Reviewerly/exHarmony huggingface dataset:
Estimating Air Pollution Related Solar Insolation Reduction in the Assessment of the Commercial and Industrial Rooftop Solar PV Potential

Air pollution is a serious issue and it has been becoming increasingly urgent over last year, mainly as a result of its effects on human health. Recently, however, a number of scientific papers has appeared reporting on air pollution effects in other fields such as the photovoltaic energy generation and, notably, on the relation between the reduction of the solar insolation reaching the PV systems and PM2.5 concentrations in the air. In this study, the rooftop solar PV potential of commercial and industrial (C&I) buildings at regional scale has been estimated tacking into account the spatially distributed solar insolation reduction factor, due to the PM2.5 in the air. High resolution LiDAR data and advanced digital surface modeling techniques have been used for determining the available suitable rooftop area and estimating the technical solar PV potential of the C&I rooftops. For the C&I study area of Aversa Nord (South Italy), we find that the suitable rooftops have annually a total electric power potential of 50.75 \( \text{GWh}/year \). For this area, an annual average PM2.5 concentrations of about 13 μg/m3 results in a nearly 5% annual solar insolation reduction. Thus, if properly located, the large scale rooftop PV systems could significantly decrease primary energy consumption and contribute to reduce the CO2 emissions.
Hierarchical and Robust Pathology Image Reading for High-Throughput Cervical Abnormality Screening

Cervical smear screening is an imaging-based cancer detection tool which is of pivotal importance for the early-stage warning. A computer-aided screening system can automatically decide if the images with cervical cells are classified as "abnormal" or "normal", and then alert pathologists. It can significantly reduce the workload for human experts and is therefore highly-demanded in clinical practice. Most of the screening methods are based on automatic cervical cell detection and classification, but the accuracy is generally limited due to the high variation of cell appearance and lacking of the context information from the surroundings. Here we propose a novel and complete framework for pathology image classification, which can provide a robust screening performance. We commence by implementing the cervical cell detection method to the pathology image from the whole-slide image (WSI) and extract representative patches from the detected "abnormal" cells with corresponding confidence information. The patches are fed into our novel classification model for a more comprehensive analysis, and conduct the classification for the overall target pathology image. It can be demonstrated in experiments that our two-stage method can effectively suppress the errors from cell-level classification, and provide a robust way for pathology image classification.
Spatial Keyframe Extraction Of Mobile Videos For Efficient Object Detection At The Edge

Advances in federated learning and edge computing advocate for deep learning models to run at edge devices for video analysis. However, the captured video frame rate is too high to be processed at the edge in real-time with a typical model such as CNN. Any approach to consecutively feed frames to the model compromises both the quality (by missing important frames) and the efficiency (by processing redundantly similar frames) of analysis. Focusing on outdoor urban videos, we utilize the spatial metadata of frames to select an optimal subset of frames that maximizes the coverage area of the footage. The spatial keyframe extraction is formulated as an optimization problem, with the number of selected frames as the restriction and the maximized coverage as the objective. We prove this problem is NP-hard and devise various heuristics to solve it efficiently. Our approach is shown to yield much better hit-ratio than conventional ones.
AR-Supported Induction Cooker AR-SI: One Step before the Food Robot

In recent years we are facing with new arising concepts in the gastronomy sector as a consequence of the new advancements in industry 4.0. Preparing food, cooking, serving and other gastronomic processes have evolved with the new trends blending IoT (Internet of Things), AR (Augmented reality), VR (Virtual Reality) and even robotics. In this paper we present an innovative concept named AR-SI. It's an AR-supported induction cooker presented as a preliminary study of a step before the food robot. AR-SI cooker is a holistic solution implemented with IoT and smart weight control which makes it possible to establish connected and distributed food practicing and adaptive power control in addition to AR support. The induction-based cooking is provided as an electronic kitchen appliance which forms the basis of cooking layout. This innovative holistic approach qualifies the AR-SI cooker through a virtual assistant that can be used for a distinguished interactive food preparation and training experience. The meal recipe is uttered explaining how to prepare the ingredients and cook with prescribed weight control. The recipe is animated by 3D graphics in augmented form on the actual cooker for the preparation of delicious meals.
Self-recursive Contextual Network for Unsupervised 3D Medical Image Registration

In this paper, we propose a self-recursive contextual network for unsupervised 3D medical image registration. Current learning-based registration methods refine an initial deformation field either through cascaded stages or in a coarse-to-fine manner, which improve results at the cost of a rapidly increased number of parameters. Aiming to achieve both elevation of performance and reduction of parameters, we design a novel pyramid structure with a self-recursive scheme and contextual components. Specifically, we adopt a weight-sharing generator to refine the deformation fields recursively across different levels of the pyramid. Meanwhile, we introduce a spatial pyramid pooling module in the feature extractor to capture richer contextual information, as well as a dilated receptive module for the post-processing of the deformation field. Evaluated on two benchmark datasets for 3D medical image registration, our method outperforms the state-of-the-art pyramid network with 39% less parameters, and competes to multi-stage cascaded registration networks with significantly less parameters and faster running speed.
A Unified Framework for Piecewise Semantic Reconstruction in Dynamic Scenes via Exploiting Superpixel Relations

This paper presents a novel framework for dense piecewise semantic reconstruction in dynamic scenes containing complex background and moving objects via exploiting superpixel relations. We utilize two kinds of superpixel relations: motion relations and spatial relations, each having three subcategories: coplanar, hinge, and crack. Spatial relations provide constraints on the spatial locations of neighboring superpixels and thus can be used to reconstruct dynamic scenes. However, spatial relations can not be estimated directly with epipolar geometry due to moving objects in dynamic scenes. We synthesize the results of semantic instance segmentation and motion relations to estimate spatial relations. Given consecutive frames, we mainly develop our method in five main stages: preprocessing, motion estimation, superpixel relation analysis, reconstruction and refinement. Extensive experiments on various datasets demonstrate that our method outperforms competitors in reconstruction quality. Furthermore, our method presents a feasible way to incorporate semantic information in Structure-from-Motion (SFM) based reconstruction pipelines.
Design of an Efficient Deep Neural Network for Multi-level Classification of Breast Cancer Histology Images

Breast cancer is the second most prominent cause of death among women, and it is a major health problem across the world over the past many years. In this work, four hundred different biopsy images were collected from the Bio-imaging 2018 breast histology classification challenge by participating Grand Challenge on breast cancer histology images (BACH) for multiclass breast cancer histology image classification using deep learning technique. Computer-aided detection or diagnosis (CAD) system plays an important role in the detection and to increase death survival rate of women suffering from breast cancer, which can decrease death rate among women. The main purpose of this paper is to develop a CAD system which can be used to detect whether the input biopsy image fits one of the four different classes like benign, in situ, Invasive and Malignant. We have incorporated an efficient lightweight neural network such as MobileNet2.10ex instead of normal convolution neural network for feature extraction and at top a fully connected deep neural network is designed to do classification among different classes. The confusion matrix is developed in order to draw the model accuracy of both techniques. It is observed that MobleNet2.10ex is giving about 88.92 % training accuracy at 257 epochs and the corresponding loss value is 0.2249. In order to progress the performance during pre-processing, a staining technique was used to remove staining from a digital scan of histology microscopic image of fine-needle aspirate (FNA) slides and feature invariant approach such as data augmentation technique was used to rotate each image in pipeline about the axis 45° apart three times in order to increase dataset and prediction accuracy. The model was trained till 280 epochs to evaluate training and validation accuracy using Google Colab having strong GPU 2496 CUDA cores, 12 GB GDDR5 VRAM and 12.6 GB RAM.
A Case Study Validation of the Pair-estimation Technique in Effort Estimation of Mobile App Development Using Agile Processes

In the software development process, effort estimation is essential and a critical phase for the development team in order to ensure that development tasks will be delivered within the planned time. This paper presents a confirmatory case study in an IT company, which examined its current estimation techniques and its process in the context of mobile app development in the Agile process. Moreover, the study provides, and has validated, a proposed estimation technique, namely Pair-estimation, in order to enhance the accuracy of the existing technique. This study, in addition, presents the effectiveness of estimation factors/predictors in supporting the development team to manage, estimate and create subtasks for their user stories.
Path-Following Model Predictive Control of Ballbots

This paper introduces a novel approach for model predictive control of ballbots for path-following tasks.Ballbots are dynamically unstable mobile robots which are designed to balance on a single ball.The model presented in this paper is a simplified version of a full quaternion-based model of ballbots' underactuated dynamics which is suited for online implementation.Furthermore, the approach is extended to handle nearby obstacles directly in the MPC formulation.The presented controller is validated through simulation on a high fidelity model as well as through real-world experiments on a physical ballbot system.
Combined Priority and Path Planning through a Double-layer Structure for Multiple Robots

In this paper, we address the problem of both priority and path planning for multiple robots. Many relevant studies showed that priority assigned to robots has a significant impact on the planning results, such as whether a solution can be found or the global cost. We propose a double-layer structure that combines priority planning and path planning. A modified A* algorithm is introduced to get collision-free paths as a path planner layer. A tree-like searching method that conducting in configuration space of priority is designed as a priority planner layer, while the mode communicating with the path planner layer is also defined to abstract heuristic information to guide searching direction. This new approach is implemented in simulation and represents good performance both in success rate and global paths cost.
Enhancing Pre-trained Language Representation for Multi-Task Learning of Scientific Summarization

This paper aims to extract summarization and keywords from scientific articles simultaneously, while abstract extraction (AE) and key extraction (KE) are considered as auxiliary tasks to each other. For the data scarcity in scientific AE and KE tasks, we propose a multi-task learning framework which uses huge unlabeled data to learn scientific language representation (pre-training) and uses smaller annotated data to transfer the learned representation to AE and KE (fine-tuning). Although the pre-trained language model performs well in universal natural language tasks, its capacity still has a margin of improvement for specific tasks. Inspired by this intuition, we use another two tasks keyword masking and key sentence prediction before the fine-tuning phase to enhance the language representation for AE and KE. This language representation enhancing stage uses the same labeled data but different optimization objectives with the fine-tuning phase. In order to evaluate our model, we develop and release a high-quality annotated corpus for scientific papers with keywords and abstract. We conduct comparative experiments on this dataset, and experimental results show that our multi-task learning framework achieves the state-of-the-art performance, proving the effectiveness of the language model enhancing mechanism.
Monte Carlo ensemble correlation coefficient for association detection

Data characteristics are often summarized and represented by a set of variables. Identifying the relationship between these variables is crucial for prediction, hypothesis testing, and decision making. The relation between two variables is often quantified using a correlation factor. Once the correlation between a response and an independent variable is quantified, it can be used to make predictions regarding response variable for the observed factor. That is, if two variables are correlated, by observing one, we can make predictions about the other one. A more accurate prediction can be made where there is strong relationship between variables. Several correlation factors have been introduced. Among them, Pearson's Correlation Coefficient has been commonly used, while Distance Correlation and Maximal Information Coefficient have been recently introduced to address the shortcomings of Pearson's Correlation Coefficient. These correlation coefficients are developed to measure associations in different trends. For example, Pearson's Correlation is used when dealing with linear trends while Spearman's correlation is used when dealing with monotonic trends. However, in many applications, the underlying relationship is not obvious to determine the appropriate choice of the correlation coefficient. In this paper, we compare these factors through a series of simulations and we propose a single generic factor by aggregating these factors for general applications.
Integrating Informativeness, Representativeness and Diversity in Pool-Based Sequential Active Learning for Regression

In many real-world machine learning applications, unlabeled samples are easy to obtain, but it is expensive and/or time-consuming to label them. Active learning is a common approach for reducing this data labeling effort. It optimally selects the best few samples to label, so that a better machine learning model can be trained from the same number of labeled samples. This paper considers active learning for regression (ALR) problems. Three essential criteria - informativeness, representativeness, and diversity - have been proposed for ALR. However, very few approaches in the literature have considered all three of them simultaneously. We propose three new ALR approaches, with different strategies for integrating the three criteria. Extensive experiments on 12 datasets in various domains demonstrated their effectiveness.
How far should I watch? Quantifying the effect of various observational capabilities on long-range situational awareness in multi-robot teams

In our previous work, we showed that individual robots within a multi-robot team can gain long-distance situational awareness from passive observations of a single nearby neighbor without any explicit robot-to-robot communication. However, that prior work was developed only in simulation, and performance was not measured for real robot teams in physical space with realistic hardware limitations. Toward this end, we studied the performance of these methods in real robot scenarios with methods using more sophisticated techniques in machine learning to mitigate practical implementation problems. In this study, we further extend that work by characterizing the effects of changing history length and sensor range. Rather than finding that increasing history length and sensor range always yield better estimation performance, we find that the optimal history length and sensor range varies depending on the distance between the estimating robot and the robot being estimated. For estimation problems where the estimation target is nearby, longer histories actually degrade performance, and so sensor ranges could be increased instead. Conversely, for farther targets, history length is as valuable or more valuable than sensor range. Thus, just as optimal shutter speed varies with light availability and speed of the subject, passive situational awareness in multi-robot teams is best achieved with different strategies depending on proximity to locations of interest. All studies use the teams of Thymio II physical, two-wheeled robots in laboratory environments <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Data and models used are available at https://github.com/PavlicLab/ACSOS2020_ReTLo_Extension.git.
Towards Robust Word Embeddings for Noisy Texts

Research on word embeddings has mainly focused on improving their performance on standard corpora, disregarding the difficulties posed by noisy texts in the form of tweets and other types of non-standard writing from social media. In this work, we propose a simple extension to the skipgram model in which we introduce the concept of bridge-words, which are artificial words added to the model to strengthen the similarity between standard words and their noisy variants. Our new embeddings outperform baseline models on noisy texts on a wide range of evaluation tasks, both intrinsic and extrinsic, while retaining a good performance on standard texts. To the best of our knowledge, this is the first explicit approach at dealing with these types of noisy texts at the word embedding level that goes beyond the support for out-of-vocabulary words.
IncreAIBMF: Incremental Learning for Encrypted Mobile Application Identification

Mobile application identification, as the fundamental technique in the field of network security and management, suffers from a critical problem, namely ‘encrypted traffic’. The proven methods for encrypted traffic identification have a major drawback, which is new come applications continue to suffer from catastrophic forgetting, a dramatic decrease in overall performance when training with new app classes added incrementally. This is due to the current model requiring the entire dataset, consisting of all the samples from the old and the new classes, to update the model. The updating requirement becomes easily unsustainable as the number of apps grows, To address the issue, we propose IncreAIBMF framework to learn deep neural networks incrementally, using new apps data and only a small exemplar set corresponding to samples from the old apps. The key idea behind IncreAIBMF is an incremental learning framework which possesses new application identification ability by incorporating the cross-distilled loss, which can not only learn the new app classes and also retain the previous knowledge corresponding to the old app classes. Our experiment results show that IncreAIBMF achieves 87.3% on Macro Precision, 87.8% on F1 Score and 88.9% on Macro Recall, respectively, on the real-world traces that consists of 50 mobile applications, supports the early prediction, and is robust to the scale of the app classes. Besides, the basic variant of IncreAIBMF, AIBMF is superior to the state-of-the-art methods in terms of identification performance.
The Prediction of Stock Market Trends Using the Hybrid Model SVM-ICA-GA

In this paper, the trends and the timings of stock market of Japanese Candlestick are predicted and analyzed empirically by developing the hybrid model that uses the three prominent techniques of artificial intelligence, i.e., SVM, ICA and genetic algorithms. In order to conduct the effective technical analysis of stock market—support vector machines (SVM) are used with genetic algorithms and imperialist competition algorithms (ICA). ICA is used to indicate the stock market timing and to optimize the SVM parameters, whereas GA is used to select the best features in addition to SVM parameters optimization. The input data of a model is generated using the two very important approaches—raw-based and signal-based. The results of the paper indicate that the performance of SVM-IC-GA is far better than the existing feed forward static neural network techniques of the existing literature.
Novel View Synthesis With Skip Connections

Novel view synthesis is the task of synthesizing an image of an object at an arbitrary viewpoint given one or a few views of the object [1]. The output image of novel view synthesis exhibits a significant structural change from the input. Because of the large change, the skip connections or U-Net architecture, which can sustain the multi-level characteristics of the input images, cannot be directly utilized for the novel view synthesis [2]. In this paper, we investigate several variations of skip connection on two widely used novel view synthesis modules, pixel generation [1] and flow prediction [3]. For pixel generation, we find that the combination of the skip connections with flow-based hard attention is helpful. On the other hand, flow prediction enjoys marginal benefit from skip connections in deeper layers. Our pipeline suggests how to make use of skip connections on tasks that involve large geometric changes.
Sightseeing Application Based on Location Marking and Convolutional Neural Network Building Recognition

In this research we observe the realization of the Kazan sightseeing system developed in the process of scientific research. There were suggested the system including augmented reality location marking and neural network recognition, which is completely new for the Kazan development market. There were identified the basic goals and tasks, that should have been solved throughout the development of the information system, were mentioned as the benefits and advantages of a such development project implementation. During the research different models and architectures of convolutional neural networks are observed and evaluated. The evaluation results of a developed application show that the proposed sightseeing system has significantly improved the tourist experiences.