Reviewerly/exHarmony huggingface dataset:
Diverse title generation for Stack Overflow posts with multiple-sampling-enhanced transformer

Stack Overflow is one of the most popular programming communities where developers can seek help for their encountered problems. Nevertheless, if inexperienced developers fail to describe their problems clearly, it is hard for them to attract sufficient attention and get the anticipated answers. To address such a problem, we propose M3NSCT5, a novel approach to automatically generate multiple post titles from the given code snippets. Developers may take advantage of the generated titles to find closely related posts and complete their problem descriptions. M3NSCT5 employs the CodeT5 backbone, which is a pre-trained Transformer model with an excellent language understanding and generation ability. To alleviate the ambiguity issue that the same code snippets could be aligned with different titles under varying contexts, we propose the maximal marginal multiple nucleus sampling strategy to generate multiple high-quality and diverse title candidates at a time for the developers to choose from. We build a large-scale dataset with 890,000 question posts covering eight programming languages to validate the effectiveness of M3NSCT5. The automatic evaluation results on the BLEU and ROUGE metrics demonstrate the superiority of M3NSCT5 over six state-of-the-art baseline models. Moreover, a human evaluation with trustworthy results also demonstrates the great potential of our approach for real-world applications.
Learning-based padding: From connectivity on data borders to data padding

Padding is employed vastly in convolutional neural networks (CNNs) to maintain the output size. In current padding schemes, the input feature maps are padded using quite simple strategies, e.g., constant zero values in zero padding, which is the most common padding choice. In this work, we propose to use learning-based paradigms to calculate the padding data from the connectivity of the input images on the data borders. Two different modules, i.e., learning-based padding by convolution (LPC) and learning-based padding by attention (LPA), are designed to obtain the padding data from the interdependencies of the image border data along the channel and spatial dimensions, respectively. The designed LPC or LPA is formulated as a generic, plug-and-play unit, which can be a direct replacement for the conventional padding technique. Extensive experiments on image classification and semantic segmentation tasks show that the proposed padding schemes can consistently obtain higher accuracy than standard padding schemes, in various deep network backbones. The codes and trained modules are available at https://github.com/ICSResearch/LP.
An inexact subgraph matching algorithm for subpart retrieval in NC process reuse

With the wide application of CAD/CAM technology, huge amounts of process data are generated each year, which are valuable to be referred to and reused. For the reuse of these historical process data, the retrieval of the subpart model is the key, as similar structures usually correspond to similar NC processes. However, the existing subpart retrieval methods are only practical to the parts with simple structures or fixed geometry. This research proposes a subpart model retrieval method based on inexact subgraph matching, which can be used for parts of arbitrary complexity. By representing the part model as an attribute graph, the task is first transformed into an inexact subgraph matching task where some degree of matching difference is accepted to retrieve similar subparts. Next, this research proposes VF3-light-N, an inexact subgraph matching algorithm that operates on attribute graphs. The idea of VF3-light-N is to quantify the mismatch of both nodes and edges based on matching error first. Then use the empty map to remove the nodes that cannot be matched, so that the remaining nodes and edges can be matched exactly. Thus, VF3-light-N can search out all the matched subgraphs within a certain noise threshold. A case study based on aircraft structural parts is carried out, and the result shows that the proposed method can efficiently retrieve subparts from complex part models. Furthermore, it is believed that VF3-light-N can be also used in other inexact subgraph matching applications, such as feature recognition.
A Two-Stage End-to-End Deep Learning Framework for Pathologic Examination in Skin Tumor Diagnosis

Neurofibromas (NFs), Bowen disease (BD), and seborrheic keratosis (SK) are common skin tumors. Pathologic examination is the gold standard for diagnosis of these tumors. Current pathologic diagnosis is primarily based on microscopic observation, which is laborious and time-consuming. With digitization, artificial intelligence can be used to improve the efficiency of pathologic diagnosis. This research aims to develop an end-to-end extendable framework for the diagnosis of skin tumor based on pathologic slide images. NF, BD, and SK were selected as target skin tumors. A two-stage skin cancer diagnosis framework is proposed in this article, which consists of two parts: patches-wise diagnosis, and slide-wise diagnosis. Patches-wise diagnosis compares different convolutional neural networks to extract features and distinguish categories from patches generated in whole slide images. Slide-wise diagnosis combines attention graph gated network model prediction with post-processing algorithm. This approach can fuse information from feature-embedding learning and domain knowledge to draw conclusions. Training, validation, and testing were performed on NF, BD, SK, and negative samples. Accuracy and receiver operating characteristic curves were used to evaluate the classification performance. This study investigated the feasibility of skin tumor diagnosis from pathologic images and may be the first instance of applying deep learning to address these three types of tumor diagnoses in skin pathology.
Consistency analysis of focal stack-based light field reconstruction

Light field camera with a microlens array can realize spatio-angular joint sampling of light ray field at the cost of a trade-off between spatial and angular resolutions. Alternatively, focal stack-based light field reconstruction can computationally retrieve full-pixel-resolution light fields in object space by virtue of the transport-of-intensity property in an image sequence recorded at different focal depths in image space. However, traditional camera imaging generally involves a nonlinear mapping between object and image spaces. The inconsistency of image-space recording and object-space reconstruction will reduce the accuracy of reconstructed light fields. In this work, we focus on analyzing and addressing the problem caused by the object-image space inconsistency for high-resolution, high-accuracy focal stack-based light field reconstruction. With a pre-calibrated light field camera as a reference, light field reconstructions in object and image spaces are experimentally compared and discussed from different aspects, such as digital refocusing, viewpoint switching, angular resolution, and depth range and sampling rate. All experimental results demonstrate that the light field reconstruction accuracy can be significantly improved when satisfying the object-image space consistency, which can serve as a mechanism for the realization and application of high-quality computational light field imaging and measurement in the situation of nonlinear recording and reconstruction.
The Path Planning and Location Method of Inspection Robot in a Large Storage Tank Bottom

With the development of robot technology, inspection robots have been applied to the defect detection of large tanks. However, the existing path planning algorithm of the tank bottom detection robot is easy to fall into the local minimum, and the path is not smooth. Besides, the positioning of the tank bottom detection robot is not accurate. This article proposes a path planning and location algorithm for the large tank bottom detection robot. Specifically, we design a preset spiral path according to the shape of the tank bottom, and a rotating potential field (RPF) near the obstacle is added to avoid the problem of path planning falling into a local minimum. We obtained accurate and smooth planning results. Compared with the state-of-the-art, the RPF method reduced the average RMSE by 9.49%. In addition, by measuring the acoustic emission distance, the three-point positioning algorithm can be used to achieve the calculation of the robot position detection in the proposed method, and the average positioning error on the spiral path is only 0.0748 ± 0.0032.
Modular architectures to deterministically generate graph states

Graph states are a family of stabilizer states which can be tailored towards various applications in photonic quantum computing and quantum communication. In this paper, we present a modular design based on quantum dot emitters coupled to a waveguide and optical fiber delay lines to deterministically generate N-dimensional cluster states and other useful graph states such as tree states and repeater states. Unlike previous proposals, our design requires no two-qubit gates on quantum dots and at most one optical switch, thereby, minimizing challenges usually posed by these requirements. Furthermore, we discuss the error model for our design and demonstrate a fault-tolerant quantum memory with an error threshold of 0.53% in the case of a 3d graph state on a Raussendorf-Harrington-Goyal (RHG) lattice. We also provide a fundamental upper bound on the correctable loss in the fault-tolerant RHG state based on the percolation theory, which is 1.24 dB or 0.24 dB depending on whether the state is directly generated or obtained from a simple cubic cluster state, respectively.
ColorMedGAN: A Semantic Colorization Framework for Medical Images

Colorization for medical images helps make medical visualizations more engaging, provides better visualization in 3D reconstruction, acts as an image enhancement technique for tasks such as segmentation, and makes it easier for non-specialists to perceive tissue changes and texture details in medical images in diagnosis and teaching. However, colorization algorithms have been hindered by limited semantic understanding. In addition, current colorization methods still rely on paired data, which is often not available for specific fields such as medical imaging. To address the texture detail of medical images and the scarcity of paired data, we propose a self-supervised colorization framework based on CycleGAN(Cycle-Consistent Generative Adversarial Networks), treating the colorization problem of medical images as a cross-modal domain transfer problem in color space. The proposed framework focuses on global edge features and semantic information by introducing edge-aware detectors, multi-modal discriminators, and a semantic feature fusion module. Experimental results demonstrate that our method can generate high-quality color medical images.
Evolution of visual data captioning Methods, Datasets, and evaluation Metrics: A comprehensive survey

Automatic Visual Captioning (AVC) generates syntactically and semantically correct sentences by describing important objects, attributes, and their relationships with each other. It is classified into two categories: image captioning and video captioning. It is widely used in various applications such as assistance for the visually impaired, human-robot interaction, video surveillance systems, scene understanding, etc. With the unprecedented success of deep-learning in Computer Vision and Natural Language Processing, the past few years have seen a surge of research in this domain. In this survey, the state-of-the-art is classified based on how they conceptualize the captioning problem, viz., traditional approaches that cast visual description either as retrieval or template-based description and deep learning approaches. A detailed review of existing methods, highlighting their pros and cons, societal impact as the number of citations, architectures used, datasets experimented on and GitHub link is presented. Moreover, the survey also provides an overview of the benchmark image and video datasets and the evaluation measures that have been developed to assess the quality of machine-generated captions. It is observed that dense or paragraph generation and Change Image Captioning (CIC) are stimulating the research community more due to the near-to-human abstraction ability. Finally, the paper explores future directions in the area of automatic visual caption generation.
Anatomically-guided deep learning for left ventricle geometry generation with uncertainty quantification based on short-axis MR images

Recent advances in deep learning have greatly improved the ability to generate analysis models from medical images. In particular, great attention is focused on quickly generating models of the left ventricle from cardiac magnetic resonance imaging (cMRI) to improve the diagnosis and prognosis of millions of patients. However, even state-of-the art frameworks present challenges, such as discontinuities of the cardiac tissue and excessive jaggedness along the myocardial walls. These geometrical features are often anatomically incorrect and may lead to unrealistic results once the geometrical models are employed in computational analyses. In this work, we propose an anatomically-guided deep learning model to overcome these limitations while preserving the advantages of state-of-the-art frameworks, such as computational efficiency, robustness, and generalization capabilities. Our novel anatomically-guided neural networks are formed by a UNet followed by a B-spline head, which acts as a regularization layer during training. The B-spline head aggregates the prediction into a single connected region, removes any undesired tissue islands, and produces a smooth continuous contour. In addition, the introduction of the B-spline head contributes to achieve a robust uncertainty quantification of the left ventricle inner and outer walls. Our results show that the proposed model generates anatomically consistent geometries while achieving an agreement with the ground truth images comparable to state-of-the-art frameworks and simultaneously improving the geometry uncertainty quantification in comparison to classic UNet models. The examples presented here, as well as source codes, are all open-source under the GitHub repository https://github.com/CBL-UCF/unet_ag.
A Personalized Course Resource Recommendation Method Based on Deep Learning in an Online Multi-Modal Multimedia Education Cloud Platform

Aiming at the problem that unstructured text in online multi-modal multimedia education is easy to cause error propagation, this paper proposes a personalized course resource recommendation method using deep learning in online multi-modal multimedia education cloud platform. First, the word vector of the text is obtained from the course data set by using the BERT pre-training model, and its semantic information in different contexts is analyzed. Then, the more complex representation of each word is extracted through the long short-term memory network (LSTM), in which the multi-head attention layer adds different weights to different word vector to better capture the key information in the sentence. Finally, the CRF layer is used to identify sentence entities, and the Sigmoid layer is used to extract relations, thus completing personalized course resource recommendation, which is significantly improved compared with other models. Experimental analysis shows that the algorithm is effective in personalized course resource recommendation.
An Evaluation Study of User Authentication in the Malaysian FinTech Industry With uAuth Security Analytics Framework

The increased cyberattack frequency and ferocity have alerted the fintech industry in detecting existential security threats and risks. Various authentication mechanisms have been deployed to countermeasure cyberattacks; whether these deployed solutions fulfil the security and technical standards has not been significantly investigated. This article proposed an uAuth security analytics framework to evaluate the deployed user authentication mechanisms. Subsequently, the technical evaluation study covered ten major commercial banks in Malaysia, whereas 120 respondents aged 18 to 25 participated in the user awareness study. The result found that mobile banking enforces more robust user authentication mechanisms than internet banking in Malaysia. As 80% of the Malaysia fintech systems only ranked as Level 3 of the uAuth security analytics framework, the authors urge Malaysia fintech industry to enhance their authentication factor, login and transaction verification methods, password policy, as well as readiness for quantum-safe security technologies.
Problematizing Realism: Confronting Colonial Perspectives and Engaging Complexities of Landscape Through “Ghost Textures”

Abstract Realistic virtual reality visualizations of a physical place, whether historic or contemporary, can exude a sense of certainty about their accuracy. They can also evade complexity by excluding underrepresented narratives about place such as those held by indigenous and other marginalized communities. Through engaging with a framework of complexity, ethics, and humility, we propose a technique of immersive virtual storytelling (IVS) that foregrounds uncertainty through a combination of realistic material treatments and translucent, ephemeral “ghost textures.” By applying these treatments to two virtual three-dimensional models telling a pre- and post-construction story of a World War II-era rifle range, we were able to test the use of these treatments in exposing gaps in archival data. This highlighted the predominance of a colonial lens in guiding historic site visualization, raising a question of how other narratives might be included without conforming to a colonial framework.
Impact of Jitter and Packet loss on Enhanced Voice Serviced for Voice over NR

The EVS (Enhanced Voice Service) as a promising speech coding technology aims to provides better voice quality and compression efficiency to deal with the challenge from Voice-over-IP and Over-The-Top voice services. To achieve better experience for speech users, 5G network should ensure the lower jitter and the less probability of packet loss for voice service. Due to the 5G coverage profile is much smaller than the existing LTE network, how to ensure the quality of voice service in poor cellular coverage is the mainly challenge for operators. To address this issue, some new features for speech codec, i.e., the RAN assisted codec rate adaptation and application-layer redundancy had been introduced in 3GPP. However, since the speech coding rate reduction is the main method of these features, it is still controversial whether these features have negatively affect the quality of voice service. To this end, this paper analyses the influence factors for jitter and packet-loss together with the voice quality evaluation methodologies first. Then the performance comparison between these new features proposed by 3GPP and the tradition handover-based solution were evaluated via laboratory test. The experiments results show that the performance of handover-based solution outperforms that of new features for speech codec in case of UE locates at the cell edge.
On forecasting of COVID-19 transmission in Saudi Arabia and Egypt using reservoir computing model

This work is devoted to introduce a reliable, fast and highly accurate reservoir computer machine learning scheme to forecast time evolution of COVID-19 pandemic. In particular, the COVID-19 official data related to susceptible cases, confirmed cases, and recovered cases in Egypt and Saudi Arabia are collected. They employed as the training data for suggested reservoir computer (RC) model. Then, detailed simulation experiments are carried out within specified time periods. The evolution of COVID-19 in Egypt and Saudi Arabia are predicted on the subsequent times intervals and compared with real validation test data. The forecasting accuracy is improved by computing the optimal output matrix which minimizes the normalized root mean square errors (NRMSEs). The performance of RC scheme is evaluated when different-size training data, different-size test data, and different number of internal nodes are used. The comparisons with the robust LSTM deep learning techniques are performed. It is shown that the presented RC-based forecasting technique is more accurate for long-time forecasting, faster, and has lower computational cost.
Deep interactive image segmentation based on region and Boundary-click guidance

In interactive image segmentation, the target object of interest can be extracted based on the guidance of user interactions. One of the main goals in this task is to reduce the user interaction burden and ensure satisfactory segmentation with as few interactions as possible. Thanks to the development of deep learning technology, neural network-based interactive approaches have significantly improved the segmentation performance through powerful feature representation. Only limited point (click) interaction is required for the user to complete the segmentation. This paper mainly follows the deep learning-based interactive segmentation methods and explores more efficient interaction strategies and effective segmentation models. We further simplify user interaction to two clicks, where the first click is utilized to select the target region and the other aims to determine the target boundary. Based on the region and boundary clicks, an interactive two-stream network structure is naturally derived to learn the region and boundary features of interest. Furthermore, we also construct an information diffusion module to better propagate the region and boundary-click labels, which helps to enhance the similarity within the region and the discrimination between boundaries. Vast experiments on the popular GrabCut, Berkeley, DAVIS, MS COCO and SBD datasets verified the effectiveness of the proposed method.
Ontology-based hybrid commonsense reasoning framework for handling context abnormalities in uncertain and partially observable environments

Ambient intelligence (AmI) systems aim to provide users with context-aware assistance services intended to improve the quality of their lives in terms of autonomy, safety, and well-being. Taking the uncertainty and partial observability of these environments into account is of major importance for context recognition and, more specifically, to detect and solve context abnormalities such as those related to the user's behavior or those related to context attribute prediction. In this paper, an ontology-based framework integrating machine learning and probabilistic planning within commonsense reasoning is proposed to recognize the user's context and abnormalities associated with it. The reasoning is performed using event calculus in answer set programming (ECASP); ECASP allows for abductive and temporal reasoning, which results in an eXplainable AI (XAI) approach. A context ontology is proposed to axiomatize the reasoning and introduce the notion of probabilistic fluents into the EC formalism in order to perform probabilistic reasoning. The reasoning incorporates probabilistic planning based on a partially observable Markov decision process (POMDP) to solve knowledge incompleteness. To evaluate the proposed framework, real-life scenarios, based on the Orange4Home and SIMADL public datasets are implemented and discussed.