Reviewerly/exHarmony huggingface dataset:
HessHist: A Hessian‐matrix weighted histogram for image contrast enhancement

For image contrast enhancement operation, it is a keypoint to obtain more natural enhanced results and keep more details without distortion. In this paper, a novel image Hessian-matrix weighted histogram for image contrast enhancement is proposed, which can improve the contrast of smooth regions and simultaneously restrain the contrast of texture regions. In the proposed method, the multi-scale fractional-order Hessian-matrix is firstly utilized to detect and quantify the texture information of the input image, which explores the regions that should be contrasted or should be restrained. Then, the strong texture regions are suppressed by a designed suppress function. Finally, the information on unsuppressed regions and suppressed texture regions will be count by a histogram, which is termed as Hessian-matrix weighted Histogram (HessHist) in this paper. According to HessHist, the corresponding cumulative distribution function will realize the contrast enhancement operation on the input image. For real-time application, the integral images are introduced for fast computation of the HessHist. Experimental results show that the proposed HessHist-based image enhancement algorithm preserves more details of input image without distortion, and is competitive with state-of-the-art image enhancement algorithms in both subjective visual perception and objective evaluation metrics.
The K-Sparse Lsr for Subspace Clustering Via 0-1 Integer Programming

Subspace clustering is a powerful technology for clustering high-dimensional data according to the underlying subspaces. Least Squares Regression (LSR) is a classical subspace clustering model because of its explicit solution and the good grouping effect that ensure the coefficient of close samples to be similar. However, one obvious limitation of it is lacking the sparsity. To introduce appropriate sparsity into LSR, we introduce an ℓ 0 norm based inequality constraint directly without any relaxation into the LSR. Since the ℓ 0 norm constrained optimization problem is difficult to solve, we first propose an appropriate variable substitution to transform the ℓ 0 norm constraint into an equivalent 0-1 integer constraint and then replace the 0-1 integer constraint with two continuous constraints. The obtained model with 0-1 integer constraint is the new model proposed in this paper, named k -sparse LSR (KSLSR), which can be effectively solved by the alternating direction method of multipliers (ADMM), an advanced solution for 0-1 integer programming. The proposed model is proved to have both sparsity and the grouping effect, which are beneficial for improving the clustering accuracy. Experimental results show the effectiveness and robustness of the proposed subspace clustering model.
The unreliable mediator

Abstract Within the array of sounding devices that have been deployed in the making of sound art, the speaker has a special status. Although not ubiquitous, the extent of the speaker’s influence on sound art cannot be underestimated, and yet for many artists the speaker remains an obstinate, unyielding thing, acknowledged for its function but not embraced for its form. Sound art has frequently positioned itself within the discourse of contemporary sculpture and installation art and, as such, both the affordances of sound itself and the materials of its technical apparatus need to be understood as different dimensions of what constitutes its medium. This brief survey of the uses of speakers in sound art uses Lucier’s Music on a Long Thin Wire as a case study in which the speaker is utilized not just for its ability to convey sonic meaning generated elsewhere but also as a thing in its own right.
Pedestrian Anomaly Detection Method using Autoencoder

It is significant that video surveillance system is adopted for the city security. For the detection of pedestrian anomaly behavior, an autoencoder-based detection model was trained using image sequences with few or no anomalous events in the framework of unsupervised learning. Deep neural networks constructed from convolution autoencoders process video frames in an unsupervised manner for capturing the spatial information of the data. These spatial structures are then combined together to form the data representation. Three-layer convolution long short-term memory model (Long Short Term Memory, LSTM) is adopted for a temporal encoder to learn the encoding spatial structure. Finally, these feature data are scored to determine whether the pedestrian's behavior is normal according to evaluation criteria. The proposed model is trained on various datasets, which demonstrates its effectiveness and robustness.
Discounted Selfish Mining: Is It Profitable?

A rational miner may gain from withholding blocks and strategically timing the release to orphan the others' blocks, which questions the existence of honest majority and blockchain concensus. This paper reconciles the discrepancy between the absence of block-withholding attacks in practice and the profits as suggested by the literature. When the miner is impatient, selfish mining is a long-term deviation, and the short-term frictions incentivize honest mining. We present a continuous-time model that incorporates discounted cash flows, a new risk of being temporarily orphaned and endogenous difficulty adjustment. We show that, the gain from selfish mining relies on a quick difficulty adjustment, and the miner is most concerned with maintaining the new difficulty in long run. A small risk would increase the threshold computation power to attack by 36%. A protocol that includes the orphan blocks in difficulty recalculation defenses such attacks.
Towards the use of robotic systems at nuclear facilities

Abstract The article addresses the matter of the mobile robotic systems (MRS) application at nuclear industry and nuclear energy facilities. Three main types of tasks solved with the help of MRS in order to ensure safety and reduce the radiation load on personnel are described. The scope of application and general requirements for such MRS, typical technological operations, control modes and human-robot interaction are discussed. It is shown that highly specialized robots are mainly used and that their design doesn’t imply a prompt replacement of the payload and adaptation to various tasks. It is noted that most of the modern MRS designed to operate at radiation-hazardous facilities are still teleoperated. The necessity of creating multifunctional modular reconfigurable MRS of various standard-size groups for use at nuclear facilities and increasing the level of their autonomy is substantiated. Key words Mobile robotic system, UGV, nuclear facility, teleoperation, supervisory control. Acknowledgements This work was carried out within the framework of the state assignment of the Ministry of Education and Science of Russia No. 075-00913-21-00 dated December 25, 2020 «Development of proposals for improving the state scientific, technical and industrial policy and popularizing achievements in the field of robotics based on an analysis of trends in the development of robotics in Russia and abroad».
Open-source OCR Engine Integration with Greek Dictionary

short-paper Share on Open-source OCR Engine Integration with Greek Dictionary Authors: Alkiviadis Tsimpiris Department of Computer, Informatics and Telecommunications Engineering, International Hellenic University, Greece Department of Computer, Informatics and Telecommunications Engineering, International Hellenic University, GreeceView Profile , Dimitrios Varsamis Department of Computer, Informatics and Telecommunications Engineering, International Hellenic University, Greece Department of Computer, Informatics and Telecommunications Engineering, International Hellenic University, GreeceView Profile , Charalampos Strouthopoulos Department of Computer, Informatics and Telecommunications Engineering, International Hellenic University, Greece Department of Computer, Informatics and Telecommunications Engineering, International Hellenic University, GreeceView Profile , George Pavlidis University Campus at Kimmeria, ATHENA - Research and Innovation Centre in Information, Communication and Knowledge Technologies, Greece University Campus at Kimmeria, ATHENA - Research and Innovation Centre in Information, Communication and Knowledge Technologies, GreeceView Profile , Kiourt Chairi University Campus at Kimmeria, ATHENA - Research and Innovation Centre in Information, Communication and Knowledge Technologies, Greece University Campus at Kimmeria, ATHENA - Research and Innovation Centre in Information, Communication and Knowledge Technologies, GreeceView Profile Authors Info & Claims PCI 2021: 25th Pan-Hellenic Conference on InformaticsNovember 2021 Pages 436–441https://doi.org/10.1145/3503823.3503903Online:22 February 2022Publication History 0citation24DownloadsMetricsTotal Citations0Total Downloads24Last 12 Months24Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Computer Modeling of Zn and Ni Distribution in Technogenic Soils

Abstract Based on computer modeling using geochemical parameters, new results of the interdependent distribution of mobile forms of heavy metals Zn and Ni in contaminated soil have been obtained. Modeling in Golden Software SURFER 8 made it possible to identify soil contamination halos - laterally module - in the zone of a hazardous gas station facility in the city. A three-dimensional skeleton model of the distribution of mobile forms of HM was obtained depending on the key parameter of soil organic carbon - Corg. The revealed picture of the distribution of metals adequately reflects the specificity of the individual chemical properties of Zn, Ni and clearly demonstrates the different efficiency of binding of their mobile forms with soil organic matter. The proposed approach of computer geochemical modeling can be recommended as a methodological guide for identifying the characteristics of behavior in the soil environment for other heavy metals. The revealed empirical dependencies prove the need to change approaches in the ecological assessment of territories in the conditions of technogenesis. Research also has a promising development, allows one to obtain new geochemical patterns and associations of elements in technogenic landscapes, which contributes to the development of the fundamental foundations of geochemical science.
Exploring a New Framework to Support Online Learning

The COVID-19 pandemic forced many teachers to connect with their students remotely in 2020. This sent educators who were unprepared for the shift to teaching online scrambling for resources to address this new challenge. In addition to finding resources, teachers were faced with developing new strategies to reach learners outside of the classroom walls. This chapter describes a research-based practical framework for how teachers can use the internet more effectively to support remote learning with elementary age students.
Blurriness Guided Underwater Salient Object Detection

There is little work done for underwater saliency objection detection (SOD), but it is vital to artificial intelligence-driven underwater analysis. Recent research has shown that depth information would increase SOD accuracy, but it may not be accessible to most RGB datasets. Since image blurriness could be an estimate of underwater scene depth [1], we propose to use a self-derived blurriness cue and fuse it into the RGB stream to boost SOD accuracy. Experimental results demonstrate the effectiveness of the proposed method. Our work would also contribute a public underwater SOD dataset to the field of underwater SOD.
Efficient On-Device Incremental Learning by Weight Freezing

On-device learning has become a new trend for edge intelligence systems. In this paper, we investigate the on-device in-cremental learning problem, which targets to learn new classes on top of a well-trained model on the device. Incremental learning is known to suffer from catastrophic forgetting, i.e., a model learns new classes at the cost of forgetting the old classes. Inspired by model pruning techniques, we propose a new on-device incremental learning method based on weight freezing. The weight freezing in our framework plays two roles: 1) preserving the knowledge of the old classes; 2) boosting the training procedure. By means of weight freezing, we build up an efficient incremental learning framework which combines knowledge distillation to fine-tune the new model. We conduct extensive experiments on CIFAR100 and compare our method with two existing methods. The experimental results show that our method can achieve higher accuracy after incrementally learning new classes.
Semantic Guided Single Image Reflection Removal

Reflection is common when we see through a glass window, which not only is a visual disturbance but also influences the performance of computer vision algorithms. Removing the reflection from a single image, however, is highly ill-posed since the color at each pixel needs to be separated into two values belonging to the clear background and the reflection, respectively. To solve this, existing methods use additional priors such as reflection layer smoothness, double reflection effect, and color consistency to distinguish the two layers. However, these low-level priors may not be consistently valid in real cases. In this paper, inspired by the fact that human beings can separate the two layers easily by recognizing the objects and understanding the scene, we propose to use the object semantic cue, which is high-level information, as the guidance to help reflection removal. Based on the data analysis, we develop a multi-task end-to-end deep learning method with a semantic guidance component, to solve reflection removal and semantic segmentation jointly. Extensive experiments on different datasets show significant performance gain when using high-level object-oriented information. We also demonstrate the application of our method to other computer vision tasks.
Single Underwater Image Enhancement Using Integrated Variational Model

Underwater images often suffer from various degradation problems such as blurring, fog, low contrast, and color distortion because the light is absorbed and scattered when travelling through water. To solve these issues, we establish a novel framework combing variational methods and pyramid technology to improve image quality in the frequency domain. Two novel variational models, the adaptive variational contrast enhancement (AVCE) and total Laplacian model, are designed with the aim of enhancing the contrast of foreground and preserving texture features at different scales. In order to solve these two models efficiently, we also exploit two optimal algorithms based on gradient descent method (GDM) and alternating direction method of multipliers (ADMM). In addition, fast Fourier transform (FFT) is applied for further accelerating the calculation procedure. Extensive experiments demonstrate that our approach achieves a good performance on contrast enhancement, color correction, and texture enlargement for underwater images. Qualitative and quantitative comparisons with several state-of-the-art methods further validate the superiority of our proposed method.
Convolutional Recommended Neural Network system based on user reviews for movies

Nowadays, every user purchases a ticket based on the movie&#x0027;s story ranking. Some users enjoy horror films, fight films, and other genres; similarly, users can choose a film and purchase a ticket. In our proposed model, we use a Neural Network with a recommended system to provide better movies to users based on movie ratings and feedback. We can get better accuracy and have better movies by using Neural Networks based on the user&#x0027;s previous movie experience. We improved the accuracy and prediction level of the movies in our proposed model based on user feedback and ratings. Compared to the existing movie system, our proposed model provides better accuracy and recommends that the user enjoy the tickets booking.
Application of Neural Network in Pixel Art Creation: Bi-directional Conversion between Photo and Pixel Art with GAN Base Model

Pixel art was first developed in the last century when computers have limited memory and processing power, and is in wide use in the game industry now. However, pixel art is a highly stylized art form that requires skilled human artists to create and the creation process is time consuming. This paper proposes an automatic way to convert photos to pixel drawings and the reverse with unsupervised, GAN based neural networks.
Optimized Parametric Inference between Multilevel Physical Representations in Multigrid Ensemble Kalman Filter

Essential features of the multigrid Ensemble Kalman Filter (G. Moldovan , G. Lehnasch , L. Cordier , M. Meldi , A multigrid /ensemble Kalman filter strategy for assimilation of unsteady flows , Journal of Computational Physics 443-110481 ) recently proposed for Data Assimilation of fluid flows are investigated and assessed in the present article. The analysis is focused on the improvement in performance due to the inner loop. In this step, data from solutions calculated on the higher resolution levels of the multigrid approach is used as surrogate observation to improve the model prediction on the coarsest levels of the grid. The latter represents the level of resolution used to run the ensemble members for global Data Assimilation. The method is tested over two classical one-dimensional problems, namely the linear advection problem and the Burger's equation. The analyses encompass a number of different aspects such as different grid resolutions. The results indicate that the contribution of the inner loop is essential in obtaining accurate flow reconstruction and global parametric optimization. These findings open exciting perspectives of application to grid-dependent reduced-order models extensively used in fluid mechanics applications for complex flows, such as Large Eddy Simulation (LES).
Sound Art and Time

Abstract This chapter explores the relationship between sound art and time, focusing on a set of works that mark milestones in the history of sound art and that explicitly engage varieties of temporality. It begins with a discussion of Thomas Edison’s invention of phonography, which allowed sound to be captured and gave it an untimely existence. It then turns to the work of John Cage, analysing it by way of Henri Bergson’s philosophy of time. The essay goes on to discuss the notions of time, duration, and eternity in Maryanne Amacher’s radio works, Max Neuhaus’s installations, La Monte Young’s Dream House, Jem Finer’s Longplayer, and other works.
Generating and Controlling Diversity in Image Search

In our society, generations of systemic biases have led to some professions being more common among certain genders and races. This bias is also reflected in image search on stock image repositories and search engines, e.g., a query like “male Asian administrative assistant” may produce limited results. The pursuit of a utopian world demands providing content users with an opportunity to present any profession with diverse racial and gender characteristics. The limited choice of existing content for certain combinations of profession, race, and gender presents a challenge to content providers. Current research dealing with bias in search mostly focuses on re-ranking algorithms. However, these methods cannot create new content or change the overall distribution of protected attributes in photos. To remedy these problems, we propose a new task of high-fidelity image generation conditioning on multiple attributes from imbalanced datasets. Our proposed task poses new sets of challenges for the state-of-the-art Generative Adversarial Networks (GANs). In this paper, we also propose a new training framework to better address the challenges. We evaluate our framework rigorously on a real-world dataset and perform user studies that show our model is preferable to the alternatives.
Indexicals and Kaplan’s Cases

In this chapter, I explain and discuss Kaplan’s cases of contingent a priori truths involving pure indexicals and demonstratives, especially those containing the ‘dtaht’ operator combined with descriptions or demonstrations. I discuss two fundamental assumptions behind Kaplan’s cases, namely, that contexts of utterances are only proper contexts, and that indexicals and demonstratives always take their semantic values from the contexts of utterances (and never from other contexts). I conclude the chapter with a contrast between Kripke’s and Kaplan’s conception of contingent a priori truths.
EMOTIONAL CONTEXT OF THE ACCURATE AND INACCURATE METACOGNITIVE MONITORING LEVELS

Purpose. The aim of the paper is to identify what emotions arise after obtaining information about the correct or incorrect estimations of the performance in a short knowledge test. To do this a short pilot experimental research was conducted outlining some peculiarities of the accurate and inaccurate metacognitive monitoring levels in university students’ learning activity. Methods and procedure. The theoretical and comparative practical methods of studying the accurate and inaccurate metacognitive monitoring levels of university students have been used in the study. The participants (n = 77, Mage = 17.62) answered knowledge information questions of three types (multiple-choice, open-answer, and ‘Yes’/‘No’) set up both in Ukrainian and in English and performed prospective and retrospective metacognitive judgements of learning. Calibration procedure helped to define average indicators of both the illusion of knowing and the illusion of not knowing, as well as adequate levels of metacognitive monitoring. After the announcement of the correct answers the participants noted their emotions that arose when the answer were correct / incorrect. Results. The results showed the occurrence of emotions with positive and negative affect both in the accurate (the adequate level) and inaccurate metacognitive monitoring levels (the illusions of knowing and not knowing). According to the data, in total, the examples of the emotional status of the highest frequency were mainly with the positive affect. The illusion of not knowing was expressed by 5 main samples of emotional status of negative affect (‘disappointed’, ‘surprised’, ‘offended’, ‘inattentive’, and ‘sad’), 8 samples of emotions of positive affect, though the value of them was not very high in almost all the cases (‘normal’, ‘good’, ‘glad’, ‘calm’, ‘nice’, ‘proud’, ‘satisfied’, and ‘unsurprised’), and 2 of neutral affect as well. Students with the adequate level of their RCJs showed only positive and neutral emotions of different value. Conclusions. The current results continue to expand an investigation of metacognitive monitoring reliability factors. Still the presented ideas serve more as a framework for future study and their pure implications still need thorough further researches.