Reviewerly/exHarmony huggingface dataset:
Heterogeneous image matching based on phase consistency

The Image matching is an important step in image registration and image fusion. To solve the problem that SAR images and optical image matching are sensitive to radiation distortion, a novel heterogeneous image matching algorithm is proposed based on image frequent-domain wavelet decomposition based on phase congruency. Firstly, morphological denoising was carried out for image pairs, and then wavelet decomposition was performed by log-Gabor filter to obtain phase consistency of pixel points (PC graph). Then, feature descriptors were established based on the maximum index graph. Double-point matching was used as the search strategy for feature matching, and the random sampling consensus algorithm was used to eliminate false matching. Compare with traditional SIFT method. Experimental results show that the proposed algorithm has better matching effect than SIFT method and is an effective heterogeneous image matching algorithm
Scalable Planning in Multi-Agent MDPs

Multi-agent Markov Decision Processes (MMDPs) arise in a variety of applications including target tracking, control of multi-robot swarms, and multiplayer games. A key challenge in MMDPs occurs when the state and action spaces grow exponentially in the number of agents, making computation of an optimal policy computationally intractable for medium- to large-scale problems. One property that has been exploited to mitigate this complexity is transition independence, in which each agent’s transition probabilities are independent of the states and actions of other agents. Transition independence enables factorization of the MMDP and computation of local agent policies but does not hold for arbitrary MMDPs. In this paper, we propose an approximate transition dependence property, called δ-transition dependence and develop a metric for quantifying how far an MMDP deviates from transition independence. Our definition of δ-transition dependence recovers transition independence as a special case when δ is zero. We develop a polynomial time algorithm in the number of agents that achieves a provable bound on the global optimum when the reward functions are monotone increasing and submodular in the agent actions. We evaluate our approach on two case studies, namely, multi-robot control and multi-agent patrolling example.
MTMS: A Fact-Corrected Summarization model Based on Multitask Learning and Multimodal Fusion

Data-driven large-scale neural network models have now become the dominant paradigm for text summarization tasks. However, the factual inconsistency problem remains a very challenging challenge in the field of text summarization. To alleviate this problem, we introduce multitask learning with multimodal fusion into the text summarization domain and propose the MTMS model. The model effectively models multimodal data fusion by introducing image modal data to assist in correcting factual errors, averaging multi-directional noise through multi-task learning, and by a special hierarchical attention fusion mechanism. Experiments show that the MTMS model is significantly effective in correcting factual errors without sacrificing ROUGE scores.
Highlighting High-quality Content as a Moderation Strategy: The Role of<i>New York Times</i>Picks in Comment Quality and Engagement

News commenting is a prevalent form of online interaction, but it is fraught with issues, such as a low quality of discussion that often takes place. While various moderation methods can be used to maintain online discussion quality, one moderation strategy that is underexplored is for professional moderators to mark high-quality posts that are further highlighted in the interface. In this work, we look at the impact of New York Times (NYT) Picks. We present an analysis of more than 13 million NYT comments, examining the quality and frequency of commenting on the site in response to NYT Picks. The findings offer evidence that NYT Picks are associated with an increase in the quality of first-time receivers’ next approved comment, as well as the commenting frequency during commenters’ early tenure on the site. The quality boost associated with receiving a Pick attenuates after subsequent picks and diminishes over time as the user continues commenting but is still higher than commenters who do not receive Picks. Visible comment quality has a relatively small but significant positive correlation with the quality of the next comment, and exposure to Pick badges is also positively correlated with subsequent higher-quality approved comments, albeit to a lesser extent. Our results underscore the potential for news organizations to adopt the moderation strategy of highlighting professionally selected high-quality comments to improve overall community quality. We discuss the implications of our findings and offer design opportunities for comment sections that could further enhance quality in online discourse.
Mining the Semantic Web with Machine Learning: Main Issues that Need to Be Known

The Semantic Web (SW) is characterized by the availability of a vast amount of semantically annotated data collections. Annotations are provided by exploiting ontologies acting as shared vocabularies. Additionally ontologies are endowed with deductive reasoning capabilities which allow to make explicit knowledge that is formalized implicitly. Along the years a large number of data collections have been developed and interconnected, as testified by the Linked Open Data Cloud. Currently, seminal examples are represented by the numerous Knowledge Graphs (KGs) that have been built, either as enterprise KGs or open KGs, that are freely available. All of them are characterized by very large data volumes, but also incompleteness and noise. These characteristics have made the exploitation of deductive reasoning services less feasible from a practical viewpoint, opening up to alternative solutions, grounded on Machine Learning (ML), for mining knowledge from the vast amount of information available. Actually, ML methods have been exploited in the SW for solving several problems such as link and type prediction, ontology enrichment and completion (both at terminological and assertional level), and concept leaning. Whilst initially symbol-based solutions have been mostly targeted, recently numeric-based approaches are receiving major attention because of the need to scale on the very large data volumes. Nevertheless, data collections in the SW have peculiarities that can hardly be found in other fields. As such the application of ML methods for solving the targeted problems is not straightforward. This paper extends [20], by surveying the most representative symbol-based and numeric-based solutions and related problems, with a special focus on the main issues that need to be considered and solved when ML methods are adopted in the SW field as well as by analyzing the main peculiarities and drawbacks for each solution.
FPGA-Based Implementation of the Stereo Matching Algorithm Using High-Level Synthesis

Stereo vision finds a wide range of applications in automotive, object detection, robot navigation, agriculture mapping, and others. Stereo matching is a stereo algorithm targeted to identify the corresponding pixels from two or more images. This study shows the implementation of stereo matching using the Sum of Absolute Difference (SAD) algorithm to extract the object's depth or disparity from stereo images. Our key objective revolves around the implementation of the stereo matching algorithm for a small field-programmable gate array (FPGA)—requiring relatively few resources while maintaining the processing speed as well as disparity map. For meeting this requirement, we used small window buffers to compute the stereo matching. The occluded pixels were reduced by introducing secondary consistency checking implementation. From the results of experiments performed using the Zynq UltraScale+ ZCU102 FPGA board with SDSoC compiler, the processing speed for computing the stereo matching algorithm with a 4×4 window buffer was 0.038 s for an image size of 486×720 pixels and 0.051 s for 375×1242 pixels resolution. The proposed design needed 1% each of BRAM and FF and 7% of LUT. An 18% reduction in the pixel errors has been observed when employing the secondary consistency matching on the post-processing.
Analysis of the Modern “International Harmonized Format” of the Theory of Concrete Creep

The purpose of the study is to analyze the main directions of the considered theory of concrete creep. This theory is the basis for calculating buildings and structures, taking into account the time factor, when considering the long-term resistance of reinforced concrete structures. Methods. Using the rules of mathematics, principles of mechanics and the results of solid experiments to reveal: the fallacy of the superposition principle, which ultimately leads to the construction of incorrect kernels of integral equations; illegal substitution of instantaneous nonlinear concrete properties, creep properties (minute creep); non-stationary instant elastic properties of concrete, transforming into a model of Maxwell's theory of creep, leading to an error of up to 300%; only linear creep is considered, leading to an error of 200–500%.; the principle of "algebraization" of integral equations naturally leads to significantly contradictory results in the calculations of structures. Results. We have found that the basics of the theory under consideration grossly violate the rules of mathematics, the principles of mechanics and the results of solid experiments.
Sentimental Analysis On Covid-19 Tweets using Bidirectional Encoder Representations Transformers

The Coronavirus epidemic has wreaked havoc on countries all over the world. People from all over the Globe have flocked to social network to voice their thoughts and feelings about the situation that has reached epidemic proportions. The need of this paper is to exemplify how social media users feel about COVID-19 in an extremely brief span of time, Twitter saw an unusual surge in tweets on the new Coronavirus. This study presents a global Sentiment Analysis of tweets related to COVID-19, as well as how people’s sentiment in multiple nations has varied. The research study focuses on a period of time from march 2020 to april 2020. In the Sentiment Analysis, we fed dataset to different algorithms and estimate the best performance among them. As in secondly we also found the reliability on BERT model. Comparatively, BERT gave foremost accuracy amidst all. Besides, the accuracy of mentioned algorithms are well represented.
Robustness Analysis for VGG-16 Model in Image Classification of Post-Hurricane Buildings

Hurricane is a destructive natural disaster that causes a dramatic loss of life and damage to property. Locating houses damaged by hurricane manually is quite time-consuming and labor intensive. To accelerate the recovery process from the hurricane, AI-based methods such as deep learning networks are widely applied in the field. We hope to use deep learning network models combined with satellite images to quickly classify the houses after the disaster. VGG-16 is one of the most popular used models in the detection of hurricane-damaged houses. To increase the accuracy of model classification, we focus on the robustness analysis of the VGG-16 model on the classification of satellite images of hurricane damage and no-damage houses to see what factors play an important role in the classification decision of the model. In our study, two factors were mainly studied about the influence on the robustness of the model: the distribution of the histogram and the shape of the house. Experiments show that changing one of these two factors will greatly decrease the classification accuracy of the model.
Image Encryption Algorithm based on 2D Hyper Chaotic Map and Trigonometric Functions

We present a new method for encrypting images using the piecewise linear chaotic map in this study (2D Hyper chaotic Map). We abstracted and mathematically turned the plaintext image into a cylinder to create a series of overlaid circles. This creativity allows us to process and encrypt the image in a novel way, employing trigonometric processes. To disrupt the angles created by our transformation, as a pseudo-random generator, we employed a 2D Hyper chaotic Map. In both the plaintext picture and the secret key, our approach has a high sensitivity. Furthermore, the results suggest that the qualities of our approach operate exceptionally well, as seen by high scores.
The additive index of polynomials over finite fields

In this paper we introduce the additive analogue of the index of a polynomial over finite fields. We show that every polynomial P(x)∈Fq[x] can be expressed uniquely in its additive index form such that P(x)=f(L(x))+M(x) where L(x),M(x) are p-linearized polynomials over Fq, deg⁡(M)<deg⁡(L), L(x) splits completely over Fq and L is of the maximal degree. As applications, we study several problems in the theory of polynomials over finite fields in terms of their additive indices, such as value set sizes, bounds on multiplicative character sums, and characterization of permutation polynomials.
Immersive Collaborative VR Application Design

Due to the recent COVID-19 pandemic, there has been a renewed interest in expanding the capabilities of remote collaboration tools. Studies show the importance of noticing peripheral cues, pointing to or manipulating real-world objects in face-to-face meetings. This case study investigated the opportunities of combining traditional video conferencing with a multi-user VR platform to enable the interactive collaborative design of a VR training experience between multiple stakeholders working from their homes. In this article, the authors reflect on the experience and contribute a fully online and immersive collaborative design workflow for future VR development projects. The authors believe this workflow is of benefit for remote collaboration in general, but particularly in severely restricted environments when face-to-face meetings are impossible.
Performance Evaluation and Improvement of a Cloud-Native Data Analysis System Application

Cloud-native computing is a new software approach in building and running flexible, scalable, and resilient applications. Cloud-native application is easy to increase the resource requirement dynamically when deteriorated performance is from resource constraints. However, applications having low usage but excessive CPU throttling would be introduced and result in performance degradation. We evaluate a cloud-native data analysis system suffering from a CPU throttling problem, and a procedure is proposed to improve the overall response time performance.
Educational Windows Presentation Foundation and XAML Application for Information Protection based on the Cryptographic Methods – part II

In this article, a desktop application for data security is proposed. The main goal of the work is the development of training software to simplify and help with the basic methods of encoding and decoding information. It can be useful to students and specialists working in the field of cryptography and using methods for information protection. It incorporates two classes of methods - Substitution and Transposition Ciphers. Three types of user roles are implemented in the application: administrator, teacher and student. In reality, the administrator is separate, because they are not present as a role in the application itself but instead have access to the code and can make changes to the interface and functions in the application.The following software tools have been used for the design of the application: C#, .NET Framework, Windows Presentation Foundation and XAML, and Entity Framework.The application offers the following functions: (i) functions, related to the registration and login of various user profiles in the application; (ii) functions, realizing the cryptographic methods included in the application – encryption and decryption and (iii) some additional functions such as: providing statistics for the current use of the application by students and teachers, as well as a history of their previous actions.The functionality and workability of the implemented ciphers are illustrated with examples that show messages being initially encoded and then decoded. The final results confirm the efficiency of the application.
Clustered voxel real-time global illumination

Real-time global illumination is an extremely challenging problem because of its intrinsic complexity due to the interplay between complex geometry, multiple light bounces, and stringent real-time frame-rate requirements. In this paper, we present a new technique that enables the real-time computation of global illumination in generic scenes for diffuse surfaces and static geometry. Our technique combines a voxel-based representation of the scene, a voxel-clustering algorithm, and an iterative light-propagation algorithm based on the resulting clusters. Although our implementation is currently designed to handle the first (and main) bounce, the technique allows for multiple light bounces. Summing up all these components results in a flexible algorithm capable of providing global illumination effects and on-the-fly illumination computations.
NAPNEA: A Cost Effective Neonatal Apnea Detection System

Sleep apnea is a prevalent and life-threatening problem, especially in infants. Napnea is a cost-effective neonatal (sleep) apnea detection system that aims to provide affordable alternative methods for continuous respiration monitoring and apnea detection. For infants diagnosed with sleep apnea, the current monitoring systems rely on sticky electrodes wired to a cardio-respiratory monitor or expensive smart devices. Napnea is a compact and affordable solution for apnea monitoring utilizing a soft, smart e-textile chest belt, integrated with a smartphone app.
Security policies by design in NoSQL document databases

The importance of data security is currently increasing owing to the number of data transactions that are continuously taking place. Large amounts of data are generated, stored, modified and transferred every second, signifying that databases require an appropriate capacity, control and protection that will enable them to maintain a secure environment for so much data. Big Data is becoming a prominent trend in our society, and increasing amounts of data, including sensitive and personal information, are being loaded into NoSQL and other Big Data technologies for analysis and processing. However, current security approaches do not take into account the special characteristics of these technologies, leaving sensitive and personal data unprotected and consequently risking considerable financial losses and brand damage. In this paper, we focus on NoSQL document databases and present a proposal for the design and implementation of security policies in this type of databases. We first follow the concept of security by design in order to propose a metamodel that allows the specification of both the structure and the security policies required for document databases. We also define an implementation model by analysing the implementation features provided by a specific NoSQL document database management system (MongoDB). Having obtained the design and implementation models, we follow the model-driven development philosophy and propose a set of transformation rules that allow the automatic generation of the final implementation of security policies. We additionally provide a technological solution in which the Eclipse Modelling Framework environment is employed in order to implement both the design metamodel (Emfatic) and the transformations (Epsilon, EGL). Finally, we apply the proposed framework to a case study carried out in the airport domain. This proposal, in addition to saving development time and costs, generates more robust solutions by considering security by design. This, therefore, abstracting the designer from both specific aspects of the target tool and having to choose the best strategies for the implementation of security policies.
Recognition of Signs and Movement Epentheses in Russian Sign Language

Automated translation from sign languages used by the hearing-impaired people worldwide is an important but so far unresolved task ensuring universal communication in the society. In our paper we propose an original approach towards recognition of Russian Sign Language (RSL) based on extraction of components: handshape and palm orientation, location, path and local movement, as well as non-manual component. We detail the development of the dataset for subsequent training of the artificial neural network (ANN) that we construct for the recognition. We further consider two approaches towards continuous sign language recognition, which are based on sequential search of candidate events for the next sign start and the complete identification of the speech elements – the actual signs, resting state of the signer, combinatorial changes in the parameters of the signs and the epentheses.
Digital infrastructure policies for data security and privacy in smart cities

The availability of large amounts of data is vital for the working of smart cities, which need to process heterogeneous information on citizens and the surrounding environment for enabling smart services. Since those data collections can include personal/sensitive information, ensuring security and privacy of the data collected and produced in a smart city is a key problem to be addressed. Two of the main pillars for protecting data privacy and security are anonymization and encryption, which however need to be carefully designed and adopted for ensuring effective protection while not compromising the possibility of performing analysis, a central aspect in smart cities. In this chapter, we address the problem of protecting large data collections in the context of smart cities and illustrate possible approaches for effectively anonymizing data and for encrypting them, while permitting to perform computations.
System Weaning Food Product Using Forward Chaining Method

The provision of complementary feeding (MP-ASI) is one of the important periods in the first thousand days of a child's life. The transition period, which takes place at the age of 6 months to 24 months, is quite vulnerable for children's growth. The selection of quality MP-ASI is influenced by the mother's level of knowledge about food ingredients and the nutritional adequacy of the baby. Although instant MP-ASI is now easy to obtain, complete with information on nutritional values, the percentage of the Nutrient Adequacy Rate (RDA) for each baby is different, depending on the baby's weight, age and type of allergy. So that more knowledge is needed to determine the right MP-ASI menu according to the nutritional needs of infants. With these problems, the author makes a web-based MP-ASI menu recommendation information system, which uses the PHP programming language, and the MySQL database. The algorithm method used to determine the MP-ASI menu recommendations according to the nutritional needs of infants is the Forward Chaining method, while the system testing method uses white box testing. The purpose of this study is to make it easier for mothers to choose the right MP-ASI menu recommendation system and information about MP-ASI that can be accessed by users through the website, where all data on the system is managed by the admin on a different website from the website that can be accessed by users. users. With this system, it is hoped that it can help the community to get the right MP-ASI menu recommendations.