Recently human–robot collaboration (HRC) has emerged as a promising paradigm for mass personalization in manufacturing owing to the potential to fully exploit the strength of human flexibility and robot precision. To achieve better collaboration, robots should be capable of holistically perceiving and parsing the information of a working scene in real-time to plan proactively and act accordingly. Although excessive attentions have been paid to human cognition in existing works of HRC, there is a lack of a holistic consideration of other crucial elements of a working scene, especially when taking a further step towards Proactive HRC. Aiming to fill the gap, this paper provides a systematic review of computer vision-based holistic scene understanding in HRC scenarios, which mainly takes into account the cognition of object, human, and environment along with visual reasoning to gather and compile visual information into semantic knowledge for subsequent robot decision-making and proactive collaboration. Finally, challenges and potential research directions that can be largely facilitated by enhanced holistic perception techniques are also discussed. 

Vision-based holistic scene understanding towards proactive human-robot collaboration

With the advent of fog and edge computing paradigms, computation capabilities have been moved toward the edge of the network to support the requirements of highly demanding services. To ensure that the quality of such services is still met in the event of users’ mobility, migrating services across different computing nodes becomes essential. Several studies have emerged recently to address service migration in different edge-centric research areas, including fog computing, multi-access edge computing (MEC), cloudlets, and vehicular clouds. Since existing surveys in this area focus on either VM migration in general or migration in a single research field (e.g., MEC), the objective of this survey is to bring together studies from different, yet related, edge-centric research fields while capturing the different facets they addressed. More specifically, we examine the diversity characterizing the landscape of migration scenarios at the edge, present an objective-driven taxonomy of the literature, and highlight contributions that rather focused on architectural design and implementation. Finally, we identify a list of gaps and research opportunities based on the observation of the current state of the literature. One such opportunity lies in joining efforts from both networking and computing research communities to facilitate future research in this area.

/pdf/a-survey-on-mobility-induced-service-migration-in-the-fog-rjq31luk4n.pdf

A Survey on Mobility-Induced Service Migration in the Fog, Edge, and Related Computing Paradigms

Multimodal action recognition techniques combine several image modalities (RGB, Depth, Skeleton, and InfraRed) for a more robust recognition. According to the fusion level in the action recognition pipeline, we can distinguish three families of approaches: early fusion, where the raw modalities are combined ahead of feature extraction; intermediate fusion, the features, respective to each modality, are concatenated before classification; and late fusion, where the modality-wise classification results are combined. After reviewing the literature, we identified the principal defects of each category, which we try to address by first investigating more deeply the early-stage fusion that has been poorly explored in the literature. Second, intermediate fusion protocols operate on the feature map, irrespective of the particularity of human action, we propose a new scheme where we optimally combine modality-wise features. Third, as most of the late fusion solutions use handcrafted rules, prone to human bias, and far from real-world peculiarities, we adopt a neural learning strategy to extract significant features from data rather than assuming that artificial rules are correct. We validated our findings on two challenging datasets. Our obtained results were as good or better than their literature counterparts.

Early, intermediate and late fusion strategies for robust deep learning-based multimodal action recognition

Recognizing human activities is one of the crucial capabilities that a robot needs to have to be useful around people. Although modern robots are equipped with various types of sensors, human activity recognition (HAR) still remains a challenging problem, particularly in the presence of noisy sensor data. In this work, we introduce a multimodal graphical attention-based HAR approach, called Multi-GAT, which hierarchically learns complementary multimodal features. We develop a multimodal mixture-of-experts model to disentangle and extract salient modality-specific features that enable feature interactions. Additionally, we introduce a novel message-passing based graphical attention approach to capture cross-modal relation for extracting complementary multimodal features. The experimental results on two multimodal human activity datasets suggest that Multi-GAT outperformed state-of-the-art HAR algorithms across all datasets and metrics tested. Finally, the experimental results with noisy sensor data indicate that Multi-GAT consistently outperforms all the evaluated baselines. The robust performance suggests that Multi-GAT can enable seamless human-robot collaboration in noisy human environments.

Multi-GAT: A Graphical Attention-Based Hierarchical Multimodal Representation Learning Approach for Human Activity Recognition

Automatic text categorization is the operation of sorting out the text documents into pre-defined text categories using some machine learning algorithms. Normally, it defines the most important approaches to organizing and making the use of a large volume of information exists in unstructured form. Nowadays, text categorization is becoming an extensively researched field of text mining and processing of languages. Word sense, semantic relationships among terms, text documents and categories are quite essential in order of enhancing the performances of categorization. Various surveys on text categorization have already been available which involve techniques of various text representation schemes to such extent but do not include several approaches that have been explored in text categorization over the standard techniques. Here, an exhaustive analysis of different text categorization approaches over the conventional approaches has been undertaken. This survey paper explores a wide variety of algorithms used for categorizing text documents and tries to assemble the existing works into three basic fields: conventional methods, fuzzy logic-based methods, deep learning-based methods. Further, conventional methods have been categorized into three fields: text categorization using handcrafted features, text categorization using nature-inspired algorithms and text categorization using graph-based methods. Furthermore, this survey provides a clear idea about the available libraries used for different algorithms, availability of datasets, categorization technologies explored in various non-Indian and Indian languages as well.

Text categorization: past and present

To fluently collaborate with people, robots need the ability to recognize human activities accurately. Although modern robots are equipped with various sensors, robust human activity recognition (HAR) still remains a challenging task for robots due to difficulties related to multimodal data fusion. To address these challenges, in this work, we introduce a deep neural network-based multimodal HAR algorithm, HAMLET. HAMLET incorporates a hierarchical architecture, where the lower layer encodes spatio-temporal features from unimodal data by adopting a multi-head self-attention mechanism. We develop a novel multimodal attention mechanism for disentangling and fusing the salient unimodal features to compute the multimodal features in the upper layer. Finally, multimodal features are used in a fully connect neural-network to recognize human activities. We evaluated our algorithm by comparing its performance to several state-of-the-art activity recognition algorithms on three human activity datasets. The results suggest that HAMLET outperformed all other evaluated baselines across all datasets and metrics tested, with the highest top-1 accuracy of 95.12% and 97.45% on the UTD-MHAD [1] and the UT-Kinect [2] datasets respectively, and F1-score of 81.52% on the UCSD-MIT [3] dataset. We further visualize the unimodal and multimodal attention maps, which provide us with a tool to interpret the impact of attention mechanisms concerning HAR.

HAMLET: A Hierarchical Multimodal Attention-based Human Activity Recognition Algorithm

In the literature, automated Bangla article classification has been studied, where several supervised learning models have been proposed by utilizing a large textual data corpus. Despite several comprehensive textual datasets are available for different languages, a few small datasets are curated on Bangla language. As a result, a few works address Bangla document classification problem, and due to the lack of enough training data, these approaches could not able to learn sophisticated supervised learning model. In this work, we curated a large dataset of Bangla articles from different news portals, which contains around 3,76,226 articles. This huge diverse dataset helps us to train several supervised learning models by utilizing a set of sophisticated textual features, such as word embeddings, TF-IDF. In this works, our learning model shows promising performance on our curated dataset, compared to state-of-the-art works in Bangla article classification. Furthermore, we deployed our proposed Bangla content classifier as a web application: bard2018.pythonanywhere.com and the video demo of this application is available here: bit.lylBARD_ VIDEO_DEMO. Additionally, we open-sourced the BARD dataset(bit.lyIBARD_DATASET) and source code of this work(bit.lvlBARD SC)’

BARD: Bangla Article Classification Using a New Comprehensive Dataset

Mobile Cloud Computing (MCC) improves the performance of a mobile application by executing it at a resourceful cloud server that can minimize execution time compared to a resource-constrained mobile device. Virtual Machine (VM) migration in MCC brings cloud resources closer to a user so as to further minimize the response time of an offloaded application. Such resource migration is very effective for interactive and real-time applications. However, the key challenge is to find an optimal cloud server for migration that offers the maximum reduction in computation time. In this paper, we propose a Genetic Algorithm (GA) based VM migration model, namely GAVMM, for heterogeneous MCC system. In GAVMM, we take user mobility and load of the cloud servers into consideration to optimize the effectiveness of VM migration. The goal of GAVMM is to select the optimal cloud server for a mobile VM and to minimize the total number of VM migrations, resulting in a reduced task execution time. Additionally, we present a thorough numerical evaluation to investigate the effectiveness of our proposed model compared to the state-of-the-art VM migration policies.

A genetic algorithm for virtual machine migration in heterogeneous mobile cloud computing

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Multi-GAT: A Graphical Attention-Based Hierarchical Multimodal Representation Learning Approach for Human Activity Recognition

HAMLET: A Hierarchical Multimodal Attention-based Human Activity Recognition Algorithm

HAMLET: A Hierarchical Multimodal Attention-based Human Activity Recognition Algorithm

BARD: Bangla Article Classification Using a New Comprehensive Dataset

A genetic algorithm for virtual machine migration in heterogeneous mobile cloud computing