Frame Difference Research Articles

MEDIA FRAMING OF TECHNOLOGICAL ADVANCEMENT AND HUMAN RIGHTS TO SAUDI ARABIA'S NEOM PROJECT

This research examines how Asharq al-Awsat and ALQST media framed the NEOM infrastructure project in Saudi Arabia. NEOM is a futuristic city project that is part of Saudi Arabia's Vision 2030. The project is designed to be the center of technological innovation, environmental sustainability, and economic development in the northwestern region of Saudi Arabia. It also explores the framing tools used and their impact on public perception and global outlook. This research uses a qualitative approach with Gamson and Modigliani’s framing analysis. The results show that Asharq al-Awsat tends to frames the NEOM project as a form of technological progress and innovation that provides economic benefits. Meanwhile, ALQST highlights the negative impacts such as human rights violations and environmental damage. These framing differences affected public perception and political discourse regarding the project. This research expands the understanding of how the media frames large infrastructure projects in the Middle East region. In addition, it reveals the implications of framing for public perception and international policy regarding infrastructure projects and human rights.

Understanding COVID-19 Media Framing: Comparative Insights from Germany, the US, and the UK During Omicron

ABSTRACT This study examines the media framing of COVID-19 reporting in Germany, the United States, and the United Kingdom during the period of the highly contagious Omicron variant from December 2021 to March 2022. By identifying issue-specific media frames using hierarchical cluster analysis, the study gains insights into the distinctive characteristics of journalistic roles within each country and sheds light on the similarities and differences in media framing. Findings reveal that the German Süddeutsche Zeitung prioritizes the economic and Omicron impact on society and reports rather emotionally through evaluations and blame on social issues but frames otherwise quite neutral, reflecting its role in opinion formation and information dissemination. Conversely, the American New York Times takes an objective and informative approach, except similar to the Süddeutsche Zeitung when dealing with social impacts of the pandemic, addressing Omicron, economic impacts, and vaccination challenges while emphasizing shared burdens of the pandemic. The British Guardian gives equal weight to economic and social consequences and the Omicron impact and also raises the issues concerning the healthcare sector. Blame and negative evaluations cross all frames and newspapers, although (emotionally charged) evaluations and accusations are most prevalent in the Guardian, often directed at unvaccinated people and politics.

Framing Effects of Emphasis and Form in Emergemcy Communication: A Study of Preventive Behaviors During the Covid-19 Pandemic in Japan

ABSTRACT This study examines the impact of frame differences on preventive behaviors in COVID-19 pandemic information. Survey experiments using large samples of Japanese participants led to the detection of framing effects of the emphasis levels and forms of information. Information framed with high severity was found to intensively influence citizens in refraining from going out. Moreover, this study demonstrates that episodic information facilitates preventive behaviors in discretionary activities. The episodic information has more intensively impacted through heuristic processing by providing exemplars. This study contributes to the reevaluation of government practices on information dissemination using various forms and intensity levels of information.

Disrupting the national education policy framework: covid-19 measures during the pandemic

ABSTRACT This research note seeks to describe and categorise the responses to the covid-19 pandemic in education in four Canadian provinces between March 2020 and June 2021. It shows that there was a significant variation in the measures that were adopted. Indeed, New Brunswick and Ontario’s social distancing measures in school were much stricter than that of Québec and Alberta. Additionally, Ontario and to a lesser extent Alberta relied on remote learning to a greater degree than the other two provinces. This variation cannot be simply brushed off because of the difference in the number of cases and rather suggests that, in times of crisis, provincial policy responses tend to vary due to framing differences. This research note contributes to federalism literature, exploring the role of subnational governments across an extended period of the pandemic.

Automated waste detection system for river surveillance in Jakarta using background substraction method

Abstract In Jakarta, the issue of river pollution due to indiscriminate waste disposal poses serious environmental and safety concerns, often leading to flooding during the rainy season. Manual surveillance by human resources has proven ineffective in addressing the escalating scale of the problem. This study presents an automated waste detection system for river surveillance in Jakarta, especially inorganic waste, utilizing video processing techniques, specifically background subtraction and frame differencing. We collected and analyzed video data from 13 rivers, including the Ciliwung, Angke, and Pesanggrahan rivers, during October and November 2023. The system’s performance was evaluated based on its accuracy in detecting waste objects, with detection rates varying significantly across different rivers. High detection accuracies were achieved in the Cipinang (93%) and Malang (90%) rivers, while lower accuracies were noted in rivers like Grogol (25%) and Cakung (17%). The overall average detection rate was 60%. These results highlight the system’s strengths in cleaner, less dynamic environments and its challenges in more complex conditions. Future research should focus on enhancing algorithm robustness, incorporating adaptive thresholding, and integrating multi-sensor data to improve detection accuracy.

Rise of Digital Influencer Marketing

Virtual influencer marketing is a global emerging phenomenon in social media research. Research on traditional influencer marketing and practices has been the locus of attention for years, but fairly scarce research deals with the rise and influence of virtual influencers, also known as artificial intelligence influencers (AIIs) or computer-generated influencers (CGIs). In this article, an explorative, qualitative single case analysis of the Japanese CGI influencer Imma is presented to identify how the CGI is visually constructed on Instagram in a marketing campaign. Two supplementary semiotic lenses are applied, i.e. a visual compositional analysis and a semiotic image analysis. The results indicate that the CGI influencer Imma is visually presented by and large with the same characteristics and attributes as found in similar studies of human influencers. Moreover, the findings indicate subtle yet essential differences in the visual framing of the CGI character to enhance a human-like appearance. Concludingly, this study has preliminary implications for research, practitioners, prospective marketers, and future innovation leaders in the virtual marketing industry, and it is proposed to consider reconceptualizing our understanding of virtual influencers and acknowledging the rise of Virtual Influencer Marketing as a field in its own right.

Application of posture estimation optimization algorithm in the analysis of college air volleyball teaching movements

The advent of computer technology and the modernization of sports education have led to an increasing reliance on intelligent technology in the field of sports education. To improve the application effect of intelligent technology in physical education, a volleyball motion analysis technology combined with pose estimation optimization algorithm is designed. During the process, Kinect collects the joint data of the research object, which is then combined with the commonly used background subtraction and frame difference optimization techniques. This results in the construction of a background model. The background update number in the initial frame is utilized as the reference value. The contour edge is smoothed through the application of a one-dimensional Gaussian kernel function, and a teaching action guidance system is designed. The experimental results showed that the average accuracy of the research method reached 79.7 % and the average recall rate reached 75.2 %. The average relative error of the method was 4.13 % when comparing the accuracy of human body model. The research method is validated to accurately capture and analyze volleyball motion, which can provide some technical help for sports teaching.

‘New worlds open up’: A qualitative study on Wonder Lab practices in Dutch paediatric palliative care with parents, healthcare professionals, and students

Within paediatric palliative care, it is essential for families and providers to have open, equal, and trusting relationships. In practice, however, building relationships can be challenging. Investing in better understanding the differences in each other's frames of reference and underlying values seems important. Wonder Lab practices provide a space to explore these differences by focusing together on life phenomena in curious and Socratic ways. Wonder Labs were organised with parents, healthcare professionals, and students involved in Dutch paediatric palliative care. The aim of this study was to develop an understanding of how participants experienced participating in Wonder Labs. We conducted twenty in-depth interviews with Wonder Lab participants and used inductive thematic analysis for data interpretation. Five themes were identified: Slowing down, Appreciating stories, Becoming vulnerable, Opening up and diving in, and Reframing perspectives. Participating in Wonder Labs allowed mothers, healthcare professionals, and students to contribute to deepening experiences and gain an expanded understanding of what is at play in caring for children with life-limiting and life-threatening conditions. Through working in pluralised groups, frames of reference and understandings complemented each other and could change. Participants often adopted a more open attitude towards others involved in care after participating and adapted day-to-day practices. Deliberating within paediatric palliative care on sensitive issues and their underlying personal and professional beliefs and values must be part of working together, without specific care situations being the catalyst. This may foster the mutual understanding needed in searching for quality of life, death, and bereavement.

Research on Sintering Machine Axle Fault Detection Based on Wheel Swing Characteristics

During the sintering process in iron production, wheel swing is a sign of sintering machine trolley axle faults, which may lead to the wheel falling off and affect the production operation of the sintering machine system in serious cases. To solve this problem, this paper proposes a fault detection and localization method based on the You Only Look Once version 9 (YOLOv9) object detection algorithm and frame difference method for detecting sintering machine trolley wheel swing. The wheel images transmitted from the camera were sent to a trolley wheel and side panel number detection model that was trained on YOLOv9 for recognition. The wheel recognition boxes of the previous and subsequent frames were fused into the wheel region of interest. In the wheel region of interest, the difference operation was carried out. The result of the difference operation was compared with the preset threshold to determine whether the trolley wheel swings. When a wheel swing fault occurs, the image of the side plate at the time of the fault is collected, and the number on the side plate is identified so as to accurately locate the faulty trolley and to assist the field personnel in troubleshooting the fault. The experimental results show that this method can detect wheel swing faults in the industrial field, and the detection accuracy of wheel swing faults was 93.33%. The trolley side plate numbers’ average precision was 99.2% in fault localization. Utilizing the aforementioned method to construct a system for detecting wheel swing can provide technical support for fault detection of the trolley axle on the sintering machine.

Evaluation of seedless wavelet-based optical flow velocimetry for schlieren images

In harsh flow environments, traditional particle-based velocimetry methods face challenges. This study explores the use of seedless schlieren images for velocimetry through a novel algorithm, namely, wavelet-based optical flow velocimetry (wOFV). Various data term constraints for wOFV were examined. It is found that the data term derived from the integrated continuity equation (ICE) outperformed the conventional displaced frame difference constraint and the schlieren-tailored constraints (SE and SSE). Evaluation based on the root mean square error (RMSE) and turbulence energy spectrum (TES) reveals that the choice of wavelet becomes insignificant for the optimal estimated velocity field when the wavelet support length is sufficiently long. In addition, the implementation of a proper truncation in wOFV shows little dependence of the RMSE on the weighting coefficient, therefore alleviating the uncertainty associated with selecting an appropriate weighting coefficient. It is found that the retrieved flow field from schlieren images approximates a down-sampled result based on available structural scales in images. Considering the prevalence of under-resolved velocity field in practical applications, schlieren-based wOFV offers a reasonable alternative to particle-based velocimetry, particularly in harsh flow environments.

Long-term video activity monitoring and anomaly alerting of group-housed pigs

The activity level of group-housed pigs can reflect their daily behavioral patterns and health conditions. Currently, most pig farms mainly rely on manual daily patrols to monitor the activities of group-housed pigs, which is labor-intensive and inaccurate. In this paper, we propose a computer vision-based method for the automatic monitoring and alerting of group-housed pigs activity anomaly. We established a pig segmentation model based on an improved Mask R-CNN incorporating the CBAM attention mechanism and CIoU loss function for segmenting pigs from video frames. The CBAM attention mechanism enables the model to focus more effectively on target regions, while CIoU loss function guides precise learning of object boundaries, outperforming the traditional IoU loss. Grounded on the segmentation results, we invocated a frame difference method to quantify the activity level of group-housed pigs. Subsequently, we adopted the K-means clustering algorithm to explore the daily behaviors of these pigs. Furthermore, we constructed a SARIMA model using historical data to proactively forecast potential shifts in activity levels. The experimental results demonstrated that the accuracy and recall rate of the pig instance segmentation model were 98.7% and 97.9%, respectively. The SARIMA model performed well in predicting activity levels of group-housed pigs with differing health status. For the pig herds under healthy status, the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) were 6.01 and 7.67 respectively, while for the pig herds under unhealthy status, the MAE and RMSE were 6.41 and 9.30 respectively. We proposed an effectively method to monitor daily behavioral patterns and anomalies of group-housed pigs, providing valuable references for pig farm management.

Pengolahan Citra Berbasis Video Proccesing dengan Metode Frame Difference untuk Deteksi Gerak

This study discusses the detection of motion of objects in video by utilizing the Frame difference method which aims to process video so as to produce Frames on moving objects. The use of mobile cameras produces video data that is used as test data, the test data is processed with the Frame difference method so as to produce a number of Frames on moving objects in order to detect moving objects in the video because the function of this method is a form of video background reduction that is simplified by a number of pixels in the video. This method process is based on the difference between two consecutive frames in the video aimed at finding differences that occur during the detection process. When processed for detection, the absolute value in the pixel is greater than the predetermined threshold value, it will be considered as a moving object, so that the detection results from the motion detection process will form a box object on the moving object. In this study, the test data used used 20 video data samples with descriptions, 10 test data with bright quality (daytime) and 10 unlit test data (night) with the aim of being able to see how much the level of performance accuracy of the Frame difference method. The test results obtained 16 out of 20 test data that were successfully detected correctly (True Positive), there were 2 test data that resulted in a False Positive error, and 2 test data that resulted in a False Negative error. This shows that the Frame difference method can provide a fairly high level of accuracy in detecting moving objects in the video. The percentage level of accuracy with confussion matrix testing has a precission value of 88%, recal 88% and an accuracy value of 80%.

Faster R-CNN based on frame difference and spatiotemporal context for vehicle detection

Faster R-CNN based on frame difference and spatiotemporal context for vehicle detection

How framing bias impacts preferences for innovation in bone tissue engineering.

It is currently unknown if surgeons and biomaterial scientists &or tissue engineers (BS&orTE) process and evaluate information in similar or different (un)biased ways. For the gold standard of surgery to move "from bench to bedside", there must naturally be synergies between these key stakeholders' perspectives. Because only a small number of biomaterials & tissue engineering innovations have been translated into the clinic today, we hypothesised this lack of translation is rooted in the psychology of surgeons and BS&orTE. Presently, both clinicians and researchers doubt the compatibility of surgery and research in their daily routines. This has led to the use of a metaphorical expression "squaring of the circle," which implies an unsolvable challenge. As bone tissue engineering belongs to the top 5 research areas in tissue engineering we choose the field of bone defect treatment options for our bias study. Our study uses an online survey instrument for data capture: incorporating a behavioural economics cognitive framing experiment methodology. Our study sample consisted of surgeons (n=208) and BS&orTE (n=59). And we employed a convenience sampling method, with participants (conference attendants) being approached both in person and via email - 22 October 2022-13 March 2023. We find no distinct positive-negative cognitive framing differences by occupation. That is, any framing bias present in this surgical decision-making setting does not appear to differ significantly between surgeon and BS&orTE specialisation. When we explored within group differences by frames, we see statistically significant (p<0.05) results for surgeons in the positive frame ranking autologous bone graft transplantation lower compared to surgeons in the negative frame. Further, surgeons in the positive frame rank Ilizarov bone transport method higher compared to surgeons in the negative frame (p<0.05).

What’s a Black feminist doing in a field like special education?

ABSTRACT Special educators are increasingly drawing from intersectionality and Black feminist theory to make sense of the disproportionate deleterious outcomes experienced by racialized students labeled with disabilities. While intersectionality gains a stronger hold in special education discourse, agencies like the Florida Department of Education are misrepresenting Black feminist theory and intersectionality as “ranking people” based on their social identities. Audre Lorde—a member of The Combahee River Collective credited for generating an intersectional shift in feminist discourse—called on the creative use of difference to push back on the marginalization of multiply-marginalized women. Lorde asserted that explicitly attending to the diversity within human experiences challenges harmful attitudes that frame differences as markers of inferiority, deviance, or failure. In this article, I draw from Black feminism and Audre Lorde’s theorizing about difference to present a framework for educators who advocate for specialized education programming that affirm student differences.

Othering and Low Status Framing of Immigrant Cuisines in US Restaurant Reviews and Large Language Models

Identifying implicit attitudes toward food can help mitigate social prejudice due to food’s pervasive role as a marker of ethnic identity. Stereotypes about food are representational harms that may contribute to racialized discourse and negatively impact economic outcomes for restaurants. Understanding the presence of representational harms in online corpora in particular is important, given the increasing use of large language models (LLMs) for text generation and their tendency to reproduce attitudes in their training data. Through careful linguistic analyses, we evaluate social theories about attitudes toward immigrant cuisine in a large-scale study of framing differences in 2.1M English language Yelp reviews. Controlling for factors such as restaurant price and neighborhood racial diversity, we find that immigrant cuisines are more likely to be othered using socially constructed frames of authenticity (e.g., authentic, traditional), and that non-European cuisines (e.g., Indian, Mexican) in particular are described as more exotic compared to European ones (e.g., French). We also find that non-European cuisines are more likely to be described as cheap and dirty, even after controlling for price, and even among the most expensive restaurants. Finally, we show that reviews generated by LLMs reproduce similar framing tendencies, pointing to the downstream retention of these representational harms. Our results corroborate social theories of gastronomic stereotyping, revealing racialized evaluative processes and linguistic strategies through which they manifest.

In Conversation With Erin Manning: A Refusal of Neurotypicality Through Attunements to Learning Otherwise

This paper documents a conversation with Erin Manning in the first webinar of the series Doing Academica Differently: In conversation with Neuroatypicality. Drawing on her scholarship, teaching experience, as well as the more recent 3Ecologies project, Manning shows how systems serve to pathologize by framing difference from the angle of typicality and as a divergence from the norm. She argues, therefore, that it is necessary to move beyond the ontological presuppositions enacted by systems of whiteness/neurotypicality. She proposes that academic work must continue to remain open to the differential within difference, and value slow and convivial practices that texture qualities of existence as a mode rather than as gridded individual identities. By focusing on the crucial notion of value in higher education and how it might be reworked in experimental ways, Manning suggests ways of attuning for learning otherwise beyond a neurotypical frame.

Enhancing visual clarity in rainy conditions based on single-frame filtering algorithm

Vision-based driving assistance systems has made a paradigm shift in the automotive industry, especially in auto electronics, towards vehicular technology in smart cities. This technology concentrates on safeguarding the members inside and outside the car, such as pedestrians or other vehicles on the road. This paper proposes a methodology that employs a single image/frame-based rain filter for video processing. In this paper, an innovative method of frame filtering is statistically modelled to provide a clear vision on the road during rainy weather. The frame filtering displays an improvement by considering a reference frame as the input frame and it is further exploited to confiscate the contextual and atmospheric particles using the method of frame difference. Moreover, this article suggests a hybrid algorithm that accomplishes filtering utilizing L0-gradient image smoothing and weights least square smoothing along with adaptive histogram equalization to preserve and enhance the image under intense rainy conditions. With a single frame-based filtering methodology, the proposed algorithm performs well with rainy images but loses information during video processing. The proposed method displays an improvement by 36.29 % and 3.48 % in Structural Similarity Index (SSIM) and 57.15 % and 14.47 % in Gradient Magnitude Similarity Deviation (GMSD) as compared to guided filter and bilateral filter methods.

Framing of Hamas Attacks on Israel in Al-Jazeera and BBCCoverage

Abstract. Digital media provides the latest news on global issues, particularly in the realm of religion, attracting a broad audience. This research explores how international mass media outlets Al-Jazeera and BBC frame their coverage of Hamas' attack on Israel on October 7. Utilizing a qualitative descriptive research method, the study aims to comprehensively depict the framing processes employed by Al-Jazeera and BBC in reporting on the Hamas-Israel conflict. Primary data includes Al-Jazeera's article "Why the Palestinian group Hamas launched an attack on Israel? All to know" and BBC News Indonesia's piece titled "What is Hamas and why is it fighting with Israel in Gaza?" Secondary data from relevant scholarly studies supports the research. Data collection involves reading and note-taking, while data analysis follows Miles and Huberman's model—data reduction, data display, and conclusion drawing. Findings reveal that Al-Jazeera defines Hamas' attack on Israel as a defensive measure, emphasizing its causes and making moral judgments by comparing Hamas' damage to Israel's actions. Al-Jazeera recommends characterizing Israel's actions against Palestine as genocide. On the other hand, BBC portrays Hamas' assault as sudden terrorism, neglecting Israel's criminal history. In its moral assessment, BBC focuses on Hamas' crimes to support a terrorist narrative. Treatment recommendations by BBC attempt to legitimize Israel's colonization efforts by highlighting that Palestine lacks recognition as an independent state by the UN. Differences in framing in the reporting of the Hamas-Israel conflict can affect diplomacy and peace efforts in the region as well as international support for one of the parties. International mass media are not always objective in reporting conflicts because they are influenced by political and ideological interests. This can lead to criticism of media objectivity and fairness and raises questions about the media's responsibility in shaping public opinion that can influence the development of conflicts.Keywords: Framing, Mass Media, Hamas Attack, Israel, Robert Entman

Enhancing human behavior recognition with spatiotemporal graph convolutional neural networks and skeleton sequences

ObjectivesThis study aims to enhance supervised human activity recognition based on spatiotemporal graph convolutional neural networks by addressing two key challenges: (1) extracting local spatial feature information from implicit joint connections that is unobtainable through standard graph convolutions on natural joint connections alone. (2) Capturing long-range temporal dependencies that extend beyond the limited temporal receptive fields of conventional temporal convolutions.MethodsTo achieve these objectives, we propose three novel modules integrated into the spatiotemporal graph convolutional framework: (1) a connectivity feature extraction module that employs attention to model implicit joint connections and extract their local spatial features. (2) A long-range frame difference feature extraction module that captures extensive temporal context by considering larger frame intervals. (3) A coordinate transformation module that enhances spatial representation by fusing Cartesian and spherical coordinate systems.FindingsEvaluation across multiple datasets demonstrates that the proposed method achieves significant improvements over baseline networks, with the highest accuracy gains of 2.76%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} on the NTU-RGB+D 60 dataset (Cross-subject), 4.1%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} on NTU-RGB+D 120 (Cross-subject), and 4.3%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} on Kinetics (Top-1), outperforming current state-of-the-art algorithms. This paper delves into the realm of behavior recognition technology, a cornerstone of autonomous systems, and presents a novel approach that enhances the accuracy and precision of human activity recognition.