Reviewerly/exHarmony huggingface dataset:
A Novel Method for Multi-UAV Cooperative Reconnaissance Mission Planning in Denied Environment

The traditional swarm intelligence algorithm to solve the path planning in single combat style of unmanned aerial vehicle (UAV) can no longer meet the requirements of multi-UAV cooperative reconnaissance mission planning (MUCRMP) problem in denied environment for its slow convergence rate, ignorance of complex constraints and guidance to local optimization. A novel method for multi-UAV cooperative reconnaissance mission planning in denied environment (MUCRMP-DE) based on an improved synthetic heuristic algorithm is proposed to tackle these. In this paper, a hierarchical model is established with the global optimization goal of UAV's minimum radar detection time at first, including the planning of reconnaissance sequence between and within target groups, as well as relative position to targets. Then an improved synthetic heuristic algorithm is proposed to solve the model, which obtains valuable reconnaissance mission plan. For an application example of reconnaissance mission involving 68 targets, the simulation results show that the improved synthetic heuristic algorithm can suit the needs of the mission, particularly in effectively evading the detection of multiple radars. While it can also give better anti-radar attributes to the UA V and efficiently improved the convergence speed in the specific reconnaissance mission.
Optimization of Object Detection CNN With Weight Quantization and Scale Factor Consolidation

Due to the current Covid-19 pandemic, Convolutional Neural Networks (CNN) models attract attention in the applications to identify people with no masks. Developing an optimal CNN model is a challenging task especially for embedded deceives with limited hardware resources. To overcome the above challenge, we present a weight quantization technique aimed to produce compact CNN model for detection of people with mask or no mask. Its weights and feature maps are optimized using minimal fixed-point quantization at little or no sacrifice of its detection accuracy. The proposed weight quantization has been evaluated using a modified tiny-YOLOv2 model with the Mask and no-mask. Furthermore, we modified the internal model architecture to further reduce the model size and inference calculation by optimizing the order of max-pooling layers, consolidating the scale factors of batch normalization into only two pre-calculated parameters, and modifying the leaky ReLU activation function. The evaluation demonstrated that it saves more than 50 % of parameter memory and 56.21 % of inference computation.
Blockchain-Based Strategy to Avoid Fake AI in eHealth Scenarios with Reinforcement Learning

Every year the healthcare sector suffers from incorrect therapies and an increasing number of patients analysis, which causes congestion in the hospitals and, potentially, worsening of patient's clinical conditions. Extending the concept of the Decision Support System already investigated by the authors, this work advances the state of the art of Reinforcement Learning (RL) via Markov Decision Process formulation, considering an agent acting in his environment motivated by the achievement of the maximum individual objective by appropriate incentives. Transparency, security and privacy of the model are guaranteed by the adoption of Blockchain to enhance the perception of safety around medical operators improving access to hospital services. Experiments focused on the Smart Contract execution time and resources usage have proved the goodness of the proposed model considering both private and public Blockchain configurations.
Customized K-Means Clustering Based Color Image Segmentation Measuring PRI

Image segmentation has been considered as one of the most important tool for image processing in many application of computer vision, remote sensing, medical science and so on. The principle target of image segmentation is the way toward partitioning an image into various fragments that cover the whole image. However, it is very challenging task to segment or partitioning the images exactly. This paper proposes a customized k-means clustering based image segmentation method. This customized form of this technique implies k-means clustering algorithm, de-noising factor associated with velocity field of each pixel and edge distinguishing using Canny edge detector. For the evaluation, we conduct the experiment on the Berkeley segmentation dataset (BSDS300). Probabilistic rand index (PRI) is utilized for the similarity measure of segmented images and their predetermined ground truths. The experimental results reveal that the proposed method is highly capable of partitioning the images and in some cases, the outcomes of this method is better than the state-of-the-art methods.
Development of mathematical literacy in primary school using mathematical models and their animated illustrations

The article proposes a methodology for the formation of functional mathematical literacy of primary school students as a component of functional literacy. This methodology is based on mathematical models and their animation illustrations. Based on international research, it is shown that mathematical modeling is the most important universal action of a student in primary school. Examples of transformation of standard tasks for movement into tasks that are more close to reality are given, in solving which it is useful to use animation modeling, which involves the creation of children's animation for educational purposes. Animated videos illustrate the solution of non-standard mathematical problems, problems with unstructured, redundant or insufcient information. The activity of creating animation models allows students to increase the level of mathematical literacy, which is confrmed by the data of the pedagogical experiment.
SpyGAN sketch: Heterogeneous Face Matching in video for crime investigation

Automatic retrieval of faces from videos based on query images effectively helps during the investigation. When the suspect’s image is unavailable, a face sketch, drawn based on eyewitness’s memory recollection, is used to search against photos. Present research works primarily focus on Heterogeneous Face Matching (HFM) sketches to mugshot images in databases. This paper proposes a sketch-face matching in a video that includes profile faces, different illumination, and poses, using a new Generative Adversarial Network called SpyGAN. Faces in the video detected using YOLOv3 are converted into realistic sketches by the proposed SpyGAN focusing on key facial regions. The generated sketches are represented using PCA-SIFT descriptors and are matched based on the cosine distance metric. Experimental results show that the proposed methodology has achieved an accuracy of 88.9% on the Chokepoint dataset and 78% on the OWN Short face-video linked dataset and has demonstrated effectiveness over the state-of-the-art methods.
On Superspecial abelian surfaces over finite fields III

In the paper (J Math Soc Jpn 72(1):303–331, 2020), Tse-Chung Yang and the first two current authors computed explicitly the number $$|\mathrm {SSp}_2(\mathbb {F}_q)|$$ of isomorphism classes of superspecial abelian surfaces over an arbitrary finite field $$\mathbb {F}_q$$ of even degree over the prime field $$\mathbb {F}_p$$ . There it was assumed that certain commutative $$\mathbb {Z}_p$$ -orders satisfy an étale condition that excludes the primes $$p=2, 3, 5$$ . We treat these remaining primes in the present paper, where the computations are more involved because of the ramification. This completes the calculation of $$|\mathrm {SSp}_2(\mathbb {F}_q)|$$ in the even degree case. The odd degree case was previous treated by Tse-Chung Yang and the first two current authors in (Doc Math 21:1607–1643, 2016). To complete the proof of our main theorem, we give a classification of lattices over local quaternion Bass orders, which is a new input to our previous works.
Evaluating Convolutional Neural Networks for No -Reference Image Quality Assessment

In the past years, deep learning evolution has helped the development of computer vision systems. However, the quality of images plays a significant role in the effectiveness of these systems and it would be useful to know the quality of the images that are imported into our systems. No-reference image quality assessment is a challenging procedure, which tries to predict the quality of an image without using any reference image. In this paper, we evaluate the performance of widely used deep learning models in no-reference image quality assessment. To that end, we used transfer learning on 8 pre-trained models which we fit into 3 datasets related to image quality assessment. The performance of these models was studied in terms of mean absolute percentage error (MAPE). Although most of the models performed reasonably well in a MAPE range of 15% to 40% depending on the dataset used, the best performing one in a single dataset was DenseNet201 with a MAPE of 9.8%, while the overall best performing model was ResNet50.
GAN Using Capsule Network for Discriminator and Generator

In this paper, we propose Capsule GAN, which incorporates the capsule network into the structure of both discriminator and generator of Generative Adversarial Networks (GAN). Many CNN-based GANs have been studied. Among them, Deep Convolutional GAN (DCGAN) has been attracting particular attention. Other examples include convolutional GAN, auxiliary classifier GAN, Wasserstein GAN (WGAN) which uses Wasserstein distance to prevent mode collapse during the learning process, and Wasserstein GAN-gp (WGAN-gp). However, image generation by GAN is not stable and prone to mode collapse. As a result, the quality of the generated images is not satisfactory. It is expected to generate better quality images by incorporating a capsule network, which compensates for the shortcomings of CNN, into the structure of GAN. Therefore, in this paper, we propose two approaches to generate images with better quality by incorporating the capsule network into GAN. The experimental results show that the proposed method is superior to the conventional method.
Design Methodology For Using Blockchain In Swarm Robotics

Swarm robotics is an evolving realm that aims to achieve a common goal in coordinated way. But the current communication and control mechanism between the robots is not secure, efficient, capable, and it is more centralized. Blockchain, a decentralized, transparent, and immutable P2P communication technology, is the right fit to pair with swarm robotics. This paper discusses the current challenges in paring blockchain with swarm robotics and the applied methodology for the amalgamation of these futuristic technologies.
Indonesian Stemmer for Ambiguous Word based on Context

Stemming is a technique to remove affixes from a word to obtain a stem. One problem in stemming Indonesian was the morphological ambiguous word, such as “memerah” (blush or milking) which can be stemmed to “merah” (red) or “perah“ (milk). Previous deterministic stemmers did not handle this ambiguity. Meanwhile, the nondeterministic approach succeeded in finding the word candidates, but it could not choose the correct stem due to the absence of context. In this research, the nondeterministic approach was improved by adding the context, namely words that precede and follow the ambiguous word. Like nondeterministic approach, the affix rule checker in the proposed stemmer generated several word candidates from the input word. Then, the candidates are selected using two steps. First, they filtered by root word dictionary, list of compound words, and list of rare words. Second, if there were still more than a word candidate, the best word was the one with the highest average similarity score from the word to its context and the ambiguous word itself. To calculate the similarity score of words, a word model derived from word2vec training was used. Experiment results showed that the proposed stemmer accuracy was 99.65% on average. Whereas the deterministic and nondeterministic stemmer reached a 98.36% and 99.32% average.
Image texture synthesis method based on the trend function constraint

Texture synthesis technology can not only be used for texture transmission and repair damaged photos, but also has a broad application prospect in large-scale virtual scene rendering. In order to solve the problems of seam aliasing in texture mapping and considering the self-similarity of texture, this paper defines the trend function based on the biased random walk algorithm, divides the motion unit with different seed points and constructs the adjacency matrix, and calculates by matching labels Random walk stepping probability to realize texture synthesis. Compared with the background image segmentation results, it can be seen that the texture image generated by the algorithm in this paper presents similarity and continuity visually, and shows strong randomness at the same time.
On the analysis of power law distribution in software component sizes

Abstract Component‐based software development (CBSD) is an active area of research. Ascertaining the quality of components is important for overall software quality assurance in CBSD. One of the important metrics for measuring defects, analyzability, efforts, and cost in CBSD is component size. The paper presents an analytical model based on maximization of Tsallis entropy to obtain closed form expression for component size distribution (maximum Tsallis entropy component size distribution, MTECSD) in steady state. It is found that the component size distribution follows power law asymptotically. A procedure based on generalized Jensen–Shannon measure is developed to estimate model parameters. A detailed analysis of many popular probability distributions along with MTECSD is carried out on many diverse real data sets of component‐based softwares. The analysis reveals that lognormal and MTECSD distributions fit well to component sizes in many software conforming the presence of power law behavior. The software whose component size distributions are described by MTECSD are in equilibrium implying that new defects in these software systems occur occasionally. Power law behavior in component sizes also imply high variation leading to difficulty in software analyzability. The precise knowledge of component size distribution also provides an alternative method to compute efforts and cost estimates by modified COCOMO model.
Image‐based locating and guiding for unmanned aerial vehicles using scale invariant feature transform, speeded‐up robust features, and oriented fast and rotated brief algorithms

Abstract This study provides a method for determining the location information from a photograph taken by an Unmanned Aerial Vehicle (UAV). In today's defense industry, the goal is to design a reliable navigation system that can operate even when the Internet and GPS systems are unavailable. In the proposed system, it is expected that there is an image database in which each image has its own metadata in JavaScript Object Notation format that defines the geolocation information for the corresponding image. In real‐time, UAV flies captures an image and uses a matching algorithm to compare it with each and every photo in the database. The image with the highest match rate in the database and its metadata is returned. The UAV extracts its own location information from the metadata. For image matching, Scale Invariant Feature Transform, Speeded‐Up Robust Features, and Oriented FAST and Rotated BRIEF algorithms are used. The proposed system is tested and evaluated by using a real‐world dataset obtained from Flickr's city images. The proposed approach is proved to work with a success rate of 97.14%.
Survey on dialogue systems including slavic languages

Slavic languages pose a challenge to the researchers in the domain of dialogue technology. A relatively free word order with a large degree of inflection, such as conjugation of verbs, and declension of adjectives, pronouns, and nouns are exhibited by the Slavic languages, which has a significant impact on the size of lexical inventories that significantly complicate the design of dialogue systems. This article conducts an empirical study on the state-of-the-art dialogue systems within Slavic languages. Moreover, we review the existing models in recent dialogue systems, pinpoint the current main challenges and identify potential research directions of practical and intelligent systems within low-resourced languages.
Security and performance evaluation of master node protocol based reputation blockchain in the bitcoin network

Bitcoin is a digital currency based on a peer-to-peer network to propagate and verify transactions. Bitcoin is gaining wider adoption than any previous crypto-currency. However, the mechanism of peers randomly choosing logical neighbours without any knowledge about the underlying physical topology can cause a delay overhead in information propagation which makes the system vulnerable to double spend attacks. Aiming at alleviating the propagation delay problem, this paper introduces a proximity-aware extension to the current Bitcoin protocol, named Master Node Based Clustering (MNBC). The ultimate purpose of the proposed protocol, which is based on how clusters are formulated and how nodes can define their membership, is to improve the information propagation delay in the Bitcoin network. In the MNBC protocol, physical internet connectivity increases as well as the number of hops between nodes decreases through assigning nodes to be responsible for maintaining clusters based on physical Internet proximity. Furthermore, a reputation-based blockchain protocol is integrated with MNBC protocol in order to securely assign a master node for every cluster. We validate our proposed methods through a set of simulation experiments and the findings show how the proposed methods run and their impact in optimising the transaction propagation delay.
Decision Support System Application with Survey 360 Degree Feedback

The quality of human resources is one of the supporting factors to improve the progress and sustainability of an organization or company. Work culture is one indicator of the good and bad quality of human resources. Therefore, it is important to apply a work culture to every existing human resource. Rumah Zakat is one of the largest socials (non-profit) organizations in Indonesia that has implemented a work culture transformation that has been implemented since 2017. Because of the importance of transforming work culture at Rumah Zakat, it is necessary to evaluate the implementation of work culture. However, the evaluation is still not maximally carried out in implementing the application of work culture with the 360 Degree Feedback assessment concept because there is no system or application that can support the implementation of the assessment of the application of work culture in real time (online) and can directly process the data of the assessment results and provide decision results from the implementation of the application. work culture. Therefore, an application or decision support system for evaluating the application of employee work culture at Rumah Zakat was built using a web-based Multifactor Evaluation Process (MFEP) method to facilitate the process of evaluating the application of employee work culture objectively with the concept of 360 Degree Feedback based on weights and assessment indicators that have been assessed. determined based on each employee rank. The results of the data presented in the application show that in semester 1 of 2019 out of 459 respondents, 68.4&#x0025; were above the standard and 22&#x0025; met the standard.
Smart-mDAG: An Intelligent Scheduling Method for Multi-DAG Jobs

Job scheduling is a fundamental problem in cloud data center, which plays an essential role in the makespan, resource utilization and maintenance of scheduling security, it has received widespread attention. With the rapid increase of jobs' amount, higher requirements are put forward for scheduling efficiency and makespan. Meantime dependencies between tasks are closely related to makespan and throughput, and these dependent tasks form multiple DAG structures. Heuristic algorithms have limitation on adjusting the scheduling policy according to diverse dependencies, thus resulting the extension of makespan. In this paper, we propose an intelligent scheduling method for multi-DAG jobs using deep reinforcement learning, called Smart-mDAG. It is a job-specific scheduling method that adjusts the scheduling policy based on diverse dependencies to minimize the makespan. Firstly, we convert dependencies to numeric form through a feature extraction module to obtain the dependent information from the DAG. Secondly, we use cascaded neural networks to implement the fusion of scheduling information, so we can obtain the fitness between machines and tasks. With Alibaba Cluster Data V2018, we evaluate the performance of Smart-mDAG on a five-machines small cluster. The result shows that compared to control algorithms, Smart-mDAG can shorten the makespan for 70% jobs, and the optimal makespan of single job can be decreased to 65% of the past.
Question Classification Framework for Helpdesk Ticketing Support System using Machine Learning

One of the elements that contribute to the nonuniformity of the question data in Helpdesk Ticketing Support (HTS) System is the diversity of services and users. Most questions that were asked in the HTS are in various forms and sentence styles but usually offer the same meaning. Various state-of-the-art machine-learning approaches have recently been used to automate the question classification process. Question classification, according to the researchers, is important to solve problems like helpdesk tickets being forwarded to the wrong resolver group and causing the ticket transfer process to take effect, and to associate a help desk ticket with its correct service from the start, reducing ticket resolution time, saving human resources, and improving user satisfaction. The key findings in the exploration results revealed that in HTS, tickets with a high number of transfer transactions take longer to complete than tickets with no transfer transaction. Thus, this research aims to develop an automated question classification model for the HTS and proposes to apply the supervised machine learning methods: Naïve Bayes (NB) and Support Vector Machine (SVM). The domain will use a readily available dataset from IT Unit. It is expected that this study will have a significant impact on the productivity of technical and system owners in dealing with the increasing number of comments, feedbacks, and complaints presented by end-users. This paper will present related works and research frameworks for automated question classification for HTS.
Generative Image Inpainting with Dilated Deformable Convolution

The image generation and completion model complement the missing area of the image to be repaired according to the image itself or the information of the image library so that the repaired image looks very natural and difficult to distinguish from the undamaged image. The difficulty of image generation and completion lies in the reasonableness of image semantics and the clear and true texture of the generated image. In this paper, a Wasserstein generative adversarial network with dilated convolution and deformable convolution (DDC-WGAN) is proposed for image completion. A deformable offset is added based on dilated convolution, which enlarges the receptive field and provides a more stable representation of geometric deformation. Experiments show that the DDC-WGAN method proposed in this paper has better performance in image generation and complementation than the traditional generative adversarial complementation network.