Reviewerly/exHarmony huggingface dataset:
A fractional-order regularization with sparsity constraint for blind restoration of images

Blind image deconvolution is one of the most challenging problems in image restoration. Inspired by the work on sparsity constraint and deblurring of blind motion, we propose a model with fractional-order regularization and sparsity constraint for blind restoration and construct split Bregman combining an iterative thresholding algorithm. Fractional-order penalty term Bqβ(Lp(Ω))(0<β≤1) in Besov space is expanded by wavelet basis and computed using iterative thresholding algorithm. The regularized terms of blur kernel under tight wavelet frame systems are solved by the split Bregman method. Numerical experiments show that our algorithm can effectively remove different kinds of blur without requiring any prior information of the blur kernels and obtain higher signal-to-noise ratios and lower relative errors. In addition, fractional-order derivative in Besov space can preserve both edges and smoothness better than the integer-order derivative.
Monte Carlo Tree Search as a Tool for Self-Learning and Teaching People to Play Complete Information Board Games

Artificial intelligence allows computer systems to make decisions similar to those of humans. However, the expert knowledge that artificial intelligence systems have is rarely used to teach non-expert humans in a specific knowledge domain. In this paper, we want to explore this possibility by proposing a tool which presents and explains recommendations for playing board games generated by a Monte Carlo Tree Search algorithm combined with Neural Networks. The aim of the aforementioned tool is to showcase the information in an easily interpretable way and to effectively transfer knowledge: in this case, which movements should be avoided, and which action is recommended. Our system displays the state of the game in the form of a tree, showing all the movements available from the current state and a set of their successors. To convince and try to teach people, the tool offers a series of queries and all information available about every possible movement. In addition, it produces a brief textual explanation for those which are recommended or not advisable. To evaluate the tool, we performed a series of user tests, observing and assessing how participants learn while using this system.
Feature Adaption with Predicted Boxes for Oriented Object Detection in Aerial Images

Object detection is a fundamental research field in computer vision. Arbitrary-oriented objects inevitably appear in face, natural scene text, and aerial image detection, which have attracted widespread attention recently. However, existing rotation detectors still suffer from the feature misalignment problem, due to the fixed convolution kernel adopted in detecting arbitrary-oriented and deformed objects. In this paper, we propose a novel method, One-stage Feature Adaption Network (OFA-Net), for oriented object detection in aerial images. A feature adaption module, implemented by the deformable convolution and the align convolution, is proposed to refine the feature maps according to the predicted offsets and decoded boxes. Furthermore, specific to the long-existing periodic angle regression problem in the detection, the box regression branch is decoupled into the size branch and the angle branch, with a new periodic loss in the angle regression branch to leverage the periodic orientation of the object. Extensive experiments demonstrate the effectiveness of our approach, achieving promising results compared with state-of-the-art methods in three benchmark datasets, DOTA, HRSC2016, and UCAS-AOD.
Feasibility of Augmented Reality in the Scope of Commission of Industrial Robot Plants

This paper analyses the synergetic potential of Augmented Reality (AR) and offline programming (OLP) in the commission of industrial robots. We discuss that AR could utilise existing programming paradigms present in the OLP to simplify the online commission in manufacturing plants with its ability to freely combine digital and real content. Focussing on the complementary use of AR and OLP a theoretically promising use case is derived. The use case is implemented in a simplified mock-up and a user study is conducted to practicably evaluate the feasibility. Ten participants commissioned an offline created program using the developed AR application. As nine out of ten participants could achieve a valid commission by using the developed AR application, the study indicates the feasibility of the presented use case.
MEED: A Multimodal Event Extraction Dataset

Multimodal tasks are gradually attracting the attention of the research community, and the lack of multimodal event extraction datasets restricts the development of multimodal event extraction. We introduce the new Multimodal Event Extraction Dataset (MEED) to fill the gap, we define event types and argument roles that can be used on multimodal data, then use controllable text generation to generate the textual modality based on visual event extraction dataset. In this paper, we aim to make full use of multimodal resources in the event extraction task by constructing a large-scale and high-quality multimodal event extraction dataset and promote researches in the field of multimodal event extraction.
Domain-Aware Word Segmentation for Chinese Language: A Document-Level Context-Aware Model

Word segmentation is an essential and challenging task in natural language processing, especially for the Chinese language due to its high linguistic complexity. Existing methods for Chinese word segmentation, including statistical machine learning methods and neural network methods, usually have good performance in specific knowledge domains. Given the increasing importance of interdisciplinary and cross-domain studies, one of the challenges in cross-domain word segmentation is to handle the out-of-vocabulary (OOV) words. Existing methods show unsatisfactory performance to meet the practical standard. To this end, we propose a document-level context-aware model that can automatically perceive and identify OOV words from different domains. Our method jointly implements a word-based and a character-based model and then processes the results with a newly proposed reconstruction model. We evaluate the new method by designing and conducting comprehensive experiments on two real-world datasets (e.g., news from different domains). The results demonstrate the superiority of our method over the state-of-the-art models in handling texts from different domains. Importantly, when doing the word segmentation under the cross-domain scenario, our proposed method can improve the performance of OOV words recognition.
Using Behavioral Biometrics of Fingerprint Authentication to Investigate Physical and Emotional User States

Time-series data can play a significant role in designing useful applications. One can be the collection of mobile sensor data for biometric-based access protection using behavioral information to identify if the user’s access is intentional or spoofed. Such application can be very useful in protecting the victim’s data from being compromised (e.g., the device locks automatically in response). However, identification of correct features and a useful machine learning algorithm can impose limitations on achieving reliable accuracy for use in real-life applications. In a previous work, a novel method was used to extract descriptive numerical features (FAST18) and then transform the features into unique image representations using feature to image transformation (FIT) algorithm, for convolutional neural network (CNN) classification. That work covered intentional and spoofed access classification. In this work, we investigated the problem by introducing more classes, including three intentional (normal, angry, and sad) and two spoofed (forced and asleep). We reported accuracy and true-positive-rate for individual users and all users. Results show high distinction between intentional and spoofed categories, confirming reliability of protection against malicious access behaviors. In addition, the three intentional behaviors were reasonably separated, but with lower accuracy. When training was restricted to three classes (normal, forced, and asleep), accuracy exceeded 95% with small overlap between forced and asleep. Extending the fingerprint application demonstrates the FAST18-FIT method strength and reliability to test other applications, including the study of behaviors and emotion-based actions through utilizing EEG brain signals.
Efficient Deep Learning Architecture for Facemask Detection

Since COVID-19 pandemic all over the world, wearing mask become mandatory in public space. In order to enforce the new normal behaviour, regulators need to ensure every person is wearing a mask in order to avoid the spreading of the viruses. Before the pandemic, there were a number of closed-circuit televisions (CCTV) installed in public space for security purposes. The research aims to identify algorithms with acceptable classification quality and at the same time low computing complexity. This research aims to identify the algorithm to identify Face Mask. This research uses two public datasets, the first dataset has two labels with and without mask, and the second dataset consists of three labels (facemask, improper use of facemask and proper use of facemask). This research examines some well known deep learning architectures which are VGG, MobileNet, MobileNetV2, EfficientNet B0, NasNetMobile. A modification of VGG to reduce the number of parameters is also examined. An evaluation of the classification performance and execution time in the testing set is carried out on binary and three class dataset. According to the experiments, Modified VGG with 7 layers with 1.6 Million parameters consistently achieves fastest performance. The classification performance for three class dataset is achieved by Modified VGG (CVGG-7) at 100% while for the binary facemask classification is achieved by MobileNetV2 at 99.7%
A Cost-Effective Framework for Running Industrial Big Data Analysis Applications in Public Clouds

Nowadays, the improvement of the data acquisition capability of the Industrial Internet of Things (IIoT) systems has brought about higher data throughput. Users can deploy industrial big data analysis applications on cloud platforms in a pay-as-you-go way to deal with the unstable data generation and data analysis workloads in the IIoT scenario. To procure stable and flexible computing resources, as a traditional pay-as-you-go cloud instance procurement option, on-demand instances are widely used, but their expensive prices also significantly increase users’ cost burden. To reduce the data analysis cost, in this article, we establish a per-job cost-effective framework adopting spot instances, on-demand instances, and cloud storage for industrial big data analysis applications. In our framework, we take advantage of spot instances, which are computing instances provided at low prices under a pay-as-you-go model, to achieve cost savings. However, using spot instances carries the risk of being interrupted. Therefore, we propose to use a checkpointing mechanism to back up intermediate results to cloud storage to reduce the potential loss caused by spot instance interruptions. Considering the time sensitivity of industrial big data analysis applications, we use on-demand instances as alternative computing resources after spot instance interruptions to ensure that users’ jobs can be completed without high time latencies. Evaluation results show that our framework can achieve cost savings as well as minimize time latencies for users’ jobs.
Geometric Fitting in Problem of Spatial Position of Object Determination by Image

The article deals with the problem of determining the spatial position and spatial orientation of an object from its image. The task is reduced to 3D reconstruction in space of a finite set of points from its central projection. Simple mathematical reasoning allows us to conclude that with a small motion in the space of a finite set of points, its central projection remains unique. This provides a theoretical basis for developing an algorithm for the above reconstruction. The article proposes an algorithm for reconstructing a finite set of points in space based on minimizing the Hausdorff distance function between a given flat set and an auxiliary finite set of a 3D model of an object. To minimize this function, a combined method for the numerical calculation of the minimum point is proposed, in which each subsequent method uses the calculated point as the initial one. In addition, the article proposes an algorithm for choosing the starting point of the iterative minimization algorithm, based on the known spatial dimensions of the object under study.
EntrapNet: A Blockchain-Based Verification Protocol for Trustless Computing

In this article, we propose a blockchain-based computing verification protocol, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EntrapNet</i> , for distributed shared computing networks, an emerging underlying network for many Internet of Things (IoT) applications. EntrapNet borrows the idea from the practice of entrapment in criminal law to reduce the possibility of receiving incorrect computing results from trustless service providers who have offered the computing resources. Furthermore, we mathematically optimize EntrapNet to deal with the fundamental tradeoff of a network: security and efficiency. We present an asymptotic optimal solution to this optimization. It will be seen that EntrapNet can be performed as an independent and low-cost layer atop of any trustless network that requires outsourced computing, thus making secure computing affordable and practical.
Cortical Vessel Segmentation for Neuronavigation Using Vesselness-Enforced Deep Neural Networks

We propose in this paper an efficient method to segment cortical vessels in craniotomy images acquired by the surgical microscope. Our method uses a vesselness-enforced convolutional neural network to classify each pixel of a craniotomy image as a vessel or surrounding tissue. This permits training the network not only on appearance-based features but also on geometrical-based constraints that will ensure the continuity of the vascular trees. Our solution uses neural style transfer to generate new instances of images from manually labeled data leading to augment the training dataset in an anatomically semantic manner. The generated images improve the generalization of our model to various types of cortical surface appearances and vascular geometries. We conducted experiments on real images from human patients that demonstrate that accurate intraoperative cortical vessel segmentation can be achieved.
Distortion-Aware Room Layout Estimation from A Single Fisheye Image

Omnidirectional images of 180° or 360° field of view provide the entire visual content around the capture cameras, giving rise to more sophisticated scene understanding and reasoning and bringing broad application prospects for VR/AR/MR. As a result, researches on omnidirectional image layout estimation have sprung up in recent years. However, existing layout estimation methods designed for panorama images cannot perform well on fisheye images, mainly due to lack of public fisheye dataset as well as the significantly differences in the positions and degree of distortions caused by different projection models. To fill theses gaps, in this work we first reuse the released large-scale panorama datasets and reproduce them to fisheye images via projection conversion, thereby circumventing the challenge of obtaining high-quality fisheye datasets with ground truth layout annotations. Then, we propose a distortion-aware module according to the distortion of the orthographic projection (i.e., OrthConv) to perform effective features extraction from fisheye images. Additionally, we exploit bidirectional LSTM with two-dimensional step mode for horizontal and vertical prediction to capture the long-range geometric pattern of the object for the global coherent predictions even with occlusion and cluttered scenes. We extensively evaluate our deformable convolution for room layout estimation task. In comparison with state-of-the-art approaches, our approach produces considerable performance gains in real-world dataset as well as in synthetic dataset. This technology provides high-efficiency and low-cost technical implementations for VR house viewing and MR video surveillance. We present an MR-based building video surveillance scene equipped with nine fisheye lens can achieve an immersive hybrid display experience, which can be used for intelligent building management in the future.
Principal Component Analysis as a Dimensionality Reduction and Data Preprocessing Technique

Nowadays, dealing with high dimension data has become common in many fields such as network security, image processing, medical diagnosis, biometric systems, e-commerce, etc. In data base with higher dimension the direct use of data is costlier in terms of its storage, processing, mathematical computations etc. Moreover, in many applications with wireless networks, Received Signal Strength (RSS) needs to be processed to remove the redundant readings. In this paper, we discussed Principal Component Analysis (PCA) as a dimensionality reduction technique. PCA can be done either by calculating the covariance matrix or by Singular Value Decomposition (SVD). We used covariance matrix calculation due to its less complexity. Simulation results verify that the correlated input data sequence is sorted to uncorrelated principal components (PC’s) with decreasing variation and some of first PC’s can represent nearly all data sets.
Thermoplasmonic Nanomagnetic Logic Gates

In advancing beyond the von Neumann architecture, nanomagnetic logic offers an attractive approach to in-memory computation by exploiting the energy hierarchy of coupled nanomagnets. However, low-power operation is largely incompatible with deterministic logic, because the temperature required for fast operation reduces the coupling too much. This study overcomes that problem using local, selective plasmon-assisted photoheating of individual nanomagnets to drive a logic gate deterministically toward the desired low-energy output state, on nanosecond time scales. This approach demonstrates the possibility of computational metamaterials and could have immediate technological relevance.
A New Concept of Electronic Text Based on Semantic Coding System for Machine Translation

In the field of machine translation of texts, the ambiguity in both lexical (dictionary) and structural aspects is still one of the difficult problems. Researchers in this field use different approaches, the most important of which is machine learning in its various types. The goal of the approach that we propose in this article is to define a new concept of electronic text, which makes the electronic text free from any lexical or structural ambiguity. We used a semantic coding system that relies on attaching the original electronic text (via the text editor interface) with the meanings intended by the author. The author defines the meaning desired for each word that can be a source of ambiguity. The proposed approach in this article can be used with any type of electronic text (text processing applications, web pages, email text, etc.). Thanks to the approach that we propose and through the experiments that we have conducted using it, we can obtain a very high accuracy rate. We can say that the problem of lexical and structural ambiguity can be completely solved. With this new concept of electronic text, the text file contains not only the text but also with it the true sense of the exact meaning intended by the writer in the form of symbols. These semantic symbols are used during machine translation to obtain a translated text completely free of any lexical and structural ambiguity.
Semantic Segmentation with Enhanced Temporal Smoothness Using CRF in Aerial Videos

UAV videos are attracting widespread interest due to their cost effectiveness and wide applications in monitoring environmental changes, disaster management etc. Recently, computer vision algorithms are utilized to analyse UAV videos and act as decision support systems. To this end, the context of the scene plays a prominent role in improving the performance of the decision support systems. The context of the video scenes is generally realized by using a video semantic segmentation algorithm. The success of video semantic segmentation algorithms relies on temporal consistency for which estimation of temporal correspondence is a necessity. Optical flow based methods are popularly used in literature for establishing temporal correspondence which are expensive for video semantic segmentation. In this regard, a new Conditional Random Field frame work is presented in this paper for UAV video semantic segmentation. A new pairwise potential energy term is proposed which uses long range temporal information required for temporally consistent labels. Further, the proposed method selectively applies CRF inference which reduces the CRF computation and is independent of optical flow estimation. The proposed algorithm achieved an mIoU of 0.89 on ManipalUAVid dataset.
Null Space Based Efficient Reinforcement Learning with Hierarchical Safety Constraints

Reinforcement learning is inherently unsafe for use in physical systems, as learning by trial-and-error can cause harm to the environment or the robot itself. One way to avoid unpredictable exploration is to add constraints in the action space to restrict the robot behavior. In this paper, we propose a null space based framework of integrating reinforcement learning methods in constrained continuous action spaces. We leverage a hierarchical control framework to decompose target robotic skills into higher ranked tasks (e. g., joint limits and obstacle avoidance) and lower ranked reinforcement learning task. Safe exploration is guaranteed by only learning policies in the null space of higher prioritized constraints. Meanwhile multiple constraint phases for different operational spaces are constructed to guide the robot exploration. Also, we add penalty loss for violating higher ranked constraints to accelerate the learning procedure. We have evaluated our method on different redundant robotic tasks in simulation and show that our null space based reinforcement learning method can explore and learn safely and efficiently.
Classification of Long-Tailed Data Based on Bilateral-Branch Generative Network with Time-Supervised Strategy

In the face of the long-tailed data distribution that widely exists in real-world datasets, this paper proposes a bilateral-branch generative network model. The data of the second branch is constructed by resampling the generative network training method to improve the data quality. A bilateral-branch network model is used to curb the risk of gradient explosion and to avoid over-fitting and under-fitting with the combined effect of different data branches. Meanwhile, Time-supervised strategy is introduced to improve the model's operational efficiency and ability to cope with extreme conditions by supervising and collaboratively controlling of the bilateral-branch generative network with time-invariant parameters. Time supervised strategy could ensure the accuracy of the model while reducing the number of iterations. Experimental results on two publicly available datasets, CIFAR10 and CIFAR100, show that the proposed method effectively improves the performance of long-tail data classification.