Reviewerly/exHarmony huggingface dataset:
Intelligent Content Based Image Retrieval Model Using Adadelta Optimized Residual Network

Content based image retrieval (CBIR) system produces useful representation of images with the consideration of the visual features of the images like color, texture, shapes, etc. An important requirement of CBIR is ensuring useful retrieval of images for the applied query images (QI). Therefore, this paper presents a new CBIR model using Adadelta optimized residual network, image retrieval against query images. The proposed model involves ResNet 50 based feature extractor to derive a useful set of features. Besides, Adadelta optimizer is applied to effectually tune the hyperparameter of the ResNet-50 model to improve the retrieval performance. In addition, Euclidean distance is employed as a similarity metric to identify the highly similar images that exist in the database with respect to the applied QI. The use of Adadelta optimizer helps to considerably boost the retrieval outcomes. For validating the improved retrieval outcomes of the proposed model, an extensive set of simulations take place on benchmark datasets and the obtained outcomes highlighted the supremacy of the proposed model over the other techniques.
A Parallelization Algorithm for Real-Time Path Shortening of High-DOFs Manipulator

The paths generated by sampling-based path planning are generally not smooth and often generate multiple unnecessary robot posture changes in the task space. To mitigate such issues with a planned path from sampling-based path planners, shortcut-based path shortening algorithms are commonly adopted in the field of robot manipulator path planning as a post-processing step. In this paper, we analyze shortcut-based algorithms and propose a new approach based on the idea of parallelism for faster path shortening so that it can be more applicable in environments where a path has to be generated as quickly as possible to avoid collisions with other moving objects around the manipulator. Through performance comparisons in simulations, it is shown that the proposed approach can obtain a well-shortened as well as much smooth path compared to the original path faster than conventional shortcut-based algorithms and an optimization-based approach developed for collision-free path generation.
A Nash-game approach to Blind Image Deblurring

Images are undeniably a vital part in our life, they are now used in all of kind of fields, hence, image processing became a must. Common image processing include image enhancement, restoration, encoding, and compression. Restoring is a process to improve an degraded image quality, to sharpen an image, and remove the noise. A major cause of image degradation is blur. There are many types of blur: motion blur, the most common, is a blur due to subject movement, or a camera shake. Blind deconvolution is the reconstruction of a sharp clean version of a blurred image without prior knowledge of the blur kernel (PSF) or the original image. In this paper, we will study the Nash game as a blind deconvolution technique for image deblurring, and we will test this method by presenting some numerical examples, and comparing it with other methods predefined in MATLAB, or existing in the literature.
Aggregation of Incentivized Learning Models in Mobile Federated Learning Environments

We propose a reputation and budget-constrained selection methodology along with an auction-driven incentive scheme in a Federated Learning (FL) setting. The reputation score is built on the performance metrics of the local models, and the incentive aims to ensure all participants are rewarded according to the quality of their contributions. With the dynamic adjustment of the user compensation to distribute the benefits more fairly, the proposed incentive increases user utilities with the increasing platform budget. The incentive achieves positive user utility without compromising the aggregated model accuracy. Moreover, the platform utility remains consistent with respect to the baselines.
Policy space identification in configurable environments

Abstract We study the problem of identifying the policy space available to an agent in a learning process, having access to a set of demonstrations generated by the agent playing the optimal policy in the considered space. We introduce an approach based on frequentist statistical testing to identify the set of policy parameters that the agent can control, within a larger parametric policy space. After presenting two identification rules (combinatorial and simplified), applicable under different assumptions on the policy space, we provide a probabilistic analysis of the simplified one in the case of linear policies belonging to the exponential family. To improve the performance of our identification rules, we make use of the recently introduced framework of the Configurable Markov Decision Processes, exploiting the opportunity of configuring the environment to induce the agent to reveal which parameters it can control. Finally, we provide an empirical evaluation, on both discrete and continuous domains, to prove the effectiveness of our identification rules.
Pose-Guided Hierarchical Semantic Decomposition and Composition for Human Parsing

Human parsing is a fine-grained semantic segmentation task, which needs to understand human semantic parts. Most existing methods model human parsing as a general semantic segmentation, which ignores the inherent relationship among hierarchical human parts. In this work, we propose a pose-guided hierarchical semantic decomposition and composition framework for human parsing. Specifically, our method includes a semantic maintained decomposition and composition (SMDC) module and a pose distillation (PC) module. SMDC progressively disassembles the human body to focus on the more concise regions of interest in the decomposition stage and then gradually assembles human parts under the guidance of pose information in the composition stage. Notably, SMDC maintains the atomic semantic labels during both stages to avoid the error propagation issue of the hierarchical structure. To further take advantage of the relationship of human parts, we introduce pose information as explicit guidance for the composition. However, the discrete structure prediction in pose estimation is against the requirement of the continuous region in human parsing. To this end, we design a PC module to broadcast the maximum responses of pose estimation to form the continuous structure in the way of knowledge distillation. The experimental results on the look-into-person (LIP) and PASCAL-Person-Part datasets demonstrate the superiority of our method compared with the state-of-the-art methods, that is, 55.21% mean Intersection of Union (mIoU) on LIP and 69.88% mIoU on PASCAL-Person-Part.
GeoAI in the US Geological Survey for topographic mapping

Abstract Geospatial artificial intelligence (GeoAI) can be defined broadly as the application of artificial intelligence methods and techniques to geospatial data, processes, models, and applications. The application of these methods to topographic data and phenomena is a focus of research in the US Geological Survey (USGS). Specifically, the USGS has researched and developed applications in terrain feature extraction, hydrographic network extraction, and semantic modeling. This article is a documentation of the recent work and current state of research and development. The article helps define the accomplishments and directions of research and applications in fields of GeoAI for topographic mapping within the USGS and more broadly.
Learning to Discover Reflection Symmetry via Polar Matching Convolution

The task of reflection symmetry detection remains challenging due to significant variations and ambiguities of symmetry patterns in the wild. Furthermore, since the local regions are required to match in reflection for detecting a symmetry pattern, it is hard for standard convolutional networks, which are not equivariant to rotation and reflection, to learn the task. To address the issue, we introduce a new convolutional technique, dubbed the polar matching convolution, which leverages a polar feature pooling, a self-similarity encoding, and a systematic kernel design for axes of different angles. The proposed high-dimensional kernel convolution network effectively learns to discover symmetry patterns from real-world images, overcoming the limitations of standard convolution. In addition, we present a new dataset and introduce a self-supervised learning strategy by augmenting the dataset with synthesizing images. Experiments demonstrate that our method outperforms state-of-the-art methods in terms of accuracy and robustness.
Scalable Algorithms for Convex Clustering

Convex clustering is an appealing approach to many classical clustering problems. It stands out among standard methods as it enjoys the existence of a unique global optimal solution. Despite this advantage, convex clustering has not been widely adopted, due to its computationally intensive nature. To address this obstacle, especially in the “big data” setting, we introduce a Scalable cOnvex cLustering AlgoRithm via Parallel Coordinate Descent Method (SOLAR-PCDM) that improves the algorithm’s scalability by combining a parallelizable algorithm with a compression strategy. This idea is in line with the rise and ever increasing availability of high performance computing systems built around multi-core processors, GPU-accelerators, and computer clusters. SOLARPCDM consists of two parts. In the first part, we develop a method called weighted convex clustering to recover the solution path by formulating a sequence of smaller equivalent optimization problems. In the second part, we utilize the Parallel Coordinate Descent Method (PCDM) to solve a specific convex clustering problem. We demonstrate the correctness and scalability of our algorithm on both simulated and real data examples.
Probabilistic Indexing and Search for Hyphenated Words

Hyphenated words are very frequent in historical manuscripts. Reliable recognition of (the prefix and suffix fragments of) these words is problematic and has not been sufficiently studied so far. If the aim is to transcribe text images, a sufficiently accurate character-level recognition of the fragments might be an admissible transcription result. However, if the goal is to allow searching for words or "keyword spotting", this is not acceptable at all because users need to query entire words, rather than possible fragments of these words. The situation becomes even worse if the aim is to index images for lexicon-free searching for any arbitrary text. To start with, this makes it necessary to know whether the concatenation of two-word fragments may constitute a regular word, or each fragment is instead a word by itself. We propose a probabilistic model to deal with these complications and present a first development of this model, based only on lexicon-free probabilistic indexing of the text images. Albeit preliminary, it already allows to very accurately find both entire and hyphenated forms of arbitrary query words by using just the entire forms of the words. Experiments carried out on a representative part of a huge historical collection of the National Archives of Finland, confirm the usefulness of the proposed methods.
Violin vibration state determined from laser streak patterns

One of the main objectives of violin acoustics is a detailed analysis of the vibration state of the instrument, the source of its sound. The goal of this work was to extend a straightforward technique, naked-eye and non-destructive, to detect details of violin vibrations: laser streak patterns. Essentially, the experimental set-up only requires harmonically driving a violin in resonance, illuminating its surface with a handheld laser, and focusing on the speckle plane. The streak patterns obtained from signature modes of the violin revealed details hardly available using other real-time techniques. For example, results showed the behavior of the island —the area between the f-holes— changing from rigid rocking in CBR to bending motion in B1- and B1+; in fact, the high sensitivity of the streak patterns even allowed detecting specific areas with major deflections on the island: the soundpost side in B1-, and the bassbar side in B1+. Therefore, employing streak patterns as a tool for real-time analysis of violins is encouraged not only for researchers but also for violin makers.
Residual Triplet Attention Network for Single-Image Super-Resolution

Single-image super-resolution (SISR) techniques have been developed rapidly with the remarkable progress of convolutional neural networks (CNNs). The previous CNNs-based SISR techniques mainly focus on the network design while ignoring the interactions and interdependencies between different dimensions of the features in the middle layers, consequently hindering the powerful learning ability of CNNs. In order to address this problem effectively, a residual triplet attention network (RTAN) for efficient interactions of the feature information is proposed. Specifically, we develop an innovative multiple-nested residual group (MNRG) structure to improve the learning ability for extracting the high-frequency information and train a deeper and more stable network. Furthermore, we present a novel lightweight residual triplet attention module (RTAM) to obtain the cross-dimensional attention weights of the features. The RTAM combines two cross-dimensional interaction blocks (CDIBs) and one spatial attention block (SAB) base on the residual module. Therefore, the RTAM is not only capable of capturing the cross-dimensional interactions and interdependencies of the features, but also utilizing the spatial information of the features. The simulation results and analysis show the superiority of the proposed RTAN over the state-of-the-art SISR networks in terms of both evaluation metrics and visual results.
Local Features-Based Watermarking for Image Security in Social Media

The last decade shows an explosion of using social media, which raises several challenges related to the security of personal files including images. These challenges include modifying, illegal copying, identity fraud, copyright protection and ownership of images. Traditional digital watermarking techniques embed digital information inside another digital information without affecting the visual quality for security purposes. In this paper, we propose a hybrid digital watermarking and image processing approach to improve the image security level. Specifically, variants of the widely used Least-Significant Bit (LSB) watermarking technique are merged with a blob detection algorithm to embed information into the boundary pixels of the largest blob of a digital image. The proposed algorithms are tested using several experiments and techniques, which are followed by uploading the watermarked images into a social media site to evaluate the probability of extracting the embedding watermarks. The results show that the proposed approaches outperform the traditional LSB algorithm in terms of time, evaluation criteria and the percentage of pixels that have changed.
BESURE: Blockchain-Based Cloud-Assisted eHealth System with Secure Data Provenance

In this paper, we investigate actual cloud-assisted electronic health (eHealth) systems in terms of security, efficiency, and functionality. Specifically, we propose a password-based subsequent-key-locked encryption mechanism to ensure the confidentiality of outsourced electronic health records (EHRs). We also propose a blockchain-based secure EHR provenance mechanism by designing the data structure of the EHR provenance record and deploying a public blockchain and smart contract to secure both EHRs and their provenance records. With the two mechanisms, we develop BESURE (blockchain-based cloud-assisted eHealth system with secure data provenance) to provide a secure EHR storage service with efficient provenance. Security analysis and comprehensive performance evaluation are conducted to demonstrate that BESURE is secure and efficient.
Multi-Class Text Classification: Model Comparison and Selection

The objective of text classification is to categorize documents into a specific number of predefined categories. We can easily imagine the issue of arranging documents, not by topic, but rather by and large assessment, e.g. deciding if the sentiment of a document is whether positive or negative. While working on a supervised machine learning problem with a defined dataset, there are many classifiers that can be used in text classification. Utilizing dataset of stack overflow questions, answers, and tags as information, we find that standard machine learning systems completely beat human-delivered baselines. These majorly include Naive Bayes Classifier for multinomial models, Linear Support Vector Machine, Logistic Regression, Word to vector (Word2vec) and Logistic Regression, Document to vector (Doc2vc) and logistic regression, Bag of Words (BOW) with Keras. Our paper is a detailed examination and comparison of accuracies among these algorithms.
On Blass Translation for Leśniewski’s Propositional Ontology and Modal Logics

In this paper, we shall give another proof of the faithfulness of Blass translation (for short, B-translation) of the propositional fragment $$\mathbf{L}_1$$ of Leśniewski’s ontology in the modal logic $$\mathbf{K}$$ by means of Hintikka formula. And we extend the result to von Wright-type deontic logics, i.e., ten Smiley-Hanson systems of monadic deontic logic. As a result of observing the proofs we shall give general theorems on the faithfulness of B-translation with respect to normal modal logics complete to certain sets of well-known accessibility relations with a restriction that transitivity and symmetry are not set at the same time. As an application of the theorems, for example, B-translation is faithful for the provability logic $$\mathbf{PrL}$$ (= $$\mathbf{GL}$$ ), that is, $$\mathbf{K}$$ $$+$$ $$\Box (\Box \phi \supset \phi ) \supset \Box \phi $$ . The faithfulness also holds for normal modal logics, e.g., $$\mathbf{KD}$$ , $$\mathbf{K4}$$ , $$\mathbf{KD4}$$ , $$\mathbf{KB}$$ . We shall conclude this paper with the section of some open problems and conjectures.
Automatic Multilingual Stopwords Identification from Very Small Corpora

Tools for Natural Language Processing work using linguistic resources, that are language-specific. The complexity of building such resources causes many languages to lack them. So, learning them automatically from sample texts would be a desirable solution. This usually requires huge training corpora, which are not available for many local languages and jargons, lacking a wide literature. This paper focuses on stopwords, i.e., terms in a text which do not contribute in conveying its topic or content. It provides two main, inter-related and complementary, methodological contributions: (i) it proposes a novel approach based on term and document frequency to rank candidate stopwords, that works also on very small corpora (even single documents); and (ii) it proposes an automatic cutoff strategy to select the best candidates in the ranking, thus addressing one of the most critical problems in the stopword identification practice. Nice features of these approaches are that (i) they are generic and applicable to different languages, (ii) they are fully automatic, and (iii) they do not require any previous linguistic knowledge. Extensive experiments show that both are extremely effective and reliable. The former outperforms all comparable approaches in the state-of-the-art, both in terms of performance (Precision stays at 100% or nearly so for a large portion of the top-ranked candidate stopwords, while Recall is quite close to the maximum reachable in theory.) and in smooth behavior (Precision is monotonically decreasing, and Recall is monotonically increasing, allowing the experimenter to choose the preferred balance.). The latter is more flexible than existing solutions in the literature, requiring just one parameter intuitively related to the balance between Precision and Recall one wishes to obtain.
Be Specific, Be Clear: Bridging Machine and Human Captions by Scene-Guided Transformer

Automatically generating natural language descriptions for images, i.e., image captioning, is one of the primary goals for multimedia understanding. The recent success of deep neural networks in image captioning has been accompanied by region-based bottom-up-attention features. Region-based features are representative of the contents of local regions while lacking an overall understanding of images, which is critical to more specific and clear language expression. Visual scene perception can facilitate overall understanding and provide prior knowledge to generate specific and clear captions of objects, object relations, and overall image scenes. In this paper, we propose a Scene-Guided Transformer (SG-Transformer) model that leverages the scene-level global context to generate more specific and descriptive image captions. SG-Transformer adopts an encoder-decoder architecture. The encoder aggregates global scene context as external knowledge with object region-based features in attention learning to facilitate object relation reasoning. It also incorporates high-level auxiliary scene-guided tasks towards more specific visual representation learning. Then the decoder integrates both object-level and scene-level information refined by the encoder for an overall image perception. Extensive experiments on MSCOCO and Flickr30k benchmarks show the superiority and generality of SG-Transformer. Besides, the proposed scene-guided approach can enrich object-level and scene graph visual representations in the encoder and generalize to both RNN- and Transformer-based architectures in the decoder.
Achievable Resolution Limits for the Noisy Adaptive 20 Questions Problem

We study the achievable performance of adaptive query procedures for the noisy 20 questions problem with measurement-dependent noise over a unit cube of finite dimension. The performance criterion that we consider is the minimal resolution, defined as the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L$</tex> <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</inf> norm between the estimated and the true values of the random location vector of a target, given a finite number of queries constrained by an excess-resolution probability. Specifically, we derive the achievable resolution of an adaptive query procedure based on the variable length feedback code by Polyanskiy et al. (TIT 2011). Furthermore, we verify our theoretical results with numerical simulations and compare the performance of our considered adaptive query procedure with that of certain state-of-the-art algorithms, such as the sorted posterior matching algorithm by Chiu and Javadi (ITW 2016). In particular, we demonstrate that the termination strategy adopted in our adaptive query procedure can significantly enhance the asymptotic performance of adaptive query procedures, especially at moderate to large excess-resolution probability constraints.