Usage Of Features Research Articles

Understanding the role of personality traits on Facebook intensity

The present study aims to examine the influence of the five personality traits (extraversion, neuroticism, agreeableness, openness and conscientiousness) on both Facebook usage and users' preferences for specific Facebook features. Hierarchical regression analysis was used for hypothesis testing. Survey data from 367 university students in Greece indicate that, agreeable individuals use Facebook to express their orientation to other people rather than to themselves. Extroverts use Facebook as a relationship building mechanism, whereas neurotics strive to bring out the best of themselves. Openness and conscientiousness do not seem to affect significantly Facebook use.

Classification of Abnormal Masses in Ultrasonic Elastography Breast Images

Abstract The specificity of US (ultrasound) B-mode imaging in detecting benign masses is less due to overlap of features of malignant and benign masses. The intensity variations in a US B-mode image are due to variation in the texture of the underlying tissues, but in a US elastography image relates to strain value in tissues and have more potential in tissue differentiation. This study aims to develop a new scheme to automatically determine a seed point in an US elastography image based on mean strain value, thereby speeding up the segmentation process and to use elasticity features extracted from the segmented images to improve the classification rate. A new method of mean strain value calculation within the abnormal region is introduced to develop a new automatic method of selecting a seed point that helps in the precise segmentation of the lesion area. A neural network is used to classify the abnormal masses from texture, shape and two new elasticity features extracted from the segmented regions. The new approach of automatic seed point selection used on a dataset of 62 US elastography images achieved an accuracy of 90.32%. The average Tanimotto coefficient of US elastography images segmented using this approach is 0.75123. The classifier provided a sensitivity, specificity and classification accuracy of 80.00%, 86.67%, and 84.00% respectively. This is the first study that applies mean strain value for automatic seed point selection in US elastography images, reducing time consumed by the user and usage of elasticity features for classification of abnormal breast masses.

Improving miRNA Target Prediction Using CLASH Data.

In this chapter, we present a computational method, TarPmiR, for miRNA target prediction. TarPmiR is based on emerging features of miRNA-target interactions learned from CLASH (crosslinking, ligation and sequencing of hybrids) data. First, we introduce miRNA target prediction, delineate existing methods for miRNA target prediction, and discuss their usage and limitations. Next, we describe available CLASH data, the learning of new miRNA binding features from CLASH data, and the usage of CLASH features in miRNA target prediction. Finally, we detail the computational pipeline of TarPmiR, discuss its performance compared with existing computational methods for miRNA target prediction, and present its installation and usage for miRNA target prediction. This chapter will facilitate the common understanding of CLASH data, new characteristics of miRNA-target interactions, and the use of the CLASH based miRNA target prediction tool TarPmiR.

Understanding the role of personality traits on Facebook intensity

The present study aims to examine the influence of the five personality traits (extraversion, neuroticism, agreeableness, openness and conscientiousness) on both Facebook usage and users' preferences for specific Facebook features. Hierarchical regression analysis was used for hypothesis testing. Survey data from 367 university students in Greece indicate that, agreeable individuals use Facebook to express their orientation to other people rather than to themselves. Extroverts use Facebook as a relationship building mechanism, whereas neurotics strive to bring out the best of themselves. Openness and conscientiousness do not seem to affect significantly Facebook use.

Apneic Events Detection Using Different Features of Airflow Signals

Apneic-event based sleep disorders are very common and affect greatly the daily life of people. However, diagnosis of these disorders by detecting apneic events are very difficult. Studies show that analyzes of airflow signals are effective in diagnosis of apneic-event based sleep disorders. According to these studies, diagnosis can be performed by detecting the apneic episodes of the airflow signals. This work deals with detection of apneic episodes on airflow signals belonging to Apnea-ECG (Electrocardiogram) and MIT (Massachusetts Institute of Technology) BIH (Bastons’s Beth Isreal Hospital) databases. In order to accomplish this task, three representative feature sets namely classic feature set, amplitude feature set and descriptive model feature set were created. The performance of these feature sets were evaluated individually and in combination with the aid of the random forest classifier to detect apneic episodes. Moreover, effective features were selected by OneR Attribute Eval Feature Selection Algorithm to obtain higher performance. Selected 28 features for Apnea-ECG database and 31 features for MITBIH database from 54 features were applied to classifier to compare achievements. As a result, the highest classification accuracies were obtained with the usage of effective features as 96.21% for Apnea-ECG database and 92.23% for MIT-BIH database. Kappa values are also quite good (91.80 and 81.96%) and support the classification accuracies for both databases, too. The results of the study are quite promising for determining apneic events on a minute-by-minute basis.

Studies Concerning the Usage of the Intrinsic Morphological and Chemical Features of Some Common and Document Paper Types as Security Items

The paper addresses the usage of the intrinsic security features which is a challenge for lowering the document protection costs. Three types of papers were investigated using ESEM-EDS and ED(P)-XRFS in order to identify and characterize overt and covert intrinsic items. The paper shows that some of the intrinsic morphological features of document paper can be successfully used as taggants. The chemical compositions can be used as internal marks for forensic authentication of the ballpoint pen imprints. Two new ways are proposed for enhancing the document safety protection: the usage of the fractal dimension of the paper pattern and the intentional admixture into paper mass of cheaper substances as taggants in conjunction with SEM and XRF techniques.

Users' preferences and design recommendations to promote engagements with mobile apps for diabetes self-management: Multi-national perspectives.

BackgroundMobile phone applications (apps) offer motivation and support for self-management of diabetes mellitus (DM), but their use is limited by high attrition due to insufficient consideration of end-users perspectives and usability requirements. This study aimed to examine app usage and feature preferences among people with DM, and explore their recommendations for future inclusions to foster engagement with diabetes apps.MethodsThe study was conducted internationally on adults with type 1 or type 2 DM using online questionnaire (quantitative) to investigate usage and preferences for app features that support diabetes self-management and semi structured telephone interview (qualitative) to explore suggestions on fostering engagement and specific educational information for inclusion into diabetes apps. Survey and interview data were analysed using descriptive/ inferential statistics and inductive thematic analysis respectively.ResultsA total of 217 respondents with type 1 DM (38.25%) or type 2 DM (61.8%), from 4 continents (Australia, Europe, Asia and America) participated in the survey. About half of the respondents (48%) use apps, mainly with features for tracking blood glucose (56.6%), blood pressure (51.9%) and food calories (48.1%). Preferred features in future apps include nutrient values of foods (56.7%), blood glucose (54.8%), physical exercise tracker (47%), health data analytics (42.9%) and education on diabetes self-management (40.6%). Irrespective of the type of DM, participants proposed future apps that are user friendly, support healthy eating, provide actionable reminders and consolidate data across peripheral health devices. Participants with type 1 DM recommended customised features with news update on developments in the field of diabetes. Nominated specific educational topics included tips on problem solving, use of insulin pump therapy, signs of diabetes complication and transitioning from paediatric into adult care.ConclusionsThe study has highlighted patients’ perspectives on essential components for inclusion in diabetes apps to promote engagement and foster better health outcomes.

Simulation of precipitation time series using tree-rings, earlywood vessel features, and artificial neural network

Precipitation forecasting plays a key role in natural resource management, agriculture management, and water requirement provision. Hence, dendroclimatology methods and artificial neural network (ANN) are used to estimate precipitation values in the past times. Moreover, a geographic information system (GIS) can be applied as a tool to demonstrate the spatial variation of precipitation. In this study, earlywood vessel features and tree-rings of Siberian elm species were used to simulate precipitation. The vessel features, the tree-ring extent, and the secondary data of climatologic station from the different sites were studied. At first, cross-dating and standardization of the tree-rings and vessels were performed. Then, time series analysis was done. In the next step, the relations between vessel chronologies and tree-rings with the precipitation were defined. The input parameters were selected tree-ring width and vessel features for the modeling, whereas precipitation of the growing season was selected as the output. The model or network was trained and verified by taking a case study of Fomanat plain (northern part of Iran). The results showed that the combinatory usage of vessel features and tree-rings extent in precipitation simulating can increase simulation capability. Further, different isohyets were generated using the simulated precipitation values, an interpolation technique in a GIS system. The results showed that the highest precipitation and the lowest precipitation have occurred in 1926 and 1986 during the last century.

Relationship between the degree exponent and its associated timeseries’ hurst exponent of a static scale-free network — Under the viewpoint of mutual representation between timeseries and network graph

During the past decade, bridging the gap between time series and complex networks, which aims to investigate the usage of network features to depict geometry of different type of timeseries becomes a hot topic. In this brief paper, the internal relationship between a typical static scale-free networks and its associated nonlinear timeseries (under the viewpoint of duality analysis of networks and time series) was investigated. A ubiquitous process to discuss such a problem are introduced. It is found that, the scale-free degree exponent has linear relationship with its associated timeseries’ hurst exponent. Research in this direction will provide new methods and powerful tools for analysis of the causal relationships between complex structures and temporal behaviors of a complex dynamical system. Also, it is useful for further investigation on structure and function analysis of complex networks via classical timeseries analysis methods.

Analyzing Attitude of Second Screen Social Media Messages

We analyze more than 3,000,000 Twitter, 800,000 Instagram, and 50,000 Tumblr posts concerning a single major in-real-life event, Super Bowl XLIX, to determine attitude. We consider three event phases ( Pre , During , and Post ). Findings show link-based recommendations and undirected broadcast patterns positively correlate with attitude in the Pre and Post phases, respectively. The usage of these specific features highlights the differing information needs of viewers during these phases, specifically the sharing of information in the Pre phase and the sharing of opinions in the Post phase. The volume of postings indicates a negative attitude for all social media platforms, demonstrating that adverse information is more likely to be shared than positive information; this finding contradicts prior findings. This second screens phenomenon research is important in identifying the sharing information on multiple social media platforms during an in real life event.

A Multiview Multimodal System for Monitoring Patient Sleep

Clinical observations indicate that during critical care at the hospitals, a patient's sleep positioning and motion have a significant effect on recovery rate. Unfortunately, there is no formal medical protocol to record, quantify, and analyze motion of patients. There are very few clinical studies that use manual analysis of sleep poses and motion recordings to support medical benefits of patient positioning and motion monitoring. Manual processes do not scale, are prone to human errors, and put strain on an already taxed healthcare workforce. This study introduces multimodal, multiview motion analysis and summarization for healthcare (MASH). MASH is an autonomous system, which addresses these issues by monitoring healthcare environments and enabling the recording and analysis of patient sleep-pose patterns. MASH uses three RGB-D cameras to monitor patients in a medical intensive care unit (ICU) room. The proposed algorithms estimate pose direction at different temporal resolutions and use keyframes to efficiently represent pose transition dynamics. MASH combines deep features computed from the data with a modified version of hidden Markov model (HMM) to flexibly model pose duration and summarize patient motion. The performance is evaluated in ideal (BC: bright and clear/occlusion-free) and natural (DO: dark and occluded) scenarios at two motion resolutions and in two environments: a mock-up and a medical ICU. The usage of deep features is evaluated and their performance compared with engineered features. Experimental results using deep features in DO scenes increase performance from $\text{86.7}\%$ to $\text{93.6}\%$ , while matching the classification performance of engineered features in BC scenes. The performance of MASH is compared with HMM and C3D. The overall overtime tracing and summarization error rate across all methods increased when transitioning from the mock-up to the the medical ICU data. The proposed keyframe estimation helps achieve a $\text{78}\%$ transition classification accuracy.

Interaction and Engagement with an Anxiety Management App: Analysis Using Large-Scale Behavioral Data.

BackgroundSAM (Self-help for Anxiety Management) is a mobile phone app that provides self-help for anxiety management. Launched in 2013, the app has achieved over one million downloads on the iOS and Android platform app stores. Key features of the app are anxiety monitoring, self-help techniques, and social support via a mobile forum (“the Social Cloud”). This paper presents unique insights into eMental health app usage patterns and explores user behaviors and usage of self-help techniques.ObjectiveThe objective of our study was to investigate behavioral engagement and to establish discernible usage patterns of the app linked to the features of anxiety monitoring, ratings of self-help techniques, and social participation.MethodsWe use data mining techniques on aggregate data obtained from 105,380 registered users of the app’s cloud services.ResultsEngagement generally conformed to common mobile participation patterns with an inverted pyramid or “funnel” of engagement of increasing intensity. We further identified 4 distinct groups of behavioral engagement differentiated by levels of activity in anxiety monitoring and social feature usage. Anxiety levels among all monitoring users were markedly reduced in the first few days of usage with some bounce back effect thereafter. A small group of users demonstrated long-term anxiety reduction (using a robust measure), typically monitored for 12-110 days, with 10-30 discrete updates and showed low levels of social participation.ConclusionsThe data supported our expectation of different usage patterns, given flexible user journeys, and varying commitment in an unstructured mobile phone usage setting. We nevertheless show an aggregate trend of reduction in self-reported anxiety across all minimally-engaged users, while noting that due to the anonymized dataset, we did not have information on users also enrolled in therapy or other intervention while using the app. We find several commonalities between these app-based behavioral patterns and traditional therapy engagement.

Emotionally Oriented Analysis of the Experiences of Visually Impaired People on Facebook

With technological advancements, there has been a vast increase in the number of companies that fight over their market share. In search of a differentiating factor, companies are investing more and more in their products’ emotional designs. This researched work has evaluated the affects that are caused in visually impaired people when using Facebook's features and then compared them with the experiences of sighted users. To do that, these two types of Facebook users were subjected to a questionnaire that was based on the PANAS affect scale. Once the information was collected, statistics were employed so as to evaluate both users’ feelings. The results have shown that there were significant statistical differences between the sighted and the visually impaired users when the “affects” were evaluated by using the PANAS tool. The five “negative affects” that were selected (Irritability, Uselessness, Frustration, Sadness, and Confusion) were largely more relevant for the blind people in most of the evaluated features. This has indicated some serious accessibility problems. However, a high frequency of the five “positive affects” that were considered (Satisfaction, Pleasantness, Surprise, Excitement, Interest, and Determination) were additionally observed for both of these two groups. These results were interpreted as feelings of both social inclusion and social exclusion, indicating the possibility of exploring technological devices that were unavailable not long ago. After analyzing their experiences in their usage of the Facebook features, the findings have also highlighted the many differing emotions that are felt by the visually impaired and the sighted users. The resulting outcomes have indicated that there are some issues that are still open to problems and difficulties. Moreover, these issues involve human-computer interactions. Nevertheless, fortunately, there is light at the end of the tunnel, as will be revealed.

Semantic function of the choral counterpoint in Canticum sacrum by Igor Stravinsky

Background. The substantiation of the logic of stylistic synthesis in the light of the choral component of the dramaturgy of this composition opens up the prospect of studying choral counterpoint of the twentieth century as universals and also helps to identify patterns of artistic thinking. Objectives. The article is devoted to the identification of semantic function of the choral counterpoint in the context of the overall dramaturgy in Canticum Sacrum ad honorem Sancti Marci Nominis by Stravinsky. Methods. The author used hermeneutic, structural-functional, semantic research methods. The performing approach to the studying fragments of Stravinsky’s compositions reflects the specificity of choral art. Results. Choral counterpoint in the context of the overall dramaturgy Canticum sacrum develops into a separate layer, which is more than others, depends on strict norms of counterpoint. Stravinsky, relying on the traditions of the cantata genre, distinguishes the choral component into a peculiar counterpoint, which contrasts with others layers of the dramaturgy (orchestra, organ, soloists). It has been found that the solo parts of the score have a more subjective embodiment, in which there are both an expressive recitation of lyric lines (Song of Solomon 4:16, 5:1) and an emotional appeal to the God (Mark 9:23-24) while the choral layer of dramaturgy represents the main tenets of the Faith: Preaching the Gospel and Three Christian Virtues – Love, Faith and Hope. The counterpoint as a reflection of the idea of “absolute harmony” in the context of the composition is the Divine Word and the main precept with which there is sacrificial Love, Faith and Hope for the salvation. Features of the pitch organization are the result of the composer’s reference to the serialism. The combination of the principles of the classical 12-tone technique with the elements of serialism determines the specific interpretation of melodic material, intervallic structure and voice leading. Conclusions. Analysis of the fragments of Canticum sacrum score set that the choral counterpoint performs the function of objectification to express basic postulate ideas. That is why the norms of poetics of the strict counterpoint are the dominant stylistic components in the choral writing of Stravinsky. They appear in the symmetry or proportionality of the forms, the partial usage of the textural features of the samples of this stylistic direction, as well as the whole apparatus of canonic techniques.

Semantic function of the choral counterpoint in Canticum sacrum by Igor Stravinsky

Background. The substantiation of the logic of stylistic synthesis in the light of the choral component of the dramaturgy of this composition opens up the prospect of studying choral counterpoint of the twentieth century as universals and also helps to identify patterns of artistic thinking. Objectives. The article is devoted to the identification of semantic function of the choral counterpoint in the context of the overall dramaturgy in Canticum Sacrum ad honorem Sancti Marci Nominis by Stravinsky. Methods. The author used hermeneutic, structural-functional, semantic research methods. The performing approach to the studying fragments of Stravinsky’s compositions reflects the specificity of choral art. Results. Choral counterpoint in the context of the overall dramaturgy Canticum sacrum develops into a separate layer, which is more than others, depends on strict norms of counterpoint. Stravinsky, relying on the traditions of the cantata genre, distinguishes the choral component into a peculiar counterpoint, which contrasts with others layers of the dramaturgy (orchestra, organ, soloists). It has been found that the solo parts of the score have a more subjective embodiment, in which there are both an expressive recitation of lyric lines (Song of Solomon 4:16, 5:1) and an emotional appeal to the God (Mark 9:23-24) while the choral layer of dramaturgy represents the main tenets of the Faith: Preaching the Gospel and Three Christian Virtues – Love, Faith and Hope. The counterpoint as a reflection of the idea of “absolute harmony” in the context of the composition is the Divine Word and the main precept with which there is sacrificial Love, Faith and Hope for the salvation. Features of the pitch organization are the result of the composer’s reference to the serialism. The combination of the principles of the classical 12-tone technique with the elements of serialism determines the specific interpretation of melodic material, intervallic structure and voice leading. Conclusions. Analysis of the fragments of Canticum sacrum score set that the choral counterpoint performs the function of objectification to express basic postulate ideas. That is why the norms of poetics of the strict counterpoint are the dominant stylistic components in the choral writing of Stravinsky. They appear in the symmetry or proportionality of the forms, the partial usage of the textural features of the samples of this stylistic direction, as well as the whole apparatus of canonic techniques.

Fast genre classification of web images using global and local features

To effectively mine the contents embedded in web images, it is useful to classify the images into different types so that they can be fed to different procedures for detailed analysis. The authors herein propose a hierarchical algorithm for efficiently classifying web images into four classes. Their algorithm consists of two stages: the first stage extracts global features reflecting the distributions of color, edge and gradient, and uses a support vector machine (SVM) classifier for preliminary classification. Images assigned low confidence by the first stage classifier are processed by the second stage, which further extracts local texture features represented in the bag-of-words framework and uses another SVM classifier for final classification. In addition, they design two fusion strategies to train the second-stage classifier and generate the final prediction depending on the usage of local features in the second stage. To validate the effectiveness of proposed method, they built a database containing more than 55,000 images from various sources. On their test image set, they obtained an overall classification accuracy of 98.4% and the processing speed is over 27 fps on an Intel(R) Xeon(R) central processing unit (2.90 GHz).

Expert-Like Use of Hedges and Boosters in Research Articles Written by Polish and English Native-Speaker Writers

The present study compares the use of main interpersonal metadiscourse markers - hedges and boosters - in a corpus of 40 research articles from the area of applied linguistics, written in English by native speakers and Polish writers. Used as communicative strategies, these words and expressions increase (boosters) or reduce (hedges) the force of arguments. In order to gain an in-depth insight and to achieve greater precision, in the analysis the author utilizes a concordance tool WordSmith 6.0 (Scott 2012). The results point to important discrepancies in the usage of these text features by authors representing different native languages and cultures. The study has important implications for developing competence in writing for publication in English as a Foreign Language.

Touchable Apps: Exploring the Usage of Touch Features and Their Impact on Engagement

Touch has become an inseparable element of mobile platforms. This study examines the use of different touch features and the impact of these touch gestures on consumer engagement with a mobile shopping app. We focus on three informational touch features that are common among shopping apps: touch to zoom in on a page (zoom-page), to view product details (product-view), and to be directed to outside links (open-webpage). We develop a two-level model that captures (1) consumers' decisions to stay with or leave an app and (2) their use of touch features. Our main results empirically demonstrate the strong explanatory power of informational touch gestures, especially their dwell time, in consumer app browsing decisions, whereas navigational touch gestures do not significantly affect app stay likelihood. A longer dwell time and early use of zoom-page within a session encourage the stay. Moreover, we observe strong synergy and antergy (negative synergy) among these touch gestures. The cumulative dwell time and temporal progression of touch gestures affect subsequent touch feature usage. Managerially, our results suggest that an early intervention that encourages the use of zoom-page increases app stay likelihood, and marketers may apply our model to quantify the impact of such interventions on consumer browsing decisions at the individual level. The results also shed light on how marketers can infer the stage of the shopping process based on touch gestures (segmentation) and guide consumers through the purchase funnel by promoting the use of zoom-page and product-view. Lastly, the findings provide insights into how marketers can promote the use of open-webpage, which has the lowest baseline usage rate yet is crucial for transactions, based on the synergy among touch gestures and through improving the non-native browsing experience.

User attitudes and behaviors toward personalized control of privacy settings on smartphones

SummaryThe fine‐grained access control has been proved to be a reliable tool to ensure preserving of privacy of end users. In fog computing, one of the challenges is to understand users' attitudes and behaviors toward personalized control. However, few of studies have given a clear view on users' perception of the burden of interactivity when they set complex privacy settings. To this end, we conducted a user study including a lab study with 26 participants and an evaluation with 223 participants. From the lab study, we found that participants were satisfied with improved privacy settings but did not adapt well to complex personalized interfaces. We proposed effective methods to assist users to balance between the full control and the additional interaction burden, including sorting, recommendations, and establishing profiles. After this lab study, we organized a survey evaluation additionally to explore users' current usage of privacy features. Results from the evaluation showed that the principle reason that users failed to use privacy features was that they were not appropriately aware of these features. A key conclusion is that privacy settings should not only let users take over the control of smartphones but also inform them of the knowledge on privacy practices.

An Automatic Parameter Decision System of Bilateral Filtering with GPU-Based Acceleration for Brain MR Images.

Bilateral filters have been extensively utilized in a number of image denoising applications such as segmentation, registration, and tissue classification. However, it requires burdensome adjustments of the filter parameters to achieve the best performance for each individual image. To address this problem, this paper proposes a computer-aided parameter decision system based on image texture features associated with neural networks. In our approach, parallel computing with the GPU architecture is first developed to accelerate the computation of the conventional bilateral filter. Subsequently, a back propagation network (BPN) scheme using significant image texture features as the input is established to estimate the GPU-based bilateral filter parameters and its denoising process. The k-fold cross validation method is exploited to evaluate the performance of the proposed automatic restoration framework. A wide variety of T1-weighted brain MR images were employed to train and evaluate this parameter-free decision system with GPU-based bilateral filtering, which resulted in a speed-up factor of 208 comparing to the CPU-based computation. The proposed filter parameter prediction system achieved a mean absolute percentage error (MAPE) of 6% and was classified as "high accuracy". Our automatic denoising framework dramatically removed noise in numerous brain MR images and outperformed several state-of-the-art methods based on the peak signal-to-noise ratio (PSNR). The usage of image texture features associated with the BPN to estimate the GPU-based bilateral filter parameters and to automate the denoising process is feasible and validated. It is suggested that this automatic restoration system is advantageous to various brain MR image-processing applications.