Reviewerly/exHarmony huggingface dataset:
Decomposition Makes Better Rain Removal: An Improved Attention-Guided Deraining Network

Rain streaks in the air show diverse characteristics with different shapes, directions, densities, even the complex overlapped phenomenon, causing great challenges for the deraining task. Recently, deep learning based image deraining methods have been extensively investigated due to their excellent performance. However, most of the existing algorithms still have limitations in removing rain streaks while preserving rich textural details under complicated rain conditions. To this end, we propose to decompose rain streaks into multiple rain layers and individually estimate each of them along the network stages to cope with the increasing abstracts. To better characterize rain layers, an improved non-local block is designed to exploit the self-similarity of rain information by learning the holistic spatial feature correlations while reducing the calculation complexity. Moreover, a mixed attention mechanism is applied to guide the fusion of rain layers by focusing on the local and global overlaps among these rain layers. Extensive experiments on both synthetic rainy/rain-haze/raindrop datasets, real-world samples, the haze, and low-light scenarios show substantial improvements both on quantitative indicators and visual effects over the current state-of-the-art technologies. The source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/kuihua/IADN</uri> .
Neural Design Network: Graphic Layout Generation with Constraints

Graphic design is essential for visual communication with layouts being fundamental to composing attractive designs. Layout generation differs from pixel-level image synthesis and is unique in terms of the requirement of mutual relations among the desired components. We propose a method for design layout generation that can satisfy user-specified constraints. The proposed neural design network (NDN) consists of three modules. The first module predicts a graph with complete relations from a graph with user-specified relations. The second module generates a layout from the predicted graph. Finally, the third module fine-tunes the predicted layout. Quantitative and qualitative experiments demonstrate that the generated layouts are visually similar to real design layouts. We also construct real designs based on predicted layouts for a better understanding of the visual quality. Finally, we demonstrate a practical application on layout recommendation.
Lightweight blockchain framework for location-aware peer-to-peer energy trading

Peer-to-peer (P2P) energy trading, as a new approach for energy management in smart grids, facilitates integration of a large number of small-scale producers and consumers into energy markets. Decentralized management of these new market participants is challenging in terms of market settlement, participant reputation and consideration of grid constraints. This paper proposes a blockchain-enabled framework for P2P energy trading among producer and consumer agents in a smart grid. A fully decentralized market settlement mechanism is designed, which does not rely on a centralized entity to settle the market and encourages producers and consumers to negotiate on energy trading with their nearby agents truthfully. To this end, the electrical distance of agents is considered in the pricing mechanism to encourage agents to trade with their neighboring agents. In addition, a reputation factor is considered for each agent, reflecting its past performance in delivering the committed energy. Before starting the negotiation, agents select their trading partners based on their preferences over the reputation and proximity of the trading partners. An Anonymous Proof of Location (A-PoL) algorithm is proposed that allows agents to prove their location without revealing their real identity. The practicality of the proposed framework is illustrated through several case studies, and its security and privacy are analyzed in detail.
$O(N^3)$ Measurement Cost for Variational Quantum Eigensolver on Molecular Hamiltonians

Variational quantum eigensolver (VQE) is a promising algorithm for near-term quantum machines.It can be used to estimate the ground state energy of a molecule by performing separate measurements of O(N 4 ) terms.This quartic scaling appears to be a significant obstacle to practical applications.However, we note that it empirically reduces to O(N 3 ) when we partition the terms into linear-sized commuting families that can be measured simultaneously.We confirm these empirical observations by studying the MIN-COMMUTING-PARTITION problem at the level of the fermionic Hamiltonian and its encoding into qubits.Moreover, we provide a fast, precomputable procedure for creating linearly sized commuting partitions by solving a round-robin scheduling problem via flow networks.In addition, we demonstrate how to construct the quantum circuits necessary for simultaneous measurement, and we discuss the statistical implication of simultaneous measurement.Our results are experimentally validated by a ground state estimation of deuteron with low shot budget on a 20-qubit IBM machine.
Multi‐view frontal face image generation: A survey

Abstract Face images from different perspectives reduce the accuracy of face recognition, and the generation of frontal face images is an important research topic in the field of face recognition. To understand the development of frontal face generation models and grasp the current research hotspots and trends, existing methods based on 3D models, deep learning, and hybrid models are summarized, and the current commonly used face generation methods are introduced. Dataset, and compare the performance of existing models through experiments. The purpose of this paper is to fundamentally understand the advantages of existing frontal face generation, sort out the key issues of such generation, and look toward future development trends.
Memetic Harris Hawks Optimization: Developments and perspectives on project scheduling and QoS-aware web service composition

Harris hawks optimization (HHO) is one of the leading optimization approaches due to its efficacy and multi-choice structure with time-varying components. The HHO has been applied in various areas due to its simplicity and outstanding performance. However,the original HHO can be improved and evolved in terms of convergence trends, and it is prone to local optimization under certain circumstances. Therefore, the performance and robustness of the algorithm need to be further improved. In our research, based on the core principle of evolutionary methods, we first developed an elite evolutionary strategy (EES) and then utilized it to advance HHO’s convergence speed and ability to jump out of the local optimum. We named such an enhanced hybrid algorithm EESHHO in this paper. To verify the effectiveness and robustness of the EESHHO, we tested it on 29 numerical optimization test functions, including 23 classic basic test functions and 6 composite test functions from the IEEE CEC2017 special session. Moreover, we apply the EESHHO on resource-constrained project scheduling and QoS-aware web service composition problems to further validate the effectiveness of EESHHO. The experimental results show that proposed EESHHO has faster convergence speed and better optimization performance by comparing it with other mainstream algorithms. The supplementary info and answers to possible queries will be publicly available at https://aliasgharheidari.com/publications/EESHHO.html. Also, the codes and info of HHO are available at: https://aliasgharheidari.com/HHO.html.
Path Planning Using an Improved A-star Algorithm

There has been a heated issue in the field of robotics about how to plan the shortest path of a map with obstacles. This papers mainly analysis the issues about the path planned are not the shortest path by the A-star algorithm under certain conditions. An effective method is proposed which is called an improved A-star algorithm. The improved A-star algorithm is proposed to solve path planning under certain conditions, which can find a shorter path in contrast of other related methods. The simulation results show that the proposed algorithms are effective to settle above the issue, in which the path generated by the improved A-star algorithm has a shorter path than the path of other improved A-star algorithms.
I Can't Breathe

In this paper, three Black women in HCI and CSCW share their experiences of being Black women academics enduring a global pandemic that is disportionately impacting the Black community while simultaneously experiencing the civil unrest due to racial injustice and police brutality. Using Black feminist epistemologies as a theoretical framework and auto-ethnography and testimonial authority as both methodology and epistemic resistance, the authors exercise epistemic agency to testify to their lived intersectional experiences and the various fronts on which they fight to be seen, to be heard, and to live. Additionally, they advocate for more inclusionary policies of Black women and other marginalized populations within the CSCW and HCI communities. We conclude with a call to action for both communities to: 1) stand in solidarity with Blacks in computing; and 2) acknowledge, disavow, and dismantle Whiteness and oppressive power structures in the field of computing, specifically HCI and CSCW.
Cascaded Parsing of Human-Object Interaction Recognition

This paper addresses the task of detecting and recognizing human-object interactions (HOI) in images. Considering the intrinsic complexity and structural nature of the task, we introduce a cascaded parsing network (CP-HOI) for a multi-stage, structured HOI understanding. At each cascade stage, an instance detection module progressively refines HOI proposals and feeds them into a structured interaction reasoning module. Each of the two modules is also connected to its predecessor in the previous stage, enabling efficient cross-stage information propagation. The structured interaction reasoning module is built upon a graph parsing neural network (GPNN), which efficiently models potential HOI structures as graphs and mines rich context for comprehensive relation understanding. In particular, GPNN infers a parse graph that i) interprets meaningful HOI structures by a learnable adjacency matrix, and ii) predicts action (edge) labels. Within an end-to-end, message-passing framework, GPNN blends learning and inference, iteratively parsing HOI structures and reasoning HOI representations (i.e., instance and relation features). Further beyond relation detection at a bounding-box level, we make our framework flexible to perform fine-grained pixel-wise relation segmentation; this provides a new glimpse into better relation modeling. A preliminary version of our CP-HOI model reached 1st place in the ICCV2019 Person in Context Challenge, on both relation detection and segmentation. In addition, our CP-HOI shows promising results on two popular HOI recognition benchmarks, i.e., V-COCO and HICO-DET.
Generative Partial Multi-View Clustering With Adaptive Fusion and Cycle Consistency

Nowadays, with the rapid development of data collection sources and feature extraction methods, multi-view data are getting easy to obtain and have received increasing research attention in recent years, among which, multi-view clustering (MVC) forms a mainstream research direction and is widely used in data analysis. However, existing MVC methods mainly assume that each sample appears in all the views, without considering the incomplete view case due to data corruption, sensor failure, equipment malfunction, etc. In this study, we design and build a generative partial multi-view clustering model with adaptive fusion and cycle consistency, named as GP-MVC, to solve the incomplete multi-view problem by explicitly generating the data of missing views. The main idea of GP-MVC lies in two-fold. First, multi-view encoder networks are trained to learn common low-dimensional representations, followed by a clustering layer to capture the shared cluster structure across multiple views. Second, view-specific generative adversarial networks with multi-view cycle consistency are developed to generate the missing data of one view conditioning on the shared representation given by other views. These two steps could be promoted mutually, where the learned common representation facilitates data imputation and the generated data could further explores the view consistency. Moreover, an weighted adaptive fusion scheme is implemented to exploit the complementary information among different views. Experimental results on four benchmark datasets are provided to show the effectiveness of the proposed GP-MVC over the state-of-the-art methods.
Scale Equivariance Improves Siamese Tracking

Siamese trackers turn tracking into similarity estimation between a template and the candidate regions in the frame. Mathematically, one of the key ingredients of success of the similarity function is translation equivariance. Non-translation-equivariant architectures induce a positional bias during training, so the location of the target will be hard to recover from the feature space. In real life scenarios, objects undergo various transformations other than translation, such as rotation or scaling. Unless the model has an internal mechanism to handle them, the similarity may degrade. In this paper, we focus on scaling and we aim to equip the Siamese network with additional built-in scale equivariance to capture the natural variations of the target a priori. We develop the theory for scale-equivariant Siamese trackers, and provide a simple recipe for how to make a wide range of existing trackers scale-equivariant. We present SE-SiamFC, a scale-equivariant variant of SiamFC built according to the recipe. We conduct experiments on OTB and VOT benchmarks and on the synthetically generated T-MNIST and S-MNIST datasets. We demonstrate that a built-in additional scale equivariance is useful for visual object tracking.
Decision-making and biases in cybersecurity capability development: Evidence from a simulation game experiment

We developed a simulation game to study the effectiveness of decision-makers in overcoming two complexities in building cybersecurity capabilities: potential delays in capability development; and uncertainties in predicting cyber incidents. Analyzing 1479 simulation runs, we compared the performances of a group of experienced professionals with those of an inexperienced control group. Experienced subjects did not understand the mechanisms of delays any better than inexperienced subjects; however, experienced subjects were better able to learn the need for proactive decision-making through an iterative process. Both groups exhibited similar errors when dealing with the uncertainty of cyber incidents. Our findings highlight the importance of training for decision-makers with a focus on systems thinking skills, and lay the groundwork for future research on uncovering mental biases about the complexities of cybersecurity.
Proof-of-Contribution consensus mechanism for blockchain and its application in intellectual property protection

Blockchain has received a lot of attention recently for its characteristics of decentralization, immutability, traceability, etc., making it a promising technology for the development of various applications, especially the management of various digital information. However, most current blockchain systems exhibit problems such as high computational overhead and centralization of power. Reliance on cryptocurrency in many public blockchain-based applications is another factor that has hindered the application of blockchain technology in areas other than the financial sectors. This paper proposes a new blockchain consensus mechanism based on the contributions of participants. The proposed consensus mechanism, which is called proof-of-contribution (PoC), quantifies user behaviors and actions in a blockchain-based application as contribution values calculated through an algorithm. The node that has the highest contribution value in each round of consensus gets the right to generate the next new block. PoC preserves the properties of decentralization and resistance to hard fork and does not rely on cryptocurrency, making it attractive over cryptocurrency-based consensus mechanisms like proof-of-work (PoW) for a wide variety of applications that do not have to involve cryptocurrency. Contribution values can be abstracted from applications and used in the underlying blockchain consensus process to improve the security and trustworthiness of the applications. Intellectual property (IP) protection is one such application to apply the blockchain technology and the PoC consensus mechanism. However, existing blockchain-based IP protection systems are mostly developed based on public blockchain platforms such as the Ethereum while some others are developed as consortium blockchains, thus exhibiting inherent defects such as high system requirements, long consensus delay, and low user participation. In this paper, we will use IP protection as the application scenario to illustrate the development of our PoC consensus mechanism and will compare PoC to some existing consensus mechanisms. Experimental results will show that the proposed PoC consensus mechanism preserves most of the main security characteristics of blockchain and is superior to existing consensus mechanisms, making it more secure and efficient to use blockchain technology for digital information management.
Adversarial Attacks and Defenses on Graphs

Deep neural networks (DNNs) have achieved significant performance in various tasks. However, recent studies have shown that DNNs can be easily fooled by small perturbation on the input, called adversarial attacks.
SmartBugs

Over the last few years, there has been substantial research on automated analysis, testing, and debugging of Ethereum smart contracts. However, it is not trivial to compare and reproduce that research. To address this, we present SmartBugs, an extensible and easy-to-use execution framework that simplifies the execution of analysis tools on smart contracts written in Solidity, the primary language used in Ethereum. SmartBugs is currently distributed with support for 10 tools and two datasets of Solidity contracts. The first dataset can be used to evaluate the precision of analysis tools, as it contains 143 annotated vulnerable contracts with 208 tagged vulnerabilities. The second dataset contains 47,518 unique contracts collected through Etherscan. We discuss how SmartBugs supported the largest experimental setup to date both in the number of tools and in execution time. Moreover, we show how it enables easy integration and comparison of analysis tools by presenting a new extension to the tool SmartCheck that improves substantially the detection of vulnerabilities related to the DASP10 categories Bad Randomness, Time Manipulation, and Access Control (identified vulnerabilities increased from 11% to 24%).
Multi-branch and Multi-scale Attention Learning for Fine-Grained Visual Categorization

ImageNet Large Scale Visual Recognition Challenge (ILSVRC) is one of the most authoritative academic competitions in the field of Computer Vision (CV) in recent years. But applying ILSVRC’s annual champion directly to fine-grained visual categorization (FGVC) tasks does not achieve good performance. To FGVC tasks, the small inter-class variations and the large intra-class variations make it a challenging problem. Our attention object location module (AOLM) can predict the position of the object and attention part proposal module (APPM) can propose informative part regions without the need of bounding-box or part annotations. The obtained object images not only contain almost the entire structure of the object, but also contains more details, part images have many different scales and more fine-grained features, and the raw images contain the complete object. The three kinds of training images are supervised by our multi-branch network. Therefore, our multi-branch and multi-scale learning network(MMAL-Net) has good classification ability and robustness for images of different scales. Our approach can be trained end-to-end, while provides short inference time. Through the comprehensive experiments demonstrate that our approach can achieves state-of-the-art results on CUB-200-2011, FGVC-Aircraft and Stanford Cars datasets. Our code will be available at https://github.com/ZF1044404254/MMAL-Net.
Complementary Pseudo Labels for Unsupervised Domain Adaptation On Person Re-Identification

In recent years, supervised person re-identification (re-ID) models have received increasing studies. However, these models trained on the source domain always suffer dramatic performance drop when tested on an unseen domain. Existing methods are primary to use pseudo labels to alleviate this problem. One of the most successful approaches predicts neighbors of each unlabeled image and then uses them to train the model. Although the predicted neighbors are credible, they always miss some hard positive samples, which may hinder the model from discovering important discriminative information of the unlabeled domain. In this paper, to complement these low recall neighbor pseudo labels, we propose a joint learning framework to learn better feature embeddings via high precision neighbor pseudo labels and high recall group pseudo labels. The group pseudo labels are generated by transitively merging neighbors of different samples into a group to achieve higher recall. However, the merging operation may cause subgroups in the group due to imperfect neighbor predictions. To utilize these group pseudo labels properly, we propose using a similarity-aggregating loss to mitigate the influence of these subgroups by pulling the input sample towards the most similar embeddings. Extensive experiments on three large-scale datasets demonstrate that our method can achieve state-of-the-art performance under the unsupervised domain adaptation re-ID setting.
A Systematic Review of Cybersecurity Risks in Higher Education

The demands for information security in higher education will continue to increase. Serious data breaches have occurred already and are likely to happen again without proper risk management. This paper applies the Comprehensive Literature Review (CLR) Model to synthesize research within cybersecurity risk by reviewing existing literature of known assets, threat events, threat actors, and vulnerabilities in higher education. The review included published studies from the last twelve years and aims to expand our understanding of cybersecurity’s critical risk areas. The primary finding was that empirical research on cybersecurity risks in higher education is scarce, and there are large gaps in the literature. Despite this issue, our analysis found a high level of agreement regarding cybersecurity issues among the reviewed sources. This paper synthesizes an overview of mission-critical assets, everyday threat events, proposes a generic threat model, and summarizes common cybersecurity vulnerabilities. This report concludes nine strategic cyber risks with descriptions of frequencies from the compiled dataset and consequence descriptions. The results will serve as input for security practitioners in higher education, and the research contains multiple paths for future work. It will serve as a starting point for security researchers in the sector.