Reviewerly/exHarmony huggingface dataset:
A Transformer-Based Framework for Scene Text Recognition

Scene Text Recognition (STR) has become a popular and long-standing research problem in computer vision communities. Almost all the existing approaches mainly adopt the connectionist temporal classification (CTC) technique. However, these existing approaches are not much effective for irregular STR. In this research article, we introduced a new encoder-decoder framework to identify both regular and irregular natural scene text, which is developed based on the transformer framework. The proposed framework is divided into four main modules: Image Transformation, Visual Feature Extraction (VFE), Encoder and Decoder. Firstly, we employ a Thin Plate Spline (TPS) transformation in the image transformation module to normalize the original input image to reduce the burden of subsequent feature extraction. Secondly, in the VFE module, we use ResNet as the Convolutional Neural Network (CNN) backbone to retrieve text image features maps from the rectified word image. However, the VFE module generates one-dimensional feature maps that are not suitable for locating a multi-oriented text on two-dimensional word images. We proposed 2D Positional Encoding (2DPE) to preserve the sequential information. Thirdly, the feature aggregation and feature transformation are carried out simultaneously in the encoder module. We replace the original scaled dot-product attention model as in the standard transformer framework with an Optimal Adaptive Threshold-based Self-Attention (OATSA) model to filter noisy information effectively and focus on the most contributive text regions. Finally, we introduce a new architectural level bi-directional decoding approach in the decoder module to generate a more accurate character sequence. Eventually, We evaluate the effectiveness and robustness of the proposed framework in both horizontal and arbitrary text recognition through extensive experiments on seven public benchmarks including IIIT5K-Words, SVT, ICDAR 2003, ICDAR 2013, ICDAR 2015, SVT-P and CUTE80 datasets. We also demonstrate that our proposed framework outperforms most of the existing approaches by a substantial margin.
Deep Q-Learning-Based Dynamic Management of a Robotic Cluster

The ever-increasing demands for autonomy and precision have led to the development of heavily computational multi-robot system (MRS). However, numerous missions exclude the use of robotic cloud. Another solution is to use the robotic cluster to locally distribute the computational load. This complex distribution requires adaptability to come up with a dynamic and uncertain environment. Classical approaches are too limited to solve this problem, but recent advances in reinforcement learning and deep learning offer new opportunities. In this paper we propose a new Deep Q-Network (DQN) based approaches where the MRS learns to distribute tasks directly from experience. Since the problem complexity leads to a curse of dimensionality, we use two specific methods, a new branching architecture, called Branching Dueling Q-Network (BDQ), and our own optimized multi-agent solution and we compare them with classical Market-based approaches as well as with non-distributed and purely local solutions. Our study shows the relevancy of learning-based methods for task mapping and also highlight the BDQ architecture capacity to solve high dimensional state space problems. Note to Practitioners—A lot of applications in industry like area exploration and monitoring can be efficiently delegated to a group of small-size robots or autonomous vehicles with advantages like reliability and cost in respect of single-robot solutions. But autonomy requires high and increasing compute-intensive tasks such as computer-vision. On the other hand small robots have energy constraints, limited embedded computing capacities and usually restricted and/or unreliable communications that limit the use of cloud resources. An alternative solution to cope with this problem consists in sharing the computing resources of the group of robots. Previous work was a proof of concept limited to the parallelisation of a single specific task. In this paper we formalize a general method that allows the group of robots to learn on the field how to efficiently distribute tasks in order to optimize the execution time of a mission under energy constraint. We demonstrate the relevancy of our solution over market-based and non-distributed approaches by means of intensive simulations. This successful study is a necessary first step towards distribution and parallelisation of computation tasks over a robotic cluster. The next steps, not tested yet, will address hardware in the loop simulation and finally a real-life mission with a group of robots.
A critical evaluation of handling uncertainty in Big Data processing

Big Data is a modern economic and social transformation driver all over the world. The selection has reached a tipping point in terms of big technological advancements that could usher in new ones. It's about making decisions, taking charge of our health, our cities, our wallets, and our education. While the amount, diversity, speed, and reliability of data has increased, so has the complexity of the data (Amzal et al., 2006). Our ability to identify the "value of information through Big Data" will have the most impact. As a result of the resolution of large data concerns, Big Data is revolutionising the IT sector Software vendors in many industries are unable to move forward with dependability and scalability, Information on the project. With the help of relational databases, the explanation for why business requires adequate atomic transactions in the business sector. As a result, to benefit from both technology, advantages, and solutions. Big Data, which refers to databases that are too huge to be processed with current database administration tools, is gaining traction in a variety of significant applications, including internet search, business, social networking, genomics, and meteorology [2]. This concept highlights key research challenges and the promise of data-driven optimization that organically integrates fuzzy, machine learning, and deep learning for decision-making under uncertainty, and identifies potential research opportunities in the business field of Bayesian optimization under uncertainty through a modern data lens. Big Data is a big issue for science and database data mining. Here, we look at the fascinating activities that my community has been doing at this conference to talk about the Big Data problem. As a result, the central focus is linking Big Data to individuals in numerous ways.
User-Centric Digital Assistance with Smart Tools for Manual Assembly Processes

To efficiently leverage digitalization potentials for large proportions of manual processes, workers must be integrated in the digital world in a seamless manner. This paper contributes to the ongoing research on Digital Assistance Systems. In particular, manual assembly processes in aircraft production are scrutinized to identify specific requirements for such a system. Following the resulting analysis, we propose a concept for user-centric digital assistance that uses smart, localized hand tools and provides workers with information based on a digital twin of the product and the production process. The proposed digital assistance system accurately tracks the tool position and enables event-based and bidirectional data flow between workers and a digital twin. The tools are automatically configured with the correct process parameters based on their current position. Quality and progress are documented and ultimately operations and everyday efforts are simplified. The concept is examined for its potentials on productivity and quality in manual assembly processes. The results show that further development of the concept promises significant improvements in manual work.
Pushing Yourself Harder: The Effects of Mobile Touch Modes on Users’ Self-Regulation

Mobile health interventions are widely used to facilitate individuals’ management of their health behavior. A notable issue is that health interventions with obvious persuasive intent may cause negligence and reactance. In this study, we propose a subtle but powerful way to bolster self-regulation in maintaining healthy behavior by leveraging embodied interaction design. Our study shows that bodily actions in interacting with digital devices can trigger thoughts about prior associated experiences and, thus, be strategically designed to affect individuals’ judgments, decisions, and behavior. Specifically, in three experiments, we find that firmly pressing a touchscreen during mobile interaction (as compared with gently tapping a touchscreen) can activate users’ approach motivation and, thus, induce their preference for a healthy over a tasty beverage, lead to more challenging exercise goals and more exercise, and reduce personal hygiene lapses after receiving hygiene education. Hence, designers of digital health products may consider designing interaction with pressing gestures to facilitate users’ self-regulation and attainment of health-related goals. Policymakers can also encourage the adoption of relevant app designs to improve citizens’ health wellbeing.
SBCSim

Change management involves a set of changes within entities that poses a risk to the stability of the entity. Similarity index helps to analyze and prioritize the changes, but it provides inaccurate results due to non-consideration of type of similarity. Existing types of similarities also lack accuracy in comparing the versions due to history slicing in case of renaming or shifting. Thus, the proposed combination of fuzzy and hybrid techniques uses modified similarity index that helps in prioritizing the changes in the entities based on the proposed nominal similarity classifications. It reduces the cost of change management by determining risky entities. Results shows that 15% of files, 19% of classes, and 66% of methods pose risk with decrease of 35.29% and 52.53% unstable/risky entities at file and entity level respectively as compared to understand tool. Moreover, MSI captures similarities between entities where SI fails in GIT repositories, thereby enhancing the process of engineering change order.
Few-shot learning-based RGB-D salient object detection: A case study

RGB-D salient object detection (SOD) aims at detecting general attention-grabbing objects from paired RGB and depth image inputs, and recently has attracted increasing research attention. Despite that many advanced RGB-D SOD models are proposed, almost all of them focus on developing models in a fully supervised manner with a small training dataset that typically has only hundreds of RGB-D samples. This may inevitably incur poor generalizability of these models when being applied to real-world scenarios and applications. To narrow such a gap, we make the first attempt of treating RGB-D SOD as a few-shot learning (FSL) problem, and improve it by introducing extra prior knowledge from a closely related task, i.e., RGB SOD. Inspired by the general taxonomy of FSL techniques, we investigate from two perspectives, namely model and data, of transferring additional knowledge from the RGB SOD dataset to enhance RGB-D SOD performance. For the former, we employ multi-task learning with parameter sharing to constrain the model space, whereas for the latter, we propose to generate the depth from RGB by using an off-the-shelf depth estimator. Representative middle-fusion and late-fusion models are trialed and validated under such a FSL setup. Our experimental results and analyses confirm the feasibility of promoting RGB-D SOD via FSL techniques, while comparative study on different FSL techniques and detection strategies is conducted. We hope this work can serve as a catalyst for bringing RGB-D saliency detection into real applications, as well as for inspiring future works that apply few-shot learning to saliency detection and other multi-modal detection tasks.
Human Centered Explainability for Intelligent Vehicles – A User Study

Advances in artificial intelligence (AI) are leading to an increased use of algorithm-generated user-adaptivity in everyday products. Explainable AI aims to make algorithmic decision-making more transparent to humans. As future vehicles become more intelligent and user-adaptive, explainability will play an important role ensuring that drivers understand the AI system's functionalities and outputs. However, when integrating explainability into in-vehicle features there is a lack of knowledge about user needs and requirements and how to address them. We conducted a study with 59 participants focusing on how end-users evaluate explainability in the context of user-adaptive comfort and infotainment features. Results show that explanations foster perceived understandability and transparency of the system, but that the need for explanation may vary between features. Additionally, we found that insufficiently designed explanations can decrease acceptance of the system. Our findings underline the requirement for a user-centered approach in explainable AI and indicate approaches for future research.
Motion Video Recognition in Speeded-Up Robust Features Tracking

Motion video recognition has been well explored in applications of computer vision. In this paper, we propose a novel video representation, which enhances motion recognition in videos based on SURF (Speeded-Up Robust Features) and two filters. Firstly, the detector scheme of SURF is used to detect the candidate points of the video because it is an efficient faster local feature detector. Secondly, by using the optical flow field and trajectory, the feature points can be filtered from the candidate points, which enables a robust and efficient extraction of motion feature points. Additionally, we introduce a descriptor, called MoSURF (Motion Speeded-Up Robust Features), based on SURF (Speeded-Up Robust Features), HOG (Histogram of Oriented Gradient), HOF (Histograms of Optical Flow), MBH(Motion Boundary Histograms), and trajectory information, which can effectively describe motion information and are complementary to each other. We evaluate our video representation under action classification on three motion video datasets namely KTH, YouTube, and UCF50. Compared with state-of-the-art methods, the proposed method shows advanced results on all datasets.
Self-designed hierarchical network based hand shape intelligent recognition and evaluation for Chinese zither performing

A proper hand shape is the foundation for professional musical instrument performance. In this paper, the image recognition technology based on artificial intelligence is introduced into the hand shape recognition of performing Chinese zither, which is referred to as Zheng in Chinese, for the first time to realize the function of hand shape intelligent evaluation and the self-designed hierarchical network is proposed to recognize and evaluate correct hand shape for Zheng performing. The intra-class difference is larger than the inter-class difference for Zheng performing hand shape image, which belongs to fine-grained image. Therefore, we use the first layer network to determine four classes of images acquired from different viewpoints. Meanwhile, the feature maps from different convolutional blocks of this layer are concatenated as the input of the second layer, which performs fine classification of Zheng performing hand shape images. Consequently, the learning ability of the network can be improved and the complexity of the network can be reduced at the same time. We design an experimental paradigm for instrumentalist hand shape performance evaluation, formulate a Zheng hand shape evaluation merit based on image recognition, and construct a Chinese zither hand shape Dataset (CZ-dataset V3) for the real scene. The experiments show that the method proposed in this paper can effectively improve the recognition accuracy of fine-grained hand shape images and the result is consistent with the evaluation of professional advisors, which realizes the perfect combination of the intelligent image recognition and the hand shape evaluation for Chinese traditional instrument performing.
A meta-learning configuration framework for graph-based similarity search indexes

Similarity searches retrieve elements in a dataset with similar characteristics to the input query element. Recent works show that graph-based methods have outperformed others in the literature, such as tree-based and hash-based methods. However, graphs are highly parameter-sensitive for indexing and searching, which usually demands extra time for finding a suitable trade-off for specific user requirements. Current approaches to select parameters rely on observing published experimental results or Grid Search procedures. While the former has no guarantees that good settings for a dataset will also perform well on a different one, the latter is computationally expensive and limited to a small range of values. In this work, we propose a meta-learning-based recommender framework capable of providing a suitable graph configuration according to the characteristics of the input dataset. We present two instantiations of the framework: a global instantiation that uses the whole meta-database to train meta-models and a dataset-similarity-based instantiation that relies on clustering to generate meta-models tailored to datasets with similar characteristics. We also developed generic and tuned versions of the instantiations. The generic versions can satisfy user requirements in orders of magnitude faster than the traditional Grid Search. The tuned versions provide more accurate predictions at a higher cost. Our results show that the tuned methods outperform the Grid Search for most cases, providing recommendations close to the optimal one and being a suitable alternative, particularly for more challenging datasets.
GCL-OSDA: Uncertainty prediction-based graph collaborative learning for open-set domain adaptation

Open-set domain adaptation (OSDA), which allows the target domain to store invisible class samples in the source domain, has recently received significant attention. In this paper, we propose a new unsupervised OSDA classification framework using an evidential network and multi-binary classifier and consider their jointly selected samples as a pseudo-labelled sample set of an unknown class. Specifically, this study designed an evidential network based on the D-S evidence theory to predict the degree of belief that a sample belongs to an unknown class. By selecting samples with high-uncertainty, false positive samples can be removed, which improves the reliability of unknown sample selection. Then, to better explore the intra-class relationship, an open-set graph convolutional network (OSGC) is proposed to extract distinguishable features of known and unknown samples in a weighted adversarial adaptation manner. Moreover, this paper presents a graph collaborative learning strategy to retrain the unknown recognition module (URM) with high confidence pseudo-labelled samples, which is predicted by the graph convolution network (GCN), where the target known class distribution is learned. Experimental results show that the proposed method outperforms state-of-the-art OSDA algorithms on three benchmark datasets and maintains a high recognition accuracy for unknown classes over a wide range of openness.
Robust Image Hashing Combining 3D Space Contour and Vector Angle Features

Abstract In this paper, an image hashing scheme combining 3D space contour (TDSC) features with vector angle (VA) features is proposed. The proposed algorithm extracts the 3D contours of the local component variation features of the image and the expression changes of the local component of the image in the form of a 3D VA to improve the performance. First, the gray component of the color image is used to construct a 3D space and the contour change features of the local component of the gray image are extracted using multi-perspectives. Then, the opposite color component and the brightness component Y of the YCbCr color space are extracted from the input image. The angular features of several image components are, respectively, extracted in the 3D space. Finally, the TDSC features are combined with the VA features to obtain image hashing. The simulations demonstrate and validate that the proposed image hashing scheme not only has better classification performance compared with the other image hashing techniques but is also equipped with the performance of tamper localization.
DPS Clustering: New Results

The results presented in this paper are obtained as part of the continued development and research of clustering algorithms based on the discrete mathematical analysis. The article briefly describes the theory of Discrete Perfect Sets (DPS-sets) that is the basis for the construction of DPS-clustering algorithms. The main task of the previously constructed DPS-algorithms is to search for clusters in multidimensional arrays with noise. DPS-algorithms have two stages: the first stage is the recognition of the maximum perfect set of a given density level from the initial array, the second stage is the partitioning of the result of the first stage into connected components, which are considered to be clusters. Study of qualities of DPS-algorithms showed that, in a number of situations in the first stage, the result does not include all clusters which have practical sense. In the second stage, partitioning into connected components can lead to unnecessarily small clusters. Simple variation of parameters in DPS-algorithms does not allow for eliminating these drawbacks. The present paper is devoted to the construction on the basis of DPS-algorithms of their new versions, more free from these drawbacks.
A Hybrid Methodology to Improve Speaking Skills in English Language Learning Using Mobile Applications

The main objective of this research is a working example of how a hybrid methodology combining traditional methodologies and mobile devices can be used to contribute to the literature on mobile learning in teaching English as a second language. This work was carried out because, in many Latin American countries, students are taught English as a second language throughout their primary and secondary education. However, at the end of their studies, most students are unable to communicate with other people in English, let alone with native speakers. Moreover, it must be taken into account that nowadays English is the most widely used language in international communications, business transactions, finance and science. The professional who knows how to communicate in English has a positive differentiator in his or her professional profile and can easily access more relevant positions in any institution. For this purpose, a review of different methodologies for teaching oral expression in English has been carried out. Metrics have also been used to choose an effective mobile application to reinforce English speaking. These analyzed methodologies have been combined with the use of a mobile application to propose a hybrid methodology that contemplates an eight-week class guide. Due to the characteristics of mobile learning, this work can help to motivate students in their learning and in improving their communicative skills in the English language. High school teachers can use this methodology as an innovation in their educational programs.