Reviewerly/exHarmony huggingface dataset:
RLTiering: A Cost-Driven Auto-Tiering System for Two-Tier Cloud Storage Using Deep Reinforcement Learning

The cloud storage boom has prompted providers to offer two storage tiers, i.e., hot and cold tiers, which are respectively purpose-built to provide the lowest cost for frequent and infrequent access patterns. However, for cloud users, it is non-trivial to determine cost-effective tiers because it is hard to obtain future access patterns in advance and is difficult to predict them exactly. The lack of future information poses a risk of increasing costs instead of saving costs. This is not the only challenge encountered when it comes to cost optimization. In this article, we take Amazon S3 as an example to analyze the pricing of two-tier cloud storage and derive several major challenges faced by cost optimization. Then, assuming a priori knowledge of future access patterns, we propose an optimal offline algorithm based on dynamic programming to determine cost-effective tiers for each time slot. Further, to handle online workload arrivals, we formulate the problem using Markov decision processes and propose RLTiering based on deep reinforcement learning. Eventually, the cost performance of RLTiering is evaluated based on real-world traces and prevalent Amazon S3 pricing, and the results show that it achieves significant cost-savings.
Classification of Corn Diseases using Random Forest, Neural Network, and Naive Bayes Methods

Abstract Corn is one of the staple foods consumed by many people after rice plants, especially in Indonesia. High consumer demand requires corn production in large quantities to meet these needs. However, corn production is not always in large quantities due to several factors, namely diseases in corn plants. Unhealthy corn plants can reduce the amount of production. Healthy and unhealthy corn plants can be identified manually, but this method is not efficient, so in this study, it is proposed to classify corn diseases using the Random Forest, Neural Network, and Nave Bayes methods. The dataset used is a collection of corn leaf images taken from farmers’ fields in the Madura Region with four target classes, namely healthy, gray leaf spot, blight, and common rust. Based on the test results, the classification using the Neural Network method provides a better accuracy value than the other two methods in classifying corn leaf datasets, namely the AUC value reaches 90.09%, classification accuracy is 74.44%, f1 score is 72.01%, precision is 74.14% and recall by 74.43%.
The impact of AI-based conversational agent on the firms’ operational performance: Empirical evidence from a call center

Artificial intelligence (AI) based conversational agent is widely used in the service industry. Although some studies have investigated the impact of AI on customers, little research has documented the concrete effects of AI-based conversational agents on firms' operational performance. To fill this gap, the main goal of this paper is to investigate the impacts of AI-based conversational agent on firms' operational performance. In addition, the moderating effect of time blocks is also investigated. We address questions using the event study method and a proprietary data set from a telecom firm in China. Results show that the introduction of an AI-based conversational agent increases the average call length and has no significant influence on call numbers. Moreover, there is a heterogeneous effect among different time blocks. Our findings provide important implications for the operational management of call center.
Multi‐domain autonomous driving dataset: Towards enhancing the generalization of the convolutional neural networks in new environments

In this paper, a large-scale dataset called the Iran Autonomous Driving Dataset (IADD) is presented, aiming to improve the generalization capability of the deep networks outside of their training domains. The IADD focuses on 2D object detection and contains more than 97,000 annotated images, covering six common object classes in the field of autonomous vehicles. To improve the generalization of the models, a wide variety of driving conditions and domains, including the city and suburban road settings, adverse weather conditions, and various traffic flows, are presented in the IADD images. The results of exhaustive evaluations conducted on several state-of-the-art convolutional neural networks reveal that not only the trained architectures have performed successfully on test data of the IADD, but also they have upheld high precision in the assessments of generalization capability. In order to challenge the models, broad range of simulations have been performed in the CARLA software environment; which due to the synthetic nature of the simulated images, severe domain shifts have been observed between the CARLA and the IADD. Also, the cross-domain evaluation results have confirmed the efficacy of the IADD in enhancing the generalization ability of the deep learning models. The dataset is available in: https://github.com/ahv1373/IADD .
Oral SARS‐CoV‐2 reduction by local treatment: A plasma technology application?

The SARS-CoV-2 pandemic reemphasized the importance of and need for efficient hygiene and disinfection measures. The coronavirus' efficient spread capitalizes on its airborne transmission routes via virus aerosol release from human oral and nasopharyngeal cavities. Besides the upper respiratory tract, efficient viral replication has been described in the epithelium of these two body cavities. To this end, the idea emerged to employ plasma technology to locally reduce mucosal viral loads as an additional measure to reduce patient infectivity. We here outline conceptual ideas of such treatment concepts within what is known in the antiviral actions of plasma treatment so far.
Multiscale Cascaded Attention Network for Saliency Detection Based on ResNet

Saliency detection is a key research topic in the field of computer vision. Humans can be accurately and quickly mesmerized by an area of interest in complex and changing scenes through the visual perception area of the brain. Although existing saliency-detection methods can achieve competent performance, they have deficiencies such as unclear margins of salient objects and the interference of background information on the saliency map. In this study, to improve the defects during saliency detection, a multiscale cascaded attention network was designed based on ResNet34. Different from the typical U-shaped encoding-decoding architecture, we devised a contextual feature extraction module to enhance the advanced semantic feature extraction. Specifically, a multiscale cascade block (MCB) and a lightweight channel attention (CA) module were added between the encoding and decoding networks for optimization. To address the blur edge issue, which is neglected by many previous approaches, we adopted the edge thinning module to carry out a deeper edge-thinning process on the output layer image. The experimental results illustrate that this method can achieve competitive saliency-detection performance, and the accuracy and recall rate are improved compared with those of other representative methods.
Recent Advances in Artificial Intelligence and Tactical Autonomy: Current Status, Challenges, and Perspectives

This paper presents the findings of detailed and comprehensive technical literature aimed at identifying the current and future research challenges of tactical autonomy. It discusses in great detail the current state-of-the-art powerful artificial intelligence (AI), machine learning (ML), and robot technologies, and their potential for developing safe and robust autonomous systems in the context of future military and defense applications. Additionally, we discuss some of the technical and operational critical challenges that arise when attempting to practically build fully autonomous systems for advanced military and defense applications. Our paper provides the state-of-the-art advanced AI methods available for tactical autonomy. To the best of our knowledge, this is the first work that addresses the important current trends, strategies, critical challenges, tactical complexities, and future research directions of tactical autonomy. We believe this work will greatly interest researchers and scientists from academia and the industry working in the field of robotics and the autonomous systems community. We hope this work encourages researchers across multiple disciplines of AI to explore the broader tactical autonomy domain. We also hope that our work serves as an essential step toward designing advanced AI and ML models with practical implications for real-world military and defense settings.
Development of a Large-Scale Roadside Facility Detection Model Based on the Mapillary Dataset

The detection of road facilities or roadside structures is essential for high-definition (HD) maps and intelligent transportation systems (ITSs). With the rapid development of deep-learning algorithms in recent years, deep-learning-based object detection techniques have provided more accurate and efficient performance, and have become an essential tool for HD map reconstruction and advanced driver-assistance systems (ADASs). Therefore, the performance evaluation and comparison of the latest deep-learning algorithms in this field is indispensable. However, most existing works in this area limit their focus to the detection of individual targets, such as vehicles or pedestrians and traffic signs, from driving view images. In this study, we present a systematic comparison of three recent algorithms for large-scale multi-class road facility detection, namely Mask R-CNN, YOLOx, and YOLOv7, on the Mapillary dataset. The experimental results are evaluated according to the recall, precision, mean F1-score and computational consumption. YOLOv7 outperforms the other two networks in road facility detection, with a precision and recall of 87.57% and 72.60%, respectively. Furthermore, we test the model performance on our custom dataset obtained from the Japanese road environment. The results demonstrate that models trained on the Mapillary dataset exhibit sufficient generalization ability. The comparison presented in this study aids in understanding the strengths and limitations of the latest networks in multiclass object detection on large-scale street-level datasets.
Driver Take-Over Behaviour Study Based on Gaze Focalization and Vehicle Data in CARLA Simulator

Autonomous vehicles are the near future of the automobile industry. However, until they reach Level 5, humans and cars will share this intermediate future. Therefore, studying the transition between autonomous and manual modes is a fascinating topic. Automated vehicles may still need to occasionally hand the control to drivers due to technology limitations and legal requirements. This paper presents a study of driver behaviour in the transition between autonomous and manual modes using a CARLA simulator. To our knowledge, this is the first take-over study with transitions conducted on this simulator. For this purpose, we obtain driver gaze focalization and fuse it with the road's semantic segmentation to track to where and when the user is paying attention, besides the actuators' reaction-time measurements provided in the literature. To track gaze focalization in a non-intrusive and inexpensive way, we use a method based on a camera developed in previous works. We devised it with the OpenFace 2.0 toolkit and a NARMAX calibration method. It transforms the face parameters extracted by the toolkit into the point where the user is looking on the simulator scene. The study was carried out by different users using our simulator, which is composed of three screens, a steering wheel and pedals. We distributed this proposal in two different computer systems due to the computational cost of the simulator based on the CARLA simulator. The robot operating system (ROS) framework is in charge of the communication of both systems to provide portability and flexibility to the proposal. Results of the transition analysis are provided using state-of-the-art metrics and a novel driver situation-awareness metric for 20 users in two different scenarios.
Study of Ageing in Complex Interface Interaction Tasks: Based on Combined Eye-Movement and HRV Bioinformatic Feedback

Human-computer interaction tends to be intelligent and driven by technological innovation. However, there is a digital divide caused by usage barriers for older users when interacting with complex tasks. To better help elderly users efficiently complete complex interactions, a smart home's operating system's interface is used as an example to explore the usage characteristics of elderly users of different genders. This study uses multi-signal physiological acquisition as a criterion. The results of the study showed that: (1) Older users are more attracted to iconic information than textual information. (2) When searching for complex tasks, female users are more likely to browse the whole page before locating the job. (3) Female users are more likely to browse from top to bottom when searching for complex tasks. (4) Female users are more likely to concentrate when performing complex tasks than male users. (5) Males are more likely to be nervous than females when performing complex tasks.
Design of Quick Search Method for Key Feature Images in Mobile Networks

Abstract In order to promote the efficiency of image retrieval in mobile network and realize the fast query of key images, this paper designs a quick search method of key feature images in mobile networks. The key features of retrieved images are extracted by rotation invariant local binary method. According to the extracted key features of the image, the query target image is processed by coarse quantization then the distance of the key features of the image is calculated and retrieved. Finally, non-exhaustive search method is used to achieve quick search of key feature images in mobile network. Experimental results show that this method can effectively extract specific images, The desired image can be quickly searched by reserved key features, and the F-score value of quick search is higher than 0.9.
A hybrid imbalanced classification model based on data density

Imbalanced data are widely available in the real world, and it is difficult to effectively identify the minority class instances in imbalanced data. Various imbalanced classification models have been proposed. However, these models neglect the data density and the location of instances which can be important factors affecting classification performance. To tackle this issue, this paper proposes a hybrid imbalanced classification model based on data density (HICD). In data-level, the density-based resampling method is presented. The data partition Algorithm is given, which divides the data space into five regions based on the data density. The corresponding subsets are generated by sampling from the divided regions to improve the recognition of different classes of instances. In algorithm-level, we construct the corresponding ensemble models for different classes of instances. In addition, the model selection Algorithm is presented. On this basis, an appropriate model is selected for each instance based on its distribution. The performance of the proposed HICD was evaluated on 18 imbalanced datasets in the real world in terms of recall, the area under the roc curve (AUC), and G-mean. The experimental results validate that our method has better performance than other competitive algorithms in imbalanced classification.
Using a Smart Living Environment Simulation Tool and Machine Learning to Optimize the Home Sensor Network Configuration for Measuring the Activities of Daily Living of Older People

This paper describes a methodology to optimize the home sensor network to measure the Activities of Daily Living (ADLs) of older people using Machine Learning (ML) applied to synthetic data generated via a newly developed Smart Living Environment (SLE) simulation tool. A home sensor network consisting of Passive InfraRed (PIR) and door sensors allows people to age in place, avoiding invasiveness of the technology by keeping track of the older users’ behaviour and health conditions. However, it is difficult to identify a priori the optimal sensor network configuration to measure users’ behaviour. To ensure better user acceptability without losing measurement accuracy, the authors proposed a methodology to optimize the home sensor network consisting of simulating human activities, and therefore sensor activations, in the reconstructed SLE and analysing the datasets generated through ML. Four ML classifiers, namely the Decision Tree (DT), Gaussian Naïve Bayes (GNB), Support Vector Machine (SVM) and K-Nearest Neighbors (KNN), were tested to measure the accuracy of ADL classification. Optimization analysis was made, providing the most suitable home sensor network configuration for two home environment case studies by exploiting the DT classifier results, as it proved to achieve the highest mean accuracy (over 94%) in measuring ADLs.
StreetScouting: A Deep Learning Platform for Automatic Detection and Geotagging of Urban Features from Street-Level Images

Urban environments are evolving rapidly in big cities; keeping track of these changes is becoming harder. Information regarding urban features, such as the number of trees, lights, or shops in a particular region, can be crucial for tasks, such as urban planning, commercial campaigns, or inferring various social indicators. StreetScouting is a platform that aims to automate the process of detecting, visualizing, and exporting the urban features of a particular region. Recently, the advent of deep learning has revolutionized the way many computer vision tasks are tackled. In this work, we present StreetScouting, an extensible platform for the automatic detection of particular urban features of interest. StreetScouting utilizes several state-of-the-art computer vision approaches including Cascade R-CNN and RetinaFace architectures for object detection, the ByteTrack method for object tracking, DNET architecture for depth estimation, and DeepLabv3+ architecture for semantic segmentation. As a result, the platform is able to detect and geotag urban features from visual data. The extracted information can be utilized by many commercial or public organizations, eliminating the need for manual inspection.
A Lightweight Deep Learning Architecture for Vegetation Segmentation using UAV-captured Aerial Images

The unmanned aerial vehicle (UAV)-captured panoptic remote sensing images have great potential to promote robotics-inspired intelligent solutions for land cover mapping, disaster management, smart agriculture through automatic vegetation detection, and real-time environmental surveillance. However, many of these applications require faster execution of the tasks to get the job done in real-time. In this regard, this article proposes a lightweight convolutional neural network (CNN) architecture, also known as LW-AerialSegNet, that helps preserve the network's feed-forward nature by increasing the intermediate layers to gather more crucial features for the segmentation task. Moreover, the network uses the concept of densely connected architecture and depth-wise separable convolution mechanisms to reduce the number of parameters of the model that can be deployed in the internet of things (IoT) edge devices to perform real-time segmentation. A UAV-based image segmentation dataset NITRDrone Dataset and Urban Drone dataset (UDD) are used to evaluate the proposed architecture. It has achieved an intersection over union (IoU) of 82% and 71% on the NITRDrone datasets UDD, respectively, thereby illustrating its superiority among the considered state-of-the-art mechanisms. The experimental results indicate that the implementation of depth-wise separable convolutions helps reduce the number of trainable parameters significantly, making it suitable to be applied on edge-computing devices at a smaller scale. The proposed architecture can be deployed in real-life settings on a UAV to extract objects such as vegetation and road lines, hence can be used in mapping urban areas, agricultural lands, etc.
Probabilistic fusion of crowds and experts for the search of gravitational waves

The acquisition of training labels in machine learning classification tasks is expensive. In the last years, crowdsourcing has emerged as a popular approach to label a training set. Crowdsourcing shares the labeling effort among a large number of (possibly non-expert) annotators. Moreover, in many realistic applications, a limited number of expert labels can also be collected to complement the crowdsourcing ones. Such combination of (millions of) crowdsourced and (a few) expert labels is precisely the setting in the GravitySpy project. The goal of GravitySpy is to enhance the detection of gravitational waves, which provide a new way of exploring the early universe in astrophysics (their first detection got the 2017 Physics Nobel prize). In this work, we propose a new probabilistic crowdsourcing model based on sparse Gaussian Processes (GPs) which allows for the integration of expert labels. To the best of our knowledge, this is the first probabilistic GP-based method that tackles this setting. We demonstrate that the resulting objective function to be optimized is a natural fusion of the crowdsourcing and the standard sparse GP classification objectives. Desirable theoretical properties of the crowdsourcing method, translate in a mathematical sound manner into the new method. The new algorithm is implemented in TensorFlow. A controlled experiment illustrates the properties and behavior of the proposed method. We also show that it performs as theoretically expected in a well-known real-world crowdsourcing dataset. Finally, its application to GravitySpy obtains 92.58% overall accuracy and 92.27% test-likelihood, outperforming all previous methods in the literature.
AutoML technologies for the identification of sparse classification and outlier detection models

Automated machine learning (AutoML) technologies offer powerful methods to automate the choice of meta-parameters and the instantiations of components of the machine learning training pipelines, such as an optimum form of data preprocessing or a suitable strength of model regularization. Besides given training data, AutoML relies on a suitable learning objective or scoring function and a search space in which to optimize the choices. Currently, most AutoML technologies focus on a single objective, which is related to the expected accuracy of the found model as evaluated according to the chosen learning objective. Additional desired characteristics such as model sparsity for an increased model efficiency and interpretability can be integrated as additional penalty terms in the objective function. Yet, this leads to one solution only, and does not mediate in between accuracy and sparsity as two usually contradicting objectives. In this contribution, we are interested in AutoML technologies which explore the full Pareto-front of sparse versus accurate models rather than a single average only. Since it is not guaranteed that architectural and meta-parameter choices stay constant along the full Pareto-front, averaging the two objectives is not necessarily optimal in this realm. Hence we propose how to treat this challenge by a novel iterative pipeline, which combines an AutoML method with feature selection technologies. We compare this technology to alternatives including baselines in two relevant modeling tasks, classification and outlier detection. We demonstrate the performance of these strategies for a couple of benchmark tasks. 1