Social Interactions for Detecting Stress Based Issues

2021 ◽  
Vol 7 (3) ◽  
Author(s):  
S Nagaraju ◽  
B. Prabhakara Reddy
Keyword(s):  
Social Interactions ◽  
Large Scale ◽  
Internet Site ◽  
Web Based ◽  
Proposed Model ◽  
Large Scale Dataset ◽  
Community Information ◽  
Stress States ◽  
Proactive Care ◽  
The Relationship

Mental stress is showing harmfulness to human health leads abnormal stress in chronology with this may lose our mental health for proactive care. With recognizable pieces of proof of web-based media, individuals cannot share their everyday exercises and collaborate with companions via web-based media stages, making it happing to use online informal community information for stress identification. We find that users stress state is closely associated with thereupon of his/her friends in social media, which we employ a large-scale dataset from real-world social platforms to systematically study the relationship between users’ stress states and social interactions. We first define a gaggle of stress-related comments, images, and social attributes from various aspects, then proposed a plot. Research results saying that the proposed model can improve the detection performance. With the help of enumeration, we build an internet site for the users to spot their stress rate level and may check other related activities.

scholarly journals An Attention-Based Model Using Character Composition of Entities in Chinese Relation Extraction

Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 79 ◽  
Author(s):  
Xiaoyu Han ◽  
Yue Zhang ◽  
Wenkai Zhang ◽  
Tinglei Huang
Keyword(s):  
Language Processing ◽  
Large Scale ◽  
Named Entity Recognition ◽  
Relation Extraction ◽  
Entity Recognition ◽  
Additional Information ◽  
Named Entity ◽  
Proposed Model ◽  
The Relationship ◽  
Crucial Part

Relation extraction is a vital task in natural language processing. It aims to identify the relationship between two specified entities in a sentence. Besides information contained in the sentence, additional information about the entities is verified to be helpful in relation extraction. Additional information such as entity type getting by NER (Named Entity Recognition) and description provided by knowledge base both have their limitations. Nevertheless, there exists another way to provide additional information which can overcome these limitations in Chinese relation extraction. As Chinese characters usually have explicit meanings and can carry more information than English letters. We suggest that characters that constitute the entities can provide additional information which is helpful for the relation extraction task, especially in large scale datasets. This assumption has never been verified before. The main obstacle is the lack of large-scale Chinese relation datasets. In this paper, first, we generate a large scale Chinese relation extraction dataset based on a Chinese encyclopedia. Second, we propose an attention-based model using the characters that compose the entities. The result on the generated dataset shows that these characters can provide useful information for the Chinese relation extraction task. By using this information, the attention mechanism we used can recognize the crucial part of the sentence that can express the relation. The proposed model outperforms other baseline models on our Chinese relation extraction dataset.

scholarly journals CodeMatcher: Searching Code Based on Sequential Semantics of Important Query Words

2022 ◽  
Vol 31 (1) ◽  
pp. 1-37
Author(s):  
Chao Liu ◽  
Xin Xia ◽  
David Lo ◽  
Zhiwe Liu ◽  
Ahmed E. Hassan ◽  
...  
Keyword(s):  
Large Scale ◽  
Fuzzy Search ◽  
Code Search ◽  
Proposed Model ◽  
Accuracy Measure ◽  
Indexing Technique ◽  
Google Search ◽  
The Relationship ◽  
Sequential Semantics

To accelerate software development, developers frequently search and reuse existing code snippets from a large-scale codebase, e.g., GitHub. Over the years, researchers proposed many information retrieval (IR)-based models for code search, but they fail to connect the semantic gap between query and code. An early successful deep learning (DL)-based model DeepCS solved this issue by learning the relationship between pairs of code methods and corresponding natural language descriptions. Two major advantages of DeepCS are the capability of understanding irrelevant/noisy keywords and capturing sequential relationships between words in query and code. In this article, we proposed an IR-based model CodeMatcher that inherits the advantages of DeepCS (i.e., the capability of understanding the sequential semantics in important query words), while it can leverage the indexing technique in the IR-based model to accelerate the search response time substantially. CodeMatcher first collects metadata for query words to identify irrelevant/noisy ones, then iteratively performs fuzzy search with important query words on the codebase that is indexed by the Elasticsearch tool and finally reranks a set of returned candidate code according to how the tokens in the candidate code snippet sequentially matched the important words in a query. We verified its effectiveness on a large-scale codebase with ~41K repositories. Experimental results showed that CodeMatcher achieves an MRR (a widely used accuracy measure for code search) of 0.60, outperforming DeepCS, CodeHow, and UNIF by 82%, 62%, and 46%, respectively. Our proposed model is over 1.2K times faster than DeepCS. Moreover, CodeMatcher outperforms two existing online search engines (GitHub and Google search) by 46% and 33%, respectively, in terms of MRR. We also observed that: fusing the advantages of IR-based and DL-based models is promising; improving the quality of method naming helps code search, since method name plays an important role in connecting query and code.

scholarly journals An Engagement Model Based on User Interest and QoS in Video Streaming Systems

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Xiaoying Tan ◽  
Yuchun Guo ◽  
Mehmet A. Orgun ◽  
Liyin Xue ◽  
Yishuai Chen
Keyword(s):  
Large Scale ◽  
Fog Computing ◽  
User Engagement ◽  
Mobile Video ◽  
User Interest ◽  
User Behaviors ◽  
Proposed Model ◽  
Large Scale Dataset ◽  
Qoe Model

With the surging demand on high-quality mobile video services and the unabated development of new network technology, including fog computing, there is a need for a generalized quality of user experience (QoE) model that could provide insight for various network optimization designs. A good QoE, especially when measured as engagement, is an important optimization goal for investors and advertisers. Therefore, many works have focused on understanding how the factors, especially quality of service (QoS) factors, impact user engagement. However, the divergence of user interest is usually ignored or deliberatively decoupled from QoS and/or other objective factors. With an increasing trend towards personalization applications, it is necessary as well as feasible to consider user interest to satisfy aesthetic and personal needs of users when optimizing user engagement. We first propose an Extraction-Inference (E-I) algorithm to estimate the user interest from easily obtained user behaviors. Based on our empirical analysis on a large-scale dataset, we then build a QoS and user Interest based Engagement (QI-E) regression model. Through experiments on our dataset, we demonstrate that the proposed model reaches an improvement in accuracy by 9.99% over the baseline model which only considers QoS factors. The proposed model has potential for designing QoE-oriented scheduling strategies in various network scenarios, especially in the fog computing context.

scholarly journals 3D Simplification Methods and Large Scale Terrain Tiling

2020 ◽  
Vol 12 (3) ◽  
pp. 437
Author(s):  
Ricard Campos ◽  
Josep Quintana ◽  
Rafael Garcia ◽  
Thierry Schmitt ◽  
George Spoelstra ◽  
...  
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Level Of Detail ◽  
Web Based ◽  
European Area ◽  
Levels Of Detail ◽  
Triangulated Irregular Networks ◽  
Large Scale Dataset ◽  
Irregular Networks ◽  
Elevation Data

This paper tackles the problem of generating world-scale multi-resolution triangulated irregular networks optimized for web-based visualization. Starting with a large-scale high-resolution regularly gridded terrain, we create a pyramid of triangulated irregular networks representing distinct levels of detail, where each level of detail is composed of small tiles of a fixed size. The main contribution of this paper is to redefine three different state-of-the-art 3D simplification methods to efficiently work at the tile level, thus rendering the process highly parallelizable. These modifications focus on the restriction of maintaining the vertices on the border edges of a tile that is coincident with its neighbors, at the same level of detail. We define these restrictions on the three different types of simplification algorithms (greedy insertion, edge-collapse simplification, and point set simplification); each of which imposes different assumptions on the input data. We implement at least one representative method of each type and compare both qualitatively and quantitatively on a large-scale dataset covering the European area at a resolution of 1/16 of an arc minute in the context of the European Marine Observations Data network (EMODnet) Bathymetry project. The results show that, although the simplification method designed for elevation data attains the best results in terms of mean error with respect to the original terrain, the other, more generic state-of-the-art 3D simplification techniques create a comparable error while providing different complexities for the triangle meshes.

scholarly journals APPLICATION OF THE EXPONENTIAL GREY MODEL ON THE MAINTENANCE COST PREDICTION FOR A LARGE SCALE HOSPITAL / EKSPONENTINIO PILKOJO MODELIO TAIKYMAS PROGNOZUOJANT DIDELĖS LIGONINĖS EKSPLOATACINES SĄNAUDAS

2012 ◽  
Vol 15 (4) ◽  
pp. 379-392
Author(s):  
Tzu-Ping Lo ◽  
Sy-Jye Guo ◽  
Chin-Te Chen
Keyword(s):  
Large Scale ◽  
Grey Model ◽  
Maintenance Cost ◽  
Exponential Series ◽  
Limited Budget ◽  
Proposed Model ◽  
Cost Distribution ◽  
Hospital Building ◽  
The Cost ◽  
The Relationship

Realizing the maintenance cost distribution and predicting the future tendency are important for facility managers to efficiently arrange the limited budget. This paper collects 16,228 maintenance records of a representative hospital in Taiwan and further analyzes the cost distribution. Besides, by calculating the maintenance cost of per square meter of floor area per year (dollar/m2/year) and comparing with the previous studies, this paper also points out the relationship between maintenance cost and the operation ages. moreover, this paper establishes a hybrid grey model termed as EGM(1,1), which adopting exponential series to identify the residual error series resulted from grey model, to predict the maintenance cost. The repair cost of hospital building from 1998 to 2006 is adopted to demonstrate the applicability and practicability of EGM(1,1). Results show that the proposed model can predict the tendency precisely. Santrauka Norint efektyviai išdėstyti ribotą biudžetą, pastatų ūkio valdytojai turi suprasti eksploatacijos sąnaudų pasiskirstymą ir sudaryti ateities tendencijų prognozes. Šiame darbe surinkti 16 228 įrašai apie reprezentacinės Taivano ligoninės eksploataciją ir jais remiantis analizuojamas sąnaudų pasiskirstymas. Apskaičiavus metines eksploatacijos sąnaudas vienam kvadratiniam metrui (doleriai/m2/metus) ir palyginus jas su ankstesniais tyrimais, darbe taip pat parodomas ryšys tarp eksploatacijos sąnaudų ir objekto amžiaus. Be to, darbe sudaromas hibridinis pilkasis modelis, pavadintas EGM(1,1), kuriame naudojant eksponentines eilutes nustatomos liktinės paklaidų eilutės, gautos pilkajame modelyje, taip siekiant prognozuoti eksploatacines sąnaudas. Naudojant 1998–2006 m. ligoninės pastato remontui išleistą sumą pristatomas EGM(1,1) taikymas ir praktiškumas. Rezultatai rodo, kad pasiūlytas modelis tendencijas gali prognozuoti tiksliai.

scholarly journals A Quick Measure of Theory of Mind in Autistic Adults: Decision Accuracy, Latency and Self-Awareness

2021 ◽  
Author(s):  
Neil Brewer ◽  
Robyn L. Young ◽  
Jade Eloise Norris ◽  
Katie Maras ◽  
Zoe Michael ◽  
...  
Keyword(s):  
Theory Of Mind ◽  
Social Interactions ◽  
Large Scale ◽  
Forced Choice ◽  
Self Awareness ◽  
Web Based ◽  
Decision Accuracy ◽  
Response Characteristics ◽  
Convergent And Divergent Validity ◽  
Autistic Adults

AbstractAutistic adults often experience difficulties in taking the perspective of others, potentially undermining their social interactions. We evaluated a quick, forced-choice version of the Adult Theory of Mind (A-ToM) test, which was designed to assess such difficulties and comprehensively evaluated by Brewer et al. (2017). The forced-choice version (the A-ToM-Q) demonstrated discriminant, concurrent, convergent and divergent validity using samples of autistic (N = 96) and non-autistic adults (N = 75). It can be administered in a few minutes and machine-scored, involves minimal training and facilitates large-scale, live, or web-based testing. It permits measurement of response latency and self-awareness, with response characteristics on both measures enhancing understanding of the nature and extent of perspective taking difficulties in autistic individuals.

Predicting microRNA–disease associations from lncRNA–microRNA interactions via Multiview Multitask Learning

2020 ◽  
Author(s):  
Yu-An Huang ◽  
Keith C C Chan ◽  
Zhu-Hong You ◽  
Pengwei Hu ◽  
Lei Wang ◽  
...  
Keyword(s):  
Noncoding Rna ◽  
Large Scale ◽  
Side Information ◽  
Complete Information ◽  
Proposed Model ◽  
Disease Associations ◽  
Area Under Roc Curve ◽  
Multiview Representation ◽  
The Relationship ◽  
Deep Learning Model

Abstract Motivation Identifying microRNAs that are associated with different diseases as biomarkers is a problem of great medical significance. Existing computational methods for uncovering such microRNA-diseases associations (MDAs) are mostly developed under the assumption that similar microRNAs tend to associate with similar diseases. Since such an assumption is not always valid, these methods may not always be applicable to all kinds of MDAs. Considering that the relationship between long noncoding RNA (lncRNA) and different diseases and the co-regulation relationships between the biological functions of lncRNA and microRNA have been established, we propose here a multiview multitask method to make use of the known lncRNA–microRNA interaction to predict MDAs on a large scale. The investigation is performed in the absence of complete information of microRNAs and any similarity measurement for it and to the best knowledge, the work represents the first ever attempt to discover MDAs based on lncRNA–microRNA interactions. Results In this paper, we propose to develop a deep learning model called MVMTMDA that can create a multiview representation of microRNAs. The model is trained based on an end-to-end multitasking approach to machine learning so that, based on it, missing data in the side information can be determined automatically. Experimental results show that the proposed model yields an average area under ROC curve of 0.8410+/−0.018, 0.8512+/−0.012 and 0.8521+/−0.008 when k is set to 2, 5 and 10, respectively. In addition, we also propose here a statistical approach to predicting lncRNA-disease associations based on these associations and the MDA discovered using MVMTMDA. Availability Python code and the datasets used in our studies are made available at https://github.com/yahuang1991polyu/MVMTMDA/.

scholarly journals Spatiality Preservable Factored Poisson Regression for Large-Scale Fine-Grained GPS-Based Population Analysis

2019 ◽  
Vol 33 ◽  
pp. 1142-1149 ◽  
Author(s):  
Masamichi Shimosaka ◽  
Yuta Hayakawa ◽  
Kota Tsubouchi
Keyword(s):  
Poisson Regression ◽  
Large Scale ◽  
Population Analysis ◽  
Empirical Evaluation ◽  
Fine Grained ◽  
Point Of Interest ◽  
Proposed Model ◽  
Gps Traces ◽  
Relationship Of ◽  
The Relationship

With the wide use of smartphones with Global Positioning System (GPS) sensors, the analysis of the population from GPS traces has been actively explored in the last decade. We propose herein a brand new population prediction model to capture the population trends in a fine-grained point of interest (POI) densely distributed over large areas and understand the relationship of each POI in terms of spatiality preservation. We propose a new framework, called Spatiality Preservable Factorized Regression (SPFR), to realize this model. The SPFR is inspired by the success of the recently proposed bilinear Poisson regression and the concept of multi-task learning with factorization approach and the graph proximity regularization. Given that the proposed model is written simply in terms of optimization, we achieve scalability using our model. The results of our empirical evaluation, which used a massive dataset of GPS logs in the Tokyo region over 32 M count logs, show that our model is comparable to the stateof-the-art methods in terms of capturing the population trend across meshes while retaining spatial preservation in finer mesh areas.

scholarly journals One does not simply track students: the relationship between teachers’ perceived public track regard and their job satisfaction in a context of rigid tracking

2021 ◽  
Author(s):  
Lorenz Dekeyser ◽  
Mieke Van Houtte ◽  
Charlotte Maene ◽  
Peter A.J. Stevens
Keyword(s):  
Job Satisfaction ◽  
General Education ◽  
Large Scale ◽  
Social Categorization ◽  
The Self ◽  
Categorization Theory ◽  
School Tracking ◽  
Large Scale Dataset ◽  
Self Reports ◽  
The Relationship

AbstractAlthough there is a wealth of research on the educational and broader outcomes of tracking in education, there is virtually no research that investigates teachers’ track identities on such outcomes. Building on research that focuses on the determinants of teachers’ job satisfaction, tracking outcomes and social categorization theory, this study tests the relationship between the perceived public regard of a teachers’ track and their job satisfaction, in a Belgian context of within- (vocational, technical and general education tracks) and between-school tracking (multilateral versus categorical schools). Data of the Belgian SIS (School, Identity and Society)-survey, a large-scale dataset gathered in 2017, containing the self-reports of 324 teachers, clustered in 43 secondary schools is used to test particular hypotheses regarding this relationship. The results of a multilevel analysis show that the relationship between teachers’ public track regard and their job satisfaction varies according to the track they teach and whether they work in a categorical or multilateral school. The findings highlight the importance of carrying out further research on tracked identities in education.

Coworking spaces: a new way of achieving productivity

2018 ◽  
Vol 16 (4) ◽  
pp. 452-466 ◽  
Author(s):  
Salvador Bueno ◽  
Gonzalo Rodríguez-Baltanás ◽  
M. Dolores Gallego
Keyword(s):  
Social Interactions ◽  
Positive Influence ◽  
Professional Relationships ◽  
Web Based ◽  
Content Type ◽  
Doing Business ◽  
Potential Factors ◽  
Coworking Spaces ◽  
The Relationship ◽  
Practical Implications

Purpose This paper aims to explore the relationship between coworking spaces and productivity. Design/methodology/approach A research model was designed to carry out the analysis. Specifically, this model attempts to reveal the influence of social interactions and the coworking environment on productivity. Furthermore, three moderated variables were incorporated into the model: gender, age and level of education. A Web-based survey was conducted. Findings The findings confirm the positive influence of social interactions and coworking environment on productivity. Research limitations/implications There are two limitations. First, it is based on the perception of coworkers. It would be interesting to add the perception of coworking space managers to provide more solid findings. The second limitation is that it has not suggested any additional potential factors which could affect productivity. Practical implications Implications of this study are grouped into two categories. First, from an academic perspective, it contributes to the development of knowledge about the increasing use of coworking spaces. Second, from a managerial perspective, this paper highlights how environmental factors and the facilities of a workplace can help to achieve better conditions for productivity, in particular in coworking spaces. Social implications Furthermore, the use of social interactions in professional relationships can be understood as an alternative way to carry out new ways of doing business. Originality/value This paper contributes to the enrichment of knowledge-concerning coworking spaces developed a pioneering study.

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