Reviewerly/exHarmony huggingface dataset:
Real-Time Web Map Construction Based on Multiple Cameras and GIS

Previous VideoGIS integration methods mostly used geographic homography mapping. However, the related processing techniques were mainly for independent cameras and the software architecture was C/S, resulting in large deviations in geographic video mapping for small scenes, a lack of multi-camera video fusion, and difficulty in accessing real-time information with WebGIS. Therefore, we propose real-time web map construction based on the object height and camera posture (RTWM-HP for short). We first consider the constraint of having a similar height for each object by constructing an auxiliary plane and establishing a high-precision homography matrix (HP-HM) between the plane and the map; thus, the accuracy of geographic video mapping can be improved. Then, we map the objects in the multi-camera video with overlapping areas to geographic space and perform the object selection with the multi-camera (OS-CDD) algorithm, which includes the confidence of the object, the distance, and the angle between the objects and the center of the cameras. Further, we use the WebSocket technology to design a hybrid C/S and B/S software framework that is suitable for WebGIS integration. Experiments were carried out based on multi-camera videos and high-precision geospatial data in an office and a parking lot. The case study’s results show the following: (1) The HP-HM method can achieve the high-precision geographic mapping of objects (such as human heads and cars) with multiple cameras; (2) the OS-CDD algorithm can optimize and adjust the positions of the objects in the overlapping area and achieve a better map visualization effect; (3) RTWM-HP can publish real-time maps of objects with multiple cameras, which can be browsed in real time through point layers and hot-spot layers through WebGIS. The methods can be applied to some fields, such as person or car supervision and the flow analysis of customers or traffic passengers.
A peer-to-peer market for utility exchanges in Eco-Industrial Parks using automated negotiations

Prosumer companies exchanging utility surpluses in Eco-Industrial Parks (EIPs) is a promising alternative for industrial symbiosis to lower investment risks, take advantage of existing capacity and cut emissions. To incentivize participation in EIPs, a trustful mechanism is needed to negotiate and enforce terms of exchange. In this work, a peer-to-peer market of software agents representing different companies in automated negotiations of utility exchanges within an EIP is proposed. Agents will learn to strategically negotiate terms of exchanges while considering private preferences, necessities, and the public negotiation context of this market. Design details for the market comprising technological enablers such as Internet of Things, Blockchain, Smart Contracts and Decentralized Applications are presented. Moreover, the influence of learning context-dependent strategies on negotiation outcomes and market efficiency is analyzed. Results obtained are promising since social welfare and near equilibrium conditions emerge despite the selfish nature of participating companies and individual contextual perceptions of negotiation agents.
Optimal circular dichroism sensing with quantum light: Multiparameter estimation approach

The measurement of circular dichroism (CD) has widely been exploited to distinguish the different enantiomers of chiral structures. It has been applied to natural materials (e.g. molecules) as well as to artificial materials (e.g. nanophotonic structures). However, especially for chiral molecules the signal level is very low and increasing the signal-to-noise ratio is of paramount importance to either shorten the necessary measurement time or to lower the minimum detectable molecule concentration. As one solution to this problem, we propose here to use quantum states of light in CD sensing to reduce the noise below the shot noise limit that is encountered when using coherent states of light. Through a multi-parameter estimation approach, we identify the ultimate quantum limit to precision of CD sensing, allowing for general schemes including additional ancillary modes. We show that the ultimate quantum limit can be achieved by various optimal schemes. It includes not only Fock state input in direct sensing configuration but also twin-beam input in ancilla-assisted sensing configuration, for both of which photon number resolving detection needs to be performed as the optimal measurement setting. These optimal schemes offer a significant quantum enhancement even in the presence of additional system loss. The optimality of a practical scheme using a twin-beam state in direct sensing configuration is also investigated in details as a nearly optimal scheme for CD sensing when the actual CD signal is very small. Alternative schemes involving single-photon sources and detectors are also proposed. This work paves the way for further investigations of quantum metrological techniques in chirality sensing.
Specific Emitter Identification Based on Deep Adversarial Domain Adaptation

To solve the problem that the neural network-based specific emitter identification (SEI) method is limited by the training scene and has poor generalization ability in the complex scene, an SEI method based on deep adversarial domain adaptation (DADA) is proposed. DADA integrates deep neural networks and adversarial methods into the domain adaptation problem of transfer learning. It trains the feature extractor by maximizing the domain discrimination loss and transfers the high-quality deep radio frequency fingerprint (RFF) features extracted under weak interference conditions to the unlabeled emitter of the same class affected by the channel and noise. At the same time, the loss of the sample classifier is minimized, and the deep RFF features of unlabeled emitters can be accurately classified and identified. By repeating the above-mentioned adversarial process, the network realizes unsupervised transfer learning based on deep RFF features. The analysis of the identification results of 20 CC2530 devices under the Rayleigh channel and different signal-to-noise ratio (SNR) conditions proves that DADA can effectively improve the network's identification performance for unlabeled emitters under different conditions and has good performance of adaptability and generalization.
Decentralized Reputation

In this work we develop a privacy-preserving reputation scheme for collaborative systems such as P2P networks in which peers can represent themselves with different pseudonyms when interacting with others. All these pseudonyms, however, are bound to the same reputation token, allowing honest peers to maintain their good record even when switching to a new pseudonym while preventing malicious ones from making a fresh start.
An intelligent chaotic clonal optimizer

Increasing complexity of real world problems motivated an area to explore efficient optimization methods to solve such problems. Existing optimization algorithms cannot solve all type of problems efficiently (NFL theorem), so new algorithms are proposed to find the better solutions for such complex optimization problems. However, their efficiency and performance can be still improved. Therefore, to follow this vital purpose, in this paper, a novel metaheuristic algorithm, called intelligent clonal optimizer (ICO), is proposed to solve continuous optimization problems. In the proposed algorithm, the initial population is generated through the chaos theory to enhance its exploration capability. It lacks any crossover operator. Instead, a novel clonal operator copying candidate solutions according to their fitness in a self-adaptive way is proposed. Cloning each parent is carried out by two methods, and according to these methods, each offspring is located near the parent or in direction of temporary target. The offsprings are classified to two classes. In addition, a novel conservative selection operator is proposed. According to this operator, the new population is selected from two classes of offsprings and current population by maintaining population diversity. The performance of the ICO algorithm is assessed on 39 well-known unimodal, multimodal, fixed-dimensional multimodal, composite and CEC2019 benchmark functions as well as three engineering application problems. Results of the proposed ICO are compared to sixteen state-of-art metaheuristic algorithms in three categories including the most well-known and recently developed algorithms and the best performer of IEEE CEC competitions using statistical analysis, scalability analysis, Wilcoxon Signed-Rank Test, Friedman test, computational time analysis and convergence analysis. The obtained results proved that ICO performs better than state-of-art metaheuristics in sense of scalability and accurate convergence. According to average rank of Friedman test, the proposed ICO is firstly ranked among others.
Agile Requirements Engineering: From User Stories to Software Architectures

Most agile practitioners employ user stories for capturing requirements, also thanks to the embedding of this notation within development and project management tools. Among user story users, circa 70% follow a simple template: As a role, I want to action, so that benefit. User stories’ popularity among practitioners and their template-based structure make them ideal candidates for the application of natural language processing techniques. In our research, we have found that circa 50% of real-world user stories contain easily preventable linguistic defects. To mitigate this problem, we have created tool-supported methods that facilitate the creation of better user stories. This tutorial combines previous work of the RE-Lab@UU into a pipeline for working with user stories: (1) The basics of creating user stories and their use in requirements engineering; (2) How to improve user story quality with the Quality User Story Framework and the AQUSA tool; (3) How to generate conceptual models from user stories using the Visual Narrator tool and analyze them for possible ambiguity and inconsistency; and (4) How to link requirements to architectures via the RE4SA model. Our approach is demonstrated with results obtained from 20+ software companies employing user stories.
Improved Sensor Model for Realistic Synthetic Data Generation

Synthetic, i.e., computer generated-imagery (CGI) data is a key component for training and validating deep-learning-based perceptive functions due to its ability to simulate rare cases, avoidance of privacy issues and easy generation of huge datasets with pixel accurate ground-truth data. Recent simulation and rendering engines simulate already a wealth of realistic optical effects, but are mainly focused on the human perception system. But, perceptive functions require realistic images modeled with sensor artifacts as close as possible towards the sensor the training data has been recorded with.
Auto-adaptive Fault Prediction System for Edge Cloud Environments in the Presence of Concept Drift

6G networks are envisioned to be trustworthy systems that can self-maintain their reliability and service availability. The ability to automatically recognize and predict any faults or failures that occur while delivering services is a step towards realizing such systems. Real-time analytics based on Artificial Intelligence (AI)/Machine Learning (ML) techniques provides the required functionality. However, in the highly dynamic and complex environment of 6G services, the distribution of data feeding to the ML models is subject to change over time (i.e., concept drift), which makes persistent model accuracy challenging without any frequent model retraining. To address this issue, in this paper, we propose a system that predicts the faults in an edge cloud environment, where the prediction models are trained and automatically adapted to the concept drifts via Transfer Learning (TL). To present the effectiveness of our system, we implemented an edge cloud testbed and introduced CPU over-utilization and network congestion fault to it in the presence of concept drift. We used Convolutional Neural Network (CNN), Long-Short Term Memory (LSTM), and a combination of them as fault prediction models. For detecting the drifts, we have implemented and compared four drift detection methods, and we utilized three TL scenarios for adapting the model to the drift. Our results indicate the superiority of our system in maintaining a persistent accuracy in the presence of concept drift compared to a prediction system without a drift handling entity.
An improved unscented Kalman filter for nonlinear systems with one-step randomly delayed measurement and unknown latency probability

In this paper, an improved unscented Kalman filter is presented to achieve state estimation for nonlinear systems with one-step randomly delayed measurement and unknown latency probability. Firstly, a Bernoulli random variable is introduced to describe the situation of randomly delayed measurement. Then, a joint prior probability density function is obtained. Finally, in the improved unscented Kalman filter algorithm, the state vector and the latency probability are estimated by utilizing a variational Bayesian technique, and the covariance matrices are computed by using the unscented transform. A univariate nonstationary growth model and a Lorenz system are included to verify that the proposed algorithm can not only implement enhanced estimation accuracy compared with the existing methods but also provide the latency probability accurately.
Online Coverage Planning for an Autonomous Weed Mowing Robot With Curvature Constraints

Thearea used for grazing cattle constitutes about one-third of the land in United States. In this paper, we present the design of Cowbot, an autonomous weed mowing robot for maintaining cow pastures. Cowbot is designed to operate in rugged environments and provides a cost-effective method for weed control in cow pastures. Path planning for the Cowbot is challenging since weed distribution on pastures is unknown. Given an onboard sensor with a limited field of view, online planning is necessary to detect weeds and plan paths to mow them. We study the general online coverage planning problem for an autonomous mower with curvature and field of view constraints. We develop two algorithms that are able to utilize new information on weed detection to optimize path length and ensure coverage. We deploy our algorithms on the Cowbot and perform field experiments to validate the suitability of our methods for real-time path planning. We also perform extensive simulation experiments which show that our algorithms result in up to 60% reduction in path length as compared to boustrophedon and random-search based coverage paths.
Ontology-based Traceability System for Interoperable Data Acquisition in Battery Cell Manufacturing

In order to support the transformation of energy and transportation sectors, costs and environmental impacts of battery cell need to be reduced. Data acquisition plays a major role in generating transparency within the complex system of battery manufacturing and enables its improvement. This paper presents a methodology for the development of an ontology-based traceability system of data acquired along the battery cell manufacturing chain. This system provides interrelations between data, data sources, and corresponding entities enabling an interoperable data acquisition. A data basis generated with this ontology-based traceability system supports and eases data analytics applications in battery cell manufacturing.
Physical Layer Security against an Informed Eavesdropper in Underwater Acoustic Channels: Reconciliation and Privacy Amplification

We propose a strategy for generating the same crypto-key between two trusted underwater acoustic nodes (Alice and Bob) without revealing it to an eavesdropper (Eve). Our work builds upon the results of [1] where a methodology for generating a string of bits for Alice, Bob and Eve based on channel feature extraction and quantization is discussed. In this paper, we aim to reconcile the respective bits of Alice and Bob while minimizing the information leaked to Eve. To this end, we examine various Reed Solomon (RS) codes and measure the reconciliation rate of Alice, Bob and Eve. Additionally, we propose the Secure Hash Algorithm-3 (SHA-3) as means to eliminate any information that Eve acquires during reconciliation. We evaluate our reconciliation and privacy amplification strategies with bits generated from real underwater acoustic channel probe exchanges between Alice and Bob and Bellhop-simulated channels for Eve. Our analysis confirms that appropriate combinations of channel features and RS codes lead to a computationally secure generation of a 256-bit crypto-key according to the principles of the National Institute of Standards and Technology (NIST), even if Eve is informed about the RS encoder and the SHA-3 function.
CBCIoT: A Consensus Algorithm for Blockchain-Based IoT Applications

Internet of things is widely used in the current era to collect data from sensors and perform specific tasks through processing according to the requirements. The data collected can be sent to a blockchain network to create secure and tamper-resistant records of transactions. The combination of blockchain with IoT has huge potential as it can provide decentralized computation, storage, and exchange for IoT data. However, IoT applications require a low-latency consensus mechanism due to its constraints. In this paper, CBCIoT, a consensus algorithm for blockchain-based IoT applications, is proposed. The primary purpose of this algorithm is to improve scalability in terms of validation and verification rate. The algorithm is developed to be compatible with IoT devices where a slight delay is acceptable. The simulation results show the proposed algorithm’s efficiency in terms of block generation time and transactions per second.
Effective Deep Features for Image Splicing Detection

In the last few years, Digital image forgery (DIF) detection has become a prominent subject. Image splicing is a frequent approach for making digital image forgeries. Image splicing creates forged images that are hard to detect immediately. The detection accuracy of most existing image splicing detection algorithms is low, thus there is room for improvement. Therefore, this research provides an image splicing detection (ISD) method based on deep learning. The proposed image splicing detection has three stages: (1) RGB image conversion and image size fitting are examples of image pre-processing. (2) Using the pre-trained CNN AlexNet model, we extract the final discriminative feature for a preprocessed image. (3) Finally, the generated feature representation is used to train a Canonical Correlation Analysis (CCA) classifier for binary classification (authentic/forged). The accuracy of the proposed approach using a pre-trained AlexNet model based deep features with CCA classifier is equal to 98.79 % when evaluated on the CASIA v1.0 splicing image forgery database. In comparison, the proposed surpassed existing methods. In the future, the proposed could be applied to other types of image forgery, such as image retouching.
Challenges of Resolving Merge Conflicts: A Mining and Survey Study

In collaborative software development, merge conflicts arise when developers integrate concurrent code changes. Practitioners seek to minimize the number of merge conflicts because resolving them is difficult, time consuming, and often an error-prone task. Despite a substantial number of studies investigating merge conflicts, the challenges in merge conflict resolution are not well understood. Our goal is to investigate which factors make merge conflicts longer to resolve in practice. To this end, we performed a two-phase study. First, we analyzed 66 projects containing around 81 thousand merge scenarios, involving 2 million files and over 10 million chunks. For this analysis, we use rank correlation, principal component analysis, multiple regression model, and effect-size analysis to investigate which independent variables (e.g., number of conflicting chunks and files) mostly influence our dependent variable (i.e., time to merge). We found that the number of chunks, lines of code, conflicting chunks, developers involved, conflicting lines of code, conflicting files, and the complexity of the conflicting code influence the merge conflict resolution time. Second, we surveyed 140 developers from our subject projects aiming at cross-validating our results from the first phase of our study. As main results, (i) we found that committing small chunks makes merge conflict resolution faster when leaving other independent variables untouched, (ii) we found evidence that merge scenario characteristics (e.g., the number of lines of code or chunks changed in the merge scenario) are stronger correlated with our dependent variable than merge conflict characteristics (e.g., the number of lines of code or chunks in conflict), (iii) we devise a taxonomy of four types of challenges in merge conflict resolution, and (iv) we observed that the inherent dependencies among conflicting and non-conflicting code is one of the main factors influencing the merge conflict resolution time.
Intelligent algorithm for detection of dengue using mobilenetv2‐based deep features with lymphocyte nucleus

Abstract Dengue is a vector‐borne disease that is highly endemic in countries located in tropical regions. It can cause severe complications and can even lead to death in the case of delayed diagnosis. Detection of dengue is done by manually examining the platelets and lymphocytes in Leishman's stained peripheral blood smear (PBS) images. PBS examination is considered the gold standard for diagnosing various haematological disorders. However, manual analysis of the PBS is labour‐intensive, tedious, and time‐consuming, requiring a skilled and experienced haematologist. Today, soft computing methods and artificial intelligence have made their way into every science and technology branch. One such area which has adopted this approach is digital pathology, for automatically identifying and diagnosing various diseases. The main objective of this work was to design an intelligent algorithm to classify normal and dengue patients with the help of digital microscopic blood smear images. A total of 94 normal and dengue‐infected PBSs were acquired at a magnification of 100×. Grey‐level segmentation based on Otsu's thresholding was used for the segmentation of the nucleus of lymphocytes. Distinct features from the nucleus that differentiated infected cells from normal were extracted using a pre‐trained MobileNetV2 network and local binary pattern. Significant features were selected using the ReliefF algorithm. Subsequently, these features were fed to the support vector machine (SVM) classifier. Our proposed system gave an accuracy, sensitivity, and specificity of 95.74%, 98.14%, and 92.50%, respectively. Hence, the developed intelligent model with deep and hand‐crafted features can be valuable for dengue diagnosis.
ASFlow: Unsupervised Optical Flow Learning With Adaptive Pyramid Sampling

We present an unsupervised optical flow estimation method by proposing an adaptive pyramid sampling in the deep pyramid network. Specifically, in the pyramid downsampling, we propose a Content-Aware Pooling (CAP) module, which promotes local feature gathering by avoiding cross region pooling, so that the learned features become more representative. In the pyramid upsampling, we propose an Adaptive Flow Upsampling (AFU) module, where cross edge interpolation can be avoided, producing sharp motion boundaries. Equipped with these two modules, our method achieves the best performance for unsupervised optical flow estimation on multiple leading benchmarks, including MPI-Sintel, KITTI 2012 and KITTI 2015. Particularly, we achieve EPE=1.5 on KITTI 2012 and F1=9.67% KITTI 2015, which outperform the previous state-of-the-art methods by 16.7% and 13.1%, respectively.