Reviewerly/exHarmony huggingface dataset:
CharityChain - A Charity App Built on Blockchain

Donors are suspicious of how their funds are being spent. Charities are subject to the same risks. Embezzlement, for example, is a sort of commercial fraud. and financial mismanagement by executives Blockchain technology has recently become popular. Technology has been used in a variety of fields. The Using blockchain technology, you can speed up the process. Making the process of giving and receiving money more transparent It's true. a single platform for tracking donations is required that will keep track of all gifts, transactions, and other information as well as contributors The goal of this article is to provide an overview of the construction of a blockchain-based tracking infrastructure donation. The goal of this article is to provide an overview of the construction of a blockchain-based tracking infrastructure donation. The system, which is based on blockchain technology, gives contributors, charity foundations, and recipients with transparent accounting of operations. On a philanthropic platform, a transparent contribution channel should be offered, allowing public users and donors to see and monitor where, when, and to whom charitable gifts were distributed.
Assessing Scientific Research Papers with Knowledge Graphs

In recent decades, the growing scale of scientific research has led to numerous novel findings. Reproducing these findings is the foundation of future research. However, due to the complexity of experiments, manually assessing scientific research is laborious and time-intensive, especially in social and behavioral sciences. Although increasing reproducibility studies have garnered increased attention in the research community, there is still a lack of systematic ways for evaluating scientific research at scale. In this paper, we propose a novel approach towards automatically assessing scientific publications by constructing a knowledge graph (KG) that captures a holistic view of the research contributions. Specifically, during the KG construction, we combine information from two different perspectives: micro-level features that capture knowledge from published articles such as sample sizes, effect sizes, and experimental models, and macro-level features that comprise relationships between entities such as authorship and reference information. We then learn low-dimensional representations using language models and knowledge graph embeddings for entities (nodes in KGs), which are further used for the assessments. A comprehensive set of experiments on two benchmark datasets shows the usefulness of leveraging KGs for scoring scientific research.
Transforming historical research practices – a digital infrastructure for the VOC archives (GLOBALISE)

The GLOBALISE project aims to unlock c. 5 million pages from the archives of the Dutch East India Company (VOC) for in-depth research. Funded by the Dutch Research Council 1 , GLOBALISE is a collaboration of the Huygens Institute, the International Institute of Social History and the Digital Infrastructure department of the Royal Netherlands Academy of Arts and Sciences Humanities Cluster; the Computational Linguistics &amp; Text Mining Lab of the Vrije Universiteit Amsterdam; the CREATE research programme of the University of Amsterdam; and the Dutch National Archives. The project team consists of historians, computational linguists, data specialists and software developers. We expect to deliver a first prototype of the research infrastructure in early 2024; all tools and data will be open access available by the end of 2026. As all of this will be a large-scale undertaking, we invite researchers and the wider interested public to work with us, especially to provide data, help annotate or enrich the VOC materials. In return, they get early access to our data and tools. More information about contributing to GLOBALISE can be found on the project website: https://globalise.huygens.knaw.nl/ .
Trending Topics in Software Engineering

Software Engineering (SE) is evolving to make the best out of the constantly changing technological trends, ranging from development to deployment to management and decommissioning of software systems. In this new column Trending Topics in Software Engineering, we aim at providing insights, reports, and outlooks on how researchers and practitioners around the world are working (or planning to work) on those trends. We intend to collect the challenges they are facing or foresee, and explore them in future issues. Our ultimate ambition is to stimulate fruitful discussion in our columns to contribute to identifying the next SE trends, while illustrating them to our audience.
Adaptive rubber-banding system of dynamic difficulty adjustment in racing games

Dynamic difficulty adjustment (DDA) is a highly significant research direction in game AI. Rubber-banding is one of the DDA techniques extensively used in racing games. This paper improves the poor adaptability of the rubber-banding system combined with the game design and development techniques. An Adaptive Rubber-Banding System (ARBS) based on ARBS relationship curve, modular mechanism, as well as detail processing schemes, is implemented in Unreal Engine 4. The paper builds various test programs and compares each system in terms of the number of players, gameplay mode, and track distance. Experimental results demonstrate that ARBS is more adaptive and provides a better experience for players. Game designers and developers are thus able to employ ARBS to design and implement AI systems suitable for racing games effectively.
Continuous Result Delta Evaluation of IR Systems

Classical evaluation of information retrieval systems evaluates a system in a static test collection. In the case of Web search, the evaluation environment (EE) is continuously changing and the hypothesis of using a static test collection is not representative of this changing reality. Moreover, the changes in the evaluation environment, as the document set, the topics set, the relevance judgments, and the chosen metrics, have an impact on the performance measurement [1, 4]. To the best of our knowledge, there is no way to evaluate two versions of a search engine with evolving EEs.
Intelligent Virtual Research Environment for Natural Language Processing (IvrE-NLP)

In the 21st century, natural language processing (NLP) has obtained much prominence for human–machine interaction (HMI). With this interest in natural language processing (NLP) has grown significantly, numerous NLP tools (e.g., morphology, the tagger, and a parser, etc.) have been developed all over the world. Despite having huge importance and requirements, we have noticed gaps for having a comprehensive single framework or platform, which encompass all NLP-related tools and technologies for promoting the research in NLP and sharing the knowledge and resources among NLP researchers required for understanding and building the solution for HMI. Our objective is to apply Software engineering in natural language processing with the concept of an object-oriented model by using a collection of reusable objects by defining the communication protocol, consisting of a set of rules that must be applied to exchange data between two NLP modules. We proposed state of art ivrE—A virtual environment for creating, modifying, executing, and analyzing various NLP solutions and technology. The proposed idea is broadly based on to define own ivrE-NLP object framework model that permits the developer to create, modify, and execute the application and analyze their outcomes by operations on visual representations of the modules. A variety of NLP-based applications (tools, modules, and plugins) already exist, they can publish into store available with environment so it can be used by research community at large. To develop complete NLP framework or platform, we require much more than just assembling or collecting these tools or modules at one place, no matter how good any tool or module is working individually. It requires not only the standards and a set of protocols, but also requires a compliant composition than a pre-defined algorithms and their implementation. In brief, we require a comprehensive open framework to bundle, manage, and integrate set of NLP tools, modules, components, applications, algorithms, and define their associated rules, comprehensive data structures, and knowledge.
Single-shot 3D shape measurement based on RGB dot patterns and stereovision

One-shot projection structured light 3D measurement is a method to establish the stereo matching relationship and reconstruct 3D shape by projecting one pattern. However, the traditional stereo matching algorithm does not solve the problem of low matching accuracy and matching efficiency, which fundamentally limits the accuracy of 3D measurement. As the projector and imaging systems have daily higher resolution and imaging quality, RGB dots projection has more application prospects because of its ability to establish a stereo matching relationship through one projection. In this work, we proposed a single-shot 3D measurement method using line clustering stereo matching, and model correction methods. The projected RGB dots are extracted by slope differenced distribution and area constrained erosion method. Area constrained erosion can solve the problem of the segmented connected blobs caused by insufficient projection resolution. The clustering stereo matching method is utilized to coarse match the segmented center red points. A model correction method is utilized to restore and constrain the pattern that cannot be imaged. Experimental results demonstrated that our method achieves the best accuracy of about 0.089mm, better than the traditional disparity and RGB line method, which may shed light on the proposed method can accurately reconstruct the 3D surface.
An Image Denoising in Spatial Domain using Bilateral Filter

The use of bilateral filters is one of the most efficient and resource-saving image processing techniques. This method does not use edge smoothing to filter the image, but it does use non-linear spatial averaging. The characteristics of the filters in the filtering process outlined above are quite important. The outputs and results are dramatically affected by even minor changes in filter parameter values. The author contributed two pieces to this publication. The author has contributed to the study of parameter selection of bilateral filters that are optimal in nature in the context of picture denoising applications. The second contribution focuses on expanding the current work, the bilateral filters.
PrimaryAI

There is growing awareness of the central role that artificial intelligence (AI) plays now and in children's futures. This has led to increasing interest in engaging K-12 students in AI education to promote their understanding of AI concepts and practices. Leveraging principles from problem-based pedagogies and game-based learning, our approach integrates AI education into a set of unplugged activities and a game-based learning environment. In this work, we describe outcomes from our efforts to co design problem-based AI curriculum with elementary school teachers.
Moving Vehicle Detection in Satellite Video via Background Subtraction and Global–Local Features Fusion Faster R-CNN

With the successful operation of the Jilin-1 constellation and other staring satellites in recent years, satellite videos have become new resources for real-time tasks such as surveillance. However, it is difficult to detect small objects in satellite videos due to the high resolution of the satellite video, and the low contrast between the objects and background. This paper develops a novel small moving vehicle detection scheme by exploiting the spatial–temporal information of satellite video. The proposed method consists of four stages: (i) pre-processing, to filter, adjust the contrast, and register the satellite video frame by frame; (ii) extracting the candidate moving vehicle region based on the improved ViBe method; (iii) detecting the vehicle both moving and stationary by the global–local features fusion Faster R-CNN; and (iv) merging the detected results from previous stages to propose the final results, accidental miss detection will be recalled by the inter frame compensation module. Experimental results show that the proposed method demonstrates better performance even when compared to some state-of-the-art methods.
SatMVS: A Novel 3D Reconstruction Pipeline for Remote Sensing Satellite Imagery

Recently, 3D reconstruction based on satellite imagery has been a hot topic in the remote sensing community. Its output called the digital surface model (DSM) can be widely used in urban planning, military navigation, and so on. Nowadays, almost all satellite image 3D reconstruction pipelines are based on traditional stereo matching algorithms which have low accuracy and long runtime. In contrast, the neural networks based on multi-view stereo (MVS) have shown great reconstruction performance in the computer vision community. To transfer the advanced MVS neural networks to the remote sensing community, we propose a novel 3D reconstruction pipeline called SatMVS. First, the input satellite images and their rational polynomial camera parameters (RPC) are cropped into small tiles according to the designated output DSM region. Second, the RPC parameters are converted to the projection matrix for the homography transform which is the core step in MVS neural networks. Third, the advanced MVS neural network is applied to estimate height maps from satellite images. At last, all inferred height maps from small tiles are converted to 3D points in Universal Transverse Mercator (UTM) coordinate system and fused to get the final complete DSM. In order to train and test SatMVS, we build a novel satellite imagery 3D reconstruction dataset called SatMVS3D dataset, which contains satellite images, RPC parameters, and height map ground truth that covers about 3km2. The experimental results on the SatMVS3D dataset demonstrate that our proposed pipeline can provide robust reconstruction performance.
EFEKTIVITAS MEDIA WORDWALL UNTUK MENINGKATKAN HASIL BELAJAR EKONOMI SISWA MAN 1 LAMONGAN

This study aims to analyse the effect of Wordwall learning media in increase economics learning outcomes for students in class XI IPS 1 MAN 1 Lamongan. The subjects of this study were 24 students of class XI IPS 1 MAN 1 Lamongan. This research was an experimental study with one-group pre-test post-test design.The analytical technique used reliability test, normality test, homogenity test, N-Gain test, and also Wilcoxon test. Based on the results of the Wilcoxon test analysis on the pre-test and post-test data of the experimental group showed a sig (2-tailed) value of 0.001 &lt; 0, 05, which means that there is a significant difference between before and after treatment. It can be concluded that Wordwall learning media (X) has a significant effect on student learning outcomes of XI IPS 1MAN 1 Lamongan (Y).
Parting With Illusions About Synthetic Data

Lack of labeled data is an omnipresent issue in deep learning for computer vision. Generating synthetic data seems to offer a simple solution for this problem. Once a data generator is set up, arbitrary amounts of fully-labeled data can be created. This data is then used to train a neural network which finally solves a problem on real data, e.g. by detecting cars.We argue that leveraging synthetic data like that rarely works in practice. Synthetic images often have a significant domain gap to real data, leading to reduced performance on the target domain. In experiments on several synthetic-to-real benchmarks including Sim10k to CityScapes, we show that state-of-the-art domain adaptation methods trained on thousands of synthetic images are usually outperformed by ordinary supervised learning on 14 to 70 images from the target domain.
Robust and Provably Secure Attribute-Based Encryption Supporting Access Revocation and Outsourced Decryption

Attribute based encryption (ABE) is a cryptographic technique allowing fine-grained access control by enabling one-to-many encryption. Existing ABE constructions suffer from at least one of the following limitations. First, single point of failure on security meaning that, once an authority is compromised, an adversary can either easily break the confidentiality of the encrypted data or effortlessly prevent legitimate users from accessing data; second, the lack of user and/or attribute revocation mechanism achieving forward secrecy; third, a heavy computation workload is placed on data user; last but not least, the lack of adaptive security in standard models. In this paper, we propose the first single-point-of-failure free multi-authority ciphertext-policy ABE that simultaneously (1) ensures robustness for both decryption key issuing and access revocation while achieving forward secrecy; (2) enables outsourced decryption to reduce the decryption overhead for data users that have limited computational resources; and (3) achieves adaptive (full) security in standard models. The provided theoretical complexity comparison shows that our construction introduces linear storage and computation overheads that occurs only once during its setup phase, which we believe to be a reasonable price to pay to achieve all previous features.
Reviews Gone South: A Subversive Experiment on Participatory Design Canons

In this paper, we present a performative exercise aimed at revisiting, from Latin American "Southern" perspectives, two seminal articles in Participatory Design (PD): "Co-creation and the new landscapes of design" by Sanders & Stappers, and "Participation in Design Things" by Ehn. The goal is to turn peer review into a critical tool to inform established knowledge in PD with Latin American perspectives, using five principles: citational justice, epistemic justice, emancipation, relationality and positionality. Through this exercise we offer reflections, experiences, and literature to critically expand PD's core as the field moves to global contexts. We discuss two tensions in this move: 1) design as socio-historical production, and 2) emancipatory commitments of participation when 'turning South'. We end with a critical reflection about the potential and limitations of our reformulation of peer review and its Southern politics. We offer these reflections to PD as a way to invite radical political solidarities across borders.
Decentralising the Digital Economy: The Blockchain and Competition Remedy

The global economy is in a precarious situation. The digital ecosystem has been a major contributor in sustaining the global economy and mitigating both the financial and social crises that are plaguing the world. The merit and reliability of technology have been reinforced in light of recent events. The digital economy is not perfect, to say the least, with major concerns regarding rampant anti-competitive conduct by the few enterprises that dominate the market. Competition law around the world has proven to be inefficient in dealing with these technological giants and before a remedy could be found, blockchain technology has started gaining traction which has the potential to change the face of not just the digital economy, but society as we know it. The virtue of decentralisation has long been considered a desirable state for the digital economy and blockchain offers the means to achieve this, and much more. This paper explores how competition law and blockchain both seek decentralisation and aims to ascertain how they can help each other achieve this in a way that builds a sustainable digital economy. The paper makes an exploratory analysis of the various interactions between competition laws and blockchain, based on which we conclude that regulating blockchain through competition laws can address the pressing needs of the global digital economy.
The Economic, Social, and Political Dimensions of Platform Studies in Education

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share MailTo Twitter LinkedIn Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Alyssa Napier, Abigail Orrick; The Economic, Social, and Political Dimensions of Platform Studies in Education. Harvard Educational Review 1 June 2022; 92 (2): 206–208. doi: https://doi.org/10.17763/1943-5045-92.2.206 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest Search
Multiple Challenges Facing Auditing and Assurance During the Covid-19 in Jordan

The study aimed to identify the challenges and problems that faced the Jordanian auditor when providing audit and assurance services in light of the Corona pandemic and its impact on performing tasks, collecting evidence, issuing the report, and proposing proposals that contribute to reducing these problems or challenges in light of the spread of mutants of the virus. The length of stay and the uncertainty associated with it. To achieve the objectives of the study, challenges and problems were divided into two periods, the first period, which includes the sudden start of the virus during the year 2020 and how it affects the audits of financial statements for the year 2019 and confirmation. That follows the 2021 period and the emergence of mutations from the Covid virus (Delta and Amicoron) and the impact of the mutation on auditing, assurance and financial statements. To conduct this study, the researcher used the inductive approach from reviewing the professional literature issued by professional organizations and professional companies related to the virus and its impact on auditing and confirmation processes and financial statements, and formulating a questionnaire about the expected challenges and mechanisms of assistance to overcome. A random sample was chosen from among these Jordanian audit offices, where The distribution of 180 questionnaires to them through the e-mail installed with the association of auditors, where the number of questionnaires that were answered was 160-questionnaires. The study recommends the need to prepare a guide to guide auditors on the implementation of audits in light of the pandemic and the spread of the virus, and to hold training workshops by professional associations and audit offices on the mechanism for carrying out audit tasks, the mechanism for evaluating audit and assurance risks, preparing the report and modifying it, and using alternative methods in Gathering evidence and moving towards evidence of the use of electronic techniques in collecting evidence.
Smart Wheelchair Management System for Disabled People

Over the recent years, the field of integrating human machine interface to power wheelchairs for autonomous driving control has gained great attention by the scientific and engineering communities. The fundamental objective of the human—wheelchair interface is to permit the individual to control the versatility of the seat in least exertion and with more vigour and security. A large variety of electric powered wheelchairs do not meet the needs of a substantial proportion of users because of requirement of muscle force and accuracy. An assistive component for executing well-heeled wheelchair motion for seriously incapacitated individuals is introduced. For making the lives of disabled people easier and less dependent, the wheelchair has been incorporated with eye control interface for the movement and direction control. For the safety of the patient, obstacle avoidance and fall alert mechanisms have been included. Also, body temperature and heart rate are two parameters that are considered for health monitoring of the patient.