Reviewerly/exHarmony huggingface dataset:
Cooperative Planning for an Unmanned Combat Aerial Vehicle Fleet Using Reinforcement Learning

In this study, reinforcement learning (RL)-based centralized path planning is performed for an unmanned combat aerial vehicle (UCAV) fleet in a human-made hostile environment. The proposed method provides a novel approach in which closing speed and approximate time-to-go terms are used in the reward function to obtain cooperative motion while ensuring no-fly-zones (NFZs) and time-of-arrival constraints. Proximal policy optimization (PPO) algorithm is used in the training phase of the RL agent. System performance is evaluated in two different cases. In case 1, the warfare environment contains only the target area, and simultaneous arrival is desired to obtain the saturated attack effect. In case 2, the warfare environment contains NFZs in addition to the target area and the standard saturated attack and collision avoidance requirements. Particle swarm optimization (PSO)-based cooperative path planning algorithm is implemented as the baseline method, and it is compared with the proposed algorithm in terms of execution time and developed performance metrics. Monte Carlo simulation studies are performed to evaluate the system performance. According to the simulation results, the proposed system is able to generate feasible flight paths in real-time while considering the physical and operational constraints such as acceleration limits, NFZ restrictions, simultaneous arrival, and collision avoidance requirements. In that respect, the approach provides a novel and computationally efficient method for solving the large-scale cooperative path planning for UCAV fleets.
IoT based smart surveillance monitoring by using model-based human action recognition design

In recent years, with the rapid development in IP camera usage, massive video surveillance data are produced at an unprecedented speed. IP camera is one of the most important elements in In Internet of things (IoT) and smart cities, in this era. IoT Based Smart Surveillance provides resources over the Internet and allows a plethora of applications to be deployed to provide services for different applications. Traditional solutions to deal with the big video data would require a large amount of computing and storage resources. one of the major bottleneck being faced the computational cost of spatio-temporal deep neural networks making them run as fast as their 2D counterparts while preserving accuracy on video recognition benchmarks. To this end, we present the new model-based human action recognition design. In this method we use fixed dimensional representation for actions in video clips of varying lengths to decrees computational complexes. This representation, simplify clustering and comparing between videos. In addition, the methods based on CNN usually need manual annotation at the beginning and the meaning of characteristics is an abstract wonder and subjective phenomenon and hence a manual annotation of attributes is highly inconsistent [1]. Anyhow our proposed smart surveillance system is unsupervised and don't need inconsistent manual annotation of video clips attributes. Many researchers believe smart surveillance system is a critical component of smart cities and can be a solid foundation for future applications in intelligent surveillance systems focuses on human action Recognition.
Seasonal prediction of Indian summer monsoon onset with echo state networks

Abstract Although the prediction of the Indian Summer Monsoon (ISM) onset is of crucial importance for water-resource management and agricultural planning on the Indian sub-continent, the long-term predictability—especially at seasonal time scales—is little explored and remains challenging. We propose a method based on artificial neural networks that provides skilful long-term forecasts (beyond 3 months) of the ISM onset, although only trained on short and noisy data. It is shown that the meridional tropospheric temperature gradient in the boreal winter season already contains the signals needed for predicting the ISM onset in the subsequent summer season. Our study demonstrates that machine-learning-based approaches can be simultaneously helpful for both data-driven prediction and enhancing the process understanding of climate phenomena.
EDLAB: A Benchmark for Edge Deep Learning Accelerators

This article introduces a new end-to-end benchmark to evaluate the performance of edge deep learning accelerators. — <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Sai Manoj, George Mason University</i>
Blockchain and Sustainability: A Tertiary Study

Blockchain is an emerging technology with potential to address issues related to sustainability. Literature reviews on blockchain and sustain ability exist, but there is a need to consolidate existing results, in particular, in terms of Sustainable Development Goals (SDG). This extended abstract presents an ongoing tertiary study based on existing literature reviews to investigate the relationship between blockchain and sustain ability in terms of SDGs. Results from a pilot analysis of 18 reviews using thematic analysis are presented.
HoloDesigner: A mixed reality tool for on-site design

Designing with the surrounding environment has been consistently encouraged in architectural design and urban planning since the modernist movement. However, on-site design work is challenging for designers, because the current design tools can hardly merge virtual three-dimensional (3D) models with real surrounding environments for visualization and interaction. To address this challenge, we designed and developed a mixed reality (MR) tool called HoloDesigner. In this paper, we describe the development process and core components of HoloDesigner. Through an on-site design application at a community park, we demonstrated its main workflow and functionalities. The results proved that HoloDesigner could successfully render on-site 3D visualization as well as real-time interactions to control 3D models in the real surrounding environment. This research offers new insights on supporting on-site design work with MR technologies and contributes to overcoming the limitations of current MR tools for on-site visualization and interaction.
Grammarly’s Tone Detector: Helping Students Write Pragmatically Appropriate Texts

Grammarly’s Tone Detector is included in the free version of the application and is available for major phone and computer program platforms. Its strength is in helping students compose pragmatically appropriate texts which could substantially increase their confidence and the feeling of autonomy. It accomplishes this by providing writers with tone indications of their text paired with emojis to help navigate the sometimes opaque waters of pragmatics. However, this tool does not provide indications of which words or phrases contribute to an indicated tone, nor does it offer suggestions for improvement. It is a perpetual beta tool in that it asks for user input to continually adapt and learn to the changing communicative situation. Although there are some areas for improvement, it is a useful tool that teachers could recommend to their students to support their ability to raise their confidence about the pragmatic appropriateness when writing emails and other texts where pragmatics are important.
Forecasting Cryptocurrency Prices Using Ensembles-Based Machine Learning Approach

This paper is devoted to the problems of the short-term forecasting cryptocurrencies time series using machine learning approach. We applied two the most powerful ensembles methods: Random Forests (RF) and Gradient Boosting Machine (GBM). For testing models we used the daily close prices of three the most capitalized coins: Bitcoin (BTC), Ethereum (ETH) and Ripple (XRP), and as a features were selected the past price information and technical indicators (moving average). To check the efficiency of these models we made out-of-sample forecast for three cryptocurrencies by using one step ahead technique. As the accuracy rate for our models we were selected Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) metrics. According to comparative analysis of the predictive ability of the RF and GBM both models showed the same order of accuracy for the out-of-sample dataset prediction, although boosting also was somewhat more accurate. Computer experiments have confirmed the feasibility of using the machine learning ensembles approaches considered for the short-term forecasting of cryptocurrencies time series. Built models and their ensembles can be used as the basis algorithms for automated Internet trading systems.
Modeling declensions without declension features. The case of Russian

Abstract This paper presents an analysis of the Russian declension in Nanosyntax (Starke 2009, 2018). The analysis has two theoretically important aspects. First, it makes no reference to language-particular declension features. This allows one to maintain the idea that morphosyntactic features are drawn from a set provided by the UG, i.e., language invariant. The analysis also does not use contextual rules. In order to correctly pair the right ending with a particular root, the analysis only relies on specifying each marker for the features it spells out. The correct pairing of roots and affixes falls out from such a specification and the Nanosyntax model of spellout.
Leveraging Large-Scale Weakly Labeled Data for Semi-Supervised Mass Detection in Mammograms

Mammographic mass detection is an integral part of a computer-aided diagnosis system. Annotating a large number of mammograms at pixel-level in order to train a mass detection model in a fully supervised fashion is costly and time-consuming. This paper presents a novel self-training framework for semi-supervised mass detection with soft image-level labels generated from diagnosis reports by Mammo-RoBERTa, a RoBERTa-based natural language processing model fine-tuned on the fully labeled data and associated mammography reports. Starting with a fully supervised model trained on the data with pixel-level masks, the proposed framework iteratively refines the model itself using the entire weakly labeled data (image-level soft label) in a self-training fashion. A novel sample selection strategy is proposed to identify those most informative samples for each iteration, based on the current model output and the soft labels of the weakly labeled data. A soft cross-entropy loss and a soft focal loss are also designed to serve as the image-level and pixel-level classification loss respectively. Our experiment results show that the proposed semi-supervised framework can improve the mass detection accuracy on top of the supervised baseline, and outperforms the previous state-of-the-art semi-supervised approaches with weakly labeled data, in some cases by a large margin.
User Experience in VR Fashion Product Shopping: Focusing on Tangible Interactions

It is necessary to focus on a VR environment centered on a tangible interaction (TI), which provides and interacts with the user experience (UX) with various sensational forms. Therefore, this study attempted to present UX evaluation items for VR fashion product shopping environment through focusing on the TI. In addition, the developed factors were evaluated for validity through empirical experiments and attempted to explore the possibility of using a VR shopping UX evaluation methodology. As a result of factor analysis of items related to VR shopping, six factors were extracted, and each factor was named as intention to use, playfulness, sharpness, telepresence, interactivity, and usability, respectively. As a result of a t-test for the difference in UX between immersive VR and non-immersive VR, it was found that there were significant differences in intention to use, playfulness, sharpness, and telepresence. As a result of performing a multiple regression analysis to analyze the effect of TI on the VR fashion shopping UX, it was found that interaction, playfulness, and telepresence are UX factors that are formed under the influence of TI in an immersive VR. In addition, intention to use, sharpness, telepresence, and usability were found to be factors experienced as an effect of TI in a non-immersive VR.
Transfer-RLS method and transfer-FORCE learning for simple and fast training of reservoir computing models

Reservoir computing is a machine learning framework derived from a special type of recurrent neural network. Following recent advances in physical reservoir computing, some reservoir computing devices are thought to be promising as energy-efficient machine learning hardware for real-time information processing. To realize efficient online learning with low-power reservoir computing devices, it is beneficial to develop fast convergence learning methods with simpler operations. This study proposes a training method located in the middle between the recursive least squares (RLS) method and the least mean squares (LMS) method, which are standard online learning methods for reservoir computing models. The RLS method converges fast but requires updates of a huge matrix called a gain matrix, whereas the LMS method does not use a gain matrix but converges very slow. On the other hand, the proposed method called a transfer-RLS method does not require updates of the gain matrix in the main-training phase by updating that in advance (i.e., in a pre-training phase). As a result, the transfer-RLS method can work with simpler operations than the original RLS method without sacrificing much convergence speed. We numerically and analytically show that the transfer-RLS method converges much faster than the LMS method. Furthermore, we show that a modified version of the transfer-RLS method (called transfer-FORCE learning) can be applied to the first-order reduced and controlled error (FORCE) learning for a reservoir computing model with a closed-loop, which is challenging to train.
Removal of Zinc (II) ions from industrial wastewater by adsorption on to activated carbon produced from pine cone

Recently heavy metal pollution is consider among the most essential environmental issues. Due to their impact, the removing of heavy metals from the environment is a high demanding concern. Heavy metals adsorption is a new technology for wastewater treatment that contain different selected heavy metals types. This study aim to adopted adsorbents with low cost application, in this technique a pine cones used as plant wastes adsorbents in Industrial wastewater treatment by implemented batch technique adsorbent dosage and adsorbate initial concentration). The plant waste sample of (pine cone) was used to test its efficiency which that provide the optimum performance in removing heavy metals and other pollutants (Turbidity, TSS and TDS). Activated carbon produced from Pine cones Turkish Pine (ACPC) was adopted as an adsorbent for the Zinc ions removal from wastewater with industrial pollutants. Surface analysis was preform to measure the pore size distribution and specific surface area of the activated carbon produced from pine cone. Optimal parameters, pH, equilibrium time, adsorbent dosage, temperature and mix speed were investigated. Using Langmuir, Freundlech and Elvich isotherm models, equilibrium data were evaluated. Langmuir isotherm model provided the highest fit to the equilibrium data.
Robust two-dimensional capped<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e3300" altimg="si3.svg"><mml:msub><mml:mrow><mml:mi>l</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn><mml:mo>,</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:math>-norm linear discriminant analysis with regularization and its applications on image recognition

Two-dimensional linear discriminant analysis (2DLDA) is an effective matrix-based supervised dimensionality reduction method that expresses 2D data directly. However, 2DLDA magnifies the influence of outliers and noise since the construction of 2DLDA is based on squared Frobenius norm. To overcome its sensitivity, this paper investigates a two-dimensional capped l2,1-norm linear discriminant analysis, called 2DCLDA. The application of capped l2,1-norm makes 2DCLDA identify outliers and suppress the effect of noise effectively. To avoid singularity and give a stable performance, a regularization term is also considered in the between-class scatter. 2DCLDA is solved through a series of generalized eigenvalue problems, with each subproblem can be deemed as a weighted 2DLDA with regularization. It is proved that the proposed iteration algorithm monotonously decreases the objective of 2DCLDA. At last, 2DCLDA with its related approaches on several face image databases is compared, and the experimental results demonstrate the superiority of 2DCLDA, especially for noise data.
Diffuser: Multi-View 2D-to-3D Label Diffusion for Semantic Scene Segmentation

Semantic 3D scene understanding is a fundamental problem in computer vision and robotics. Despite recent advances in deep learning, its application to multi-domain 3D semantic segmentation typically suffers from the lack of extensive enough annotated 3D datasets. On the contrary, 2D neural networks benefit from existing large amounts of training data and can be applied to a wider variety of environments, sometimes even without need for retraining. In this paper, we present ‘Diffuser’, a novel and efficient multi-view fusion framework that leverages 2D semantic segmentation of multiple image views of a scene to produce a consistent and refined 3D segmentation. We formulate the 3D segmentation task as a transductive label diffusion problem on a graph, where multi-view and 3D geometric properties are used to propagate semantic labels from the 2D image space to the 3D map. Experiments conducted on indoor and outdoor challenging datasets demonstrate the versatility of our approach, as well as its effectiveness for both global 3D scene labeling and single RGB-D frame segmentation. Furthermore, we show a significant increase in 3D segmentation accuracy compared to probabilistic fusion methods employed in several state-of-the-art multi-view approaches, with little computational overhead.
VideoTrain: A Generative Adversarial Framework for Synthetic Video Traffic Generation

Unlike the traditional Internet application such as web browsing and peer-to-peer(P2P), video streaming has been dominating the global network traffic for the past few years, raising many challenges for network providers. With the popularity of interactive videos, a.k.a 360° videos, resource requirement for video streaming has been further increased. Prior identification of these video traffic is useful for effective provisioning of network resources, yet it is difficult due to the end-to-end encryption of data. However, with the recent advances in Machine Learning (ML) methods, prior identification of these resource-demanding traffic types has become viable. Nonetheless, they require more training data, without which leads to poor performance. Collecting more training data may also pose issues related to delayed training time. To remedy this problem, in this paper, we propose a novel Generative Adversarial Network (GAN) based data generation solution to synthesise video streaming data targeting 360°/normal video classification. Taking over 600 actual video traces and generating ≈ 30000 new traces, our post-classification results show that we can achieve 5 - 15% of accuracy improvement compared to only having actual traces.
Private Data Sharing in a Secure Cloud-based Application for Acute Stroke Care

Acute stroke care demands fast procedures and collaboration of different healthcare organisations and professionals. The development of cloud computing and the wide adoption of electronic health records (EHR) foster healthcare systems to improve data availability and potentially enhance acute-stroke care quality. However, the design of a secure and privacy-preserving EHR cloud-based application is challenging. This paper presents the leading security and privacy requirements for healthcare applications and contextualises each requirement in the acute stroke care use case. Moreover, we deploy the ASCLEPIOS eHealth Cloud-based framework to address each requirement in designing an EHR cloud-based application for data sharing during acute stroke care. Our initial prototype combines the ASCLEPIOS framework with a public cloud service infrastructure and a private signature scheme. The prototype meets the security and privacy requirements since it protects the EHR data against unauthorised access, data breaches, and data exposure on public cloud providers. Moreover, we present preliminary results and discussions about the usability and overhead of the ASCLEPIOS framework.
Improvement of Optical Flow Estimation by Using the Hampel Filter for Low-End Embedded Systems

Owing to the recent advances in the field of deep-learning-based approaches, state-of-the-art performance has been achieved for optical flow estimation. However, non-deep-learning-based improvement in the optical flow estimation performance is still required because many platforms, such as small UGVs and drones, involve constraints that make mounting GPUs difficult. Thus, in this study, we do not apply deep learning methods; rather, we improve the accuracy of the optical flow pipeline by optimizing only its fundamental parameters. The optical flow estimation performance is influenced by the number of coarse-to-fine estimation pyramids, the filter applied to each pyramid level, the window size, and the graduated nonconvexity (GNC) step number. No significant differences from the previous research were achieved by optimizing the parameters heuristically because they have already been optimized. Therefore, we decided to change the filter applied in each pyramid, which is the most important factor in determining the optical flow estimation performance. As a result of verification, the optical flow with the median filter did not show good optical flow estimation performance due to oversmoothing of the image boundary, and the optical flow with the weighted median filter proposed to overcome this drawback could not well address the complexity of the equation and deal with the large computation cost. The proposed Hampel filter shows better performance by minimizing the loss of the original image in the image smoothing process and reduced computational complexity compared to the weighted median filter. The principle of the Hampel filter is similar to that of the median filter, except that if the reference pixel is statistically close to the pixel median in the window, it works by preserving the original pixel value. This is usually used for filtering outliers in a 1D signal but has never been applied for 2D image filters. This study expanded the application of the existing Hampel filter to include 2D images. Compared to the conventional median filter, this filter demonstrates relatively good performance, as it can considerably reduce the loss in the image data and improve the excessive edge smoothing effect such that it can better identify motion. The performance-related improvements achieved by this research were verified using the KITTI Vision Benchmark Suite, and the simulation results demonstrate that the Hampel filter can replace the existing filter and that it has strengths related to multi-object detection, background recognition, and the identification of moving objects at the edges of images.