Reviewerly/exHarmony huggingface dataset:
Contact Tracing in Turkey’s Struggle with COVID-19: Its Scope, Components, and Stages

In COVID19 pandemic started on 31/12/2019, the first reported case and death were observed in Turkey on March 11th and 17th, respectively. As this letter is written, 80.783.674 cases and 1.764.863 deaths around the world, and 2.147.578 cases and 19.878 deaths in Turkey were observed. Non-pharmaceutical interventions such as contact tracing (CT), quarantine/isolation, school closures, curfews were applied in different combinations. The experience gained from the beginning of the pandemic has shown that CT has strategic importance in combating the pandemic. Deaths per population and per case rankings show that Turkey has achieved considerable success in the struggle against the pandemic. More than 6000 CT teams each consisting of 3 staff and reached the cases and their contacts in less than 32 hours with a rate of 99.6% play an important role in this success. In this letter, the scope, components, stages of CT applied in Turkey are provided.
Aesthetic Evaluation of Images Based on Deep and Shallow Feature Fusion Network Models

In the image aesthetics evaluation, the last layer of the deep network is usually utilized for classification of high-level semantic features, and there is a problem of poor classification performance using this traditional deep learning method. To address this problem, this paper proposes an image aesthetic evaluation based on a deep and shallow feature fusion network model. Improved image aesthetic classification methods allow the extraction of deep semantic aesthetic features and shallow detailed aesthetic features. Experimental validation is carried out based on the AVA dataset, and the results show that the network model has a higher classification accuracy than extracting only high-level features.
Pedestrian Detection in Infrared Thermal Images Based on Raised Cosine Distribution

We propose a simple and fast image thresholding approach for detecting pedestrians in an infrared thermal image. The approach uses the raised cosine distribution function as an analogous function to the one-dimensional histogram of the infrared thermal image. Experiments are conducted on the infrared thermal images gathered from the standard infrared image datasets to describe the performance of the proposed method. The performance is evaluated by comparing the results with state-of-art methods.
Load Balancing Using Firefly Approach

Load balancing is an important metric for enhancing system performance. The distributed nature and heterogeneity of cloud resources makes scheduling and load balancing a challenging task. Scheduling of jobs to the appropriate virtual machines (VMs) can be done using different mechanisms, but balancing the load is the major problem that occurs due to fluctuation of load, and different VM specifications. This leads to imbalanced resource utilization and performance degradation of the system. This paper proposes a scheme for maximizing resource utilization by balancing the load across cloud system by distributing jobs to reliable VMs using firefly approach for better performance. CloudSim tool is used to simulate the scheme and results show that the scheme performs better than existing work.
Decision Support System for Covid19 Affected Family Cash Aid Recipients Using the Naïve Bayes Algorithm and the Weight Product Method

The purpose of this study was to predict the recipients of cash assistance and to evaluate Naïve Bayes in predicting recipients of cash assistance from families affected by Covid19.This study uses the Naïve Bayes algorithm to calculate the accuracy and classification of cash transfer recipient data.The data used is logical data then processed and calculated.The variables used are Age, Income, College Status, and labels using two classes, namely Cannot and Can.From the results of this study, it can be concluded that the recipients of cash assistance in Village X can be predicted Naïve Bayes using a training value of 10%.Based on the results of the evaluation using confusion matrix and testing the accuracy of Naïve Bayes is 67%.For the calculation of the Weighted Product method using variables Age, Income, Education, Working Status, Family Status and there are two alternatives, namely Cannot and Can.From the Weighted Product calculation, it produces a Vector S ranking value of 2.24 and Vector V of 0.66, which states that families affected by Covid19 have the right to receive cash assistance.
Semi-structured information in the field of artificial intelligence and information security: processing results

Abstract This article is devoted to the semantic analysis of weakly structured information in the field of “Artificial intelligence and information security”. The methodology of this research included two stages and is based on the meta-analysis of existing studies. The received results allow development of further methodological recommendations on semi-structured information and artificial intelligence.
PoAS: Enhanced Consensus Algorithm for Collaborative Blockchain Intrusion Detection System

Signature-based Collaborative Intrusion Detection System (CIDS) deeply depends on the reliability of nodes to produce attack signatures. Each node in the network is responsible to produce a new attack signature to be distributed with other nodes. There is an issue that exists in CIDS highlighted in this paper, it is to maintain trust between the nodes while distributing the attack signatures. Recently, researchers found that blockchain has great potential to solve those problems. A consensus algorithm in the blockchain is capable to improve trust between the nodes. The object of this work to is design an extension of the Chain-based consensus algorithm to satisfy the requirement. The extensions name it as Proof of Attack Signature (PoAS). In the evaluation, the outcomes showed that the PoAS consensus algorithm successfully produces trusted attack signatures, and improved the robustness of CIDS, when compared to the network without the PoAS.
Hand tracking from monocular RGB with dense semantic labels

Recent years have seen a renewed interest in RGB-based hand tracking, as opposed to the depth-based tracking that has dominated the field since the introduction of commodity depth cameras. This trend has been driven by the ability of convolutional neural networks to process large quantities of image data. In this paper, we propose an approach to hand tracking that operates on sets of dense semantic labels. A full pipeline for RGB-based hand tracking is presented. This pipeline uses convolutional neural networks to produce a per-pixel semantic map of the scene before optimising the state of a kinematic model according to this semantic map using a tracking algorithm based on Different Evolution (DE). This technique allows us to simultaneously localise the hand in 3D space and recover the pose, and requires only monocular RGB input. We apply our technique to a benchmark dataset, reporting semantic segmentation and 3D pose tracking results, which we compare to the current state of the art. We also compare our DE-based algorithm to an equivalent one based on Particle Swarm Optimisation (PSO) and show that it is superior.
Timing attacks and local timing attacks against Barrett’s modular multiplication algorithm

Abstract Montgomery’s and Barrett’s modular multiplication algorithms are widely used in modular exponentiation algorithms, e.g. to compute RSA or ECC operations. While Montgomery’s multiplication algorithm has been studied extensively in the literature and many side-channel attacks have been detected, to our best knowledge no thorough analysis exists for Barrett’s multiplication algorithm. This article closes this gap. For both Montgomery’s and Barrett’s multiplication algorithm, differences of the execution times are caused by conditional integer subtractions, so-called extra reductions. Barrett’s multiplication algorithm allows even two extra reductions, and this feature increases the mathematical difficulties significantly. We formulate and analyse a two-dimensional Markov process, from which we deduce relevant stochastic properties of Barrett’s multiplication algorithm within modular exponentiation algorithms. This allows to transfer the timing attacks and local timing attacks (where a second side-channel attack exhibits the execution times of the particular modular squarings and multiplications) on Montgomery’s multiplication algorithm to attacks on Barrett’s algorithm. However, there are also differences. Barrett’s multiplication algorithm requires additional attack substeps, and the attack efficiency is much more sensitive to variations of the parameters. We treat timing attacks on RSA with CRT, on RSA without CRT, and on Diffie–Hellman, as well as local timing attacks against these algorithms in the presence of basis blinding. Experiments confirm our theoretical results.
Research on the Blockchain Derivatives: Based on Time Series of Token Price Risk and Yield

Aiming at the new lending derivatives in the cryptocurrency market, this paper studies the value fluctuations and risk characteristics of Bitcoin as a basic product through Time Series Analysis and establishment of GARCH models. Based on the linkage between the value of financial derivatives and basic products, this paper analyzes the characteristics of cryptocurrency lending derivatives. The results show that Bitcoin yield has peak fat-tail characteristic and volatility clustering characteristic. Bitcoin has higher risk and leverage than traditional financial products. Investors have a higher response to the falling yields that have a greater impact on the market. As a cryptocurrency, Bitcoin has risk-compensating characteristics, and its price fluctuations last a long time. The results of this paper provide an important reference for the establishment of risk management mechanism and related policy regulations in the cryptocurrency market, and also provide risk warnings to market participants.
Development of 3D Animation Based Hydrocarbon Learning Media

Abstract The learning process of chemistry on the hydrocarbon compound material taught by the teacher currently uses 2D-based media (blackboard). Actually, there is nothing wrong with using this blackboard for teachers in teaching, but there are some materials that need to be explained more concretely through direct media or IT-based media. In addition, students also do not understand the learning being taught because there are no facilities for practice so that students still use their imagination in practical learning. From the information obtained through interviews with chemistry teachers, the teacher’s desire to make IT-based media is very high, but they are constrained by the ability to design it. The research method used is the 4D version of Research and Development (RnD) (Define, Design, Develop, Disseminate). The results showed that the products produced were valid, practical, and effective for use as a medium in learning hydrocarbons with the respective values of validity, practicality and effectiveness were 0.83, 0.92, and 0.94.
An Art of Review on Conceptual based Information Retrieval

Basically keywords are used to index and retrieve the documents for the user query in a conventional information retrieval systems. When more than one keywords are used for defining the single concept in the documents and in the queries, inaccurate and incomplete results were produced by keyword based retrieval systems. Additionally, manual interventions are required for determining the relationship between the related keywords in terms of semantics to produce the accurate results which have paved the way for semantic search. Various research work has been carried out on concept based information retrieval to tackle the difficulties that are caused by the conventional keyword search and the semantic search systems. This paper aims at elucidating various representation of text that is responsible for retrieving relevant search results, approaches along with the evaluation that are carried out in conceptual information retrieval, the challenges faced by the existing research to expatiate requirements of future research. In addition, the conceptual information that are extracted from the different sources for utilizing the semantic representation by the existing systems have been discussed.
Hybrid Video and Image Hashing for Robust Face Retrieval

Video face retrieval (VFR) is an appealing and practical computer vision task, which aims to search particular character from masses of videos like in TV-Series. The challenges of this task mainly lie in two aspects, i.e. faces in such videos contain complex appearance variations with uncontrollable shooting environment and searching from big data usually requires high efficiency in both space and time. To fulfill this task, current works typically proceed in a learning to hash manner by fusing single-frame features within a video to obtain the video representation and further embedding it into Hamming space to yield video binary codes. The feature fusion stage has inevitably discarded too much frame information and leads to less discriminative video codes. In this paper, we propose Hybrid Video and Image Hashing (HVIH) to learn more effective binary codes for face videos. Specifically, we fully exploit the dense frame features rather than simply discarding them after the video level fusion and jointly optimize binary codes for the video and its composed frames in adapted supervised manners. To achieve more robust video representation, we introduce a module of video center alignment to ensure the binary codes location of the video and its frames to be as compact and consistent as possible in the Hamming space, which naturally facilitates both tasks of video-to-video retrieval and image-to-video retrieval. Extensive experiments on two challenging video face databases demonstrate the superiority of our approach over the state-of-the-art.
A Gesture Recognition Framework Based on Multi-frame Super-resolution Image Sequence

This paper presents a gesture recognition framework based on multi-frame super-resolution image sequence, aiming at solving the low recognition efficiency problem caused by the motion blur during fast gesture transformation and the complex variable visual backgrounds in military operation scenarios. The framework includes three modules: the gesture detection and alignment, the features extraction and fusion, as well as the gesture reconstruction and recognition. Through these three modules, the gesture within each image of the image sequence can be quickly located and aligned, and then the feature information of multiple images can be fused in pixel-level. Finally, the fused features are used to build a gesture reconstruction module to perform high-resolution gesture reconstruction and recognition. On top of that, we build a new video hand dataset named "PHV1000", which covers more than a dozen military operation gestures and fills the gap in this field, and we demonstrate the superior performance of our method in this dataset.
Performance analysis of SOTA Transformer Network in Numerical Expression Calculation

Computing Numerical expressions by analyzing the sentences is an intricate task in the domain of Natural Language Processing (NLP). Moreover, the noisy ("in-word") and erroneous representation of numerical expressions make the task more complex. In this work we have studied the performance of Transformer network models for solving numerical expressions. We have reported performance measurements for the task both in the word-level and character-level transformers. Observations reveal that though the transformer networks perform outstanding in sequence understanding tasks, the models under-perform on more logical calculative numerical evaluations.
The Ability to Adapt jBatik Software Technology for Traditional Batik Craftsmen

The purpose of this study is to find out the adaptability of traditional batik craftsmen in using jBatik software technology to create motif development. The traditional batik industry in Tuban, East Java was choosen as an example of case because it is considered to represent other traditional batik industries in Java. In previous studies, the use of jBatik software has been tested and resulted in the development of several designs of traditional Tuban batik motifs. This study was conducted to analyze the factors driving as well as inhibiting traditional batik craftsmen in adapting jBatik software technology through an experimental approach by referring to the diffusion of innovation theory. This is useful as one of the solutions today as an effort to measure the readiness level of traditional batik craftsmen to adapt technology, as well as being a way of self-evaluation for the technology to adapt to the needs of traditional batik craftsmen in Indonesia.
Preserving cardinals and weak forms of Zorn’s lemma in realizability models

Abstract We develop a technique for representing and preserving cardinals in realizability models, and we apply this technique to define a realizability model of Zorn’s lemma restricted to an ordinal.
Bibliometric Analysis of Water Conservancy Talent Development Research in China

Abstract In order to study the overall status of the research on water conservancy talent development in China, 6135 articles on the water conservancy talent development published in the past 30 years were downloaded from CNKI. Taking these articles as the sample, the time distribution, hot topic, topic cluster and research frontier of the water conservancy talent development research were quantitatively analysed with CiteSpace software. The results indicate that the scale of water conservancy talent development research has increased significantly in the past 30 years, but it has not become a popular school in the field of water conservancy research. Moreover, the research on the development of water conservancy talent involves a wide range of topics, including water conservancy reform, talent management in enterprises, talent education, rural water conservancy talent development, but the research appears extensive on the whole, so the future research can develop in a more refined way.
End-To-End Graph-Based Deep Semi-Supervised Learning with Extended Graph Laplacian

The process of labeling samples costs time and resources but unlabeled samples are easier to obtain. Recently, graph-based deep semi-supervised learning (GDSSL) training a deep network using a small number of labeled samples and the abundant unlabeled samples has been demonstrated to be promising on image classification tasks. These methods construct a graph to represent the structure of the input data (or hidden features). The successes of these GDSSL algorithms depend upon the structure of the similarity graph. However, existing GDSSL approaches construct the graph using predefined rules (such as knn graph) or fixed similarity measures (such as Gaussian kernel), which may limit the potential of GDSSL. In this paper, we move further in this direction to propose a novel end-to-end GDSSL approach which fully optimizes the whole graph without such limitations. To this end, we concatenate two neural networks (feature network and similarity network) together to learn the categorical label and semantic similarity, respectively, and train the networks with a new regularization term, the extended graph Laplacian, to minimize a unified objective function. Extensive experiments on several benchmark datasets demonstrate that our approach could outperform existing approaches on image classification. Furthermore, as a side-product, the similarity network could give faithful semantic similarity measure of samples, which is not possessed by other GDSSL approaches.
Image Dehazing Based on Multiple Scattering Model

Most of the exiting hazing algorithms are established on the assumptions of single scattering of light by atmospheric particles, but in real world, the multiple scattering is obvious and cannot be ignored. It is insufficient to describe the real atmospheric scattering phenomenon just considering the single atmospheric scattering. To obtain a better dehazing effect, we first analyze the multiple atmospheric scattering in detail and propose an image degradation model based on multiple scattering. Second, we propose a new improved method on the basis of dark channel priori. The proposed algorithm uses the semi-reverse algorithm to determine the foggy area, and then estimates the atmospheric light A from the most concentrated area; and then estimated transmission with the minimum channel of RGB, and finally according to the multiple scattering model to recover a clear image. Experimental results demonstrate that our approach can achieve good results for image containing sky regions and has low computational complexity. Furthermore, the proposed algorithm can eliminate the blurring effect, while improves the contrast and sharpness of the hazy image.
Attention Based Encoder-decoder Network for Cardiac Semantic Segmentation

The heart is one of the most important organs of humans. Accurate cardiac segmentation can improve the efficiency of cardiac diagnosis, and thus boost the clinical applications in the future. Although deep learning has enormous success in recent years, medical cardiac segmentation is still an unsolved problem due to the unique characteristics of medical images obtained by computerized tomography or magnetic resonance imaging. In this paper, we propose a new cardiac segmentation method for CT images. Our key idea is to introduce attention mechanisms, both in the spatial domain and channels, to improve the recent UNet++ network structure for medical images. The spatial and channel attention mechanisms can guide the network to focus on important information, which is effective for medical images, thereby improving segmentation details and accuracy. We evaluated our method on our own four-chamber heart dataset, and experiments demonstrate that the proposed method can obtain encouraging segmentation performance, and higher accuracy results compared with other methods.
An Introduction to Machine Learning for Panel Data: Decision Trees, Random Forests, and Other Dendrological Methods

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Self-supervised Attribute-Aware Refinement Network for Low-Quality Text Recognition

Scene texts collected from unconstrained environments encompass various types of degradation, including low-resolution, cluttered backgrounds, and irregular shapes. Training a model for text recognition with such types of degradations is notoriously hard. In this work, we analyze this problem in terms of two attributes: semantic and a geometric attribute, which are crucial cues for describing low-quality text. To handle this issue, we propose a new Self-supervised Attribute-Aware Refinement Network (SAAR-Net) that addresses these attributes simultaneously. Specifically, a novel text refining mechanism is combined with self-supervised learning for multiple auxiliary tasks to solve this problem. In addition, it can extract semantic and geometric attributes important to text recognition by introducing mutual information constraint that explicitly preserves invariant and discriminative information across different tasks. Such learned representation encourages our method to evidently generate a clear image, thus leading to better recognition performance. Extensive results demonstrate the effectiveness in refinement and recognition simultaneously.