Reviewerly/exHarmony huggingface dataset:
Conceptualization, Modeling, Visualization, and Evaluation of Specialized Domain Ontologies for Nano-energy as a Domain

The growing demand for data to be defined in a way that is understandable by both computers and humans has encouraged researchers to look at ontologies. Nano-energy is an area where there are currently no ontologies; as a result, an ontology in this domain was thought to be essential. This paper provides a nano-energy ontology model from the energy area viewpoint, focusing on sustainable energy. It is the outcome of detailed domain analysis. WebVOWL is used to visualize the ontology, and WebProtege is used to convert it to owl format. Ultimately, the semantic approach was used to test the domain ontologies in terms of both quantity and quality, and with the best, reuse ratio in a class of 0.012 is obtained.
MRDA-Net: Multiscale Residual Dense Attention Network for Image Denoising

Deep learning methods have become prevalent in image denoising. Existing deep-learning approaches can mainly be divided into two categories: encoder-decoder and high-resolution models, wherein high-resolution models have superior resolution capabilities in detail description and restoration. In this study, we propose a network, namely, multi-scale residual dense attention network, that takes advantage of the context and attention information of images. Specifically, a residual dilated dense module containing dilated dense convolutional layers is employed to enlarge the receptive field of the proposed network. Then, we train such module to learn multi-scale features of images. A feature aggregation module is sequentially designed with dual attention blocks, yielding multi-scale feature maps effectively. We aggregate the multi-scale maps by a concatenation operation. Finally, a simple convolutional layer is adopted to generate residual images. With the residual learning strategy, clean images are obtained implicitly. During the experiment tests, we firstly carry out ablation studies to demonstrate the function of each proposed module. Then, blind and non-blind denoising procedures are carried out. Additionally, the total parameters are considered for analyzing the complexity of the proposed network. Comprehensive experiments show our method is better than several selected state-of-the-art denoising ones by the measure of PSNR and SSIM.
Eye-movement Analysis and Prediction using Deep Learning Techniques and Kalman Filter

Eye movement analysis has gained significant atten-tion from the eye-tracking research community, particularly for real-time applications. Eye movement prediction is predominantly required for the improvement of sensor lag. The previously introduced eye-movement approaches focused on classifying eye movements into two categories: saccades and non-saccades. Al-though these approaches are practical and relatively simple, they confuse fixations and smooth pursuit by putting them up within the non-saccadic category. Moreover, Eye movement analysis has been integrated into different applications, including psychology, neuroscience, human attention analysis, industrial engineering, marketing, advertising, etc. This paper introduces a low-cost eye-movement analysis system using Convolutional Neural Network (CCN) techniques and the Kalman filter to estimate and analyze eye position. The experiment results reveal that the proposed system can accurately classify and predict eye movements and detect pupil position in frames, notwithstanding the face tracking and detection. Additionally, the obtained results revealed that the overall performance of the proposed system is more efficient and effective comparing to Recurrent Neural Network (RNN).
Automatic Sickle Cell Anaemia Detection Using Image Processing Technique

Sickle cell anaemia is a congenital red blood cell disorder which depicts an absence of sufficient red blood cells that are fit for transporting oxygen all over body. Usually, ordinary red bloods are disc shaped. But in case of sickle cell anaemia (SCA), the red blood cell is formed in crescent shape instead of disc shapes. A novel methodology is introduced in this study to detect, classify and count the sickle cells available in the red blood cell images to start early identification and treatment. In the developed methodology, the sample images of blood containing both normal and sickle cells are collected. The pre-processing consists of grey scale image conversion and noise filtering using median filter. Watershed segmentation aids in partitioning the images distinctly so that normal cells and sickle cells can be detected. Region properties are calculated to find the number of sickle cells in the given image. Amongst the various region properties, centroid plays an important role for the detection and counting the number of sickle cells available in the image. This automated recognition will especially be useful to the medical experts as a decision support system.
Offline Handwritten Character Recognition of Gujarati Characters Using Convolutional Neural Network

Handwritten character recognition (HCR) system recognizes characters in handwritten documents. There are two types of the HCR—Online handwritten character recognition, which uses data from real-time machines, and offline handwritten character recognition, which uses data from scanned documents or photographs. As different people have different ways of writing, offline handwriting recognition is difficult in comparison. A number of studies have already been conducted in different languages such as Hindi, Kannada, Telugu, Tamil, Bangla etc. However, there is still an insufficient quantity of academic work done on Gujarati script, which is the primary purpose of this paper. In this study, we present a novel image character dataset in Gujarati, which has 60 images per character. Handwritten Gujarati characters are recognized using the Convolutional Neural Network (CNN). The accuracy of recognizing handwritten Gujarati characters is greater than 90%.
A Novel Method for Cataract Detection and Segmentation Using Nakagami Distribution

Early detection of cataract is considered as an important solution to prevent vision loss. An automatic detection of cataract is proposed in this work with the help of histogram approach. In the beginning, noises occur in an image which is also referred to as impulse noise. To eliminate this noise a non-linear type of median filter is matched especially for the morphological filter. These filtering methods help to extract the content of the image by edge detection and segmentation. The quality of the image is evaluated the image enhancing can be obtained by a histogram approach. A normalization method can be used to enhance the image which is also called Contrast stretching. To make morphological functions effective a top-hat filter is used to segment the cataract part in the given image. Nakagami distributions are usually used for extracting required important information of ultrasound details by matching histograms from the radio frequency signals. The extracted information from the Nakagami distribution is obtained by parameter values. The recent techniques used to improve the given image quality in histogram modification method are done by Intentional Camera Movement (ICM) and Unintentional Camera Movement (UCM) to recognize the real image more precisely. In the proposed method the result shows the noise reduction and a better contrast in the output image through parameters values such as Mean Squared Error (MSE) obtained as 17.23 and Peak-Signal-to-Noise Ratio (PSNR) obtained as 35.8.
Gradient-Based Optimizer for Structural Optimization Problems

Meta-heuristic algorithms are stochastic search methods that have been used for quite a long time to solve complex, non-linear optimization problems for which exact methods are usually very costly or don’t exist at all. Gradient-based optimizer (GBO) is a recently introduced meta-heuristic algorithm that is based on the gradient-based Newton’s method. One component of the GBO algorithm is the Gradient Search rule (GSR) which guides the algorithm to promising regions and helps it converge to the best solution. The other component is the Local Escaping Operator (LEO) which helps the algorithm avoid local optima. To test the performance of the GBO algorithm, we use it to optimize three structural optimization problems. The first problem is to minimize the weight of corrugated bulkheads for a tanker. The second problem is to minimize the cost of a tubular column which is supposed to carry a compressive load. The third problem is to minimize the cost of a simply supported reinforced concrete beam subjected to a specific load. These are complex and constrained problems that test the ability of an optimization algorithm to explore the promising regions of the search space and then converge to find the best solution. To further judge the performance of the GBO algorithm, its best solutions and statistical results for these problems are compared to the results of two other well-known algorithms from the literature.
Deep Neural Sequence to Sequence Lexical Substitution for the Polish Language

The aim of this paper is to investigate the applicability of language models to the problem of lexical substitution in a strongly inflected language. For this purpose, we focus on pre-trained models based on transformer architectures, in particular BERT and BART. We present a solution in the form of the BART-based sequence-to-sequence model. Then we propose and explore a number of approaches to generate an artificial dataset for lexical substitution, using the adapted PLEWiC dataset as a reference. During this study we focus on Polish as an example of a strongly inflected language.
A Proxy Re-signcryption Scheme with Delegation Property

Recently, proxy re-encryption (PRE) cryptosystem has been increasingly suggested in many fields, including the cloud and fog computing paradigm. In the PRE paradigm, in which the only confidentiality of security features of the transmitted message is guaranteed, the proxy entity can re-encrypt the message without exposing the corresponding plaintext. However, other security features such as integrity and authenticity are not guaranteed with using PRE cryptosystem. Thus, in a true assumption, the proxy entity is indicated as a semi-trusted entity, mostly in fog computing where fog nodes, such as the road side unit (RSU), are considered untrusted. The malicious RSU fog node can easily modify the integrity of the encrypted message. This paper proposes a proxy re-signcryption scheme with delegation property (PRSCD), in which the security features of data integrity, confidentiality, and authenticity are completely guaranteed during the transmission process. Thus, the PRSCD is proved in CCA on security models. An analysis of security illustrates that the PRSCD is able to prevent a malicious RSU fog node from performing a number of security challenges.
Towards an Enhanced Framework to Facilitate Data Security in Cloud Computing

Cloud- and associated edge computing are vital technologies for online sharing of computing resources with respect to processing and storage. The SaaS provisioning of services and applications on a pay-per-use basis removes the responsibility of managing resources from organisations which in turn translates to cost savings by reducing capital expenditure for such organisation. Naturally, any online and distributed environment incurs security challenges, and while ordinary users might not be concerned by the unknown whereabouts of their data in the cloud, the opposite may hold for organisations or corporates. There are numerous interventions that attempt to address the challenge of security on the cloud through various frameworks, yet cloud security remains a challenge since the emergence of cloud technology. This research highlights and critically analyses the structure of and mechanisms associated with three prominent cloud security frameworks in the literature to evaluate how each of them addresses the challenges of cloud security. On the strength of a set of qualitative propositions defined from the analyses, we develop a comprehensive cloud security framework that encompasses some components of the studied frameworks, aimed at improving on data and information security in the cloud.
Towards Bridging the Gap Between Knowledge Graphs and Chatbots

Chatbots are nowadays being applied widely in different life domains. One major reason for this trend is the mature development process that is supported by large companies and sophisticated conversational platforms. However, the required development steps are mostly done manually while transforming existing knowledge bases into interaction configurations, s.t., algorithms integrated into the conversational platforms are enabled to learn the intended interaction patterns. However, already existing domain knowledge may get vanished while transforming a structured knowledge base into a “flat” text representation without references backwards. In this paper, we aim for an automatic process dedicated to generating interaction configurations for a conversational platform (Google Dialogflow) from an existing domain-specific knowledge base. Our ultimate goal is to generate chatbot configurations automatically, s.t., the quality and efficiency are increased.
ROLE OF ORGANIZATION CULTURE ON ORGANIZATIONAL AGILITY FOR DIGITAL TRANSFORMATION IN XYZ GOVERNMENT ORGANIZATION

The current situation of VUCA, where changes are more directed at customer needs, speed, accuracy, and sustainability. The presence of technology and the almost simultaneous COVID-19 pandemic requires organizations to have agility in the digital transformation process. This change in situation was also brought about by the XYZ organization, an organization engaged in digital security management. The purpose of this research is to be able to create the right model for talent management within the organization in order to encourage organizations to be more agile and able to go through digital transformation well through organizational culture and knowledge sharing. This research was conducted using quantitative methods. Data were obtained through filling out online questionnaires to 259 members of the XYZ organization. The results showed that there was a significant and positive influence of organizational culture on organizational agility. In addition, knowledge sharing has a significant and positive effect on organizational culture. Meanwhile, the perception of organizational support has a significant and positive effect on both knowledge sharing and organizational culture. This research shows that it is important for organizations to build a good organizational culture in supporting each other to become an agile organization in changing situations like today.
Emotional Communication Between Chatbots and Users: An Empirical Study on Online Customer Service System

In a digital environment, chatbots act as customer service agents to assist consumers in making decisions. Improving the service efficiency of chatbots has aroused widespread concern in the industry and academia. Based on the computer as a social actor (CASA) and emotional contagion theory, this study explores the influence mechanism of quality assessment in the process of communication between users and human-machine customer service, investigates the relationship between chatbot performance and user perception, and constructs an assessment model for the quality of communication process. It adopts partial least squares (PLS) structural equation modelling (SEM) to evaluate the research model and hypothesis. Based on 163 samples, the results show that, in the process of communication, users’ perception of the robot’s ability, especially the accuracy and effectiveness of the robot, will significantly affect users’ evaluation of the quality of communication. At the same time, the language style of the chatbot has little impact on the evaluation of the quality of communication. The results of this study provide important insights into the rational use of human-computer interaction in e-commerce and lay a foundation for understanding the service mechanism and related theories of online service agents in artificial intelligence.
Automatic Modulation Classification Based on One-Dimensional Convolution Feature Fusion Network

Abstract Deep learning method has been gradually applied to Automatic Modulation Classification (AMC) because of its excellent performance. In this paper, a lightweight one-dimensional convolutional neural network module (OnedimCNN) is proposed. We explore the recognition effects of this module and other different neural networks on IQ features and AP features. We conclude that the two features are complementary under high and low SNR. Therefore, we use this module and probabilistic principal component analysis (PPCA) to fuse the two features, and propose a one-dimensional convolution feature fusion network (FF-Onedimcnn). Simulation results show that the overall recognition rate of this model is improved by about 10%, and compared with other automatic modulation classification (AMC) network models, our model has the lowest complexity and the highest accuracy.
From Scene Flow to Visual Odometry Through Local and Global Regularisation in Markov Random Fields

We revisit pairwise Markov Random Field (MRF) formulations for RGB-D scene flow and leverage novel advances in processor design for real-time implementations. We consider scene flow approaches which consist of data terms enforcing intensity consistency between consecutive images, together with regularisation terms which impose smoothness over the flow field. To achieve real-time operation, previous systems leveraged GPUs and implemented regularisation only between variables corresponding to neighbouring pixels. Such systems could estimate continuously deforming flow fields but the lack of global regularisation over the whole field made them ineffective for visual odometry. We leverage the GraphCore Intelligence Processing Unit (IPU) graph processor chip, which consists of 1216 independent cores called tiles, each with 256 kB local memory. The tiles are connected to an ultrafast all-to-all communication fabric which enables efficient data transmission between the tiles in an arbitrary communication pattern. We propose a distributed formulation for dense RGB-D scene flow based on Gaussian Belief Propagation which leverages the architecture of this processor to implement both local and global regularisation. Local regularisation is enforced for pairs of flow estimates whose corresponding pixels are neighbours, while global regularisation is defined for flow estimate pairs whose corresponding pixels are far from each other on the image plane. Using both types of regularisation allows our algorithm to handle a variety of in-scene motion and makes it suitable for estimating deforming scene flow, piece-wise rigid scene flow and visual odometry within the same system.
Mobile Applications in Education

The purpose of the study is to consider the features of the use of mobile applications for the development of professional competencies, in particular cognitive skills, and their general impact on the effectiveness of learning. There were 845 students from 2 countries participating in the research. The students were divided into two groups: one group was trained in accordance with the curriculum developed based on mobile applications while the curriculum of the second group was not changed. The results of the reinterview showed a significant increase in the cognitive abilities of respondents in Group 1 as 81% of them indicated a strong effect of the use of mobile applications on the development of cognitive skills. The analysis of the results also showed an increase in the students' motivation to use mobile applications as compared to the survey conducted at the first stage the list of professional competencies and percentage changed.
Smart Speakers and Functional Diversity: A Scoping Review

Introduction Smart speakers (SS) are widely spread in contemporary society. Artificial intelligence in these conversational devices, empowered by virtual assistants and connectivity with different equipment, enable solutions for adapting more accessible smart environments for those with functional diversity; Objectives To map the evidence on the use of SS by people with disabilities, describing its potential and possibilities for intervention; Methods A scoping review based on articles extracted from PubMed/Medline, B-On, Scopus, and Web of Science was performed in February 2021. Peer-reviewed articles published from 2016 to 2021, in Portuguese and English, were scrutinised. Included studies focused on using SS available in the general market (Google Nest/Home, Amazon Echo, Invoke, HomePod) as an intervention agent in the home or Environment Control Units (ECU), in populations with diverse conditions, covering individuals with sensory, cognitive, emotional and motor impairments. Results Different approaches and user requirements were identified. The devices were applied as an only solution or as an element of more complex systems, interacting with other devices or software; Conclusions These devices have diverse intervention potential with proven functional improvements for people with physical, cognitive, sensory, and emotional limitations. SS can replace expensive devices with closed systems created for niche populations aimed at specific forms of disability. Since customisation is a relevant feature, the application of SS does not dispense technical support in the process.
Application of GOA-GDOP in aircraft collaborative detection

The cooperative detection of aircraft has been widely concerned by scholars at home and abroad in recent years. This paper mainly studies the positioning accuracy of the sensor-carrying aircraft in collaborative detection. By using Grasshopper Optimisation Algorithm (GOA), Taking Geometrical Dilution of Precision (GDOP) as the objective function, the relative position relationship between aircraft is determined, the optimal detection configuration is planned, and the feasibility of the algorithm is analyzed under limited flight area conditions, the positioning accuracy of collaborative detection is further improved.
Structural Supervision for Word Alignment and Machine Translation

Syntactic structure has long been argued to be potentially useful for enforcing accurate word alignment and improving generalization performance of machine translation.Unfortunately, existing wisdom demonstrates its significance by considering only the syntactic structure of source tokens, neglecting the rich structural information from target tokens and the structural similarity between the source and target sentences.In this work, we propose to incorporate the syntactic structure of both source and target tokens into the encoder-decoder framework, tightly correlating the internal logic of word alignment and machine translation for multitask learning.Particularly, we won't leverage any annotated syntactic graph of the target side during training, so we introduce Dynamic Graph Convolution Networks (DGCN) on observed target tokens to sequentially and simultaneously generate the target tokens and the corresponding syntactic graphs, and further guide the word alignment.On this basis, Hierarchical Graph Random Walks (HGRW) are performed on the syntactic graphs of both source and target sides, for incorporating structured constraints on machine translation outputs.Experiments on four publicly available language pairs verify that our method is highly effective in capturing syntactic structure in different languages, consistently outperforming baselines in alignment accuracy and demonstrating promising results in translation quality.