Tokyo International Airport (HANEDA), the 4th largest airport in the world, chose the German SBD provider to equip their self-service installation with the Materna IPS One ID journey.

RecordForAll, audio recording and editing software for podcasters, took home top honors at the recent 2007 Shareware Industry Awards ceremony, by recieiving the award for the Best Sound Program. The Shareware Industry Awards are the Oscars of the software industry, recognizing outstanding software programs sold utilizing a marketing method that allows users to try the software prior to making a purchase decision.

This work describes an online handwritten character recognition system working in combination with an offline recognition system. The online input data is also converted into an offline image, and in parallel recognized by both online and offline strategies. Features are proposed for offline recognition and a disambiguation step is employed in the offline system for the samples for which the confidence level of the classier is low. The outputs are then combined probabilistically resulting in a classier out-performing both individual systems. Experiments are performed for Kannada, a South Indian Language, over a database of 295 classes. The accuracy of the online recognizer improves by 11% when the combination with offline system is used.

In this paper, we propose a novel dexterous technique for fast and accurate recognition of online handwritten Kannada and Tamil characters. Based on the primary classifier output and prior knowledge, the best classifier is chosen from set of three classifiers for second stage classification. Prior knowledge is obtained through analysis of the confusion matrix of primary classifier which helped in identifying the multiple sets of confused characters. Further, studies were carried out to check the performance of secondary classifiers in disambiguating among the confusion sets. Using this technique we have achieved an average accuracy of 92.6% for Kannada characters on the MILE lab dataset and 90.2% for Tamil characters on the HP Labs dataset.

In order to further improve the teaching quality of multimedia teaching in school daily teaching, a classroom facial expression emotion recognition model is proposed based on convolutional neural network. VGGNet and CliqueNet are used as the basic expression emotion recognition methods, and the two recognition models are fused while the attention module CBAM is added. Simulation results show that the designed classroom face expression emotion recognition model based on V-CNet has high recognition accuracy, and the recognition accuracy on the test set reaches 93.11%, which can be applied to actual teaching scenarios and improve the quality of classroom teaching.

To improve the effectiveness of online training for music education, it was investigated how to improve the pulse-coupled neural network in image processing for spectral image segmentation. The study proposes a two-scale descent method to achieve oblique spectral correction. Subsequently, a convolutional neural network was optimised using a two-channel feature fusion recognition network for music theory notation recognition. The results showed that this image segmentation method had the highest accuracy, close to 98%, and the accuracy of spectral tilt correction was also as high as 98.4%, which provided good image pre-processing results. When combined with the improved convolutional neural network, the average accuracy of music theory symbol recognition was about 97% and the highest score of music majors was improved by 16 points. This shows that the method can effectively improve the teaching effect of online training in music education and has certain practical value.

Action recognition plays a vital role in analysing human body behaviour and has significant implications for research and education. However, traditional recognition methods often suffer from issues such as inaccurate time and spatial feature vectors. Therefore, this study addresses the problem of inaccurate recognition of aerobic gymnastics action image data and proposes a visualised three-dimensional convolutional neural network algorithm-based action recognition model. This model incorporates unsupervised visualisation methods into the traditional network and enhances data recognition capabilities through the introduction of a random noise perturbation enhancement algorithm. The research results indicate that the data augmented with noise perturbation achieves the lowest mean square error, reducing the error value from 0.3352 to 0.3095. The use of unsupervised visualisation analysis enables clearer recognition of human actions, and the algorithm model is capable of accurately recognising aerobic movements. Compared to traditional algorithms, the new algorithm exhibits higher recognition accuracy and superior performance.

The conventional methods for identifying English online course learning behaviours have the problems of low recognition accuracy and high time cost. Therefore, a multimodal data fusion-based method for identifying English online course learning behaviours is proposed. Firstly, the analytic hierarchy process is used for decision fusion of multimodal data of learning behaviour. Secondly, based on the fusion results of multimodal data, weight coefficients are set to minimise losses and extract learning behaviour features. Finally, based on the extracted learning behaviour characteristics, the optimal classification function is constructed to classify the learning behaviour of English online courses. Based on the transfer information of learning behaviour status, the identification of online course learning behaviour is completed. The experimental results show that the recognition accuracy of the proposed method is above 90%, and its recognition accuracy is and can shorten the recognition time of learning behaviour, with high practical application reliability.

In order to improve the standardisation of mutual information index, accuracy rate and recall rate of student classroom behaviour recognition method, this paper proposes a student's classroom behaviour recognition method based on abstract hidden Markov model (HMM). After cleaning the students' classroom behaviour data, improve the data quality through interpolation and standardisation, and then divide the types of students' classroom behaviour. Then, in support vector machine, abstract HMM is used to calculate the output probability density of support vector machine. Finally, according to the characteristic interval of classroom behaviour, we can judge the category of behaviour characteristics. The experiment shows that normalised mutual information (NMI) index of this method is closer to one, and the maximum AUC-PR index can reach 0.82, which shows that this method can identify students' classroom behaviour more effectively and reliably.

Aim/Purpose: This study presents a novel deep learning-based framework designed to enhance spontaneous micro-expression recognition by effectively increasing the amount and variety of data and balancing the class distribution to improve recognition accuracy. Background: Micro-expression recognition using deep learning requires large amounts of data. Micro-expression datasets are relatively small, and their class distribution is not balanced. Methodology: This study developed a framework using a deep learning-based model to recognize spontaneous micro-expressions on a person’s face. The framework also includes several technical stages, including image and data preprocessing. In data preprocessing, data augmentation is carried out to increase the amount and variety of data and class balancing to balance the distribution of sample classes in the dataset. Contribution: This study’s essential contribution lies in enhancing the accuracy of micro-expression recognition and overcoming the limited amount of data and imbalanced class distribution that typically leads to overfitting. Findings: The results indicate that the proposed framework, with its data preprocessing stages and deep learning model, significantly increases the accuracy of micro-expression recognition by overcoming dataset limitations and producing a balanced class distribution. This leads to improved micro-expression recognition accuracy using deep learning techniques. Recommendations for Practitioners: Practitioners can utilize the model produced by the proposed framework, which was developed to recognize spontaneous micro-expressions on a person’s face, by implementing it as an emotional analysis application based on facial micro-expressions. Recommendation for Researchers: Researchers involved in the development of a spontaneous micro-expression recognition framework for analyzing hidden emotions from a person’s face are playing an essential role in advancing this field and continue to search for more innovative deep learning-based solutions that continue to explore techniques to increase the amount and variety of data and find solutions to balancing the number of sample classes in various micro-expression datasets. They can further improvise to develop deep learning model architectures that are more suitable and relevant according to the needs of recognition tasks and the various characteristics of different datasets. Impact on Society: The proposed framework could significantly impact society by providing a reliable model for recognizing spontaneous micro-expressions in real-world applications, ranging from security systems and criminal investigations to healthcare and emotional analysis. Future Research: Developing a spontaneous micro-expression recognition framework based on spatial and temporal flow requires the learning model to classify optimal features. Our future work will focus more on exploring micro-expression features by developing various alternative learning models and increasing the weights of spatial and temporal features.

Aim/Purpose: The objective of this paper is to review the vast literature of user-centric in-formation science and inform about the emerging themes in information behaviour science. Background: The paradigmatic shift from system-centric to user-centric approach facilitates research on the cognitive and individual information processing. Various information behaviour theories/models emerged. Methodology: Recent information behaviour theories and models are presented. Features, strengths and weaknesses of the models are discussed through the analysis of the information behaviour literature. Contribution: This paper sheds light onto the weaknesses in earlier information behaviour models and stresses (and advocates) the need for research on social information behaviour. Findings: Prominent information behaviour models deal with individual information behaviour. People live in a social world and sort out most of their daily or work problems in groups. However, only seven papers discuss social information behaviour (Scopus search). Recommendations for Practitioners : ICT tools used for inter-organisational sharing should be redesigned for effective information-sharing during disaster/emergency times. Recommendation for Researchers: There are scarce sources on social side of the information behaviour, however, most of the work tasks are carried out in groups/teams. Impact on Society: In dynamic work contexts like disaster management and health care settings, collaborative information-sharing may result in decreasing the losses. Future Research: A fieldwork will be conducted in disaster management context investigating the inter-organisational information-sharing.

Aim/Purpose: The amount of information published has increased dramatically due to the information explosion. The issue of managing information as it expands at this rate lies in the development of information extraction technology that can turn unstructured data into organized data that is understandable and controllable by computers Background: The primary goal of named entity recognition (NER) is to extract named entities from amorphous materials and place them in pre-defined semantic classes. Methodology: In our work, we analyze various machine learning algorithms and implement K-NN which has been widely used in machine learning and remains one of the most popular methods to classify data. Contribution: To the researchers’ best knowledge, no published study has presented Named entity recognition for the Kikuyu language using a machine learning algorithm. This research will fill this gap by recognizing entities in the Kikuyu language. Findings: An evaluation was done by testing precision, recall, and F-measure. The experiment results demonstrate that using K-NN is effective in classification performance. Recommendation for Researchers: With enough training data, researchers could perform an experiment and check the learning curve with accuracy that compares to state of art NER. Future Research: Future studies may be done using unsupervised and semi-supervised learning algorithms for other resource-scarce languages.

Congcong FANG,Yun JIN,Guanlin CHEN,Yunfan ZHANG,Shidang LI,Yong MA,Yue XIE, Vol.E107-D, No.11, pp.1463-1467
Currently, an increasing number of tasks in speech emotion recognition rely on the analysis of both speech and text features. However, there remains a paucity of research exploring the potential of leveraging large language models like GPT-3 to enhance emotion recognition. In this investigation, we harness the power of the GPT-3 model to extract semantic information from transcribed texts, generating text modal features with a dimensionality of 1536. Subsequently, we perform feature fusion, combining the 1536-dimensional text features with 1188-dimensional acoustic features to yield comprehensive multi-modal recognition outcomes. Our findings reveal that the proposed method achieves a weighted accuracy of 79.62% across the four emotion categories in IEMOCAP, underscoring the considerable enhancement in emotion recognition accuracy facilitated by integrating large language models.
Publication Date: 2024/11/01

The technology to put a name to a face is now free or cheap to use, so it is mostly a matter of ethics and propriety about whether to exercise the ability or not.

The post Two Harvard students created face recognition glasses. It wasn’t hard. appeared first on Boston.com.

Iris recognition technology is quickly emerging as the preeminent biometric solution for PIAM — here’s why.

Washington — NIOSH has unveiled a beta version of an interactive, PC-based simulation software tool aimed at improving hazard recognition in mines.

Explore the technological and use case dynamics that are reshaping the LPR landscape, and opening new revenue opportunities for security integrators.

Nedap and Tuxen & Associates will offer a free webinar introducing ANPR Lumo on May 7 at 3 p.m. CT. 

Washington — The Federal Motor Carrier Safety Administration is seeking public comment on a freight-carrier alliance petition regarding the use of hair samples as a drug-testing method for commercial motor vehicle drivers.

Washington — The Federal Motor Carrier Safety Administration has denied a petition calling on the agency to recognize hair samples as an alternative drug-testing method for truckers, reasserting a long-standing position that it lacks the statutory authority to do so.

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Orlando, FL — The National Safety Council, during the 2024 NSC Safety Congress & Expo, honored 14 professional drivers who have helped make roads and communities safer.

Mounting research into causes of dementia and cognitive decline have produced the recognition that two of the potentially modifiable risk factors into these conditions are diet and exercise. In a recent study at King’s College, London 418 adults age 65 and up were tested every two to three years over a 12-year period. Results revealed that cognitive decline and Alzheimer’s Disease were linked to levels of neural stem cell death. Importantly, underlying which was low levels of vitamin D, carotenoids, and lipids

Children's Geographies; 06/01/2023
(AN 164286258); ISSN: 14733285
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The BRICS summit in Kazan has formalized Russia's leadership in real decolonization and creation of developed economies based on its own strength. First steps have been named and are clear to everyone. Russia accepted as a new leader in the world The fact that 32 heads of state and UN Secretary General Antonio Guterres gathered in Kazan, Russia, during the times of the special military operation has demonstrated the decline of US influence in the world and the emergence of Russia as its leader. A country with 3.5 percent of global GDP in purchasing power parity was able to withstand harsh sanctions and three years of war with NATO at the hands of Ukrainians. The West that dominates the world economy with 40 percent is clearly losing the game to Russia on all fronts. It is about the ability, desire and readiness to defend one's national interests on the battlefield and mobilize one's economy. This is valued and respected in the world, and no one saw this in the Western camp.

In the recent 2-3 years, an increasing number of malls and retail chains have adopted real-time video analytics and facial recognition to enhance security, customer experience and footfall analysis.

Some of these technologies are showcased this week at the NRF Protect Conference in Long Beach, California.

Human tRNA (uracil-5-)-methyltransferase 2 homolog A (TRMT2A) is the dedicated enzyme for the methylation of uridine 54 in transfer RNA (tRNA). Human TRMT2A has also been described as a modifier of polyglutamine (polyQ)-derived neuronal toxicity. The corresponding human polyQ pathologies include Huntington's disease and constitute a family of devastating neuro­degenerative diseases. A polyQ tract in the corresponding disease-linked protein causes neuronal death and symptoms such as impaired motor function, as well as cognitive impairment. In polyQ disease models, silencing of TRMT2A reduced polyQ-associated cell death and polyQ protein aggregation, suggesting this protein as a valid drug target against this class of disorders. In this paper, the 1.6 Å resolution crystal structure of the RNA-recognition motif (RRM) from Drosophila melanogaster, which is a homolog of human TRMT2A, is described and analysed.

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The same artificial intelligence technique typically used in facial recognition systems could help improve prediction of hailstorms and their severity, according to a new, National Science Foundation-funded study. Instead of zeroing in on the features of an individual face, scientists trained a deep learning model called a convolutional neural network to recognize features of individual storms that affect the formation of hail and how large the hailstones will be, both of which are notoriously difficult to predict. The promising results highlight the importance of taking into account a storm's entire structure, something that's been challenging to do with existing hail-forecasting techniques.

Image credit: Carlye Calvin

A new NAE Perspective by Adiba M. Proma, Robert M. Wachter, and Ehsan Hoque discusses how helping people change their behaviors may be where technology can have its greatest impact on climate change.

A facial recognition system misidentifies a woman in London as a shoplifter, igniting fresh concerns over the technology's accuracy and reliability

PITTSBURGH (September 2, 2016) ... The potential to develop "materials that compute" has taken another leap at the University of Pittsburgh's Swanson School of Engineering, where researchers for the first time have demonstrated that the material can be designed to recognize simple patterns. This responsive, hybrid material, powered by its own chemical reactions, could one day be integrated into clothing and used to monitor the human body, or developed as a skin for "squishy" robots.

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The use of the technology will also require individual's consent, the CAC said in a statement. It added that non-biometric identification solutions should be favored over facial recognition in cases where such methods are equally effective.

The American Bakers Association program salutes safety practices and awareness.

For the second consecutive year, Grupo Bimbo received recognition for its actions to mitigate the effects of climate change at a global level.

Evans Roofing Company, Inc., with its subsidiaries Charles F. Evans (union) and CFE, Inc. (non-union), is a building envelope contractor licensed in 46 states. Charles F. Evans and CFE, Inc. are the only commercial roofing and wall panel contractors to hold the VPP STAR mobile work force designation.

Awarded a Spot in the Pioneer 250 Category, this Compliance-Certified Managed IT Services Provider Continues to Lead with Exceptional IT Services, Support and Cloud Security Solutions

Weeldi's Automation Studio enables non-developers to maintain stable automated interfaces with vendor web portals, SaaS applications and websites globally automating invoice and report downloads, MACD ordering and PCI compliant payments.

LogicalDOC is proud to wrap up Q1 2024 with huge success and accolades from the Gartner Digital Markets brands - Software Advice, and GetApp. Our product was recognized in various flagship reports in Q1

The Need for Identity Verification in Sports Betting Platforms

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With a consultative sales approach, BT Partners sets a benchmark for success, focusing on understanding client needs and delivering tailored solutions.

The American Health Council is proud to Honor Dr. Dan Andreae to the Board of "Best in Medicine" for his compassion, and willingness to make a difference in the world of medical advancement