Reviewerly/exHarmony huggingface dataset:
Controlling the energy gap of a tunable two-level system by ac drive

We study the influence of a strong off-resonant driving signal to the energy levels of a superconducting flux qubit both experimentally and theoretically. In the experiment, we carry out a three-tone spectroscopy. This allows us to directly observe the modification of the qubit's energy levels by the dynamical Stark shift caused by the driving signal. A theoretical treatment including corrections from both rotating and counter-rotating frames, allowed us to completely explain the observed experimental results and to reconstruct the influence of the strong driving to the dissipative dynamics as well as to the coupling constants of the qubit. As one potential application, the tunability of the minimal energy-level splitting of a superconducting qubit by a microwave-induced dynamical Stark shift can help to overcome the parameter spread induced by the microfabrication of superconducting artificial quantum circuits.
Penetration Testing Framework for IoT

In the Internet of Things (IoT) environment, objects are connected on a network to share data. However, most of the IoT devices are developed and deployed with poor security consideration. As a result, these devices become a target of attacks. A solution for ensuring the safety and security of a network system is Penetration testing. In this study, we propose a framework for automated and flexible penetration testing for IoT network. Most of the available penetration testing methods are experts based, that select tool and process manually. This kind of Pen-test is a costly, time-consuming and inefficient. Also, the existing automated penetration testing doesn't consider the interaction between system components; it works by testing each component of a system separately. Individual component testing can lead to a security gap that makes the Pen-test inefficient since many low severity vulnerabilities on different inter-connected components can lead the system to an insecure state. Moreover, in some cases testing the individual components can claim that the particular component is secure, but if these individual components are connected in one system, it makes this system insecure. Due to such shortages, our framework will test the End-to-End target system (i.e., end devices, wireless communication, the control unit, then communication to the cloud server, and finally communication from the cloud to end user through mobile app or webpage). The proposed framework will automatically gather the information of the target IoT network and then perform various kinds of penetration testing through the network. Then it will summarize the results of Pentest and gives the recommendations to secure the system.
Deep Learning for Remote Sensing Image Super-Resolution

The aim of image super-Resolution (SR) is to enhance image resolution while still retain the integrity of the original image. There are many ongoing types of research on image super-resolution for natural images, but any a few on remote sensing images. In this paper, we proposed deep learning-based image super-resolution techniques, including convolutional neural network (CNN) and generative adversarial network (GAN) to enhance the resolution of remote sensing images by a factor 4. In CNN, it learns an end to end mapping from low-resolution image to high-resolution image whereas, in GAN, the model learns the mapping guided by the GAN loss and gives the sharper appearance in high-resolution images. Our experimental results show that visually GAN models perform well but are inferior to other models in terms of image quality metrics, whereas quantitatively CNN models outperform other super-resolution models.
Bounded privacy-utility monotonicity indicating bounded tradeoff of differential privacy mechanisms

Differential privacy can achieve the tradeoff between privacy and utility by using privacy metric and utility metric. However, since privacy metric and utility metric may not be bounded, differential privacy can not provide the bounded tradeoff. Moreover, there is no unified method to indicate the bounded tradeoff of differential privacy in the current work. To this end, we proposed the bounded privacy-utility monotonicity indicating the bounded tradeoff of differential privacy. First, we gave the definition of the bounded tradeoff of differential privacy, and we presented the bounded privacy-utility monotonicity of differential privacy based on computational indistinguishability. Second, we theoretically proved the bounded privacy-utility monotonicity of several differential privacy mechanisms based on the bounded metrics of modulus of characteristic function and normalized entropy, including the Laplace mechanism, discrete Laplace mechanism, Gaussian mechanism, exponential mechanism, optimal mechanism, and quaternary mechanism. We also showed that these mechanisms had the bounded privacy-utility monotonicity in the multivariate case. Third, our numerical results further demonstrated that these several differential privacy mechanisms obtained the bounded privacy-utility monotonicity. Finally, we gave an instance of achieving the bounded tradeoff of differential privacy mechanisms based on the bounded privacy-utility monotonicity under semi-honest model, and we discussed the goal of optimization of the bounded tradeoff of differential privacy based on the bounded privacy-utility monotonicity under semi-honest model. Therefore, the bounded privacy-utility monotonicity can be used to indicate the bounded tradeoff of differential privacy under semi-honest model. Furthermore, the bounded privacy-utility monotonicity plays an important role of optimizing the bounded tradeoff of differential privacy under semi-honest model.
Single Image Super-Resolution: From Discrete to Continuous Scale Without Retraining

Convolutional neural network (CNN)-based single image super-resolution (SR) methods have achieved superior performance on some discrete-scaling factors, including 2, 3, and 4.However, the scaling factors for SR should be continuous and not discrete in practical applications.Previous CNN-based SR models usually yield poor results for non-integer-scaling factors and are sometimes even worse than results derived from the conventional bicubic method.To extend CNN-based SR models to continuous scale, this paper proposes a multiple-scaling-based SR (MSSR) method that combines an integer-scaling-factor SR and once or twice non-integer-scaling-factor SR without retraining networks.For a non-integer-scaling factor, the MSSR method first computes an optimal integer-scaling factor according to the data similarity and choose the corresponding pre-trained model for the next stage.Then, an existing CNN-based model is used to perform the integer-scaling-factor SR.Finally, the output is scaled to the target size.The proposed MSSR method can extend a variety of existing CNN-based SR models from discrete to continuous-scaling factors.Experimental results with six CNN-based SR models demonstrated that the MSSR method could effectively improve the performance of existing CNN-based SR models for continuous-scaling-factor SR without retraining networks.Furthermore, the comparison with a magnification-arbitrary method, called Meta-SR, shows that the proposed MSSR method usually outperforms Meta-SR for scaling factors greater than or equal to 2.
Keywords Extraction Method for Technological Demands of Small and Medium-Sized Enterprises Based on LDA

Keywords extraction technology is used in the technological demands of SMEs. It can match the demands to the scientific research team quickly and accurately. This is an effective method to promote industry-university-research linkage. But how to extract effective information from a wide range of technological demands information is a challenging task. For this issue, the paper is based on the text of the technological demands of SMEs, and proposes a keywords extraction method based on LDA. Firstly, natural language processing technology is used to preprocess text. Then, multi-feature weighting and Latent Dirichlet Allocation (LDA) theme model are fused to extract keywords. Thirdly, the method chooses the best keywords through post-processing. Finally, the method is verified by experiments, and the feasibility of the method is demonstrated by actual cases. The method was tested on Chinese ScientistIn dataset. The experimental results show that the F-value is higher than that of Term Frequency - Inverse Document Frequency (TF - IDF) and LDA. It also shows that the method can improve the intelligence of the scientific and technological collaboration platform. The algorithm works best when the F-value is 0.79 at K=3.
The Application of Modular Arithmetic for Matrix Calculations

The article considers the application of the Residue Number System (RNS) for matrix calculations. This can be useful to improve the performance and reliability of calculations in digital signal processing and artificial neural networks. We have reviewed the algorithms for calculating the scalar product. Their modeling and comparison with the standard means of the NumPy library of the Python are carried out. Methods of translation between the positional numeral system and RNS are described and simulated. Conclusions about the applicability of methods for calculations on Python are made. We have present the fault-tolerant scheme for error detection and correction. The examples of error occurrence and their correction by the presented scheme are considered.
Requirements for adopting software process lines

A Software Process Line (SPrL) is potentially suitable for constructing software development methodologies by reusing core assets. However, adopting this approach without prior assessment of its suitability can lead to failure. The aim of this paper is to identify a set of requirements that can be used for deciding whether to adopt the SPrL approach in an organization. Identification of the requirements was accomplished in two stages: the characteristics important in method tailoring were first identified via a Systematic Mapping Study (SMS) that focused on analyzing 43 primary studies; the degree of importance of the identified characteristics was then determined using a questionnaire survey in which 31 experts participated. By analyzing the results of the SMS and the survey, we have identified 12 product-related, 22 project-related, and 10 organization-related requirements. In addition to these requirements, we have also identified two relevant requirements by studying previous research on Software Product Lines (SPL) and Business Process Lines (BPL). The requirements thus identified can help organizations decide on whether to adopt the SPrL approach: the more an organization satisfies the requirements, the more frequently method tailoring occurs in that organization, and hence, the more justified it is to adopt the SPrL approach.
Use of blockchain for enabling Construction 4.0

Blockchain, known as the ‘Trust Protocol’, is the amalgamation of technologies for secure decentralized data management and value transaction, with characteristics that provide security, anonymity, provenance, immutability and purpose of data without any third-party organization in control of data transactions. This creates novel opportunities in digital applications and ecosystems and is particularly disruptive in cyber-physical systems, the Internet of Things, cloud computing, cognitive computing and other automation and data exchange processes relevant to the Fourth Industrial Revolution. Distributed Ledgers Technologies, with and without blockchain are highly relevant in the application of Virtual Environments, like Mixed Reality, and BIM when discussing the construction sector, in which projects suffer fragmentation in processes, services and firms, with disconnect between design and construction. Blockchain can subvert these effects through use of open and transparent transactions, revolutionizing construction and building operations, leading to delivery of higher quality, more sustainable assets. Arguably, blockchain is central to cyber-physical systems. From the beginning, the protocol of cryptocurrencies embedded possibilities for programmable money and computational contracts. This ‘tokenized economy’ offers a tremendous variety of possible transaction and applications yet to be discovered and exploited. Decentralization of money and payments has moved to decentralization of markets and contemplates the transfer of many other kinds of assets beyond cryptocurrency. Developments in blockchain leverage novel categories, distinctions, and understandings of value allocation, and standard classifications and definitions are still emerging. Some of the terminology that broadly refers to the blockchain 2.0 space includes Smart Contracts, Smart Property, Decentralized Applications and Decentralized Autonomous Organizations. However, not all processes need an economy or a payments system, or peer-to-peer exchange, or decentralization, or robust public record keeping. Blockchain shows that there are at least three different levels of information. The first level is dumb, unenhanced, unmodulated data. The second is socially recommended data, data elements enriched by social network peer recommendation, made possible by networked internet models. The newly proposed level three is blockchain consensus-validated data, data’s highest recommendation level based on group consensus-supported accuracy and quality. This chapter focuses on the evolution, application and future directions of blockchain technology in the context of cyber-physical systems and Construction 4.0.
System Analysis and Design Using Secure Software Development Life Cycle Based On ISO 31000 and STRIDE. Case Study Mutiara Ban Workshop

Nowadays there are many security problems encountered in information systems. Mutiara Ban Workshop makes a safe system using the SSDLC (Secure Software Development Lifecycle) method. SSDLC is a model used by organizations to build safe applications. The SSDLC process determines how to integrate security into the software development process. ISO 31000 risk management and STRIDE are processes in SSDLC that handle security issues. In ISO 3100 risk management the existing processes establish context, risk identification, risk analysis, risk evaluation, risk care and communication that have their respective functions in overcoming security issues. Then, STRIDE is a process of classifying security issues divided into six classes, namely spoofing, destruction, rejection, information disclosure, denial of service, increase in privileges. The purpose of this study is to analyze and evaluate software published by Mutiara Ban's workshop using SSDLC (Secure Software Development Life Cycle) method and analyze STRIDE threat modeling. The results of the study is system design with information technology consisting of a list of risks and factors that contribute to information technology in Mutiara Ban Workshop and the design can be implemented.
Determination of the Center of Radial Distortion for a Camera Lens

A precise determination of the center of radial distortion (CoD) improves the characterization of the distortion introduced by the lens, and hence, supports a better description of the perspective projection process. Nonetheless, its value is often neglected and approximated by the center of the image. In our research, we aim to determine a lens CoD with high accuracy using derivative instruments. To that end, we introduce a method that bounds the region containing the CoD within one pixel of uncertainty by characterizing the residual of a straight line (RSL) fitting process. The procedure is further refined to obtain an estimate for the CoD position by first inferring straight lines (ISL) crossing it and then finding the intersection between these lines. Later on, we explore the introduction of the division model assumption (DMA) for lens distortion in the estimation of the CoD. The RSL approach becomes relevant as we compare the ISL and DMA methods with alternatives from the literature as it provides a reference value containing the CoD. In our experiments, we take images of a straightness gauge by moving a camera with a coordinate measuring machine at regular and accurate steps. At each location, we estimate undistorted lines at different orientations of the camera and find the CoD from their intersections. Our approach permits the establishment of the CoD with high accuracy for a camera lens with radial distortion.
Hybrid neural tagging model for open relation extraction

Open Relation Extraction (ORE) task remains a challenge to obtain a semantic representation by discovering arbitrary relations from the unstructured text. Conventional methods heavily depend on feature engineering or syntactic parsing, which are inefficient or error-cascading. Recently, leveraging supervised deep learning methods to address the ORE task is a promising way. However, there are two main challenges: (1) The lack of enough labeled corpus to support supervised training; (2) The exploration of specific neural architecture that adapts to the characteristics of open relation extracting. In this paper, we build a large-scale, high-quality training corpus in a fully automated way. And wedesign a tagging scheme to assist in transforming the ORE task into a sequence tagging processing. Furthermore, we propose a hybrid neural network model (HNN4ORT) for open relation tagging. The model employs the Ordered Neurons LSTM to encode potential syntactic information to capture the associations among the arguments and relations. It also emerges a novel Dual Aware Mechanism, including Local-aware Attention and Global-aware Convolution. The dual awarenesses complement each other. Takes the sentence-level semantics as a global perspective, and at the same time, the model implements salient local features to achieve sparse annotation. Experiment results on various testing sets show that our model achieves state-of-the-art performance compared toconventional methods or other neural models.
Future Uncertainty-Based Control for Relative Navigation in GPS-Denied Environments

In a nonlinear system where the separation principle does not hold, control actions at the current time can have a significant effect on estimation accuracy in the future. To properly control this type of system with a model predictive controller requires an accurate estimate of the future uncertainty of the system. To compute this estimate, we propose a definition of the nonlinear observability matrix with respect to a future state and establish its connection with the filtering Cramer-Rao lower bound at the future state. We also define a sensitivity function that correlates noise on control inputs to errors on the state estimate. As a demonstrative example, we consider the problem of relative navigation of two unmanned aerial systems in Global Positioning System denied environments. We demonstrate using Monte Carlo simulations that by controlling to optimize our derived observability and controllability metrics, we achieve significantly improved estimation performance over a trajectory optimized for an uncertainty estimate that does not consider input noise.
Mutual Complementarity: Multi-Modal Enhancement Semantic Learning for Micro-Video Scene Recognition

Scene recognition is one of the hot topics in micro-video understanding, where multi-modal information is commonly used due to its efficient representation ability.However, there are some challenges in the usage of multi-modal information because the semantic consistency among multiple modalities in micro-videos is weaker than in traditional videos, and the influences of multi-modal information in micro-videos are always different.To address these issues, a multi-modal enhancement semantic learning method is proposed for micro-video scene recognition in this study.In the proposed method, the visual modality is considered the main modality whereas other modalities such as text and audio are considered auxiliary modalities.We propose a deep multi-modal fusion network for scene recognition with enhanced the semantics of auxiliary modalities using the main modality.Furthermore, the fusion weight of multimodal can be adaptively learned in the proposed method.The experiments demonstrate the effectiveness of enhancement and adaptive weight learning in the multi-modal fusion of the micro-video scene recognition.
Fine-Grained Image Classification Combined with Label Description

Fine-grained image classification faces huge challenges because fine-grained images are similar overall, and the distinguishable regions are difficult to find. Generally, in this task, label descriptions contain valuable semantic information that is accurately compatible with discriminative features of images (i.e., the description of the "Rusty Black Bird" corresponding to the morphological characteristics of its image). Bringing these descriptions into consideration is benefit to discern these similar images. Previous works, however, usually ignore label descriptions and just mine informative features from images, thus the performance may be limited. In this paper, we try to take both label descriptions and images into consideration, and we formalize the classification task into a matching task to address this issue. Specifically, Our model is based on a combination of Convolutional Neural Networks (CNN) over images and Graph Convolutional Networks(GCN) over label descriptions. We map the resulting image representations and text representations to the same dimension for matching and achieve the purpose of classification through the matching operation. Experimental results demonstrate that our approach can achieve the best performance compared with the state-of-the-art methods on the datasets of Stanford dogs and CUB-200-2011.
Reconciling Feature-Reuse and Overfitting in DenseNet with Specialized Dropout

Recently convolutional neural networks (CNNs) achieve great accuracy in visual recognition tasks. DenseNets become one of the most popular CNN models due to its effectiveness in the feature-reuse. However, like other CNN models, DenseNets also face the overfitting problem if not more severe. Existing dropout methods can be applied but not effective. In particular, the property of the feature-reuse in DenseNets will be impeded, and the dropout effect will be weakened by the spatial correlation inside feature maps. To address these problems, we craft the design of a specialized dropout method from three aspects, the dropout location, the dropout granularity, and the dropout probability. The insights attained here could potentially be applied as a general approach for boosting the accuracy of other CNN models with similar shortcut connections. Experimental results show that DenseNets with our specialized dropout method yield better accuracies compared to vanilla DenseNets and state-of-the-art CNN models, and such accuracy boost increases with the model depth.
Review on Supervised Learning Techniques

In most of the application domains, few tasks can be solved by supervised learning where they can be used to label the data patterns from the existing data. This paper reviews various supervised learning techniques like decision trees, rule-based learners, lazy learners such as NNC, and a comparison of major supervised learning mechanisms like neural networks and support vector machines. The strengths and weakness of unsupervised learning techniques are also compared. This paper reviews about various supervised learning techniques strengths and weakness, brief review of unsupervised techniques, and navigation to semi-supervised learning.