An integrated semantic-based approach in concept based video retrieval

Multimedia content has been growing quickly and video retrieval is regarded as one of the most famous issues in multimedia research. In order to retrieve a desirable video, users express their needs in terms of queries. Queries can be on object, motion, texture, color, audio, etc. Low-level representations of video are different from the higher level concepts which a user associates with video. Therefore, query based on semantics is more realistic and tangible for end user. Comprehending the semantics of query has opened a new insight in video retrieval and bridging the semantic gap. However, the problem is that the video needs to be manually annotated in order to support queries expressed in terms of semantic concepts. Annotating semantic concepts which appear in video shots is a challenging and time-consuming task. Moreover, it is not possible to provide annotation for every concept in the real world. In this study, an integrated semantic-based approach for similarity computation is proposed with respect to enhance the retrieval effectiveness in concept-based video retrieval. The proposed method is based on the integration of knowledge-based and corpus-based semantic word similarity measures in order to retrieve video shots for concepts whose annotations are not available for the system. The TRECVID 2005 dataset is used for evaluation purpose, and the results of applying proposed method are then compared against the individual knowledge-based and corpus-based semantic word similarity measures which were utilized in previous studies in the same domain. The superiority of integrated similarity method is shown and evaluated in terms of Mean Average Precision (MAP)

Combination of semantic word similarity metrics in video retrieval

Multimedia Information Retrieval is one of the most challenging issues. Search for knowledge in the form of video is the main focus of this study. In recent years, there has been a tremendous need to query and process large amount of video data that cannot be easily described. There is a mismatch between the low-level interpretation of video frames and the way users express their information needs. This issue leads to the problem named semantic gap. To bridge semantic gap, concept-based video retrieval has been considered as a feasible alternative technique for video search. In order to retrieve a desirable video shot, a query should be defined based on users’ needs. In spite of the fact that query can be on object, motion, texture, color and so on, queries which are expressed in terms of semantic concepts are more intuitive and realistic for end users. Therefore, a concept-based video retrieval model based on the combination of the knowledge-based and corpus-based semantic word similarity measures is proposed with respect to bridging semantic gap and supporting semantic queries. In this study, Latent Semantic Analysis (LSA) which is a corpus-based semantic similarity measure is compared to previously utilized corpus-based measures. In addition, we experiment a combination of LSA with a knowledge-based semantic similarity measure in order to improve the retrieval effectiveness. For evaluation purpose, TRECVID 2005 dataset is utilized as well. As experimental results show, combination of knowledge-based and corpus-based outperforms individual one with MAP of 16.29%

Functional dissociation of behavioral effects from acetylcholine and glutamate released from cholinergic striatal interneurons

In the striatum, cholinergic interneurons (CINs) have the ability to release both acetylcholine and glutamate, due to the expression of the vesicular acetylcholine transporter (VAChT) and the vesicular glutamate transporter 3 (VGLUT3). However, the relationship these neurotransmitters have in the regulation of behavior is not fully understood. Here we used reward-based touchscreen tests in mice to assess the individual and combined contributions of acetylcholine/glutamate co-transmission in behavior. We found that reduced levels of the VAChT from CINs negatively impacted dopamine signalling in response to reward, and disrupted complex responses in a sequential chain of events. In contrast, diminished VGLUT3 levels had somewhat opposite effects. When mutant mice were treated with haloperidol in a cue-based task, the drug did not affect the performance of VAChT mutant mice, whereas VGLUT3 mutant mice were highly sensitive to haloperidol. In mice where both vesicular transporters were deleted from CINs, we observed altered reward-evoked dopaminergic signalling and behavioral deficits that resemble, but were worse, than those in mice with specific loss of VAChT alone. These results demonstrate that the ability to secrete two different neurotransmitters allows CINs to exert complex modulation of a wide range of behaviors

New frontiers in translational research: Touchscreens, open science, and the mouse translational research accelerator platform

© 2020 International Behavioural and Neural Genetics Society and John Wiley & Sons Ltd Many neurodegenerative and neuropsychiatric diseases and other brain disorders are accompanied by impairments in high-level cognitive functions including memory, attention, motivation, and decision-making. Despite several decades of extensive research, neuroscience is little closer to discovering new treatments. Key impediments include the absence of validated and robust cognitive assessment tools for facilitating translation from animal models to humans. In this review, we describe a state-of-the-art platform poised to overcome these impediments and improve the success of translational research, the Mouse Translational Research Accelerator Platform (MouseTRAP), which is centered on the touchscreen cognitive testing system for rodents. It integrates touchscreen-based tests of high-level cognitive assessment with state-of-the art neurotechnology to record and manipulate molecular and circuit level activity in vivo in animal models during human-relevant cognitive performance. The platform also is integrated with two Open Science platforms designed to facilitate knowledge and data-sharing practices within the rodent touchscreen community, touchscreencognition.org and mousebytes.ca. Touchscreencognition.org includes the Wall, showcasing touchscreen news and publications, the Forum, for community discussion, and Training, which includes courses, videos, SOPs, and symposia. To get started, interested researchers simply create user accounts. We describe the origins of the touchscreen testing system, the novel lines of research it has facilitated, and its increasingly widespread use in translational research, which is attributable in part to knowledge-sharing efforts over the past decade. We then identify the unique features of MouseTRAP that stand to potentially revolutionize translational research, and describe new initiatives to partner with similar platforms such as McGill\u27s M3 platform (m3platform.org)

Geographic distribution and time trends of water-pipe use among Iranian youth and teenage students : A meta-analysis and systematic review

Water-pipe tobacco smoking is harmful to health, yet its rate of prevalence remains uncertain. Recent evidence has shown that the prevalence of water-pipe smoking among students is higher than in the general population. In this study, a systematic review of related literature on water-pipe use was conducted, and for this purpose, 76 articles were examined in the study. In this vein, geographic distribution and time trends of water-pipe consumption in Iran were considered. The results of this study showed that lifetime, last-year, and last-month prevalence of water-pipe smoking use among Iranian students were 28.78 (25.07–32.49), 20.84 (16.01–25.66), and 16.36 (11.86–20.85), respectively. The results also showed a wide variation by the region and sex in Iran. This study has shown the importance of addressing public prevention and alerting programs in schools and universities.acceptedVersionPeer reviewe

Concept-based video retrieval system using integration of similarity measures

There is a tremendous need to query and process large amount of video data that cannot be easily described textually. Although a query can be on object, motion, texture, color and so on, queries which are expressed in terms of semantic concepts are more intuitive and realistic for end users. However, most videos are not fully annotated; hence results of queries which are solely based on the available annotations would not be exhaustive. Furthermore, annotations of a video shot depends on the annotators perception, thus if several people were to annotate the videos, each might have different perceptions, which results with various tagging semantic concepts. Thus, there is a need to match closely the semantics of the desired query term to the available annotations. This paper describes a concept-based video retrieval model for supporting queries using concepts which are not available in the annotated video. The similarity mapping is based on the integration of the knowledge-based and corpus-based semantic word similarity measures for supporting semantic queries.The system prototype was able to demonstrate the retrieval of the 100 top ranked video shots which are semantically similar to a new concept

A comparative study in classification techniques for unsupervised record linkage model

Problem statement: Record linkage is a technique which is used to detect and match duplicate records which are generated in data integration process. A variety of record linkage algorithms with different steps have been developed in order to detect such duplicate records. To find out whether two records are duplicate or not, supervised and unsupervised classification techniques are utilized in different studies. In order to utilize the supervised classification algorithms without consuming a lot of time for labeling data manually, a two step method which selects the training data automatically has been proposed in previous studies. However, the effectiveness of different classification techniques is the issue which should be taken into accounts in record linkage systems in order to classify records more accurately. Approach: To determine and compare the effectiveness of different supervised classification techniques in an unsupervised manner, some of the prominent classification methods are applied in duplicate records detection. Duplicate detection and classification of records in two real world datasets, namely Cora and Restaurant is experimented by Support Vector Machines, Naïve Bayes, Decision Tree and Bayesian Networks which are regarded as some prominent classification techniques. Results: As experimental results show, while Support Vector Machines outperforms with F-measure of 96.27% in Restaurant dataset, for Cora dataset, the effectiveness of Naïve Bayes is the best and it leads to an improvement with F-measure of 89.7%. Conclusion/Recommendation: The result of detecting duplicate records with different classification techniques tends to fluctuate depending on the dataset which is used. Moreover, Support Vector Machines and Naïve Bayes outperform other methods in our experiments

High level semantic concept retrieval using a hybrid similarity method

In video search and retrieval, user’s need is expressed in terms of query. Early video retrieval systems usually matched video clips with such low-level features as color, shape, texture, and motion. In spite of the fact that retrieval is done accurately and automatically with such low-level features, the semantic meaning of the query cannot be expressed in this way. Moreover, the limitation of retrieval using desirable concept detectors is providing annotations for each concept. However, providing annotation for every concept in real world is very challenging and time consuming, and it is not possible to provide annotation for every concept in the real world. In this study, in order to improve the effectiveness of the retrieval, a method for similarity computation is proposed and experimented for mapping concepts whose annotations are not available onto the annotated and known concepts. The TRECVID 2005 data set is used to evaluate the effectiveness of the concept-based video retrieval model by applying the proposed similarity method. Results are also compared with previous similarity measures used in the same domain. The proposed similarity measure approach outperforms other methods with the Mean Average Precision (MAP) of 26.84% in concept retrieval

The Antimicrobial and Anti-Biofilm Effects of <i>Hypericum perforatum</i> Oil on Common Pathogens of Periodontitis: An In Vitro Study

The antibacterial and anti-biofilm effects of Hypericum perforatum oil against the common pathogens of periodontitis (Escherichia coli, Streptococcus mutans, Staphylococcus aureus, Enterococcus faecalis, Porphyromonas gingivalis) was investigated. Disk diffusion (DD), minimum inhibitory concentration (MIC), and minimum bactericidal concentration (MBC) approaches were applied to test the antimicrobial effects. In order to determine the anti-biofilm effects, the amount of bacterial biofilm formation was assessed using the microtiter plate technique. The anti-biofilm effects were then confirmed by determining the minimum biofilm inhibitor concentration (MBIC). The MIC, MBC, MBIC, and DD values were 64, 256, 512 μg/mL, and 14 mm for Staphylococcus aureus; 128, 256, 512 μg/mL, and 16 mm for Streptococcus mutans; 256, 512, 256 μg/mL, and 20 mm for Escherichia coli; 32, 128, 512 µg/mL, and 16 mm for Enterococcus faecalis; and 64, 128, 256 µg/mL, and 15 mm for Porphyromonas gingivalis, respectively. According to our results, Hypericum perforatum oil has antibacterial and anti-biofilm properties against the common bacteria associated with periodontitis