Reviewerly/exHarmony huggingface dataset:
Super-resolution reconstruction of remote sensing images based on Swin Transformer fusion attention network

Image super-resolution reconstruction technology in remote sensing can improve the spatial resolution of remote sensing images with the breakthrough of physical hardware limitations. With the development of deep learning technology, more and more algorithms proposed in the field of natural images are applied to the field of remote sensing super-resolution. Due to the large difference in the size of the objects in remote sensing images and the high complexity of the image, the reconstructed image will be blurred when the algorithm in the field of natural images is directly used. To address this problem, this paper proposes a shallow feature extraction feature fusion with multiple convolutions, followed by the extraction of high-frequency information using the Swin Transformer module with a fusion attention mechanism. The edge details of the image are extracted using the gradient of the image in the final reconstruction process, and complementary fusion is performed at the end of the network, which can effectively supplement the lack of shallow features caused by the deep network. Finally, experiments show that the proposed model obtains satisfactory reconstruction results of remote sensing images.
An Adam-adjusting-antennae BAS Algorithm for Refining Latent Factor Analysis Model

Extracting the latent information in high-dimensional and incomplete matrices is an important and challenging issue. The Latent Factor Analysis (LFA) model can well handle the high-dimensional matrices analysis. Recently, Particle Swarm optimization (PSO)-incorporated LFA models have been proposed to tune the hyper-parameters adaptively with high efficiency. However, the incorporation of PSO causes the premature problem. To address this issue, we propose a sequential Adam-adjusting-antennae BAS (A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> BAS) optimization algorithm, which refines the latent factors obtained by the PSO-incorporated LFA model. The A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> BAS algorithm consists of two sub-algorithms. First, we design an improved BAS algorithm which adjusts beetles’ antennae and step-size with Adam; Second, we implement the improved BAS algorithm to optimize all the row and column latent factors sequentially. With experimental results on two real high-dimensional matrices, we demonstrate that our algorithm can effectively solve the premature convergence issue.
A Herd Foraging-Based Adaptive Coverage Path Planning in Unbounded Environments

Coverage path planning is important in helping us perform such tasks as map construction and criminal capture. As an outstanding method, predator-prey coverage path planning employs a predator-prey mechanism to enable a robot to adaptively cover an arbitrary 2-D surface with dynamic obastacles. However, it is designed for bounded environments only and cannot work in unbounded environments. Inspired by the foraging behavior of herds in nature, this work proposes an adaptive coverage path planning algorithm suitable for unbounded environments, called herd foraging-based coverage path planning. It employs a virtual herd to control the overall coverage direction of a robot, allowing it to be applied in unbounded environments. The experimental results demonstrate its effectiveness in unbounded environments.
Deep learning based audio and video cross-modal recommendation

With the rapid development of the Internet multimedia industry, audio and video cross-modal retrieval and recommendation have become a compelling topic in deep learning. However, most methods still have certain problems. For example, it takes a lot of effort to find suitable background music for the video, and the recommendations mode of the audio-visual database platform are too singular. This article will take the matching problem of video and music as the starting point in optimizing the music retrieval mode. To improve the multimedia website recommendation mechanism, a video and audio cross-domain recommendation model KATLN is proposed. We have incorporated the ideas of domain adaptation and attention mechanism, obtained rich item feature representations, captured users’ finer-grained interest preferences, and improved the accuracy of modeling. Through experiments, it can be seen that, compared with the existing mainstream algorithms, the KATLN proposed in this paper has outstanding performance and is more prominent in accurately grasping user preferences.
Knowledge Graph based Question Pair Matching for Domain-Oriented FAQ System

The matching methods of question pair in the most FAQ system are less optimized for the specific domains, where proper nouns and irregular expressions always exist in the questions. To address the above problems, we propose a knowledge filtering method and the FK-BERT (FAQ-oriented knowledge-enabled BERT) model, which make full use of the domain knowledge graph. In the knowledge filtering stage, the semantic relationships between candidate entities and question sentences are thoroughly evaluated to choose the applicable entities. Furthermore, FK-BERT model considers both the relationships between entities within a single question, as well as the relationships of entities between two questions. The experimental results show that the joint use of knowledge filtering and FK-BERT model can improve the performance of FAQ systems for the domain of operating system.
Are Male Candidates Better than Females? Debiasing BERT Resume Retrieval System

Advanced language models like BERT have great performances in various Natural Language Processing tasks. However, recent researches have shown that language models can learn gender biases from corpus, and lead to discriminatory decisions and unfair allocation of resources. We proposed a measure of gender bias in BERT resume retrieval system, by performing job searches on a group of resumes with different genders but consistent abilities. We proposed to calculate the average ranking and Discounted Cumulative Gain (DCG) scores of male and female resumes, and found that men outperformed women even though the two resumes were identical except for gender. This shows BERT has gender stereotypes, and its resume retrieval systems prefer male candidates. Therefore, we also proposed a regularized debiasing method to promote gender equity. Referring to Densifier method, we can get the subspace vector encoding bias semantics through matrix transformation of word vector difference between occupational and gender words. By defining the correlation between BERT word vector and gender bias subspace as the loss term, we can remove the bias semantics in BERT, and also avoid it learning stereotypes even trained on an unfair data set. After regularized debiased, the gender ranking gap of BERT was reduced by an average of 61.8%, while DCG scores were reduced by 53.9%.
Recommending View Bundles in Data Marketplaces

For companies to have a competitive advantage, they need to extract relevant information from data and for that, they need to complement their own data with other data sources. Data marketplaces are platforms on which data providers and data consumers do business. However, every data interaction incurs a monetary cost. Therefore, data consumers are interested in buying a set of interesting views that fit their (goal and) budget. Views allow data to be represented visually, enabling users to make sense of patterns and insights. Besides allowing for easier cost control, buying a set of views bundled together increases the chance of finding what consumers want over buying them view-by-view. Selecting the suitable views to compose an interesting bundle is non-trivial, due to the vast number of view combinations that potentially meet the data consumer’s needs. In this paper, we address the problem of view bundle recommendation in data marketplaces, in which the utility of a bundle depends on the interplay among candidate views. We propose the use of Self-Organizing Maps as a means to compute this interplay and use a Genetic Algorithm to design near-optimal bundles. Our empirical results demonstrate that our approach can effectively aid data consumers to find relevant view bundles under budget constraints.
Loc-VAE: Learning Structurally Localized Representation from 3D Brain MR Images for Content-Based Image Retrieval

Content-based image retrieval (CBIR) systems are an emerging technology that supports reading and interpreting medical images. Since 3D brain MR images are high dimensional, dimensionality reduction is necessary for CBIR using machine learning techniques. In addition, for a reliable CBIR system, each dimension in the resulting low-dimensional representation must be associated with a neurologically interpretable region. We propose a localized variational autoencoder (Loc-VAE) that provides neuroanatomically interpretable low-dimensional representation from 3D brain MR images for clinical CBIR. Loc-VAE is based on $\beta-$VAE with the additional constraint that each dimension of the low-dimensional representation corresponds to a local region of the brain. The proposed Loc-VAE is capable of acquiring representation that preserves disease features and is highly localized, even under high-dimensional compression ratios (4096:1). The low-dimensional representation obtained by Loc-VAE improved the locality measure of each dimension by 4.61 points compared to naive $\beta-$VAE, while maintaining comparable brain reconstruction capability and information about the diagnosis of Alzheimer’ s disease.
Fusional Modality and Distribution Alignment Learning for Visible-Infrared Person Re-Identification

The Visible-Infrared Person Re-Identification (VI-ReID) task aims to retrieval pedestrian images with the same labels across different modalities. VI-ReID is a very challenging task due to the huge intra-modality variation and cross-modality gap. Existing methods are mainly based on the feature alignment to mitigate the modality’s gap, however, using only feature-level constraints does not mitigate cross-modality gap well. We propose a fusional modality and distribution alignment learning network (FMADALNet) to mitigate modality’s gap and align modality’s distribution to learn modality-shared feature representations. FMADALNet contains a lightweight fusional modality generation module (FMGM). FMGM constructs a fusional modality that incorporates heterogeneous image features and contains only modality-shared information to mitigate modality gap at the pixel-level. In addition, to mitigate the differences in the distribution of the different modalities, we design a Hetero-center Maximum Mean Discrepancy loss (HcMMD), which reduces the differences in the distribution of the different modalities in a displaying manner. Extensive experimental results on two public datasets show that our proposed method achieves impressive performance compared to state-of-the-art methods.
ACADIA: Efficient and Robust Adversarial Attacks Against Deep Reinforcement Learning

Existing adversarial algorithms for Deep Reinforcement Learning (DRL) have largely focused on identifying an optimal time to attack a DRL agent. However, little work has been explored in injecting efficient adversarial perturbations in DRL environments. We propose a suite of novel DRL adversarial attacks, called ACADIA, representing AttaCks Against Deep reInforcement leArning. ACADIA provides a set of efficient and robust perturbation-based adversarial attacks to disturb the DRL agent's decision-making based on novel combinations of techniques utilizing momentum, ADAM optimizer (i.e., Root Mean Square Propagation, or RMSProp), and initial randomization. These kinds of DRL attacks with novel integration of such techniques have not been studied in the existing Deep Neural Networks (DNNs) and DRL research. We consider two well-known DRL algorithms, Deep-Q Learning Network (DQN) and Proximal Policy Optimization (PPO), under Atari games and MuJoCo where both targeted and non-targeted attacks are considered with or without the state-of-the-art defenses in DRL (i.e., RADIAL and ATLA). Our results demonstrate that the proposed ACADIA outperforms existing gradient-based counterparts under a wide range of experimental settings. ACADIA is nine times faster than the state-of-the-art Carlini & Wagner (CW) method with better performance under defenses of DRL.
Effect of Hamming Distance on Performance of ECOC with Estimated Binary Classifiers

Error-Correcting Output Coding (ECOC) is a method for constructing a multi-valued classifier using a combination of binary classifiers. The effectiveness of ECOC for multivalued classification problems has been demonstrated by many experimental evaluations. Therefore, classification performance have strongly depended on the data under consideration, and it is not clear what kind of combinations of binary classifiers have good performance. Motivated by this fact, the authors have clarified the best combination of binary classifiers that makes ECOC, assuming a situation in which each binary classifier can estimate the true posterior probability. They also have proposed a total framework for analytical evaluation when a binary classifier outputs an estimated posterior probability that approximates the true posterior probability. These studies established a framework for evaluating the theoretical performance of ECOC.Based on these findings, this study discusses the theoretical performance of ECOC from the upper bound perspective. The results showed that increasing the Hamming distance between code words can blackuce the error rate. We then evaluate various combinations of binary classifiers based on analytical evaluation.
GNN-EA: Graph Neural Network with Evolutionary Algorithm

Recently, Graph Neural Networks (GNNs) have shown great promise in addressing various tasks with non-Euclidean data. Encouraged by the successful application on discovering convolutional and recurrent neural networks, Neural Architecture Search (NAS) is extended to alleviate the complexity of designing appropriate task-specific GNNs. Unfortunately, existing graph NAS methods are usually susceptible to unscalable depth, redundant computation, constrained search space and some other limitations. In this paper, we present an evolutionary graph neural network architecture search strategy, involving inheritance, crossover and mutation operators based on fine-grained atomic operations. Specifically, we design two novel crossover operators at different granularity levels, GNNCross and LayerCross. Experiments on three different graph learning tasks indicate that the neural architectures generated by our method exhibit comparable performance to the handcrafted and automated baseline GNN models.
A Comparative Study on Deep Feature Extraction Approaches for Visual Tracking of Industrial Robots

Owing to several breakthroughs in deep-learning research, visual object trackers based on deep learning algorithms have been the de facto method in the tracking community. However, the performance of this category of trackers is largely influenced by the richness and quality of the target feature representation. Thus, to extract features with high discriminative power, special attention must be paid to the feature extraction component of the tracker. Despite the significance of feature extraction paradigm in deep learning-based trackers, limited studies have been dedicated to comparatively examining the feature learning and representation capabilities of various existing deep feature extraction approaches in visual tracking. In this paper, we present a comparative study on two different deep feature extraction methods in visual tracking of industrial robots in video. This work examines and visualizes the feature representation capability of two commonly employed feature extraction methods in deep learning-based tracking; namely, Convolutional Neural Network (CNN)-based and Transformer-based feature extraction methods. To demonstrate the effectiveness of the deep learning-based tracking paradigm, this study adopts a Siamese-based tracker that is trained end-to-end offline with ILSVRC15 datasets as well as a Transformer-based tracking architecture that is also pre-trained on the ImageNet dataset. This study also contributes a dataset consisting of images of industrial robots, which was employed for evaluating the two adopted trackers. From this study, we could conclude that the feature learning and representation of the transformer-based feature extraction pipeline largely improved the overall tracking performance of the tracker.
Cooperation of Human and Active Learning based AI for Fast and Precise Complaint Management

In highly competitive markets, customer loyalty plays an increasingly important role for companies. An important aspect is the recording and processing of customer complaints, on the one hand for problem-solving and on the other hand for internal process optimization. The complaint handling process extends over several phases, in which different people with varying expertise and experience may be involved, which poses a major challenge in order to achieve Consistent quality in the recording process of complaints. To address this issue, this work presents a robust active learning based AI system that allows using existing expert knowledge to create more reliable classifications in a shorter amount of time. The implemented prototype shows a decrease of up to 86% in the average processing time of complaints, with an average increase of up to 37% in the classification accuracy.
Adaptive Prediction of Resources and Workloads for Cloud Computing Systems with Attention-based and Hybrid LSTM

Currently, cloud computing service providers face big challenges in predicting large-scale workload and resource usage time series. Due to the difficulty in capturing nonlinear features, traditional forecasting methods usually fail to achieve high performance in predicting resource usage and workload sequences. Much noise implicit in the original sequences of resources and workloads is another reason for their low performance. To address these problems, this work proposes a hybrid prediction model named SABG that integrates an adaptive Savitzky-Golay (SG) filter, Attention mechanism, Bidirectional and Grid versions of Long and Short Term Memory (LSTM) networks. SABG adopts an adaptive SG filter in the data pre-processing to eliminate noise and extreme points in the original time series. It uses bidirectional and grid LSTM networks to capture bidirectional features and dimension ones, respectively. Then, it utilizes an attention mechanism to explore importance of different data dimensions. SABG aims to predict resource usage and workloads in highly variable traces in cloud computing systems. Extensive experimental results demonstrate that SABG achieves higher-accuracy prediction than several benchmark prediction approaches with datasets from Google cluster traces.
On Security of Proof-of-Policy (PoP) in the Execute-Order-Validate Blockchain Paradigm

Attacks on consensus protocols against a blockchain system are often caused by inner malicious nodes, and inject valid but malicious transactions or blocks to the blockchain by exploiting the consensus protocol. Much attention is paid to attacks such as the 51% attack on the Proof-of-Work (PoW) and long range attack on Proof-of-Stake (PoS) on the consensus protocol in a public blockchain, where the attack cost is high. There is no much systematic work on the attacks on the consensus protocol in a permissioned blockchain. In this paper, we perform a holistic security study of the “execute-order-validate” paradigm used by a permissioned blockchain system such as Hyperledger Fabric. We first systematically present the consensus protocol in the execute-order-validate blockchain paradigm and abstract the consensus protocol as Proof-of-Policy (PoP). We then analyze the chaincode deployment process of Fabric and show it can be exploited to deploy malicious chaincode to launch collusion attacks against PoP. The collusion attacks do not incur high computational cost or monetary cost like attacks on PoW and PoS. The scale of a permissioned blockchain system is often limited, and there is no built-in penalty for such attacks. Therefore, the risk of those collusion attacks is high compared with those against public blockchain systems. We build a Fabric test network to validate the attacks. A large-scale analysis is performed on 7036 Fabric projects on GitHub to evaluate the attack generality.
Detecting DNS hijacking by using NetFlow data

DNS hijacking represents a security threat to users because it enables bypassing existing DNS security measures.Several malware families exploit this by changing the client DNS configuration to point to a malicious DNS resolver.Following the assumption that users will never actively choose to use a resolver that is not well-known, our paper introduces the idea of detecting client-based DNS hijacking by classifying public resolvers based on whether they are well-known or not.Furthermore, we propose to use NetFlow-based features to classify a resolver as well-known or malicious.By characterizing and manually labelling the 405 resolvers seen in four weeks of NetFlow data from a national ISP, we show that classification of both well-known and malicious servers can be made with an AUROC of 0.85.
Approaches to Modeling the Machine and Tractor Fleet of an Agricultural Organization in the Conditions of Digitalization

In recent years, large agricultural organizations and agricultural holdings have increasingly used field digitization systems, telemetry, information methods of fleet management, elements of precision farming. However, the pace of development of digital technologies in most medium and small forms of management in the industry remains low. The study was conducted in order to study the state of digitalization of the machine and tractor fleet of an agricultural enterprise and assess the prospects for its development, identify problems and develop incentive measures taking into account the introduction of digital technologies. The use of digital developments for the machine and tractor fleet allows management to obtain real-time information about the performance of technological operations, resource costs, and with the help of objective independent control, allows continuously recording the main parameters of the technological process and transmitting them to the place of demand, which increases the operational efficiency of management. In order to accelerate the process of introducing digital technologies, it is necessary to stimulate the training of specialists in digital technologies, increase state funding for research on this issue, subsidize investments aimed at developing the infrastructure of digitalization of the agricultural sector.