Reviewerly/exHarmony huggingface dataset:
Ottoman OCR: Printed Naskh Font

We present an OCR tool developed for printed Ottoman documents in naksh font as part of a project named End-to-End Conversion of Ottoman Documents to Modern Turkish This tool uses a deep learning model trained with a data set containing original and synthetic documents. We conducted an experimental comparison of this tool named Osmanlica.com with Tesseract Arabic, Tesseract Persian, Abby Finereader, Miletos and Google Docs OCR tools (or models) using a test data set comprised of 21 pages of original documents. With 88,64% raw, 95,92% normalized and 97,18% joined character recognition accuracy rates, Osmanlica.com outperformed the other tools with a marked difference. Osmanlica.com also achieved 58% word recognition accuracy which is the only rate over 50% among the OCR tools compared. We shared the test data set, ground truth, OCR outputs and the test program written in Python using difflib at osmanlica.com/test for independent verification.
Extractive Text Summarization Based on Selectivity Ranking

Extractive summarization of text documents deals with automatic creation of a summary by combining the most salient sentences extracted from the original text document into a more concise form. In this paper, we introduce a novel graph-based method for extractive summarization that transforms a given text into a graph of interconnected sentences and employs computationally efficient selectivity measure to measure the importance of graph nodes. In turn, the text summary is build upon the sentences corresponding to the most important graph nodes. The edges in the graph are based on three measures of similarity between sentences, i.e., Mihalcea’s, Jaccard and Cosine. The first among the three leads to best performance, which also compares favorably to the performance of three alternative summarization methods of TF-IDF, centroid and TextRank. The results confirm that selectivity of nodes in a text graph build using the Mihalcea’s similarity between sentences is a computationally efficient and well performing method for extractive summarization.
Processes, Systems &amp; Tests: Defining Contextual Equivalences

In this position paper, we would like to offer and defend a new template to study equivalences between programs -- in the particular framework of process algebras for concurrent computation.We believe that our layered model of development will clarify the distinction that is too often left implicit between the tasks and duties of the programmer and of the tester. It will also enlighten pre-existing issues that have been running across process algebras as diverse as the calculus of communicating systems, the $\pi$-calculus -- also in its distributed version -- or mobile ambients.Our distinction starts by subdividing the notion of process itself in three conceptually separated entities, that we call \emph{Processes}, \emph{Systems} and \emph{Tests}. While the role of what can be observed and the subtleties in the definitions of congruences have been intensively studied, the fact that \emph{not every process can be tested}, and that \emph{the tester should have access to a different set of tools than the programmer} is curiously left out, or at least not often formally discussed.We argue that this blind spot comes from the under-specification of contexts -- environments in which comparisons takes place -- that play multiple distinct roles but supposedly always \enquote{stay the same}.We illustrate our statement with a simple Java example, the \enquote{usual} concurrent languages, but also back it up with $\lambda$-calculus and existing implementations of concurrent languages as well.
An efficient multiclass classifier for classification of Alzheimer's disease/mild cognitive impairment/Normal subjects

Abstract Typically, in sparse representation‐based classifiers, the weight associated with each training sample is ignored, resulting in reduced accuracy. Moreover, individual binary classifiers solved a multiclass problem. It requires more time as multiple runs are needed to compute the accuracy. In this paper, we propose a novel optimal sparse representation‐based classifier. It solves the ternary classification problem with improved accuracy in a single run. The ternary classification considers Alzheimer's disease versus mild cognitive impairment versus normal control in a single run. A two‐stage sparse representation model is used to design the proposed classifier. To update the weight coefficients, we suggest a regularized Levenberg–Marquardt learning. It allows selecting a subset of significant training samples. To determine the appropriate subset size, we investigate an objective function in terms of classification accuracy. For optimization, we suggest a hybrid particle swarm optimization–squirrel search technique. The experiment conducted on the Alzheimer's Disease Neuroimaging Initiative database shows our method outperforms other state‐of‐the‐art methods in terms of computation time and accuracy. The use of different training–testing partition ratios makes the proposed method immune to biased results, overfitting, and underfitting difficulties. Moreover, results are obtained from 100 iterations to confirm its stability. The suggested model may be helpful for further research in medical image analysis.
A Multi-factor Approach for Cloud Security

Cloud computing is known for its complexity regarding different models of deployment and services that it offers. However, security remains a massive hindrance in its development. Hence, a multi-factor approach to secure the cloud environment proposed by this paper relies on authentication and auditing as the fundamental elements for sustaining the privacy of information in the cloud environment. These are needful assets to counter various threats and attacks on the cloud service provider as well as at the user end. This paper proposes a multi-factor approach through which a user's identity is verified securely; as well as a means to build trust between the client and the cloud service provider by allowing proper visibility of the user's activities.
Identifying Technology Trends for Blockchain Applications in Industry 4.0 Domain: A Patent Perspective

This study aims to identify the technology trends for the application of Blockchain technology across the industry 4.0 technological landscape. The study achieves the aim using patent descriptive analysis. The study highlights the patent publication trends, major technological categories for the collected data, along with citation analytics for top patents as well as companies. Major technological trends are Blockchain applications across Internet of Things, Artificial Intelligence and record management systems using cloud architectures.
List of Tony Hoare’s Publications

chapter Share on List of Tony Hoare’s Publications Author: Cliff Jones Search about this author Authors Info & Claims Theories of Programming: The Life and Works of Tony HoareOctober 2021 Pages 395–410https://doi.org/10.1145/3477355.3477375Online:02 October 2021Publication History 0citation2DownloadsMetricsTotal Citations0Total Downloads2Last 12 Months2Last 6 weeks0 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 AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Assessing the Role of Gaze Tracking in Optimizing Humans-In-The-Loop Telerobotic Operation Using Multimodal Feedback

A key challenge in achieving effective robot teleoperation is minimizing teleoperators' cognitive workload and fatigue. We set out to investigate the extent to which gaze tracking data can reveal how teleoperators interact with a system. In this study, we present an analysis of gaze tracking, captured as participants completed a multi-stage task: grasping and emptying the contents of a jar into a container. The task was repeated with different combinations of visual, haptic, and verbal feedback. Our aim was to determine if teleoperation workload can be inferred by combining the gaze duration, fixation count, task completion time, and complexity of robot motion (measured as the sum of robot joint steps) at different stages of the task. Visual information of the robot workspace was captured using four cameras, positioned to capture the robot workspace from different angles. These camera views (aerial, right, eye-level, and left) were displayed through four quadrants (top-left, top-right, bottom-left, and bottom-right quadrants) of participants' video feedback computer screen, respectively. We found that the gaze duration and the fixation count were highly dependent on the stage of the task and the feedback scenario utilized. The results revealed that combining feedback modalities reduced the cognitive workload (inferred by investigating the correlation between gaze duration, fixation count, task completion time, success or failure of task completion, and robot gripper trajectories), particularly in the task stages that require more precision. There was a significant positive correlation between gaze duration and complexity of robot joint movements. Participants' gaze outside the areas of interest (distractions) was not influenced by feedback scenarios. A learning effect was observed in the use of the controller for all participants as they repeated the task with different feedback combination scenarios. To design a system for teleoperation, applicable in healthcare, we found that the analysis of teleoperators' gaze can help understand how teleoperators interact with the system, hence making it possible to develop the system from the teleoperators' stand point.
Trust in Smart Personal Assistants: A Systematic Literature Review and Development of a Research Agenda

Smart Personal Assistants (SPA) fundamentally influence the way individuals perform tasks, use services and interact with organizations. They thus bear an immense economic and societal potential. However, a lack of trust - rooted in perceptions of uncertainty and risk - when interacting with intelligent computer agents can inhibit their adoption. In this paper, we conduct a systematic literature review to investigate the state of knowledge on trust in SPAs. Based on a concept-centric analysis of 50 papers, we derive three distinct research perspectives that constitute this nascent field: user interface-driven, interaction-driven, and explanation-driven trust in SPAs. Building on the results of our analysis, we develop a research agenda to spark and guide future research surrounding trust in SPAs. Ultimately, this paper intends to contribute to the body of knowledge of trust in artificial intelligence-based systems, specifically SPAs. It does so by proposing a novel framework mapping out their relationship.
Augmented Reality Application for Solar System Learning: A Research in Progress

This paper presents a research in progress of a mobile application development that features the use of Augmented Reality (AR). In recent times, the use of technology in education has gained much interest among researchers and practitioners. Augmented Reality (AR) is one of the technologies that has attracted most attention. The use of AR has been shown to produce positive improvement to both teachers and students in a learning environment. For students, the use of AR provides them with an immersive environment thus making their learning experience more enjoyable. AR based learning application may also help teachers in explaining complex topics such as the concept of solar system. Thus, this paper proposes a development of a mobile application to aid students in learning process with the help of AR. The development of this mobile application utilizes the solar system topic as a medium of proof of concept.
Single Image Dehazing in 3D Space with More Stable GANs

In this paper, an end-to-end generative adversarial networks in the 3D space (3D-DehazeGAN) to recover the hazy-free images is proposed. To improve the performance of the GAN, a new improving method by adopting the integral control is proposed to decrease the steady state error and enhance its stability. To take advantage of existed networks' excellent dehazing ability of thin haze, a convolutional neural network with 3D convolution layers is proposed as the dehazing module of 3D-DehazeGAN. 3D-DehazeGAN completes the dehazing task by removing local thin haze constantly. To realize the mutual mapping of 2D and 3D space, dimension extension and dimension reduction modules are designed. The experimental results demonstrated 3D-DehazeGAN obtained higher SSIM and PSNR scores compared to 2D-DehazeGAN dehazing with 2D convolution layers and the new improving method ensured a faster convergent rate and better valid performances compared to other methods.
Scene text detection with improved receptive field and adaptive feature fusion

Regression-based text detection methods are currently the research focuses due to their simple network structure and fast inference speed. However, most of them suffer from limited receptive field of convolutional neural network and simplistic feature-fusing in feature pyramid. As a consequence, the previous algorithms still have many shortcomings, such as difficulty in accurately detecting long texts and the inconsistency across different feature scales. To address these two problems, we first incorporate a densely connected atrous convolutional module into the feature extraction network, accordingly the receptive field is enlarged, and in turn the extraction of high-level semantic information is strengthened. Secondly, we weight and re-fuse the features from different levels of the feature pyramid, which can filter conflicting information at various levels to maintain the scale invariance of features. Extensive experiments have been made on ICDAR2015 and MSRA-TD500 datasets, and the experimental results have proved the effectiveness of the method.
A Model-Based Approach to Solve the Sparse Reward Problem

For reinforcement learning (RL) algorithms, the sparsity of reward has always been a problem to be solved. Because reinforcement learning cannot get effective feedback in most cases, the agent is difficult to learn effectively. We propose a model-based algorithm to create extra rewards and increase reward density to make it easy to learning. To reshape rewards, we use model error as an extra reward and add it to the return. We test our approach in the Google Research Football Environment, and our algorithm gets good results.
Autonomous Aerial Dual-Target Following Among Obstacles

In contrast to recent developments in online motion planning to follow a single target with a drone among obstacles, a multi-target case with a single chaser drone has been hardly discussed in similar settings. Following more than one target is challenged by multiple visibility issues due to the inter-target occlusion and the limited field-of-view in addition to the possible occlusion and collision with obstacles. Also, reflecting multiple targets into planning objectives or constraints increases the computation load and numerical issues in the optimization compared to the single target case. To resolve the issues, we first develop a visibility score field for multiple targets incorporating the field-of-view limit and inter-occlusion between targets. Next, we develop a fast sweeping algorithm used to compute the field for the suitability of real-time applications. Last, we build an efficient hierarchical planning pipeline to output a chasing motion for multiple targets ensuring key objectives and constraints. For reliable chasing, we also present a prediction algorithm to forecast the movement of targets considering obstacles. The online performance of the proposed algorithm is extensively validated in challenging scenarios, including a large-scale simulation, and multiple real-world experiments in indoor and outdoor scenes. The full code implementation of the proposed method is released here: https://github.com/icsl-Jeon/dual_chaser.
Breast masses classification using contour shape descriptors based on Beam Angle Statistics

Mammography is an important diagnostic tool for early detection of breast cancer. Masses in mammogram are characterized by their shape and margin of their contours. A typical contour of benign mass is round and oval, whereas a malignant mass has irregular and spiculated. Many studies in literature have shown that features extracted from 1D signature of 2D mass contour can distinguish benign and malignant lesion. The features extracted from 1D signature results in low dimensional feature vector and in turn doesn't require feature selection step for classification. The proposed research study applies a Beam angle statistics (BAS) to convert 2D contour into 1D signature. Further, the features are extracted from the 1D BAS signatures in order to classify masses. K-nearest neighbor (KNN), support vector machines (SVM), Adaboost decision tree and artificial neural networks (ANN) are employed to categorize breast masses into benign and malignant using features of BAS signature. The proposed methodology was tested on 147 contours, obtaining accuracy of 88.8% accuracy. The proposed system was also compared with recent literature described related breast cancer analysis.
Designing Plots for Multiplayer Games with the Use of Graph Transformation Rules

The paper presents a new application of the graph model of the game world and graph rules describing players’ actions in this world. This time, this model and rules are used to design storylines in multiplayer role-playing games. The proposed model is suitable for collaborative plot design, as the set of rules describing game actions can be divided into subsets corresponding to particular quests, which are then assigned to different plot writers. The main contributions of this paper are: firstly, a new type of graph transformation rules for construction of storylines in games that require competition or collaboration between multiple players, and secondly, a prototype networked application to validate the proposed ideas and to serve as a playtesting tool for the designed sets of rules. In the presented application, the graph world model is stored in a Neo4j graph database server. Application instances running on players’ computers communicate with this server. The paper presents the application’s operation on an example of a simple two-player game.
State-of-the-art OCR Algorithm for Translation Guiding: An Online-offline Combinational Model

State-of-the-art OCR algorithm for the translation guiding considering the online-offline combinational model is studied in this paper. The difficulty of the handwritten character recognition is mainly due to the different writing habits and writing styles of different people, to deal with this challenge, this paper proposes the novel clustering based OCR framework. The neural computation is combined for the data structure analysis and the image analytic model. The designed model is applied to the smart translation guiding. The online-offline combinational model is designed and tested to validate the performance.
Learned buffer management

Buffer management is a key issue in database systems and operating systems. Recently, with the advance of machine learning, it has been a new frontier to use machine learning techniques to improve buffer management. This paper makes a preliminary study on learned buffer management, aiming to (1) present a conceptual design for learned buffer management and (2) verify whether a machine learning model can help improve the efficiency of buffer management. We first present a framework for learned buffer management, which includes a feature vector-generating Generator, a labeling Predictor, and a decision-making Replacer. Finally, we conduct a preliminary experiment on two tailor-made workloads and the results suggest the efficiency of the learned buffer manager.
Study of selected properties of red maple wood (Acer rubrum) from the experimental plot of the forest arboretum in Rogów

Study of selected properties of red maple wood (Acer rubrum) form the experimental plot of the forest arboretum in Rogów. As part of the work, investigation on the dendrometric, physical and mechanical properties of red maple trees and its wood from the Forest Experimental Plant in Rogów has been carried out. The obtained results of the research on the species experimentally introduced in Rogów were compared with the features of the Acer rubrum from the area of natural occurrence in North America. The results of the investigation showed that the trees from the Arboretum area have a lower height, a much smaller trunk diameter and their physical and mechanical properties are weaker than the maple wood grown in native conditions. Despite the above statements, the significant influence of the location of the wood in the trunk (distance to the core) on its density, acoustic properties and static modulus of elasticity, bending and compression strength along the fibers are noted.
Ultrasound Variational Style Transfer to Generate Images Beyond the Observed Domain

The use of deep learning in medical image analysis is hindered by insufficient annotated data and the inability of models to generalize between different imaging settings. We address these problems using a novel variational style-transfer neural network that can sample various styles from a computed latent space to generate images from a broader domain than what was observed. We show that using our generative approach for ultrasound data augmentation and domain adaptation during training improves the performance of the resulting deep learning models, even when tested within the observed domain.
An Online Platform for Testing and Evaluating Random Number Generators

This article introduces a new online platform for testing binary random number generators. The growing share of low-complex devices in IoT networks increases the demand for basic authorization and authentication tools, the critical block of which is a secure random number generator. Communication devices, therefore, require designers to carry out time-consuming tests and acquire specialist knowledge of statistical testing in evaluation of their results. To meet the current requirements, we have created a test platform to assess the quality of random strings produced by the generator. The presented solution, based on the proprietary evaluation metric, provides feedback on the properties of the uploaded random sequences. Clear interface provides ease of use and by machine learning in the platform’s backend, along with the increase of processed data, the improved quality of the interpretation delivered by the system is ensured. The operation of the platform has been confirmed experimentally, based on the analysis of hardware generators producing random strings with known properties.