Reviewerly/exHarmony huggingface dataset:
Spatial Temporal Video Enhancement Using Alternating Exposures

High-speed video acquisition under poor illumination conditions is a challenging task. Imaging using long exposure can ensure brightness and suppress noise. However, the captured images may be blurry due to fast object movements or camera shakes. Imaging with short exposure can record sharp textures, but the high camera gain may cause noticeable noise. To alleviate this dilemma, we design a camera system using alternating exposures, where frames expose cyclically in a short-long way. The system consists of restoration and interpolation modules to reconstruct sharp, noise-reduced, high-frame-rate frames from low-frame-rate alternate-exposed input images. We design an optical-flow-based alternate-complementary alignment architecture for spatial enhancement, which effectively aligns the short-exposed and long-exposed images in a two-stage progressive way. Moreover, it explores complementary information from short-exposed and long-exposed inputs to ensure consistency between outputs. We propose a flow-enhanced frame interpolation module for temporal enhancement, which refines the intermediate flows and reconstructs the intermediate images based on the restored images of the alignment network and warped input neighboring frames. The whole network with two modules is end-to-end jointly learnable. We first evaluate the algorithm on simulation data. To demonstrate practicality, we then test it on real data by setting up a prototype camera. We propose an effective spatial degradation regularization strategy to reduce the domain gap between simulation and real data. Besides, we extend our method by integrating multi-frame exposure fusion technology to reduce overexposure areas in real scenarios. Experimental results show that our method performs favorably against state-of-the-art methods on both synthetic data and real-world data.
Deep learning applied to breast imaging classification and segmentation with human expert intervention

Automatic classification and segmentation of tumors in breast ultrasound images enables better diagnosis and planning treatment strategies for breast cancer patients.We collected 953 breast ultrasound images from two open-source datasets and classified them with help of an expert radiologist according to BI-RADS criteria. The data was split into normal, benign and malignant classes. We then used machine learning to develop classification and segmentation algorithms.We found 3.92% of the images across the open-source datasets had erroneous classifications. Post-radiologist intervention, three algorithms were developed based on the classification categories. Classification algorithms distinguished images with healthy breast tissue from those with abnormal tissue with 96% accuracy, and distinguished benign from malignant images with 85% accuracy. Both algorithms generated robust F1 and AUROC metrics. Finally, the masses within images were segmented with an 80.31% DICE score.Our work illustrates the potential of deep learning algorithms to improve the accuracy of breast ultrasound assessments and to facilitate automated assessments.
Control Program Design of Autonomous Vehicle Robot Using Grammatical Evolution

The automatic design of the control program of the vehicle robot is presented in this study. LEGO MINDSTORM EV3 and RVW Level Builder are used for this study. The control program of EV3 is designed automatically by using Grammatical Evolution (GE), which is one of the evolutionary algorithms. The generated program is applied for controlling EV3. The results show that the robot behavior in the experiment of EV3 was roughly equivalent to it in simulator. Therefore, the proposed algorithm based on GE can generate the control program of robot automatically.
Artificial Bee Colony Algorithm and Bat Algorithm for Solving Travel Salesman Problem

There are many algorithms for optimization meta heuristic have developed on swarm intelligence-based that depending in the nature of design. One of them, Bat Algorithm (BA), is based on the "echolocation behaviors" of bats. Micro bat used echolocation to specify the prey, avoid obstacles and locate their roosting crevices in the dark. Another algorithm is Artificial Bee Colony (ABC) is a new optimization algorithm which depend on the "bee behavior" towards colony to search about the food. In this paper, presents the BA and ABC algorithms steps for solving TSP then try to search about best solution depending on the parameters for both algorithms. The results show the ABC is best performance than BA for finding the best tour quickly compared with other by consuming time lesser than by effecting on the convergence speed for searching the solution. Furthermore, The BA required more parameters to achieve each output efficiently and need using improved control strategy to balance between exploitation and exploration that consuming more time for it.
Skeleton-based Action Recognition with Attention and Temporal Graph Convolutional Network

Human skeleton sequences provide important information for human action recognition. Compared with video data, it has lower sensitivity to light and lower data redundancy. Many traditional skeleton-based methods generally believe that all joints in a skeleton sequence are of the same contribution for an action. This paper proposes a novel action recognition method based on an attention and temporal graph convolutional network (ATGCN). The network could automatically learn spatial and temporal features from skeleton data. The attention mechanism is applied to weight joints to obtain their contributions in an action. Experiments conducted NTU RGB+D and WorkoutUOW-18 datasets have demonstrated the effectiveness of the proposed method.
Universal intraensemble method using nonlinear AI techniques for regression modeling of small medical data sets

Regression modeling of a small data set is an important task in many industries. It is critical in medicine in cases of insufficient historical data to conduct effective intellectual analysis. The small number of modern approaches to solving this problem do not always provide satisfactory results. In addition, the existing tools that are either very simple, which can lead to erroneous predictions, or tools that are quite complex, which can lead to problems such as overfitting, create the need to select a large number of additional parameters, the definition of distribution laws for data augmentation techniques, and so on. This chapter proposes universal intraensemble methods to improve the accuracy of processing short sets of medical data. The main idea of the method is a combination of data augmentation and elements of ensemble learning. This approach improves the generalization properties of the method and therefore increases the prediction accuracy. The developed method is based on the use of arbitrary nonlinear computational intelligence tools. The authors present various algorithmic implementations of the proposed method using both machine learning algorithms and artificial neural networks. Experimental modeling was performed on two short data sets from different fields of medicine. High prediction accuracy was established with a significant increase in the training time of each of the algorithms due to both a significant increase in the sample size and the need to process double the number of inputs. The increase in the prediction accuracy for each of the developed algorithms in comparison with the basic tools, which are the basis of the work of each specific algorithmic implementation of the method, has been experimentally established. The method can operate with very short data samples. In addition, it is scaled by the use of a large number of different nonlinear computational intelligence tools that are based on the principles of machine learning. The method can be applied in various fields for which it is necessary to solve classification or regression tasks based on short and very short data sets.
A MCDM-based performance of classification algorithms in breast cancer prediction for imbalanced datasets

The choice of a suitable classification algorithm is extremely important to worry about in different areas involving cancer diagnosis. It associates more than one benchmark for selecting the correct classification algorithm, so the appropriate choice called for multiple criteria decision making (MCDM). Several techniques of MCDM adjudicator may help in ranking of numerous classifiers. The main motive is to propose a two-step approach for determining conclusive ranking of classification algorithms utilising three MCDM techniques and Spearman's rank correlation coefficient (SRCC). Twenty classification algorithms are ranked utilising 12 performance benchmarks on seven breast cancer microarray datasets. Primary ranking generated on classifier's results using technique for order preference by similarity to ideal solution (TOPSIS), VlseKriterijumska optimizacija I Komprominso resenje (VIKOR) and grey relational coefficient (GRC) are nonconclusive. Thus, spearman's rank correlation coefficient is used to settle the differences in primary ranking and produce secondary ranking which resulted in identifying common top classifiers among seven micro-array gene expression datasets.
Developing a topic-driven method for interdisciplinarity analysis

This study explores the topic-based interdisciplinarity in the research domain of literacy. A text corpus of keywords was generated through a deep keyword generation model from abstracts of 346,387 articles published in 296 disciplines from 1917 to 2021. Dirichlet-Multinomial Regression topic modeling, interdisciplinarity indices, and network analysis were employed to analyze the collected corpus. Topic modeling uncovered 15 dominant research topics in the literacy field, as well as their up-and-down trends from 2000 to 2021. For each topic, keywords were then replaced with disciplines, and interdisciplinarity was measured using four indices: variety, balance, disparity, and diversity. Finally, the interdisciplinarity of each topic, connectivity between topics, and topic trends were comprehensively analyzed on the keyword co-occurrence network. Our methodology reaches beyond connectivity limited to a few disciplines and provides insight into the direction of collaboration between disciplines centered on a research domain. Moreover, the study's deep keyword generation model has methodological implications for forming a corpus spanning numerous disciplines as a bottom-up approach.
Deterministic distribution of multipartite entanglement in a quantum network by continuous-variable polarization states

Quantum network plays a vitally important role in the practical application of quantum information, which requires the deterministic entanglement distribution among multiple remote users. Here, we propose a feasible scheme to deterministically distribute quadripartite entanglement by continuous-variable (CV) polarization states. The quantum server prepares the quadripartite CV polarization entanglement and distributes them to four remote users via optical fiber. In this way, the measurement of CV polarization entanglement is local oscillation free, which makes the long distance entanglement distribution in commercial optical fiber communication networks possible. Furthermore, both the Greenberger-Horne-Zeilinger-like (GHZ-like) and cluster-like polarization entangled states can be distributed among four users by controlling the beam splitter network in quantum server, which are confirmed by the extended criteria for polarization entanglement of multipartite optical modes. The protocol provides the direct reference for experimental implementation and can be directly extended to quantum network with more users, which is essential for a metropolitan quantum network.
Comparisons of Different Clustering Algorithms for Privacy of Online Social Media Network

Online Social Networks (OSN) connect billions of users with direct consequences to offline activities. As of late, OSNs have seen critical development and accepted much consideration in exploration. These Networks have consistently been a significant part of daily life; however, since an ever-increasing number of individuals are associated with the internet, their online partners satisfy an undeniably significant job. Users use online applications where they end up sharing lots of information including personal data. Hence this data including huge information is money-making for data owners and they share data with third parties like advertisers who are ethical users. This data also consists of personal data. As data can be used for unethical purposes by unauthorized users which leads to different attacks and illegal use of data and personal information. It is fundamental to first anonymize users’ data before imparting it to any of the third parties like advisers. Anonymization preserves data privacy. However, anonymization prompts data loss, which by implication influences the data utility. Balancing data privacy and utility of information is an open research issue. Different clustering algorithms can be applied for anonymizing social network data. Comparison of clustering algorithms leads to the best algorithm to provide k-anonymity to social media networks.
The Effect of Learning Methods and Vocabulary Mastery on English Speaking Ability

The research title is effects of the learning method and vocabulary mastery toward speaking skill (Experiments on x grade students of private vocational schools in Bogor). The purpose of this study is to find the influence of learning methods and vocabulary mastery on speaking skills. This research used Experimental research methods. An inferential analysis is done by ANOVA statistics (Analysis of Variance). The results of the study conclude: (1) This is a significant effect on the learning method toward speaking skills in private vocational high school in Bogor. This can be proved by the value of sig 0,000 &lt;0,05 and count F 43,431, so it can be concluded that there is a significant difference in speaking skills in groups of students who were given Chinese Whisper learning methods. (2) There is a significant effect on vocabulary mastery toward speaking skills in the private vocational schools in Bogor. It can be proven by the sig value 0.002 &lt;0.05 and count F 10.447 which means that there is a significant difference in the ability to speak in groups of students who have a high and low vocabulary. (3) There is interactive a significant effect of the learning method and vocabulary mastery toward speaking skill in the private vocational school in Bogor. This can be proved by the sig value 0.003 &lt;0.05 and count F 9.736
Cross-Site Scripting (XSS) and SQL Injection Attacks Multi-classification Using Bidirectional LSTM Recurrent Neural Network

E-commerce, ticket booking, banking, and other web-based applications that deal with sensitive information, such as passwords, payment information, and financial information, are widespread. Some web developers may have different levels of understanding about securing an online application. The two vulnerabilities identified by the Open Web Application Security Project (OWASP) for its 2017 Top Ten List are SQL injection and Cross-site Scripting (XSS). Because of these two vulnerabilities, an attacker can take advantage of these flaws and launch harmful web-based actions. Many published articles concentrated on a binary classification for these attacks. This article developed a new approach for detecting SQL injection and XSS attacks using deep learning. SQL injection and XSS payloads datasets are combined into a single dataset. The word-embedding technique is utilized to convert the word’s text into a vector. Our model used BiLSTM to auto feature extraction, training, and testing the payloads dataset. BiLSTM classified the payloads into three classes: XSS, SQL injection attacks, and normal. The results showed great results in classifying payloads into three classes: XSS attacks, injection attacks, and non-malicious payloads. BiLSTM showed high performance reached 99.26% in terms of accuracy.
FASTA – A Stream Cipher for Fast FHE Evaluation

In this paper we propose Fasta, a stream cipher design optimised for implementation over popular fully homomorphic encryption schemes. A number of symmetric encryption ciphers have been recently proposed for FHE applications, e.g. the block cipher LowMC, and the stream ciphers Rasta (and variants), FLIP and Kreyvium. The main design criterion employed in these ciphers has typically been to minimise the multiplicative complexity of the algorithm. However, other aspects affecting their efficient evaluation over common FHE libraries are often overlooked, compromising their real-world performance. Whilst Fasta may also be considered as a variant of Rasta, it has its parameters and linear layer especially chosen to allow efficient implementation over the BGV scheme, particularly as implemented in the HElib library. This results in a speedup by a factor of 25 compared to the most efficient publicly available implementation of Rasta. Fasta 's target is BGV, as implemented in HElib. However the design ideas introduced in the cipher could also be potentially employed to achieve improvements in the homomorphic evaluation in other popular FHE schemes/libraries. We do consider such alternatives in this paper (e.g. BFV and BGVrns, as implemented in SEAL and PALISADE), but argue that, unlike BGV in HElib, it is more challenging to make use of their parallelism in a Rasta-like stream cipher design.
Systematic Literature Review on Corporate Information Technology Governance in Indonesia using Cobit 2019

The development of Information Technology as a data communication medium is growing very rapidly. The role of information technology is of course very crucial. However, the problem that often occurs in companies or agencies is the incompatibility of technology expectations with technology that already exists in a company. Therefore, it is necessary to manage the use of information technology. This study is a literature review that aims to analyze the IT governance of companies or agencies in Indonesia that use Cobit 2019. This study uses the Systematic Literature Review (SLR) method, to answer Research Questions (RQ): RQ1 related to the company or agency that is the case study when evaluating governance using Cobit 2019, RQ2 related to the type of journal publication and year of publication of the article, and RQ3 related to recommendations from recommended domains from journal case studies. The results of the study obtained as many as 18 journal articles and proceedings were selected in the search process according to the established criteria. The results of a specific study in the context of the main question of this study, namely the governance system of 18 companies or agencies have used Cobit 2019 (answer from RQ1). The selected relevant article types include 18 articles sourced from 7 journal articles and 11 proceedings articles with a range of publication years from 2019 to 2021 (answer from RQ2). From the selected articles, it was identified that the use of the Deliver, Service, and Support (DSS) domain was the most dominant in the implementation of Cobit 2019 in companies or agencies in Indonesia. The results of the study are further described in this article.
Behavior Reasoning for Opponent Agents in Multi-Agent Learning Systems

One important component of developing autonomous agents lies in the accurate prediction of their opponents’ behaviors when the agents interact with others in an uncertain environment. Most recent study focuses on first constructing predictive types (or models) of the opponents, considering their various properties of interest, and subsequently using these models to predict their behaviors accordingly. However, as the possible type space can be rather large, it is time-consuming, and sometimes even infeasible, to predict the actual behaviors of opponents with all candidate types. Thus, in this paper a tractable opponent behavior reasoning approach is proposed that facilitates ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$a$</tex-math></inline-formula> ) extraction of a small yet representative summary of all candidates using sub-modular-type maximization, and accordingly, ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$b$</tex-math></inline-formula> ) identification of the most appropriate type for real-time behavior prediction based on multi-armed bandits. In addition, we propose a knowledge-transfer scheme through demonstration learning to synchronize subject agents’ knowledge about their opponents’ behaviors. This further reduces the burden of reasoning with all models of their opponents from the perspective of individual subject agents. We integrate the new behavior prediction and reasoning method into a state-of-the-art evolutionary multi-agent framework, namely a memetic multi-agent system (MeMAS), and demonstrate empirical performance in two problem domains.
A Methodology for Extracting Power-Efficient and Contrast Enhanced RGB Images

Smart devices have become an integral part of people's lives. The most common activities for users of such smart devices that are energy sources are voice calls, text messages (SMS) or email, browsing the World Wide Web, streaming audio/video, and using sensor devices such as cameras, GPS, Wifi, 4G/5G, and Bluetooth either for entertainment or for the convenience of everyday life. In addition, other power sources are the device screen, RAM, and CPU. The need for communication, entertainment, and computing makes the optimal management of the power consumption of these devices crucial and necessary. In this paper, we employ a computationally efficient linear mapping algorithm known as Concurrent Brightness & Contrast Scaling (CBCS), which transforms the initial intensity value of the pixels in the YCbCr color system. We introduce a methodology that gives the user the opportunity to select a picture and modify it using the suggested algorithm in order to make it more energy-friendly with or without the application of a histogram equalization (HE). The experimental results verify the efficacy of the presented methodology through various metrics from the field of digital image processing that contribute to the choice of the optimal values for the parameters a,b that meet the user's preferences (low or high-contrast images) and green power. For both low-contrast and low-power images, the histogram equalization should be omitted, and the appropriate a should be much lower than one. To create high-contrast and low-power images, the application of HE is essential. Finally, quantitative and qualitative evaluations have shown that the proposed approach can achieve remarkable performance.
Deploying Hadoop Architecture Using Ansible and Terraform

Management of excessive data in the Big Data world is difficult. Software frameworks and clusters help in the management of the data. Although the setting up of the cluster consumes greater time and effort to implement for the distributed data storage and processing. This problem can be automated with the usage of various Development Operation tools. Terraform is a powerful tool for Provisioning and Ansible is used for Configuration management and Application deployment tool. The proposed work is about using this tool to completely automate the process of setting up the Hadoop cluster with no human intervention. Ansible provides Infrastructure as a Code. There are many pre-created modules available in Ansible to make our task simpler and easier. The main feature of these modules is that they are idempotent. Making efficient use of these modules in building the code is foundation of this work. Ansible is preferred over the other tools because it is agentless and it uses SSH protocol to connect to Linux systems and Win RM protocol to connect to Windows systems. The entire architecture can be deployed with in no time using the automation of the Ansible and Terraform over the cloud servers.