Reviewerly/exHarmony huggingface dataset:
Novel Competitive Swarm Optimizer for Sampling-Based Image Matting Problem

In this paper, a novel competitive swarm optimizer (NCSO) is presented for large-scale global optimization (LSGO) problems. The algorithm is basically motivated by the particle swarm optimizer (PSO) and competitive swarm optimizer (CSO) algorithms. Unlike PSO, CSO neither recalls the personal best position nor global best position to update the elements. In CSO, a pairwise competition tool was presented, where the element that fails the competition are updated by learning from the winner and the winner particles are just delivered to the succeeding generation. The suggested algorithm informs the winner element by an added novel scheme to increase the solution superiority. The algorithm has been accomplished on high-dimensional CEC2008 benchmark problems and sampling-based image matting problem. The experimental outcomes have revealed improved performance for the projected NCSO than the CSO and several metaheuristic algorithms.
Accuracy Comparison of UV-filtered Indonesian Banknotes Denomination Recognition Systems

As technology progresses, monetary transaction systems around the world are being continuously developed. Artificial intelligence as part of machine learning, especially, emerges as a new trend being used in transactions automation. This research is written with a purpose to propose a comprehensive comparison of accuracy, in recognizing denomination of authentic Indonesian Banknotes (Rupiah) using image processing methods and machine learning algorithms. This research is comparing accuracy between some classification systems designed using several known classifiers, using three kinds of image resolutions. From this research, KNN produced 100% accuracy, while the accuracy for SVM varied between 12.5 to 100% depending on the kernel used.
Hyperspectral Stripes Removal With Wavelet-Domain Low-Rank/Group-Sparse Decomposition

Pushbroom acquisition of hyperspectral imagery is prone to striping artifacts in the along-track direction. A hyperspectral destriping algorithm is proposed such that subbands of a 2D wavelet transform most effected by pushbroom stripes-namely, those with spatially vertical orientation-are the exclusive focus of destriping. The proposed method features an iterative image decomposition composed of a low-rank model for the stripes coupled with a group-sparse prior on the wavelet coefficients of the subbands in question. Experimental results on both synthetically striped imagery demonstrate superior image quality for the proposed method as compared to other state-of-the-art methods.
Supervised Generative Adversarial Cross-Modal Hashing by Transferring Pairwise Similarities for Venue Discovery

Venue discovery using real-world multimedia data has not been investigated thoroughly. We are referring to business and travel locations as venues in this study and aim to improve the efficiency of venue discovery by hashing. Most existing supervised cross-modal hashing methods map data in different modalities to Hamming space, where the semantic information is exploited to supervise data of different modalities during the training stage. However, previous works neglect pairwise similarity between data in different modalities, which lead to degraded performance of hashing function learning. To address this issue, we propose a supervised Generative Adversarial Cross-modal Hashing method by Transferring Pairwise Similarities (SGACH-TPS). This work has three significant contributions: i) we propose a model for making efficient venue discovery, ii) the supervised generative adversarial network can construct a hash function to map multimodal data to a common hamming space. iii) a simple transfer training strategy for the adversarial network is suggested to supervise data in different modalities where the pairwise similarity is transferred to the fine-tuning stage of training. Evaluation on the new WikiVenue dataset confirms the superiority of the proposed method.
Technologies of reconstruction and procedural generation of three-dimensional content

Abstract The research is aimed at finding and developing approaches to procedural content generation that meet the conditions of universality and optimality. The use of modular objects is considered as one of the possible solutions and a model for their construction is proposed. This model allows you to create single-tier modular objects, taking into account the possibility of combining modules according to specified rules, which ensures the correctness of their construction. For the proposed model, an algorithm for generating modular objects based on a given contour was developed and implemented. This algorithm provides the ability to generate objects in real time, which allows you to quickly make changes and thereby optimize the process of generating content and make it more flexible.
Implementation of fuzzy controller in different embedded systems

This article presents the implementation of different embedded systems in the development of fuzzy logic for its application in the control of a furnace with measures to 100°C for the optimization of preheating in surface mounted devices. Fuzzy logic accepts multiple variables that the device must process efficiently, so it is very important to make the appropriate choice of the embedded system to achieve adaptability to any problem presented. In addition, the observations of the development of fuzzy logic within the program are discussed.
Endomorphism rings of supersingular elliptic curves over <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:msub><mml:mrow><mml:mi mathvariant="double-struck">F</mml:mi></mml:mrow><mml:mrow><mml:mi>p</mml:mi></mml:mrow></mml:msub></mml:math>

Let p>3 be a fixed prime. For a supersingular elliptic curve E over Fp, a result of Ibukiyama tells us that End(E) is a maximal order O(q) (resp. O′(q)) in End(E)⊗Q indexed by a (non-unique) prime q satisfying q≡3mod8 and the quadratic residue (pq)=−1 if 1+π2∉End(E) (resp. 1+π2∈End(E)), where π=((x,y)↦(xp,yp) is the absolute Frobenius. Let qj denote the minimal q for E whose j-invariant j(E)=j and M(p) denote the maximum of qj for all supersingular j∈Fp. Firstly, we determine the neighborhood of the vertex [E] with j∉{0,1728} in the supersingular ℓ-isogeny graph if 1+π2∉End(E) and p>qjℓ2 or 1+π2∈End(E) and p>4qjℓ2: there are either ℓ−1 or ℓ+1 neighbors of [E], each of which connects to [E] by one edge and at most two of which are defined over Fp. We also give examples to illustrate that our bounds are tight. Next, under GRH, we obtain explicit upper and lower bounds for M(p), which were not studied in the literature as far as we know. To make the bounds useful, we estimate the number of supersingular elliptic curves with qj<cp for c=4 or 12. In the appendix, we compute M(p) for all p<2000 numerically. Our data show that M(p)>p except p=11 or 23 and M(p)<plog2⁡p for all p.
Analysis of Information Leakage from MCU using Neural Network

Electromagnetic leakage from an operating microcontroller unit (MCU) can be intercepted and analyzed to deduce the instructions being processed. This is helpful to understanding eavesdropping and to later protecting against it. In this work, harnessing a deep neural network, we analyze the massive electromagnetic (EM) leakage information to extract the instructions run in a microcontroller unit (MCU). An EM leakage acquisition environment is built, and the leakage signal of an MCU is collected. A multi-layer convolutional neural network is constructed for the side channel analysis and identification. The recognition accuracy is over 95% in the training phase, and more than 75% in the prediction phase. This experiment proves that the electromagnetic leakage emitted can be collected by appropriate methods and analyzed by deep learning technology. It can effectively deduce the instructions running in an MCU, which lays a foundation for the protection against eavesdropping.
A Partitional Cooperative Coevolutionary Group Search Optimization Approach for Data Clustering

Clustering algorithms are important methods for exploratory data analysis with application in many fields, such as data mining, image understanding, text analysis, and so on. Partitional clustering models are the most popular clustering methods, but these approaches suffer from some limitations, like the sensibility to algorithm initialization and the lack of mechanisms to help them escaping from local minima points. In this work, a new partitional cooperative coevolutionary algorithm based on Group Search Optimization (GSO) is proposed (CCGSO) to deal with clustering task. Evolutionary Algorithms (EAs), such as GSO, are nature-inspired meta-heuristics known for their capabilities to find global optima solutions even when dealing with hard optimization problems. However, many EAs are based on competitive behavior among population individuals, although it is known that sometimes cooperation may result in better solutions then sheer competition. Experiments are executed on real benchmark data sets to evaluate the performance of proposed algorithm in comparison to other well-known competitive partitional evolutionary clustering methods from literature. Experimental results show that CCGSO is able to achieve better average performances when dealing with clustering task than competitive evolutionary clustering approaches.
Matching Observations to Distributions: Efficient Estimation via Sparsified Hungarian Algorithm

Suppose we are given observations, where each observation is drawn independently from one of k known distributions. The goal is to match each observation to the distribution from which it was drawn. We observe that the maximum likelihood estimator (MLE) for this problem can be computed using weighted bipartite matching, even when n, the number of observations per distribution, exceeds one. This is achieved by instantiating n duplicates of each distribution node. However, in the regime where the number of observations per distribution is much larger than the number of distributions, the Hungarian matching algorithm for computing the weighted bipartite matching requires O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) time. We introduce a novel randomized matching algorithm that reduces the runtime to O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) by sparsifying the original graph, returning the exact MLE with high probability. Next, we give statistical justification for using the MLE by bounding the excess risk of the MLE, where the loss is defined as the negative log-likelihood. We test these bounds for the case of isotropic Gaussians with equal covariances and whose means are separated by a distance η, and find (1) that ≫log k separation suffices to drive the proportion of mismatches of the MLE to 0, and (2) that the expected fraction of mismatched observations goes to zero at rate O((log k) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> /η <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ).
Deep-Learning-Based Image Tagging for Semantic Image Annotation

Many multimedia images, which are generated in countless ways, must be semantically stored and managed to search for relevant images. Therefore, studies on image annotation have been conducted and the field is developing at a steady pace. In existing approaches, the user inputs the tag for the image directly to obtain the semantic tag. In this study, the tag is input to the image using a convolutional neural network, which can be used for deep learning. Thus, it the hassle of entering tags for images is eliminated. Through deep learning, the user can automatically input the tag for the image, and the entered tags can be expanded to the resource description framework model in Linked Tag. Finally, the user can perform semantic-based image search using the SPARQL query language.
Analysist Web-Based of Laundry Information System

Laundry is business engaged in services that are now commonly found. The high demand for laundry services makes laundry owners in Sungai Bambu, North Jakarta look for ways to keep transaction records neatly organized and not lose records or damage that is often experienced by customers or owners. In addition, employees must calculate the total price of the weight of the items to be washed, so that it takes a long time. Laundry also provides a variety of laundry services that can be added at any time. Besides making manual reports. To overcome this, laundry service information system is designed using the waterfall method built with Visual Studio as an interface and SQL Server as data storage. Design of this information system will make it easy to collect transaction data and minimize data loss or damage, generate automatic reports, and can perform special laundry services.
Video Search Reranking with Relevance Feedback Using Visual and Textual Similarities

In video search reranking, in addition to the well-known semantic gap, the intent gap, which is the gap between the representation of the users' demand and the real search intention, is becoming a major problem restricting the improvement of reranking performance. To address this problem, we propose video search reranking based on a semantic representation by multiple tags. In the proposed method, we use relevance feedback, which the user can interact with by specifying some example videos from the initial search results. We apply the relevance feedback to reduce the gap between the real intent of the users and the video search results. In addition, we focus on the fact that multiple tags are used to represent video contents. By vectorizing multiple tags associated with videos on the basis of the Word2Vec algorithm and calculating the centroid of the tag vector as a collective representation, we can evaluate the semantic similarity between videos by using tag features. We conduct experiments on the YouTube-8M dataset, and the results show that our reranking approach is effective and efficient.
Depth-Wise Based Convolutional Neural Network for Street Imagery Digit Number Classification

We present a lightweight convolutional neural network model for street view house numbers recognition and classification. Our ultimate goal is to build a high performance, compact and memory efficient deep learning model for classifying digits of real-world house numbers which is suitable for deployment into devices with limited computational powers. Our model applied world class ground breaking convolution neural network architecture for embedded vision proposed as backbone architecture. The backbone architecture uses a streamlined deep learning architecture with depth-wise separable convolutions in performing computer vision tasks. We performed extensive experiments with high degree hyper parameters turning to increase the mode's accuracy, minimize training and validation loss respectively. In order to improve the model accuracy and performance, we deployed random image argumentation technique and batch normalization process during training. We obtained 99.6% training accuracy and demonstrated the effectiveness of our model on randomly selected samples of the street view test dataset and achieved a commendable accuracy of 92.0%.
Using Participatory Design in the Development of a New Musical Interface

The design of New Musical Interfaces (NMI) often involves an intersection of disciplines: NMI design, Human-computer Interaction (HCI) and Interaction Design (IxD). This intersection has brought into NMI design approaches and philosophies arising from the focus of HCI and IxD on human-centred design. Not unexpectedly, a number of challenges and questions have arisen along with a significant tension related to the nature of user involvement in the design process. This project adopted an approach informed by participatory design where musicians became integral to the design process. Through this process a deeper understanding of the needs of musicians beyond usability considerations was developed. Whilst this understanding is bounded by the specifics of the design context, this project is submitted as a case study that may provide some guidance in the processes adopted by others when designing NMIs.
Vision-based tile recognition algorithms for robot grasping task in EAST

During the operation of the experimental advanced superconducting tokamak (EAST), tiles installed on the vacuum vessel break and fall onto the divertor. To ensure high experimental performance, a robot removes the broken tiles and replaces them with fine tiles. However, one of the key issues for this task is to distinguish fallen tiles from other tiles for grasping. In addition, discrete spots caused by burn and corrosion on the tiles’ surface also bring challenges in vision-based recognition. Here, a novel breakpoint detection algorithm is proposed that recognizes fallen tiles. Experiments are performed to demonstrate the recognition and catch processes of fallen tiles and to test the robustness of the algorithm.
FS-Net: Medical Image Denoising via Local Receptive Field Smoothing Network

Noise is one of the most challenging tasks in medical images which is happening during transmission and acquisition, and as a result, it complicates a subsequent medical image analysis task. This paper proposes a novel local receptive field smoothing network (FS-Net) for medical image denoising by introducing smoothing priors of receptive field of intermediate CNN layers. First, for a receptive field of a CNN, each of its values smoothing properties are retained by weighting their local neighborhoods (i.e., after smoothing, each new neurons are the weighted average of its neighborhoods). The weights are calculated using the similarity of their corresponding receptive field (i.e., the similarity between the receptive field used to calculate a receptive fields value and the receptive field used to calculate their corresponding neighborhoods) and this keeps the smoothing properties. Second, by applying this smoothing prior on receptive fields of intermediate CNN layers, an end to end FSNet which directly approximate the latent clean images from its noisy version is designed. The receptive fields smoothing helps to train deeper architecture and improves the reconstructed image quality. Experimental results on Breast Mammography images reveal that the proposed method outperforms other states of the art denoising methods.
Deep Residual-Dense Attention Network for Image Super-Resolution

Recently, a great variety of CNN-based methods have been proposed for single image super-resolution. But how to restore more high-frequency details is still an unsolved issue. It is easy to find that the low-frequency information is similar in a pair of low-resolution and high-resolution images. So the model only needs to pay more attention to the high-frequency information to restore more realistic images which have abundant details and meet human visual system better. In this paper, we propose a deep residual-dense attention network (RDAN) for image super-resolution. Specially, we propose a channel attention module to change the weight of each channel and a spatial attention module to rescale the region weight in a channel map, which can make the model focus more on the high-frequency information. Experimental results on five benchmark datasets show that RDAN is superior to those state-of-the-art methods for both accuracy and visual performance.
2D-Deep Neural Network and Its Online Rapid Learning

In the paper, the 2D-deep neural network and the algorithm for its online learning are proposed. This system allows reducing the number of adjustable weights due to the rejection of the vectorization-devectorization operations. As a result, it saves the information that is contained between columns and rows of data inputs presented as 2D matrix.
ANALYSIS OF INTERNATIONAL EXPERIENCE IN PREPARATION OF FUTURE TECHNICAL PROFESSIONALS

To date, the training system for future IT professionals on information security and integrated information security systems is quite conservative and has some inertia that must be taken into account when reforming the higher education system, including the cybersecurity training system (information security), in line with the goals and objectives of Ukraine's European integration in higher education in the context of the Bologna Process and the transition to new lists of training areas for professionals in information technology. Given that the training of future information security professionals is relatively new (since it was launched in Ukraine only in 1997 in accordance with the law of Ukraine No. 537-V) and the most dynamic in its activity, the relevance of the development of information security training system is even more relevant. That is why, in this article, we considered how the training of future information security specialists is carried out in institutions of higher education of leading countries in the world. In particular, we have considered countries such as the United States of America, the German Federal Republic, the United Kingdom of Great Britain and Northern Ireland, the French Republic, the Republic of Austria and the Republic of Poland. Basically, in this article we have identified the directions in which future information security specialists are trained in the leading countries of the world, reviewed and analyzed educational programs for the training of information security specialists and disciplines related to professional disciplines. In addition, we identified the main differences in training in a particular country and made a comparative analysis between the training of future information security professionals in Ukraine and the leading countries of the world.
Dual-Path Recurrent Network for Image Super-Resolution

Recently, significant progress has been made for the task of image super-resolution (SR). However, existing methods show that stacking convolution layers blindly leads to overwhelming parameters and high computational complexities. Besides, the conventional feed-forward architectures can hardly fully exploit the mutual dependencies between low- and high-resolution images. Motivated by these observations, we first propose a novel architecture by taking advantage of recursive learning. Based on dual-path block (DPB), we enhance the network performance simply by increasing the number of DPB recursions and avoiding additional parameters. Moreover, in contrast to most convolutional neural network (CNN) methods with one state, our network is endowed with two states (low-resolution state and high-resolution state) transformed mutually. We exploit the relationship between the two states by introducing a back-projection operation which calculates the differences between the two states for a better result. Extensive experiments show that our method outperforms the state-of-the-art methods.