Learning to Predict Page View on College Official Accounts With Quality-Aware Features

At present, most of departments in colleges have their own official accounts, which have become the primary channel for announcements and news. In the official accounts, the popularity of articles is influenced by many different factors, such as the content of articles, the aesthetics of the layout, and so on. This paper mainly studies how to learn a computational model for predicting page view on college official accounts with quality-aware features extracted from pictures. First, we built a new picture database by collecting 1,000 pictures from the official accounts of nine well-known universities in the city of Beijing. Then, we proposed a new model for predicting page view by using a selective ensemble technology to fuse three sets of quality-aware features that could represent how a picture looks. Experimental results show that the proposed model has achieved competitive performance against state-of-the-art relevant models on the task for inferring page view from pictures on college official accounts.

Pictures Library of Smoking Cravings: Development and Verification of Smokers and Non-smokers

Background: The cue-induced craving by addiction related materials is commonly employed in addiction research; however, no existing standardized picture database based on the expectation model of craving has been developed. We prepared and validated a Pictures Library of Smoking Cravings (PLSC) in this study.Methods: We captured pictures 366 smoking and 406 control pictures (matched in content). We selected 109 smoking pictures and 115 control pictures and asked participants to provide ratings of craving, familiarity, valence, and arousal induced in them. Participants were divided into three groups: non-smokers (n = 211), light smokers (n = 504), and heavy smokers (n = 101).Results: The results showed that smoking pictures evoked a greater craving, familiarity, and arousal than control pictures in smokers (ps < 0.01). In addition, craving caused by smoking pictures was positively associated with the Fagerström test for nicotine dependence score in dependent smokers.Conclusions: Overall, the contemporary results showed that PLSC is effective and can be used in smoking-related studies.

The Sexual OBjectification and EMotion database: A free stimulus set and norming data of sexually objectified and non-objectified female targets expressing multiple emotions

Sexual objectification – perceiving or treating a woman as a sexual object – is a widespread phenomenon. Studies on sexual objectification and its consequences have grown dramatically over the last decades covering multiple and diverse areas of research. However, research studying sexual objectification might have limited internal and external validity due to the lack of a controlled and standardized picture database. Moreover, there is a need to extend this research to other fields including the study of emotions. Therefore, in this paper we introduce the SOBEM Database, a free tool consisting of 280 high-resolution pictures depicting objectified and non-objectified female models expressing a neutral face and three different emotions (happiness, anger, and sadness) with different intensity. We report the validation of this dataset by analyzing results of 134 participants judging pictures on the six basic emotions and on a range of social judgments related to sexual objectification. Results showed how the SOBEM can constitute an appropriate instrument to study both sexual objectification per se and its relation with emotions. This database could therefore become an important instrument able to improve the experimental control in future studies on sexual objectification and to create new links with different fields of research.

Query-Adaptive Small Object Search using Object Proposal and Shape-Aware Descriptor

To distinguish little and smooth organized items in a huge picture database. Dissimilar to well-known techniques investigating a rough picture equal pairwise comparability, the hunt is intended to abuse the likeness actions by the proposition equal. A successful diagram-based question extension system is intended to survey every one of these better-coordinated recommendations against every one of its neighbors inside a similar picture for exact confinement. Joined with a shapemindful element descriptor Edge Bob, a lot of increasingly shrewd edge-loads and hub utility measures, the proposed hunt technique can deal with differing view edges, enlightenment conditions, miss happening, and impediment proficiently. In picture recovery, include extraction of any information pictures in the dataset has been handled. The highlights of pictures have been grouped utilizing a sack of highlights. On the off chance that Order esteem is gotten least then the picture has been recovered. The proposed one can profit from the current in speed and precision and better execution

Effects of Imagery as Visual Stimuli on the Physiological and Emotional Responses

Study of emotions has gained interest in the field of sensory and consumer research. Accurate information can be obtained by studying physiological behavior along with self-reported-responses. The aim was to identify physiological and self-reported-responses towards visual stimuli and predict self-reported-responses using biometrics. Panelists (N = 63) were exposed to 12 images (ten from Geneva Affective PicturE Database (GAPED), two based on common fears) and a questionnaire (Face scale and EsSense). Emotions from facial expressions (FaceReaderTM), heart rate (HR), systolic pressure (SP), diastolic pressure (DP), and skin temperature (ST) were analyzed. Multiple regression analysis was used to predict self-reported-responses based on biometrics. Results showed that physiological along with self-reported responses were able to separate images based on cluster analysis as positive, neutral, or negative according to GAPED classification. Emotional terms with high or low valence were predicted by a general linear regression model using biometrics, while calm, which is in the center of emotion dimensional model, was not predicted. After separating images, positive and neutral categories could predict all emotional terms, while negative predicted Happy, Sad, and Scared. Heart Rate predicted emotions in positive (R2 = 0.52 for Scared) and neutral (R2 = 0.55 for Sad) categories while ST in positive images (R2 = 0.55 for Sad, R2 = 0.45 for Calm).