Reviewerly/exHarmony huggingface dataset:
Floquet-engineered light-cone spreading of correlations in a driven quantum chain

We investigate the light-cone-like spread of electronic correlations in a laser-driven quantum chain. Using the time-dependent density matrix renormalization group, we show that high-frequency driving leads to a Floquet-engineered spread velocity that determines the enhancement of density-density correlations when the ratio of potential and kinetic energies is effectively increased both by either a continuous or a pulsed drive. For large times we numerically show the existence of a Floquet steady state at not too long distances on the lattice with minimal heating. Intriguingly, we find a discontinuity of dynamically scaled correlations at the edge of the light cone, akin to the discontinuity known to exist for quantum quenches in Luttinger liquids. Our work demonstrates the potential of pump-probe experiments for investigating light-induced correlations in low-dimensional materials and puts quantitative speed limits on the manipulation of long-ranged correlations through Floquet engineering.
Computer Adaptive Testing Using Upper-Confidence Bound Algorithm for Formative Assessment

There is strong support for formative assessment inclusion in learning processes, with the main emphasis on corrective feedback for students. However, traditional testing and Computer Adaptive Testing can be problematic to implement in the classroom. Paper based tests are logistically inconvenient and are hard to personalize, and thus must be longer to accurately assess every student in the classroom. Computer Adaptive Testing can mitigate these problems by making use of Multi-Dimensional Item Response Theory at cost of introducing several new problems, most problematic of which are the greater test creation complexity, because of the necessity of question pool calibration, and the debatable premise that different questions measure one common latent trait. In this paper a new approach of modelling formative assessment as a Multi-Armed bandit problem is proposed and solved using Upper-Confidence Bound algorithm. The method in combination with e-learning paradigm has the potential to mitigate such problems as question item calibration and lengthy tests, while providing accurate formative assessment feedback for students. A number of simulation and empirical data experiments (with 104 students) are carried out to explore and measure the potential of this application with positive results.
Optimal design of complementary metal-oxide-semiconductor analogue circuits: An evolutionary approach

This article proposes an efficient approach to design two different CMOS analogue circuits, namely p-channel Metal-Oxide-Semiconductor (PMOS) driver-based two-stage comparator and n-channel Metal-Oxide-Semiconductor (NMOS) driver-based folded-cascode operational amplifier, using a modified version of particle swarm optimization (PSO) known as craziness-based PSO (CRPSO) algorithm. PSO replicates the behaviour of bird flocking, and it requires a fewer number of control parameters. The drawbacks of PSO are premature convergence and stagnation problem. Due to these drawbacks, the PSO is improved to CRPSO for the optimal design of analogue circuits. The near-global optimal solution has been achieved by using the CRPSO. The CRPSO is used to attain the optimal sizes of the MOS devices for the individual circuits, which lead to the minimization of the total MOS area occupied by the circuit. SPICE simulation has also been carried out with the optimal device dimensions achieved from the CRPSO algorithm. Statistical analysis and the process corner analysis have also been performed for the above mentioned circuits. SPICE-based results confirm that the CRPSO-based approach is a better technique compared to the other earlier reported methods for both the circuits.
Overview of one-Class Classification

One-class classification, which was tested successfully in unbalanced sample classification problems, is one of the hotspots in pattern recognition research. This paper first analysis the commonly used one-class classification methods, then classifies these methods into three categories: boundary based method, re-construction based method and border based method. Finally, a series of testing experiments based on artificial database are designed to test the advantages and disadvantages of these methods from the aspect of learning ability, classification decision and algorithm complexity.
Stereoscopic video saliency detection based on spatiotemporal correlation and depth confidence optimization

Effective stereoscopic video saliency detection is challenging due to the diversity of visual contrast and the variability of object motion. In this paper, we propose a novel stereoscopic video saliency detection method based on spatiotemporal correlation and depth confidence optimization. First, spatial saliency is obtained by color contrast computation and further enhancing spatial correlation between neighbors. Second, temporal saliency is calculated by propagating motion information between frames. The special propagation in sequential frames and reverse sequential frames augments the temporal correlation. Furthermore, a depth confidence optimization is proposed to fuse the spatial saliency and temporal saliency to generate optimal saliency map adaptively. Experimental results on three datasets demonstrate the efficiency of the proposed saliency method.
Efficient blind deblurring under high noise levels

The goal of blind image deblurring is to recover a sharp image from a motion blurred one without knowing the camera motion. Current state-of-the-art methods have a remarkably good performance on images with no noise or very low noise levels. However, the noiseless assumption is not realistic considering that low light conditions are the main reason for the presence of motion blur due to requiring longer exposure times. In fact, motion blur and moderate to high noise often appear together. Most works approach this problem by first estimating the blur kernel k and then deconvolving the noisy blurred image. In this work, we first show that current state-of-the-art kernel estimation methods based on the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> gradient prior can be adapted to handle high noise levels while keeping their efficiency. Then, we show that a fast non-blind deconvolution method can be significantly improved by first denoising the blurry image. The proposed approach yields results that are equivalent to those obtained with much more computationally demanding methods.
Multi-robot path planning based on improved artificial potential field and B-spline curve optimization

This paper mainly studies the path planning algorithm for multi-robot. It introduces a gain constraint and a random factor to improve the artificial potential field method, which respectively suppresses the path oscillation and avoid the local minimum. Based on the improved artificial potential field method, the B-spline curve optimization is used to smooth the planned path. The simulation results show that the algorithm improvement made in this paper is effective in solving the multi-robot path planning problem.
ISAR Resolution Enhancement Using Residual Network

Following the breakthroughs in natural image super-resolution task using faster and deeper neural networks, a method for inverse synthetic aperture radar (ISAR) resolution enhancement using residual network is proposed in this paper. Our method learns an end-to-end mapping between low/high-resolution ISAR images. The mapping is represented as a residual network that takes the low-resolution ISAR image as its input and outputs the high-resolution one. In our residual network, residual blocks without batch normalization (BN) layers are applied to retain the range of magnitude for ISAR images, parametric rectified linear unit (PReLU) is used as an activation function to adaptively learn negative coefficient. In addition, we propose a new evaluation index for ISAR resolution enhancement performance called IMV replacing 3dB bandwidth. Simulation and experimental results indicate that this method can achieve higher quality high-resolution ISAR image than traditional sparsity-driven method, which can improve the estimation accuracy of scattering points and recover weak scattering points.
Adversarial approach to domain adaptation for reinforcement learning on dialog systems

Domain adaptation using source domain data is preferable in the development of Deep Q Network (DQN) based dialog systems, because of the training cost for additional target domains. However, the inherent domain shift hinders feature-space level generalization, which degrades performance. We introduce Adversarial Calibrator based Transfer learning (ACT) to aid the coherent training of the target domain feature extractor network. In ACT, the feature extractor of the target domain is considered as a generator, which is trained to be matched with a pre-stored latent feature set of the source domain with adversarial training losses. We verified ACT with the PyDial framework, conducting domain adaptation experiment with the following domains: San Francisco Restaurant (SFR), Cambridge Restaurant (CR), and Laptop11 (LAP). The result demonstrates that ACT outperforms β-VAE based adaptation between the pair of distinct domains (SFR-LAP), and even outperforms individual learning between the pair of similar domains (SFR-CR).
An efficient queries processing model based on Multi Broadcast Searchable Keywords Encryption (MBSKE)

Cloud computing is a technology which has enabled many organizations to outsource their data in an encrypted form to improve processing times. The public Internet was not initially designed to handle massive quantities of data flowing through millions of networks. Thus, the rapid increase in broadcast users and growth in the amount of broadcasted information leads to a decrease in the speed of sending queries and receiving encrypted data from the cloud. In order to address this issue, Next Generation Internet (NGI) is being developed, capable of high speeds, while maintaining data privacy. This research proposes a novel search algorithm, entitled Multi-broadcast Searchable Keywords Encryption, which processes queries through a set of keywords. This set of keywords is sent from the users to the cloud server in an encrypted form, thus hiding all information about the user and the content of the queries from the cloud server. The proposed method uses a caching algorithm and provides an improvement of 40% in terms of runtime and trapdoor. In addition, the method minimizes computational costs, complexity, and maximizes throughput, in the cloud environment, whilst maintaining privacy and confidentiality of both the user and the cloud. The cloud returns encrypted query results to the user, where data is decrypted using the user's private keys.
Reinforcement Learning for Personalized Dialogue Management

Language systems have been of great interest to the research community and have recently reached the mass market through various assistant platforms on the web. Reinforcement Learning methods that optimize dialogue policies have seen successes in past years and have recently been extended into methods that personalize the dialogue, e.g. take the personal context of users into account. These works, however, are limited to personalization to a single user with whom they require multiple interactions and do not generalize the usage of context across users. This work introduces a problem where a generalized usage of context is relevant and proposes two Reinforcement Learning (RL)-based approaches to this problem. The first approach uses a single learner and extends the traditional POMDP formulation of dialogue state with features that describe the user context. The second approach segments users by context and then employs a learner per context. We compare these approaches in a benchmark of existing non-RL and RL-based methods in three established and one novel application domain of financial product recommendation. We compare the influence of context and training experiences on performance and find that learning approaches generally outperform a handcrafted gold standard.
Framework for a Single Global Repository of Child Abuse Materials

Abstract Against the tide of a huge amount of data, law enforcement agencies barely manage to focus on undiscovered child abuse materials ( CAM s) to identify victims, abusers, and crime scenes. Currently, to alleviate this burden to some extent, there are international and national repositories of CAM s in place, such as Interpol's International Child Sexual Exploitation Database ( ICSE DB ), NCMEC 's Child Recognition and Identification System ( CRIS ), and the UK 's Child Abuse Image Database ( CAID ). This article introduced these global, regional, and national repositories and other related transnational initiatives comprehensively and also critiques the current trend of setting up more repositories and additional initiatives at local and regional levels. Later, the author elaborates on a plan for a single global repository governed by an international organization. Despite a significant decrease in the costs for a country to be actively involved in the fight against online child sexual abuse and emergence of new possibilities for more effective prevention strategies and investigational methods, such an idea of global centralization might face insurmountable opposition from current stakeholders, particularly because of organizational concerns and technological challenges.
Path planning of the dual-arm robot based on VT-RRT algorithm

This paper presents a variable step size trunk rapidly-exploring random trees (VT-RRT) algorithm for path planning of a dual-arm robot. First, we establish the DH parameter simulation model of the dual-arm robot which is composed of two UR5 single-arm robots. Then, in the working space of the dual-arm robot, several spheres are randomly added to constitute an obstacle environment. Finally, we propose a new improved algorithm by transforming the search space of random nodes in RRT algorithm and adaptively adjusting the step size according to the target position. The simulation results show that the VT-RRT algorithm effectively increases the search efficiency, decreases the iteration step size, and reduces the path planning time in comparison to the basic rapidly-exploring random trees (RRT) algorithm. At the same time, the robustness is also greatly increased.
Social open innovation platform design for science teaching by using pythagorean fuzzy analytic hierarchy process

The education system is very important for the society. In this study, we try to define and prioritize the requirements of an n online platform which can be used as a source for collecting feedback from stakeholders of an educating system. The involved stakeholders are the course content generators , teachers, students and families of the students. In the application three experts used linguistic terms to evaluate the performance indicators and the weights are calculated using Pythagorean Fuzzy Analytic Hierarch Process (AHP) Method.
Fully Accountable Data Sharing for Pay-as-You-Go Cloud Scenes

Many enterprises and individuals prefer to outsource data to public cloud via various pricing approaches. One of the most widely-used approaches is the pay-as-you-go model, where the data owner hires public cloud to share data with data consumers, and only pays for the actually consumed services. To realize controllable and secure data sharing, ciphertext-policy attribute-based encryption (CP-ABE) is a suitable solution, which can provide fine-grained access control and encryption functionalities simultaneously. But there are some serious challenges when applying CP-ABE in pay-as-you-go. First, the decryption cost in ABE is too heavy for data consumers. Second, ABE ciphertexts probably suffer distributed denial of services (DDoS) attacks, but there is no solution that can eliminate the security risk. At last, the data owner should audit resource consumption to guarantee the transparency of charge, while the existing method is inefficient. In this work, we propose a general construction named fully accountable ABE (FA-ABE), which simultaneously solves all the challenges by supporting all-sided accountability in the pay-as-you-go model. We formally define the security model and prove the security in the standard model. Also, we implement an instantiate construction with the self-developed library <inline-formula><tex-math notation="LaTeX">$\mathsf{ libabe}$</tex-math></inline-formula> . The experiment results indicate the efficiency and practicality of our construction.
Speaker-Independent Speech-Driven Visual Speech Synthesis using Domain-Adapted Acoustic Models

Speech-driven visual speech synthesis involves mapping acoustic speech features to the corresponding lip animation controls for a face model. This mapping can take many forms, but a powerful approach is to use deep neural networks (DNNs). The lack of synchronized audio, video, and depth data is a limitation to reliably train DNNs, especially for speaker-independent models. In this paper, we investigate adapting an automatic speech recognition (ASR) acoustic model (AM) for the visual speech synthesis problem. We train the ASR-AM on ten thousand hours of audio-only transcribed speech. The ASR-AM is then adapted to the visual speech synthesis domain using ninety hours of synchronized audio-visual speech. Using a subjective assessment test, we compared the performance of the AM-initialized DNN to a randomly initialized model. The results show that viewers significantly prefer animations generated from the AM-initialized DNN than the ones generated using the randomly initialized model. We conclude that visual speech synthesis can significantly benefit from the powerful representation of speech in the ASR acoustic models.
Electromagnetic Watermarking: exploiting IEMI effects for forensic tracking of UAVs

We introduce for the first time the concept of electromagnetic watermarking and investigate its practical application in the scope of forensic tracking of unmanned aerial vehicles. As they spread out in civilian contexts, they pose new threats for critical infrastructures. Neutralization techniques are designed, but the collateral damages are often not acceptable. Instead, a watermarking system which inserts information into the target in order to enable the verification of its presence at the incident can be an interesting solution. Electromagnetic watermarking exploits the effects of intentional electromagnetic interference to embed a watermark remotely into non cooperating targets.
Enhanced Fiducial Marker Based Precise Landing for Quadrotors

Fiducial markers are landmarks typically used for pose estimation. However, the detection rate of existing fiducial marker based approaches is relatively low. To speed up the marker detection without sacrificing accuracy or robustness, this paper proposes a novel enhanced fiducial marker detection-tracking algorithm wherein the ROI (region of interest) based kernelized correlation filter (KCF) tracking strategy and the existing Apriltag2 algorithm are integrated together to form a new detection-tracking framework. More specifically, by selecting the appropriate ROI aera from the fiducial marker, the KCF tracking algorithm is employed to track the ROI area and the AprilTag2 algorithm is adopted to detect the selected area to obtain the pose estimation. Accordingly, the relative pose and the IMU measurements are fused in an Extended Kalman Filter (EKF) to obtain the full state estimation. On this basis, a nonlinear cascade P-PID controller is introduced to achieve the precise landing task. Comparative experiments are conducted to show that the detection rate of the proposed approach is up to 3-7 times faster than other methods without any parallel computation technique. In addition, the accuracy of the real-time landing is less than 5cm.
Hybrid System for Information Extraction from Social Media Text: Drug Abuse Case Study

Social media are becoming widely used in the healthcare field as a patients-caregivers communication tool giving birth to new sources of information rich with the knowledge that may improve this field. Therefore, social media data analysis becomes a real business requirement for healthcare industrials and data scientists. However, regarding their complexity and unstructured character, existing natural language processing tools cannot succeed their exploitation. In the literature, a wide range of approaches appeared based on dictionaries, linguistic patterns and machine learning having their strengths and weaknesses. In this work, we propose a hybrid system combining the above approaches by taking the advantage of each of them to extract structured and salient drug abuse information from health-related tweets. We improve the system accuracy by real time update of the domain dictionary. We collected 1000000 tweets and we conducted different experiments showing the advantage of hybridization on efficient information extraction from social media data.
Redirected Jumping

Redirected walking (RDW) techniques provide a way to explore a virtual space that is larger than the available physical space by imperceptibly manipulating the virtual world view or motions. These manipulations may introduce conflicts between real and virtual cues (e.g., visual-vestibular conflicts), which can be disturbing when detectable by users. The empirically established detection thresholds of rotation manipulation for RDW still require a large physical tracking space and are therefore impractical for general-purpose Virtual Reality (VR) applications. We investigate Redirected Jumping (RDJ) as a new locomotion metaphor for redirection to partially address this limitation, and because jumping is a common interaction for environments like games. We investigated the detection rates for different curvature gains during RDJ. The probability of users detecting RDJ appears substantially lower than that of RDW, meaning designers can get away with greater manipulations with RDJ than with RDW. We postulate that the substantial vertical (up/down) movement present when jumping introduces increased vestibular noise compared to normal walking, thereby supporting greater rotational manipulations. Our study suggests that the potential combination of metaphors (e.g., walking and jumping) could further reduce the required physical space for locomotion in VR. We also summarize some differences in user jumping approaches and provide motion sickness measures in our study.
CNN Model for Screen Content Image Quality Assessment Based on Region Difference

Deep learning methods for image quality assessment (IQA) is becoming more and more popular, and a variety of convolutional neural network(CNN) models for natural image quality assessment (NI-QA) have been proposed. Screen content image quality assessment (SCI-QA) is a relatively new topic compared to natural image quality assessments. Screen content image is different from natural images. It usually consists of pictorial regions and textual regions, which results in different visual perceptions of different regions in a SCI. In the CNN models for IQA, they usually take image patches as input of the network. Therefore, we propose a method to segment the distorted image into image patches and then distinguish them into pictorial patches and textual patches. Due to the different characteristics of those two regions, the quality scores of pictorial and textual regions are separately predicted by the CNN model, and then are fused to indicate the whole quality degree of distorted SCIs. The scores are fused according to different weights that are calculated by the local activity degree of SCIs. The experimental results in the SIQAD database validate the effectiveness of the proposed CNN-based method compared to several advanced IQA methods.