Reviewerly/exHarmony huggingface dataset:
Spaces of Dyadic Distributions

This paper studies spaces of distributions on a dyadic half-line, which is the positive half-line equipped with bitwise binary addition and Lebesgue measure. We prove the nonexistence of a space of dyadic distributions which satisfies a number of natural requirements (for instance, the property of being invariant with respect to the Walsh–Fourier transform) and, in addition, is invariant with respect to multiplication by linear functions. This, in particular, is evidence that the space of dyadic distributions suggested by S. Volosivets in 2009 is optimal. We also show applications of dyadic distributions to the theory of refinement equations and wavelets on the dyadic half-line.
Interactive Photo Editing on Smartphones via Intrinsic Decomposition

Abstract Intrinsic decomposition refers to the problem of estimating scene characteristics, such as albedo and shading, when one view or multiple views of a scene are provided. The inverse problem setting, where multiple unknowns are solved given a single known pixel‐value, is highly under‐constrained. When provided with correlating image and depth data, intrinsic scene decomposition can be facilitated using depth‐based priors, which nowadays is easy to acquire with high‐end smartphones by utilizing their depth sensors. In this work, we present a system for intrinsic decomposition of RGB‐D images on smartphones and the algorithmic as well as design choices therein. Unlike state‐of‐the‐art methods that assume only diffuse reflectance, we consider both diffuse and specular pixels. For this purpose, we present a novel specularity extraction algorithm based on a multi‐scale intensity decomposition and chroma inpainting. At this, the diffuse component is further decomposed into albedo and shading components. We use an inertial proximal algorithm for non‐convex optimization (iPiano) to ensure albedo sparsity. Our GPU‐based visual processing is implemented on iOS via the Metal API and enables interactive performance on an iPhone 11 Pro. Further, a qualitative evaluation shows that we are able to obtain high‐quality outputs. Furthermore, our proposed approach for specularity removal outperforms state‐of‐the‐art approaches for real‐world images, while our albedo and shading layer decomposition is faster than the prior work at a comparable output quality. Manifold applications such as recoloring, retexturing, relighting, appearance editing, and stylization are shown, each using the intrinsic layers obtained with our method and/or the corresponding depth data.
Securing Distributed Database Using Elongated RSA Algorithm

Securing data, management of the authorised access of the user and maintaining the privacy of the data are some of the problems relating with the stored data in the database. The security of the data stored is considered as the major concern which is to be managed in a very serious manner as the users are sensitive about their shared data. The user's data can be protected by the process of cryptography which is considered as the conventional method. Advanced Encryption Standard (AES), Data Encryption Standard(DES), Two Fish, Rivest Shamir Adleman Algorithm (RSA), Attribute Based Encryption (ABE), Blowfish algorithms are considered as some of the cryptographic algorithms. These algorithms are classified into symmetric and asymmetric algorithms. Same key is used for the encryption and decoding technique in symmetric key cryptographic algorithm whereas two keys are used for the asymmetric ones. In this paper, the implementation of one of the asymmetric algorithm RSA with the educational dataset is done. To secure the distributed database, the extended version of the RSA algorithm is implemented as the proposed work.
Four-direction global matching with cost volume update for stereovision

The accuracy and speed of semi-global matching (SGM) make it widely used in many computer vision problems. However, SGM often struggles in dealing with pixels in the homogeneous regions and also suffers from streak artefacts for weak smoothness constraints. Meanwhile, we observe that the global method usually fails in occluded areas. The disparities for occluded pixels are typically the average of the disparity of nearby pixels. The local method can propagate the information into occluded pixels with a similar color. In this paper, we propose a novel, to the best of our knowledge, four-direction global matching with a cost volume update scheme to cope with textureless regions and occlusion. The proposed method makes two changes in the recursive formula: a) the computation process considers four visited nodes to enforce more smooth constraints; b) the recursive formula integrates cost filtering to propagate reliable information farther in nontextured regions. Thus, our method can inherit the speed of SGM, properly avoid streaking artefacts, and deal with the occluded pixel. Extensive experiments in stereo matching on Middlebury demonstrate that our method outperforms typical SGM-based cost aggregation approaches and other state-of-the-art local methods.
Write Like You: Synthesizing Your Cursive Online Chinese Handwriting via Metric‐based Meta Learning

Abstract In this paper, we propose a novel Sequence‐to‐Sequence model based on metric‐based meta learning for the arbitrary style transfer of online Chinese handwritings. Unlike most existing methods that treat Chinese handwritings as images and are unable to reflect the human writing process, the proposed model directly handles sequential online Chinese handwritings. Generally, our model consists of three sub‐models: a content encoder, a style encoder and a decoder, which are all Recurrent Neural Networks. In order to adaptively obtain the style information, we introduce an attention‐based adaptive style block which has been experimentally proven to bring considerable improvement to our model. In addition, to disentangle the latent style information from characters written by any writers effectively, we adopt metric‐based meta learning and pre‐train the style encoder using a carefully‐designed discriminative loss function. Then, our entire model is trained in an end‐to‐end manner and the decoder adaptively receives the style information from the style encoder and the content information from the content encoder to synthesize the target output. Finally, by feeding the trained model with a content character and several characters written by a given user, our model can write that Chinese character in the user's handwriting style by drawing strokes one by one like humans. That is to say, as long as you write several Chinese character samples, our model can imitate your handwriting style when writing. In addition, after fine‐tuning the model with a few samples, it can generate more realistic handwritings that are difficult to be distinguished from the real ones. Both qualitative and quantitative experiments demonstrate the effectiveness and superiority of our method.
Deep Learning for Word-Level Handwritten Indic Script Identification

In this work, we present a novel method that uses convolutional neural networks (CNNs) for multi scale and multi spectral feature extraction. The model is not only limited to conventional spatial domain representation but also multilevel two dimensional discrete Haar wavelet transform, where image representations are scaled to a variety of different sizes. These are then used to train different CNNs to select features. To be precise, we use 10 different CNNs that select a set of 10240 features, i.e. 1024/CNN. With this, 11 different handwritten scripts are identified, where 1000 words per script are used. In our test, we have achieved a maximum script identification accuracy of 94.73% using multi-layer perceptron (MLP). Our results outperform several state of the art techniques.
Runtime Verification of Multi-Agent Self-Adaptive System

Self-adaptive system (SAS) used in the field needs to comply with strict quality requirements. In order to study the adaptation of multi-agent system to dynamic changes at runtime, an adaptive runtime verification method based on multi-agent system is proposed. The characteristic of this method is to convert the environmental impact factors in the multi-agent system into a probabilistic form, and perform runtime verification in a formal way to enable the system to regulate itself. The use of the method is discussed through a real case. The experimental results show that the method is suitable for an adaptive system with multiple agents.
Research on Explainable Artificial Intelligence Techniques: An User Perspective

Artificial intelligence (AI) has become part of our daily lives and is commonly used in a wide range of industries. However, most of users have little visibility and knowledge about how AI systems reach a particular decision and their outcomes in the various fields where AI and machine learning are being applied. Many of the algorithms' results cannot be understood and explained in terms of how and why a specific decision was taken. With the advance of artificial intelligence researches, several techniques and approaches have emerged over the last few years, but only a few studies have been produced with regards to the end-user perspective and comprehension of the algorithms outcomes. This paper aims to evaluate the comprehensibility of the explanations from the perspective of the different types of users, aiming to generate confidence and understanding of the results produced by the AI.
S2Dedup

Secure deduplication allows removing duplicate content at third-party storage services while preserving the privacy of users' data. However, current solutions are built with strict designs that cannot be adapted to storage service and applications with different security and performance requirements.
Handwritten Word Recognition Using Fuzzy Matching Degrees

Abstract Handwritten text recognition systems interpret the scanned script images as text composed of letters. In this paper, efficient offline methods using fuzzy degrees, as well as interval fuzzy degrees of type-2, are proposed to recognize letters beforehand decomposed into strokes. For such strokes, the first stage methods are used to create a set of hypotheses as to whether a group of strokes matches letter or digit patterns. Subsequently, the second-stage methods are employed to select the most promising set of hypotheses with the use of fuzzy degrees. In a primary version of the second-stage system, standard fuzzy memberships are used to measure compatibility between strokes and character patterns. As an extension of the system thus created, interval type-2 fuzzy degrees are employed to perform a selection of hypotheses that fit multiple handwriting typefaces.
Cost Affinity Learning Network for Stereo Matching

Existing stereo matching methods mainly tend to directly aggregate features output from Convolutional Neural Network to obtain more discriminative cost features, but ignore the affinity of each element in the cost feature which also plays a key role in enhancing the cost feature. In this work, we propose a novel cost affinity learning network(CAL-Net) whose Affinity Enhanced Module(AEM) extracts the affinity of the elements in the cost feature and reconstructs a more discriminative feature. In addition, CAL-Net designs a Disparity Weight Loss(DWL) to guide training. Specifically, AEM takes the advatange of the self-attention mechanism to learn internal affinity between different elements and exploits it to reconstruct the cost feature for emphasizing informative elements. DWL calculates the adaptive weight according to disparity error. As the error decreases, the weight gradually increases and enables the network to gradually transit from the pixel level disparity to sub-pixel level. Experiments demonstrate that CAL-Net boosts the performance, especially in textureless and reflective regions, and achieves better results on Scene Flow and KITTI 2012 benchmarks than some typical related methods.
TABBA: A Novel Feature Selection Method Based on Binary Bat Algorithm and T-Test

Discovering disease-related genes from high-dimensional biomedical data is currently a hot issue. However, most biomedical data has a large number of irrelevant or redundant features, which makes the data difficult to utilize directly. For this problem, an improved binary bat algorithm based on T-test and variable step size adaptive algorithm (TABBA) is proposed. The T-test is employed to generate the initial population. Then a variable step size adaptive algorithm is introduced to accelerate convergence and avoid falling into the local optimum. The performance of TABBA is compared with original binary bat algorithm (BBA), variable step size adaptive binary bat algorithm (ABBA) and other optimization algorithms. The results in four public biomedical datasets confirm that TABBA is superior compared to benchmark methods in term of accuracy and the number of selected features.
Learning, compression, and leakage: Minimising classification error via meta-universal compression principles

Learning and compression are driven by the common aim of identifying and exploiting statistical regularities in data, which opens the door for fertile collaboration between these areas. A promising group of compression techniques for learning scenarios is normalised maximum likelihood (NML) coding, which provides strong guarantees for compression of small datasets — in contrast with more popular estimators whose guarantees hold only in the asymptotic limit. Here we consider a NMLbased decision strategy for supervised classification problems, and show that it attains heuristic PAC learning when applied to a wide variety of models. Furthermore, we show that the misclassification rate of our method is upper bounded by the maximal leakage, a recently proposed metric to quantify the potential of data leakage in privacy-sensitive scenarios.
DBWGIE-MR: A density-based clustering algorithm by using the weighted grid and information entropy based on MapReduce

The main target of this paper is to design a density-based clustering algorithm using the weighted grid and information entropy based on MapReduce, noted as DBWGIE-MR, to deal with the problems of unreasonable division of data gridding, low accuracy of clustering results and low efficiency of parallelization in big data clustering algorithm based on density. This algorithm is implemented in three stages: data partitioning, local clustering, and global clustering. For each stage, we propose several strategies to improve the algorithm. In the first stage, based on the spatial distribution of data points, we propose an adaptive division strategy (ADG) to divide the grid adaptively. In the second stage, we design a weighted grid construction strategy (NE) which can strengthen the relevance between grids to improve the accuracy of clustering. Meanwhile, based on the weighted grid and information entropy, we design a density calculation strategy (WGIE) to calculate the density of the grid. And last, to improve the parallel efficiency, core clusters computing algorithm based on MapReduce (COMCORE-MR) are proposed to parallel compute the core clusters of the clustering algorithm. In the third stage, based on disjoint-set, we propose a core cluster merging algorithm (MECORE) to speed-up ratio the convergence of merged local clusters. Furthermore, based on MapReduce, a core clusters parallel merging algorithm (MECORE-MR) is proposed to get the clustering algorithm results faster, which improves the core clusters merging efficiency of the density-based clustering algorithm. We conduct the experiments on four synthetic clusters. Compared with H-DBSCAN, DBSCAN-MR and MR-VDBSCAN, the experimental results show that the DBWGIE-MR algorithm has higher stability and accuracy, and it takes less time in parallel clustering.
The Real Blockchain Game Changer

The single critical output of a blockchain is creating trust where previously impossible. While this feature delivers compelling value for many use cases (bitcoin for money, standards setting and data sharing for permissioned blockchains, audit trails and protection against liability in supply chains), the most novel use case has been something unexpected: the birth of a new type of social structure to provide goods and services. The early examples of this new type of social structure have shown themselves to be incredibly effective at providing those services to their users.
Unraveling the Potential of Artificial Intelligence-Driven Blockchain Technology in Environment Management

Blockchain as an emerging technology provides a ray of hope to the most intensive environmental issues facing the planet. Blockchain with its decentralized business model has great relevance not only in the field of finance but also in environmental sustainability. The World Economic Forum has identified blockchain as a repair mechanism to the most challenging global environmental issues. It is a highly promising technology gaining traction in diverse fields. Blockchain through this technology unveils its capabilities as a decentralized ledger of all dealings across peer-to-peer networks, where participants can ratify transactions without any central authority. Blockchain technology is an indestructible electronic ledger of transactions designed to record and store everything of value in addition to financial transactions. Blockchain can ensure a shift to cleaner and more resource conserving decentralized solutions, unravel natural capital and to empower communities. This chapter attempts to study the applicability of blockchain in protecting and sustaining the global environment at various levels including life on land, life below the earth and climate changes. Deployment of blockchain technology is needed in areas like climate change, biodiversity conservation and healthy water bodies to overcome the threats they face.
MobiDIV: A Privacy-Aware Real-Time Driver Identity Verification on Mobile Phone

As car hire and sharing service is popular in the transportation market, secure driver identity verification is attracting more attention. However, the current verification mechanism focuses on performing authentication operations in the cloud server before drivers get access to the car, which results in potential privacy security issues. In this article, we present a privacy-aware architecture, MobiDIV, which is a client-only scheme, where all sensitive data are processed locally on the driver’s smartphone. To achieve real-time and robust driver identification during the driving life cycle, an efficient face feature extractor is proposed in MobiDIV. Specifically, two three-stream neural networks using the proposed efficient SqueezeNet structure are trained on our synthesized data set for different in-car uncertainties (pose, motion blur, nonalignment and low illumination). During authentication, only an adaptable embedding model is selected and conducted on phone for continuous feature extraction. The anomaly operation monitoring algorithm is then applied to the optical signal generated by phone flash for secure identity reidentification and verification failure message transmission. This allows us to further ensure the privacy of driver facial images without compromising on the real-time identity verification. We perform extensive experiments on various data sets. Compared to most SOTA deep neural networks on real-world open data sets, we achieve similar verification accuracy with fewer parameters and floating-point calculations. On the challenging synthetic test data sets, we even achieve a higher average verification accuracy. To assess the MobiDIV in-depth, the proposed model is integrated in car-sharing platform ICICV-E100 and the obtained results show the feasibility of our system.
Blind decision making: Reinforcement learning with delayed observations

Many applications, such as robotics, are increasingly utilizing Reinforcement Learning (RL). The current implementation of RL typically assumes that the state update from the previous actions happens instantaneously, and thus can be used for making future decisions. However, this may not always be true. When the state update is not available, the decision is taken partly in the blind since it cannot rely on the current state information. This paper proposes an approach, where the delay in the knowledge of the state can be used, and the decisions are made based on the available information which may not include the current state information. One approach could be to include the actions after the last-known state as a part of the state information, however, that leads to an increased state-space making the problem complex and slower in convergence. The proposed algorithm gives an alternate approach where the state space is not enlarged, as compared to the case when there is no delay in the state update. Evaluations on the control environments further illustrate the improved performance of the proposed algorithm.
Artist guided generation of video game production quality face textures

We develop a high resolution face texture generation system which uses artist provided appearance controls as the conditions for a generative network. Artists are able to control various elements in the generated textures, such as the skin, eye, lip, and hair color. This is made possible by reparameterizing our dataset to the same UV mapping, allowing us to utilize image-to-image translation networks. Although our dataset is limited in size, only 126 samples in total, our system is still able to generate realistic face textures which strongly adhere to the input appearance attribute conditions because of our training augmentation methods. Once our system has generated the face texture, it is ready to be used in a modern game production environment. Thanks to our novel SuperResolution and material property recovery methods, our generated face textures are 4K resolution and have the associated material property maps required for raytraced rendering.
Developing a lateral topographic density model for Brazil

Topographic density distribution models are used in many geodetic and geophysical problems. Due to the difficulty in developing a high-resolution three-dimensional density model, many studies are focusing on the development of lateral density models and, despite the existence of global models, it is understood that these are less detailed and need to be improved to be used in regional and local studies. In this context, this research develops a distribution model of the lateral density of topographic masses for Brazil (LTD_Brazil, with 30 arc-seconds grid spacing), associated with their uncertainties, considering more detailed information from geological maps and apparent density values of rock types. The model results indicated an average density of 2459 kg/m³, ranging from 1625 to 3400 kg/m³, with standard deviations ranging from 8 to 351 kg/m³. This average density value has great influence of large Phanerozoic sedimentary basins (Amazonas/Solimões/Acre, Alto Tapajós, Parecis, Parnaíba and Paraná basins) located in Brazil. Furthermore, comparing the LTD_Brazil with a global model (UNB_TopoDensT), it was possible to suggest that the differences (ranging from −500 to 600 kg/m3), more significant in metamorphic and igneous rocks regions, are associated with greater detail of the lithological units used and accuracy of the model.