Reviewerly/exHarmony huggingface dataset:
Enhancement of Curve-Fitting Image Compression Using Hyperbolic Function

Image compression is one of the most interesting fields of image processing that is used to reduce image size. 2D curve-fitting is a method that converts the image data (pixel values) to a set of mathematical equations that are used to represent the image. These equations have a fixed form with a few coefficients estimated from the image which has been divided into several blocks. Since the number of coefficients is lower than the original block pixel size, it can be used as a tool for image compression. In this paper, a new curve-fitting model has been proposed to be derived from the symmetric function (hyperbolic tangent) with only three coefficients. The main disadvantages of previous approaches were the additional errors and degradation of edges of the reconstructed image, as well as the blocking effect. To overcome this deficiency, it is proposed that this symmetric hyperbolic tangent (tanh) function be used instead of the classical 1st- and 2nd-order curve-fitting functions which are asymmetric for reformulating the blocks of the image. Depending on the symmetric property of hyperbolic tangent function, this will reduce the reconstruction error and improve fine details and texture of the reconstructed image. The results of this work have been tested and compared with 1st-order curve-fitting, and standard image compression (JPEG) methods. The main advantages of the proposed approach are: strengthening the edges of the image, removing the blocking effect, improving the Structural SIMilarity (SSIM) index, and increasing the Peak Signal-to-Noise Ratio (PSNR) up to 20 dB. Simulation results show that the proposed method has a significant improvement on the objective and subjective quality of the reconstructed image.
Global Image Thresholding Adaptive Neuro-Fuzzy Inference System Trained with Fuzzy Inclusion and Entropy Measures

Thresholding algorithms segment an image into two parts (foreground and background) by producing a binary version of our initial input. It is a complex procedure (due to the distinctive characteristics of each image) which often constitutes the initial step of other image processing or computer vision applications. Global techniques calculate a single threshold for the whole image while local techniques calculate a different threshold for each pixel based on specific attributes of its local area. In some of our previous work, we introduced some specific fuzzy inclusion and entropy measures which we efficiently managed to use on both global and local thresholding. The general method which we presented was an open and adaptable procedure, it was free of sensitivity or bias parameters and it involved image classification, mathematical functions, a fuzzy symmetrical triangular number and some criteria of choosing between two possible thresholds. Here, we continue this research and try to avoid all these by automatically connecting our measures with the wanted threshold using some Artificial Neural Network (ANN). Using an ANN in image segmentation is not uncommon especially in the domain of medical images. However, our proposition involves the use of an Adaptive Neuro-Fuzzy Inference System (ANFIS) which means that all we need is a proper database. It is a simple and immediate method which could provide researchers with an alternative approach to the thresholding problem considering that they probably have at their disposal some appropriate and specialized data.
An improved RDF data Clustering Algorithm

Linked data has been officially approved as one of the best useful sources for background information that publishing and connecting structured data on the Web. Therefore, Machine Learning community should respond with a clear package of methods and best practices to bring this type of data into the field. Relatively little attention has been paid in the literature to the possible union of both linked data and Machine Learning. In order to overcome this carelessness and achieve this joining, we should focus little more on RDF data. The main purpose of this article, is to present a typical Machine Learning Pipeline on RDF data and an improved version of RDF clustering algorithm based on Candidate Description by using different similarity measures and agglomerative hierarchical techniques.
Adaptive Structured Light with Scatter Correction for High-Precision Underwater 3D Measurements

High-precision underwater 3D cameras are required to automate many of the traditional subsea inspection, maintenance and repair (IMR) operations. In this paper we introduce a novel multi-frequency phase stepping (structured light) method for high-precision 3D estimation even in turbid water. We introduce an adaptive phase-unwrapping procedure which uses the phase-uncertainty to determine the highest frequency that can be reliably unwrapped. Light scattering adversely affects the phase estimate. We propose to remove the effect of forward scatter with an unsharp filter and a model-based method to remove the backscatter effect. Tests in varying turbidity show that the scatter correction removes the adverse effect of scatter on the phase estimates. The adaptive frequency unwrapping with scatter correction results in images with higher accuracy and precision and less phase unwrap errors than the Gray-Code Phase Stepping (GCPS) approach.
Sentiment Analysis Based Requirement Evolution Prediction

To facilitate product developers capturing the varying requirements from users to support their feature evolution process, requirements evolution prediction from massive review texts is in fact of great importance. The proposed framework combines a supervised deep learning neural network with an unsupervised hierarchical topic model to analyze user reviews automatically for product feature requirements evolution prediction. The approach is to discover hierarchical product feature requirements from the hierarchical topic model and to identify their sentiment by the Long Short-term Memory (LSTM) with word embedding, which not only models hierarchical product requirement features from general to specific, but also identifies sentiment orientation to better correspond to the different hierarchies of product features. The evaluation and experimental results show that the proposed approach is effective and feasible.
To what extent the medical device software regulations can be achieved with agile software development methods? XP—DSDM—Scrum

For medical device software development organizations, it is very challenging to maintain both conformance to the strict regulatory requirements enforced by the safety-critical nature of the domain and achieve efficiency in software development. Agile software development methods provide promising solutions to overcome the efficiency issues and the challenges of traditional software development approaches in the domain. Previously, we investigated to what extent the regulatory requirements defined in MDevSPICE® (the software process assessment framework for medical device software development) are met through using eXtreme Programming (XP) and Scrum and what additional practices have to be performed to ensure safety and regulatory compliance in the medical device software development domain. In this paper, we extended the research to include the Dynamic Systems Development Method (DSDM) which covers the whole software development life cycle. Here, we provide a comprehensive and quantitative analysis of XP’s and DSDM’s suitability for medical device software development and briefly discuss Scrum from the same perspective. We provide the coverage ratios of processes and practices defined in MDevSPICE® when these agile software development methods are implemented.
Mean Curvature Is a Good Regularization for Image Processing

Ill-posed problems are very common in many image processing and computer vision tasks. To solve such problems, a regularization must be imposed. In this paper, we argue why mean curvature is a good regularization for these tasks. From a geometry point of view, we show that minimizing mean curvature is to assume that the ground truth is a piece-wise minimal surface. From a statistics point of view, we show that the mean curvature from natural images is sparse. From an optimization point of view, we show that the gradient of mean curvature regularization can be numerically approximated by very simple filters. This fact significantly simplifies the optimization procedure. Thus, the link between these filters and the gradient of mean curvature regularization is established in this paper. In summary, mean curvature is a proper regularization for various ill-posed problems in image processing and computer vision.
Lightweight Anonymous Geometric Routing for Internet of Things

Mobile service computing relies on efficient and secure data transfer. Geometric routing, which guarantees scalability, efficiency, and mobility, is a promising routing scheme for mobile services in the resource-constrained Internet of Things (IoT). However, the private data transmitted by geometric routing may be eavesdropped by the malicious node and the privacy-preserving becomes one of the important issues for the mobile service in IoTs. Due to the resource constrains, approaches using encryption or hashing are not suitable for geometric routing. In this paper, we propose a lightweight anonymous geometric routing (LAGER) to protect the node-related private data. Instead of using the encryption or hashing approach, LAGER adopts a coordinate confusion mechanism which provides each node an anonymous coordinate of virtual node, such that the private data is decoupled from the node coordinate. The malicious node cannot determine which node the data belongs to even if it obtains the private data. To support the data transmission with anonymous coordinates, LAGER adopts a hybrid routing scheme by combining the greedy routing and a source routing. The experiment results show that LAGER provides effective anonymity with acceptable costs of scalability and efficiency.
A Novel Calibration Method of Articulated Laser Sensor for Trans-Scale 3D Measurement

The articulated laser sensor is a new kind of trans-scale and non-contact measurement instrument in regular-size space and industrial applications. These sensors overcome many deficiencies and application limitations of traditional measurement methods. The articulated laser sensor consists of two articulated laser sensing modules, and each module is made up of two rotary tables and one collimated laser. The three axes represent a non-orthogonal shaft architecture. The calibration method of system parameters for traditional instruments is no longer suitable. A novel high-accuracy calibration method of an articulated laser sensor for trans-scale 3D measurement is proposed. Based on perspective projection models and image processing techniques, the calibration method of the laser beam is the key innovative aspect of this study and is introduced in detail. The experimental results show that a maximum distance error of 0.05 mm was detected with the articulated laser sensor. We demonstrate that the proposed high-accuracy calibration method is feasible and effective, particularly for the calibration of laser beams.
A Heuristic Elastic Particle Swarm Optimization Algorithm for Robot Path Planning

Path planning, as the core of navigation control for mobile robots, has become the focus of research in the field of mobile robots. Various path planning algorithms have been recently proposed. In this paper, in view of the advantages and disadvantages of different path planning algorithms, a heuristic elastic particle swarm algorithm is proposed. Using the path planned by the A* algorithm in a large-scale grid for global guidance, the elastic particle swarm optimization algorithm uses a shrinking operation to determine the globally optimal path formed by locally optimal nodes so that the particles can converge to it rapidly. Furthermore, in the iterative process, the diversity of the particles is ensured by a rebound operation. Computer simulation and real experimental results show that the proposed algorithm not only overcomes the shortcomings of the A* algorithm, which cannot yield the shortest path, but also avoids the problem of failure to converge to the globally optimal path, owing to a lack of heuristic information. Additionally, the proposed algorithm maintains the simplicity and high efficiency of both the algorithms.
Quantum Indistinguishability of Random Sponges

In this work we show that the sponge construction can be used to construct quantum-secure pseudorandom functions. As our main result we prove that random sponges are quantum indistinguishable from random functions. In this setting the adversary is given superposition access to the input-output behavior of the construction but not to the internal function. Our proofs hold under the assumption that the internal function is a random function or permutation. We then use this result to obtain a quantum-security version of a result by Andreeva, Daemen, Mennink, and Van Assche (FSE'15) which shows that a sponge that uses a secure PRP or PRF as internal function is a secure PRF. This result also proves that the recent attacks against CBC-MAC in the quantum-access model by Kaplan, Leurent, Leverrier, and Naya-Plasencia (Crypto'16) and Santoli, and Schaffner (QIC'16) can be prevented by introducing a state with a non-trivial inner part. The proof of our main result is derived by analyzing the joint distribution of any q input-output pairs. Our method analyzes the statistical behavior of the considered construction in great detail. The used techniques might prove useful in future analysis of different cryptographic primitives considering quantum adversaries. Using Zhandry's PRF/PRP switching lemma we then obtain that quantum indistinguishability also holds if the internal block function is a random permutation.
New Non-Binary Quantum Codes Derived From a Class of Linear Codes

In this short paper, we consider the non-binary quantum codes construction from a class of linear codes, which are not self-orthogonal over finite fields. As the computational results, new quantum codes [[8, 0, ≥ 4]] <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> , [[8, 2, ≥ 3]] <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> , [[11, 1, ≥ 5]] <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> , [[10, 0, ≥ 5]] <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> , and [[16, 6, ≥ 4]] <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> are obtained.
Evolving Knowledge Graphs

Many practical applications have observed knowledge evolution, i.e., continuous born of new knowledge, with its formation influenced by the structure of historical knowledge. This observation gives rise to evolving knowledge graphs whose structure temporally grows over time. However, both the modal characterization and the algorithmic implementation of evolving knowledge graphs remain unexplored. To this end, we propose EvolveKG, a framework that reveals cross-time knowledge interaction with desirable performance of storage and computation. The novelty of EvolveKG lies in Derivative Graph - a static weighted snapshot of evolution at a certain time. Particularly, each weight quantifies knowledge effectiveness with a temporarily decaying function of consistency and attenuation, two proposed factors depicting whether or not the effectiveness of a fact fades away with time. Thanks to the cross-time interaction, EvolveKG allows future knowledge prediction by virtue of the influence from the historical ones. Empirically tested under two real datasets, the superiority of EvolveKG is confirmed via its prediction accuracy.
Receding-Horizon Trajectory Planning Algorithm for Passively Safe On-Orbit Inspection Missions

This paper presents a sampling-based receding-horizon inspection algorithm that allows observation of a tumbling target vehicle while guaranteeing an arbitrary low risk of collision between the chaser performing the inspection and the target. The proposed algorithm relies on a simple heuristic search of the best inspection trajectory, and it can be suited to both onboard implementation as an autonomous guidance module and offline trajectory design. The main strength of the algorithm resides in its ability to quickly solve black-box optimal control problems, exploiting an input sampling approach that is commonly used in robotic motion planning. A new and computationally simple method to verify the safety of the two vehicles is also presented and implemented into the algorithm. The proposed method allows taking into account relative navigation and maneuver execution errors, as well as the effects of unmodeled perturbations. The results obtained with the algorithm are compared to the ones obtained with a classic inspection strategy making use of inclined football orbits. It is shown that the proposed guidance algorithm consistently ensures the completion of the inspection mission, and it takes advantage of the characteristics of the natural dynamics of the relative motion to outperform the classic approach.
Obstacle information aided target tracking algorithms for angle‐only tracking of a highly maneuverable target in three dimensions

This study considers the angle-only target tracking problem, which is to estimate the target information (position and velocity) based on both the azimuth and the elevation angle of the target. Incorporating obstacle information into maneuvering target tracking algorithms can produce a better performance when the target's maneuver is caused by avoiding collision with obstacles. Incorporating obstacle information, the target tracking algorithms are developed to track a highly maneuverable target in three dimensions. This study demonstrates the effectiveness of the proposed obstacle information aided tracking algorithms using simulations.
A Buffer Overflow Prediction Approach Based on Software Metrics and Machine Learning

Buffer overflow vulnerability is the most common and serious type of vulnerability in software today, as network security issues have become increasingly critical. To alleviate the security threat, many vulnerability mining methods based on static and dynamic analysis have been developed. However, the current analysis methods have problems regarding high computational time, low test efficiency, low accuracy, and low versatility. This paper proposed a software buffer overflow vulnerability prediction method by using software metrics and a decision tree algorithm. First, the software metrics were extracted from the software source code, and data from the dynamic data stream at the functional level was extracted by a data mining method. Second, a model based on a decision tree algorithm was constructed to measure multiple types of buffer overflow vulnerabilities at the functional level. Finally, the experimental results showed that our method ran in less time than SVM, Bayes, adaboost, and random forest algorithms and achieved 82.53% and 87.51% accuracy in two different data sets. The method presented in this paper achieved the effect of accurately predicting software buffer overflow vulnerabilities in C/C++ and Java programs.
Complex-Valued Ordinary Differential Equation Modeling for Time Series Identification

Time series identification is one of the key approaches to dealing with time series data and discovering the change rules.Therefore, time series forecasting can be treated as one of the most challenging issues in this field.In order to improve the forecasting performance, we propose a novel time series prediction model based on a complex-valued ordinary differential equation (CVODE) to predict time series.A multi expression programming (MEP) algorithm is utilized to optimize the structure of the CVODE model.So as to achieve the optimal complex-valued coefficients, a novel optimization algorithm based on a complex-valued crow search algorithm (CVCSA) is proposed.The chaotic Mackey-Glass time series, small-time scale traffic measurements, Nasdaq-100 index, and Shanghai stock exchange composite index are utilized to evaluate the performance of our method.The results prove that our proposed method could predict more accurately than state-of-the-art real-valued neural networks and an ordinary differential equation (ODE).The CVCSA has faster convergence speed and stronger optimization ability than the crow search algorithm (CSA) and particle swarm optimization (PSO).
A Fundamental Performance Limitation for Adversarial Classification

Despite the widespread use of machine learning algorithms to solve problems of technological, economic, and social relevance, provable guarantees on the performance of these data-driven algorithms are critically lacking, especially when the data originates from unreliable sources and is transmitted over unprotected and easily accessible channels. In this letter, we take an important step to bridge this gap and formally show that, in a quest to optimize their accuracy, binary classification algorithms-including those based on machine-learning techniques-inevitably become more sensitive to adversarial manipulation of the data. Further, numerical evidence suggests that the accuracy-sensitivity tradeoff depends solely on the statistics of the data, and cannot be improved by tuning the algorithms or increasing their complexity.
A Secret Sharing Scheme for Quantum Gray and Color Images Based on Encryption

This paper is concerned with the better security of quantum image secret sharing (QISS) algorithm. The improved QISS (IQISS) scheme is implemented on both quantum gray image (FRQI) and quantum color image (MCQI). The new IQISS scheme comprises efficient sharing process and recovering process. The core idea of the sharing process is to combine encryption and measurement for two types of quantum secret images to acquire the quantum shadow images. In the recovering process, strip operation is firstly utilized on the shadow images. Afterwards, the decryption algorithm is used to recover the original quantum secret image. Experiments demonstrate that significant improvements in the security are in favor of the proposed approach.
Performance Perspective on Private Distributed Ledger Technologies for Industrial Networks

Blockchain-based Distributed Ledger Technology (DLT) is a novel paradigm to create tamper-resistant execution environments and data storage for distributed applications on top of a peer-to-peer network. This technology has shown to be of interest in many use-cases, especially in industrial processes where multiple shareholders would like to process and share data in a secure and accountable way. In this work, we evaluate the performance of a DLT-based system via modeling and a quantitative performance evaluation, focusing on the impact of the underlying communication network. Our numerical evaluation is based on the Hyperledger Fabric DLT framework, its benchmarking tool Caliper, and a dedicated test bed, where network properties such as latency or packet loss can be artificially influenced. Our experiments show that the validation of the transactions in a DLT-based system is the main contributor in the transaction latency. We also demonstrate that the properties of the communication network can influence the performance largely, even in the case where only one of the participants in the DLT system has poor network access.