Reviewerly/exHarmony huggingface dataset:
Denoising Graph Inference Network for Document-Level Relation Extraction

Relation Extraction (RE) is to obtain a predefined relation type of two entities mentioned in a piece of text, e.g., a sentence-level or a document-level text.Most existing studies suffer from the noise in the text, and necessary pruning is of great importance.The conventional sentence-level RE task addresses this issue by a denoising method using the shortest dependency path to build a long-range semantic dependency between entity pairs.However, this kind of denoising method is scarce in document-level RE.In this work, we explicitly model a denoised document-level graph based on linguistic knowledge to capture various long-range semantic dependencies among entities.We first formalize a Syntactic Dependency Tree forest (SDT-forest) by introducing the syntax and discourse dependency relation.Then, the Steiner tree algorithm extracts a mention-level denoised graph, Steiner Graph (SG), removing linguistically irrelevant words from the SDT-forest.We then devise a slide residual attention to highlight word-level evidence on text and SG.Finally, the classification is established on the SG to infer the relations of entity pairs.We conduct extensive experiments on three public datasets.The results evidence that our method is beneficial to establish long-range semantic dependency and can improve the classification performance with longer texts.
Re-modeling the solar diffuse fraction in Taiwan on basis of a typical-meteorological-year data

An objective approach to determine a dataset from which a correlation model of solar diffuse fraction is fitted and tested is proposed. A typical meteorological year (TMY) of global radiation data that is measured in Tainan, Taiwan over 10 years (2011–2020) is used to construct the training dataset and the remaining data (90% data base) is used as a test dataset. Two multiple-predictor and one single-predictor correlation models for the hourly diffuse fraction are developed by using more theoretically rigorous techniques for determining the breaking points for segmentation (excluding the modified Boland-Ridley-Lauret (BRL) model) and how many significant predictors required in each segmented interval for the regression model with piece-wise linear multiple-predictor correlation. The performance of each developed correlation model is superior to that of the existing same-type model using a part (one year or two years) of the same data base. The re-modeled piece-wise linear multiple-predictor correlation model has the best long-term performance of the three developed correlation models. The modified BRL model on basis of the TMY data is second. The re-modeled Liu-Jordan-type (single predictor) model allows real time prediction and has a simpler form than the other two models but prediction accuracy is inferior.
Towards Correlated Data Trading for High-Dimensional Private Data

The commoditization of private data has become an attractive research topic with the emergence of Big Data era. In this paper, we study the trading of high-dimensional private data with differential privacy guarantee. We propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Cheap</i> , which is a novel Correlated data trading framework for High-dimEnsionAl Private data. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Cheap</i> first models data correlations among high-dimensional user attributes, and builds an initial attribute clustering scheme. Combined with this scheme, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Cheap</i> devises a novel data perturbation mechanism by solving optimal attribute clustering ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OAC</i> ) problem, in order to improve data utility of traded data and further generate a privacy-preserving high-dimensional dataset with close joint distribution with the original one. It then quantifies privacy loss based on near-optimal attribute cluster scheme due to the NP-hardness of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OAC</i> problem, and further compensates data owners by running auction in a cost-effective way. We evaluate the performance of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Cheap</i> on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">UserBehavior</i> dataset and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Obesity</i> dataset, respectively. Our evaluation and analysis demonstrate that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Cheap</i> well balances data utility and privacy protection, and achieves all desired economic properties of budget balance, individual rationality and truthfulness.
Quality-based band selection for hyperspectral image visualization

Displaying a hyperspectral image on a trichromatic monitor poses the problem of reducing the large number of bands to just three colour channels. In this letter, a new visualization method is proposed, based on an image quality score computed on each band of the hyperspectral image. A detail enhancement post-processing step increases the prominence of small-scale information in the visualization. The experimental results obtained on three widely used hyperspectral datasets prove the effectiveness of the proposed approach in producing high-quality visualizations from the point of view of the chosen objective metrics.
A Novel Deception-Based Scheme to Secure the Location Information for IoBT Entities

The Internet of Battlefield Things (IoBT) is a subdomain of the Internet of Things (IoT) that applies to a battlefield. Due to the limited communication infrastructure, the entities in the battlefield communicate with each other using Device-2-Device (D2D) communication. D2D is used to send and request extremely sensitive data as well as the location information of other entities. Thus, to execute tasks and avoid information leaks, it is critical to protect the location data of soldiers and equipment. To the best of our knowledge, location privacy issues in IoBT have not been fully investigated. The attackers are expected to probe the network before attacking valuable assets, giving them an advantage over the defenders. This advantage can be offset by using deception-based methods. In this study, we propose a deception-based scheme to enhance the location information security of IoBT nodes. A novel encryption method is proposed along with dummy identities and dummy packets. We develop a mathematical model to evaluate the proposed scheme in terms of safety time, probability of failure, and probability of identifying the real packet. The obtained results demonstrate that the proposed method is efficient in reducing the probability of identifying the real location of the communicating entities. We develop NetLogo simulation to validate the mathematical model.
A Double Stream Person Re-Identification Method Based on Attention Mechanism and Multi-Scale Feature Fusion

Person re-identification is the key technology for designing a intelligent video surveillance system to retrieve and recongise a specific person in a series of videos. It mainly plays a role in multiple non-overlapping camera monitoring environments to determine whether the target person of interest that has appeared under a camera appears again under others. However, the image data, in the real scene, taken by the surveillance camera may be occluded or blurred, which increased the difficulty of identifying the pedestrian posturn and then lead to dramatically decrease the accuracy of recognition. To solve the above problems, we propose a dualbranch multi-scale feature fusion network, which improves the expression ability of pedestrian features under partial occlusion by learning discriminative pedestrian features. The feature erasure mechanism is introduced into the branch network to effectively enhance the fine-grained feature learning ability of the network. By embedding the lightweight attention module into Resnet-50, the image sequence features of the channel dimension will be extracted, and the interference caused by the cluttered back ground information will be suppressed. In the training phase, the average pooling layer and the maximum pooling layer of different kernels and strides are utilized for different residual stages, and the mixed pooling strategy and mixed loss function of different kernels are designed. Finally, a novel hybrid loss function which combines triplet loss and ID loss is presented to train the network model. By comparison with the existing representative methods on Market1501, DukeMTMC reID and MTMS17 datasets, the experimental results show that the features extracted by the proposed method are more discriminative and with high recognition accuracy.
A Comparative Study of Situation Awareness-Based Decision-Making Model Reinforcement Learning Adaptive Automation in Evolving Conditions

Situation-awareness-based decision-making (SABDM) models constructed using cognitive maps and goal-direct task analysis techniques have been successfully used in decision support systems in safety-critical and mission-critical environments such as air traffic control and electrical energy distribution. Reinforcement learning (RL) and other machine learning techniques are used to automate situational awareness mental model parameter adjustments, reducing the expert work on the initial configuration and long-term maintenance without affecting the mental model’s structure and maintaining the situation-awareness-based decision-making model’s cognitive and explainability characteristics. Real-world models should evolve to cope with changes in the environmental conditions. This study evaluates the application of reinforcement learning as an online adaptive technique to adjust the situational-awareness mental model’s parameters under evolving conditions. We conducted evaluation experiments using real-world public datasets to compare the performance of the SABDM model with that of the reinforcement learning adaptation technique (SABDM/RL) and other adaptive machine learning methods under distinct concept drift evolving conditions. We measured the techniques’ overall and dynamic performance to understand how well they adapt to evolving environmental conditions. The experiments show that SABDM/RL performs similarly to modern online adaptive machine learning classification methods with the support of concept drift detection techniques while maintaining the mental model strength of the situation awareness-based systems.
Non-Relaxation Deep Hashing Method for Fast Image Retrieval

Deep hashing methods utilize an end-to-end framework to mutually learn feature representations and hash codes, thereby achieving a better retrieval performance. Traditional supervised hashing methods adopt handcrafted features for hashing function learning and then generate hash codes through classification and quantization. The lack of adaptability and independence of the quantization procedure leads to low retrieval accuracy of supervised hashing methods with handcrafted features in image retrieval. In this study, a non-relaxation deep hashing method for fast image retrieval is proposed. In this method, a differentiable host thresholding function is used to encourage hash-like codes to approach -1 or 1 non-linearly at the output of the convolutional neural, instead of the symbol function for quantization used in the traditional method. The output of the host thresholding function is directly used to compute the network training error, and a loss function is elaborately designed with the norm to constrain each bit of the hash-like code to be as binary as possible. Finally, a symbol function is added outside the trained network model to generate binary hash codes for image storage and retrieval in a low-dimensional binary space. Extensive experiments on two large-scale public datasets show that our method can effectively learn image features, generate accurate binary hash codes, and outperform state-of-the-art methods in terms of the mean average precision.
Qualitative Clustering of Software Repositories Based on Software Metrics

Software repositories contain a wealth of information about the aspects related to software development process.For this reason, many studies analyze software repositories using methods of data analytics with a focus on clustering.Software repository clustering has been applied in studying software ecosystems such as GitHub, defect and technical debt prediction, software remodularization.Although some interesting insights have been reported, the considered studies exhibited some limitations.The limitations are associated with the use of individual clustering methods and manifesting in the shortcomings of the obtained results.In this study, to alleviate the existing limitations we engage multiple cluster validity indices applied to multiple clustering methods and carry out consensus clustering.To our knowledge, this study is the first to apply the consensus clustering approach to analyze software repositories and one of the few to apply the consensus clustering to software metrics.Intensive experimental studies are reported for software repository metrics data consisting of a number of software repositories each described by software metrics.We revealed seven clusters of software repositories and relate them to developers' activity.It is advocated that the proposed clustering environment could be useful for facilitating the decision making process for business investors and open-source community with the help of the Gartner's hype cycle.
Privacy-Preservation Techniques for IoT Devices: A Systematic Mapping Study

The Internet of Things (IoT) is becoming a pervasive technology entangled in everyday life. The objective of IoT is to provide ubiquitous access to numerous devices and machines on service providers. However, IoT-based devices may expose a user to various privacy and security threats. Privacy-preservation techniques focus on securing any type of data transfer between different parties. We aim to deliver the current state of the art in terms of privacy-preservation techniques used for IoT devices that have been discussed in the literature. Therefore, we carried out a systematic identification of the privacy-preservation techniques for IoT devices that have been described in the cybersecurity domain. To do so, we followed rigorous guidelines to define our research protocol to increase the repeatability and reliability of our results. A set of ten research questions was created to drive the analysis of our study. This research work comprehensively analyzes and discusses the privacy-preservation techniques for IoT devices published in five different academic venues. We identified 260 studies, mostly published between 2017 and 2021, that were systematically selected from an initial set of 1394 papers. The most active research areas in privacy-preservation techniques for IoT devices discuss cryptography techniques to improve the authentication process to access IoT devices. The majority of authors presented privacy-preservation techniques for IoT that involved inventory privacy threats and discussed privacy interference attacks. We comprehensively analyze and discuss the trends, gaps, and possible future research directions of the privacy-preservation techniques used for IoT devices published in different academic venues.
Tagging Multi-Label Categories to Points of Interest From Check-In Data

Category tag of Point Of Interest (POI) serves a crucial role in numerous Location Based Services (LBS) like finding a place of interest, POI recommendation, location-based advertisement, etc. However, it has been found that many POIs (around 30%) do not have any category information. Manual tagging of these categories is a very costly affair as it requires a physical visit to each POI. Hence, an automated method for category tagging of POIs has been identified as an important research problem. The problem of category tagging of POIs is still in its primary stage, requiring further exploration. In this work, we solve the problem in three broad steps i). feature identification, ii). feature extraction, and iii). tag assignment. For the first step, we consider sequential features, temporal features, and spatial features, which can be constructed using check-in data. For the second step, we generate POI embedding representing its features, using a graph-based unsupervised approach. For the final step, the problem is considered a multi-label classification problem. For this, we have used Classifier Chain algorithms which preserves inter-tag correlation. Comparison with state-of-the-art methodologies along with multiple variants of features and classification algorithms show a significant improvement of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$5-34\%$</tex-math></inline-formula> by standard metric of average F1-score.
Deinterleaving of radar pulse based on implicit feature

In the complex countermeasure environment, the pulse description words (PDWs) of the same type of multi-function radar emitters are similar in multiple dimensions. Therefore, it is difficult for conventional methods to deinterleave such emitters. In order to solve this problem, a pulse deinterleaving method based on implicit features is proposed in this paper. The proposed method introduces long short-term memory (LSTM) neural networks and statistical analysis to mine new features from similar PDW features, that is, the variation law (implicit features) of pulse sequences of different radiation sources over time. The multi-function radar emitter is deinterleaved based on the pulse sequence variation law. Statistical results show that the proposed method not only achieves satisfactory performance, but also has good robustness.
Attentive Adversarial Collaborative Filtering

Generative adversarial nets (GANs) have enjoyed considerable success in computer vision and attracted much attention from recommender systems. However, due to the discrete nature of items, it is infeasible to graft GANs directly onto recommendation models. Although several methods have taken steps forward, their training processes are slow-convergent, time-consuming, or even unstable. This article proposes a novel framework named attentive adversarial collaborative filtering (AACF) and an efficient training strategy to improve GANs in recommender systems. There are two distinct novelties over previous work. First, AACF is a differentiable generative adversarial framework that introduces an attention mechanism and “virtual items” to bridge the gap between the generator and the discriminator. Owing to the intrinsic differentiability, AACF can be stably optimized with gradient descent methods. Second, the efficient training strategy substantially reduces computational complexity. It is capable of efficiently training and scaling up the AACF model to large datasets. Extensive experiments on various datasets demonstrate the effectiveness, fast convergence, stability, and scalability of AACF. Since our ideas are general in nature, they will open a path to stably and efficiently train GANs in the research areas with discrete data. The implementation 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/zhongchuansun/AACF</uri> .
WHU-Stereo: A Challenging Benchmark for Stereo Matching of High-Resolution Satellite Images

Stereo matching of high-resolution satellite images (HRSI) is still a fundamental but challenging task in the field of photogrammetry and remote sensing. Recently, deep learning (DL) methods, especially convolutional neural networks (CNNs), have demonstrated tremendous potential for stereo matching on public benchmark datasets. However, datasets for stereo matching of satellite images are scarce. To facilitate further research, this paper creates and publishes a challenging dataset, termed WHU-Stereo, for stereo matching DL network training and testing. This dataset is created by using airborne LiDAR point clouds and high-resolution stereo imageries taken from the Chinese GaoFen-7 satellite (GF-7). The WHU-Stereo dataset contains more than 1700 epipolar rectified image pairs, which cover six areas in China and includes various kinds of landscapes. We have assessed the accuracy of ground-truth disparity maps, and it is proved that our dataset achieves comparable precision compared with existing state-of-the-art stereo matching datasets. To verify its feasibility, in experiments, the hand-crafted SGM stereo matching algorithm and recent deep learning networks have been tested on the WHU-Stereo dataset. Experimental results show that deep learning networks can be well trained and achieves higher performance than hand-crafted SGM algorithm, and the dataset has great potential in remote sensing application. The WHU-Stereo dataset can serve as a challenging benchmark for stereo matching of high-resolution satellite images, and performance evaluation of deep learning models. Our dataset is available at https://github.com/Sheng029/WHU-Stereo
Global–local contrastive multiview representation learning for skeleton-based action recognition

Skeleton-based human action recognition has been drawing more interest recently due to its low sensitivity to appearance changes and the accessibility of more skeleton data. However, the skeletons captured in practice are sensitive to the view of an actor, given the occlusion of different human-body joints and the errors in human joint localization. Each view is noisy and incomplete, but important factors, such as motion and semantics, should be shared between all views in action representation learning. We support the classic hypothesis that a powerful representation is one that models view-invariant factors, and so does unsupervised learning. Therefore, we study this hypothesis under the framework of contrastive multiview learning, where we learn a representation for action recognition that aims to maximize the mutual information between different views of the same action sequence. Apart from that, a global–local contrastive loss is proposed to model the multi-scale co-occurrence relationships in both spatial and temporal domains. Extensive experimental results show that the proposed method significantly boosts the performance of unsupervised skeleton-based human action methods on three challenging benchmarks of PKUMMD, NTU RGB+D 60, and NTU RGB+D 120.
Hierarchical interaction and pooling network for co-salient object detection

Co-salient object detection (CoSOD) aims to detect common and salient objects across the given image group. Due to the particularity of CoSOD, images in the given group are processed synergistically to excavate the relevance between them. Inspired by the tracking methods, previous works tend to utilize pixel-wise correspondences to measure the relevance. However, because of the complexity of the image groups, the obtained feature maps could be easily affected by the common interference. Moreover, current works tend to utilize classification labels to ensure intra-group coherence and inter-group separability, which may cause some overfitting problems. In this paper, we propose the hierarchical interaction and pooling network to alleviate the above problems. We first design a pyramid pooling interaction module and perform convolution with dimension permutation, making full use of convolution and multi-receptive field information. We further propose the coherence confirmation module along with the four-branch architecture. Without the classification labels, the module achieves comparable or even better performance. Extensive experiments demonstrate that the proposed method can detect common and salient objects more accurately and achieves the new state-of-the-art.