current methods in 3D human shape reconstruction only focus on single RGB images, losing all the temporal information. In contrast, we propose temporally refined Graph U-Nets, including an image-level module and a video-level module, to solve this problem. The image-level module is Graph U-Nets for human shape and pose estimation from images, where the Graph Convolutional Neural Network (Graph CNN) helps the information communication of neighboring vertices, and the U-Nets architecture enlarges the receptive field of each vertex and fuses high-level and low-level features. The video-level module is a small Residual Temporal Graph CNN (Residual TG-CNN), which learns temporal dynamics from both structural and temporal neighbors. The temporal dynamics of each vertex are continuous in the temporal dimension and highly relevant to the structural neighbors, so it is helpful to diminish the ambiguity of the body in single images by fusing temporal dynamics. Our algorithm makes full use of labels from image-level datasets and refines the image-level results through video-level module. Evaluated on Human3.6 M and 3DPW datasets, our model produces accurate 3D human meshes and achieves superior 3D human pose estimation accuracy when compared with state-of-the-art methods.
Seasonal-Periodic Subgraph Mining in Temporal Networks

\emphSeasonal periodicity is a frequent phenomenon for social interactions in temporal networks. A key property of this behavior is that it exhibits periodicity for multiple particular periods in temporal networks. Mining such seasonal-periodic patterns is significant since it can indicate interesting relationships between the individuals involved in the interactions. Unfortunately, most previous studies for periodic pattern mining ignore the seasonal feature. This motivates us to explore mining seasonal-periodic subgraphs, and the investigation presents a novel model, called maximal σ-periodic $ømega$-seasonal k-subgraph. It represents a subgraph with size larger than k and that appears at least σ times periodically in at least $ømega$ particular periods on the temporal graph. Since seasonal-periodic patterns do not satisfy the anti-monotonic property, we propose a weak version of support measure with an anti-monotonic property to reduce the search space efficiently. Then, we present an effective mining algorithm to seek all maximal σ-periodic $ømega$-seasonal k-subgraphs. Experimental results on real-life datasets show the effectiveness and efficiency of our approach.
Intent-Driven Similarity in E-Commerce Listings

Discovering similarities between online listings is a common backend task being used across different downstream experiences in eBay. Our baseline unstructured listing similarity method relies on measuring the semantic textual similarity between the embedding vectors of listing titles. However, we discovered that even with the latest contextualized embedding methods, our similarity fails to give the proper weight to the key tokens in the title that matter. This often results in identifying listing similarities that are not sufficient, which later hurts the downstream experiences. In this paper we present a method we call "Listing2Query", or "L2Q", which uses a Sequence Labeling approach to learn token importance from our users? search queries and on-site behaviour. We used pairs of listing titles and their matching search queries, and leveraged a contextualized character language model, to train L2Q as a bidirectional recurrent neural network to produce token importance weights. We demonstrate that plugging these weights into relatively straightforward listing similarity methods is a simple way to significantly improve the similarity results, even to the extent that it consistently outperforms those created by popular representations such as BERT. Notably, this approach is not reserved to only large online marketplaces but can be generalized to other cases that include a search-driven experience and a recall set of short documents.