Sign language recognition is one of the most challenging applications in machine learning and human-computer interaction. Many researchers have developed classification models for different sign languages such as English, Arabic, Japanese, and Bengali; however, no significant research has been done on the general-shape performance for different datasets. Most research work has achieved satisfactory performance with a small dataset. These models may fail to replicate the same performance for evaluating different and larger datasets. In this context, this paper proposes a novel method for recognizing Bengali sign language (BSL) alphabets to overcome the issue of generalization. The proposed method has been evaluated with three benchmark datasets such as `38 BdSL’, `KU-BdSL’, and `Ishara-Lipi’. Here, three steps are followed to achieve the goal: segmentation, augmentation, and Convolutional neural network (CNN) based classification. Firstly, a concatenated segmentation approach with YCbCr, HSV and watershed algorithm was designed to accurately identify gesture signs. Secondly, seven image augmentation techniques are selected to increase the training data size without changing the semantic meaning. Finally, the CNN-based model called BenSignNet was applied to extract the features and classify purposes. The performance accuracy of the model achieved 94.00%, 99.60%, and 99.60% for the BdSL Alphabet, KU-BdSL, and Ishara-Lipi datasets, respectively. Experimental findings confirmed that our proposed method achieved a higher recognition rate than the conventional ones and accomplished a generalization property in all datasets for the BSL domain.

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BenSignNet: Bengali Sign Language Alphabet Recognition Using Concatenated Segmentation and Convolutional Neural Network

• A Gaussian mixed distribution label-based model is proposed for students’ head pose estimation. • The model can learn the discriminative facial features in each angle image. • Experimental results show that the GMDL model outperforms other existing models. Students’ head pose estimation is a very difficult task since the training data is insufficient for many head pose angles. In this study, we consider each head pose image as a Gaussian mixed distribution other than the traditional hard label, in which the adjacent head pose images can provide supplementary information for the target image. Specifically, the Gaussian mixed distribution covers the current head pose image and its adjacent 8 head pose images. Each label of head pose image describes the similar degree between the current image and its adjacent head pose images. Then, a novel network architecture is proposed by constructing the Gaussian mixed distribution which learns more discriminative facial features. The extensive evaluations on two public HPE databases show that the proposed GMDL model obtains the better performance compared with the conventional algorithms. In practice, the proposed model can be utilized to estimate learners’ head pose angle for attention understanding in the instruction and learning scenarios. 

GMDL: Toward precise head pose estimation via Gaussian mixed distribution learning for students’ attention understanding

Low-quality remotely sensed images (RSIs) are not beneficial for the analysis of many activities including agricultural growth, resident migration, forest fire, and etc. Many previous enhancement schemes improve their quality via changing their illumination. However, these approaches often fail in detail and brightness preservation as well as contrast improvement due to that the information from a single image is limited. To address this issue, an enhancement framework, named as global-local enhancement network (GLE-Net), is proposed to correct the intensity via learning extra information from collected training data, including the following three key steps: first, RSIs are decomposed by the discrete wavelet transformation (DWT) method into the low-frequency component and the detail components. Then, the low-frequency component is improved by the global enhancement network while the detail components are enhanced by the local enhancement network in parallel. Finally, the enhanced components are employed to produce high-quality images with the inverse DWT (IDWT) method. The quantitatively and qualitatively comparable experiments on both synthetic and real-world RSIs validate that the proposed GLE-Net method performs well on preserving brightness and fine details, and even outperforms the state-of-the-arts.

Learning a Contrast Enhancer for Intensity Correction of Remotely Sensed Images

Accurate fire identification can help to control fires. Traditional fire detection methods are mainly based on temperature or smoke detectors. These detectors are susceptible to damage or interference from the outside environment. Meanwhile, most of the current deep learning methods are less discriminative with respect to dynamic fire and have lower detection precision when a fire changes. Therefore, we propose a dynamic convolution YOLOv5 fire detection method using a video sequence. Our method first uses the K-mean++ algorithm to optimize anchor box clustering; this significantly reduces the rate of classification error. Then, the dynamic convolution is introduced into the convolution layer of YOLOv5. Finally, pruning of the network heads of YOLOv5’s neck and head is carried out to improve the detection speed. Experimental results verify that the proposed dynamic convolution YOLOv5 fire detection method demonstrates better performance than the YOLOv5 method in recall, precision and F1-score. In particular, compared with three other deep learning methods, the precision of the proposed algorithm is improved by 13.7%, 10.8% and 6.1%, respectively, while the F1-score is improved by 15.8%, 12% and 3.8%, respectively. The method described in this paper is applicable not only to short-range indoor fire identification but also to long-range outdoor fire detection.

/pdf/a-robust-fire-detection-model-via-convolution-neural-1qw57ko9.pdf

A Robust Fire Detection Model via Convolution Neural Networks for Intelligent Robot Vision Sensing

Extensive user check-in data incorporating user preferences for location is collected through Internet of Things (IoT) devices, including cell phones and other sensing devices in location-based social network. It can help traveling enterprises intelligently predict users' interests and preferences, provide them with scientific tourism paths, and increase the enterprises income. Thus, successive point-of-interest (POI) recommendation has become a hot research topic in augmented Intelligence of Things (AIoT). Presently, various methods have been applied to successive POI recommendations. Among them, the recurrent neural network-based approaches are committed to mining the sequence relationship between POIs, but ignore the high-order relationship between users and POIs. The graph neural network-based methods can capture the high-order connectivity, but it does not take the dynamic timeliness of POIs into account. Therefore, we propose an <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I nteraction-enhanced and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T ime-aware <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G raph <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C onvolution <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N etwork (ITGCN) for successive POI recommendation. Specifically, we design an improved graph convolution network for learning the dynamic representation of users and POIs. We also designed a self-attention aggregator to embed high-order connectivity into the node representation selectively. The enterprise management systems can predict the preferences of users, which is helpful for future planning and development. Finally, experimental results prove that ITGCN brings better results compared to the existing methods.

/pdf/interaction-enhanced-and-time-aware-graph-convolutional-nm68wjx8.pdf

Interaction-Enhanced and Time-Aware Graph Convolutional Network for Successive Point-of-Interest Recommendation in Traveling Enterprises

Recommendation accuracy is a fundamental problem in the quality of the recommendation system. In this paper, we propose an efficient deep matrix factorization with review feature learning for the industrial recommender system (EDMF). Two characteristics in user's review are revealed. First, interactivity between the user and the item, which can also be considered as the former's scoring behavior on the latter, is exploited in a review. Second, the review is only a partial description of the user's preferences for the item, which is revealed as the sparsity property. Specifically, in the first characteristic, EDMF extracts the interactive features of onefold review by convolutional neural networks with word attention mechanism. Subsequently, L0 norm is leveraged to constrain the review considering that the review information is a sparse feature, which is the second characteristic. Furthermore, the loss function is constructed by maximum a posteriori estimation theory, where the interactivity and sparsity property are converted as two prior probability functions. Finally, the alternative minimization algorithm is introduced to optimize the loss functions. Experimental results on several datasets demonstrate that the proposed methods, which show good industrial conversion application prospects, outperform the state-of-the-art methods in terms of effectiveness and efficiency.

Duantengchuan Li

Papers

EDMF: Efficient Deep Matrix Factorization with Review Feature Learning for Industrial Recommender System

Multi-perspective social recommendation method with graph representation learning