Reviewerly/exHarmony huggingface dataset:
Attention and Prediction-Guided Motion Detection for Low-Contrast Small Moving Targets

Small target motion detection within complex natural environments is an extremely challenging task for autonomous robots. Surprisingly, the visual systems of insects have evolved to be highly efficient in detecting mates and tracking prey, even though targets occupy as small as a few degrees of their visual fields. The excellent sensitivity to small target motion relies on a class of specialized neurons, called small target motion detectors (STMDs). However, existing STMD-based models are heavily dependent on visual contrast and perform poorly in complex natural environments, where small targets generally exhibit extremely low contrast against neighboring backgrounds. In this article, we develop an attention-and-prediction-guided visual system to overcome this limitation. The developed visual system comprises three main subsystems, namely: 1) an attention module; 2) an STMD-based neural network; and 3) a prediction module. The attention module searches for potential small targets in the predicted areas of the input image and enhances their contrast against a complex background. The STMD-based neural network receives the contrast-enhanced image and discriminates small moving targets from background false positives. The prediction module foresees future positions of the detected targets and generates a prediction map for the attention module. The three subsystems are connected in a recurrent architecture, allowing information to be processed sequentially to activate specific areas for small target detection. Extensive experiments on synthetic and real-world datasets demonstrate the effectiveness and superiority of the proposed visual system for detecting small, low-contrast moving targets against complex natural environments.
Recent Trends in LA-Based Research: A Topic Analysis of CELA Abstract Content

Galen Newman, Dongying Li, Zhihan Tao and Rui Zhu Galen Newman is Associate Professor at Texas A&M University. He also serves as Coordinator of the Bachelor of Landscape Architecture Program and he is Associate Department Head. His research interests include urban regeneration, community resilience, landscape performance, land use science, and spatial analytics. Dongying Li is Assistant Professor at Texas A&M University. Her research interests include health systems design, ecosystem services, active living, and data modeling. Zhihan Tao is a PhD student in the Urban and Regional Science Program at Texas A&M University. His research interests include spatial analysis, low-impact development, landscape performance, and unmanned aerial vehicles. Rui Zhu is a PhD student in the Urban and Regional Science Program at Texas A&M University. Her research interests include design for health, spatial analytics, landscape performance, and advanced digital representation.
Pengenalan English Vocabulary Pada Anak Usia Dini Melalui English For Kids

Given the English vocabulary instructional method used by teachers of PAUD Qurrotul Uyun Pamekasan is not groundbreaking and varied since they constantly teach the English vocabulary from the textbook, additionally they never employ a modern technology tool such as smartphone gadget. Despite the fact that they frequently bring the gadget when the teaching learning porcoses in the class is occurring. In view of that the learning atmosphere becomes unappealing, the pupils becomes passive and dependent learners. Hence the aim of community service activity is to give pupils of PAUD Qurrotul Uyun Pamekasan training regarding English vocabulary learning employing English For Kids application. Method used in this community service is training by utilizing steps with activity theoretically and practically. The results are to be able to enhance pupils’ leaning motivation, to be able to lessen pupils’ boredom while learning, as well as to be able to create pulpils become autonomous pupils.
Application of Data Envelopment and Internet of Things Technology for Asset Value Evaluation

Assets are an essential part of an enterprise’s production and an indispensable economic entity for production and operation. The valuation of assets is one such area that has received significant attention in recent years. However, there exist numerous issues from various perspectives. These issues require us to study and explore them further. This article discusses the role of Internet of Things (IoT) and data envelopment methods to improve and develop asset evaluation. Based on the concept of asset evaluation, this paper designs an asset evaluation framework using the IoT. This framework can provide an all-weather and no-delay preparation basis for the asset appraisal work. Moreover, this paper uses data envelopment and particle swarm optimization (PSO) algorithm to clean and reclassify asset data. The objective of this algorithm is to solve the nonlinear function optimization problems associated with asset value evaluation. Finally, we verify the effectiveness and practicability of the model by comparing experimental simulation and manual evaluation.
Blockchain-Based Distributed Data Integrity Auditing Scheme

Cloud storage technology enables users to outsource local data to cloud service provider (CSP). In spite of its copious advantages, how to ensure the integrity of data has always been a significant issue. A variety of provable data possession (PDP) scheme have been proposed for cloud storage scenarios. However, the participation of centralized trusted third-party auditor (TPA) in most of the previous work has brought new security risks, because the TPA is prone to the single point of failure. Furthermore, the existing schemes do not consider the fair arbitration and lack an effective method to punish the malicious behavior. To address the above challenges, we propose a novel blockchain-based decentralized data integrity auditing scheme without the need for a centralized TPA. By using smart contract technique, our scheme supports automatic compensation mechanism. DO and CSP must first pay a certain amount of ether for the smart contract as deposit. The CSP gets the corresponding storage fee if the integrity auditing is passed. Otherwise, the CSP not only gets no fee but has to compensate DO whose data integrity is destroyed. Security analysis shows that the proposed scheme can resist a variety of attacks. Also, we implement our scheme on the platform of Ethereum to demonstrate the efficiency and effectiveness of our scheme.
Closing the Transactional Distance in an Online Graduate Course through the Practice of Embedded Librarianship

Using the practice of embedded librarianship, a professor from the Graduate School of Education and the Education Librarian at Rutgers, The State University of New Jersey collaborated to investigate its impact on the inherent transactional distance that exists in an online graduate course. Using Michael G. Moore’s three relational distances existing in the online environment—“teacher-student,” “student-student,” “student-course content”—the authors added two areas—”instructor-librarian” and “student-librarian”—that can close the transactional distance in online courses. Through course activities, structure, and access to the embedded librarian and specific library resources, students had increased opportunities for engagement, thereby reducing transactional distance.
Hindi Chhattisgarhi Machine Translation System Using Statistical Approach

Machine Translation is a subfield of Natural language Processing (NLP) which uses to translate source language to target language. In this paper an attempt has been made to make a Hindi Chhattisgarhi machine translation system which is based on statistical approach. In the state of Chhattisgarh there is a long awaited need for Hindi to Chhattisgarhi machine translation system for converting Hindi into Chhattisgarhi especially for non Chhattisgarhi speaking people. In order to develop Hindi Chhattisgarhi statistical machine translation system an open source software called Moses is used. Moses is a statistical machine translation system and used to automatically train the translation model for Hindi Chhattisgarhi language pair called as parallel corpus. A collection of structured text to study linguistic properties is called corpus. This machine translation system works on parallel corpus of 40,000 Hindi-Chhattisgarhi bilingual sentences. In order to overcome translation problem related to proper noun and unknown words, a transliteration system is also embedded in it. These sentences are extracted from various domains like stories, novels, text books and news papers etc. This system is tested on 1000 sentences to check the grammatical correctness of sentences and it was found that an accuracy of 75% is achieved.
User Representations as a Design Resource

The study of how the understanding of usages and users is achieved and turned into the characteristics of products comprises ‘the sociology of user representation’ in Science and Technology Studies. Whilst the early research on the topic was foremost a critique of designers’ imposition of theirimagination and preferences on prospective users, research has since discovered a richer research landscape in accomplishing the difficult task of anticipating the future contexts and identities of users. Our paper continues this line of work by examining a situation where first-hand access to users is blocked from human-centred design-oriented designers. Constructing an array of complementary user representations helps them to bridge the previously accumulated knowledge on users in their trade to the envisioned technology. The complementarities in the handful of key user segment representations and what is represented in their explicated form allowed the design team to make reasoned and accountable design decisions.
Open science in practice: 300 published research ideas and outcomes illustrate how RIO Journal facilitates engagement with the research process

Since Research Ideas and Outcomes was launched in late 2015, it has stimulated experimentation around the publication of and engagement with research processes, especially those with a strong open science component. Here, we zoom in on the first 300 RIO articles that have been published and elucidate how they relate to the different stages and variants of the research cycle, how they help address societal challenges and what forms of engagement have evolved around these resources, most of which have a nature and scope that would prevent them from entering the scholarly record via more traditional journals. Building on these observations, we describe some changes we recently introduced in the policies and peer review process at RIO to further facilitate engagement with the research process, including the establishment of an article collections feature that allows us to bring together research ideas and outcomes from within one research cycle or across multiple ones, irrespective of where they have been published.
Adopting DOI in Legal Citation: A Roadmap for the Legal Academy

A Digital Object Identifier (DOI) is a unique string of numbers, letters, and symbols used to identify web-based information assets such as articles, multimedia items, and datasets. A digital object minted with a DOI will be persistently discoverable through this identifier, as long as it lives on the Web. DOIs are already ubiquitous in citations in the medical and scientific literature, primarily because the discovery of, access to, and linkages between the scholarship in these disciplines happens almost exclusively online. As is true with most content on the web, scholarly content in the sciences is published on multiple platforms and may be archived in multiple locations. In light of the fact that one may be hard-pressed to create a reliable static URL that other researchers can refer to under these circumstances, those who publish in the sciences have arrived at a consensus that DOIs are the gold standard for making research outputs easier to find, use, and share.Why, then, has the legal academy largely eschewed DOIs for legal citation? Discussions are certainly taking place, but currently there are no practical guidelines for implementing DOIs in legal citations. The Bluebook takes no position on them and authors and law review editors either ignore them or are largely unaware of their benefits.This paper argues that the implementation and development of a standard for DOI in legal citation is long past due. It will lay out a roadmap for legal scholars, institutions, and vendors for implementing DOI, with helpful tips for authors, librarians, and law journals on minting DOIs; and will provide examples for the Bluebook on what a rule for integrating DOI in legal citation might look like.
A Day-Ahead Power Output Forecasting of Three PV Systems Using Regression, Machine Learning and Deep Learning Techniques

The forecasting of output solar power improves the quality, reliability and stability of power system. The aim of this research is day-ahead prediction of PV output power for 3 solar systems. The three PV systems are polycrystalline, monocrystalline and thin-film systems. A deep learning technique (RNN-LSTM) is proposed for day-ahead prediction of solar power output. The regression [GPR, GPR(PCA) and machine learning [SVR, SVR(PCA)] techniques are also developed. The forecasting accuracy is compared based on accuracy measurement parameters such as RMSE, MSE, correlation coefficient (R) and coefficient of determination (R2). One-year data for 2016 is considered for analysis. 70% of data is utilized for training and 30% for validation and testing. It is found that deep learning technique has better forecasting accuracy than other developed techniques in terms of lower (RMSE, MSE) and higher (R, R2), for day head forecasting of PV power output.
Crime Prevention System: Crashing Window Sound Detection Using AI Processor

This paper introduces the AI system that is used as crime prevention system at house or store. This system checks intrusion by detecting the sound of crashing windows. The proposed system consists with a micro controller unit (MCU) and a AI processor. We employ various sounds such as crashing windows, crashing plates, and other sounds that occur in everyday life to verify the proposed system. In order to find the optimized learning model, we employ simulation model of the AI processor and implement the optimal learning model. We tested our proposed system and the result shows accuracy up to 91%.
Analysis of Code Refactoring Impact on Software Quality: A Scientific Explanation

Code refactoring is a “Technique used for restructuring an existing source code, improving its internal structure without changing its external behaviour”. It is the process of changing a source code in such a way that it does not alter the external behaviour of the code yet improves its internal structure. It is a way to clean up code that minimizes the chances of introducing bugs. Refactoring is a change made to the internal structure of a software component to make it easier to understand and cheaper to modify, without changing the observable behaviour of that software component. Bad smells indicate that there is something wrong in the code that have to refactor. There are different tools that are available to identify and remove these bad smells. It is a technique that change our source code in a more readable and maintainable form by removing the bad smells from the code. Refactoring is used to improve the quality of software by reducing the complexity. In this paper bad smells are found and perform the refactoring based on these bad smell and then find the complexity of program and compare with initial complexity. The complexity of our project is reduced after apply the refactoring and improve the quality of our project. This paper shows that when refactoring is performed the complexity of software decrease and easily understandable.
The rubber hand illusion in children: What are we measuring?

The rubber hand illusion (RHI) is a much-studied bodily illusion that has been used in a wide number of populations to investigate the plasticity of the mental body representation. In adult participants, the wide adoption of the illusion has led to a proliferation of experimental variations of the illusion, and with that, considerable apparent inconsistencies in both empirical results and conceptual interpretations. In turn, this makes it challenging to integrate empirical findings and to identify what those findings together can tell us about the representation of the body in the brain. More recently, scientists have started applying the illusion to populations of children, in order to better understand how body representations develop in both typically developing children and in clinical populations. With this field now starting to expand, we believe it is both urgent and important to prevent unintended methodological variability from hindering the consistency of the paediatric literature as it has the adult literature. With this aim in mind, we review the 12 currently available paediatric RHI studies, and summarise their key methodological choices and conceptual definitions. We highlight a number of important discrepancies, particularly where seemingly equivalent analysis choices might significantly affect the interpretation of results, and make recommendations for future studies. We hope this will allow this important and emerging field to benefit from the synergy that results from multiple studies using convergent and consistent empirical methods.
Physical Aspects of Posturography in Virtual Reality

Virtual reality is a modern information technology consisting in a computer creation of an image which presents scenes with the participation of objects with the possibility of their movements in virtual space and interaction.The basis of the scene is a mathematical and/or physical model.If the observer can influence the observed scene, then the observation turns into a computer game.A force plate measures the position of the resultant pressure force on the feet (COP) of a person standing on the platform and thus evaluates the involuntary and voluntary swaying of the body, which is constantly losing and regaining its balance.A visual-motor feedback has been used in posturographic research by linking the movements of the COP of a person standing on a force plate with a movement of a virtual marker on the screen against the background of simple geometric figures.Thus, the patient is playing a computer game for which the location of the COP is the information input.The patient's task is to lean forward according to the virtual image of the geometric figure on the screen and maintain the COP marker in the given field for a few seconds.The force of inertia gives the moment of a force about the axis of rotation at the support point of the force plate.Thus, the COP is influenced by inertia forces which results in spurious COP movements at vigorous or abrupt deflections.A piece of Python software has been developed to assess the magnitude of the influence of inertia forces.The model assumes specifying the projection of the centre of gravity (COG) point at the location of the computer mouse instead.The model gives qualitative results which are very close to actual posturographic recordings.
Dynamic, Incremental, and Continuous Detection of Cyberbullying in Online Social Media

The potentially detrimental effects of cyberbullying have led to the development of numerous automated, data-driven approaches, with emphasis on classification accuracy. Cyberbullying, as a form of abusive online behavior, although not well-defined, is a repetitive process, i.e., a sequence of aggressive messages sent from a bully to a victim over a period of time with the intent to harm the victim. Existing work has focused on harassment (i.e., using profanity to classify toxic comments independently) as an indicator of cyberbullying, disregarding the repetitive nature of this harassing process. However, raising a cyberbullying alert immediately after an aggressive comment is detected can lead to a high number of false positives. At the same time, two key practical challenges remain unaddressed: (i) detection timeliness, which is necessary to support victims as early as possible, and (ii) scalability to the staggering rates at which content is generated in online social networks. In this work, we introduce CONcISE , a novel approach for timely and accurate Cyberbullying detectiON in online social media SEssions. CONcISE is a two-stage online approach designed to reduce the time to raise a cyberbullying alert by sequentially examining comments as they become available over time, and minimizing the number of feature evaluations necessary for a decision to be made for each comment. Extensive experiments on a real-world Instagram dataset with <?TeX $\sim \!4M$?> users and <?TeX $\sim \!10M$?> comments demonstrate the effectiveness, scalability, and timeliness of our approach and its benefits over existing methods. Additional experiments using a Twitter dataset offer evidence in support of the potential generalizability of CONcISE to other social media platforms.
An Efficient Ensemble Binarized Deep Neural Network on Chip with Perception-Control Integrated

Lightweight UAVs equipped with deep learning models have become a trend, which can be deployed for automatic navigation in a wide range of civilian and military missions. However, real-time applications usually need to process a large amount of image data, which leads to a very large computational complexity and storage consumption, and restricts its deployment on resource-constrained embedded edge devices. To reduce the computing requirements and storage occupancy of the neural network model, we proposed the ensemble binarized DroNet (EBDN) model, which implemented the reconstructed DroNet with the binarized and ensemble learning method, so that the model size of DroNet was effectively compressed, and ensemble learning method was used to overcome the defect of the poor performance of the low-precision network. Compared to the original DroNet, EBDN saves more than 7 times of memory footprint with similar model accuracy. Meanwhile, we also proposed a novel and high-efficiency hardware architecture to realize the EBDN on the chip (EBDNoC) system, which perfectly realizes the mapping of an algorithm model to hardware architecture. Compared to other solutions, the proposed architecture achieves about 10.21 GOP/s/kLUTs resource efficiency and 208.1 GOP/s/W energy efficiency, while also providing a good trade-off between model performance and resource utilization.
Depth Map Super-Resolution Using Guided Deformable Convolution

Depth maps acquired by low-cost sensors have low spatial resolution, which restricts their usefulness in many image processing and computer vision tasks. To increase the spatial resolution of the depth map, most state-of-the-art depth map super-resolution methods based on deep learning extract the features from a high-resolution guidance image and concatenate them with the features from the depth map. However, such simple concatenation can transfer unnecessary textures, known as texture copying artifacts, of the guidance image to the depth map. To address this problem, we propose a novel depth map super-resolution method using guided deformable convolution. Unlike standard deformable convolution, guided deformable convolution obtains 2D kernel offsets of the depth features from the guidance features. Because the guidance features are not explicitly concatenated with the depth features but are used only to determine the kernel offsets for the depth features, the proposed method can significantly alleviate the texture copying artifacts in the resultant depth map. Experimental results show that the proposed method outperforms the state-of-the-art methods in terms of qualitative and quantitative evaluations.