Reviewerly/exHarmony huggingface dataset:
The Symmetric and Asymmetric Effect of Remittances on Financial Development: Evidence from South Africa

Investigating the remittance-financial development relationship is an ongoing endeavor among economists and policy makers. Building and improving on the existing work, this study considers the possibility that the relation between remittances and financial development is potentially asymmetric. This study applies the linear ARDL and captures the possibility of an asymmetrical relationship by applying the non-linear Autoregressive Model (NARDL). Using NARDL, an attempt is made to estimate the short-run and long-run asymmetric responses of financial development through positive and negative partial sum decompositions of changes in remittances. To assess the robustness of the ARDL and NARDL estimates, a battery of long-run robustness tests were employed, including the linear and nonlinear versions of the fully modified ordinary least squares (FMOLS). Annual data series from 1980 to 2017, derived from the World Development Indicators, Fred Economic data and Penn World Tables were used for this study. The ARDL results reveal a positive and significant impact of remittances on financial development, whereas NARDL estimations suggest a both positive and negative shock of remittances on financial development in the long run: a percentage (%) increase in the remittances brings about 0.121568 percent increase in financial development, whereas a one-percent decrease in remittances produces a 0.33363 percent decrease in financial development.
Collaborative Filtering Model of Graph Neural Network Based on Random Walk

This paper proposes a novel graph neural network recommendation method to alleviate the user cold-start problem caused by too few relevant items in personalized recommendation collaborative filtering. A deep feedforward neural network is constructed to transform the bipartite graph of user–item interactions into the spectral domain, using a random wandering method to discover potential correlation information between users and items. Then, a finite-order polynomial is used to optimize the convolution process and accelerate the convergence of the convolutional network, so that deep connections between users and items in the spectral domain can be discovered quickly. We conducted experiments on the classic dataset MovieLens-1M. The recall and precision were improved, and the results show that the method can improve the accuracy of recommendation results, tap the association information between users and items more effectively, and significantly alleviate the user cold-start problem.
FloorUSG: Indoor floorplan reconstruction by unifying 2D semantics and 3D geometry

This paper proposes a multistage floorplan reconstruction approach from RGB images and a dense 3D mesh, called FloorUSG, by combining 2D plane instances and 3D plane primitives. In primitive detection, plane instances inferred from images complement the results of traditional 3D plane detection well. And in the optimization, both the plane confidence and the geometric quality of data are considered to select the optimal subset from the candidates. Different from existing methods that rely on delicate corner detection from a planar graph or pure geometric 3D plane detection, our framework accurately recovers the location of the floorplan via 2D–3D primitive fusion. Experimental results indicate that our method has the ability to recover detailed structures of scenes of different scales and can reconstruct the floorplan from imperfect data with high robustness compared to the state-of-the-art algorithms.
The Bitcoin Halving Cycle Volatility Dynamics and Safe Haven-Hedge Properties: A MSGARCH Approach

This paper introduces a unique perspective towards Bitcoin safe haven and hedge properties through the Bitcoin halving cycle. The Bitcoin halving cycle suggests that Bitcoin price movement follows specific sequences, and Bitcoin price movement is independent of other assets. This has significant implications for Bitcoin properties, encompassing its risk profile, volatility dynamics, safe haven properties, and hedge properties. Bitcoin's institutional and industrial adoption gained traction in 2021, while recent studies suggest that gold lost its safe haven properties against the S&P500 in 2021 amid signs of funds flowing out of gold into Bitcoin. Amid multiple forces at play (COVID-19, halving cycle, institutional adoption), the potential existence of regime changes should be considered when examining volatility dynamics. Therefore, the objective of this study is twofold. The first objective is to examine gold and Bitcoin safe haven and hedge properties against three US stock indices before and after the stock market selloff in March 2020. The second objective is to examine the potential regime changes and the symmetric properties of the Bitcoin volatility profile during the halving cycle. The Markov Switching GARCH model was used in this study to elucidate regime changes in the GARCH volatility dynamics of Bitcoin and its halving cycle. Results show that gold did not exhibit safe haven and hedge properties against three US stock indices after the COVID-19 outbreak, while Bitcoin did not exhibit safe haven or hedge properties against the US stock market indices before or after the COVID-19 pandemic market crash. Furthermore, this study also found that the regime changes are associated with low and high volatility periods rather than specific stages of a Bitcoin halving cycle and are asymmetric. Bitcoin may yet exhibit safe haven and hedge properties as, at the time of writing, these properties may manifest through sustained adoption growth.
Augmented Behavioral Annotation Tools, with Application to Multimodal Datasets and Models: A Systematic Review

Annotation tools are an essential component in the creation of datasets for machine learning purposes. Annotation tools have evolved greatly since the turn of the century, and now commonly include collaborative features to divide labor efficiently, as well as automation employed to amplify human efforts. Recent developments in machine learning models, such as Transformers, allow for training upon very large and sophisticated multimodal datasets and enable generalization across domains of knowledge. These models also herald an increasing emphasis on prompt engineering to provide qualitative fine-tuning upon the model itself, adding a novel emerging layer of direct machine learning annotation. These capabilities enable machine intelligence to recognize, predict, and emulate human behavior with much greater accuracy and nuance, a noted shortfall of which have contributed to algorithmic injustice in previous techniques. However, the scale and complexity of training data required for multimodal models presents engineering challenges. Best practices for conducting annotation for large multimodal models in the most safe and ethical, yet efficient, manner have not been established. This paper presents a systematic literature review of crowd and machine learning augmented behavioral annotation methods to distill practices that may have value in multimodal implementations, cross-correlated across disciplines. Research questions were defined to provide an overview of the evolution of augmented behavioral annotation tools in the past, in relation to the present state of the art. (Contains five figures and four tables).
Design of a Company-Engagement Model for Procurement and Supply Management Classes

Background: There are opportunities to enrich procurement and supply management classes to be more international and to engage with industry more deeply. Methods: Using a design science method this paper develops a course design for teaching procurement that enriches existing approaches to company engagement in courses. Results: Beyond the use of stand-alone guest lectures, case studies, or in-company projects, the design involved semester-long collaboration around core topics for the course, involving company lectures and multiple student projects aligned with the course topics, engaging multiple levels of managers from around the globe. Based upon an evaluation of the first-year collaboration, improvements were identified that were implemented in year two of the collaboration. Conclsuions: The design brings course learning into a real-world context and supports student skill development through project work.
Monocular-Vision-Based Moving Target Geolocation Using Unmanned Aerial Vehicle

This paper develops a framework for geolocating a ground moving target with images taken from an unmanned aerial vehicle (UAV). Unlike the usual moving target geolocation approaches that rely heavily on a laser rangefinder, multiple UAVs, prior information of the target or motion assumptions, the proposed framework performs the geolocation of a moving target with monocular vision and does not have any of the above restrictions. The proposed framework transforms the problem of moving target geolocation to the problem of stationary target geolocation by matching corresponding points. In the process of corresponding point matching, we first propose a Siamese-network-based model as the base model to match corresponding points between the current frame and the past frame. Besides the introduction of a base model, we further designed an enhanced model with two outputs, where a row-ness loss and a column-ness loss are defined for achieving a better performance. For the precision of corresponding point matching, we propose a compensation value, which is calculated from the outputs of the enhanced model and improves the accuracy of corresponding point matching. To facilitate the research on corresponding point matching, we constructed a dataset containing various aerial images with corresponding point annotations. The proposed method is shown to be valid and practical via the experiments in simulated and real environments.
Betty: Enabling Large-Scale GNN Training with Batch-Level Graph Partitioning

The Graph Neural Network (GNN) is showing outstanding results in improving the performance of graph-based applications. Recent studies demonstrate that GNN performance can be boosted via using more advanced aggregators, deeper aggregation depth, larger sampling rate, etc. While leading to promising results, the improvements come at a cost of significantly increased memory footprint, easily exceeding GPU memory capacity. In this paper, we introduce a method, Betty, to make GNN training more scalable and accessible via batch-level partitioning. Different from DNN training, a mini-batch in GNN has complex dependencies between input features and output labels, making batch-level partitioning difficult. Betty introduces two noveltechniques, redundancy-embedded graph (REG) partitioning and memory-aware partitioning, to effectively mitigate the redundancy and load imbalances issues across the partitions. Our evaluation of large-scale real-world datasets shows that Betty can significantly mitigate the memory bottleneck, enabling scalable GNN training with much deeper aggregation depths, larger sampling rate, larger training batch sizes, together with more advanced aggregators, with a few as a single GPU.
GZKP: A GPU Accelerated Zero-Knowledge Proof System

Zero-knowledge proof (ZKP) is a cryptographic protocol that allows one party to prove the correctness of a statement to another party without revealing any information beyond the correctness of the statement itself. It guarantees computation integrity and confidentiality, and is therefore increasingly adopted in industry for a variety of privacy-preserving applications, such as verifiable outsource computing and digital currency.
Revealing and Classification of Deepfakes Video's Images using a Customize Convolution Neural Network Model

Deepfake has been exploited in recent years despite its widespread usage in a variety of areas to create dangerous material such as fake movies, rumors, and false news by changing or substituting the face information of the sources and so poses enormous security concerns to society. Research on active detection & prevention technologies is critical as deepfake continues to evolve. Deepfake has been a blessing, but we've taken advantage of it by utilizing it to swap faces. Deepfake is a new subdomain of Artificial Intelligence (AI) technology in which one person's face is layered over another person's face, which is becoming more and more popular on social networking sites. Deepfake pictures and videos can now be created much more quickly and cheaply due to ML (Machine Learning), which is a primary component of deepfakes. Despite negative connotations attached to the term "deepfakes," technology is increasingly being used in commercial & individual contexts. New technical advancements have made it more difficult to distinguish between deepfakes and images that have been digitally manipulated. The rise of deepfake technologies has sparked a growing sense of unease. The primary goal of this project is to properly distinguish deepfake pictures from real images using deep learning techniques. In this study, we implemented a customized CNN algorithm to identify deepfake pictures from a video dataset and conducted a comparative analysis with two other methods to determine which way was superior. The Kaggle dataset was used to train & test our model. Convolutional neural networks (CNNs) have been used in this research to distinguish authentic & deepfake images by training three distinct CNN models. A customized CNN model, which includes several additional layers such as a dense layer, MaxPooling, as well as a dropout layer, has also been developed and implemented. This method follows the frames extraction, face feature extraction, data preprocessing, and classification phases in determining whether Real or Fake images in the video reflect the objectives. Accuracy, loss, and the area under the receiver operating characteristic (ROC) curve were used to characterize the data. Customized CNN outperformed all other models, achieving 91.4% accuracy, a reduced loss value of 0.342, as well as an AUC of 0.92. Besides, we obtained 85.2% testing accuracy from the CNN and 95.5% testing accuracy from the MLP-CNN model.
Detection of Reading Impairment from Eye-Gaze Behaviour using Reinforcement Learning

Experimental psychology and neuroscience reveal that decision-behavior plays a dominant role in human-selective-attention when it comes to reading, object and scene detection. Difficulties in reading are easily reflected by eye-movement patterns. Hence, modelling eye-gaze behaviour for normal readers and people with reading impairments can greatly help in contrasting reading strategies used, which can in turn help in early identification and diagnosis of impairments such as dyslexia. This paper introduces a novel method of formulating a reinforcement learning model that is explainable, and can obtain the sequence of gaze targets based on recorded observations of dyslexic and non-dyslexic children. Results reveal that despite being a less sophisticated model, it is able to obtain the optimal reading policy of the ideal reader, from a set of good and poor readers with the help of a strong reward system and Q-Learning agent.
Targeted aspect-based emotion analysis to detect opportunities and precaution in financial Twitter messages

Microblogging platforms, of which Twitter is a representative example, are valuable information sources for market screening and financial models. In them, users voluntarily provide relevant information, including educated knowledge on investments, reacting to the state of the stock markets in real-time and, often, influencing this state. We are interested in the user forecasts in financial, social media messages expressing opportunities and precautions about assets. We propose a novel Targeted Aspect-Based Emotion Analysis (tabea) system that can individually discern the financial emotions (positive and negative forecasts) on the different stock market assets in the same tweet (instead of making an overall guess about that whole tweet). It is based on Natural Language Processing (nlp) techniques and Machine Learning streaming algorithms. The system comprises a constituency parsing module for parsing the tweets and splitting them into simpler declarative clauses; an offline data processing module to engineer textual, numerical and categorical features and analyse and select them based on their relevance; and a stream classification module to continuously process tweets on-the-fly. Experimental results on a labelled data set endorse our solution. It achieves over 90% precision for the target emotions, financial opportunity, and precaution on Twitter. To the best of our knowledge, no prior work in the literature has addressed this problem despite its practical interest in decision-making, and we are not aware of any previous nlp nor online Machine Learning approaches to tabea.
Automatic generation of scientific papers for data augmentation in document layout analysis

Document layout analysis is an important task to extract information from scientific literature. Deep-learning solutions for document layout analysis require large collections of training data that are not always available. We generate a large number of synthetic pages to subsequently train a neural network to perform document object detection. The proposed pipeline allows users to deal with less common layouts for which it is not easy to find large annotated datasets. High-quality annotations for a small collection of papers are obtained through a semi-automatic approach. Then, a generative model, based on LayoutTransformer, is used to generate plausible layouts that are subsequently populated with random information to perform data augmentation. We evaluate the proposed method considering scientific articles with two different types of layouts: double and single columns. For double-column papers, we improve detection by 1% starting from 385 manually annotated scientific articles. For single-column papers, we improve detection by 49% starting from 218 articles.
Modulation of light by coherent perfect polarization rotation in Ce:YIG

We present a scheme in which light is modulated through coherent perfect activity. By irradiating two coherent beams from both sides to a magnetic material and controlling their relative phases, we can manipulate the output light. Through coherent perfect polarization rotation process, large polarization rotation angles can be observed, which makes possible the light modulation. It is realized that this coherent process provides the light modulation by using a very low intensity coherent light.
VISHIEN-MAAT: Scrollytelling visualization design for explaining Siamese Neural Network concept to non-technical users

The past decade has witnessed rapid progress in AI research since the breakthrough in deep learning. AI technology has been applied in almost every field; therefore, technical and non-technical end-users must understand these technologies to exploit them. However existing materials are designed for experts, but non-technical users need appealing materials that deliver complex ideas in easy-to-follow steps. One notable tool that fits such a profile is scrollytelling, an approach to storytelling that provides readers with a natural and rich experience at the reader's pace, along with in-depth interactive explanations of complex concepts. Hence, this work proposes a novel visualization design for creating a scrollytelling that can effectively explain an AI concept to non-technical users. As a demonstration of our design, we created a scrollytelling to explain the Siamese Neural Network for the visual similarity matching problem. Our approach helps create a visualization valuable for a short-timeline situation like a sales pitch. The results show that the visualization based on our novel design helps improve non-technical users' perception and machine learning concept knowledge acquisition compared to traditional materials like online articles.
Research on Recommendation System of Online Chinese Learning Resources Based on Multiple Collaborative Filtering Algorithms (RSOCLR)

This study is an innovative study on intelligent online language teaching that is based on the development of online Chinese learning resources and learner needs. The recommendation technology of collaborative filtering algorithm is used to find, analyze and recommend online Chinese learning resources that meet the needs of learners and teachers. This study assists learners and teachers in quickly searching for various learning resources under specified conditions, analyzing the resources obtained in detail, and providing results based on machine learning and big data sample analysis. The research is structured as follows to highlight the characteristics of Chinese learning resources and the personalized demand orientation of recommendation results: (1) Based on the personalized demand orientation of learners’ language learning and the goal oriented theoretical standards of Chinese teaching, a recommendation system based on multiple collaborative filtering hybrid algorithms is designed; (2) Evaluate the operation of the recommendation system using teaching practice; (3) This article investigates the influence of online language learning from technical factors and the operation mechanism of Chinese learning resource recommendation. The results of the experiments show that this hybrid approach has some advantages when it comes to recommending Chinese learning resources.HIGHLIGHTSA system for recommendating online Chinese learning resources is constructed.The recommendation results based on learners’ personalized needs and learning goal oriented design have high accuracy.The recommendation system based on hybrid mode has a high recommendation effect.The recommendation system can find and predict learners’ learning needs.Cultural differences, mental states, and learning psychology from evaluators may lead to differences in recommendation results.
iSGAN: An Improved SGAN for Crowd Trajectory Prediction from Surveillance Videos

AI and computer vision are making strides every day with newer solutions to real-world problems and emerging challenges. An extremely important and useful application of computer vision is surveillance systems for security in open or closed spaces. This research work investigates a security application by forecasting crowd trajectories using surveillance videos. Generative Adversarial Networks(GANs) are commonly used for video processing applications like future frame synthesis. Social GAN(SGAN) is one of the recent models that has been used for crowd trajectory prediction. However, SGAN is a two stream architecture that keeps the background and foreground separate and predicts the foreground changes only, keeping the background static. In this paper, a novel GAN architecture called iSGAN is proposed for crowd trajectory prediction. The proposed model is an improvement of SGAN and it incorporates the onestream architecture of improved Video GAN (iVGAN) into SGAN. The one-stream architecture doesn't separate the backgroungd and foreground and thus allowing to captiure the dynamics of the video (camera zoom, pan, lighting change, obstacles etc.). The proposed iSGAN model has been assessed on the benchmark ETH Pedestrian dataset and it is found that the iSGAN Model outperforms other existing trajectory prediction models.