Reviewerly/exHarmony huggingface dataset:
Rescaling-Assisted Super-Resolution for Medium-Low Resolution Remote Sensing Ship Detection

Medium-low resolution (M-LR) remote sensing ship detection is a challenging problem due to the small target sizes and insufficient appearance information. Although image super resolution (SR) has become a popular solution in recent years, the ability of image SR is limited since much information is lost in input images. Inspired by the powerful information embedding ability of the encoder in image rescaling, in this paper, we introduce image rescaling to guide the training of image SR. Specifically, we add an adaption module before the SR network, and use the pre-trained rescaling network to guide the optimization of the adaption module. In this way, more information is embedded in the adapted M-LR images, and the subsequent SR module can utilize more information to achieve better performance. Extensive experimental results demonstrate the effectiveness of our method on image SR. More importantly, our method can be used as a pre-processing approach to improve the detection performance.
Simple way to incorporate loss when modeling multimode-entangled-state generation

We show that the light generated via spontaneous four-wave mixing or parametric down conversion in multiple, coupled, lossy cavities is a multimode squeezed thermal state. Requiring this state to be the solution of the Lindblad master equation results in a set of coupled first-order differential equations for the time-dependent squeezing parameters and thermal photon numbers of the state. The benefit of this semianalytic approach is that the number of coupled equations scales linearly with the number of modes and is independent of the number of photons generated. With this analytic form of the state, correlation variances are easily expressed as analytic functions of the time-dependent mode parameters. Thus, our solution makes it computationally tractable and relatively straightforward to calculate the generation and evolution of multimode entangled states in multiple coupled, lossy cavities, even when there are a large number of modes and/or photons.
Method for evaluating plan recovery strategies in dynamic multi-agent environments

Plan execution in dynamic environments can be affected by unexpected events leading to failures. Research on multi-agent planning area presents recovery strategies with replanning and repairing with evaluation based simply on average values. Thus, in this work, we propose a statistical method to evaluate plan recovery strategies in dynamic environments using a domain-independent approach. To validate the proposed method, we conducted simulated experiments with varying the number of agents, goals, actions, failure probability, and agents’ coupling levels. The evaluation metrics include plan length and planning time. The results highlight with at least 94% certainty that repairing planning time is lower than replanning, and replanning builds plans with fewer actions than repairing. Considering plan recovery strategies in dynamic multi-agent environments, we demonstrate that repairing presents better results as it is faster, but replanning builds better plans as the final plan length is strongly correlated to failure occurrence.
Research on Intelligent Retrieval Method of Teaching Resources on Large-Scale Network Platform

With the increase in information on various cloud computing platforms, there are more and more teaching documents and videos, which provide sufficient resources for people to learn. Facing the large-scale digital teaching resources, how to quickly and accurately retrieve the required content has become an important research direction in the information field. Especially in the face of heterogeneous, dynamic, and large-scale teaching resources stored in the cloud computing platform, the traditional cloud computing resource retrieval has poor performance and low work efficiency. To solve this problem, a cloud computing platform retrieval method based on genetic algorithm is proposed, which is suitable for intelligent retrieval of teaching resources. Firstly, the teaching resource storage system based on cloud computing platform is analyzed, and the overall architecture of the system and the network topology of cloud storage data are given. Then, a resource retrieval method suitable for cloud computing platform is designed by genetic algorithm, and the convergence performance of genetic algorithm is improved by ant colony algorithm. Finally, the selection algorithm in genetic algorithm is optimized by using random numbers and increasing the number of cycles. The experimental results show that the proposed intelligent retrieval method has greatly improved the Recall and Precision compared with the traditional retrieval methods.
Efficient Parallel Thinning of 3d Objects on the Body-centered Cubic Lattice

We consider thinning methods to extract one dimensional skeletons from discrete objects defined on the body-centered cubic (bcc) lattice. In Strand (2004), a condition has been given that guarantees the preservation of the object’s topology in such a thinning process. In this paper, we present stronger conditions that even allow the topological invariant point removal in a parallelized process. These conditions for p -simplicity can be efficiently evaluated which leads to a very fast thinning process. We show that p -simplicity is a new concept that cannot be obtained by adapting the checking plane conditions of Tsao and Fu to the bcc lattice. Furthermore, we introduce distance information and an optional pruning mechanism into the thinning process to improve the quality of the resulting skeletons. The presented results show that our method generates high quality skeletons that reproduce the symmetries of the models even under the condition of added noise and contain only very few spurious branches. The presented running times demonstrate the linear run-time behavior of our algorithm and the speedup that is achieved by the parallelization. • Curve-like skeletons of closed 3D objects are used for many different purposes in computer graphics. • Thinning is a method to extract the skeleton which requires the object to be discretized on a lattice. • Only for thinning on the cubic grid parallel algorithms are available. • We present the first parallel thinning algorithm on the bcc lattice. • Our method generates high quality skeletons that contain only very few spurious branches. • The skeletons reproduce the symmetries of the models even under the condition of added noise.
A hybrid Genetic–Grey Wolf Optimization algorithm for optimizing Takagi–Sugeno–Kang fuzzy systems

Abstract Nature-inspired optimization techniques have been applied in various fields of study to solve optimization problems. Since designing a Fuzzy System (FS) can be considered one of the most complex optimization problems, many meta-heuristic optimizations have been developed to design FS structures. This paper aims to design a Takagi–Sugeno–Kang fuzzy Systems (TSK-FS) structure by generating the required fuzzy rules and selecting the most influential parameters for these rules. In this context, a new hybrid nature-inspired algorithm is proposed, namely Genetic–Grey Wolf Optimization (GGWO) algorithm, to optimize TSK-FSs. In GGWO, a hybridization of the genetic algorithm (GA) and the grey wolf optimizer (GWO) is applied to overcome the premature convergence and poor solution exploitation of the standard GWO. Using genetic crossover and mutation operators accelerates the exploration process and efficiently reaches the best solution (rule generation) within a reasonable time. The proposed GGWO is tested on several benchmark functions compared with other nature-inspired optimization algorithms. The result of simulations applied to the fuzzy control of nonlinear plants shows the superiority of GGWO in designing TSK-FSs with high accuracy compared with different optimization algorithms in terms of Root Mean Squared Error (RMSE) and computational time.
MC-GEN: Multi-level clustering for private synthetic data generation

With the development of machine learning and data science, data sharing is very common between companies and research institutes to avoid data scarcity. However, sharing original datasets that contain private information can cause privacy leakage. A reliable solution is to utilize private synthetic datasets which preserve statistical information from original datasets. In this paper, we propose MC-GEN, a privacy-preserving synthetic data generation method under differential privacy guarantee for machine learning classification tasks. MC-GEN applies multi-level clustering and differential private generative model to improve the utility of synthetic data. In the experimental evaluation, we evaluated the effects of parameters and the effectiveness of MC-GEN. The results showed that MC-GEN can achieve significant effectiveness under certain privacy guarantees on multiple classification tasks. Moreover, we compare MC-GEN with three existing methods. The results showed that MC-GEN outperforms other methods in terms of utility.
AMDCNet: An attentional multi-directional convolutional network for stereo matching

Stereo matching refers to finding the correspondence of a point in the real world between two different storage mediums (e.g., intensity images, depth images, three-dimensional points). There are existing stereo matching methods in the literature, but they exhibit two shortcomings. Firstly, during the feature region extraction of stereo matching, these methods require measuring the distance of regions, but measuring the texture distribution of the region is difficult and might lead to the failure of matching. Secondly, the templates used in these methods are rectangles with a fixed size, while most of the natural images exhibit rich information and are more suitable for flexible templates. In this paper, we propose an attentional multi-directional convolutional network (AMDCNet) for circumventing these issues. Our AMDCNet approach consists of three stages: extract the visual sensitivity factor, construct the multi-directional aggregation template and utilize left–right consistency detection to optimize. We evaluate our approach using standard images in the Middlebury test dataset, Scene Flow and KITTI 2015. Experimental results show that AMDCNet can reduce the mismatch rate, and also show significant improvement in accuracy compared with some classical method. In some scenarios, it surpasses some advanced methods based on deep learning. The model code, dataset, and results of the experiments in this paper are available at: https://github.com/WangHewei16/Attentional-Multi-Directional-Convolution-Network.
Belief Revision and Computational Argumentation: A Critical Comparison

This paper aims at comparing and relating belief revision and argumentation as approaches to model reasoning processes. Referring to some prominent literature references in both fields, we will discuss their (implicit or explicit) assumptions on the modeled processes and hence commonalities and differences in the forms of reasoning they are suitable to deal with. The intended contribution is on one hand assessing the (not fully explored yet) relationships between two lively research fields in the broad area of defeasible reasoning and on the other hand pointing out open issues and potential directions for future research.
High-Precision Voltage Measurement for Optical Quantum Computation

This paper presents a theoretical study into the use of optical systems for quantum computation. The study results pertain to quantum sampling and quantum communication and provide a basis for further research and the development of a physical implementation. We propose an optical superstructure that can implement specific computation processes and algorithms. The superstructure is composed of nonlinear optical units, such as beta barium borate crystals. The units are positioned in series, powered by a pulse laser pump, and culminate in a beam splitter that generates the output state of a number of entangled photon pairs. Computation is achieved by entanglement propagation via beam splitters and adjustable phase shifters, which set related parameters. Demonstrating a two-component case, we show how a series of cosine-based components can be implemented. The obtained results open a broad front for future research. Future work should investigate the construction of a quantum optimizer using quantum sampling methods and also investigate high-precision temporal voltage measurement, which is a key procedure for the construction of high-fidelity devices.
Quantum statistics engineering in a hybrid nanoparticle-emitter-cavity system

Quantum engineering of photons is a key requirement for the development of quantum technology. Here, we put forward a scheme to investigate extensively quantum statistical characteristics of the photons and their engineering in a hybrid nanoparticle-emitter-cavity architecture through placing a nanoparticle (NP) and a quantum emitter (QE), called NP-QE molecule, into an optical cavity, where the coherent tripartite coupling emerges in the interactions among the NP, QE, and cavity. The photons are collected from the two different channels, i.e., the cavity emission and the NP-QE molecule scattering. Due to the composite nature of the tripartite dressed excitations of our fully coupled system and the destructive interference between different transition pathways for the two-photon excitation, the nontrivial quantum statistical properties, such as unconventional single-photon blockade and strong photon superbunching, can be achieved even within the bad cavity limit (without the need of the strong-coupling condition) when the NP-QE molecule is driven by an external laser not interacting the cavity mode. It is also revealed that the photon autocorrelation and cross-correlation properties are well modified by regulating the size-dependent system parameters, such as the NP-to-QE distance and the NP radius. The small-coupling strength required, the ease of parameter tuning, the relaxation of high-cavity quality factor, and the robustness to the dephasing rate in realistic scenarios all benefit the generation of single-photon sources, which has potential applications in quantum information and quantum communication.
Design and Application of Cloud Resource-Based Ideological and Political Online Course Resource Platform

Online teaching platforms have been popularized and promoted as a result of the development of network information technology, and colleges and universities encourage teachers to use a variety of network teaching platforms to innovate teaching models and improve teaching effectiveness. Using an ideological and political online course as an example, it analyzes the teaching design concepts, instructional effects, and existing problems on the online learning platform, and extracts recommendations for online course construction that have a specific reference for online course teaching. Additionally, aiming at the multiobjective cloud resource scheduling problem, this article aims to optimize the total completion time and total execution cost of the task. It does so by utilizing fuzzy mathematics, establishing a fuzzy cloud resource scheduling model, and proposing a hybrid intelligent optimization algorithm CO. The CO algorithm is validated by randomly generating cloud-computing resource scheduling data using the CloudSim simulation platform. The experimental results indicate that the CO algorithm outperforms traditional cloud resource scheduling algorithms in terms of optimization and load balancing performance.
Overflowing emerging neural network inference tasks from the GPU to the CPU on heterogeneous servers

While current deep learning (DL) inference runtime systems sequentially offload the model's tasks on to an available GPU/accelerator based on its capability, we make a case for selectively redirecting some of these tasks to the CPU and running them concurrently with the GPU doing other work. This new opportunity specifically arises for emerging DL models whose data flow graphs (DFGs) have much wider fan-outs compared to traditional ones which are invariably linear chains of tasks. By opportunistically moving some of these tasks to the CPU, we can (i) shave off service times from the critical path of the DFG, (ii) devote the GPU for more deserving tasks, and (iii) improve overall utilization of the provisioned hardware in the server. However, several factors such as its criticality in the DFG, slowdown when moved to a different hardware engine, and overheads in transferring input/output data across these engines, determine the what/when/how of tasks to be directed. While this is computationally demanding and slow to be solved optimally, through a series of rationales we derive a fast technique for task overflow from GPU to CPU. We implement this technique on a nimble heterogeneous concurrent runtime engine built on top of the state-of-the-art ONNXRuntime engine and demonstrate > 10% reduction in latency, > 19% gain in throughput, and > 9.8% savings in GPU memory usage for emerging neural network models.
Application of Deep Convolution Network Algorithm in Sports Video Hot Spot Detection

Sports videos are blowing up over the internet with enriching material life and the higher pursuit of spiritual life of people. Thus, automatically identifying and detecting helpful information from videos have arisen as a relatively novel research direction. Accordingly, the present work proposes a Human Pose Estimation (HPE) model to automatically classify sports videos and detect hot spots in videos to solve the deficiency of traditional algorithms. Firstly, Deep Learning (DL) is introduced. Then, amounts of human motion features are extracted by the Region Proposal Network (RPN). Next, an HPE model is implemented based on Deep Convolutional Neural Network (DCNN). Finally, the HPE model is applied to motion recognition and video classification in sports videos. The research findings corroborate that an effective and accurate HPE model can be implemented using the DCNN to recognize and classify videos effectively. Meanwhile, Big Data Technology (BDT) is applied to count the playing amounts of various sports videos. It is convinced that the HPE model based on DCNN can effectively and accurately classify the sports videos and then provide a basis for the following statistics of various sports videos by BDT. Finally, a new outlook is proposed to apply new technology in the entertainment industry.
Implications and interrelations of litho-boundaries and vicinity of lineaments for hydrothermal alteration zones under remote sensing and GIS environment

The applicability and significance of remote sensing images varies according to capability of synoptic view. These synoptic views of satellite images have tremendous utility to solve many geological enigmas due to constraints of limitations of investigations (manual/ mapping error) in the field of geology. To prepare more accurate remote locations, structural maps such as lineament, fold, faults, shear zone, geomorphology, lithology and mineralogy of remote sensing image helps to solve these problems. In the research work of the hydrothermal alteration minerals zoning and identification completed through spectroscopic and advanced visible-infrared imaging spectroscopy new generation (AVIRIS-NG) hyperspectral remote sensing data. To establish intercorrelation and implications of lineaments with hydrothermal alteration zone of the study area prepared lineament map through manual and auto extraction using Landsat 7 ETM+ and ASTER digital elevation model (DEM) remote sensing data with the applications of algorithms such as Sobels filtering and "LINE" tool of PCI Geomatica. The networks of lineaments have a significant role in demarcation of potential mineral zones. To get vicinity of lineaments, prepared a lineament density map through extracted lineaments. In the final output the vicinity of lineaments density, mineral sample locations, mineral map and regolith zones have shown strong correlation among each other. So, the conclusion is that there is a strong significance in intercorrelation of hydrothermal alteration zones, lineaments and litho-boundaries.
A penalty function semi-continuous thresholding methods for constraints of hashing problems

The Hashing process is an effective tool for handling large-scale data (for example, images, videos, or multi-model data) retrieval problems. To get better retrieval accuracy, hashing models usually are imposed with three rigorous constraints, i.e., discrete binary constraint, uncorrelated condition, and the balanced constraint, which will lead to being ‘NP-hard’. In this study, we divide the whole constraints set into the uncorrelated (orthogonality) constraint and the binary discrete balance constraint and propose a fast and accurate penalty function semi-continuous thresholding (PFSCT) hash coding algorithm based on forward–backward algorithms. In addition, we theoretically analyze the equivalence between the relaxed model and the original problems. Extensive numerical experiments on diverse large-scale benchmark datasets demonstrate comparable performance and effectiveness of the proposed method. • A fast and accurate penalty function semi-continuous thresholding (PFSCT) hash coding algorithm based on forward–backward algorithms is established in the work, by dividing the whole constraints set into the uncorrelated (orthogonality) constraint and the binary discrete balance constraint. • We theoretically analyze the equivalence between the relaxed model and the original problems. • Extensive numerical experiments on diverse large-scale benchmark datasets, including Cifar10, NUS-WIDE and SUN397, demonstrate comparable performance and effectiveness of the proposed method.
Fast Gradient Scaled Method for Generating Adversarial Examples

Though deep neural networks have achieved great success on many challenging tasks, they are demonstrated to be vulnerable to adversarial examples, which fool neural networks by adding human-imperceptible perturbations to the clean examples. As the first generation attack for generating adversarial examples, FGSM has inspired many follow-up attacks. However, the adversarial perturbations generated by FGSM are usually human-perceptible because FGSM modifies the pixels by the same amplitude through computing the sign of the gradients of the loss. To this end, we propose the fast gradient scaled method (FGScaledM), which scales the gradients of the loss to the valid range and can make adversarial perturbation to be more human-imperceptible. Extensive experiments on MNIST and CIFAR-10 datasets show that while maintaining similar attack success rates, our proposed FGScaledM can generate more fine-grained and more human-imperceptible adversarial perturbations than FGSM.
Storywork &amp; Reciprocity: On the Design of an Audio Documentary that Extends HCI Research back to Participants

Participatory design means building reciprocal relationships with research participants and opening up conversations with larger communities. We describe and reflect on our design-led research of creating an hour-long audio documentary. Participants' desires to better understand the experiences of others within our study is our key motivation. We see the creation of the documentary as an important reciprocal step to invite further participation within a longer-term, multi-year project. Rooted in a decolonial perspective on translating academic knowledge to the general public, our work builds on our own prior fieldwork with 9 people with blindness on their reminiscence experiences. The audio documentary aims to deliver insights from research findings and inquiries, inspired by participants' stories through their voices in the interview recordings. The documentary serves as a gift for participation and a genuine invitation for future research. We conclude with opportunities for future HCI research and practice.