Reviewerly/exHarmony huggingface dataset:
Adaptive Self-Calibration of Fisheye and Wide-Angle Cameras

In this paper, a plumb line method of highly adaptable self-calibration has been proposed and tested on multiple fisheye and wide-angle camera systems. In an industrial perspective, the calibration of heavy distortion lens (fisheye, wide-angle) is a scuffle of bottleneck factors such as specificity, cost and precision. To resolve this problem, an adaptive and automatic estimation of distortion parameters by employing parameter optimization techniques on scene geometrical attributes such as straight lines and edges was proposed. Experimental results demonstrate the quantitative comparison of the proposed method on simulated ground-truth data and real data with previous self-calibration techniques and OpenCV traditional checkerboard methods in terms of Peak-Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Metric (SSIM). Additionally, the proposed method can preserve almost all the image pixels which is the primary reason behind employing wide-angle and fisheye cameras. Finally, a practical qualitative analysis has been carried out by estimating distortion parameters under various scenarios such as indoors, outdoors, day, dawn, traffic conditions etc.
Privacy-Preserving Broker-ABE Scheme for Multiple Cloud-Assisted Cyber Physical Systems

Cloud-assisted cyber-physical systems (CCPSs) integrate the physical space with cloud computing. To do so, sensors on the field collect real-life data and forward it to clouds for further data analysis and decision-making. Since multiple services may be accessed at the same time, sensor data should be forwarded to different cloud service providers (CSPs). In this scenario, attribute-based encryption (ABE) is an appropriate technique for securing data communication between sensors and clouds. Each cloud has its own attributes and a broker can determine which cloud is authorized to access data by the requirements set at the time of encryption. In this paper, we propose a privacy-preserving broker-ABE scheme for multiple CCPSs (MCCPS). The ABE separates the policy embedding job from the ABE task. To ease the computational burden of the sensors, this scheme leaves the policy embedding task to the broker, which is generally more powerful than the sensors. Moreover, the proposed scheme provides a way for CSPs to protect data privacy from outside coercion.
A practical solution to clone problem in anonymous information system

Cloning user's identity is always a thorny problem for an information system, especially for an anonymous system. With the development of big data applications, clone behaviors sometimes even become attacks on these systems. But until now, there has been no very satisfactory anti-clone scheme in the anonymous system. After analyzing the problems in existing anti-clone schemes, without any assumptions about physical security, we provide a practical solution to the clone problem in anonymous authentication system. In our scheme, the authentication is not only related to user's private key, but also related to user's current state, which is constantly updated by the system. Therefore, the authentication trajectories of user and clone will inevitably overlap, and it results in information leakage so as to indentify clone behaviors and revoke clone user's credential. Meanwhile, we prove that honest users are truly anonymous and their login behaviors are unlinkable with complete security proofs. According to the analysis of the system function and the system efficiency, our scheme is much more efficient and has the best anti-clone properties comparing with the existing schemes.
Differentially Privacy-preserving Social IoT

With the sophisticated network infrastructure, the breadth of Open Data and the increased smartphones, the popularity of the IoT (Internet of Things) has risen sharply. The Internet of Things bridges the gap between the physical world and the virtual world. The Internet of Things provides intelligent data services to humans through the integration of physical objects and information networks. The impact of users' social connections and interactions on the Internet of Things and privacy protection has been ignored. Inevitably, these users are exposed to a serious risk of privacy breaches in the presence of untrusted service providers and malicious attackers. In this paper, we propose and implement an effective Internet of Things service with differential privacy-preserving to explore collaborative intelligence considering the social interaction of users in social networks. Compared to the outdated approach, our scheme can monitor various parameters of the entire site from the Internet of Things devices, find critical data, and send and deliver the requested service over the Internet in a fast and scalable manner. Additionally, our analysis clearly illustrates that our approach provides guidance for privacy protection in the social Internet of Things. Experiments using real data sets show that our solutions reduce costs, increase flexibility, and provide more accurate and efficient social Internet of Things devices services. We further explore the potential challenges that need to be addressed in this research area.
An anomaly detection method to improve the intelligent level of smart articles based on multiple group correlation probability models

Purpose The purpose of this paper is to detect abnormal data of complex and sophisticated industrial equipment with sensors quickly and accurately. Due to the rapid development of the Internet of Things, more and more equipment is equipped with sensors, especially more complex and sophisticated industrial equipment is installed with a large number of sensors. A large amount of monitoring data is quickly collected to monitor the operation of the equipment. How to detect abnormal data quickly and accurately has become a challenge. Design/methodology/approach In this paper, the authors propose an approach called Multiple Group Correlation-based Anomaly Detection (MGCAD), which can detect equipment anomaly quickly and accurately. The single-point anomaly degree of equipment and the correlation of each kind of data sequence are modeled by using multi-group correlation probability model (a probability distribution model which is helpful to the anomaly detection of equipment), and the anomaly detection of equipment is realized. Findings The simulation data set experiments based on real data show that MGCAD has better performance than existing methods in processing multiple monitoring data sequences. Originality/value The MGCAD method can detect abnormal data quickly and accurately, promote the intelligent level of smart articles and ultimately help to project the real world into cyber space in CrowdIntell Network.
Design and Implementation of an Autonomous Robot Manipulator for Pick &amp; Place Planning

Abstract This paper focuses on advancement of an autonomous robot manipulator for pick and place applications using Artificial Intelligence Technique. The mobile robot carries a four Degree of Freedom (DOF) robotic arm for picking a particular object from a given initial position to the required target position in an unknown static environment, while tackling obstacles and implementing path planning algorithm using artificial intelligence technique. The proposed artificial intelligence technique employs the optimization strategy, based on a novel meta-heuristic approach for mobile robot path planning. The mobile manipulator involves the use of infrared, ultrasonic sensors for detecting obstacles, microcontroller for artificial intelligence software and geared motors &amp; servo motors for motion locomotion. The effectiveness of the method was tested and verified by via simulation mode on four different trajectories. From the simulation results, it was found that approximately path length and elapsed time of triangular shape, conical shape, cubical shape&amp;S- shapeobstacles environments are 70.22, 70.92, 71.29 &amp; 70.36 pixels and 117.93, 73.94, 122.86 &amp; 117.87 seconds respectively.
CascadeML: An Automatic Neural Network Architecture Evolution and Training Algorithm for Multi-label Classification (Best Technical Paper)

In multi-label classification a datapoint can be labelled with more than one class at the same time. A common but trivial approach to multi-label classification is to train individual binary classifiers per label, but the performance can be improved by considering associations between the labels, and algorithms like classifier chains and RAKEL do this effectively. Like most machine learning algorithms, however, these approaches require accurate hyperparameter tuning, a computationally expensive optimisation problem. Tuning is important to train a good multi-label classifier model. There is a scarcity in the literature of effective multi-label classification approaches that do not require extensive hyperparameter tuning. This paper addresses this scarcity by proposing CascadeML, a multi-label classification approach based on cascade neural network that takes label associations into account and requires minimal hyperparameter tuning. The performance of the CasecadeML approach is evaluated using 10 multi-label datasets and compared with other leading multi-label classification algorithms. Results show that CascadeML performs comparatively with the leading approaches but without a need for hyperparameter tuning.
Parallelization of All-Pairs-Shortest-Path Algorithms in Unweighted Graph

The design of the network topology of a large-scale parallel computer system can be represented as an order/degree problem in graph theory. To solve the order/degree problem, it is necessary to obtain an all-pairs-shortest-path (APSP) of the graph. Thus, this paper evaluates two parallel algorithms that quickly find the APSP in unweighted graphs and compares their performance. The first APSP algorithm is based on the breadth-first search (BFS-APSP) and the second is based on the adjacency matrix (ADJ-APSP). First, we develop serial algorithms and threaded algorithms using OpenMP, and show that ADJ-APSP is up to 32.34 times faster than BFS-APSP. Next, we develop hybrid-parallel algorithms using OpenMP and MPI, and show that BFS-APSP is faster than ADJ-APSP under certain conditions because the maximum number of processes in BFS-APSP is greater than in ADJ-APSP. In addition, we parallelize ADJ-APSP using a single GPU (NVIDIA Tesla V100) and achieve a speed increase of up to 16.53-fold compared to that of a single CPU. Finally, we evaluate the performance of the algorithms using 128 GPUs and achieve a computation time 101.10 times faster than that using a single GPU. Moreover, it is shown that the calculation time of both algorithms can be greatly reduced when the input graphs are symmetric.
A SENSOR FUSION SYSTEM IDENTIFYING COMPLEX EVENTS FOR LOCALISATION ESTIMATION

The focus of this study in progress is based on a non-GPS navigation technique which can be used in an environment without any infrastructure.Our proposed system combines a Wireless Body Area Network (WBAN) consisting of different sensors with Complex Event Processing (CEP).For the identification of complex events for independent navigation three use cases are provided.
DOORMOR: A Functional Prototype of a Manual Search and Rescue Robot

In this paper, we have discussed about DOORMOR - a standalone, fully functional search and rescue robot prototype which can come to aid for humans in many cases. With the help of Wi-Fi technology, this robot can be controlled within up to hundred meters. It can search for alive human bodies underneath a pile of debris, clear out debris and other obstacles, stream live video of the surrounding environment wirelessly and assist people in search and rescue operations. In this paper, we have tried to give a brief idea about DOORMOR, it's architecture, facts and features, used components and techniques used to build up the whole robot.
Forging quantum data: classically defeating an IQP-based quantum test

Recently, quantum computing experiments have for the first time exceeded the capability of classical computers to perform certain computations – a milestone termed "quantum computational advantage." However, verifying the output of the quantum device in these experiments required extremely large classical computations. An exciting next step for demonstrating quantum capability would be to implement tests of quantum computational advantage with efficient classical verification, such that larger system sizes can be tested and verified. One of the first proposals for an efficiently-verifiable test of quantumness consists of hiding a secret classical bitstring inside a circuit of the class IQP, in such a way that samples from the circuit&amp;apos;s output distribution are correlated with the secret. The classical hardness of this protocol has been supported by evidence that directly simulating IQP circuits is hard, but the security of the protocol against other (non-simulating) classical attacks has remained an open question. In this work we demonstrate that the protocol is not secure against classical forgery. We describe a classical algorithm that can not only convince the verifier that the (classical) prover is quantum, but can in fact can extract the secret key underlying a given protocol instance. Furthermore, we show that the key extraction algorithm is efficient in practice for problem sizes of hundreds of qubits. Finally, we provide an implementation of the algorithm, and give the secret vector underlying the "\$25 challenge" posted online by the authors of the original paper.
Interfacing Python with DIgSILENT Power Factory: Automation of Tasks

The DIgSILENT Power Factory is an engineering tool for the analysis of distribution, transmission, and industrial electrical power systems. “DIgSILENT” is an acronym for “Digital SImuLation of Electrical NeTworks”. This software is an advanced integrated and interactive package dedicated to electrical power system and control analysis so as to achieve planning and operational optimization objectives. The package can be interfaced with programming languages and software for performing tasks for better utility. Python is a high-level programming language with legible syntax and comes with open-source license. This paper brings the methodology for interfacing of Python with DIgSILENT Power Factory for automation of tasks. The paper provides various code snippets for interfacing Python with software package and task automation.
3D ROC Histogram: A New ROC Analysis Tool Incorporating Information on Instances

While Receiver Operator Characteristic (ROC) curves have been a standard tool in the design and evaluation of binary classification problems, they have sometimes been blamed for ignoring some vital information in the evaluation process, such as predicted scores and the amount of information about the target that each instance carries. In this paper, a new classification performance method denoted as 3D ROC histogram is proposed for extending ROC curves into 3D space. In this histogram, the x-axis and the y-axis are respectively labeled as false positive rate, and true positive rate which are the same with traditional ROC space. The z-axis serves as a quantitative index that represents vital information, and the volume of the 3D ROC histogram (V3RH) acts as a summary index. The proposed method preserves merits such as robustness with respect to class imbalance and threshold independence, and also, it provides an easy way for incorporating additional information in the evaluation process. Experiments on real-world datasets were conducted, with results that confirmed it to be a reliable measure.
Toward Optimal Selection of Information Retrieval Models for Software Engineering Tasks

Information Retrieval (IR) plays a pivotal role in diverse Software Engineering (SE) tasks, e.g., bug localization and triaging, bug report routing, code retrieval, requirements analysis, etc. SE tasks operate on diverse types of documents including code, text, stack-traces, and structured, semi-structured and unstructured meta-data that often contain specialized vocabularies. As the performance of any IR-based tool critically depends on the underlying document types, and given the diversity of SE corpora, it is essential to understand which models work best for which types of SE documents and tasks. We empirically investigate the interaction between IR models and document types for two representative SE tasks (bug localization and relevant project search), carefully chosen as they require a diverse set of SE artifacts (mixtures of code and text), and confirm that the models' performance varies significantly with mix of document types. Leveraging this insight, we propose a generalized framework, SRCH, to automatically select the most favorable IR model(s) for a given SE task. We evaluate SRCH w.r.t. these two tasks and confirm its effectiveness. Our preliminary user study shows that SRCH's intelligent adaption of the IR model(s) to the task at hand not only improves precision and recall for SE tasks but may also improve users' satisfaction.
Development of an Adaptive Trait-Aging Invariant Face Recognition System Using Convolutional Neural Networks

Trait-aging causes large intra-subject variations and negatively impacts on the accuracy of face recognition systems. With lapse in time, it returns a false match when identifying and authenticating the same individual. In this paper, an augmented database using pre-processing measures, a feature extracting algorithm, a modified classifier and an adaptive trait-aging invariant face recognition system were developed. The database was populated using data augmentation processes on the subjects got from the Face and Gesture Recognition Network Aging Database (FG-NET AD). The data augmentation process increased the amount of available data, thus making it suitable for deep learning operations. Cross-validation operations were also performed on the augmented dataset and each face image resized to fit into the adopted Convolutional Neutral Network Model (CNN). The CNN model was re-trained with FG-NET AD after it augmented, cross-validated and divided into mini-batches. The re-training was achieved using transfer learning technique. The resulting adaptive face recognition algorithm was validated using the data reserved for testing to ascertain its performance. The performance of the proposed adaptive face recognition system was evaluated using cumulative score curve and Mean Square Error (MSE). It had a 99.90% testing accuracy, a testing loss of 0.003%, Mean Absolute Error (MAE) of 0.0637, and Mean Squared Error (MSE) of 0.0158. Thus, out-performing the best result recorded in the available literature using similar database.
Ultrasound Image Despeckling and Enhancement using Modified Multiscale Anisotropic Diffusion Model in Non-Subsampled Shearlet Domain

Abstract Ultrasound imaging is undoubtedly the most used imaging modality for diagnostic purposes. Unfortunately, it is accompanied by speckle which can degrade texture information by obscuring fine details like boundaries and edges. This work presents a method for despeckling ultrasound images by treating them with multiscale modified speckle reduction anisotropic diffusion model and Non-Subsampled shearlet transform (NSST). The method involves division of images using a non-subsampled Laplacian pyramid. This results in low and high frequency image components. Modified anisotropic diffusion is used on the low frequency part. The high frequency component, as subjected to shearlet function, generates noisy coefficients in various directions. These coefficients are further subjected to NSST thresholding. The denoised low and high frequency image components are then recombined to obtain the enhanced image. This multidimensional and multidirectional method improves the qualitative characteristics of ultrasound images by not just removing speckle noise but also by preserving edges, thus resulting in effective image enhancement. Performance of the method is analysed on synthetic and real medical ultrasound images. Results reveal that the proposed method exceeds the state-of-the-art methods in the context of edge preservation and structural similarities, and thus, it is an effective aid to radiologists in their clinical diagnosis by providing an enhanced denoised image.
Research on Data Link Ontology Mapping Algorithm Based on Bayesian Network Model

Interconnection between multiple data link systems is an urgent problem for wireless control systems.Its difficulty lies in the fact that data link messages are multi-source heterogeneous, By analyzing its sub-domain characteristics, we constructs the data message domain ontology and establishes a data link message ontology model based on Bayesian network(DLMOBN).It includes the study of nodes, directed edges and node similarity probability distribution and so on, convert multi-source heterogeneous messages into mathematical models.We propose a data link message ontology mapping algorithm, the OWL syntax is used to formally describe the acquired domain ontology, extract useful information such as concepts, attributes, and instances, and then store the information in a preset data structure, k-means algorithm is used to cluster them to form ''cluster'', which is used as a classification index to classify the similarity pair as a node in the Bayesian network, and pass the concept of the lower layer between nodes to the prior concept of the upper layer.The semantic distance, the attribute, the feature and other factors of the similar pair are used to calculate the semantic similarity.Finally, the final semantic similarity value is obtained by weighting.It is verified by experiments that the method improves the recall rate and precision, reduces the time complexity.
An adaptive approach for anomaly detector selection and fine-tuning in time series

The anomaly detection of time series is a hotspot of time series data mining. The own characteristics of different anomaly detectors determine the abnormal data that they are good at. There is no detector can be optimizing in all types of anomalies. Moreover, it still has difficulties in industrial production due to problems such as a single detector can't be optimized at different time windows of the same time series. This paper proposes an adaptive model based on time series characteristics and selecting appropriate detector and run-time parameters for anomaly detection, which is called ATSDLN(Adaptive Time Series Detector Learning Network). We take the time series as the input of the model, and learn the time series representation through FCN. In order to realize the adaptive selection of detectors and run-time parameters according to the input time series, the outputs of FCN are the inputs of two sub-networks: the detector selection network and the run-time parameters selection network. In addition, the way that the variable layer width design of the parameter selection sub-network and the introduction of transfer learning make the model be with more expandability. Through experiments, it is found that ATSDLN can select appropriate anomaly detector and run-time parameters, and have strong expandability, which can quickly transfer. We investigate the performance of ATSDLN in public data sets, our methods outperform other methods in most cases with higher effect and better adaptation. We also show experimental results on public data sets to demonstrate how model structure and transfer learning affect the effectiveness.