Reviewerly/exHarmony huggingface dataset:
Artistic Robotic Painting Using the Palette Knife Technique

This paper presents a novel robotic painting system able to create artworks using the palette knife technique. The implementation of this method with a robotic system is particularly challenging, since the robot needs to precisely manipulate the palette knife to pick up and release the color on the canvas. The painting system comprises a 6-DOF collaborative robot, a camera to acquire the information on the color position, and several algorithms for the artistic rendering of the images and for the planning of the trajectories that the robot has to follow. During the painting process the user can modify multiple parameters: both software, for example, stroke position and orientation, and hardware, for example, palette knife inclination and height, to obtain different stroke effects. Finally, the experimental results are discussed by analyzing the artworks painted by the novel robotic system.
Towards a New Model to Secure IoT-based Smart Home Mobile Agents using Blockchain Technology

The Internet of Things (IoT) is becoming an indispensable part of the actual Internet and continues to extend deeper into the daily lives of people, offering distributed and critical services. Mobile agents are widely used in the context of IoT and due to the possibility of transmitting their execution status from one device to another in an IoT network, they offer many advantages such as reducing network load, encapsulating protocols, exceeding network latency, etc. Also, Blockchain Technology is growing rapidly allowing for the addition of an approved security layer in many areas. Security issues related to mobile agent migration can be resolved with the use of Blockchain. This paper aims to demonstrate how Blockchain Technology can be used to secure mobile agents in the context of the IoT using Ethereum and a Smart Contract. The transactions within the Blockchain are used to detect the malevolent mobile agents that could infiltrate the IoT systems. The proposed model aims to provide a secure migration of mobile agents to ensure security and protect the IoT applications against malevolent agents. The case of a smart home with multiple applications is applied to verify the proposed solution. The model presented in this paper could be extended to a wider selection of IoT systems outside of the smart home.
Going from double to single reading for screening exams labeled as likely normal by AI: what is the impact?

We investigated whether a deep learning-based artificial intelligence (AI) system can be used to improve breast cancer screening workflow efficiency by making a pre-selection of likely normal screening mammograms where double-reading could be safely replaced with single-reading. We collected 18,015 consecutively acquired screening exams, the independent reading assessments by each radiologist of the double reading process, and the information about whether the case was recalled and if so the recall outcome. The AI system assigned a 1-10 score to each screening exam denoting the likelihood of cancer. We simulated the impact on recall rate, cancer detection rate, and workload if single-reading would have been performed for the mammograms with the lowest AI scores. After evaluating all possible AI score thresholds, it was found that when AI scores 1 to 7 are single read instead of double read, the cancer detection rate would have remained the same (no screen-detected cancers missed –the AI score is low but the single-reader would recall the exam), recall rate would have decreased by 11.8% (from 5.35% to 4.79%), and screen reading workload would have decreased by 32.6%. In conclusion, using an AI system could improve breast cancer screening efficiency by pre-selecting likely normal exams where double-reading might not be needed.
Exquisitor at the Lifelog Search Challenge 2020

We present an enhanced version of Exquisitor, our interactive and scalable media exploration system. At its core, Exquisitor is an interactive learning system using relevance feedback on media items to build a model of the users' information need. Relying on efficient media representation and indexing, it facilitates real-time user interaction. The new features for the Lifelog Search Challenge 2020 include support for timeline browsing, search functionality for finding positive examples, and significant interface improvements. Participation in the Lifelog Search Challenge allows us to compare our paradigm, relying predominantly on interactive learning, with more traditional search-based multimedia retrieval systems.
SkyNet: a Hardware-Efficient Method for Object Detection and Tracking on Embedded Systems

Object detection and tracking are challenging tasks for resource-constrained embedded systems. While these tasks are among the most compute-intensive tasks from the artificial intelligence domain, they are only allowed to use limited computation and memory resources on embedded devices. In the meanwhile, such resource-constrained implementations are often required to satisfy additional demanding requirements such as real-time response, high-throughput performance, and reliable inference accuracy. To overcome these challenges, we propose SkyNet, a hardware-efficient neural network to deliver the state-of-the-art detection accuracy and speed for embedded systems. Instead of following the common top-down flow for compact DNN (Deep Neural Network) design, SkyNet provides a bottom-up DNN design approach with comprehensive understanding of the hardware constraints at the very beginning to deliver hardware-efficient DNNs. The effectiveness of SkyNet is demonstrated by winning the competitive System Design Contest for low power object detection in the 56th IEEE/ACM Design Automation Conference (DAC-SDC), where our SkyNet significantly outperforms all other 100+ competitors: it delivers 0.731 Intersection over Union (IoU) and 67.33 frames per second (FPS) on a TX2 embedded GPU; and 0.716 IoU and 25.05 FPS on an Ultra96 embedded FPGA. The evaluation of SkyNet is also extended to GOT-10K, a recent large-scale high-diversity benchmark for generic object tracking in the wild. For state-of-the-art object trackers SiamRPN++ and SiamMask, where ResNet-50 is employed as the backbone, implementations using our SkyNet as the backbone DNN are 1.60X and 1.73X faster with better or similar accuracy when running on a 1080Ti GPU, and 37.20X smaller in terms of parameter size for significantly better memory and storage footprint.
A social robot mediator to foster collaboration and inclusion among children

Formation of subgroups and thereby the problem of intergroup bias is well-studied in psychology.Already from the age of five, children can show ingroup preferences.We developed a social robot mediator to explore how a robot could help overcome these intergroup biases, especially for children newly arrived to a country.By utilizing an online evaluation of collaboration levels, we allow the robot to perceive and act upon the current group dynamics.We investigated the effectiveness of the robot's mediating behavior in a between-subject study with 39 children, of whom 13 children had arrived in Sweden within the last 2 years.Results indicate that the robot could help the process of inclusion by mediating the activity.The robot succeeds in encouraging the newly arrived children to act more outgoing and in increasing collaboration among ingroup children.Further, children show a higher level of prosociality after interacting with the robot.In line with prior work, this study demonstrates the ability of social robotic technology to assist group processes.
Adaptive Wavelet Based MRI Brain Image De-noising

This paper presents a unique approach for wavelet-based MRI brain image de-noising. Adaptive soft and hard threshold functions are first proposed to improve the results of standard soft and hard threshold functions for image de-noising in the wavelet domain. Then, we applied the newly emerged improved adaptive generalized Gaussian distributed oriented threshold function (improved AGGD) on the MRI images to improve the results of the adaptive soft and hard threshold functions and also to display that, this non-linear and data-driven function can works promisingly even in de-noising the medical images. The most important characteristic of this function is that it is dependent on the image since it is combined with an adaptive generalized Gaussian distribution function. Traditional thresholding neural network (TNN) and optimized based noise reduction have good results but fail to keep the visual quality and may blur some parts of an image. In TNN and optimized based image de-noising, it was required to use Least-mean-square (LMS) learning and optimization algorithms, respectively to find the optimum threshold value and parameters of the threshold functions which was time consuming. To address these issues, the improved AGGD based image de-noising approach is introduced to enhance the qualitative and quantitative performance of the above mentioned image de-noising techniques. De-noising using improved AGGD threshold function provides better results in terms of Peak Signal to Noise Ratio (PSNR) and also faster processing time since there is no need to use any Least-mean-square (LMS) learning and optimization algorithms for obtaining the optimum value and parameters of the thresholding functions. The experimental results indicate that image de-noising using improved AGGD threshold performs pretty well comparing with the adaptive threshold, standard threshold, improved wavelet threshold, and the optimized based noise reduction methods.
Comparison of artificial intelligence and empirical models for energy production estimation of 20 MWp solar photovoltaic plant at the Saharan Medium of Algeria

Purpose This study aims to estimate the electric power production of the 20 MWp solar photovoltaic (PV) plant installed in the Adrar region, South of Algeria using minimal knowledge about weather conditions. Design/methodology/approach In this study, simulation models based on linear and nonlinear approaches were used to estimate accurate energy production from minimum radiometric and meteorological data. Simulations have been carried out by using multiple linear regression (MLR) and artificial neural network (ANN) models with three basic types of neuron connection architectures, namely, feed-forward neural network, cascade-forward neural network (CNN) and Elman neural network. The performance is measured based on evaluation indexes, namely, mean absolute percentage error, normalized mean absolute error and normalized root mean square error. Findings A comparison of the proposed ANN models has been made with MLR models. The performance analysis indicates that all the ANN-based models are superior in prediction accuracy and stability, and among these models, the most accurate results are obtained with the use of CNN-based models. Practical implications The considered model will be adopted in solar PV forecasting areas as part of the operational forecasting chain based on numerical weather prediction. It can be an effective and powerful forecasting approach for solar power generation for large-scale PV plants. Social implications The operational forecasting system can be used to generate an effective schedule for national grid electricity system operators to ensure the sustainability as well as favourable trading performance in the electricity, such as adjusting the scheduling plan, ensuring power quality, reducing depletion of fossil fuel resources and consequently decreasing the environmental pollution. Originality/value The proposed method uses the instantaneous radiometric and meteorological data in 15-min time interval recorded over the two years of operation, which made the result exploits a fact that the energy production estimation of PV power generation station is comparatively more accurate.
Efficient Deep Learning Architecture for Detection and Recognition of Thyroid Nodules

Ultrasonography is widely used in the clinical diagnosis of thyroid nodules. Ultrasound images of thyroid nodules have different appearances, interior features, and blurred borders that are difficult for a physician to diagnose into malignant or benign types merely through visual recognition. The development of artificial intelligence, especially deep learning, has led to great advances in the field of medical image diagnosis. However, there are some challenges to achieve precision and efficiency in the recognition of thyroid nodules. In this work, we propose a deep learning architecture, you only look once v3 dense multireceptive fields convolutional neural network (YOLOv3-DMRF), based on YOLOv3. It comprises a DMRF-CNN and multiscale detection layers. In DMRF-CNN, we integrate dilated convolution with different dilation rates to continue passing the edge and the texture features to deeper layers. Two different scale detection layers are deployed to recognize the different sizes of the thyroid nodules. We used two datasets to train and evaluate the YOLOv3-DMRF during the experiments. One dataset includes 699 original ultrasound images of thyroid nodules collected from a local health physical center. We obtained 10,485 images after data augmentation. Another dataset is an open-access dataset that includes ultrasound images of 111 malignant and 41 benign thyroid nodules. Average precision (AP) and mean average precision (mAP) are used as the metrics for quantitative and qualitative evaluations. We compared the proposed YOLOv3-DMRF with some state-of-the-art deep learning networks. The experimental results show that YOLOv3-DMRF outperforms others on mAP and detection time on both the datasets. Specifically, the values of mAP and detection time were 90.05 and 95.23% and 3.7 and 2.2 s, respectively, on the two test datasets. Experimental results demonstrate that the proposed YOLOv3-DMRF is efficient for detection and recognition of thyroid nodules for ultrasound images.
Quantum image encryption based on Lorenz hyper-chaotic system

According to the higher security of higher-dimensional chaotic system, a new scheme of quantum image encryption and decryption based on Lorenz hyper-chaotic system is proposed. The encryption process is mainly divided into two parts: the location information scrambling process and the color information replacement process. In the location information scrambling process, the pseudo-random sequence obtained by the Lorenz hyper-chaotic system is first sorted to obtain the corresponding position index sequence, and then the rows and columns of the position information are scrambled by the index sequence. In the process of color replacement, the change of pixel value is firstly diffused to each pixel through chaotic sequence, and then the information of each pixel is confused, so as to realize the process of color information replacement. Experimental analysis shows that the encryption scheme in this paper has a good effect.
Heatmap Regression via Randomized Rounding

Heatmap regression has become the mainstream methodology for deep learning-based semantic landmark localization, including in facial landmark localization and human pose estimation. Though heatmap regression is robust to large variations in pose, illumination, and occlusion in unconstrained settings, it usually suffers from a sub-pixel localization problem. Specifically, considering that the activation point indices in heatmaps are always integers, quantization error thus appears when using heatmaps as the representation of numerical coordinates. Previous methods to overcome the sub-pixel localization problem usually rely on high-resolution heatmaps. As a result, there is always a trade-off between achieving localization accuracy and computational cost, where the computational complexity of heatmap regression depends on the heatmap resolution in a quadratic manner. In this paper, we formally analyze the quantization error of vanilla heatmap regression and propose a simple yet effective quantization system to address the sub-pixel localization problem. The proposed quantization system induced by the randomized rounding operation 1) encodes the fractional part of numerical coordinates into the ground truth heatmap using a probabilistic approach during training; and 2) decodes the predicted numerical coordinates from a set of activation points during testing. We prove that the proposed quantization system for heatmap regression is unbiased and lossless. Experimental results on popular facial landmark localization datasets (WFLW, 300W, COFW, and AFLW) and human pose estimation datasets (MPII and COCO) demonstrate the effectiveness of the proposed method for efficient and accurate semantic landmark localization. Code is available at http://github.com/baoshengyu/H3R.
Fine-Grained Breast Cancer Classification With Bilinear Convolutional Neural Networks (BCNNs)

Classification&nbsp;of histopathological images from cancers is a challenging task, even for well-trained professionals, due to the&nbsp;fine-grained&nbsp;variability of the disease. Deep Convolutional Neural Networks (CNNs) have shown great potential for many highly variable&nbsp;fine-grained&nbsp;objects. In this study, we proposed a Bilinear Convolutional Neural Networks (BCNNs) based deep learning method for fine-grained classification of breast cancer histopathological images. We evaluated our model by comparing with several deep learning algorithms for fine-grained classification. We used bilinear pooling to aggregate a large number of orderless features without consideration of the disease location. The experimental results on BreaKHis, a publicly available breast cancer dataset, showed that the proposed method is cutting-edge with accuracies of 99.24% in binary classification and 95.95% in fine-grained classification.
Designing everyday automation with well-being in mind

Nowadays, automation not only permeates industry but also becomes a substantial part of our private, everyday lives. Driven by the idea of increased convenience and more time for the "important things in life," automation relieves us from many daily chores - robots vacuum floors and automated coffee makers produce supposedly barista-quality coffee on the press of a button. In many cases, these offers are embraced by people without further questioning. However, while we save time by delegating more and more everyday activities to automation, we also may lose chances for enjoyable and meaningful experiences. In two field studies, we demonstrate that a manual process has experiential benefits over more automated processes by using the example of coffee-making. We present a way to account for potential experiential costs of everyday automation and strategies of how to design interaction with automation to reconcile experience with the advantages of a more and more powerful automation.
An investigation of biases in web search engine query suggestions

Purpose Survey-based studies suggest that search engines are trusted more than social media or even traditional news, although cases of false information or defamation are known. The purpose of this paper is to analyze query suggestion features of three search engines to see if these features introduce some bias into the query and search process that might compromise this trust. The authors test the approach on person-related search suggestions by querying the names of politicians from the German Bundestag before the German federal election of 2017. Design/methodology/approach This study introduces a framework to systematically examine and automatically analyze the varieties in different query suggestions for person names offered by major search engines. To test the framework, the authors collected data from the Google, Bing and DuckDuckGo query suggestion APIs over a period of four months for 629 different names of German politicians. The suggestions were clustered and statistically analyzed with regards to different biases, like gender, party or age and with regards to the stability of the suggestions over time. Findings By using the framework, the authors located three semantic clusters within the data set: suggestions related to politics and economics, location information and personal and other miscellaneous topics. Among other effects, the results of the analysis show a small bias in the form that male politicians receive slightly fewer suggestions on “personal and misc” topics. The stability analysis of the suggested terms over time shows that some suggestions are prevalent most of the time, while other suggestions fluctuate more often. Originality/value This study proposes a novel framework to automatically identify biases in web search engine query suggestions for person-related searches. Applying this framework on a set of person-related query suggestions shows first insights into the influence search engines can have on the query process of users that seek out information on politicians.
Security analysis of an audio data encryption scheme based on key chaining and DNA encoding

Fairly recently, a new encryption scheme for audio data encryption has been proposed by Naskar, P.K., et al. The cryptosystem is based on substitution-permutation encryption structure using DNA encoding at the substitution stage, in which the key generation is based on a key chaining algorithm that generates new key block for every plain block using a logistic chaotic map. After some several statistical tests done by the authors of the scheme, they claimed that their cryptosystem is robust and can resist conventional cryptanalysis attacks. Negatively, in this paper we show the opposite: the scheme is extremely weak against chosen ciphertext and plaintext attacks thus only two chosen plaintexts of 32 byte size are sufficient to recover the equivalent key used for encryption. The cryptosystem's shuffling process design is vulnerable which allow us recovering the unknown original plaintext by applying repeated encryptions. Our study proves that the scheme is extremely weak and should not be used for any information security or cryptographic concern. Lessons learned from this cryptanalytic paper are then outlined in order to be considered in further designs and proposals.
Zero-Cost Proxies for Lightweight NAS

Neural Architecture Search (NAS) is quickly becoming the standard methodology to design neural network models. However, NAS is typically compute-intensive because multiple models need to be evaluated before choosing the best one. To reduce the computational power and time needed, a proxy task is often used for evaluating each model instead of full training. In this paper, we evaluate conventional reduced-training proxies and quantify how well they preserve ranking between multiple models during search when compared with the rankings produced by final trained accuracy. We propose a series of zero-cost proxies, based on recent pruning literature, that use just a single minibatch of training data to compute a model's score. Our zero-cost proxies use 3 orders of magnitude less computation but can match and even outperform conventional proxies. For example, Spearman's rank correlation coefficient between final validation accuracy and our best zero-cost proxy on NAS-Bench-201 is 0.82, compared to 0.61 for EcoNAS (a recently proposed reduced-training proxy). Finally, we use these zero-cost proxies to enhance existing NAS search algorithms such as random search, reinforcement learning, evolutionary search and predictor-based search. For all search methodologies and across three different NAS datasets, we are able to significantly improve sample efficiency, and thereby decrease computation, by using our zero-cost proxies. For example on NAS-Bench-101, we achieved the same accuracy 4$\times$ quicker than the best previous result. Our code is made public at: https://github.com/mohsaied/zero-cost-nas.