Reviewerly/exHarmony huggingface dataset:
Forecasting the PV Panel Power Based on Image Processing and Historical Outputs

Solar PhotoVoltaic (PV) installment is expanding quickly around the world, but deeper integration into the electrical grid is hampered by solar power intermittency. A portion of the short-term PV fluctuation is caused by abrupt weather changes, such as cloud cover variations, which can drastically impact the PV panel output on time scales of minutes. Images of the sky can be used to provide information on the present and upcoming cloud cover to increase the accuracy of the PV power forecast. Convolutional Neural Network (CNN) is used in this study so as to link the solar panels’ power with the current sky photos. In addition, the PV panel’s historical output is utilized to enhance forecasting accuracy. Also, the sensitivity of the proposed model to the configuration of the machine learning procedure, such as the number of neurons and the width of the network, has been assessed. Furthermore, the uncertainty of the incorporated stochastic approach and the effect of different arrangements of inputs and outputs on the performance metrics have been evaluated. The proposed model achieves the root mean square error ranges from 2.37 kW to 3.43 kW for the real-data dataset.
Video Stabilization using RAFT-based Optical Flow

Video Stabilization is the basic need for modern-day video capture. Many methods have been proposed throughout the years including 2D and 3D-based models as well as models that use optimization and deep neural networks. This work describes the implementation of cutting-edge Recurrent All-Pairs Field Transforms (RAFT) for optical flow estimation in video stabilization. we use a pipeline that accommodates the large motion. It then passes the results to the optical flow for better accuracy. After that, it satisfies the inaccuracies of the optical flow and makes it robust to occlusion, Parallax, and moving objects. Our approach yields better results (visually and quantitatively) compared to other optimization and deep learning-based visual stabilization techniques.
A Cost-Effective Smart Labor Assistance Trolley for Industrial Applications

In the last couple of decades, autonomous human assistance robots have been enormously attracting the industrial sector. For this purpose, numerous researchers have contributed towards designing efficient and robust human assistance mechanisms. However, their proposed approaches do not provide a cost-effective solution due to the deployment of exorbitant sensors and sophisticated infrastructure. Besides, it was quite challenging for existing human-following robots to track their assigned human companion in different illusional states and luminous conditions while detecting obstacles and taking respective maneuvers (i.e., abrupt turns, etc.). Moreover, self-driving solutions need to take fast and real-time actions to avoid collisions in the designated environment. For this purpose, literature has shown the efficiency of YOLOv3 with respect to providing real-time results in latency-sensitive applications. Hence, to overcome this dilemma, we propose to develop an economically efficient deep learning- based smart labor assistance trolley that uses the YOLO v3 as a core detective deep learning technique with advance and efficient perception and motion planning modules. The perception module succours the autonomous trolley to precisely detect and classify the objects. While the motion planning module uses the specific intended target detection technique to follow the targeted person in crowded environment. These techniques make the autonomous trolley able to take expeditious, meticulous, and conspicuous action in real-time. The labor assistant robot detects and tracks the respective person using YOLOv3. To validate the efficiency of the proposed solution, we have performed a series of experiments considering different test cases. Our proposed work achieved a mean average precision of 0.81%.
An Analysis of Data Curation Techniques throughout the Perception Development Pipeline

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;The development of perception functions for tomorrow’s automated vehicles is driven by enormous amounts of data: often exceeding a gigabyte per second and reaching into the terabytes per hour. Data is typically gathered by a fleet of dozens of mule vehicles which multiply the data generated into the hundreds of petabytes per year. Traditional methods for fueling data-driven development would record every bit of every second of a data logging drive on solid-state drives located on a PC in the vehicle. Recorded data must then be exported from these drives using an upload station which pushes to the data lake after arriving back at the garage.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;This paper considers different techniques for curating logged data. These curation methods are performed to maximize the usefulness of the data throughout its lifecycle and minimize the amount of data necessary for perception development and validation The reduction of logged data has the effect of not only curtailing storage costs, but also minimizing latency for data availability and maximizing possible campaign drive time. Advanced techniques considered include: (a) the real-time evaluation of sensor and bus signals, (b) the application of artificial intelligence (e.g. for similarity-based image discovery and dynamic scene content detection), and (c) using function prototyping to inform the data curation process. In addition, various possible integration points for the curation techniques are considered in the data ingestion pipeline - from the cloud in the datacenter to the in-vehicle logging system on the edge. The trade-offs for each proposed techniques are considered across the various components of the data ingestion pipeline to explore the feasibility of - and to arrive at some conclusions for - designing more intelligent data collection campaigns.&lt;/div&gt;&lt;/div&gt;
8. The Evolution of Integrated Music Education From the 90’s to the Present

Abstract The article marks the most important aspects of the evolution of Romanian pre-university music education during the last 30 years. The education reform highlighted the positive aspects as well as the shortcomings in the field of music education in the integrated system. An analysis of the current state sheds light on what is valuable in the system and what can be improved so that specialized music education remains a genuine educational and cultural landmark in a constantly changing society.
Disentangling Critical Questions from Argument Schemes

Abstract Critical questions have been understood in the framework of argument schemes from their conception. This understanding has influenced the process of evaluating arguments and the development of classifications. This paper argues that relating these two notions is detrimental to research on argument schemes and critical questions, and that it is possible to have critical questions without relying on argument schemes. Two objections are raised against the classical understanding of critical questions based on theoretical and analytical grounds. The theoretical objection presents the assumptions that are embedded in the idea of argument schemes delivering questions to evaluate arguments. The analytical objection, on the other hand, exposes the shortcomings of the theory when critical questions are used to evaluate real-life argumentation. After presenting these criticisms, a new theory of critical questions is sketched. This theory takes into account the dynamics of dialectical discussions to describe the function of critical questions and their implications for evaluating arguments.
Open-source tool for model performance analysis for subpopulations

When a CAD-AI network is created and employed, both safety and effectiveness need to be guaranteed for all subgroups of the target population. We present a novel toolbox for automatic slicing and performance assessment in a generic and modular approach, helping to find the subpopulations where cautiousness is warranted, and the model may need improvement. In a first step slices are generated and saved for further analysis inspired by the existing ‘Slice Finder’ algorithm. Depending on the type of AI task (classification, object detection, segmentation, instance segmentation...) multiple metrics are evaluated. Both labeled (specificity, sensitivity...), unlabeled (outlier score, confidence score...) and user-defined metrics can be included. Optionally, the confidence interval (CI) is calculated. In a last step, the metric values and CI are used to rank the slices to quickly find the slices of interest. Custom ranking methods can be added, keeping the full process from slice generation up to and including visualization modular and customizable. We illustrate the toolbox with a dermatology classification and object detection use-case. First a single model is evaluated down to crosses of three slices where slices of interest are detected on degree three which would be difficult to find if not automated. Additionally, the usage of unlabeled metrics such as outlier score is illustrated to automatically find slices of interest.
Classification of point-of-care ultrasound in breast imaging using deep learning

Early detection of breast cancer is important to reduce morbidity and mortality. Access to breast imaging is limited in low- and middle-income countries compared to high-income countries. This contributes to advancestage breast cancer presentation with poor survival. Pocket-sized portable ultrasound device, also known as point-of-care ultrasound (POCUS), aided by decision support using deep learning-based algorithms for lesion classification could be a cost-effective way to enable access to breast imaging in low-resource settings. A previous study, where using convolutional neural networks (CNN) to classify breast cancer in conventional ultrasound (US) images, showed promising results. The aim of the present study is to classify POCUS breast images. A POCUS data set containing 1100 breast images was collected. To increase the size of the data set, a CycleConsistent Adversarial Network (CycleGAN) was trained on US images to generate synthetic POCUS images. A CNN was implemented, trained, validated and tested on POCUS images. To improve performance, the CNN was trained with different combinations of data consisting of POCUS images, US images, CycleGAN-generated POCUS images and spatial augmentation. The best result was achieved by a CNN trained on a combination of POCUS images and CycleGAN-generated POCUS images and augmentation. This combination achieved a 95% confidence interval for AUC between 93.5% – 96.6%.
Artificial intelligence for image-based breast cancer risk prediction using attention

Accurate prediction of individual breast cancer risk paves the way for personalised prevention and early detection. Whilst well-established clinical risk factors can be used to stratify the population into risk groups, the addition of genetic information and breast density has been shown to improve prediction. Machine learning enabled automatic risk prediction provides key advantages over existing methods such as the ability to extract more complex information from mammograms. However, this is a challenging area of research, partly due to the lack of data within the field, therefore there is scope for novel approaches. Our method uses Multiple Instance Learning in tandem with attention in order to make accurate, short-term risk predictions from full-sized mammograms taken prior to the detection of cancer. This approach ensures small features like calcifications are not lost in a downsizing process and the whole mammogram is analysed effectively. An attention pooling mechanism is designed to highlight patches of increased importance and improve performance. Additionally, this increases the interpretability of our model as important patches can be shown in a saliency map. We also use transfer learning in order to utilise a rich source of screen-detected cancers and evaluate whether a model trained to detect cancers in mammograms allows us also to predict risk in priors. Our model achieves an AUC of 0.635 (0.600,0.669) in cancer-free screening mammograms of women who went on to a screen-detected or interval cancer between 5 and 55 months and an AUC of 0.804 (0.777,0.830) in screen-detected cancers.
Developing a task-oriented deep convolutional neural network application towards estimating near-term breast cancer risk: preliminary work

The purpose of this study was to develop a loss function that can drive a given CNN to achieve high sensitivity (or recall) for identifying women with a high-risk of having breast cancer. The cross-entropy (CE) loss function is widely used to optimize a CNN for natural scene classification due to its stability. However, CE loss treats each class equally, thus, it may not be suitable to train the CNN to have high sensitivity performance. Therefore, we hypothesized that a loss function based on the Fβ-measure, the weighted harmonic mean of precision and recall, can improve the sensitivity of the resulting CNN model by giving more weight to the recall metric. To do so, we combined CE loss with the Fβ-measure to implement a task-oriented loss function for achieving high sensitivity performance. In this preliminary work, we used a screening mammogram dataset of 2000 scans (1000 recalled lesions;1000 normal). We extracted recalled lesion patches using radiologists’ annotations and normal patches from the center of the breast. We fine-tuned the DenseNet121 network using the image patch dataset with a data split ratio of 0.8:0.1:0.1 for training, validation, and testing. We conducted ROC analysis to evaluate the performance of our proposed model. In the test set, the model with the task-oriented loss function achieved an AUC of 0.90 compared to CE loss (AUC=0.88) alone. The ROC curve of the proposed loss function achieved (a sensitivity of 53% at 98% specificity level) higher sensitivity than the CE loss alone (a sensitivity of 41% at 98% specificity level) for a high specificity area.
A systematic study on design and development of optical communication image encryption techniques

Abstract Optical encryption techniques in over the last 10 years have become more important in the area of optical information processing. Several applications, including online payments, medical or biometric image storage or transmission, private military applications, police identification techniques, online banking platforms, governmental services, identity (ID) cards, etc., are only a few examples. Require special and reliable security in the transmission and storage of images. Since digital picture information may be readily accessed by hackers via transmission over the Internet or other communication channels, the issue of photos with unlawful usage is becoming more and more severe. A more effective and secure means of communicating images may be offered via optical encryption. The encryption mechanism and keys are needed at the receiver side in order to decrypt the original optical data. Picture security becomes a highly important subject in communication science as a result of network growth and rising need for image transmission. There are several security issues with picture data, including forgery detection and illegal access. The most straightforward method of picture protection is to encrypt it as text. The traditional text encryption algorithms are inappropriate for picture encryption because of the high correlation present in image data. Over the last two decades, a number of optical approaches have been proposed for photo encryption, with the specific advantage of processing two-dimensional (2D) image data in parallel conduction mode. Therefore, we are doing an extensive investigation on optical image encryption in this work.
A Real-Time Path Planning Algorithm Based on the Markov Decision Process in a Dynamic Environment for Wheeled Mobile Robots

A real-time path planning algorithm based on the Markov decision process (MDP) is proposed in this paper. This algorithm can be used in dynamic environments to guide the wheeled mobile robot to the goal. Two phases (the utility update phase and the policy update phase) constitute the path planning of the entire system. In the utility update phase, the utility value is updated based on information from the observable environment. Obstacles and walls reduce the utility value, pushing agents away from these impassable areas. The utility value of the goal is constant and is always only the largest. In the policy update, a series of policies can be obtained by the strategy of maximizing its long-term total reward, and the series will eventually form a path towards the goal, regardless of where the agent is located. The simulations and experiments show that it takes longer to find the first path in the beginning due to the large changes of utility value, but once the path is planned, it requires a small amount of time cost to respond to the environmental changes. Therefore, the proposed path planning algorithm has an advantage in dynamic environments where obstacles move in unpredictable ways.
Enhancing diversity in GANs via non-uniform sampling

Recent advances in Generative Adversarial Networks (GANs) have led to impressive results in generating realistic data. However, GANs training is still challenging, often leading to mode-collapse, where a certain type of samples dominates the generated output. To address this issue, we propose a novel training algorithm based on bidirectional GANs (BiGANs) that can be generalized to any implicit generative model. Our algorithm relies on a non-uniform sampling scheme, where data points in a minibatch are sampled with probability inversely proportional to their log-evidence. However, estimating log-evidence is computationally expensive. Instead, we propose to use the reconstruction error, which directly correlates with the log-evidence and only requires a BiGAN network evaluation. Additionally, we combine the aforementioned method with a regularization in the empirical distribution of the encoder that further boosts the performance. Our empirical results show that the proposed methods improve both the quality and diversity of the generated samples.
Accurate breast cancer diagnosis using a stable feature ranking algorithm

Breast cancer (BC) is one of the most common cancers among women. Since diverse features can be collected, how to stably select the powerful ones for accurate BC diagnosis remains challenging.A hybrid framework is designed for successively investigating both feature ranking (FR) stability and cancer diagnosis effectiveness. Specifically, on 4 BC datasets (BCDR-F03, WDBC, GSE10810 and GSE15852), the stability of 23 FR algorithms is evaluated via an advanced estimator (S), and the predictive power of the stable feature ranks is further tested by using different machine learning classifiers.Experimental results identify 3 algorithms achieving good stability ([Formula: see text]) on the four datasets and generalized Fisher score (GFS) leading to state-of-the-art performance. Moreover, GFS ranks suggest that shape features are crucial in BC image analysis (BCDR-F03 and WDBC) and that using a few genes can well differentiate benign and malignant tumor cases (GSE10810 and GSE15852).The proposed framework recognizes a stable FR algorithm for accurate BC diagnosis. Stable and effective features could deepen the understanding of BC diagnosis and related decision-making applications.
Humanoid in Robotics

This research paper contains detailed statistics about the robotic methods and systems. Artificial intelligence is increasing in the marketplace. The market is receiving totally depending on artificial intelligence for responsibility the multifaceted tasks. Robotics is a greatest famous division in the area of manufacturing and sciences. All engineers are taking attention to make a robot. It can do a certain task and can give optimum result for the given task. Every robotic engineer is trying to mark a robot with 0% error but it is not possible at the current technological state. It will give you the correct answer for every question without any error. It uses its database for, how it works and how it senses works. It will be enough for getting good information about robotics and devices along with the system of robots
Review of Van den Branden (2022): To task or not to task

Preview this online first article: Review of Van den Branden (2022): How to Teach an Additional Language: To task or not to task?, Page 1 of 1 < Previous page | Next page > /docserver/preview/fulltext/10.1075/itl.22015.pau/itl.22015.pau-1.gif
Four Million Segments and Counting: Building an English-Croatian Parallel Corpus through Crowdsourcing Using a Novel Gamification-Based Platform

Parallel corpora have been widely used in the fields of natural language processing and translation as they provide crucial multilingual information. They are used to train machine translation systems, compile dictionaries, or generate inter-language word embeddings. There are many corpora available publicly; however, support for some languages is still limited. In this paper, the authors present a framework for collecting, organizing, and storing corpora. The solution was originally designed to obtain data for less-resourced languages, but it proved to work very well for the collection of high-value domain-specific corpora. The scenario is based on the collective work of a group of people who are motivated by the means of gamification. The rules of the game motivate the participants to submit large resources, and a peer-review process ensures quality. More than four million translated segments have been collected so far.
Are There Pluralities of Worlds?

Abstract Indicative conditionals and configurations with neg-raising predicates have been brought up as potential candidates for constructions involving world pluralities. I argue against this hypothesis, showing that cumulativity and quantifiers targeting a plurality’s part structure cannot access the presumed world pluralities. I furthermore argue that this makes worlds special in the sense that the same tests provide evidence for pluralities in various other semantic domains.
April Showers Bring May Flowers

Given the numbers and data at our fingertips in this digital age, mathematical and digital literacy skills are imperative when it comes to understanding natural and social phenomena and making good decisions. As teachers we are responsible for helping students make sense of this information