Reviewerly/exHarmony huggingface dataset:
On the reduction of mixed Gaussian and impulsive noise in heavily corrupted color images

In this paper, a novel approach to the mixed Gaussian and impulsive noise reduction in color images is proposed. The described denoising framework is based on the Non-Local Means (NLM) technique, which proved to efficiently suppress only the Gaussian noise. To circumvent the incapacity of the NLM filter to cope with impulsive distortions, a robust similarity measure between image patches, which is insensitive to the impact of impulsive corruption, was elaborated. To increase the effectiveness of the proposed approach, the blockwise NLM implementation was applied. However, instead of generating a stack of output images that are finally averaged, an aggregation strategy combining all weights assigned to pixels from the processing block was developed and proved to be more efficient. Based on the results of comparisons with the existing denoising schemes, it can be concluded that the novel filter yields satisfactory results when suppressing high-intensity mixed noise in color images. Using the proposed filter the image edges are well preserved and the details are retained, while impulsive noise is efficiently removed. Additionally, the computational burden is not significantly increased, compared with the classic NLM, which makes the proposed modification applicative for practical image denoising tasks.
Graph Pooling Inference Network for Text-Based VQA

Effectively leveraging objects and optical character recognition (OCR) tokens to reason out pivotal scene text is critical for the challenging Text-Based Visual Question Answering (TextVQA) task. Graph-based models can effectively capture the semantic relationship among visual entities (objects and tokens) and report remarkable performance in TextVQA. However, previous efforts usually leverage all visual entities, whereas ignoring the negative effect of superfluous entities. This paper presents a Graph Pooling Inference Network (GPIN) which is an evolutionary graph learning method to purify the visual entities and capture the core semantics. It is observed that the dense distribution of reduplicative objects and the crowd of semantically dependent OCR tokens usually co-exist in the image. Motivated by this, GPIN adopts an adaptive node dropping strategy to dynamically downscale semantically closed nodes for graph evolution and update. To deepen the comprehension of scene text, GPIN is a dual-path hierarchical graph architecture that progressively aggregates the evolved object graph and the evolved token graph semantics into a graph vector that serves as visual cues to facilitate the answer reasoning. It can effectively eliminate object redundancy and enhance the association of semantically continuous tokens. Experiments conducted on TextVQA and ST-VQA datasets show that GPIN achieves promising performance compared with state-of-the-art methods.
Exploring the futuristic landscape of artificial intelligence for alpha generation: A comprehensive study

In today's world, parenting should be seen as proactive rather than reactive. Everyone's access to digital technology has increased the necessity of knowing and caring for it. Big data, the internet, and artificial intelligence-based information technology advancements have drastically altered human behaviour over time. The objective of this study was to conduct a technological analysis of millennial parents' use of artificial intelligence for their children and to investigate the many factors impacting working millennial parents' adoption of artificial intelligence for their children. This research serves both exploratory and descriptive purposes. It possesses a qualitative quality. The researcher received the primary data from the Delhi NCR region. The data was acquired with the help of a questionnaire. Parents from the millennial generation make up the respondents. Information was gathered from the parents of millennials via a questionnaire. 64 individuals responded. These are academic experts who work for academic organisations. They are research scholars, research associates, associate professors, and assistant professors. The respondents provided the researcher with well-written answers. Data analysis was carried out with the aid of R programming, in which Cronbach's alpha was evaluated for the reliability of the data. Further correlation between technological aspects and correlation Between factors impacting working millennial parents' adoption of artificial intelligence for their children. The results show that they are positive for adapting to artificial intelligence. At the moment, parents are enrolling their children in skill-development programs and buying them toys. They might merely take more away from that. Millennial parents feel protected when their kids use secure electronics. The learning and flexible nature of children's relationships with technology is another proof that artificial intelligence promotes the careers of the alpha generation. Parents employ parental control gadgets and settings to keep tabs on their kids' whereabouts online and what they're watching on YouTube or other apps. Finally, it is evident from the data gathered that the alpha generation will see great advancements in artificial intelligence.
A design for tokenization in governmental investment

Purpose The purpose of this study is to identify the possible efficiency gains in using tokenization for the execution of public expenditure on governmental investments. Design/methodology/approach Through design science research methodology, the exploratory research produced a tokenized prototype in the blockchain, through the Ernst and Young OpsChain traceability solution, allowing automated processes in the stages of public expense. A focus group composed of auditors from the public sector evaluated the possibility of improving the quality of information available in the audited entities, where the tokens created represent and register the actions of public agents in the blockchain Polygon. Findings The consensus of the experts in the focus group indicated that the use of tokenization could improve the quality of the information, since the possibility of recording the activities of public agents in the metadata of the tokens at each stage of the execution of the expenditure allows the audited entities the advantages of the information recorded on the blockchain, according to the following ranking: first the immutability of audited data, followed by reliability, transparency, accessibility and efficiency of data structures. Originality/value This research makes an empirical contribution to the real use of tokenization in blockchain technology to the public sector through a value chain in which tokens were created and moved between the wallets of public agents to represent, register and track the operations regarding public expense execution.
Evaluation of Python Error Message Interpretation: Study on Students with Different Levels of Programming Experience

Error messages are a helpful tool for everyone interested in learning a programming language. Even after learning the language, novice and experienced programmers alike, have to interact with an error message one way or another. However, there is no single programming language that is usedacross all platforms and systems, so the programmers have to write programs in many different languages. If the programming languages have similar structures, working with a new language is relatively easy. We have asked the question, whether the same effect could be observed in case of error messages. We designed an online survey which was conducted internationally to measure whether the general experience of programmers from any programming language influences the programmer’s ability to correct python error message. The survey was aimed at students who have some experience with programming. We find in comparison with novices, on average the experiencedprogrammers find and fix an error in the code given the sameerror message with its corresponding code snippet. Additionally,we see correlations between the average of correctly fixed errorsand number of programming languages that the participants hadexperience with, as well as their chosen major and their age.
Preparing students to form customized education programs for their future

Current trends including globalization, digitalization, informatization, computerization, etc., dictate new requirements for the forms, content and delivery methods of education. Education in the form of acquiring knowledge is replaced by the form of acquisition of competences. The actual principle of the quality and level of education is the competence approach, and in the field of providing educational services - the student-centered approach. The purpose of this work is to share the discussion regarding the trajectory of student's education with the necessary competencies for future professional activity, taking into account the student-centered approach to the educational process systems thinking required to solve complex problems, and dynamic optimization methods. The formation of the student's educational trajectory is based on the development of methodological, ontological and logical foundations of knowledge construction. Ontologies play a decisive role in the description model of the formation of such trajectories when a system of ontological descriptions of various training programs is created, which simultaneously becomes a means of methodology for the formation of new trajectories of knowledge. The use of the proposed principles of formation of the student's educational trajectories allows to make this process, on the one hand, more personalized, and on the other hand, transdisciplinary - which ensures the unification of students into study groups by courses, departments and faculties. The practical implementation of the step-by-step principles of building and installing educational programs takes place in the format of "Prism of Knowledge" based on the "Web-Software Complex – Editor of Learning Trajectory Scenarios". It provides the user with a toolkit with dynamic optimization methods for building various scenarios of the optimal learning trajectory taking into account innovations and future professional activities. Therefore, the given system of formation of knowledge trajectories allows building both personal knowledge bases and unification of groups of students, which in the future will contribute to the development of student-oriented education.
BlobCUT: A Contrastive Learning Method to Support Small Blob Detection in Medical Imaging

Medical imaging-based biomarkers derived from small objects (e.g., cell nuclei) play a crucial role in medical applications. However, detecting and segmenting small objects (a.k.a. blobs) remains a challenging task. In this research, we propose a novel 3D small blob detector called BlobCUT. BlobCUT is an unpaired image-to-image (I2I) translation model that falls under the Contrastive Unpaired Translation paradigm. It employs a blob synthesis module to generate synthetic 3D blobs with corresponding masks. This is incorporated into the iterative model training as the ground truth. The I2I translation process is designed with two constraints: (1) a convexity consistency constraint that relies on Hessian analysis to preserve the geometric properties and (2) an intensity distribution consistency constraint based on Kullback-Leibler divergence to preserve the intensity distribution of blobs. BlobCUT learns the inherent noise distribution from the target noisy blob images and performs image translation from the noisy domain to the clean domain, effectively functioning as a denoising process to support blob identification. To validate the performance of BlobCUT, we evaluate it on a 3D simulated dataset of blobs and a 3D MRI dataset of mouse kidneys. We conduct a comparative analysis involving six state-of-the-art methods. Our findings reveal that BlobCUT exhibits superior performance and training efficiency, utilizing only 56.6% of the training time required by the state-of-the-art BlobDetGAN. This underscores the effectiveness of BlobCUT in accurately segmenting small blobs while achieving notable gains in training efficiency.
Large‐scale spatiotemporal calculation of photovoltaic capacity factors using ray tracing: A case study in urban environments

Abstract Photovoltaics (PVs) on building facades, either building‐integrated or building‐attached, offer a large energy yield potential especially in densely populated urban areas. Targeting this potential requires the availability of planning tools such as insolation forecasts. However, calculating the PV potential of facade surfaces in an urban environment is challenging. Complex time‐dependent shadowing and light reflections must be considered. In this contribution, we present fast ray tracing calculations for insolation forecasts in large urban environments using clustering of Sun positions into typical days. We use our approach to determine time resolved PV capacity factors for rooftops and facades in a wide variety of environments, which is particularly useful for energy system analyses. The advantage of our approach is that the determined capacity factors for one geographic location can be easily extended to larger geographic regions. In this contribution, we perform calculations in three exemplary environments and extend the results globally. Especially for facade surfaces, we find that there is a pronounced intra‐day and also seasonal distribution of PV potentials that strongly depends on the degree of latitude. The consideration of light reflections in our ray tracing approach causes an increase in calculated full load hours for facade surfaces between 10% and 25% for most geographical locations.
SHPIA 2.0: An Easily Scalable, Low-Cost, Multi-purpose Smart Home Platform for Intelligent Applications

Abstract Sensors, electronic devices, and smart systems have invaded the market and our daily lives. As a result, their utility in smart home contexts to improve the quality of life, especially for the elderly and people with special needs, is getting stronger and stronger. Therefore, many systems based on smart applications and intelligent devices have been developed, for example, to monitor people’s environmental contexts, help in daily-life activities, and analyze their health status. However, most existing solutions have drawbacks related to accessibility and usability. They tend to be expensive and lack generality and interoperability. These solutions are not easily scalable and are typically designed for specific constrained scenarios. This paper tackles such drawbacks by presenting SHPIA 2.0, an easily scalable, low-cost, multi-purpose smart home platform for intelligent applications. It leverages low-cost Bluetooth Low Energy (BLE) devices featuring both BLE connected and BLE broadcast modes, to transform common objects of daily life into smart objects. Moreover, SHPIA 2.0 allows the collection and automatic labeling of different data types to provide indoor monitoring and assistance. Specifically, SHPIA 2.0 is designed to be adaptable to different home-based application scenarios, including human activity recognition, coaching systems, and occupancy detection and counting. The SHPIA platform is open source and freely available to the scientific community, fostering collaboration and innovation.
The role of regulation in the relationship between financial development and inclusive finance in Sub-Saharan Africa

This study seeks to examine the role of regulation in the relationship between financial development and financial inclusion in Sub-Saharan Africa (SSA). Based on available data, the study used 30 SSA economies from 2008 to 2020, employing a generalized method of moments. The study found that as much as financial development enhances financial inclusion directly in SSA and regulation on its own brings more people into the financial system, increasing regulations that restrict financial sector activities in the region should not be above the level of 1.6047, or it would hinder financial development from improving financial inclusion. The study recommends that the financial sector introduce user-friendly products, including low-cost financial services. Second, the central banks of SSA economies can recognise or award financial firms that are the best contributors to financial inclusion. This will encourage other financial firms to do their best. Additionally, policymakers should consider the threshold when employing regulations to enhance financial sector-induced financial inclusion in SSA. Specifically, the study indicates that at least the mean of the current two years of regulations should be computed and compared to the threshold before deciding whether to be more restrictive or not.
Analysis of User Behavior using Set-Top-Box for Internet Protocol Television-Based Application

AbstractThe growth of Internet usage has grown day by day. Many people use the internet for their commercial, education, and personal needs. Misuse of the internet has also increased exponentially. As with the rapid growth of Internet Protocol Television (IPTV), a high alarm still prevails. Much fake news and information are propagated through social media, which misguides many people. Hence tracing back of IP address is needed for appropriate monitoring. The log history stored in the service provider does not enrich the parents to monitor the same. Cache memory involved in the service provider is shared with needy people at their request. Retaining information in service providers depends upon the local norms of any region. A large amount of data handling in the service provider increases the challenges. This paper aims to save the on-view channel IP address in the local Set-Top Box (STB), where STB acts as the front device for IPTV applications. The IP address, including the monitoring frequencies and time observed, is stored in the local STB. This facilitates the parents to monitor their wards, and misuse of the internet is also minimized. The logic block added to the onboard increases the chip area by 3.97m3, and the delay is 847 ps.KEYWORDS: Internet protocol televisionSet-top-boxInternetSim-Altera Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementData sharing is not applicable to this article as no new data were created or analyzed in this study.Additional informationNotes on contributorsCammillus SCammillus, S received BE. (ECE) from Madurai Kamaraj University (2001), ME. in communication systems from Anna University (2007). Since 2007, he has been in the department of Electronics and Communication Engineering at National Engineering College, Kovilpatti. His research interest includes mobile adhoc networks, wireless networks, network security, and digital VLSI. He is currently pursuing doctorate programme at Anna University, Chennai. Corresponding author. Email: scammillus@gmail.com, cammillus@gmail.comShanmugavel SShanmugavel, S earned his bachelor's degree in electronics and communication engineering from Madras Institute of Technology in 1978. In 1989, he received his PhD from the Indian Institute of Technology (IIT), Kharagpur, in coded communication and spread spectrum techniques. In 1987, he became a lecturer in the Department of Electronics and Communication Engineering at IIT Kharagpur, and in 1991, he was promoted. He is currently at the National Engineering College in Kovilpatti, Tamil Nadu, India, in the Department of Electronics and Communication Engineering. In September 2000, he received the IETE-CDIL Award for his research paper. His research interests are mobile ad hoc networks, ATM networks, and CDMA engineering. Email: shanmugavels@inbox.lv, sethusvel@gmail.com
Triangulating NLP-based analysis of rater comments and MFRM: An innovative approach to investigating raters’ application of rating scales in writing assessment

Rater comments tend to be qualitatively analyzed to indicate raters’ application of rating scales. This study applied natural language processing (NLP) techniques to quantify meaningful, behavioral information from a corpus of rater comments and triangulated that information with a many-facet Rasch measurement (MFRM) analysis of rater scores. The data consisted of ratings on 987 essays by 36 raters (a total of 3948 analytic scores and 1974 rater comments) on a post-admission English Placement Test (EPT) at a large US university. We computed a set of comment-based features based on the analytic components and evaluative language the raters used to infer whether raters were aligned to the scale. For data triangulation, we performed correlation analyses between the MFRM measures of rater performance and the comment-based measures. Although the EPT raters showed overall satisfactory performance, we found meaningful associations between rater comments and performance features. In particular, raters with higher precision and fit to what the Rasch model predicts used more analytic components and used evaluative language more similar to the scale descriptors. These findings suggest that NLP techniques have the potential to help language testers analyze rater comments and understand rater behavior.
AUV Collision Avoidance Planning Method Based on Deep Deterministic Policy Gradient

Collision avoidance planning has always been a hot and important issue in the field of unmanned aircraft research. In this article, we describe an online collision avoidance planning algorithm for autonomous underwater vehicle (AUV) autonomous navigation, which relies on its own active sonar sensor to detect obstacles. The improved particle swarm optimization (I-PSO) algorithm is used to complete the path planning of the AUV under the known environment, and we use it as a benchmark to improve the fitness function and inertia weight of the algorithm. Traditional path-planning algorithms rely on accurate environment maps, where re-adapting the generated path can be highly demanding in terms of computational cost. We propose a deep reinforcement learning (DRL) algorithm based on collision avoidance tasks. The algorithm discussed in this paper takes into account the relative position of the target point and the rate of heading change from the previous timestep. Its reward function considers the target point, running time and turning angle at the same time. Compared with the LSTM structure, the Gated Recurrent Unit (GRU) network has fewer parameters, which helps to save training time. A series of simulation results show that the proposed deep deterministic policy gradient (DDPG) algorithm can obtain excellent results in simple and complex environments.
Simulating Gaussian boson sampling quantum computers

Abstract A growing cohort of experimental linear photonic networks implementing Gaussian boson sampling (GBS) have now claimed quantum advantage. However, many open questions remain on how to effectively verify these experimental results, as scalable methods are needed that fully capture the rich array of quantum correlations generated by these photonic quantum computers. In this paper, we briefly review recent theoretical methods to simulate experimental GBS networks. We focus mostly on methods that use phase-space representations of quantum mechanics, as these methods are highly scalable and can be used to validate experimental outputs and claims of quantum advantage for a variety of input states, ranging from the ideal pure squeezed vacuum state to more realistic thermalized squeezed states. A brief overview of the theory of GBS, recent experiments, and other types of methods are also presented. Although this is not an exhaustive review, we aim to provide a brief introduction to phase-space methods applied to linear photonic networks to encourage further theoretical investigations.