The human brain constantly learns and rapidly adapts to new situations by integrating acquired knowledge and experiences into memory. Developing this capability in machine learning models is considered an important goal of AI research since deep neural networks perform poorly when there is limited data or when they need to adapt quickly to new unseen tasks. Meta-learning models are proposed to facilitate quick learning in low-data regimes by employing absorbed information from the past. Although some models have recently been introduced that reached high-performance levels, they are not biologically plausible. We have proposed a bio-plausible meta-learning model inspired by the hippocampus and the prefrontal cortex using spiking neural networks with a reward-based learning system. Our proposed model includes a memory designed to prevent catastrophic forgetting, a phenomenon that occurs when meta-learning models forget what they have learned as soon as the new task begins. Also, our new model can easily be applied to spike-based neuromorphic devices and enables fast learning in neuromorphic hardware. The final analysis will discuss the implications and predictions of the model for solving few-shot classification tasks. In solving these tasks, our model has demonstrated the ability to compete with the existing state-of-the-art meta-learning techniques.

Recently, there has been a high demand for accelerating and improving the detection of automatic cadastral mapping. As this problem is in its starting point, there are many methods of computer vision and deep learning that have not been considered yet. In this paper, we focus on deep learning and provide three geometric post-processing methods that improve the quality of the work. Our framework includes two parts, each of which consists of a few phases. Our solution to this problem uses instance segmentation. In the first part, we use Mask R-CNN with the backbone of pre-trained ResNet-50 on the ImageNet dataset. In the second phase, we apply three geometric post-processing methods to the output of the first part to get better overall output. Here, we also use computational geometry to introduce a new method for simplifying lines which we call it pocket-based simplification algorithm. For evaluating the quality of our solution, we use popular formulas in this field which are recall, precision and F-score. The highest recall we gain is 95 percent which also maintains high Precision of 72 percent. This resulted in an F-score of 82 percent. Implementing instance segmentation using Mask R-CNN with some geometric post-processes to its output gives us promising results for this field. Also, results show that pocket-based simplification algorithms work better for simplifying lines than Douglas-Puecker algorithm.

Meta-learning techniques enable quick learning of new tasks by using few samples with utilizing prior knowledge learned from previous tasks. Gradient-based models are widely used because of their simplicity and ability to solve a wide range of problems. However, they only succeed in solving tasks with a very similar structure since they adapt the model with a shared meta-parameter across all tasks. In recent years, some models have been proposed to enhance the gradient-based models to deal with task uncertainty and heterogeneity via sharing knowledge among similar tasks by using task clustering. Nevertheless, the high-dimensional parameter space of gradient-based models hinders them from achieving their full potential in low-data regimes. Bayesian meta-learning algorithms address this issue by learning a data-dependent latent generative representation of model parameters. Our proposed model bypasses the aforementioned limitations by leveraging Bayesian algorithms as well as clustering input tasks. The final analysis demonstrates the effectiveness of the proposed model for few-shot image classification problems.

Point cloud coding solutions have been recently standardized to address the needs of multiple application scenarios. The design and assessment of point cloud coding methods require reliable objective quality metrics to evaluate the level of degradation introduced by compression or any other type of processing. Several point cloud objective quality metrics has been recently proposed to reliable estimate human perceived quality, including the so-called projection-based metrics. In this context, this paper proposes a joint geometry and color projection-based point cloud objective quality metric which solves the critical weakness of this type of quality metrics, i.e., the misalignment between the reference and degraded projected images. Moreover, the proposed point cloud quality metric exploits the best performing 2D quality metrics in the literature to assess the quality of the projected images. The experimental results show that the proposed projection-based quality metric offers the best subjective-objective correlation performance in comparison with other metrics in the literature. The Pearson correlation gains regarding D1-PSNR and D2-PSNR metrics are 17% and 14.2 when data with all coding degradations is considered.

We propose a novel keypoint voting 6DoF object pose estimation method, which takes pure unordered point cloud geometry as input without RGB information. The proposed cascaded keypoint voting method, called RCVPose3D, is based upon a novel architecture which separates the task of semantic segmentation from that of keypoint regression, thereby increasing the effectiveness of both and improving the ultimate performance. The method also introduces a pairwise constraint in between different keypoints to the loss function when regressing the quantity for keypoint estimation, which is shown to be effective, as well as a novel Voter Confident Score which enhances both the learning and inference stages. Our proposed RCVPose3D achieves state-of-the-art performance on the Occlusion LINEMOD (74.5%) and YCB-Video (96.9%) datasets, outperforming existing pure RGB and RGB-D based methods, as well as being competitive with RGB plus point cloud methods.

Background: Social networks are a good source for monitoring public health during the outbreak of COVID-19, these networks play an important role in identifying useful information. Objectives: This study aims to draw a comparison of the public’s reaction in Twitter among the countries of West Asia (a.k.a Middle East) and North Africa in order to make an understanding of their response regarding the same global threat. Methods: 766,630 tweets in four languages (Arabic, English French, and Farsi) tweeted in March 2020, were investigated. Results: The results indicate that the only common theme among all languages is “government responsibilities (political)” which indicates the importance of this subject for all nations. Conclusion: Although nations react similarly in some aspects, they respond differently in others and therefore, policy localization is a vital step in confronting problems such as COVID-19 pandemic.

Point clouds (PCs) are a powerful 3D visual representation paradigm for many emerging application domains, especially virtual and augmented reality, and autonomous vehicles. However, the large amount of PC data required for highly immersive and realistic experiences requires the availability of efficient, lossy PC coding solutions are critical. Recently, two MPEG PC coding standards have been developed to address the relevant application requirements and further developments are expected in the future. In this context, the assessment of PC quality, notably for decoded PCs, is critical and asks for the design of efficient objective PC quality metrics. In this paper, a novel point-to-distribution metric is proposed for PC quality assessment considering both the geometry and texture. This new quality metric exploits the scale-invariance property of the Mahalanobis distance to assess first the geometry and color point-to-distribution distortions, which are after fused to obtain a joint geometry and color quality metric. The proposed quality metric significantly outperforms the best PC quality assessment metrics in the literature.

Recently, point clouds have shown to be a promising way to represent 3D visual data for a wide range of immersive applications, from augmented reality to autonomous cars. Emerging imaging sensors have made easier to perform richer and denser point cloud acquisition, notably with millions of points, thus raising the need for efficient point cloud coding solutions. In such scenario, it is important to evaluate the impact and performance of several processing steps in a point cloud communication system, notably the degradations associated to point cloud coding solutions. Moreover, since point clouds are not directly visualized but rather processed with a rendering algorithm before shown on any display, the perceived quality of point cloud data highly depends on the rendering solution. In this context, the main objective of this paper is to study the impact of several coding and rendering solutions on the perceived user quality and in the performance of available objective assessment metrics. Another contribution regards the assessment of recent MPEG point cloud coding solutions for several popular rendering methods, which was never presented before. The conclusions regard the visibility of three types of coding artifacts for the three considered rendering approaches as well as the strengths and weaknesses of objective metrics when point clouds are rendered after coding.

The role of wearable technology in our daily lives is rapidly growing and many users are cumulatively becoming dependent on it. To provide insight into the future of wearable technologies and various community attitudes towards them, we implemented an in-depth quantitative investigation of opinions from academic texts (DBLP and PubMed), social media (Twitter), news media (Google News and Bing News), and entrepreneurship communities (Kickstarter and Indiegogo) over a 10-year period. Our results indicate that unlike academia, the news media, entrepreneurship communities, and social media all hold overall positive attitudes towards wearable technologies. Secondly, there are diverse perspectives towards various wearable products across different platforms. Specifically, "XR" technologies received the most attention, while "Exoskeleton" ignited the most heated debates. Thirdly, we discovered that the lifetime of a hyped wearable technology lasts approximately three years. Furthermore, the news media and entrepreneurship community's attitudes towards wearable technologies did not have a strong impact on public opinion. Finally, among all types of wearable technologies, "fashion design" and "healthcare" products were the most enlightening for the market.

Reliable quality assessment of decoded point cloud geometry is essential to evaluate the compression performance of emerging point cloud coding solutions and guarantee some target quality of experience. This paper proposes a novel point cloud geometry quality assessment metric based on a generalization of the Hausdorff distance. To achieve this goal, the so-called generalized Hausdorff distance for multiple rankings is exploited to identify the best performing quality metric in terms of correlation with the MOS scores obtained from a subjective test campaign. The experimental results show that the quality metric derived from the classical Hausdorff distance leads to low objective-subjective correlation and, thus, fails to accurately evaluate the quality of decoded point clouds for emerging codecs. However, the quality metric derived from the generalized Hausdorff distance with an appropriately selected ranking, outperforms the MPEG adopted geometry quality metrics when decoded point clouds with different types of coding distortions are considered.

During recent years, the fast proliferation of robots in people’s everyday lives calls for a profound examination of public consensus, which is the ultimate determinant of the future of this industry. This paper investigates text corpora, consisting of posts in Google News, Bing News, and Kickstarter, over an 8-year period and Twitter over a 1-year period, to quantify the public’s and media’s opinion about this emerging technology. The results of our analysis demonstrate that news platforms and the public take an overall positive position on robots. However, there is a deviation between news coverage and Twitter users’ attitudes. Among various robot types, sex robots raise the fiercest debate. Besides, based on our analysis the public and news media conceptualization of robotics has altered over recent years. More specifically, a shift from solely industrial-purpose machines, towards more social, assistive, and multi-purpose gadgets is visible.

This paper demonstrates that the instrumented indentation test (IIT), together with a trained artificial neural network (ANN), has the capability to characterize the mechanical properties of the local parts of a welded steel structure such as a weld nugget or heat affected zone. Aside from force-indentation depth curves generated from the IIT, the profile of the indented surface deformed after the indentation test also has a strong correlation with the materials' plastic behavior. The profile of the indented surface was used as the training dataset to design an ANN to determine the material parameters of the welded zones. The deformation of the indented surface in three dimensions shown in images were analyzed with the computer vision algorithms and the obtained data were employed to train the ANN for the characterization of the mechanical properties. Moreover, this method was applied to the images taken with a simple light microscope from the surface of a specimen. Therefore, it is possible to quantify the mechanical properties of the automotive steels with the four independent methods: (1) force-indentation depth curve; (2) profile of the indented surface; (3) analyzing of the 3D-measurement image; and (4) evaluation of the images taken by a simple light microscope. The results show that there is a very good agreement between the material parameters obtained from the trained ANN and the experimental uniaxial tensile test. The results present that the mechanical properties of an unknown steel can be determined by only analyzing the images taken from its surface after pushing a simple indenter into its surface.

Aspect-based sentiment analysis (ABSA) is a more detailed task in sentiment analysis, by identifying opinion polarity toward a certain aspect in a text. This method is attracting more attention from the community, due to the fact that it provides more thorough and useful information. However, there are few language-specific researches on Persian language. The present research aims to improve the ABSA on the Persian Pars-ABSA dataset. This research shows the potential of using pre-trained BERT model and taking advantage of using sentence-pair input on an ABSA task. The results indicate that employing Pars-BERT pre-trained model along with natural language inference auxiliary sentence (NLI-M) could boost the ABSA task accuracy up to 91% which is 5.5% (absolute) higher than state-of-the-art studies on Pars-ABSA dataset.

Nowadays, point clouds (PCs) are a promising representation format for immersive content and target several emerging applications, notably in virtual and augmented reality. However, efficient coding solutions are critically needed due to the large amount of PC data required for high quality user experiences. To address these needs, several PC coding standards were developed and thus, objective PC quality metrics able to accurately account for the subjective impact of coding artifacts are needed. In this paper, a scale-invariant PC geometry quality assessment metric is proposed based on a new type of correspondence, namely between a point and a distribution of points. This metric is able to reliably measure the geometry quality for PCs with different intrinsic characteristics and degraded by several coding solutions. Experimental results show the superiority of the proposed PC quality metric over relevant state-of-the-art.

An increased interest in immersive applications has drawn attention to emerging 3D imaging representation formats, notably light fields and point clouds (PCs). Nowadays, PCs are one of the most popular 3D media formats, due to recent developments in PC acquisition, namely with new depth sensors and signal processing algorithms. To obtain high fidelity 3D representations of visual scenes a huge amount of PC data is typically acquired, which demands efficient compression solutions. As in 2D media formats, the final perceived PC quality plays an importance role in the overall user experience and, thus, objective metrics capable to measure the PC quality in a reliable way are essential. In this context, this paper proposes and evaluates a set of objective quality metrics for the geometry component of PC data, which plays a very important role on the final perceived quality. Based on the popular PSNR PC geometry quality metric, novel improved PSNR-based metrics are proposed by exploiting the intrinsic PC characteristics and the rendering process that must occur before visualization. The experimental results show the superiority of the best proposed metrics over state-of-the-art, obtaining an improvement up to 32% in the Pearson correlation coefficient.

The increasing availability of point cloud data in recent years is demanding for high performance denoising methods and compression schemes. When point cloud data is directly obtained from depth sensors or extracted from images acquired from different viewpoints, imprecisions on the depth acquisition or in the 3D reconstruction techniques result in noisy point clouds which may include a significant number of outliers. Moreover, the quality assessment of point clouds is a challenging problem since this 3D representation format is unstructured and it is typically not directly visualized. In this paper, selected objective quality metrics are evaluated regarding their correlation with human quality assessment and thus human perception. As far as the authors know, this is the first paper performing the subjective assessment of point cloud denoising algorithms and the evaluation of most used point cloud objective quality metrics. Experimental results show that graph-based denoising algorithms can improve significantly the point cloud quality data and that objective metrics that model the underlying point cloud surface can correlate better with human perception.

The increasing availability of point cloud data in recent years is demanding high performance compression solutions. Naturally, methods to perform objective quality assessment of compressed point clouds are also very much needed, namely metrics to measure the geometry distortion of point clouds when positioning errors are present. This is a rather challenging problem since this 3D representation format is unstructured and it is typically not directly visualized. In this context, the objective of this paper is to perform subjective and objective quality assessment of point clouds degraded by compression artifacts and to evaluate the correlation of the most popular objective quality metrics with human perception. In this work, subjective experiments conducted at Instituto Superior Técnico (IST) are described with point clouds compressed with two different but yet promising solutions, one based on the octree representation of the 3D space and another based on the rather popular graph transform. As far as the authors know, this is the first study of this type made available and should have a key role on the future development and evaluation of point cloud coding solutions. 1

In this paper a method for extracting keypoints from human brain MR images is proposed. These keypoints are obtained based on curved structures in the brain MR images. In this method, a keypoint is center of a circle which includes circular boundaries in the image and is selected based on gradients of the image. These keypoints and their descriptors are scale and rotation invariant. The proposed method is compared with other well-known methods with repeatability measure and ROC curves. Experimental results show that proposed method performs better than other well-known methods, specially, when deformations are remarkable.