Reviewerly/exHarmony huggingface dataset:
A New Fusion-Based Agricultural Synthetic Aperture Radar Image Despeckling by Using Anisotropic Diffusion and Discrete Wavelet Transform Methods

Abstract In agricultural synthetic aperture radar (SAR), the characteristic depression is due to the speckle-noise, which is of a granular form and is multiplicative in nature, leading to common issues as the features of the images get blurred. Precise agricultural SAR images that are needed for despeckling the speckle-noise are obtained by employing despeckling methods. Preserving the fine details of the agricultural SAR images during the process of speckle-noise reduction is a very challenging task for the researcher. Most of the despeckling methods only smooth the edges but do not enhance the edges. The anisotropic diffusion, also known as Perona-Malik diffusion and two-dimensional discrete wavelet transform, is employed to eliminate the speckle-noise and preserve the fine details of the image. The efficiency and robustness of the proposed method are analyzed by its perceptible characteristics and by employing other analyzing parameters like peak-signal-to-noise ratio, structural similarity index measure, universal image quality index, and root mean square error. The robustness and execution time are determined and compared with typical filters and techniques. The proposed method is more robust and has a better ability to be used in realistic applications.
Performance Evaluation of a Lightweight IoT Authentication Protocol

Ensuring security to IoT devices is important in order to provide privacy and quality of services. Proposing a security solution is considered an important step towards achieving protection, however, proving the soundness of the solution is also crucial. In this paper, we propose a methodology for the performance evaluation of lightweight IoT-based authentication protocols based on execution time. Then, a formal verification test is conducted on a lightweight protocol proposed in the literature. The formal verification test conducted with Scyther tool proofs that the model provides mutual authentication, authorization, integrity, confidentiality, non-repudiation, and accountability. The protocol also was proven to provide protection from various attacks.
Design of coherent quantum observers in the presence of unknown inputs

In this paper, we proceed with our research on coherent quantum observers, with a focus on the scenario where there exist unknown inputs to the quantum plant. In our previous studies, we were mainly concerned with linear quantum plants in the absence of unknown inputs. However, when there are unmodeled dynamics or disturbances in quantum systems, the quantum version of Luenberger observers or Kalman filters might not be adequate. Given that no prior knowledge is assumed for the unknown inputs, we take advantage of the Hautus lemma to improve the robustness in observing a quantum system. More precisely, we consider linear quantum stochastic systems with unknown inputs involved whose dynamics correspond to open quantum harmonic oscillators. We show the procedure regarding synthesis of coherent observers for such systems that accomplish the task of simultaneously estimating system variables together with the unknown inputs, which is also illustrated by a simulation example.
A Data Mining and Ontology-Based Approach for Predicting the Research Ideas in the Wildlife Sector of Sri Lanka

Sri Lanka, is the global biodiversity hotspot, which contains a wide variety of fauna & flora. Also, wildlife is the key player in the tourism industry of Sri Lanka. But still, Sri Lankan wildlife faced many conflicts to protect their biodiversity because of a lack of technical and research support. There were significant research gaps and data resources but still, researchers retreat to select this domain as their study. This study demonstrates a novel approach to data mining to find hidden keywords and automated labeling for past research work in this area. To model topics and recognize the major keywords for future researchers, we used Latent Dirichlet Allocation (LDA) algorithms to build an ontology model to visualize the relationships between each keyword and similar keywords cluster into creative topics. These techniques are also useful for potential research ideas, for identifying hidden research gaps and to classify domain-related publications. The experiment results demonstrate the validity and efficiency as well as it will make a platform for all researchers to interact easily with the wildlife domain.
A State Transition Algorithm with Variable Local Candidate Solution Space and its application to Residue Hydrogenation Fractionation Process

Parameter Optimal State Transition Algorithm (POSTA) is a global metaheuristic method with adaptive parameter adjustment capabilities. However, its fixed local search candidate solution space easily leads to the computational redundancy and the problems that are difficult to jump out of local optimization. Therefore, a novel state transition algorithm with variable local candidate solution space is proposed in this paper, called VLCSSSTA. First, a dynamic logic of distance control is designed to speed up the search, which adaptively changes the size of the candidate solution space. Then, the larger candidate solutions are randomly generated to enhance the ability to jump out of the local optimum. Statistical research results show that the proposed algorithm has better performance than POSTA and some classic optimization algorithms in five benchmark function experiment. Finally, the proposed method is applied to the operation optimization in residue hydrogenation fractionation (RHF). The experimental results also verify the effectiveness of the proposed method.
A Comparative Study of Dictionary-based and Machine Learning-based Named Entity Recognition in Pashto

Information Extraction (IE) is the process of extracting structured information from unstructured text using natural language processing (NLP). One important sub-task of IE is the extraction of names of persons, places, and organizations, called Named Entity Recognition (NER). NER plays an important role in many NLP applications such as Question Answering, Machine Translation, and Text Summarization. It has been widely studied for high-resource languages like English. However, no research has taken place in this regard for Pashto. We hypothesized that based on the research done for English and other languages in the area of NER a system can be developed for Pashto. We have developed two NER systems for detecting names of persons, places, and organizations in Pashto text. First, a dictionary-based NER that uses three dictionaries containing names of persons, locations, and organizations, respectively. Second, a learning-based approach that uses Hidden Markov Model (HMM) for the task. We have evaluated both systems on a dataset collected from sports news. Our evaluation showed F-Measure of 82% for HMM and 60% for dictionary-based NER. Our findings highlight that HMM outperforms dictionary based NER.
Autonomic Runtime Adaptation Framework for Power Management in Large-Scale High-Performance Computing Systems

Power consumption is one of the most critical issues in the viability and sustainability of exascale computing systems. It is challenging to achieve a quintillion (10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">18</sup> ) computations per second within the sustainable power budget of 20 MW. We need to improve energy efficiency at all HPC ecosystem levels to achieve exascale performance, such as subsystem level, node level, system level, scheduling level, software level, HPC site level, and power supply level. The power consumption and performance are the conflicting optimization concerns and that we need to manage intelligently. Hence, we need an efficient and comprehensive power management framework. In this paper, we have presented an agent-based autonomic runtime adaptation framework (ARAF). We investigated the Q-learning algorithm for devising optimal operating point (V, F) based control policy. The experimental results show that the described control policy provides 5-12% of energy-saving with minimal performance degradation. The proposed architecture offers a comprehensive and efficient power management framework for next-generation HPC systems.
Diagonal Node-based Path Planning and Path Smoothing for First Responders and Rescue Robots

A key point of reducing injury rates for first responders is spending less time to reach a target in a dangerous area. First responders and rescue robots are applied in previous researches using dangerous areas search and obstacle avoidance. Unmanned aerial vehicles (UAVs) are also started to use for disaster management. Therefore, path planning for the first responders and rescue robots are needed the during dangerous area searching using UAVs. Here, path planning algorithms for the first responders and rescue robots are needed to global path planning and it is better to use different kinds of node-based algorithms depending on a known and a partially known environment. An A* algorithm is suitable in the known environment and a D* lite algorithm is proper in the partially known environment. However, long path calculation time is always an issue of the node-based global path planning. For the long path calculation time, a novel diagonal path planning algorithm is proposed in this paper. The main idea of the diagonal path planning is to use diagonal nodes instead of surrounded nodes from current node selection steps. Also, it causes a zig-zag path issue, so a path smoothing with a bounded curvature is proposed to solve the zig-zag issue. The effectiveness of the proposed algorithm is confirmed through simulations.
Using Spatial Temporal Graph Convolutional Network Dynamic Scene Graph for Video Captioning of Pedestrians Intention

Video captioning helps to understand video content at a semantic level. The scene graph method can realize structured representation of image semantics. The scene graph method can provide effective support for video captioning. In the areas of autonomous driving and Advanced Driver Assistance Systems (ADAS), intelligent systems need to make cognitive inferences about pedestrian intentions and behaviors, and the relationships between surrounding objects. This will help the system make accurate decisions in real time. In this paper, we propose a video captioning of pedestrians intention, This algorithm constructs a novel dynamic scene graph based on spatial temporal graph convolution network. The previous scene graph method solved the problem of the semantic structured representation of the image, but did not make good use of the video time series information between front and back images of consecutive frames in video. Such defects cause the existing algorithms to not understand the dynamic behavior of traffic scenes well. Our new method collaboratively extracts visual semantic features from both spatial and temporal, which effectively improves the video reasoning ability. On the video captioning dataset for pedestrian scenes, Experimental results show that novel method can significantly improve the performance of trajectory prediction.
End-to-end control of autonomous vehicles based on deep learning with visual attention

In this paper, we propose an end-to-end controller for self-driving vehicles based on visual attention. Attention strategy is used to weight the high-dimensional feature information extracted by convolutional neural networks (CNNs), and then the vehicle's velocity and steering wheel angle are predicted by different recurrent neural networks (RNNs). The end-to-end controller is trained on Comma.ai dataset and can effectively reduce the mean absolute error (MAE). The result shows that compared with other models, the end-to-end control model based on visual attention can achieve better control effects of vehicle's speed and steering wheel angle.
Audio Public opinion Analysis Model based on heterogeneous Neural Network

With the rapid development of the information age, the main body of public opinion information is no longer just structured data such as text and numbers, but the proportion of unstructured data such as audio and video has increased significantly. Therefore, the analysis and processing of unstructured public opinion data has become the key to deal with the problem of public opinion. Based on this, this paper designs an audio public opinion early warning model based on heterogeneous neural network. Heterogeneous neural network refers to a variety of different types of neural networks to participate in the completion of specified tasks under the premise of data sharing, while retaining the autonomy of each neural network. In this paper, the audio data is converted into a spectrum as an input feature. The convolution neural network can obtain the feature representation of the audio in the spectrum graph, which is used as the input of the recurrent neural network, and then the features related to the text representation are obtained. In this paper, Bert is used to modify the semantic results combined with public opinion domain knowledge, and finally the results are introduced into the shallow neural network Fasttext for public opinion early warning processing. In this paper, experiments are carried out based on the open source voice data set ST-CMDS-20170001-1 Chinese mandarin corpus and Pytorch platform. The results show that this model can effectively analyze and process public opinion of unstructured audio and video data.
Dog Noseprint Identification Algorithm

This paper proposes a dog noseprint identification system based on Gabor filter and feature matching. Given images of dog noseprints, the system determines the region of interest, pre-processes the images using adaptive thresholding, extracts features, and performs feature matching to identify dogs. To extract features, we first apply the Gabor filter with 60 directions to images. Then we employ the scale invariant feature transform (SIFT) feature extractor to obtain keypoints that are invariant to image rotation and scaling. The extracted keypoints are compared with reference key-points of a dog noseprint that needs to be identified. To improve the matching accuracy, we present several matching algorithms. Experiments show that the SIFT based identification system method surpasses other methods in terms of accuracy, while the ORB based on system outperforms other methods in terms of speed.
Designing Execution Models of Distributed System in Theoretical Courses on Information Technology

This chapter is concerned to simulation of distributed systems. There is a large number of specialized software systems designed to build a simulation model of distributed systems and conduct a simulation experiment. The chapter discusses the original software tools for the automated design of simulation models illustrating the various conceptions of educational courses on distributed systems and distributed algorithms.
Light-field imaging for distinguishing fake pedestrians using convolutional neural networks

Pedestrian detection plays an important role in automatic driving system and intelligent robots, and has made great progress in recent years. Identifying the pedestrians from confused planar objects is a challenging problem in the field of pedestrian recognition. In this article, we focus on the 2D fake pedestrian identification based on light-field (LF) imaging and convolutional neural network (CNN). First, we expand the previous dataset to 1500 samples, which is a mid-size dataset for LF images in all public LF datasets. Second, a joint CNN classification framework is proposed, which uses both RGB image and depth image (extracted from the LF image) as input. This framework can fully mine 2D feature information and depth feature information from corresponding images. The experimental results show that the proposed method is efficient to identify the fake pedestrian in a 2D plane and achieves a recognition accuracy of 97.0%. This work is expected to be used in recognition of 2D fake pedestrian and may help researchers solve other computer vision problems.
Recognition System of Activities in Daily Living Based on Deep Sensing of Water Usage

In recent years, the number of elderly people living alone has been increasing with the accelerated aging of society, and shortage of the labors of the elderly care sector becomes a serious problem. In order to solve this problem, the activity observation and recognition systems for the remote elderly care have been proposed. Many of these services often use cameras, and image analysis technology enables highly accurate recognition of activities. However, the possibility of invading the privacy of users and the high cost are regarded as problems.Therefore, in this study, we propose a new recognition system of activities in daily living based on sensing of water usage in the house because the usage pattern of the water is deeply related with the activities of the user in the house (e.g., washing, cooking, taking a bath). In order to avoid privacy issues, the proposed system obtains only information about time change of the water usage at a specific place in the house and estimates the type of activities of the people living in the house by analyzing the time-series data of the water usage utilizing the machine learning technologies. In addition, the proposed system builds a mesh network consisting of a large number of sensor nodes installed in the house using Thread communication.
Design and Development of the Robot for Aquatic Debris Using a Smartphone

Project aims to decrease the contamination in the water which is a very serious concern both for humans and water organisms. It uses a smart fish-type robotic sensor network which continuously monitors the contamination and sends the data to the control unit. The data include the contamination level and the GPS location of the contamination area. The benefits of this project are to protect the lives of water species, save lives of humans, low cost, highly reliable, easy to install and from anywhere can get the information about the contamination level. The main applications of the project are fresh water conserving and monitoring system, sewage disposal system.
WITHDRAWN: A hybrid population based incremental learning algorithm with Particle Swarm Optimization for general threshold Visual Cryptography schemes

Abstract An effortless transfer of multimedia information over the Internet is achieved due to advances in the network’s technology. Commercial identifications, similarity maps, and numerous confidential data can be transferred over the Internet. When dealing with private videos and images, security challenges must be accounted for as hackers steal information by exploiting the weak links across communication networks. There has been the development of numerous schemes for the management of the security challenges of private images and videos. The encryption of a secret image onto n shares, wherein all participants can hold one or more shares, is known as the Visual Cryptography (VC) scheme. In the probabilistic VC scheme, the secret black pixels are reconstructed incompletely thus, the display quality obtained is not consistent with human visual perception. To enhance the display quality, it is required to optimize the threshold VCS. In this work, meta heuristic algorithms, Particle Swarm Optimization (PSO), and Population-Based Incremental Learning (PBIL) are modified to optimize the VCS threshold. PSO’s abilities like simplicity, convergence speed, and optimum global searches, have made it the most frequently-employed evolutionary variant among hybrid techniques. Substitution of a population’s individuals with a set of their statistics forms the basis of the concept behind the PBIL algorithm. Proposal for a hybrid Population-Based Incremental Learning algorithm with Particle Swarm Optimization (PBIL-PSO) has been provided in this work.
Data Hiding Technique Using Differential Evolution

The method of data hiding has become one of the most trustworthy techniques to combat the malicious attacks on communication networks. Among various data hiding methods, watermark has turned one of its foremost technique, in which an image is veiled in a cover image. The paper proposes a Differential Evolution based watermark technique that eases to locate the covert region for data hiding where the unwanted attacks can almost be nullified. Least Significant Bits are used for veiling the watermark. The proposed scheme is carried out in the spatial domain. The experimental results show the competent quality of images are restored even after various attacks, as well as the watermark itself, is still in a recognizable state.
Lessons Learned from Delivering Programing Courses Online for Retraining Serbian Workforce through UNDP Programs – A Joint Project between UCSDX and MISANU

Brief overview of the Serbian IT sector employment situation is given. Through the summary of the past online learning experiences it is shown that online learning can be an effective tool to reeducate the Serbian workforce to serve the countries growing IT employment demands. The nature of the UN Development grants in Serbia is explained together with the details of the joint proposal from the University of San Diego Extension and the Mathematical Institute of the Serbian Academy of Sciences and Arts. The proposal consisted of a group of Java and Python courses that were delivered online in English to 34 students selected from more than 80 people that applied. The results presented show that the program was very effective with an extremely high satisfaction rate both in terms of the content and online delivery platform, and that the similar methods can be used in the future for further education of the IT workforce.
A Quantum Machine Learning Classifier Model for Diabetes

This paper demonstrates the advantages of quantum machine learning by using the quantum kitchen sinks (QKS) algorithm. Quantum machine learning (QML) is implemented along with the QKS to check the accuracy with the help of the hybrid algorithm. By optimizing the quantum learning, the accuracy of the algorithm is compared with the classical machine learning. The objective of the proposed work was to train a simple machine learning algorithm on a hybrid quantum computer/simulator to evaluate whether the hybrid quantum computer would be trained with higher accuracy compared to a conventional computer. The diabetics’ dataset was used to train a logistic regression model as a binary classifier. Two qubits were used for implementing the QML algorithm on a quantum simulator. The Quantum kitchen sinks employ the quantum circuits which are used to nonlinearly tranform standard inputs into features that can be utilized by numerous machine learning algorithms. The features from the dataset were extracted using the quantum kitchen sinks (QKS), which is then fed into a classical logistic regression algorithm for training. This technique of extracting features from quantum techniques and using a classical computer for prediction empowers machine learning to obtain high accuracy.
Self-orthogonal codes constructed from weakly self-orthogonal designs invariant under an action of $$M_{11}$$

In this paper we generalize the construction of binary self-orthogonal codes obtained from weakly self-orthogonal designs described in Tonchev (J Combinat Theory Ser A 52:197-205, 1989) in order to obtain self-orthogonal codes over an arbitrary field. We extend construction self-orthogonal codes from orbit matrices of self-orthogonal designs and weakly self-orthogonal 1-designs such that block size is odd and block intersection numbers are even described in Crnković (Adv Math Commun 12:607–628, 2018). Also, we generalize mentioned construction in order to obtain self-orthogonal codes over an arbitrary field. We construct weakly self-orthogonal designs invariant under an action of Mathieu group $$M_{11}$$ and, from them, binary self-orthogonal codes.