Reviewerly/exHarmony huggingface dataset:
A Deep Learning-Based Action Recommendation Model for Cryptocurrency Profit Maximization

Research on the prediction of cryptocurrency prices has been actively conducted, as cryptocurrencies have attracted considerable attention. Recently, researchers have aimed to improve the performance of price prediction methods by applying deep learning-based models. However, most studies have focused on predicting cryptocurrency prices for the following day. Therefore, clients are inconvenienced by the necessity of rapidly making complex decisions on actions that support maximizing their profit, such as “Sell”, “Buy”, and “Wait”. Furthermore, very few studies have explored the use of deep learning models to make recommendations for these actions, and the performance of such models remains low. Therefore, to solve these problems, we propose a deep learning model and three input features: sellProfit, buyProfit, and maxProfit. Through these concepts, clients are provided with criteria on which action would be most beneficial at a given current time. These criteria can be used as decision-making indices to facilitate profit maximization. To verify the effectiveness of the proposed method, daily price data of six representative cryptocurrencies were used to conduct an experiment. The results confirm that the proposed model showed approximately 13% to 21% improvement over existing methods and is statistically significant.
How Augmented Reality (AR) Can Help and Hinder Collaborative Learning: A Study of AR in Electromagnetism Education

Learning physics is often difficult for students because concepts such as electricity, magnetism and sound, cannot be seen with the naked eye. Emerging technologies such as Augmented Reality (AR) can transform education by making challenging concepts visible and accessible to novices. We present a Hololens-based augmented reality system where collaborators learn about the invisible electromagnetism phenomena involved in audio speakers, and we measure the benefits of AR technology through quantitative and qualitative methods. Specifically, we measure learning (knowledge gains and transfer) and collaborative knowledge exchange behaviors. Our results indicate that, while AR generally provides a novelty effect, specific educational AR visualizations can be both beneficial and detrimental to learning - they helped students to learn spatial content and structural relationships, but hindered their understanding of kinesthetic content. Furthermore, AR facilitated learning in collaborations by providing representational common ground, which improved communication and peer teaching. We discuss these effects, as well as identify factors that have positive impact (e.g., co-located representations, easier access to resources, better grounding) or negative impact (e.g., tunnel vision, overlooking kinesthetic feedback) on student collaborative learning with augmented reality applications.
Learning to Assess Image Quality Like an Observer

Human observers are the ultimate receivers and evaluators of the image visual information and have powerful perception ability of visual quality with short-term global perception and long-term regional observation. Thus, it is natural to design an image quality assessment (IQA) computational model to act like an observer for accurately predicting the human perception of image quality. Inspired by this, here, we propose a novel observer-like network (OLN) to perform IQA by jointly considering the global glimpsing information and local scanning information. Specifically, the OLN consists of a global distortion perception (GDP) module and a local distortion observation (LDO) module. The GDP module is designed to mimic the observer's global perception of image quality through performing classification of images' distortion categories and levels. Simultaneously, to simulate the human local observation behavior, the LDO module attempts to gather the long-term regional observation information of the distorted images by continuously tracing the human scanpath in the observer-like scanning manner. By leveraging the bilinear pooling layer to collaborate the short-term global perception with the long-term regional observation, our network precisely predicts the quality scores of distorted images, such as human observers. Comprehensive experiments on the public datasets powerfully demonstrate that the proposed OLN achieves state-of-the-art performance.
Research on vehicle obstacle avoidance path planning based on APF-PSO

The existing vehicle obstacle avoidance path planning methods generally aim at obtaining the collision-free path, ignoring the impact of the planned path on the vehicle stability in the obstacle avoidance process, so that the controlled vehicle has the risk of rollover in the obstacle avoidance process. To solve the above problems, a two-layer obstacle avoidance path planning algorithm considering path pre-planning and re-planning is proposed in this paper. In the path pre-planning layer, an improved APF algorithm with road boundary function constraints is proposed. By introducing the repulsion field adjustment factor, the shortcomings of GNRON and local optimization existing in the existing artificial potential field method are effectively solved. In the path re-planning layer, taking the rollover stability index as the constraint, a pre-planning result optimization method based on particle swarm optimization algorithm is proposed. The simulation results show that the obstacle avoidance path planning algorithm proposed in this paper can not only generate the obstacle avoidance path in real-time, but also reduce the yaw rate and yaw angle of the main vehicle in the process of obstacle avoidance, and effectively improve the rollover stability of the vehicle in the process of obstacle avoidance.
Accurate and Efficient Large-Scale Multi-Label Learning With Reduced Feature Broad Learning System Using Label Correlation

Multi-label learning for large-scale data is a grand challenge because of a large number of labels with a complex data structure. Hence, the existing large-scale multi-label methods either have unsatisfactory classification performance or are extremely time-consuming for training utilizing a massive amount of data. A broad learning system (BLS), a flat network with the advantages of succinct structures, is appropriate for addressing large-scale tasks. However, existing BLS models are not directly applicable for large-scale multi-label learning due to the large and complex label space. In this work, a novel multi-label classifier based on BLS (called BLS-MLL) is proposed with two new mechanisms: kernel-based feature reduction module and correlation-based label thresholding. The kernel-based feature reduction module contains three layers, namely, the feature mapping layer, enhancement nodes layer, and feature reduction layer. The feature mapping layer employs elastic network regularization to solve the randomness of features in order to improve performance. In the enhancement nodes layer, the kernel method is applied for high-dimensional nonlinear conversion to achieve high efficiency. The newly constructed feature reduction layer is used to further significantly improve both the training efficiency and accuracy when facing high-dimensionality with abundant or noisy information embedded in large-scale data. The correlation-based label thresholding enables BLS-MLL to generate a label-thresholding function for effective conversion of the final decision values to logical outputs, thus, improving the classification performance. Finally, experimental comparisons among six state-of-the-art multi-label classifiers on ten datasets demonstrate the effectiveness of the proposed BLS-MLL. The results of the classification performance show that BLS-MLL outperforms the compared algorithms in 86% of cases with better training efficiency in 90% of cases.
Deep Learning and Its Applications in Computational Pathology

Deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial networks (GANs), and graph neural networks (GNNs) have, over the past decade, changed the accuracy of prediction in many diverse fields. In recent years, the application of deep learning techniques in computer vision tasks in pathology has demonstrated extraordinary potential in assisting clinicians, automating diagnoses, and reducing costs for patients. Formerly unknown pathological evidence, such as morphological features related to specific biomarkers, copy number variations, and other molecular features, could also be captured by deep learning models. In this paper, we review popular deep learning methods and some recent publications about their applications in pathology.
Challenges and Opportunities for Integrating Augmented Reality and Computational Fluid Dynamics Modeling under the Framework of Industry 4.0

The industrial requirements change rapidly due to the technological advancements, and the need to immediately investigate prospective system alternatives to develop a more efficient production system design is more intense than ever. Manufacturing systems simulation as a key technology of Industry 4.0 is a powerful tool towards optimizing manufacturing processes. Computational Fluid Dynamics (CFD) simulation can help smart factories gain a competitive advantage due to low cost, low risk and the provided meaningful insights. However, while CFD tools can provide users with precise simulations, there is strong need for interactive design and analysis tools. Additionally, fluid flow behavior is a challenging problem for cognitive interpretation and visualization. Therefore, engineers/technicians need to visualize the behavior of the desired flow field to improve their cognitive ability in problem-solving. As a result, Augmented Reality (AR) technology is proposed as the solution for an enhanced virtual learning and training environment in smart factories. To address these challenges, this research work presents an Industrial Internet of Things (IIoT) framework for simulation and visualization of selected monitored values based on AR and CFD in a cloud-computing environment for the intuitive visualization of the data on smart devices. The applicability of the proposed framework has been validated in a real-life case study derived from an Original Equipment Manufacturer (OEM) supplier of Industrial refrigerator systems.
Toward Practical Usage of the Attention Mechanism as a Tool for Interpretability

Natural language processing (NLP) has been one of the subfields of artificial intelligence much affected by the recent neural revolution.Architectures such as recurrent neural networks (RNNs) and attention-based transformers helped propel the state of the art across various NLP tasks, such as sequence classification, machine translation, and natural language inference.However, if neural models are to be used in high-stakes decision making scenarios, the explainability of their decisions becomes a paramount issue.The attention mechanism has offered some transparency in the workings of otherwise black-box RNN models: attention weights (scalar values assigned input words) invite to be interpreted as the importance of that word, providing a simple method of interpretability.Recent work, however, has questioned the faithfulness of this practice.Subsequent experiments have shown that faithfulness of attention weights may still be achieved by incorporating word-level objectives in the training process of neural networks.In this article, we present a study that extends the techniques for improving faithfulness of attention based on regularization methods that promote retention of word-level information.We perform extensive experiments on a wide array of recurrent neural architectures and analyze to what extent the explanations provided by inspecting attention weights are correlated with the human notion of importance.We find that incorporating tying regularization consistently improves both the faithfulness (-0.14 F1, +0.07 Brier, on average) and plausibility (+53.6% attention mass on salient tokens) of explanations obtained through inspecting attention weights across analyzed datasets and models.
Determination of the appropriate zone on dam surface for floating photovoltaic system installation using RS and GISc technologies

This study aims to reveal suitable places where floating photovoltaic-solar power plants (FPV-SPPs) can be installed on the dam surface using the possibilities of remote sensing (RS) and geographical information science (GISc) technologies. Past satellite images from Landsat and Sentinel platforms allow researchers to analyse shoreline changes in the dam surface. Shoreline extraction is a crucial process for the FPV-SPP to stay afloat despite external constraints. In this study, changes in dam water levels were determined by classifying 20-year satellite images and analysing a 32-year global surface water dynamics dataset. The water surface area was calculated as 1,562.40 ha using the random forest (RF) algorithm and the normalized differences water index (NDWI) on Google Earth Engine (GEE) cloud platform. In addition, solar analysis was carried out with GISc using annual solar radiation maps shuttle radar topography mission (SRTM) data, which directly affects the energy production of FPV-SPPs. It has been calculated that the solar radiation on the water surface varies between 1,554 kWh/m2-year and 1,875 kWh/m2-year. These calculated values were divided into five different classes, and it was observed that 88.5% of the dam surface had a very high level of solar radiation compared to other areas. Higher efficiency will be obtained from the FPV-SPP to be installed in this region compared to the systems to be installed in other regions. It has been observed that the radiation values in other parts of the water surface are lower due to topographic shading. These analyses revealed energy zones with high production potential, thereby easing the decision-making process for investors planning to establish FPV-SPPs.
Leveraging Spatial and Photometric Context for Calibrated Non-Lambertian Photometric Stereo

The problem of estimating a surface shape from its observed reflectance properties still remains a challenging task in computer vision. The presence of global illumination effects such as inter-reflections or cast shadows makes the task particularly difficult for non-convex real-world surfaces. State-of-the-art methods for calibrated photometric stereo address these issues using convolutional neural networks (CNNs) that primarily aim to capture either the spatial context among adjacent pixels or the photometric one formed by illuminating a sample from adjacent directions.In this paper, we bridge these two objectives and introduce an efficient fully-convolutional architecture that can leverage both spatial and photometric context simultaneously. In contrast to existing approaches that rely on standard 2D CNNs and regress directly to surface normals, we argue that using separable 4D convolutions and regressing to 2D Gaussian heat-maps severely reduces the size of the network and leads to more stable predictions.Our experimental results on a real-world photometric stereo benchmark show that the proposed approach outperforms the existing published methods in accuracy. The source code for our method is available at https://github.com/DawyD/UNet-PS-4D.
Investigations in Emotion Aware Multimodal Gender Prediction Systems From Social Media Data

Gender plays a crucial role in improving the performance quality of personalized systems. Privacy and anonymity allow users to hide their details. Based on the intuition that post contents of male and female users differ, we can predict the gender of the social media account holder via their corresponding posts. These posts can be multimodal (text + image) in nature. We investigate various emotion-assisted multimodal gender prediction models in this article. The developed models use gated recurrent units (GRUs) and ResNets for extracting features from the tweets and the images, respectively. The distribution of emotion categories is related to the gender of the target person. In response to these findings, this article describes the first attempt to use multimodal (image and text posted) information for gender prediction in a multitask setting with emotion recognition as an auxiliary task. The enriched PAN-2018 dataset with gender and emotion labels is used to train gender and emotion networks. Several models were developed to improve the gender prediction task by generating better emotion-aware features. The results show that the proposed multimodal emotion detection model outperforms single-modal (text and image)-based models and state-of-the-art systems on the benchmark PAN-2018 dataset.
On the relationship between Bitcoin and other assets during the outbreak of coronavirus: Evidence from fractional cointegration analysis

This article tries to investigate the connectedness between Bitcoin and Crude Oil, S&P500 and Natural Gas with the health crisis. That is why one might apply fractional cointegration analysis on daily data over the period 01/09/2019-30/04/2020. Our results indicate the presence of fractional integration in residual series, implying the existence of a fractional cointegration relationship. A short-run joint dynamics between Bitcoin and some other assets (Crude Oil, S&P500 and Natural Gas) is nevertheless well-pronounced. Such analysis of the long and short-term dependencies between different assets could be interesting from a portfolio perspective.
Privacy-Preserving E-voting System through Blockchain Technology

In conventional voting systems, achieving transparency and reliability is challenging due to the centralized environment. With the advancement in technology, electronic voting has emerged. But in E-voting also, it is essential to maintain the privacy, confidentiality and integrity of the votes and voters, which is challenging. The blockchain technology that has triggered the start of a new era in the digital world is one possible solution. The immutability property and decentralized architecture of blockchain technology make it suitable to create a robust and secure E-voting system. It helps in conducting the elections by maintaining authentication, integrity, transparency, confidentiality and non-repudiation in the system. In this paper, we have evaluated the potential and feasibility of the blockchain system for electronic voting. We have addressed all the possible limitations in the current E-voting system. Using solidity language, we have implemented a small-scale sample E-voting system as a smart contract that includes everything from hosting the election, verifying the voters and counting the votes and have deployed it on an external network. The paper also shows how Zero-Knowledge Proof can help in creating privacy-preserving secure E-voting systems.
A Holistic View of Memory Utilization on HPC Systems: Current and Future Trends

Memory subsystem is one crucial component of a computing system. Co-designing memory subsystems becomes increasingly challenging as workloads continue evolving on HPC facilities and new architectural options emerge. This work provides the first large-scale study of memory utilization with system-level, job-level, temporal and spatial patterns on a CPU-only and a GPU-accelerated leadership supercomputer. From system-level monitoring data that spans three years, we identify a continuous increase in memory intensity in workloads over recent years. Our job-level characterization reveals different hotspots in memory usage on the two systems. Furthermore, we introduce two metrics, ’spatial imbalance’ and ’temporal imbalance’, to quantify the imbalanced memory usage across compute nodes and throughout time in jobs. We identify representative temporal and spatial patterns from real jobs, providing quantitative guidance for research on efficient resource configurations and novel architectural options. Finally, we showcase the impact of our study in informing system configurations through an upcoming NNSA CTS procurement.
A Framework of an Obstacle Avoidance Robot for the Visually Impaired People

Chaki, Sudipto Ahmed, Shamim Biswas, Milon Tamanna, IffatHuman being precepts its visual information with the help of eyes. But, some people are visually impaired and are incapable to collect visual information. That is why they have to rely on others while walking or moving somewhere. So, we have proposed a framework that can solve this issue effectively and give the blind people a scope to move around freely. Some researchers have already contributed to this issue and proposed some walker-based systems to assist visually impaired people. But, most of the walkers are either heavy or big. Heavyweight restricts them from being user-friendly, and big size makes it impossible to be used in a narrow space. In this regard, we have developed a lightweight robot that can assist blind people to find an obstacle freeway while moving. Our robot is integrated with a Microsoft Kinect camera and to process the captured images, we use the Raspberry Pi as its computational unit. By using the depth sensor of the Kinect device, the robot collects its surroundings images and is sent to the Raspberry Pi unit to analyze them to detect any obstacles coming toward. We accommodate our robot with an obstacle avoidance algorithm so that if any obstacle is detected within a certain range, the robot can automatically move either right or left depending on the intensity values of the images.
Unmanned Aerial Vehicle (UAV)-Assisted Path Planning for Unmanned Ground Vehicles (UGVs) via Disciplined Convex-Concave Programming

Thelast decade has witnessed growing research interests in the path-planning problem for unmanned ground vehicles (UGVs) equipped with RGB-D cameras or LiDARs. However, apart from their extremely high cost, these sensors may be obscured by obstacles, which makes them impractical for many complex scenarios. Besides, most of the existing works for path planning have not considered energy efficiency for UGVs that are usually constrained by limited on-board batteries. Distinct from existing methods, this work presents a vision-based unmanned aerial vehicle (UAV)-assisted cooperative system for multiple UGVs. The proposed system harnesses the broad vision of UAV and operates in both general outdoor and global positioning system (GPS)-denied indoor environments. In sharp contrast to the conventional heuristic algorithms such as the rapidly exploring random tree (RRT) algorithm and the Dijkstra methods, the proposed energy-efficient path planning for UGVs is formulated as a non-convex optimization problem by considering the collision-aware obstacle-avoidance. More specifically, the proposed path-planning scheme contains two stages, namely the semantic segmentation that localizes UGVs and obstacles in the environment by exploiting the wide-angle camera mounted on the UAV, followed by the trajectory generation in which a disciplined convex and concave programming (DCCP) algorithm is devised for the non-convex energy-minimization problem. Extensive experiments validate the effectiveness of the UAV-UGV cooperative system and the proposed DCCP-based path-planning scheme.