Reviewerly/exHarmony huggingface dataset:
Continuous Authentication of Mouse Dynamics Based on Decision Level Fusion

The demand for information security is growing with the changes of the times, and the authentication system is an important gateway to ensure information security. Password authentication is the most commonly used authentication method in modern network. However, because the password is easy to be cracked, we need to pay more attention to more authentication methods. In many authentications, the advantage of keystrokes and mouse authentication are more obvious; however, when researchers use mouse dynamics to authenticate, classifier training always require a large amount of data, and when the data is less, there may be inaccurate results. In this paper, a decision-level fusion method of the two classifiers is proposed, which reduces the strong dependence on data during training. In this method, the support vector machine optimized by genetic algorithm and k-nearest-neighbor algorithm are combined to get a lower error rate, which is lower than the error rate generated by the two methods alone.
The 2-Adic Complexity of Ding-Helleseth Generalized Cyclotomic Sequences of Order 2 and Period <i>pq</i>

This paper considers the 2-adic complexity of Ding-Helleseth generalized cyclotomic sequences of order 2 and period <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$pq$ </tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> are distinct odd primes with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathrm {gcd}(p-1,q-1)=2,p\equiv q\equiv 3\pmod 4$ </tex-math></inline-formula> . These sequences have been proved to possess good linear complexity. Our results show that the 2-adic complexity of these sequences is at least <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$pq-q-1$ </tex-math></inline-formula> . Then it is large enough to resist the attack of the rational approximation algorithm.
Aiming at QoS: A Modified DE Algorithm for Task Allocation in Cloud Computing

The Cloud computing system is characterized by large scale servers being utilized by an even larger number of users. It is a system where there is the need to frequently and efficiently schedule and manage different application tasks, with varied service requirements. One of the challenges of Cloud computing is managing the quality of service (QoS) rendered to users, specifically scheduling tasks between users and Cloud resources in a timely manner. Cloud users usually have widely diverse QoS requirements and meeting these simultaneously is also a challenge. In this paper, in order to improve on Cloud resource allocation and specifically to tailor it towards meeting varied QoS requirements of users, we proposed a new algorithm which combines Differential Evolution with the Shapley Value economic mode. This combination allows us measure the contribution of each virtual machine (VM), so as to improve the probability of obtaining a better tasks-to-resource allocation thereby improving user satisfaction. From results of conducted experiments, when compared with the traditional DE (Differential Evolution) algorithm and the conventional task-VM binding policy in CloudSim, both for allocations where special QoS requirements are required and in instances of multiple QoS requirements; the modified Shapley value based DE algorithm (SVBDA) shows significant improvement.
Detection of Landmarks by Autonomous Mobile Robots Using Camera-Based Sensors in Outdoor Environments

This paper presents the methods of increasing the probability of landmark detection using the features of the color parameters (coordinates) of the image averaged over each column of the camera matrix pixels. The first method involves selecting a jump (dip) of color parameters distribution and averaging these parameters in the background areas using shifted similar distributions for images obtained by scanning the surrounding space. This possibility occurs if the correlation radius of the color parameters on the image is several times smaller than the horizontal size of the image. The second method uses a color parameter jump on the first modes of the Hilbert-Huang transform in the location of a landmark image.
On Extension of Quantum Channels and Operations to the Space of Relatively Bounded Operators

We analyse possibility to extend a quantum operation (sub-unital normal completely positive linear map on the algebra $$\mathfrak{B}(\mathcal{H})$$ of bounded operators on a separable Hilbert space $$\mathcal{H}$$ ) to the space of all operators on $$\mathcal{H}$$ relatively bounded w.r.t. a given positive unbounded operator. We show that a quantum operation $$\Phi$$ can be uniquely extended to a bounded linear operator on the Banach space of all $$\sqrt{G}$$ -bounded operators on $$\mathcal{H}$$ provided that the operation $$\Phi$$ is $$G$$ -limited: the predual operation $$\Phi_{*}$$ maps the set of positive trace class operators $$\rho$$ with finite value of $$\textrm{Tr}\rho G$$ into itself. Assuming that $$G$$ has discrete spectrum of finite multiplicity we prove that for a wide class of quantum operations the existence of the above extension implies the $$G$$ -limited property. Applications to the theory of Bosonic Gaussian channels are considered.
Research on the Development of Cloud Computing

With the development of Internet technology, individuals and enterprises have higher and higher requirements for computing power and storage capacity, and cloud computing has emerged. This paper mainly studies the future development direction of cloud computing by analyzing the definition, classification and development process of cloud computing.
Indian Language Identification using Deep Learning

Spoken language is the most regular method of correspondence in this day and age. Endeavours to create language recognizable proof frameworks for Indian dialects have been very restricted because of the issue of speaker accessibility and language readability. However, the necessity of SLID is expanding for common and safeguard applications day by day. Feature extraction is a basic and important procedure performed in LID. A sound example is changed over into a spectrogram visual portrayal which describes a range of frequencies in regard with time. Three such spectrogram visuals were generated namely Log Spectrogram, Gammatonegram and IIR-CQT Spectrogram for audio samples from the standardized IIIT-H Indic Speech Database. These visual representations depict language specific details and the nature of each language. These spectrograms images were then used as an input to the CNN. Classification accuracy of 98.86% was obtained using the proposed methodology.
Sova: A Software-Defined Autonomic Framework for Virtual Network Allocations

With the rise of network virtualization, the workloads deployed on data center are dramatically changed to support diverse service-oriented applications, which are in general characterized by the time-bounded service response that in turn puts great burden on the data-center networks. Although there have been numerous techniques proposed to optimize the virtual network allocation in data center, the research on coordinating them in a flexible and effective way to autonomically adapt to the workloads for service time reduction is few and far between. To address these issues, in this article we propose Sova, an autonomic framework that can combine the virtual dynamic SR-IOV (DSR-IOV) and the virtual machine live migration (VLM) for virtual network allocations in data centers. DSR-IOV is a SR-IOV-based virtual network allocation technology, but its operation scope is very limited to a single physical machine, which could lead to the local hotspot issue in the course of computation and communication, likely increasing the service response time. In contrast, VLM is an often-used virtualization technique to optimize global network traffic via VM migration. Sova exploits the software-defined approach to combine these two technologies with reducing the service response time as a goal. To realize the autonomic coordination, the architecture of Sova is designed based on the MAPE-K loop in autonomic computing. With this design, Sova can adaptively optimize the network allocation between different services by coordinating DSR-IOV and VLM in autonomic way, depending on the resource usages of physical servers and the network characteristics of VMs. To this end, Sova needs to monitor the network traffic as well as the workload characteristics in the cluster, whereby the network properties are derived on the fly to direct the coordination between these two technologies. Our experiments show that Sova can exploit the advantages of both techniques to match and even beat the better performance of each individual technology by adapting to the VM workload changes.
Quantum Homomorphic Encryption Based on Quantum Obfuscation

Homomorphic encryption enables computation on encrypted data while maintaining secrecy. This leads to an important open question whether quantum computation can be delegated and verified in a non-interactive manner or not. In this paper, we affirmatively answer this question by constructing the quantum homomorphic encryption scheme with quantum obfuscation. It takes advantage of the interchangeability of the unitary operator, and exchanges the evaluation operator and the encryption operator by means of equivalent multiplication to complete homomorphic encryption. The correctness of the proposed scheme is proved theoretically. The evaluator does not know the decryption key and does not require a regular interaction with a user. Because of key transmission after quantum obfuscation, the encrypting party and the decrypting party can be different users. The output state has the property of complete mixture, which guarantees the scheme security. Moreover, the security level of the quantum homomorphic encryption scheme depends on quantum obfuscation and encryption operators.
Multistage opto-neuromorphic process system based on commercial optoelectric devices

This paper proposes a structure for opto-neuromorphic processes by using multistage connections of photo-transmitters. The basic unit consists of a light-emitting diode and a photoresistor for a non-inverting photo signal processor. By constructing a cascade structure of these basic units, multistage photo transmission of input light signal is examined in pulse signals with various periods and duty cycles as the input to the first opto-neuromorphic stage. This approach verifies that optical signals can be transmitted through our proposed structure and that by controlling the amount of the transmitted light between the basic units, the gain between stages can be controlled. It is demonstrated that this structure can be used for opto-neuromorphic processes by transmitting light signals as if transmitting signals between neurons with different weights and activation patterns. The proposed structure can be modified with an inverter circuit to build a photo-inverter and by using a combination of photo-transmitter and photo-inverters, it can serve as the basic structure to build more complicated opto-neuromorphic process systems.
NCMS: Towards accurate anchor free object detection through <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e2231" altimg="si1.svg"><mml:msub><mml:mrow><mml:mi>ℓ</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:math> norm calibration and multi-feature selection

We present simple and flexible drop-in modules in feature pyramids for general object detection, which can be easily generalized to other anchor-free detectors without introducing extra parameters, and only involves negligible computational cost on training and testing. The proposed detector, called NCMS, inserts a simple norm calibration (NC) operation between the feature pyramids and detection head to alleviate and balance the norm bias caused by feature pyramid network (FPN). Furthermore, the NCMS leverages an enhanced multi-feature selective strategy (MS) during training to assign the ground-truth to particular feature pyramid levels as supervisions, in order to obtain more discriminative representation for objects. By generalizing to the state-of-the-art FSAF module (Zhu et al., 2019), our NCMS improves it by 1.6% on COCO val set without bells and whistles. The resulting best model achieves 44.0% mAP with single-model and single-scale testing, which is a fairly competitive result.
Can Blockchain Help Resolve Modern Slavery in Supply Chains?

While blockchain was designed as a ledger for cryptocurrency transactions, it can record transactions of anything of value. Blockchain is increasingly used to prove the integrity of commodities, tracing their supply chain journey from the source to the end user. Yet, transferring this technology from a cryptocurrency context to a supply chain setting is not without difficulties. This article explores the implications for multinational and transnational companies in using blockchain as a means to address modern slavery. The research identifies five challenges: verification, inclusion, trust, privacy, and normativity.
360Cast: Foveation-Based Wireless Soft Delivery for 360-Degree Video

Wireless 360-degree video delivery provides virtual reality (VR) immersive experience where each user can freely switch his/her viewing orientation. The existing schemes of the wireless 360-degree video streaming use digital-based compression and transmission. However, they have many disadvantages in terms of video traffic and quality: cliff effect due to unstable wireless channels, large video traffic due to the extremely high resolution of the 360-degree video, and the perceptual redundancy within the transmitted video. To solve the above problems, this paper proposes a novel wireless 360-degree video transmission scheme called 360Cast. 360Cast adopts the analog-based transmission, including power allocation and analog modulation, to achieve graceful video quality improvement even in time-varying wireless channels. Here, the power allocation considers the distortion of human perception and sphere-to-plane projection to maximize the human perceptual quality at the 360-degree video playback. In addition, 360Cast predicts and transmits the user's viewport based on the recent HMD user's orientation by using dynamic linear regression (DLR) and only transmits the viewport for traffic reduction. Evaluation results show that the proposed 360Cast provides better human perceptual quality via HMD compared with the existing analog transmission schemes, i.e., SoftCast and FoveaCast, by using the integration of viewport prediction, power allocation, and analog modulation.
Optimal Experimental Design for Inverse Problems in the Presence of Observation Correlations

Optimal experimental design (OED) is the general formalism of sensor placement and decisions about the data collection strategy for engineered or natural experiments. This approach is prevalent in many critical fields such as battery design, numerical weather prediction, geosciences, and environmental and urban studies. State-of-the-art computational methods for experimental design, however, do not accommodate correlation structure in observational errors produced by many expensive-to-operate devices such as X-ray machines or radar and satellite retrievals. Discarding evident data correlations leads to biased results, poor data collection decisions, and waste of valuable resources. We present a general formulation of the OED formalism for model-constrained large-scale Bayesian linear inverse problems, where measurement errors are generally correlated. The proposed approach utilizes the Hadamard product of matrices to formulate the weighted likelihood and is valid for both finite- and infinite-dimensional Bayesian inverse problems. Extensive numerical experiments are carried out for empirical verification of the proposed approach by using an advection-diffusion model, where the objective is to optimally place a small set of sensors, under a limited budget, to predict the concentration of a contaminant in a closed and bounded domain.
Sentiment Analysis of Sina Weibo Users Under the Impact of Super Typhoon Lekima Using Natural Language Processing Tools: A Multi-Tags Case Study

Since August 10th, Super Typhoon Lekima has dealt huge damage to south-east of China and also caused great panic and anxiety among people. In order to figure out levels of people's negative feelings and how factors such as gender, age, location and publishment time affected people's emotions, this paper conducted a sentiment analysis by analyzing microblogs that people pushed on Sina Weibo. This study extracted users' contents and user information on Sina Weibo August 10th to establish a database. Then we applied to the database a word segmentation tool based on algorithms that Jieba used along with a sentiment word dictionary. Finally, with users' moments and posters classified, labeled and rated, people's sentimental negativity scores during the attack of Super Typhoon Lekima and relevance between people's negativity scores and their individual information are gained.
A cooperative camera surveillance method based on the principle of coarse-fine coupling boresight adjustment

Visual surveillance systems play a vital role in smart manufacturing scenes. In this paper, a cooperative camera surveillance method is proposed based on the coarse-fine coupling boresight adjustment principle. A master-slave camera cooperative monitoring model is established by combining a high-resolution camera with a large-field camera, the former of which is embedded with a Risley prism pair to perform high-precision boresight pointing for monitoring a local target, while the latter can observe a wide scene and guide the former to catch a region of interest. An effective boresight adjustment strategy is put forward with a method of combining fast convergence and iterative refinement. A calibration method of the master-slave camera is proposed with the combination of binocular vision and equivalent motion plane. The error distribution, adjustment efficiency and tracking accuracy of the dual field monitoring are analyzed in detail to reveal the mapping mechanism between imaging feedback and boresight adjustment. In the surveillance experiment, the root-mean-square tracking error of the fine tracking camera can achieve better than 7.04 pixels, while the adjustment steps only need about 10 times, which validates the feasibility and effectiveness of the proposed method in industrial operation surveillance.
Data-Driven Small Cell Planning for Traffic Offloading with Users’ Differential Privacy

The development of 5G network and rapid growth of mobile traffic bring lucrative opportunities for Micro Operators (μOs), the novel local operators who own local spectrum, deploy and manage small cells (e.g., femtocells) in a specific area. Collaborating with traditional mobile network operators (MNOs), μOs gain profits from helping to offload the traffic carried by macro base stations and providing the MNOs customers with seamless service. However, due to the demand uncertainty and the sensitivity of individual user's demand profile, it is challenging for μOs to allocate the resource properly to avoid the under-and over-utilized situations. To address this issue, in this paper, we propose to employ data-driven approach to characterize the demand uncertainty, exploit differential privacy protocols to protect user's demand profile, and formulate the small cell planning problem into two-stage stochastic programming optimization with the objective of minimizing the capital and operational costs of μOs. Based on the formulated problem, we develop feasible solutions and conduct extensive simulations using real-world base station accessing real cellular data (i.e., data of 4G LTE network in Zhengzhou city, China) to verify the effectiveness of the proposed model.
A Bayesian Method for Real‐Time Earthquake Location Using Multiparameter Data

Abstract A primary task of a network‐based, earthquake early warning system is the prompt event detection and location, needed to assess the magnitude of the event and its potential damage through the predicted peak ground shaking amplitude using empirical attenuation relationships. Most of real‐time, automatic earthquake location methods ground on the progressive measurement of the first P‐wave arrival time at stations located at increasing distances from the source but recent approaches showed the feasibility to improve the accuracy and rapidity of the earthquake location by using the additional information carried by the P‐wave polarization or amplitude, especially with unfavorable seismic network lay‐outs. Here, we propose an evolutionary, Bayesian method for the real‐time earthquake location which combines the information derived from the differential P‐wave arrival times, amplitude ratios and back‐azimuths measured at a minimum of two stations. As more distant stations record the P‐wave, the posterior pdf is updated and new earthquake location parameters are determined along with their uncertainty. To validate the location method, we performed a retrospective analysis of mainshocks ( M &gt; 4.5) occurred during the 2016–2017 Central Italy earthquake sequence by simulating the typical acquisition layouts of in‐land, coastal and linear array of stations. Results show that with the combined use of the three parameters, 2–4 s after the first P‐wave detection, the method converges to stable and accurate determinations of epicentral coordinates and depth even with a nonoptimal coverage of stations. The proposed methodology can be generalized and adapted to the off‐line analysis of seismic records collected by standard local networks.
Non-Commutative Graphs in the Fock Space over One-Particle Hilbert Space

In the present paper we continue our study of non-commutative operator graphs in infinite-dimensional spaces. We consider examples of the non-commutative operator graphs generated by resolutions of identity corresponding to the Heisenberg–Weyl group of operators acting on the Fock space over one-particle state space. The problem of quantum error correction for such graphs is discussed.
A Calibration Method for Mobile Omnidirectional Vision Based on Structured Light

Mobile omnidirectional structured light vision is increasingly used in scene perception and robot navigation. A wide range of information is obtained by means of the vision system by only one image and laser image features are detected and extracted easily and quickly. In this paper a novel calibration method for mobile omnidirectional camera based on structured light is presented. Firstly, a set of parallel laser planes is emitted on the walls of corridor as auxiliary targets by structured light and intersects with wall orthogonally. Secondly, the constraint relationship is analyzed between the vanishing points in fisheye images and intrinsic parameters of imaging model. Finally, effects of the laser stripes' interval and the angle between the wall which contains laser stripes and ground on calibration results are evaluated. Compared to Scaramuzza method, the calibration method shows its superiority in terms of both feasibility and efficiency. The method with the characteristic of self-calibration since the planar target is replaced by actively projected laser stripes. The result illustrates that our method has the advantages of simple and feasible operation, but result is effective and accurate. The calibration parameters are independent of the laser stripes' interval and the angle between the wall and ground. Therefore, the method of the mobile omnidirectional structured light vision presented in this paper can be applied to many areas.