Reviewerly/exHarmony huggingface dataset:
An Innovative Low-dose CT Inpainting Algorithm based on Limited-angle Imaging Inpainting Model

With the popularity of computed tomography (CT) technique, an increasing number of patients are receiving CT scans. Simultaneously, the public's attention to CT radiation dose is also increasing. How to obtain CT images suitable for clinical diagnosis while reducing the radiation dose has become the focus of researchers.To demonstrate that limited-angle CT imaging technique can be used to acquire lower dose CT images, we propose a generative adversarial network-based image inpainting model-Low-dose imaging and Limited-angle imaging inpainting Model (LDLAIM), this method can effectively restore low-dose CT images with limited-angle imaging, which verifies that limited-angle CT imaging technique can be used to acquire low-dose CT images.In this work, we used three datasets, including chest and abdomen dataset, head dataset and phantom dataset. They are used to synthesize low-dose and limited-angle CT images for network training. During training stage, we divide each dataset into training set, validation set and testing set according to the ratio of 8:1:1, and use the validation set to validate after finishing an epoch training, and use the testing set to test after finishing all the training. The proposed method is based on generative adversarial networks(GANs), which consists of a generator and a discriminator. The generator consists of residual blocks and encoder-decoder, and uses skip connection.We use SSIM, PSNR and RMSE to evaluate the performance of the proposed method. In the chest and abdomen dataset, the mean SSIM, PSNR and RMSE of the testing set are 0.984, 35.385 and 0.017, respectively. In the head dataset, the mean SSIM, PSNR and RMSE of the testing set are 0.981, 38.664 and 0.011, respectively. In the phantom dataset, the mean SSIM, PSNR and RMSE of the testing set are 0.977, 33.468 and 0.022, respectively. By comparing the experimental results of other algorithms in these three datasets, it can be found that the proposed method is superior to other algorithms in these indicators. Meanwhile, the proposed method also achieved the highest score in the subjective quality score.Experimental results show that the proposed method can effectively restore CT images when both low-dose CT imaging techniques and limited-angle CT imaging techniques are used simultaneously. This work proves that the limited-angle CT imaging technique can be used to reduce the CT radiation dose, and also provides a new idea for the research of low-dose CT imaging.
Proposal of a New Approach for Protected Deposit Area Registration in Public Administration Information Systems—A Case Study from Slovakia

Perception of the meaning and wider context in recording important information about objects that represent strategically valuable data is the basis for increasing their value and binding in order to strengthen their credibility. In recent years, emphasis has been placed on digitization and electronic data collection and their interpretation, which ensures the promotion of real-world objects. The protection of mineral wealth and the creation of protected deposit areas (PDAs) is often limited to an analogue form of documentation ensuring the raw material potential of that country. The often inefficient and insufficient way of managing data in public information systems (ISs) and their subsequent use in the customary procedural stages of other decision-making procedures of state authorities leads to the loss of relevant information in connection with such protected areas. This paper on specific studies emphasizes the need to use and follow procedures in strengthening the Slovak national concept based on data and technical compatibility supporting the exchange of information, which will support the expansion of the IS environment with data in connection with the protection of mineral wealth in the form of PDAs. As a result of the existing legislation and historical background, it is necessary to comprehensively evaluate the contexts that fundamentally enter into the content of data in the ISs of individual institutions recording fundamental information about objects in connection with the protection of deposit areas. The methods used and the analysis of input data on PDAs from the relevant information systems pointed to insufficient and incomplete records and presentation of data regarding PDAs. In this document, a solution is proposed which, as a result, consolidates the disparate way of registering PDAs and presents an organizationally more profitable way of exchanging data. It was found that only modern ISs and their filling with data, respecting the rules and principles of standardization, prioritizing the content of established, and validly reflecting data, are a high-quality basis for an interoperable environment containing the necessary information, for example, in the establishment of three-dimensional records.
Deep Reinforcement Learning Based on the Hindsight Experience Replay for Autonomous Driving of Mobile Robot

In this paper, we present a method to overcome the sparse reward problem that can occur in the autonomous driving of a mobile robot based on the deep deterministic policy gradient algorithm of reinforcement learning using simple reward engineering and the hindsight experience replay technique. The mobile robot used in the experiment is a robot operating system-based TurtleBot3, and the experimental environment was configured using Gazebo. To validate the effectiveness of the proposed technique, we present the experimental results with the application of HER.
Accurate depth image generation via overfit training of point cloud registration using local frame sets

Accurate three-dimensional perception is a fundamental task in various computer vision applications. Recently, commercial RGB-depth (RGB-D) cameras have been widely adopted as single-view depth-sensing devices owing to their efficient depth-sensing abilities. However, the depth quality of most RGB-D sensors remains insufficient owing to the inherent noise from a single-view environment. Several recent studies have focused on the single-view depth enhancement based on modern deep-learning technologies. The approaches typically train networks using high-quality benchmark depth datasets, which indicates that the quality of the supervised depth dataset is a fundamental factor for the depth enhancement system; however, such high-quality depth datasets are difficult to obtain. In this study, we developed a novel method for high-quality depth image generation based on an RGB-D stream dataset. First, we defined consecutive depth frames in a local spatial region as a local frame set. Then, the depth frames were aligned to a certain frame in the local frame set using an unsupervised point cloud registration scheme. The registration parameters were trained based on an overfit-training scheme, which was primarily used to generate an enhanced depth image for each frame set. The final depth dataset was constructed using multiple local frame sets, and each local frame set was trained independently. The primary advantage of this study is that a high-quality depth dataset can be constructed under various scanning environments using only the RGB-D stream dataset. Moreover, our proposed method can be used as a new benchmark depth dataset for accurate performance evaluations. We evaluated our dataset on previously benchmarked depth datasets and demonstrated that our method is superior to state-of-the-art depth enhancement frameworks.
Multi Domain Resource Accurate Allocation Algorithm for Wireless Communication of Internet of Things Based on Chaotic Neural Network

Aiming at the problem that it is easy to fall into local minimum in the multi domain resource allocation process of wireless communication in the Internet of things, a multi domain resource allocation algorithm of wireless communication in the Internet of things based on chaotic neural network is proposed. The effects of attenuation factor and temperature fading parameters on the chaotic characteristics of chaotic neural network are analyzed, and the network parameters are selected reasonably. This paper obtains the multi domain resources of integrated Internet of things wireless communication, updates the multi domain resources, and builds a multi domain resource configuration model through relevant network parameters. In this paper, we use data mining method to obtain the multi domain resource data of wireless communication. And the parameters of the network are appropriately selected to make the neural network appear chaotic, so the resource allocation process based on the chaotic neural network is designed. Therefore, the resource allocation process based on chaotic neural network is designed. The experimental results show that the configuration results of the algorithm are consistent with the ideal configuration results, and the shortest end-to-end delay is 10 ms, and the lowest packet loss rate is 4%.
Accurate Recommendation Algorithm of Digital Resources in Ideological And Political Mobile Teaching Based on Collaborative Filtering

At present, the recommendation of teaching resources only depends on the score of resource acquisition, which leads to low accuracy of recommended resources and low efficiency of recommendation algorithm. In order to improve the above defects, this paper will study the accurate recommendation algorithm of digital resources of ideological and political mobile teaching based on collaborative filtering. Through implicit and explicit mining of teaching resource data, the user's preference is calculated, and the resource recommendation data related to similar users is obtained. SCROM(Sharable Content Object Reference Model) is used to manage the digital resources of ideological and political education, and the calling process of resource content is simple. After the collaborative filtering algorithm is improved, the resources are accurately recommended according to the comprehensive score. In the test of the algorithm, the accuracy rate of resource recommendation by using the improved collaborative filtering algorithm is maintained above 75%, and the recommendation efficiency is greatly improved, with good recommendation effect.
A case for remote attestation in programmable dataplanes

Programmability is a double-edged sword. It can better tailor solutions to problems, optimize resource use, and inexpensively patch deployed equipment. But programmability can also be abused to undermine the security of hardware and that of its unwitting users. Remote Attestation (RA) is a class of techniques to provide integrity assurance to remote users of resources such as hardware, OSs and applications. It is used to establish well-defined trust relationships among mutually distrustful principals who provide, use or delegate remote resources. RA could benefit, for example, tenants of a data-center or users of IoT equipment such as health monitors.
Making links on your web pages last longer than you

It is common for the authors of a web page to include links to related pages on other sites. However, when users visit a page several years after it was last updated, they often find that some of the external links either do not work or point to unrelated content. To combat these problems of link rot and content drift, the solution used today is to capture a copy of the linked page when a link is created and serve this copy to users who choose to visit the link.
Extending vision ray calibration by determination of focus distances

The application of cameras as sensors in optical metrology techniques for three-dimensional topography measurement, such as fringe projection profilometry and deflectometry, presumes knowledge regarding the metric relationship between image space and object space. This relation is established by camera calibration and a variety of techniques are available. Vision ray calibration achieves highly precise camera calibration by employing a display as calibration target, enabling the use of active patterns in the form of series of phase-shifted sinusoidal fringes. Besides the required spatial coding of the display surface, this procedure yields additional full-field contrast information. Exploiting the relation between full-field contrast and defocus, we present an extension of vision ray calibration providing the additional information of the focus distances of the calibrated camera. In our experiments we achieve a reproducibility of the focus distances in the order of mm. Using a modified Laplacian based focus determination method, we confirm our focus distance results within a few mm.
Semantic Interactive Learning for Text Classification: A Constructive Approach for Contextual Interactions

Interactive Machine Learning (IML) can enable intelligent systems to interactively learn from their end-users, and is quickly becoming more and more relevant to many application domains. Although it places the human in the loop, interactions are mostly performed via mutual explanations that miss contextual information. Furthermore, current model-agnostic IML strategies such as CAIPI are limited to ’destructive’ feedback, meaning that they solely allow an expert to prevent a learner from using irrelevant features. In this work, we propose a novel interaction framework called Semantic Interactive Learning for the domain of document classification, located at the intersection between Natural Language Processing (NLP) and Machine Learning (ML). We frame the problem of incorporating constructive and contextual feedback into the learner as a task involving finding an architecture that enables more semantic alignment between humans and machines while at the same time helping to maintain the statistical characteristics of the input domain when generating user-defined counterexamples based on meaningful corrections. Therefore, we introduce a technique called SemanticPush that is effective for translating conceptual corrections of humans to non-extrapolating training examples such that the learner’s reasoning is pushed towards the desired behavior. Through several experiments we show how our method compares to CAIPI, a state of the art IML strategy, in terms of Predictive Performance and Local Explanation Quality in downstream multi-class classification tasks. Especially in the early stages of interactions, our proposed method clearly outperforms CAIPI while allowing for contextual interpretation and intervention. Overall, SemanticPush stands out with regard to data efficiency, as it requires fewer queries from the pool dataset to achieve high accuracy.
Intelligent Decision Making Based on the Combination of Deep Reinforcement Learning and an Influence Map

Almost all recent deep reinforcement learning algorithms use four consecutive frames as the state space to retain the dynamic information. If the training state data constitute an image, the state space is used as the input of the neural network for training. As an AI-assisted decision-making technology, a dynamic influence map can describe dynamic information. In this paper, we propose the use of a frame image superimposed with an influence map as the state space to express dynamic information. Herein, we optimize Ape-x as a distributed reinforcement learning algorithm. Sparse reward is an issue that must be solved in refined intelligent decision making. The use of an influence map is proposed to generate the intrinsic reward when there is no external reward. The experiments conducted in this study prove that the combination of a dynamic influence map and deep reinforcement learning is effective. Compared with the traditional method that uses four consecutive frames to represent dynamic information, the score of the proposed method is increased by 11–13%, the training speed is increased by 59%, the video memory consumption is reduced by 30%, and the memory consumption is reduced by 50%. The proposed method is compared with the Ape-x algorithm without an influence map, DQN, N-Step DQN, QR-DQN, Dueling DQN, and C51. The experimental results show that the final score of the proposed method is higher than that of the compared baseline methods. In addition, the influence map is used to generate an intrinsic reward to effectively resolve the sparse reward problem.
Vocabulary size estimates for Lithuanian native speakers

Abstract The vocabulary size estimates suggested for native speakers used to vary considerably. More recently, researchers using Yes/No tests reported numbers that are much closer together. This article seeks to answer the question of whether we can give an estimate of an adult native speaker’s vocabulary size without specifying a language. First, it presents the estimate for a native speaker’s vocabulary size in Lithuanian and explores its variation depending on the person’s age (ranging from 16 to 71 years old), place of residence, their reading for pleasure habits, and gender. Then it compares this estimate to the ones suggested for German, English, and Dutch native speakers. The analysis suggests that the estimates for different languages, at least the ones based on empirical data rather than statistical predictions, do not differ considerably.
Geometric imaginary and quasi-probability functions of multi-component Schrödinger cat state

Superposition is one of unusual features in quantum mechanics. This paper investigates the properties of multi-component Schrödinger cat state, generated by amplitude dispersion. The geometrical imaginary of the multi-component Schrödinger cat state is studied, and the entropy of the geometrical imaginary is proposed. This entropy is inverse proportional to the amplitude of the initial coherent state in the large amplitude regime. Additionally, the superposition of the multi-component cat state can be vividly displayed by the Wigner function and the Pegg–Barnett phase operator.
Quantum Ray Tracing with Simulation

Rendering is a computer graphics process that generates images from a three-dimensional scene including models, lights and cameras. Ray tracing is a category of rendering algorithms that calculates pixel colors by simulating the physical movements of light rays such as reflections, refractions and scatterings. The core problem of ray tracing is a high-dimensional numerical integration, and the classical Monte Carlo ray tracing requires a big number of rays to reduce error. By leveraging the inherent parallelism of quantum computing, we propose a framework of quantum ray tracing algorithm, and do simulation experiments to prove that it has a quadratically faster error convergence than classical Monte Carlo ray tracing.
Work-in-Progress: HeteroRW: A Generalized and Efficient Framework for Random Walks in Graph Analysis

Random walk (RW) is a common graph analysis algorithm that consists of two phases: construction and sampling. The construction phase is responsible for generating the sampling table. The sampling phase contains many walkers which wander through the whole graph to sample. However, RW is notorious for its dynamic and sparse memory access pattern, which makes existing research suffer low throughput and memory bottleneck. In addition, the variety of RW algorithms in different scenarios also brings new design challenges.This paper proposes HeteroRW, a generalized framework to accelerate RWs on FPGAs. HeteroRW first identifies the two phases’ computation characteristics and presents corresponding hardware acceleration designs, respectively. Then, HeteroRW achieves the template-based design to support a variety of RW algorithms. Finally, HeteroRW integrates a novel scheduling layer to partition the input data and perform design space exploration (DSE). Experimental results show that HeteroRW achieves 4.3x speedup over the recent FPGA implementation while effectively simplifying the accelerator customization process.
Enhancing recommender ensemble by estimating input fitness

Recommendation system is designed to tackle the information overload problem. The performance of a single recommendation system can be significantly improved if ensemble methods are used. In some recent works researchers seek ways to leverage past history for enhancing the performance of ensemble methods, The core concept rooted in this strand of research is based on exploiting user meta-data for enriching the input spaces for ensembler, however, in many user-agnostic recommendation scenarios such as ad-hoc searching where the user IDs and meta-data are absent, which makes such meta-data-driven ensembling method infeasible. In this work, we proposed a novel user-agnostic ensembling model Rec4Rec, which learns the representations of base recommenders from the input sequences via attention mechanism, in such a manner, the patterns and characteristics of input sequences are memorized within base recommender representations. The base recommender fitnesses are estimated through computing distance with input sequences and can be readily integrated with outputs of base recommenders for ensemble learning, no user-IDs or meta-data are required here as opposed to other meta-data-driven ensemble approaches. Experiment results show that Rec4Rec significantly outperforms the stacking ensembler. The embedding visualization proves that the proposed ensemble method can effectively distinguish the base recommenders. The superior performance of Rec4Rec demonstrates the importance of building representations for base recommenders of an ensemble in ad-hoc inquiries. • We embed both the item sequences and recommenders to estimate the fitness score. • Similar recommenders are close to each other in the embedding space. • Input item sequence is recommended to the best fit recommenders. • Comparing the embedding distance between input and recommenders to get the fitness scores.
Incorporating spatial context for post-OCR in map images

Extracting text from historical maps using Optical Character Recognition (OCR) engines often results in partially or incorrectly recognized words due to complex map content. Previous work utilizes lexical-based approaches with linguistic context or applies language models to correct OCR results for documents. However, these post-OCR methods cannot directly consider spatial relations of map text for correction. For example, "Mississippi" and "River" constitute the place phrase "Mississippi River" (linguistic relation), and near "highway", there are likely to exist intersected "road" to enter the "highway" (spatial relation). This paper presents a novel approach that exploits the spatial arrangement of map text using a contextual language model, BART [6] for post-processing of map text from OCR. The approach first structures word-level map text into sentences based on their spatial arrangement while preserving the spatial location of words constituting a place name and corrects imperfect OCR text using neighboring information. To train BART for capturing spatial relations in map text, we automatically generate large numbers of synthetic maps to fine-tune BART with location names and their spatial context. We conduct experiments on synthetic and real-world historical maps of various map styles and scales and show that the proposed method can achieve significant improvement over the commonly used lexical approach.