Reviewerly/exHarmony huggingface dataset:
The impact of the Covid-19 crisis on the liquidity of cryptocurrencies

We tracked the impact of the Covid-19 crisis on the liquidity of10 crypto currencies for the period from July 31, 2019 (before the crisis) to December 31, 2020 (in the Covid -19 era). We applied the vector error correction model to each crypto currency. The results show that in the short term, the COVID-19 crisis has no influence on the liquidity of cryptocurrencies except for Cardano. Similarly, in the long term, it has no impact on the liquidity of cryptocurrencies with the exception of Binance coin, Tezos and Cardano. The assumption of having a common liquidity factor implies that, in a shock of liquidity, the entire market will be affected. The cryptocurrency market, however, has proven to be different.
A Study of Role-Play Method and Human Augmentation to Enhance Digital Content Creator

This research has designed and developed a The Role-Play Method Human Augmentation Learning Model to Enhance Digital Content Creator, aiming to study and synthesize the framework for developing role-playing Method Human Augmentation Simulation Learning models to enhance digital content creators and study feasibility. The development approach is a model for managing, studying or reviewing the content of studying in covid-19 situations, there are 50 undergraduate students in digital media technology, Rajamangala University of Technology Tawan-Ok. Research content uses digital film production course content. Digital Media Technology Program in Multimedia Technology, Rajamangala University of Technology Tawan-Ok, 6 lessons, trialled in semester 2, academic year 2022, the method of conducting research is divided into 5 phases: Phase 1 synthesis of conceptual framework for developing The Role-Play Method Human Augmentation Learning Model to Enhance Digital Content Creator Phase 2 Synthesis of substanceist features. The expected benefit is that it competes with Digital Content Creator that are suitable for the context of Thailand. Educational institutions that offer courses in relevant disciplines have guided them with Role-Play learning methods to develop Digital Content Creator.
Feature and region based image search and navigation

The transmission speed of pictures is very important. With the development of science and technology, more andmorepeople are paying attention to this field in recent years. The goal of this paper is to discuss a client-server systemthat enables an interactive user to efficiently navigate through a database of related photos, using ROI (regions of interest)within each image as navigation "portals". The study's objective is to emphasise the importance of highly efficient interactive communication system. There are three approaches to be considered: The first method (existing system) istotransmit an entire image; The second method is to communicate region of interest (ROI); The third methodistocommunicate only SIFT features[14]. JPEG2000 and Scale-Invariant Feature Transform (SIFT) are powerful tool inthese three methods. This paper investigated the responsiveness of these three different methods. When faster andmoreresource-efficient methods are found, the system will become faster. In the experiment, the database of photos takenbymyself was used, and the experimental data was collected. Finally, it was concluded that method 3 is the fastest method.
Discovery of Frequent Pagesets from Weblog Using Hadoop Mapreduce Based Parallel Apriori Algorithm

Web usage mining is a critical stage in analyzing user behavior that involves mining frequently visited pages. Mining usage patterns from a big weblog file using existing frequent mining techniques are inefficient in terms of storage and performance. These algorithms did not perform well while mining big volumes of data due to CPU, storage, and main memory restrictions. To address these challenges, this paper proposes the parallel Apriori technique, which is based on Hadoop MapReduce and is an effective, extensible, and easy programming paradigm for processing large volumes of data. It employs pruning and session reduction techniques to eliminate infrequent pagesets, reducing the time required to process massive amounts of weblog data. It also employs clusters of processing nodes to improve performance. Experiments are conducted to demonstrate performance of the suggested approach using four genuine weblog data sets. Experiments reveal that our suggested system beats existing sequential association rule mining systems in finding similar pagesets, such as Apriori, Eclat, and FP-Growth.
QA4QG: Using Question Answering to Constrain Multi-Hop Question Generation

Multi-hop question generation (MQG) aims to generate com-plex questions which require reasoning over multiple pieces of information of the input passage. Most existing work on MQG has focused on exploring graph-based networks to equip the traditional Sequence-to-sequence framework with reasoning ability. However, these models do not take full advantage of the constraint between questions and answers. Furthermore, studies on multi-hop question answering (QA) suggest that Transformers can replace the graph structure for multi-hop reasoning. Therefore, in this work, we propose a novel framework, QA4QG, a QA-augmented BART-based framework for MQG. It augments the standard BART model with an additional multi-hop QA module to further con-strain the generated question. Our results on the HotpotQA dataset show that QA4QG outperforms all state-of-the-art models, with an increase of 8 BLEU-4 and 8 ROUGE points compared to the best results previously reported. Our work suggests the advantage of introducing pre-trained language models and QA module for the MQG task.
Artificial Intelligence-based Multiomics Integration Model for Cancer Subtyping

One out of every six deaths in the world is attributed to Cancer. On the whole, Cancer is a personalized and heterogeneous condition with a wide variety of subtypes, each with its characteristics and pathogeneses. Cancers differ in their clinical characteristics and survival times due to the distinct nature of similar tumors. Which renders them difficult to classify, predict and diagnose. Recently, joint analysis of cancers across multiple omics layers like genomics, epigenomics, and transcriptomics has provided a new perspective on cancer deregulation. Combined analysis of these complementary signals from different cellular functional layers has the potential to help in fine-tuned characterization, classification, and early diagnosis of cancers. However, in addition to the multitude of heterogeneous variables in omics data, multi-omics data pose many challenges, because of their disparate nature, in data integration and knowledge discovery. This study examines the challenge of multi-omics data integration using novel Deep learning (DL) methods like Deep Auto-encoder. We explore the application of Auto-encoders for multi-omics analysis to identify homogeneous cancer subtypes, sharing similar etiology, responses, and clinical outcomes.
A unified framework for abstractive summarization over prompt language model and pointer mechanism

In this paper, we propose a unified framework for an abstractive summarization method which uses the prompt language model and a pointer mechanism. The abstractive summarization problem usually includes a text encoder and a text decoder. Current methods usually employ an encoder-decoder architecture to condense and paraphrase a document. To better paraphrase a document, we propose a unified framework for an abstractive summarization model that only uses a topic-sensitive decoder. Our model has a prompt input module, a text decoder and a pointer mechanism. We apply our model to Xsum, Gigaword, and CNN/DailyMail summarization datasets, and experimental results demonstrate that our model has achieved state-of-the-art results on the Xsum dataset and comparable results on the other two datasets.
Histogram-Guided Semantic-Aware Colorization

User-guided colorization can predict the colors of a grayscale image according to user inputs, including exemplar images, local inputs and global inputs. Global inputs-based methods are probably the easiest ones to use, but are hard to distribute the input colors into correct regions, due to the lack of color-semantic correspondences. In this paper, we propose a novel histogram-guided semantic-aware colorization method, which explicitly builds the correspondences between global colors and local features with an attention mechanism and uses a differentiable histogram loss to impose the histogram of the results. Our method starts with a semantic-aware subnetwork to build the color-semantic correspondences, followed by a colorization subnetwork to reconstruct the color channels. Experiments demonstrate that our method can effectively control the results with the input histogram. Extensive visual, numerical and user study comparisons show that our method outperforms other global input-based state-of-the-art methods in color naturalness and consistency.
Domain-Agnostic Meta-Learning for Cross-Domain Few-Shot Classification

Few-shot classification requires one to classify instances of novel classes, given only a few examples of each class. Although promising meta-learning methods have been proposed recently, there is no guarantee that existing solutions would generalize to novel classes from an unseen domain. In this paper, we tackle the challenging task of cross-domain few-shot classification and propose Domain-Agnostic Meta-Learning (DAML) algorithm. Our DAML, serving as an optimization strategy, learns to adapt the model to novel classes in both seen and unseen domains by data sampled from multiple domains with desirable task settings. In our experiments, we apply DAML on three popular metric-based models under cross-domain settings. Experiments on several benchmarks (mini-ImageNet, CUB, Cars, Places, Plantae and META-DATASET) show that DAML significantly improves the generalization ability of learning models, and addresses cross-domain few-shot classification with promising results.
Graph Convolutional Networks With Autoencoder-Based Compression And Multi-Layer Graph Learning

This work aims to propose a novel architecture and training strategy for graph convolutional networks (GCN). The proposed architecture, named Autoencoder-Aided GCN (AA-GCN), compresses the convolutional features in an information-rich embedding at multiple hidden layers, exploiting the presence of autoencoders before the point-wise nonlinearities. Then, we propose a novel end-to-end training procedure that learns different graph representations per layer, jointly with the GCN weights and auto-encoder parameters. As a result, the proposed strategy improves the computational scalability of the GCN, learning the best graph representations at each layer in a data-driven fashion. Several numerical results on synthetic and real data illustrate how our architecture and training procedure compares favorably with other state-of-the-art solutions, both in terms of robustness and learning performance.
Neo-Twin Support Vector Machines for Pattern Classification

In this paper, a new variant of Twin Support Vector Machines (TSVM) termed as Neo-Twin Support Vector Machines (Neo-TSVM) has been proposed for binary pattern classification. TSVM uses hinge loss to allow optimal separation from the opposite class, casting it as a constrained optimisation problem. Neo-TSVM presents a simpler model which eliminates the constraints and cast it as an Unconstrained Minimisation Problem (UMP). Further to allow, better separation between the non-parallel hyperplanes, the notion of angle has also been introduced in the optimisation problem. For testing the efficacy of the proposed classifier, experiments have been conducted on benchmark datasets, and it is observed that the proposed classifier achieves results comparable to that of TSVM, and is also time efficient.
Synpose: A Large-Scale and Densely Annotated Synthetic Dataset for Human Pose Estimation in Classroom

Deep learning-based methods for human pose estimation require large volumes of training data to achieve superior performance. However, data acquisition in classroom environments raises privacy concerns, which will undoubtedly hinder the development of the latest deep learning techniques in education domain. Due to the absence of large, richly annotated classroom datasets, research into classroom observation has had to be done by manually collecting and annotating datasets. Unfortunately, the annotation of such data is time-consuming and challenging in over-crowded classrooms. To break through these limitations, we open source SynPose, a large, densely labeled synthetic dataset specifically designed for crowded human pose estimation in classroom and meeting scenarios. Moreover, we propose a novel CTGAN to bridge the domain gap. Comprehensive experiments on real-world classroom images show that our proposed dataset and method deliver important performance benefits compared to existing datasets, revealing the potential of SynPose for future studies.
A YOLOR Based Visual Detection of Amateur Drones

The popularity of Unmanned Aerial Vehicles (UAV) has increased considerably among today&#x2019;s technologies. Especially in the field of defense technologies, it has become very important to detect UAVs in military sheltered areas, border violations or any special area that needs protection. In this study, the recently released YOLOR was used to detect UAVs. The data set created from the UAV images was trained with the YOLOR model, and as a result of the experiments, it was experimentally proven that the YOLOR model has a 95.9% success rate and is better than other object detection models.
ProdRev: A DNN framework for empowering customers using generative pre-trained transformers

Following the pandemic, customers, preference for using e-commerce has accelerated. Since much information is available in multiple reviews (sometimes running in thousands) for a single product, it can create decision paralysis for the buyer. This scenario disempowers the consumer, who cannot be expected to go over so many reviews since its time consuming and can confuse them. Various commercial tools are available, that use a scoring mechanism to arrive at an adjusted score. It can alert the user to potential review manipulations. This paper proposes a framework that fine-tunes a generative pre-trained transformer to understand these reviews better. Furthermore, using "common-sense" to make better decisions. These models have more than 13 billion parameters. To fine-tune the model for our requirement, we use the curie engine from generative pre-trained transformer (GPT3). By using generative models, we are introducing abstractive summarization. Instead of using a simple extractive method of summarizing the reviews. This brings out the true relationship between the reviews and not simply copy-paste. This introduces an element of "common sense" for the user and helps them to quickly make the right decisions. The user is provided the pros and cons of the processed reviews. Thus the user/customer can take their own decisions.
Investigating the critical human behaviour elements and their implications for knowledge management systems: A Literature Review

A knowledge management system is required to ultimately enable an organization to realize its knowledge management objectives. The organization has several issues with this system, including deciding whether to adopt it or not, and, in the latter case, whether users would accept working with it. This study aims to determine the impact of human behavioural factors (HBFs), such as emotion, on group processes, as well as the impact of these processes on individuals' attitudes and emotions, character, values and beliefs, perspective and motivation, and knowledge management system&#x2013;cycles (KMS-Cs). In addition, knowledge management systems are adopted at both the organizational and individual levels. Many factors influence an organization&#x2019;s decision to adopt knowledge management systems, including corporate culture and top management support. Various circumstances also influence users' acceptance, and naturally, the amount of acceptance affects the knowledge management system&#x2019;s effectiveness. Although an effective knowledge management system (KMS) is a critical strategy for organizations to obtain sustained competitive advantages, little research has been undertaken to determine what factors contribute to the success of KMS.
The Open Virtual Mirror Framework for enfacement illusions

Enfacement illusions are traditionally elicited by visuo-tactile stimulation, but more active paradigms become possible through the usage of virtual reality techniques. For instance, virtual mirrors have been recently proposed to induce enfacement by visuo-motor stimulation. In a virtual mirror experiment, participants interact with an avatar that imitates their facial movements. The active control over the avatar greatly enhances the sense of agency, which is an important ingredient for successful enfacement illusion induction. Due to technological challenges, most virtual mirrors so far were limited to the imitation of the participant's head pose, i.e., its location and rotation. However, stronger experiences of agency can be expected by an increase in the avatar's mimicking abilities. We here present a new open-source framework for virtual mirror experiments, which we call the Open Virtual Mirror Framework (OVMF). The OVMF can track and imitate a large range of facial movements, including pose and expressions. It has been designed to run on standard computer hardware and easily interfaces with existing toolboxes for psychological experimentation, while satisfying the requirement of a tightly controlled experimental setup. Further, it is designed to enable convenient extension of its core functionality such that it can be flexibly adjusted to many different experimental paradigms. We demonstrate the usage of the OVMF and experimentally validate its ability to elicit experiences of agency over an avatar, concluding that the OVMF can serve as a reference for future experiments and that it provides high potential to stimulate new directions in enfacement research and beyond.
Comprehensive Survey of different Machine Learning Algorithms used for Software Defect Prediction

The software development life cycle is a long and complicated process. It consists of analysis, design, development, testing and deployment. Defect prediction is the technique of creating models that detect defective systems such as units or classes in the early stages of the process. The major goal of Software Defect Prediction is to detect defects prone in the program and thereby reduce the effort, time and cost involved to the minimum. This paper gives a comprehensive review of all the techniques to approach defect prediction. The PROMISE repository which is a public software defect prediction dataset which is owned by the National Aeronautics and Space Administration (NASA) is used. More than 30 research papers in the domain of software defect prediction were analysed and reviewed. In each paper surveyed, the processes involved in Software Defect prediction were captured. About 30 papers with different Machine Learning Algorithms were identified and entered into the table. The results were tabulated into columns like Dataset Used, Supervised Learning Algorithm, Un-supervised Learning Algorithm and Computational Intelligence. The bar graphs were generated to determine the most used Supervised Learning Algorithm, Unsupervised Learning Algorithm and Dataset used for Software Defect Prediction among all the 40 papers surveyed thoroughly.
Chunkfusion: A Learning-Based RGB-D 3D Reconstruction Framework Via Chunk-Wise Integration

Recent years have witnessed a growing interest in online RGB-D 3D reconstruction. On the premise of ensuring the reconstruction accuracy with noisy depth scans, making the system scalable to various environments is still challenging. In this paper, we devote our efforts to try to fill in this research gap by proposing a scalable and robust RGB-D 3D reconstruction framework, namely Chunk-Fusion. In ChunkFusion, sparse voxel management is exploited to improve the scalability of online reconstruction. Besides, a chunk-wise TSDF (truncated signed distance function) fusion network is designed to perform a robust integration of the noisy depth measurements on the sparsely allocated voxel chunks. The proposed chunk-wise TSDF integration scheme can accurately restore surfaces with superior visual consistency from noisy depth maps and can guarantee the scalability of online reconstruction simultaneously, making our reconstruction framework widely applicable to scenes with various scales and depth scans with strong noises and outliers. The outstanding scalability and efficacy of our ChunkFusion have been corroborated by extensive experiments. To make our results reproducible, the source code is made online available at https://cslinzhang.github.io/ChunkFusion/.
MBA-RainGAN: A Multi-Branch Attention Generative Adversarial Network for Mixture of Rain Removal

Rain severely degrades the visibility of scene objects, especially when images are captured through the glass under rainy weather. We observe three intriguing phenomena: 1) rain is a mixture of raindrops, rain streaks and rainy haze; 2) the depth from the camera determines the degree of object visibility, where objects nearby and far away are visually blocked by rain streaks and rainy haze, respectively; and 3) raindrops on the glass randomly affect the object visibility of the whole image space. However, existing solutions and benchmark datasets lack full consideration of the mixture of rain (MOR). In this paper, we originally consider that the overall object visibility is determined by MOR, and enrich the RainCityscapes by considering real-world raindrops to construct the MOR dataset, named RainCityscapes++. To solve the practical rain removal problem arisen from MOR, we formulate a new rain imaging model and propose a multi-branch attention generative adversarial network (MBA-RainGAN). Extensive experiments show clear improvements of our approach over SOTAs on RainCityscapes++.
The Stanford Drone Dataset Is More Complex Than We Think: An Analysis of Key Characteristics

Several datasets exist which contain annotated information of individuals’ trajectories. Such datasets are vital for many real-world applications, including trajectory prediction and autonomous navigation. One prominent dataset currently in use is the Stanford Drone Dataset (SDD) (Robicquet <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , 2016). Despite its prominence, discussion surrounding the characteristics of this dataset is insufficient. We demonstrate how this insufficiency reduces the information available to users and can impact performance. Our contributions include the outlining of key characteristics in the SDD, employment of an information-theoretic measure and custom metric to clearly visualize those characteristics, the implementation of the PECNet (Mangalam <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , 2020) and Y-Net (Mangalam <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , 2021) trajectory prediction models to demonstrate the outlined characteristics’ impact on predictive performance, and lastly we provide a comparison between the SDD and Intersection Drone (inD) Dataset. Our analysis of the SDD’s key characteristics is important because without adequate information about available datasets a user’s ability to select the most suitable dataset for their methods, to reproduce one another’s results, and to interpret their own results are hindered. The observations we make through this analysis provide a readily accessible and interpretable source of information for those planning to use the SDD. Our intention is to increase the performance and reproducibility of methods applied to this dataset going forward, while also clearly detailing less obvious features of the dataset for new users.