Frame Information Research Articles

Attention-Based Multi-Layered Encoder-Decoder Model for Summarizing Non-Interactive User-Based Videos

Video summarization extracts the relevant contents from a video and presents the entire content of the video in a compact and summarized form. User based video summarization, can summarize a video as per the requirement of the user. In this work, a non interactive and a perception-based video summarization technique is proposed that makes use of attention mechanism to capture user’s interest and extract relevant keyshots in temporal sequence from the video content. Here, video summarization has been articulated as a sequence-to-sequence learning problem and a supervised method has been proposed for summarization of the video. Adding layers to the existing network makes it deeper, enables higher level of abstraction and facilitates better feature extraction. Therefore, the proposed model uses a multi-layered, deep summarization encoder-decoder network (MLAVS), with attention mechanism to select final keyshots from the video. The contextual information of the video frames is encoded using a multi-layered Bidirectional Long Short-Term Memory network (BiLSTM) as the encoder. To decode, a multi-layered attention-based Long Short-Term memory (LSTM) using a multiplicative score function is employed. The experiments are performed on the benchmark TVSum dataset and the results obtained are compared with recent works. The results show considerable improvement and clearly demonstrate the efficacy of this methodology against most of the other available state-of-art methods.

Do large language models show decision heuristics similar to humans? A case study using GPT-3.5.

A Large Language Model (LLM) is an artificial intelligence system trained on vast amounts of natural language data, enabling it to generate human-like responses to written or spoken language input. Generative Pre-Trained Transformer (GPT)-3.5 is an example of an LLM that supports a conversational agent called ChatGPT. In this work, we used a series of novel prompts to determine whether ChatGPT shows heuristics and other context-sensitive responses. We also tested the same prompts on human participants. Across four studies, we found that ChatGPT was influenced by random anchors in making estimates (anchoring, Study 1); it judged the likelihood of two events occurring together to be higher than the likelihood of either event occurring alone, and it was influenced by anecdotal information (representativeness and availability heuristic, Study 2); it found an item to be more efficacious when its features were presented positively rather than negatively-even though both presentations contained statistically equivalent information (framing effect, Study 3); and it valued an owned item more than a newly found item even though the two items were objectively identical (endowment effect, Study 4). In each study, human participants showed similar effects. Heuristics and context-sensitive responses in humans are thought to be driven by cognitive and affective processes such as loss aversion and effort reduction. The fact that an LLM-which lacks these processes-also shows such responses invites consideration of the possibility that language is sufficiently rich to carry these effects and may play a role in generating these effects in humans. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Framing of 1Malaysia Development Berhad (1MDB) Financial Scandal by English and Chinese Newspapers Published in Malaysia

The study compared how the 1Malaysia Development Berhad (1MDB) financial scandal was framed in English and Chinese online newspapers published in Malaysia. Content analysis was conducted for 200 articles for two English newspapers (The Star, 50; Malaysiakini English, 50) and two Chinese newspapers (Sin Chew Daily, 50; Malaysiakini Chinese, 50). The four newspapers were similar in the reliance on episodic framing and government sources of information, and the valence of the articles. Government sources is the opinion leader in 1MDB events but space is given to the voices of the opposition, foreign entities and the public. The English newspapers and Malaysiakini Chinese have more articles with a positive valence (46%-56%) in favour of investigations to resolve the financial corruption case and about 31% of the articles had a negative valence. However, Sin Chew Daily is more critical of the investigations than the other three newspapers. There are significant differences among the newspapers in frame dimensions of news headlines. The responsibility frame is used in close to 80% of the 1MDB articles in the Chinese newspapers but only in 40%-50% of the 1MDB articles in the English newspapers. Instead the English newspapers highlight the economic consequences of 1MDB and the conflict between individuals and groups, as well as contradictions between rumour and fact. The findings suggest that framing of controversial high-profile financial corruption case may differ due to the readership of the English and Chinese newspapers. Keywords: 1MDB, framing, financial scandal, mainstream newspapers, alternative newspapers.

A scalable multi-density clustering approach to detect city hotspots in a smart city

In the field of Smart City applications, the analysis of urban data to detect city hotspots, i.e., regions where urban events (such as pollution peaks, virus infections, traffic spikes, and crimes) occur at a higher density than in the rest of the dataset, is becoming a common task. The detection of such hotspots can serve as a valuable organizational technique for framing detailed information about a metropolitan area, providing high-level spatial knowledge for planners, scientists, and policymakers. From the algorithmic viewpoint, classic density-based clustering algorithms are very effective in discovering hotspots characterized by homogeneous density; however, their application on multi-density data can produce inaccurate results. For such a reason, since metropolitan cities are characterized by areas with significantly variable densities, multi-density clustering approaches are more effective in discovering city hotspots. Moreover, the growing volumes of data collected in urban environments require the development of parallel approaches, in order to take advantage of scalable executions offered by Edge and Cloud environments. This paper describes the design and implementation of a parallel multi-density clustering algorithm aimed at analyzing high volumes of urban data in an efficient way. The experimental evaluation shows that the proposed parallel clustering approach takes out encouraging advantages in terms of execution time, speedup, and efficiency.

Multi-Scene Generalized Trajectory Global Graph Solver with Composite Nodes for Multiple Object Tracking

The global multi-object tracking (MOT) system can consider interaction, occlusion, and other ``visual blur'' scenarios to ensure effective object tracking in long videos. Among them, graph-based tracking-by-detection paradigms achieve surprising performance. However, their fully-connected nature poses storage space requirements that challenge algorithm handling long videos. Currently, commonly used methods are still generated trajectories by building one-forward associations across frames. Such matches produced under the guidance of first-order similarity information may not be optimal from a longer-time perspective. Moreover, they often lack an end-to-end scheme for correcting mismatches. This paper proposes the Composite Node Message Passing Network (CoNo-Link), a multi-scene generalized framework for modeling ultra-long frames information for association. CoNo-Link's solution is a low-storage overhead method for building constrained connected graphs. In addition to the previous method of treating objects as nodes, the network innovatively treats object trajectories as nodes for information interaction, improving the graph neural network's feature representation capability. Specifically, we formulate the graph-building problem as a top-k selection task for some reliable objects or trajectories. Our model can learn better predictions on longer-time scales by adding composite nodes. As a result, our method outperforms the state-of-the-art in several commonly used datasets.

A Lightweight Channel and Time Attention Enhanced 1D CNN Model for Environmental Sound Classification

One dimension convolutional neural networks (1D CNN) that directly take raw waveforms as input has less competition than 2D CNN recognizing environmental sound. In order to overcome its disadvantages, we propose a novel lightweight 1D CNN structure by employing attention mechanism, which has significant improvement in both accuracy and computational complexity. Concretely, (1) two attention modules are constructed along channel and time dimension separately, and combined to give an intermediate feature map, which focus on key frequency band and semantically related time frame information. (2) Without increasing training overhead, snapshot ensemble is employed to further improve performance. Results from two benchmarking datasets (UrbanSound8k, ESC-10) demonstrated that: by employing attention mechanism, our model outperforms all of the previously reported 1D CNN approaches in accuracy with less parameters. Meanwhile with improved performance gain, the proposed model is superior than most of the existing spectral-based 2D CNN approaches and competitive with SOTA performance, while with orders of magnitude parameters fewer. Overall, it indicates our model is compact and has good potential in practical resource-limited applications, such as sound recognition on embedded platform.

That I can wait: The effect of promotion framing on consumer preferences

Sales managers often struggle to create effective combinations of promotion framing and promotion reward timing. However, whether gain-framing (vs. loss-reduction framing) promotions are better suited to be combined with immediate or delayed promotions remains an open question. In three studies, we show that the gain (vs. loss-reduction) framing of promotion information activates a high (vs. low) construal level, which leads to a higher preference for delayed (vs. immediate) promotions. We also find that time perception moderates the proposed effect. Specifically, consumers with time-overestimating (vs. time-underestimating) perception are more likely to choose immediate (delayed) promotions when they are at a low (high) construal level. Our findings provide insights for marketers and retailers that consumers would like to wait for a higher reward in a longer horizon under the gain promotion frame, while they prefer an immediate reward even if the benefit is much lower under the loss-reduction promotion frame.

Characteristics of Trypsin genes and their roles in insecticide resistance based on omics and functional analyses in the malaria vector Anopheles sinensis

Trypsin is one of the most diverse and widely studied protease hydrolases. However, the diversity and characteristics of the Trypsin superfamily of genes have not been well understood, and their role in insecticide resistance is yet to be investigated. In this study, a total of 342 Trypsin genes were identified and classified into seven families based on homology, characteristic domains and phylogenetics in Anopheles sinensis, and the LY-Domain and CLECT-Domain families are specific to the species. Four Trypsin genes, (Astry2b, Astry43a, Astry90, Astry113c) were identified to be associated with pyrethroid resistance based on transcriptome analyses of three field resistant populations and qRT-PCR validation, and the knock-down of these genes significantly decrease the pyrethroid resistance of Anopheles sinensis based on RNAi. The activity of Astry43a can be reduced by five selected insecticides (indoxacarb, DDT, temephos, imidacloprid and deltamethrin); and however, the Astry43a could not directly metabolize these five insecticides, like the trypsin NYD-Tr did in earlier reports. This study provides the overall information frame of Trypsin genes, and proposes the role of Trypsin genes to insecticide resistance. Further researches are necessary to investigate the metabolism function of these trypsins to insecticides.

»Vi ser att [eleven] har behov av särskilt stöd» Om kunskapsbildning och informationshantering i skolans elevutredningar

This study engages with the discursive construction of knowledge in school documents concerning pupils in need of special pedagogical support. In these texts, information from various voices is represented and recontextualized, meaning that images of pupils are negotiated and outlined, and ultimately knowledge about pupils and their learning is constructed discursively. The aim of the study is to investigate practices of knowledge construction in these texts. The data consist of 27 documents written by teachers who teach in lower secondary school (7th–9th grades). Drawing on linguistic discourse analysis, lexico-grammatical features are analyzed from both a qualitative and a quantitative perspective. The study shows how information originating from various voices is managed and framed by linguistic means in qualitatively different ways, and how the framing of information contributes to construction of several kinds of knowledge. In the analysis, five forms of framing are identified, along with six different types of voices. By quantifying the forms of framing as related to various voices, the study maps out the legitimization and delegitimization of various voices via the construction of different kinds of knowledge. Even though information from several voices is represented in the texts, it is primarily information from either the writer him/herself or written sources, such as previous documentation about pupils, that is legitimized by the writer.

Multi-frame non-coherent track-before-detect method for weak tones in passive sonar

In order to solve the problem of performance degradation of track-before-detect (TBD) methods using single frame information when received signals exhibit a large fluctuation. A TBD method utilizing multi-frame information is proposed. Specifically, the novel measurement model and likelihood ratio function based on the non-coherent accumulation of multi-frame data are established. Multi-frame integration greatly improves the signal-to-noise ratio (SNR) of measurements. Furthermore, the derived likelihood ratio function can more effectively describe the statistical characteristics of the spectrum measurements. Combining these two advantages can improve the detection performance of weak tones for passive sonar. The simulation and experimental data results both demonstrate the advantages of the proposed method.

Mitigating Distractor Challenges in Video Object Segmentation through Shape and Motion Cues

The purpose of semi-supervised video object segmentation (VOS) is to predict and generate object masks in subsequent video frames after being provided with the initial frame’s object mask. Currently, mainstream methods leverage historical frame information for enhancing the network’s performance. However, this approach faces the following issues: (1) They often overlook important shape information, leading to decreased accuracy in segmenting object-edge areas. (2) They often use pixel-level motion estimation to guide the matching for addressing distractor objects. However, this brings heavy computation costs and struggle against occlusion or fast/blurry motion. For the first problem, this paper introduces an object shape extraction module that exploits both the high-level and low-level features to obtain object shape information, by which the shape information can be used to further refine the predicted masks. For the second problem, this paper introduces a novel object-level motion prediction module, in which it stores the representative motion features during the training stage, and predicts the object motion by retrieving them during the inference stage. We evaluate our method on benchmark datasets compared with recent state-of-the-art methods, and the results demonstrate the effectiveness of the proposed method.

A study on the factors influencing the intention to receive booster shots of the COVID-19 vaccine in China based on the information frame effect.

In the response to and prevention and control of the Novel coronavirus pneumonia, the COVID-19 vaccine does not provide lifelong immunity, and it is therefore important to increase the rate of booster shots of the COVID-19 vaccine. In the field of information health science, research has found that information frames have an impact in changing individual attitudes and health behaviors. This study focuses on the effects of different influencing factors on the public's willingness to receive the booster shots of the COVID-19 vaccine under two information frameworks. An online questionnaire was conducted to explore the effects of demographic characteristics, personal awareness, social relationships, risk disclosure, perceived booster vaccination protection rate, and duration of protection under the assumption of an information framework. T test and one-way analysis were used to testing the effect of variables. (1) The persuasion effect under the gain frame is higher than that under the loss frame (B = 0.863 vs. B = 0.746); (2) There was no significant difference in subjects' intention of booster vaccination in terms of gender, age, income, occupation, educational background and place of residence. Whether family members received booster vaccination was strongly correlated with their intention of vaccination under the loss framework (p = 0.017, M = 4.63, SD = 0.664). (3) The higher the understanding of COVID-19, the higher the degree of compliance with the government's COVID-19 prevention and control measures, and the higher the willingness to strengthen vaccination; (4) Risk disclosure has a significant impact on people's willingness to receive COVID-19 booster shots (M = 2.48, under the loss framework; M = 2.44, under the gain framework); (5) Vaccine protection rate and duration of protection have an impact on people's willingness to vaccinate. Increased willingness to vaccinate when the protection rate of booster vaccine approaches 90% (M = 4.76, under the loss framework; M = 4.68, under the gain framework). When the vaccine protection period is 2 years, people are more willing to receive a booster vaccine; and the willingness to receive a booster shot is stronger under the loss framework (M = 4.60, SD = 0.721, p = 0.879). The impact of the information framework on COVID-19 vaccination intentions is different, and the disclosure of relevant health information should focus on the impact of the information framework and content on the public's behavior toward strengthening vaccination. Therefore, in the face of public health emergencies, public health departments, healthcare institutions, and other sectors can consider adopting the Gainful Information Framework tool to disseminate health information to achieve better persuasion and promote public health behavior change enhancing public health awareness, and promoting universal vaccination.

Underwater Turbid Media Stokes-Based Polarimetric Recovery.

Underwater optical imaging for information acquisition has always been an innovative and crucial research direction. Unlike imaging in the air medium, the underwater optical environment is more intricate. From an optical perspective, natural factors such as turbulence and suspended particles in the water cause issues like light scattering and attenuation, leading to color distortion, loss of details, decreased contrast, and overall blurriness. These challenges significantly impact the acquisition of underwater image information, rendering subsequent algorithms reliant on such data unable to function properly. Therefore, this paper proposes a method for underwater image restoration using Stokes linearly polarized light, specifically tailored to the challenges of underwater complex optical imaging environments. This method effectively utilizes linear polarization information and designs a system that uses the information of the first few frames to calculate the enhanced images of the later frames. By doing so, it achieves real-time underwater Stokes linear polarized imaging while minimizing human interference during the imaging process. Furthermore, the paper provides a comprehensive analysis of the deficiencies observed during the testing of the method and proposes improvement perspectives, along with offering insights into potential future research directions.

Pro-Environmental Nationalism is Still Nationalism: How Political Identity and Prior Attitudes Affect Nationalist Framing Effects on Support for Climate Action

ABSTRACT Based on research on framing effects and biased cognition, this paper investigated the limitations of framing pro-environmental messages in nationalist terms that emphasize the importance of upholding national pride, security, and leadership in global climate mitigation – a growing tendency among pro-environmental governments. Evidence from two survey experiments in the United States (N = 1075 and N = 205, respectively) supported the hypothesis that political identities and prior attitudes drove the effects of pro-environmental nationalist frame exposure on climate attitudes by influencing which considerations remained salient in people’s minds following exposure. Study 1 showed that Republicans (versus Democrats) and nationalist participants assigned more salience to nationalist considerations following exposure, resulting in higher ethnoculturalism and, subsequently, lower climate mitigation support and higher climate scepticism. Study 2 largely replicated these results. Thus, this paper highlighted the limitations of framing pro-environmental information in nationalist terms to increase support for climate action.

Value delivery in green consumption: the effect of advertisement value proposition on consumer perception and purchase intention.

Amid the expanding green consumer market, companies are increasingly emphasizing their eco-friendly contributions in advertising. This study delves into the value delivery process within green consumption, guided by the service-dominant logic (S-D logic). A survey-based experiment with 589 responses reveals that the information frame in advertising significantly impacts consumer value perceptions and purchase intentions. Notably, the temporal frame of advertising plays a pivotal role, influencing consumers' value perceptions. Future framing enhances emotional value perceptions, while past framing promotes environmental value perceptions. This research enhances our understanding of value delivery mechanisms in green consumption and holds valuable implications for marketers of sustainable products.

Equivalence framing and its effects on truth judgments: evidence from China

Recent research based on samples from Western countries has shown that the framing of information influences judgments of its truth, with negative framed statements perceived as more credible than formally equivalent positive framed statements. However, these findings may not apply to all Chinese residents because of China’s unique news contexts that adhere to positive reporting and a collectivist culture. Although the equivalence framing also affects people’s judgment of the truth of the information in China, it differs from people in Western countries. Chinese people believe statements with positive framed statements are more credible than statements with formally equivalent negative frames. They are more likely to associate positive information with news and show no obvious reactance to the persuasive intention perceived from positive framed statements. In addition, exposure to information from non-official media is changing this particular outcome, and people with frequent access to non-official media show similar responses to those in the West along these paths.

The influence of tourism destination safety reminder information frame on tourists' safety compliance behavior

Because of the unusual environmental attributes of tourism activities, how to effectively inform and remind tourists of the risk information of tourism destinations is of great significance to guide tourists' safety compliance behavior, which is critical in preventing safety accidents for destinations. Using emotion appraisal theory, framing effect theory, and identity theory, this study designs three experiments to explore the main effect of safety reminder information frame (gain-type vs. loss-type) on safety compliance behavior, the mediating effect of tourists' empathy, and the moderating effect of family identity by treating safety reminder information as stimulus material. We found that as compared with gain-type, loss-type safety reminder information frame is more effective in encouraging safety compliance behavior among tourists. Second, tourists' empathy serves as an important mediator between safety reminder information and safety compliance behavior. Third, this study not only finds that the impact of tourists' empathy on safety compliance behavior is greater for tourists with a stronger family identity but also that family identity does matter in the impact of tourism destination safety reminder on tourists’ empathy. To summarize, loss-type safety reminder information frames are more effective in activating tourists' empathy, thereby boosting safety compliance behavior among tourists with stronger family identity. In terms of the primary driver of safety compliance behavior, it is recommended that the destination adopt the loss-type frame to edit and publish the safety reminder information to induce safety compliance behavior. These findings have significant implications for the formulation and enforcement of safety reminders in tourist destinations.

Like diamonds in the sky? Public perceptions, governance, and information framing of solar geoengineering activities in Mexico, the United Kingdom, and the United States

ABSTRACT Solar geoengineering (also known as solar radiation modification) is garnering more attention (and controversy) among media and policymakers in response to the impacts of climate change. Such debates have become more prominent following the first-ever field trials of stratospheric aerosol injection (SAI) in 2022. How the lay public perceives solar geoengineering remains unclear, however. We use nationally representative samples (N = 3013) in Mexico, United States, and United Kingdom to examine public perceptions of risks and benefits, support, and policy preferences. We also employ an information-framing design that presented individuals with media-style reports on SAI activities differing along three dimensions: location, actor, and scale and purpose. Support for SAI is found to be generally higher in Mexico; perceptions of risks and benefits do not differ between countries. Information about SAI activities has a limited effect. There is evidence that activities conducted by universities receive more support than those by start-up companies.

How Information Framing Nudges Acceptance of China's Delayed Retirement Policy: A Moderated Mediation Model of Anchoring Effects and Perceived Fairness.

China's delayed retirement policy will be prudently rolled out at the appropriate time, yet the public's acceptance of this policy is concerning. To address this issue, our endeavor explores the impact of framing and anchoring effects on policy acceptance, aiming to mitigate the populace's resistance to the new policy. We conducted two survey studies on the Chinese population aged 16-65. Achieved through an online survey, Study 1 (N = 225) demonstrated that information framing significantly influences the public's acceptance of the delayed retirement policy. It was found that perceived fairness plays a mediating role between information framing and policy acceptance. Notably, the positive frame had a more pronounced effect on acceptance than its negative counterpart, with the positive presentation being perceived as more fair. Study 2 (N = 383), utilizing a combination of online and offline approaches, revealed that the anchoring effect moderates the relationship between information framing and perceived fairness. The interaction of anchoring and framing effects significantly influences perceived fairness, subsequently promoting public policy acceptance. The interplay between anchoring and framing effects significantly shapes perceived fairness, in turn bolstering the public's receptiveness to policy. These insights offer reasonable communication strategies for the smooth advancement of new policies, further enriching the field of behavioral science.

Real-time ghost imaging algorithm on the multidimensional vector matrix Walsh transformation with spatiotemporal free-fps.

In this research, we propose a real-time spatiotemporal free-fps algorithm based on the multidimensional vector matrix Walsh transform with an adjustable ghost imaging video quality and a frame rate to address the issues of uneven imaging speed, fixed imaging frame rate, and uncomfortable appearance in real-time ghost imaging videos of moving objects. This algorithm utilizes the temporal and spatial correlation of ghost imaging videos to achieve free and adjustable video frame rates in time without being limited by DMD refresh rates. Improving the spatial information of a single frame in space enhances the smoothness of ghost imaging videos, making the appearance of ghost imaging videos more comfortable. To achieve this, a four-dimensional vector Walsh transform kernel matrix is used to transform and reconstruct the high-quality images of the target object. Then the reconstructed high-quality image is spatially interpolated to enhance spatial information. Reasonable frame rate parameters are set based on the corresponding relationship between the detection values of the adjacent frames and speckle, improving the ghost imaging video in both time and space and achieving a smooth real-time ghost imaging video with an adjustable quality and frame rate. The simulation and experimental results of moving objects show that our algorithm solves the limitation of a DMD refresh rate compared with the existing ghost imaging video methods and makes the ghost imaging video more comfortable and smoother in real time. The PSNR of the objective evaluation index is increased by 12%. Regarding a subjective evaluation, this paper proposes an adaptive parameterless evaluation algorithm (APEA) for images with different resolutions based on the NRSS, which improved the structure retention degree by 13% and the Brisque parameter evaluation by 70%. We propose an adaptive parameterless video comfort evaluation algorithm (APVCEA) to evaluate the subjective comfort of ghost imaging videos by 14% compared with traditional methods.