Reviewerly/exHarmony huggingface dataset:
Multi-Robot Multiple Camera People Detection and Tracking in Automated Warehouses

In this work a multi-robot system is presented for people detection and tracking in automated warehouses. Each Automated Guided Vehicle (AGV) is equipped with multiple RGB cameras that can track the workers’ current locations on the floor thanks to a neural network that provides human pose estimation. Based on the local perception of the environment each AGV can exploit information about the tracked people for self-motion planning or collision avoidance.Additionally, data collected from each robot contributes to a global people detection and tracking system. A warehouse central management software fuses information received from all AGVs into a map of the current locations of workers. The estimated locations of workers are sent back to the AGVs to prevent potential collision. The proposed method is based on two-level hierarchy of Kalman filters. Experiments performed in a real warehouse show the viability of the proposed approach.
A soft sensor model of <i>Pichia pastoris</i> cell concentration based on IBDA-RELM

For Pichia pastoris fermentation process with multi-operating conditions, it is difficult to predict the cell concentration under the new operating conditions by the soft sensor model established under the specific operating conditions. Inspired by the idea of transfer learning, a method based on an improved balanced distribution adaptive regularization extreme learning machine (IBDA-RELM) was proposed to solve the problem. The domain adaptation (DA) method in transfer learning is developed to reduce distribution distance by transforming data. However, the joint distribution adaptation (JDA) and the balanced distribution adaptation (BDA) in DA cannot be directly applied to regression problems. The fuzzy sets (FSs) method was proposed to solve this issue. Finally, a soft sensor model of Pichia pastoris cell concentration was realized by inputting the converted data to the RELM model. Simulation verification was carried out with three operating conditions at the scene of fermentation. The transfer effects of three DA methods, including transfer component analysis (TCA), improved joint distribution adaptation (IJDA) as well as IBDA, were compared. The predicted results show that IBDA-RELM had a better performance in the soft sensor of Pichia pastoris cell concentration under multi-operating conditions.
Vehicle Counting Network with Attention-based Mask Refinement and Spatial-awareness Block Loss

Vehicle counting aims to calculate the number of vehicles in congested traffic scenes. Although object detection and crowd counting have made tremendous progress with the development of deep learning, vehicle counting remains a challenging task, due to scale variations, viewpoint changes, inconsistent location distributions, diverse visual appearances and severe occlusions. In this paper, a well-designed Vehicle Counting Network (VCNet) is novelly proposed to alleviate the problem of scale variation and inconsistent spatial distribution in congested traffic scenes. Specifically, VCNet is composed of two major components: (i) To capture multi-scale vehicles across different types and camera viewpoints, an effective multi-scale density map estimation structure is designed by building an attention-based mask refinement module. The multi-branch structure with hybrid dilated convolution blocks is proposed to assign receptive fields to generate multi-scale density maps. To efficiently aggregate multi-scale density maps, the attention-based mask refinement is well-designed to highlight the vehicle regions, which enables each branch to suppress the scale interference from other branches. (ii) In order to capture the inconsistent spatial distributions, a spatial-awareness block loss (SBL) based on the region-weighted reward strategy is proposed to calculate the loss of different spatial regions including sparse, congested and occluded regions independently by dividing the density map into different regions. Extensive experiments conducted on three benchmark datasets, TRANCOS, VisDrone2019 Vehicle and CVCSet demonstrate that the proposed VCNet outperforms the state-of-the-art approaches in vehicle counting. Moreover, the proposed idea can be applicable for crowd counting, which produces competitive results on ShanghaiTech crowd counting dataset.
Exploration of Large Outdoor Environments Using Multi-Criteria Decision Making

We present a Multi-Criteria Decision Making (MCDM) framework specifically designed for planetary exploration. Our work is based on PROMETHEE II, which allows operators to add task-specific criteria and conditions. We extended this algorithm to improve its resource usage by reducing the number of candidate exploration goals that have to be evaluated and compared. This is crucial when given a large number of goals, as is typical for outdoor environments. In addition, we identified five different criteria for planetary exploration, including a novel criterion that we call Direction of Interest (DOI), and use a categorization of these for further resource optimizations. We thereby ensure that the CPU usage of our decision making method can meet the limited budget of a space rover. We present simulated and real-world experiments with the Lightweight Rover Unit (LRU) and show a reduction of the processing time for decision making of approx. 70%.
Distilling Reflection Dynamics for Single-Image Reflection Removal

Single-image reflection removal (SIRR) aims to restore the transmitted image given a single image shot through glass or window. Existing methods rely mainly on information extracted from a single image along with some predefined priors, and fail to give satisfying results on real-world images, due to inherent ambiguity and lack of large and diverse real-world training data. In this paper, instead of reasoning about a single image only, we propose to distill a representation of reflection dynamics from multi-view images (i.e., the motions of reflection and transmission layers over time), and transfer the learned knowledge for the SIRR problem. In particular, we propose a teacher-student framework where the teacher network learns a representation of reflection dynamics by watching a sequence of multi-view images of a scene captured by a moving camera and teaches a student network to remove reflection from a single input image. In addition, we collect a large real-world multi-view reflection image dataset for reflection dynamics knowledge distillation. Extensive experiments show that our model yields state-of-the-art performances.
A Multi-robot Task Allocation and Path Planning Method for Warehouse System

To improve the efficiency of the traditional warehouse system, a multi-robot task allocation and path planning method for the warehouse system are designed according to the function of it. In the multi-robot task allocation, considering the impact of congestion on the task execution cost, the market auction algorithm which is improved makes the probability compensation of the task execution cost, and the overall path length of the multi-robot system to complete the task is optimized. Conflict search algorithm based on D*Lite plan paths for the multi-robot system, D*Lite algorithm as the lower algorithm provides the basic paths, and the upper algorithm introduces the constraint tree to constrain the collision problems of basic paths between robots, so as to obtain the shortest total running time and safe paths. The simulation results show that the designed multi-robot task allocation and path planning method can effectively improve the operating efficiency of multi-robot in the warehouse system.
Traffic sign recognition algorithm based on feature pyramid attention

The traditional traffic sign recognition algorithm is easily affected by factors such as complex background and illumination in a real scene, which can easily lead to missed detection and misdetection, in the stage of traffic sign detection, the Feature Pyramid Spatial Attention (FPSA) module is proposed. In the process of generating the feature pyramid, the FPSA module uses high-level features as the attention information of low-level features. In order to solve the problem of sample imbalance in the dataset, the dataset is screened, and the category loss weight based on the effective number of samples is introduced when calculating the loss. Experiments conducted on 45 traffic sign categories on the TT-100K dataset prove that FPSA and the proposed category loss weights are beneficial to improve the performance of small object detection and recognition in complex backgrounds, and are obtained on the Faster R-CNN model 78.9% mAP accuracy.
Jungle party: a game about loss and grief

Death is a theme discussed in various media, such as movies, games and books. In the real world it is hard for many people to talk about the theme, mostly with kids. A lot of researchers claim that hiding and masking the death can harm the process of grief and acceptance of the fact. To help in this dialogue, the present paper brings a proposal of a digital game, in the Storytelling genre, with the goals to talk with kids about losing by using a metaphor. The conception of the game adopted the SemTh approach and participatory design. The team was composed of two designers, two developers, three HCI professionals and one narrative professional. This paper presents a first cycle of the game prototyping, which was evaluated by one psychologist, one HCI professional and by the narrative professional.
A Lightweight Modulation Classification Network Resisting White Box Gradient Attacks

Improving the attack resistance of the modulation classification model is an important means to improve the security of the physical layer of the Internet of Things (IoT). In this paper, a binary modulation classification defense network (BMCDN) was proposed, which has the advantages of small model scale and strong immunity to white box gradient attacks. Specifically, an end-to-end modulation signal recognition network that directly recognizes the form of the signal sequence is constructed, and its parameters are quantized to 1 bit to obtain the advantages of low memory usage and fast calculation speed. The gradient of the quantized parameter is directly transferred to the original parameter to realize the gradient concealment and achieve the effect of effectively defending against the white box gradient attack. Experimental results show that BMCDN obtains significant immune performance against white box gradient attacks while achieving a scale reduction of 6 times.
Offensive Language Detection in Turkish Tweets with Bert Models

As the insulting statements increase on the online platform, these negative statements create a reaction and disturb the peace of society. Offensive language detection research has been increased in recent years. This paper explores the effects of the usage of BERT models and fine-tuning techniques on offensive language detection on Turkish tweets. We emphasize the pre-trained model importance on the performance of a downstream task and the importance of the used BERT model.
An Integrated Process for Verifying Deep Learning Classifiers Using Dataset Dissimilarity Measures

The specification and verification of algorithms is vital for safety-critical autonomous systems which incorporate deep learning elements. We propose an integrated process for verifying artificial neural network (ANN) classifiers. This process consists of an off-line verification and an on-line performance prediction phase. The process is intended to verify ANN classifier generalisation performance, and to this end makes use of dataset dissimilarity measures. We introduce a novel measure for quantifying the dissimilarity between the dataset used to train a classification algorithm, and the test dataset used to evaluate and verify classifier performance. A system-level requirement could specify the permitted form of the functional relationship between classifier performance and a dissimilarity measure; such a requirement could be verified by dynamic testing. Experimental results, obtained using publicly available datasets, suggest that the measures have relevance to real-world practice for both quantifying dataset dissimilarity, and specifying and verifying classifier performance.
Blind Image Separation Method Based on Cascade Generative Adversarial Networks

To solve the challenge of single-channel blind image separation (BIS) caused by unknown prior knowledge during the separation process, we propose a BIS method based on cascaded generative adversarial networks (GANs). To ensure that the proposed method can perform well in different scenarios and to address the problem of an insufficient number of training samples, a synthetic network is added to the separation network. This method is composed of two GANs: a U-shaped GAN (UGAN), which is used to learn image synthesis, and a pixel-to-attention GAN (PAGAN), which is used to learn image separation. The two networks jointly complete the task of image separation. UGAN uses the unpaired mixed image and the unmixed image to learn the mixing style, thereby generating an image with the “true” mixing characteristics which addresses the problem of an insufficient number of training samples for the PAGAN. A self-attention mechanism is added to the PAGAN to quickly extract important features from the image data. The experimental results show that the proposed method achieves good results on both synthetic image datasets and real remote sensing image datasets. Moreover, it can be used for image separation in different scenarios which lack prior knowledge and training samples.
Efficient SE(3) Reachability Map Generation via Interplanar Integration of Intra-planar Convolutions

Convolution has been used for fast computation of reachability maps, but it has high computational costs when performing SE(3) convolution operations for general joint arrangements in industrial robots and 3D workspace. Its application is also limited to planar robots, 2D workspace, or robots with special spatial arrangements for joints. In this paper, we find that the SE(3) convolution can be decomposed into a set of SE(2) convolutions, which significantly reduces the computational complexity when computing the reachability map of high-DOF robotic manipulators in the 3D workspace. We also leverage GPU parallel computing and Fast Fourier transform to further accelerate the computation procedure. We demonstrate the time efficiency and quality of our approach using a set of numerical experiments for constructing reachability maps and also present a multi-robot plant phenotyping system that uses the computed reachability map for efficient viewpoint selection and path planning.
Research on time series characteristics of the gas drainage evaluation index based on lasso regression

The evaluation of the coal mine gas drainage effect is affected by many factors, such as flow rate, wind speed, drainage negative pressure, concentration, and temperature. This paper starts from actual coal mine production monitoring data and based on the lasso regression algorithm, features selection of multiple parameters of the preprocessed gas concentration time series to construct gas concentration feature selection based on the algorithm. The three-time smoothing index method is used to fill in the missing values. Aiming at the problem of different dimensions in the gas concentration time series, the MinMaxScaler method is used to normalize the data. The lasso regression algorithm is used to perform feature selection on the multivariable gas concentration time series, and the gas concentration time series selected by the lasso feature and the gas concentration time series without feature selection are input. The performance of the ANN algorithm for gas concentration prediction is compared and analyzed. The optimal α value and L1 norm are selected based on the grid search method to determine the strong explanatory gas concentration time series feature set of the working face, and an experimental comparison of the gas concentration prediction results before and after the lasso feature selection is performed. We verify the effectiveness of the algorithm.
Integrating External Services in DIME

We show how to extend the (application) Domain Specific Languages supported by the DIME low-code development environment to integrate functionalities hosted on heterogeneous technologies and platforms. Developers can this way utilize within DIME entire platforms like e.g. R for data analytics, and collections of services, like e.g. any REST-based microservices. In this paper we describe the current architecture of the DIME-based Digital Thread platform we are building in a collection of interdisciplinary collaborative projects, discuss the role of various DSLs in the platform, and provide a step by step tutorial for the integration of external platforms and external services in DIME. The goal is to enable a wide range of DIME adopters to integrate their application specific external services in the DIME open source platform, bootstrapping a collaborative ecosystem where the low-code activity of integrating external capabilities facilitates an increasingly no-code application development on the basis of pre-integrated Application DSLs.
Dermoscopy Images Enhancement via Multi-Scale Morphological Operations

Skin dermoscopy images frequently lack contrast caused by varying light conditions. Indeed, often low contrast is seen in dermoscopy images of melanoma, causing the lesion to blend in with the surrounding skin. In addition, the low contrast prevents certain details from being seen in the image. Therefore, it is necessary to design an approach that can enhance the contrast and details of dermoscopic images. In this work, we propose a multi-scale morphological approach to reduce the impacts of lack of contrast and to enhance the quality of the images. By top-hat reconstruction, the local bright and dark features are extracted from the image. The local bright features are added and the dark features are subtracted from the image. In this way, images with higher contrast and detail are obtained. The proposed approach was applied to a database of 236 color images of benign and malignant melanocytic lesions. The results show that the multi-scale morphological approach by reconstruction is a competitive algorithm since it achieved a very satisfactory level of contrast enhancement and detail enhancement in dermoscopy images.
Exploring Aspects Regarding Reasoning in Neuro-Symbolic Rules and Connectionist Expert Systems

Neuro-symbolic approaches combine neural and symbolic methods. This paper explores aspects regarding the reasoning mechanisms of two neuro-symbolic approaches, that is, neurules and connectionist expert systems. Both of them provide reasoning and explanation facilities. Neurules are a type of neuro-symbolic rules tightly integrating the neural and symbolic components and giving pre-eminence to the symbolic component. Connectionist expert systems give pre-eminence to the connectionist component. The aspects explored in this paper have not been previously addressed. As far as neurules are concerned, an aspect playing a role in conflict resolution (i.e., order of neurules) is explored. Experimental results show an improvement in reasoning efficiency. As far as connectionist expert systems are concerned, variations of the reasoning mechanism are explored. Experimental results show that none of the alternative versions of the reasoning mechanism outperforms the others in all cases.
Suitability of DIC and ESPI optical methods for monitoring fatigue damage development in X10CrMoVNb9-1 power engineering steel

Abstract The aim of this research was to compare the effectiveness of two different optical measurement techniques (digital image correlation—DIC and electronic speckle pattern interferometry—ESPI) during fatigue damage development monitoring in X10CrMoVNb9-1 (P91) power engineering steel for pipes. The specimens machined from the as-received pipe were subjected to fatigue loadings and monitored simultaneously using DIC and ESPI techniques. It was found that DIC technique, although characterised by lower resolution, was more effective than ESPI. DIC allows to monitor the fatigue behaviour of steel specimens and accurately indicate the area of potential failure even within the initial stage of fatigue damage development.
Ethical AI-Powered Regression Test Selection

Test automation is common in software development; often one tests repeatedly to identify regressions. If the amount of test cases is large, one may select a subset and only use the most important test cases. The regression test selection (RTS) could be automated and enhanced with Artificial Intelligence (AI-RTS). This however could introduce ethical challenges. While such challenges in AI are in general well studied, there is a gap with respect to ethical AI-RTS. By exploring the literature and learning from our experiences of developing an industry AI-RTS tool, we contribute to the literature by identifying three challenges (assigning responsibility, bias in decision-making and lack of participation) and three approaches (explicability, supervision and diversity). Additionally, we provide a checklist for ethical AI-RTS to help guide the decision-making of the stakeholders involved in the process.
Adaptive Side-Channel Analysis Model and Its Applications to White-Box Block Cipher Implementations

White-box block cipher (WBC) aims at protecting the secret key of a block cipher even if an adversary has full control over the implementations. At CHES 2016, Bos et al. proved that WBC are also threatened by side-channel analysis (SCA), e.g., differential fault analysis (DFA) and differential computation analysis (DCA). Therefore, advanced countermeasures have been proposed by Lee et al. for resisting DFA and DCA, such as table redundancy and improved masking methods, respectively. In this paper, we introduce a new adaptive side-channel analysis model which assumes that an adversary adaptively collects the intermediate values of a specific function and can mount the DFA/DCA attack with chosen inputs. In the adaptive SCA model, both theoretical analysis and experimental results show that Lee et al.’s proposed methods are vulnerable to DFA and DCA attacks. Moreover, a negative proposition is also demonstrated on the corresponding high-order countermeasures under our new model.
Anomaly Detection in Aerial Videos Via Future Frame Prediction Networks

By the virtue of high flexibility, low-cost, real-time, and high-resolution data acquisition capacity, unmanned aerial vehicles (UAVs) can be exploited for a wide range of applications, especially in surveillance, inspection, and search fields. Such applications aim to detect potential suspicious events, violent human actions from an untrimmed and lengthy UAV video. Anomaly detection methods are highly in demand because it is unrealistic for human experts to manually detect all abnormal events in image scene. However, anomaly detection methods in aerial videos are rarely studied in the remote sensing community. In this paper, We propose a future frame prediction network based on convolutional variational autoencoder networks to detect anomalous events. Compared to several models, our network has a superior performance.