Explore the vast range of titles available.

The debate on how social media use (SMU) influences adolescent well-being is mostly based on self-reports of SMU. By collecting data and screenshots donated from 374 Swiss adolescents (Meanage = 15.71; SDage = 0.82) over 2 weeks, we created “Appnomes”—app-specific usage metrics on screentime, number of activations, and number of notifications per participant per day derived, and associated them with daily hedonic and eudaimonic well-being. Longer TikTok time predicted lower eudaimonic well-being (β = − 0.08) daily but higher positive emotions (β = 0.06) the next day; longer use of WhatsApp predicted negative emotions (β = 0.06) while more screen activations for WhatsApp predicted greater feelings of connection (β = 0.08). Instagram notification was positively related to increased feeling of focused (β = 0.06) the next day. YouTube screen unlocks predicted more feeling of meaning (β = 0.07) the next day. More Snapchat screentime predicted less relaxed, less competent, and less positive emotions (with − 0.07 < β < − 0.06). Results pointed towards minimal or no effects, challenging the moral panic on the detrimental impact of SMU on teen well-being.

This study investigates whether mobile app developers need to adjust their app store strategy depending on the popularity of their mobile apps. The study draws on the long tail perspective to differentiate between niche and popular mobile apps and considers app updating frequency, revenue model, user value of mobile apps, and optimization of the mobile app content as influential elements of app store strategy. It proposes a model linking developers’ app store strategy to mobile app performance, measured through user rating data, with mobile app popularity acting as a moderator. To test its hypotheses, the study estimates a regression model with interaction effects on a dataset of mobile apps from Apple’s App Store (N = 5765). The findings reveal that, relative to popular mobile apps, niche mobile apps need to be managed more proactively through more frequent updates and efficacious optimization of the mobile app content. The findings also show that offering mobile apps in hedonic categories complements a niche positioning strategy. The study contributes a novel insight by documenting how mobile app popularity interacts with app store strategy in determining mobile app performance.

Over recent years, there are an increasing number of incidents in which archival images have been ripped. Leak tracking is one of the key problems for anti-screenshot digital watermarking of archival images. Most of the existing algorithms suffer from low detection rate of watermark, because the archival images have a single texture. In this paper, we propose an anti-screenshot watermarking algorithm for archival images based on Deep Learning Model (DLM). At present, screenshot image watermarking algorithms based on DLM can resist screenshot attacks. However, if these algorithms are applied on archival images, the bit error rate (BER) of the image watermark will increase dramatically. Archival images are ubiquitous, so in order to improve the robustness of archival image anti-screenshot, we propose a screenshot DLM “ScreenNet”. It aims to enhance the background and enrich the texture with style transfer. Firstly, a preprocessing process based on style transfer is added before the insertion of an archival image into the encoder to reduce the influence of the screenshot process of the cover image. Secondly, the ripped images are usually moiréd, so we generate a database of ripped archival images with moiréd by means of moiréd networks. Finally, the watermark information is encoded/decoded through the improved ScreenNet model using the ripped archive database as the noise layer. The experiments prove that the proposed algorithm is able to resist anti-screenshot attacks and achieves the ability to detect watermark information to leak the trace of ripped images.

Purpose: Screenshots are an important means of visualization in software documentation. One question technical communicators need to address when dealing with screenshots is whether visual signaling elements, such as arrows or frames, should be added in order to highlight relevant information. This article reports the results of an experimental study that examined whether signaling elements successfully guide visual attention of readers to relevant screenshot information as intended. A second goal was to find out whether visual signaling has a positive impact on how accurate and fast users execute the tasks which the screenshots support. Method: Two versions of a software tutorial were constructed that included screenshots with or without signaling elements. Participants' eye movements were recorded while they studied the tutorial and executed the tasks described therein. In addition to eye movement measures, accuracy of task execution and time to complete the tasks were determined as measures of overall success on the tasks. Results: Participants working with tutorials that used visual signaling executed more tasks correctly. No differences were found regarding the time needed to complete the tasks. Analysis of the eye tracking data showed that participants fixated relevant screenshot areas longer and more often if highlighted by signaling elements. Conclusions: The results provide evidence that adding signaling elements to screenshots is an effective means to guide the visual attention of users. As predicted by the Cognitive Theory of Multimedia Learning, visual signaling does not simply increase interest in pictures but helps users to select relevant information.

Various virtual goniometers have been used for photographic measurements. However, there is no single method that is both reliable and valid for measuring the forearm, wrist, and finger joints. This study aimed to investigate the criterion validity and intra- and inter-rater reliability of a virtual goniometer for assessing forearm, wrist, and finger joints using screenshots from video recordings and to calculate the standard error of measurement (SEM) and minimum detectable change (MDC). This is a clinical measurement study. Goniometric measurements were performed independently by two observers in 26 healthy participants (49 hands) using a virtual goniometer. Criterion validity was assessed by examining the agreement between virtual and manual goniometer measurements. Reliability was calculated using the intraclass correlation coefficient (ICC) to assess agreement between virtual and manual goniometers and interobserver agreement for virtual measurements. The difference between measurements was analyzed using the Student test and Bland-Altman plots. SEM and MDC were both used to determine the error associated with the measurements. Strong agreement between measurements (ICC = 0.69-0.98) and positive moderate to high correlation (r = 0.52-0.96; p < 0.001) were observed. Bland-Altman plots showed the agreement between the two measurement methods. Intra-rater (ICC = 0.80-0.99) and inter-rater reliability (ICC = 0.76-0.99) were high. SEM was low (2°-4°) and MDC ranged from 4°-12°. The virtual goniometer proved to be a valid and reliable method for measuring joint angles from screenshots. The inter-rater and intra-rater reliability of the virtual goniometer was high. The average bias between the virtual and manual goniometer was small. Measurement errors were low for forearm, wrist, and hand movements, with the largest measurement errors observed for the second and third fingers. •Joint angles can be accurately measured with screenshots.•Virtual goniometer is a valid and reliable instrument for measuring forearm, wrist, and hand joints.•Largest measurement errors were observed in the index and long fingers.

Phishing evolves rapidly nowadays, causing much damage to finance, brand reputation, and privacy. Various phishing detection methods have been proposed along with the rise of phishing, but there are still research issues. Phishing websites mainly steal users’ information through visual deception and deep learning methods have been proved very effective in computer vision applications but there is a lack in the research on visual analysis using deep learning algorithms. Moreover, most research use balanced datasets, which is not the case in a real Web environment. Therefore, this paper proposes a security indicator area (SIA) which contains most security indicators that are designed to help users identify phishing sites. The proposed method then takes screenshots of SIA and uses a convolutional neural network (CNN) as a classifier. To prove the efficiency of the proposed method, this paper carries out several comparative experiments on an unbalanced dataset with much fewer phishing sites, which increases detection difficulty but also makes the detection closer to reality. The results show that the proposed method achieves the highest F1-score among the compared methods, while providing advantages on detection efficiency and data expansibility in phishing detection.

As screenshots of copyrighted video content are spreading through the Internet without any regulation, cases of copyright infringement have been observed. Further, it is difficult to use existing forensic techniques for determining whether or not a given image was captured from a screen. Thus, we propose a screenshot identification scheme using the trace of screen capture. Since most television systems and camcorders use interlaced scanning, many screenshots are taken from interlaced videos. Consequently, these screenshots contain the trace of interlaced videos, combing artifacts. In this study, we identify a screenshot using the characteristics of combing artifacts that appear to be shaped like horizontal jagged noise and can be found around the edges. To identify a screenshot, the edge areas are extracted using the gray level co-occurrence matrix (GLCM). Then, the amount of combing artifacts is calculated in the extracted edge areas by using the similarity ratio (SR), the ratio of the horizontal noise to the vertical noise. By analyzing the directional inequality of noise components, the proposed scheme identifies the source of an input image. In the experiments conducted, the identification accuracy is measured in various environments. The results prove that the proposed identification scheme is stable and performs well.

Increasingly high levels of smartphone ownership and use pose the potential risk for addictive behaviors and negative health outcomes, particularly among younger populations. Previous methodologies to understand mobile screen time have relied on self-report surveys or ecological momentary assessments (EMAs). Self-report is subject to bias and unreliability, while EMA can be burdensome to participants. Thus, a new methodology is needed to advance the understanding of mobile screen time. The objective of this study was to test the feasibility of a novel methodology to record and evaluate mobile smartphone screen time and use: battery use screenshot (BUS). The BUS approach, defined for this study as uploading a mobile phone screenshot of a specific page within a smartphone, was utilized within a Web-based cross-sectional survey of adolescents aged 12-15 years through the survey platform Qualtrics. Participants were asked to provide a screenshot of their battery use page, a feature within an iPhone, to upload within the Web-based survey. Feasibility was assessed by smartphone ownership and response rate to the BUS upload request. Data availability was evaluated as apps per BUS, completeness of data within the screenshot, and five most used apps based on battery use percentage. Among those surveyed, 26.73% (309/1156) indicated ownership of a smartphone. A total of 105 screenshots were evaluated. For data availability, screenshots contained an average of 10.2 (SD 2.0) apps per screenshot and over half (58/105, 55.2%) had complete data available. The most common apps or functions included Safari and Home and Lock Screen. Study findings describe the BUS as a novel approach for real-time data collection focused on iPhone screen time and use among young adolescents. Although feasibility showed some challenges in the upload capacity of young teens, data availability was generally strong across this large dataset. These data from screenshots have the potential to provide key insights into precise mobile smartphone screen use and time spent per mobile app. Future studies could explore the use of the BUS methodology on other mobile smartphones such as Android phones to correlate mobile smartphone screen time with health outcomes.

By means of an overview of certain aspects of article structures in printed dictionaries and with reference to some examples from e-dictionaries a number of features of article structures in e-dictionaries are discussed. Reference is made to the positioning of articles in article stretches and functional partial article stretches. Different structural components of articles, i.e. text segments, comments and search zones are distinguished. The increased role of data-identifying entries as a type of non-typographical structural indicator in e-dictionaries receives attention as well as the fact that the traditional division of an article in two comments, typically a comment on form and a comment on semantics, cannot merely be maintained. The value of the cohesion resulting from the use of comments in printed dictionaries is much more restricted in e-dictionaries. The use of search zones and rapid access to these zones have a much more important role in the article structure of e-dictionaries. In the planning of e-dictionaries provision needs to be made for a multi-layered article structure with screenshots that display the data in a variety of search zones. Access to these search zones goes via structural indicators in an opening or further screenshot. Provision needs to be made for one lemma to occur in a comprehensive article but also in a number of restricted articles that can be retrieved from the same database. Users should also have the opportunity to design their own user profile that will allow them to consult dictionary articles structured according to their specific needs. Artikelstrukture : vanaf gedrukte na e-woordeboeke Aan die hand van 'n oorsig oor bepaalde aspekte van artikelstrukture in gedrukte woordeboeke en met verwysing na 'n aantal voorbeelde uit e-woordeboeke word sekere kenmerke van artikelstrukture in e-woordeboeke bespreek. Daar word gekyk na die plasing van artikels in artikeltrajekte asook in funksionele artikeldeeltrajekte. Verskillende struktuurkomponente van artikels, te wete tekssegmente, kommentare en soeksones word onderskei. Die groter rol van data-identifiserende inskrywings as tipe nie-tipografiese struktuurmerker in e-woordeboeke kry aandag asook die feit dat die tradisionele verdeling in twee kommentare, tipies 'n vormkommentaar en 'n semantiese kommentaar, nie sonder meer gehandhaaf word nie. Die waarde van die samehang wat kommentare in gedrukte woordeboeke meebring, is veel beperkter in e-woordeboeke. Die gebruik van soeksones en die kits-toegang daartoe speel 'n veel belangriker rol in die artikelstruktuur van e-woordeboeke. Vir e-woordeboeke moet 'n veelvlakkige artikelstruktuur met skermskote wat die data in 'n verskeidenheid soeksones vertoon, beplan word. Toegang tot soeksones geskied via struktuurmerkers in 'n openings- of verdere skermskoot. Daar moet daarvoor voorsiening gemaak word dat een lemma in 'n omvattende artikel maar ook in 'n verskeidenheid beperkte artikels kan optree wat aan dieselfde databasis onttrek word. Gebruikers moet ook die kans kry om met behulp van die opstel van 'n eie gebruikersprofiel artikels te kan raadpleeg wat in terme van hulle eie behoeftes gestruktureer is.