Reviewerly/exHarmony huggingface dataset:
A Study on Position Estimation and Path Planning Technology of Autonomous Underwater Vehicle (AUV)

In recent years, it is strongly required to utilize underwater robots for the management and investigation of various underwater artificial structures as the number of underwater artificial structures for the use of marine resources and military purposes has increased rapidly. For exploration of underwater structures using an underwater robot, navigation that estimates its own position and control creating and following a moving path are very important technologies.BR In this paper, we developed a position estimation algorithm that fusions DVL-aiding inertial navigation and a pose recognition in local area using active visible marker. We also implemented a path planning and following algorithm of underwater robot that adapted RRT (Rapidly-exploring Random Tree) and improved LOS (Line of Sight) method. As a result, in the actual marine environment experiment, the combined performance of position estimation and path tracking was measured, and the path following RMS error of the planned moving path was 0.53 m, which satisfied the target following error within 1 m.
Disobedient Antennas

Contemporarily, antennas are receding into their parent devices, minimizing or disappearing completely from sight. They are affected by the technological trend toward miniaturization of electronic devices [8]. Similarly, textile antennas are being embedded into clothing where they lay flat to the body, seamlessly integrated into the surface of the garment [7]. Yet the question arises whether textile antennas must be discrete and designed out of sight. In this paper we propose a possible alternative. Disobedient Antennas are textile interaction design examples using textile design and experimental research methods. They are textile receiver antennas that disrupt the convention of planar textile antennas [7,8] to suggest that textile antennas can be voluminous and sculptural wearable objects. Further, they occupy a performative space, suggesting body-space-object interaction by using the antennas to perceive and explore electromagnetic fields through sonic feedback. Their use opens to improvised movements and choreographies in response to electromagnetic space.
A learning approach with incomplete pixel-level labels for deep neural networks

Learning with incomplete labels in Neural Networks has been actively investigated these last years. Among different kinds of incomplete labels, we investigate incomplete pixel-level labels which are tackled in many concrete problems. One of the challenges for incomplete pixel-level labels is the missing information at local-level. Most of the current researches with incomplete labels in Neural Network focus on the incompleteness of global labels, only a few works focus on the incompleteness of local labels. To deal with the local incompleteness, we propose a learning approach which uses two dynamic weighted maps in parallel: one for object pixels and another one for background pixels. The two maps are integrated into the loss function of the target Neural Networks, to optimize the model by the present labels and to minimize the damage of the missing labels. We validate our approach on the speech balloon extraction problem in comic book images. Our approach uses the output of a balloon extraction algorithm as incomplete labels. The results are comparable with the state of the art supervised approach with manual labels. The results are very promising because our method does not require any manual labels. In addition, we apply our method to the medical image segmentation task to confirm the generalization of our approach.
Automatic Updates of Transition Potential Matrices in Dempster-Shafer Networks Based on Evidence Inputs

Sensor fusion is a topic central to aerospace engineering and is particularly applicable to unmanned aerial systems (UAS). Evidential Reasoning, also known as Dempster-Shafer theory, is used heavily in sensor fusion for detection classification. High computing requirements typically limit use on small UAS platforms. Valuation networks, the general name given to evidential reasoning networks by Shenoy, provides a means to reduce computing requirements through knowledge structure. However, these networks use conditional probabilities or transition potential matrices to describe the relationships between nodes, which typically require expert information to define and update. This paper proposes and tests a novel method to learn these transition potential matrices based on evidence injected at nodes. Novel refinements to the method are also introduced, demonstrating improvements in capturing the relationships between the node belief distributions. Finally, novel rules are introduced and tested for evidence weighting at nodes during simultaneous evidence injections, correctly balancing the injected evidenced used to learn the transition potential matrices. Together, these methods enable updating a Dempster-Shafer network with significantly less user input, thereby making these networks more useful for scenarios in which sufficient information concerning relationships between nodes is not known a priori.
Predicting IMDB Movie Rating Using Deep Learning

Automatic text classification is considered as a significant task in the current digital world where a huge volume of data is present in the form of text. So, it has been necessary to come up with efficient algorithms that could classify reviews and emotions provided by the user without human intervention. This will help others in choosing a better movie to watch. Several metrics such as accuracy, Recall, and area under the ROC curve are used to determine the efficiency of algorithms and to suggest accurate classifiers. In our paper, the efficiency of classical machine learning algorithms and deep learning architectures on the IMDB movie review dataset has been contrasted and proposed the most efficient architecture. The former part of the paper focuses on pre-processing the text documents and the latter part of the paper experiments with advanced deep learning techniques on the text.
dcgp: Differentiable Cartesian Genetic Programming made easy.

Genetic Programming (GP), is a computational technique based on the idea of representing a computer program in some dedicated data-structure (e.g. a tree) to then evolve it using genetic algorithms as to improve its ability at solving a predefined task.How good a certain program is at solving the task is typically encapsulated in its fitness: a vector of floating point values defining various aspects of the program.In a typical example, the fitness would be the program output error with respect to a predefined behaviour over a number of inputs.Generation after generation, the genetic algorithm would then improve such a fitness in the attempt to have the program behave correctly.The idea flourished in the late 80s mainly thanks to the work of John Koza (Koza, 2010), and (using the words of Koza) has always had a "hidden skeleton in the closet": the difficulty to find and evolve real valued parameters in the expressed program.A recent development called differentiable genetic programming (Izzo, Biscani, & Mereta, 2017), was introduced to address exactly this issue by allowing to learn constants in computer programs using the differential information of the program outputs as obtained using automated differentiation techniques.
Forensic analysis of TikTok application to seek digital artifacts on Android smartphone

Emerging with highlight features as a global phenomenon, TikTok - the international version of Douyin application in China market, is a social media video app for creating and sharing short lip-syncing. This social video platform has seen astounding growth by reaching 1.5 billion users in 2019 by capturing the cultural zeitgeist among teenage smartphone owners in unprecedented fashion. However, criminals have been paid attention to this platform where young users become the target of abusers or predators. In this paper, we introduce the forensic analysis of the artifacts left on Android phones by TikTok application. In more detail, we indicate a complete description of all the artifacts obtained in TikTok. Furthermore, by using the results discussed in the paper, an investigator can rebuild the list of followers, searching keywords, favorites, messages that have been exchanged by users. It is helpful to examine and determine which artifacts could be considered as evidence for criminal investigation on popular social networking application.
Whole Breast Sound Speed Measurement from US Tomography Correlates Strongly with Volumetric Breast Density from Mammography

To assess the feasibility of using tissue sound speed as a quantitative marker of breast density.This study was carried out under an Institutional Review Board-approved protocol (written consent required). Imaging data were selected retrospectively based on the availability of US tomography (UST) exams, screening mammograms with volumetric breast density data, patient age of 18 to 80 years, and weight less than 300 lbs. Sound speed images from the UST exams were used to measure the volume of dense tissue, the volume averaged sound speed (VASS), and the percent of high sound speed tissue (PHSST). The mammographic breast density and volume of dense tissue were estimated with three-dimensional (3D) software. Differences in volumes were assessed with paired t-tests. Spearman correlation coefficients were calculated to determine the strength of the correlations between the mammographic and UST assessments of breast density.A total of 100 UST and 3D mammographic data sets met the selection criteria. The resulting measurements showed that UST measured a more than 2-fold larger volume of dense tissue compared to mammography. The differences were statistically significant (P < 0.001). A strong correlation of rS = 0.85 (95% CI: 0.79-0.90) between 3D mammographic breast density (BD) and the VASS was noted. This correlation is significantly stronger than those reported in previous two-dimensional studies (rS = 0.85 vs rS = 0.71). A similar correlation was found for PHSST and mammographic BD with rS = 0.86 (95% CI: 0.80-0.90).The strong correlations between UST parameters and 3D mammographic BD suggest that breast sound speed should be further studied as a potential new marker for inclusion in clinical risk models.
Resilient Techniques Against Disruptions of Volatile Cloud Resources

On-Demand cloud resources are highly available and reliable since most common cloud service providers organize their clouds as a network of several regions (data centres) and multiple availability zones in each region. This redundant and highly distributed resource pool guarantees users high availability and reliability, even in case of disasters. In order to increase revenues, cloud service providers offer their unused computing resources for much cheaper prices than On-Demand resources, in the form of volatile cloud resources. The trade-off for the high discount is their volatile ability, i.e. lower availability and lower reliability. This means that a user can lose part or all volatile resources at any time, similar to a large-scale technology-related massive failure (disaster). This chapter introduces volatile cloud resources, their life cycle, pros and cons. It also presents several resilient techniques against volatile cloud resources’ disruptions and multiple failures.
The Most Interesting Anomalies Discovered in ZTF DR3 from the SNAD-III Workshop

The search for objects with unusual astronomical properties, or anomalies, is one of the most anticipated results to be delivered by the next generation of large scale astronomical surveys. Moreover, given the volume and complexity of current data sets, machine learning algorithms will undoubtedly play an important role in this endeavor. The SNAD team is specialized in the development, adaptation and improvement of such techniques with the goal of constructing optimal anomaly detection strategies for astronomy. We present here the preliminary results from the third annual SNAD workshop (https://snad.space/2020/) that was held on-line in 2020 July.
Approximating Gaussian Process Emulators with Linear Inequality Constraints and Noisy Observations via MC and MCMC

Adding inequality constraints (e.g. boundedness, monotonicity, convexity) into Gaussian processes (GPs) can lead to more realistic stochastic emulators. Due to the truncated Gaussianity of the posterior, its distribution has to be approximated. In this work, we consider Monte Carlo (MC) and Markov Chain Monte Carlo (MCMC) methods. However, strictly interpolating the observations may entail expensive computations due to highly restrictive sample spaces. Furthermore, having (constrained) GP emulators when data are actually noisy is also of interest for real-world implementations. Hence, we introduce a noise term for the relaxation of the interpolation conditions, and we develop the corresponding approximation of GP emulators under linear inequality constraints. We show with various toy examples that the performance of MC and MCMC samplers improves when considering noisy observations. Finally, on 2D and 5D coastal flooding applications, we show that more flexible and realistic GP implementations can be obtained by considering noise effects and by enforcing the (linear) inequality constraints.
Comparison of Data Mining Algorithm Performance on Student Savings Dataset

Abstract Sabilillah Educational Cash Unit is a unit within the sabilillah educational foundation which is engaged in education. The stored cash processing data will be utilized using data mining so that it can be used as a decision support for finding information that is useful in evaluating the data used. Various methods contained in the data mining, the authors will make a comparison of the method techniques from the data mining. The use of the decision tree and, K-means method is implemented using the Rapid Miner application, which will later be analysed of each of these methods to determine the strategy to look for students who have the potential to save the hajj savings. This research was conducted with a group of data to determine the percentage value of precision, recall and accuracy. The results of this study that the C.4.5 method has a better value than other methods on the recall and accuracy, while the K-Means method has a precision value better than the other methods.
Recognition of Historical Handwritten Kannada Characters Using Local Binary Pattern Features

Archaeological departments throughout the world have undertaken massive digitization projects to digitize their historical document corpus. In order to provide worldwide visibility to these historical documents residing in the digital libraries, a character recognition system is an inevitable tool. Automatic character recognition is a challenging problem as it needs a cautious blend of enhancement, segmentation, feature extraction, and classification techniques. This work presents a novel holistic character recognition system for the digitized Estampages of Historical Handwritten Kannada Stone Inscriptions (EHHKSI) belonging to 11th century. First, the EHHKSI images are enhanced using Retinex and Morphological operations to remove the degradations. Second, the images are segmented into characters by connected component labeling. Third, LBP features are extracted from these characters. Finally, decision tree is used to learn these features and classify the characters into appropriate classes. The LBP features improved the performance of the system significantly.
A Girsanov Type Representation of Quadratic-Exponential Cost Functionals for Linear Quantum Stochastic Systems

This paper is concerned with multimode open quantum harmonic oscillators and quadratic-exponential functionals (QEFs) as quantum risk-sensitive performance criteria. Such systems are described by linear quantum stochastic differential equations driven by multichannel bosonic fields. We develop a finite-horizon expansion for the system variables using the eigenbasis of their two-point commutator kernel with noncommuting position-momentum pairs as coefficients. This quantum Karhunen-Loeve expansion is used in order to obtain a Girsanov type representation for the quadratic-exponential functions of the system variables. This representation is valid regardless of a particular system-field state and employs the averaging over an auxiliary classical Gaussian random process whose covariance operator is defined in terms of the quantum commutator kernel. We use this representation in order to relate the QEF to the moment-generating functional of the system variables. This result is also specified for the invariant multipoint Gaussian quantum state when the oscillator is driven by vacuum fields.
Feedback-Dubins-RRT Recovery Path Planning of UUV in an Underwater Obstacle Environment

In this paper, a UUV (Unmanned Underwater Vehicle) recovery path planning method of a known starting vector and end vector is studied. The local structure diagram is designed depending on the distance and orientation information about the obstacles. According to the local structure diagram, a Rapidly exploring Random Tree (RRT) method with feedback is used to generate a 3D Dubins path that approaches the target area gradually, and the environmental characteristic of UUV reaching a specific target area is discussed. The simulation results demonstrate that this method can effectively reduce the calculation time and the amount of data storage required for planning. Meanwhile, the smooth spatial path generated can be used further to improve the feasibility of the practical application of UUV.
A solution to MPTCP’s inefficiencies under the incast problem for Data Center Networks

In recent years several multipath data transport mechanisms, such as MPTCP and XMP, have been introduced to effectively exploit the path diversity of data center networks (DCNs). However, these multipath schemes have not been widely deployed in DCNs. We argue that two key factors among others impeded their adoption: TCP incast and minimum window syndrome . First, these mechanisms are ill-suited for workloads with a many-to-one communication pattern, commonly found in DCNs, causing frequent TCP incast collapses. Second, the syndrome we discover for the first time, results in 2-5 times lower throughput for single-path flows than multipath flows, thus severely violating network fairness. To effectively tackle these problems, we propose AMP: an adaptive multipath congestion control mechanism that quickly detects the onset of these problems and transforms its multipath flow into a single-path flow. Once these problems disappear, AMP safely reverses this transformation and continues its data transmission via multiple paths. Our evaluation results under a diverse set of scenarios in a fat-tree topology with realistic workloads demonstrate that AMP is robust to the TCP incast problem and improves network fairness between multipath and single-path flows significantly with little performance loss.
Parallel Implementation of SM2 Elliptic Curve Cryptography on Intel Processors with AVX2

This paper presents an efficient and secure implementation of SM2, the Chinese elliptic curve cryptography standard that has been adopted by the International Organization of Standardization (ISO) as ISO/IEC 14888-3:2018. Our SM2 implementation uses Intel’s Advanced Vector Extensions version 2.0 (AVX2), a family of three-operand SIMD instructions operating on vectors of 8, 16, 32, or 64-bit data elements in 256-bit registers, and is resistant against timing attacks. To exploit the parallel processing capabilities of AVX2, we studied the execution flows of Co-Z Jacobian point arithmetic operations and introduce a parallel 2-way Co-Z addition, Co-Z conjugate addition, and Co-Z ladder algorithm, which allow for fast Co-Z scalar multiplication. Furthermore, we developed an efficient 2-way prime-field arithmetic library using AVX2 to support our Co-Z Jacobian point operations. Both the field and the point operations utilize branch-free (i.e. constant-time) implementation techniques, which increase their ability to resist Simple Power Analysis (SPA) and timing attacks. Our software for scalar multiplication on the SM2 curve is, to our knowledge, the first constant-time implementation of the Co-Z based ladder that leverages the parallelism of AVX2.
Cue Utilization, Phishing Feature and Phishing Email Detection

Cognitive processes are broadly considered to be of vital importance to understanding phishing email feature detection or misidentification. This research extends the current literature by introducing the concept of cue utilization as a unique predictor of phishing feature detection. First year psychology students (n = 127) undertook three tasks measuring cue utilization, phishing feature detection and phishing email detection. A multiple linear regression model provided evidence that those in a higher cue utilization typology (n = 55) performed better at identifying phishing features than those in a lower cue utilization typology (n = 72). Furthermore, as predicted by the Elaboration Likelihood Model (ELM) and Heuristic-Systematic Model (HSM), those who deliberated longer per email demonstrated an increased ability to correctly identify phishing features. However, these results did not translate into improved performance in the phishing email detection task. Possible explanations for these results are discussed, including possible limitations and areas of future research.
An Improved Differential Fault Analysis on Block Cipher KLEIN-64

KLEIN-64 is a lightweight block cipher designed for resource-constrained environment, and it has advantages in software performance and hardware implementation. Recent investigation shows that KLEIN-64 is vulnerable to di... | Find, read and cite all the research you need on Tech Science Press
MF2: A Collection of Multi-Fidelity Benchmark Functions in Python

The field of (evolutionary) optimization algorithms often works with expensive black-box optimization problems.However, for the development of novel algorithms and approaches, real-world problems are not feasible due to their high computational cost.Instead, benchmark functions such as Sphere, Rastrigin, and Ackley are typically used.These functions are not only fast to compute, but also have known properties which are very helpful when examining the performance of new algorithms.
Deep Learning for Anomaly Detection

Anomaly detection has been widely studied and used in diverse applications. Building an effective anomaly detection system requires researchers and developers to learn complex structure from noisy data, identify dynamic anomaly patterns, and detect anomalies with limited labels. Recent advancements in deep learning techniques have greatly improved anomaly detection performance, in comparison with classical approaches, and have extended anomaly detection to a wide variety of applications. This tutorial will help the audience gain a comprehensive understanding of deep learning based anomaly detection techniques in various application domains. First, we give an overview of the anomaly detection problem, introducing the approaches taken before the deep model era and listing out the challenges they faced. Then we survey the state-of-the-art deep learning models that range from building block neural network structures such as MLP, CNN, and LSTM, to more complex structures such as autoencoder, generative models (VAE, GAN, Flow-based models), to deep one-class detection models, etc. In addition, we illustrate how techniques such as transfer learning and reinforcement learning can help amend the label sparsity issue in anomaly detection problems and how to collect and make the best use of user labels in practice. Second to last, we discuss real world use cases coming from and outside LinkedIn. The tutorial concludes with a discussion of future trends.