Reviewerly/exHarmony huggingface dataset:
Research on Key Information Recognition System Based on Bayesian Classification

In order to improve the efficiency of image content retrieval, combined with computer vision technology, an intelligent key information detection and recognition system applied to images is proposed. The system is divided into four parts: positioning the text area, detecting key information, recognizing key information and broadcasting voice. Firstly, the image is preprocessed to obtain a clear binary image. Secondly, the dynamic line segmentation algorithm is used to obtain the candidate text areas of the image, and the non-text areas are filtered out with the revelatory rule. Then, the Bayesian classification algorithm is used to discriminate the candidate text region to obtain the key information. Finally, the image of key information is converted into editable text through Optical Character Recognition technology, and the text is converted into speech using text-to-speech conversion technology. The experimental results show that the recognition system of key information is simple and efficient, and the classification accuracy is good, which is suitable for image key information recognition.
Variational Bayesian Adaptive Kalman Filter for Integrated Navigation with Unknown Process Noise Covariance

In the application of integrated navigation by using Inertial measurement Units (IMUs) and Global Navigation Satellite System (GNSS), magnetometers and barometer, the process noise of IMU s is usually unstable and considered to be unknown, because temperature and vibration may cause the divergence or an incorrect result of Kalman filter algorithm, especially with the low-cost sensors. To solve the problem of unstable process noise covariance, a Variational Bayesian Adaptive Kalman Filter for integrated navigation is presented in this paper. Given that the conjugate prior distribution of unknown process noise covariance is assumed to be inverse Wishart distribution, Variational Bayesian (VB) method can estimate system state and process noise covariance simultaneously. Based on the idea of VB method, the novel Variational Bayesian adaptive Extended Kalman Filter (VBEKF -P) and Variational Bayesian adaptive Unscented Kalman Filter (VBUKF - P) are implemented and applied to compensate the instability. The comparison experimental results show that VBEKF-P and VBUKF-P can guarantee estimation accuracy and stability under the condition of underestimated process noise covariance, but with the expense of five to ten times of computational complexity.
Remote sensing image denoising technology based on FFDNet model

Convolutional neural network is widely used in the field of image denoising, and FFDNet model has excellent performance in the field of image denoising. The denoising of remote sensing image is also one of the most basic preprocessing methods of remote sensing image. In this paper, FFDNet model is applied to remote sensing image denoising. Select a remote sensing image data set (UC combined land use data set), replace the natural noise with additive Gaussian white noise (AWGN), process it with different methods, compare it with DnCNN and VDNet, and analyze the comparison results. FFDNet has better performance.
Evolving Deep Parallel Neural Networks for Multi-Task Learning

Multi-Task Learning (MTL) can perform multiple tasks simultaneously with a single model, and can achieve competitive performance for each individual task. In recent years, the Deep Neural Networks (DNNs) based models have demonstrated their advantages in the field of MTL. Yet, most of such models are commonly manually designed with expertise through performing trial and error experiments, which is prohibitively ineffective. In view of this, we design a method based on evolutionary algorithm in this paper, named EVO-MTL, to automate the parallel DNN architectures for effectively addressing the MTL problems. Specifically, our main idea is to evolve the connections between the parallel task-specific backbone networks, and then leverage the useful information contained in the tasks by fusing the task-specific features. In order to verify the effectiveness of the proposed algorithm, the experiments are designed to compare with recent MTL methods including the manually designed and automatically designed. The experimental results demonstrate that the proposed algorithm can outperform the carefully hand-designed methods. In addition, the proposed algorithm can also attain promising competitive performance in balancing multi-task conflicts compared with the DNN architecture searched by state-of-the-art automated MTL method.
Multi-step Cooperative Control Model and Application for Multi-group Users

The installation of software in university computer room is a very cumbersome thing, which requires a lot of manpower. This paper puts forward the method of remote cooperative control through the information processing of the control end, calling the Winsock interface, transmitting with UDP protocol, broadcasting the control information to the controlled end, and simulating the action at the controlled end, so as to solve the problem of installing software in university computer room System settings. Experimental results show that the proposed method has high practical value.
Fusion of handcrafted and deep transfer learning features to improve performance of breast lesion classification

Computer-aided detection and/or diagnosis schemes typically include machine learning classifiers trained using either handcrafted features or deep learning model-generated automated features. The objective of this study is to investigate a new method to effectively select optimal feature vectors from an extremely large automated feature pool and the feasibility of improving the performance of a machine learning classifier trained using the fused handcrafted and automated feature sets. We assembled a retrospective image dataset involving 1,535 mammograms in which 740 and 795 images depict malignant and benign lesions, respectively. For each image, a region of interest (ROI) around the center of the lesion is extracted. First, 40 handcrafted features are computed. Two automated feature set are extracted from a VGG16 network pretrained using the ImageNet dataset. The first automated feature set is extracted using pseudo color images created by stacking the original image, a bilateral filtered image, and a histogram equalized image. The second automated feature set is created by stacking the original image in three channels. Two fused feature sets are then created by fusing the handcrafted feature set with each automated feature set, respectively. Five linear support vector machines are then trained using a 10- fold cross-validation method. The classification accuracy and AUC of the SVMs trained using the fused feature sets performs significantly better than using handcrafted or automated features alone (p&lt;0.05). Study results demonstrate that handcrafted and automated features contain complimentary information so that fusion together create classifiers with improved performance in classifying breast lesions as malignant or benign.
High Dimensional Convolution Acceleration via Tensor Decomposition

The high-dimensional convolution, in either linear or nonlinear form, has been employed in a wide range of computer vision solutions due to its beneficial smoothing property. However, its full-kernel implementation is extremely slow. We do need a fast algorithm for this important operation. To solve this problem, we propose an acceleration pipeline assembled by three steps: [Formula: see text]-D nonlinear convolution [Formula: see text] [Formula: see text]-D linear convolution [Formula: see text] 1-D dimensional convolution [Formula: see text] 1-D recursive box filter. Thanks to the low computational complexity of box filtering, we speed up the computation significantly. Roughly speaking, our contribution is two-fold: (1) establishing the connection between the high-dimensional convolution acceleration algorithm and tensor decomposition; (2) propose total four acceleration technologies including demultiplexing–blurring–multiplexing framework, convolution decomposition, periodic tensorization and recursively box filtering to compose our acceleration pipeline under the line of the above connection. The effectiveness of these techniques is demonstrated in various comparisons and experiments. The running times of various applications are largely shortened from several minutes to fewer seconds or less.
An Exploration Study On the Dependency Among Vulnerabilities and Bugs

Security vulnerabilities are major defects in software implementation that allow malicious uses to undermine its integrity by triggering crashes, stealing information, or even taking control of the software and its underlying system. Despite the extensive research on vulnerabilities themselves, few studies have been performed on understanding the relations between security vulnerabilities and other bugs, which have attracted attention due to some recently found important vulnerabilities. In this paper, we present an exploration study on the vulnerability-bug relations in two important software projects: Firefox as the representative of browsers, and Red Hat as the representative of operating systems. In the study, we automatically extracted dependencies among vulnerability and bugs and manually investigated the character of such dependencies.
Students' attitudes to e-learning in high school biology instruction

The COVID-19 pandemic has led to a disruption in the regular process of teaching and learning. The inclusion of electronic learning (e-learning) is a measure introduced in order to preserve continuity in school education. E-learning, however, was introduced in response to the state of emergency and the pandemic and not as a pre-planned and well-prepared innovation, thus suggesting the need to research students' attitudes towards the implementation of e-learning in high school biology instruction. 255 students of two Belgrade high schools (the Zemun High School and the Fifth Belgrade High School), in which e-learning was implemented, took part in the study. Processing of the data obtained by means of a questionnaire yielded results indicating that students on the whole have a neutral attitude to e-learning and do not want it to become their sole model of learning. Moreover, they are aware only of some of the benefits provided by e-learning. The results show that there are no statistically significant differences between the attitudes of male and female students to e-learning. Similarly, students' grade, their end-of-semester grade in biology, and rating of how interesting instructional content is, do not affect their attitudes to e-learning. Based on the results, in view of the current situation in terms of health security and the need to optimize the instruction process, further continued training of teachers and students in e-learning and instruction is being planned, as well as the provision of appropriate material and other resources for this purpose.
Recognition of Compound Characters from Degraded Kannada Documents

Mapping to editable fonts in machine interpretable codes such as ASCII/ISO/Unicode of the recognized or labeled images by classifier is one of the challenging tasks and comes under post-processing stage of optical character recognition protocol. Proposed here are sequences of steps that takes any Kannada compound character retrieved from a degraded document and produces editable text of the character images. The key steps of the proposed method are simple preprocessing of degraded documents, segmentation of characters, extraction and ordering of connected components, and mapping to Unicode based on correlation analysis of the character components, and finally, editable texts of characters are obtained. Datasets employed for experimentation include 1866 characters and components in the training data, and 1651 simple, 848 multi-, and 105 complex compound characters. The accuracy of the ordering based on correlation analysis is around 82.55% for simple compound characters, 61.43% for multi-compound characters, and 57.14% for complex compound characters.
Ontology-based approach for vessel activity recognition

Recognising vessel activities like illegal fishing or human trafficking helps focusing resources and allows targeted inspections on suspicious vessels. This paper proposes an ontology-based approach for vessel activity recognition, aligned with prior works in the area of ontology-based activity recognition as well as with existing ontology design pattern including a discussion of the Common Core ontology. Existing definitions of vessel behaviours and activities are reviewed and summarised in a shared conceptualisation. The semantic description is technology-independent, supports portability and allows making a first step towards a combined data- and knowledge-driven vessel activity recognition, for an increased interpretability of information sources. A generic design pattern is proposed to cover more specific existing and potential future vessel activity definitions in a common way. We argue that the modelling and exploitation of uncertain information is a key enabler for vessel activity recognition, given the large variability of vessel behaviours and inherent uncertainty related to their intent.
Related Work and Our Approach

The problem of extracting saturated term-sets for learning domain ontologies from professional texts, describing a subject domain of interest, appeared to be under-researched. Therefore, a broader scale systematic review of the related work has been undertaken for collecting different bits of relevant knowledge about the State-of-the-Art in various fields. These ranged from Ontology Engineering, through Ontology Learning from Texts and Information Science, to Qualitative Research in Social and Medical Sciences. The analysis of these bits of knowledge helped us better understand the research gaps in our field of study.With an aim to narrow these research gaps, a vision of the approach for terminology saturation measurement and detection was proposed. This vision allowed us to formulate the research questions that needed to be answered in order to transform this visionary approach into the method, further implement it in a software pipeline, systematically evaluate the method, and make it ready for technology transfer.
Uncuffed: A Blockchain-based Secure Messaging System

Messaging systems used in critical applications, like communication in autonomous vehicles, increase the security issues that lead faster to breaches, loss, and damage. Additional requirements specify directions towards new solution approaches. The blockchain technology is proposed in this article for a simulation messaging platform based on the Bitcoin protocol to prevent personal data leakage. The selected mechanism takes advantage of the data storage decentralization of blockchain, and provides private interchange of secure messages and images. Attempts to obtain valuable data are halted by the proposed UNCUFFED system that supports two groups of users, the Miners and the Clients. Miners collect rewards as active blockchain members, whereas Clients send messages out of the platform.
Reversible Image Watermarking Based on Deep Learning

Reversible image watermarking refers to technology that can restore an image to its original state after extracting the watermark. The scheme based on prediction error expansion (PEE) can achieve greater embedding capacity and less image distortion than other methods, so it has been widely researched in recent years. Prediction results of the predictor used by PEE are still not accurate enough, which limits the development of PEE. In this paper, a reversible watermarking predictor based on a deep neural network is proposed. Compared with other predictors, the prediction error histogram generated by our proposed predictor distributes more sharply. At the same time, because of the better prediction results, the watermarked image is closer to the original image. Experimental results show that the proposed method is effective and superior to the existing methods.
Evaluating Citizen Comments in Public Consultations Using Data Mining

research-article Share on Evaluating Citizen Comments in Public Consultations Using Data Mining: Evaluating Citizen Comments in Public Consultations Using Data Mining: Analyzing Legislation Comments for the Greek General Commercial Registry Authors: Eirini Manga Department of Informatics and Computer Engineering / School of Engineering, University of West Attica, Greece Department of Informatics and Computer Engineering / School of Engineering, University of West Attica, GreeceView Profile , Nikitas Karanikolas Department of Informatics and Computer Engineering / School of Engineering, University of West Attica, Greece Department of Informatics and Computer Engineering / School of Engineering, University of West Attica, GreeceView Profile , Catherine Marinagi Department of Sciences, Agricultural University of Athens, Greece Department of Sciences, Agricultural University of Athens, GreeceView Profile , Christos Skourlas Department of Informatics and Computer Engineering / School of Engineering, University of West Attica, Greece Department of Informatics and Computer Engineering / School of Engineering, University of West Attica, GreeceView Profile Authors Info & Claims PCI 2021: 25th Pan-Hellenic Conference on InformaticsNovember 2021 Pages 430–435https://doi.org/10.1145/3503823.3503902Online:22 February 2022Publication History 0citation20DownloadsMetricsTotal Citations0Total Downloads20Last 12 Months20Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Research on path planning of UAV based on Hybrid A<sup>*</sup> algorithm

UAVs are widely used in fields such as industrial inspection, disaster relief and package transportation. Therefore, it has great significance to realize safe and smooth path planning in an unknown dynamic environment. For this reason, this paper designs a path planning system suitable for unknown environment based on the hybrid A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm by the following three modules, the planning module, the navigation module and the control module to achieve the above-mentioned functions. We built a drone model and experimental environment in the gazebo simulator, verified the performance of the system with smoothness as an indicator.
An Interval-Valued Intuitionistic Fuzzy Estimation of the Area of 2D-Figures Based on Pick’s Formula

We propose an interval-valued intuitionistic fuzzy estimation of the interior of a simple closed curve. This work is based on a previous research focused on an iterative procedure for estimation of the area surrounded by a simple closed curve in the real 2D space. Pick’s Formula is employed for calculating the area surrounded by a special types of polygons. The result is a numerical method permitting the implementation of the algorithm in any procedural programming language.
Designing Library Atmospherics for Information Delivery

Learning environment comprises providing physical environment, cultural context in which students learn, and growth and development of institutions at large. However, learning environment is influenced by multiple factors such as institutional factors, physical infrastructure tools, budget, services, ICT, marketing the services, and evaluation of the services. Given the advent of web 2.0 technologies, there is need to provide active and two-way, student centric learning environment for effective learning and gaining of skill set. The chapter explores the aspects of how a conducive learning environment can be created by designing library atmospherics for information delivery.
Human action recognition based on convolutional neural network

In the field of human action recognition, a good action classification algorithm has always been the key to measure the accuracy of action recognition. Since the trajectory of human targets is often complex, the recognition of RGB color images is restricted by many influencing factors, such as light intensity, complex background and angle. In this paper, kinect equipment is used to collect depth image data, and the image is generated into bone data. The Alexnet model in convolutional neural network is used to train and classify the data, which effectively improves the accuracy of action recognition.
The Collective Process Framework DTScrum for Integrating Design Thinking into Scrum

The rapid progression of technological capabilities and fast-changing customer demand forces today’s firms to adapt to a volatile environment and react quickly to change. Thus, the agile software development framework Scrum has already become a generally accepted framework for delivering work in small but consumable increments at a fast pace. One facet often neglected is that approaches such as like Scrum are not yet able to cope with ill-defined problems. That is to say, while software development approaches aim at developing software products iteratively and incrementally, we often still need to shift our attention first on framing the actual problem. In this context, one design discipline can help to unveil the real problem, define it, and put it into a clear customer requirement. This discipline is more known as Design Thinking and originates from the search to complement the arts and sciences. Design Thinking is utilizing knowledge from both professions alike, but in ways that are peculiarly adapted to the problems of the digital age. During the last years, it is also receiving much attention in the Software Engineering community. Despite showing obvious similarities with agile software development, little is yet known how to make effective use of Design Thinking in the context of agile approaches. In this contribution, it will be depicted how the basic principles and concepts of Design Thinking and Scrum cohere on a conceptual level while addressing the various, and to some extent, competing views and needs emerging from the professional environment. Important here are the synergies between problem understanding, reflected by Design Thinking, and problem-solving, reflected by Scrum.
Detectroops - A Street Mascarar

The proposed Detectroops system would promote public safety by measuring a person&#x0027;s temperature and assessing whether or not they are wearing a mask in public settings. The person will be able to enter public space after sterilising himself and meeting the requirements. By doing so, on every occasion the count of people attending will be taken into account. On exceeding the count limit, the officials will be notified till then they would not be allowed to enter. All of these systems are connected to a single system, which will be controlled and monitored by an official. We try to implement Detectroops by using various technologies like Deep Learning, Internet of Things. Since it is not possible to have everything under control, Detectroops are crucial in limiting the virus&#x0027;s spread and maximizing the safety of the people.