Reviewerly/exHarmony huggingface dataset:
Adjacent Context Coordination Network for Salient Object Detection in Optical Remote Sensing Images

Salient object detection (SOD) in optical remote sensing images (RSIs), or RSI-SOD, is an emerging topic in understanding optical RSIs. However, due to the difference between optical RSIs and natural scene images (NSIs), directly applying NSI-SOD methods to optical RSIs fails to achieve satisfactory results. In this article, we propose a novel adjacent context coordination network (ACCoNet) to explore the coordination of adjacent features in an encoder-decoder architecture for RSI-SOD. Specifically, ACCoNet consists of three parts: 1) an encoder; 2) adjacent context coordination modules (ACCoMs); and 3) a decoder. As the key component of ACCoNet, ACCoM activates the salient regions of output features of the encoder and transmits them to the decoder. ACCoM contains a local branch and two adjacent branches to coordinate the multilevel features simultaneously. The local branch highlights the salient regions in an adaptive way, while the adjacent branches introduce global information of adjacent levels to enhance salient regions. In addition, to extend the capabilities of the classic decoder block (i.e., several cascaded convolutional layers), we extend it with two bifurcations and propose a bifurcation-aggregation block (BAB) to capture the contextual information in the decoder. Extensive experiments on two benchmark datasets demonstrate that the proposed ACCoNet outperforms 22 state-of-the-art methods under nine evaluation metrics, and runs up to 81 fps on a single NVIDIA Titan X GPU. The code and results of our method are available at https://github.com/MathLee/ACCoNet.
Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-Resolution

Single image super-resolution (SISR) with generative adversarial networks (GAN) has recently attracted increasing attention due to its potentials to generate rich details. However, the training of GAN is unstable, and it often introduces many perceptually unpleasant artifacts along with the generated details. In this paper, we demonstrate that it is possible to train a GAN-based SISR model which can stably generate perceptually realistic details while inhibiting visual artifacts. Based on the observation that the local statistics (e.g., residual variance) of artifact areas are often different from the areas of perceptually friendly details, we develop a framework to discriminate between GAN-generated artifacts and realistic details, and consequently generate an artifact map to regularize and stabilize the model training process. Our proposed locally discriminative learning (LDL) method is simple yet effective, which can be easily plugged in off-the-shelf SISR methods and boost their performance. Experiments demonstrate that LDL outperforms the state-of-the-art GAN based SISR methods, achieving not only higher reconstruction accuracy but also superior perceptual quality on both synthetic and real-world datasets. Codes and models are available at https://github.com/csjliang/LDL.
Business Sustainability of Small and Medium Enterprises during the COVID-19 Pandemic: The Role of AIS Implementation

Small and medium enterprises (SMEs) are the pillars on which most businesses worldwide rest. Thus, without the support of qualified information systems, it can be very challenging for them to improve their performance and difficult for them to reach sustainability goals. Despite the essentiality of economic sustenance for a competitive advantage in the postmodern industrial era, Jordanian SMEs are hampered with multiple challenges, such as accounting information quality, which supports various organizational decisions. The prevalence of information technology (IT) optimizes accounting operations through accounting-based information. A computerized accounting system (accounting information system, or AIS) facilitates accurate reporting, processes large-scale transactions, and generates meaningful reporting for subsequent evaluation. Given the lack of AIS implementation in SMEs, despite its notable advantages, this study aims to investigate the AIS-implementation antecedents and their implications towards sustainable business performance among Jordanian SMEs. An integrated model was recommended based on the technology–organization–environment (TOE) framework and resource-dependency theory (RDT) for the incorporation of AIS-implementation elements and sustainable business performance into one model. A self-administered questionnaire was disseminated among 194 respondents within the context of Jordanian SMEs for data collection and evaluation using structural equation modelling (SEM). Based on the study outcomes, external pressure, compatibility, financial support, top management support (TMS), and external assistance significantly impacted AIS implementation, which subsequently catalyzed sustainable business performance. Such results could offer useful insights into how organizations could optimize AIS implementation for sustainable business performance and expand the current body of literature on IS- or IT-implementation antecedents and impacts. The implications of this study are that SMEs should develop effective AIS implementation in order to reach sustainability goals. Therefore, we recommend and encourage SMEs decision makers to utilize AIS for their businesses.
DyTox: Transformers for Continual Learning with DYnamic TOken eXpansion

Deep network architectures struggle to continually learn new tasks without forgetting the previous tasks. A recent trend indicates that dynamic architectures based on an expansion of the parameters can reduce catastrophic forgetting efficiently in continual learning. However, existing approaches often require a task identifier at test-time, need complex tuning to balance the growing number of parameters, and barely share any information across tasks. As a result, they struggle to scale to a large number of tasks without significant overhead. In this paper, we propose a transformer architecture based on a dedicated encoder/decoder framework. Critically, the encoder and decoder are shared among all tasks. Through a dynamic expansion of special tokens, we specialize each forward of our decoder network on a task distribution. Our strategy scales to a large number of tasks while having negligible memory and time overheads due to strict control of the parameters expansion. Moreover, this efficient strategy doesn't need any hyperparameter tuning to control the network's expansion. Our model reaches excellent results on CIFAR100 and state-of-the-art performances on the large-scale ImageNet100 and ImageNet1000 while having less parameters than concurrent dynamic frameworks.
Multi-ResAtt: Multilevel Residual Network With Attention for Human Activity Recognition Using Wearable Sensors

Human activity recognition (HAR) applications have received much attention due to their necessary implementations in various domains, including Industry 5.0 applications such as smart homes, e-health, and various Internet of Things applications. Deep learning (DL) techniques have shown impressive performance in different classification tasks, including HAR. Accordingly, in this article, we develop a comprehensive HAR system based on a novel DL architecture called Multi-ResAtt (multilevel residual network with attention). This model incorporates initial blocks and residual modules aligned in parallel. Multi-ResAtt learns data representations on the inertial measurement units level. Multi-ResAtt integrates a recurrent neural network with attention to extract time-series features and perform activity recognition. We consider complex human activities collected from wearable sensors to evaluate the Multi-ResAtt using three public datasets, Opportunity; UniMiB-SHAR; and PAMAP2. Additionally, we compared the proposed Multi-ResAtt to several DL models and existing HAR systems, and it achieved significant performance.
Improved LSTM-Based Time-Series Anomaly Detection in Rail Transit Operation Environments

Anomaly detection is crucial to the reliability and safety of rail transit systems. The rapid development of Internet of Things (IoT) and cloud technologies together with recent advances in machine learning offered various cloud-based data-driven approaches to automatic anomaly detection. However, the challenges introduced by the different types of equipment in rail transit systems with highly diverse data distributions and the lack of labeled anomaly data have not been sufficiently addressed. In this article, we attempt to cope with such challenges by proposing an improved long short term memory (LSTM)-based time-series anomaly detection scheme. The key elements of the proposed scheme include an improved LSTM model that may achieve more accurate time-series prediction for various rail transit devices and a method for determining an appropriate error threshold for detecting anomalies based on the prediction errors. In order to further enhance anomaly detection performance, we also propose a pruning algorithm for reducing the number of false anomalies. Our method does not rely on scarce anomaly labels but dynamically determines a threshold of prediction errors to identify anomalies; therefore, it overcomes the challenge of the extremely uneven distribution of rail transit data. We conducted extensive experiments in a real metro operation environment for performance evaluation. The experiment results prove the effectiveness of the proposed scheme and show a superior performance of the scheme compared to existing anomaly detection methods.
Cross-Domain Object Detection for Autonomous Driving: A Stepwise Domain Adaptative YOLO Approach

Supervised object detection models based on deep learning technologies cannot perform well in domain shift scenarios where annotated data for training is always insufficient. To this end, domain adaptation technologies for knowledge transfer have emerged to handle the domain shift problems. A stepwise domain adaptive YOLO (S-DAYOLO) framework is developed which constructs an auxiliary domain to bridge the domain gap and uses a new domain adaptive YOLO (DAYOLO) in cross-domain object detection tasks. Different from the previous solutions, the auxiliary domain is composed of original source images and synthetic images that are translated from source images to the similar ones in the target domain. DAYOLO based on YOLOv5s is designed with a category-consistent regularization module and adaptation modules for image-level and instance-level features to generate domain invariant representations. Our proposed method is trained and evaluated by using five public driving datasets including Cityscapes, Foggy Cityscapes, BDD100K, KITTI, and KAIST. Experiment results demonstrate that object detection performance is significantly improved when using our proposed method in various domain shift scenarios for autonomous driving applications.
Variable-Rate Deep Image Compression through Spatially-Adaptive Feature Transform

We propose a versatile deep image compression network based on Spatial Feature Transform (SFT) [45], which takes a source image and a corresponding quality map as inputs and produce a compressed image with variable rates. Our model covers a wide range of compression rates using a single model, which is controlled by arbitrary pixel-wise quality maps. In addition, the proposed framework allows us to perform task-aware image compressions for various tasks, e.g., classification, by efficiently estimating optimized quality maps specific to target tasks for our encoding network. This is even possible with a pretrained network without learning separate models for individual tasks. Our algorithm achieves outstanding rate-distortion trade-off compared to the approaches based on multiple models that are optimized separately for several different target rates. At the same level of compression, the proposed approach successfully improves performance on image classification and text region quality preservation via task-aware quality map estimation without additional model training. The code is available at the project website <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Two-Phase Multidocument Summarization Through Content-Attention-Based Subtopic Detection

Multidocument summarization problem deals with extracting main information and ideas from a set of related documents. Solution to this problem is to find an extraction strategy that aims at finding a small subset of sentences that is able to cover the most important information about the whole document set. Although a large number of machine-learning-based methods have shown great promise, the lack of high-quality training data poses an inherent obstacle to them. Furthermore, because of the proliferation of low-quality documents on the Internet, the existing summarization strategies, which are merely based on statistical features, get poor performance. In this article, we propose a new two-phase multidocument summarization strategy using content attention-based subtopic detection. First, inspired by distance dynamics-based community detection mechanism, we extract subtopics from the set of documents by having insight into their own content attention and also underlying semantic relations. Instead of complicated neural attention mechanisms, we propose a simple iteration-based content attention method to complete the subtopic detection task. Second, we formulate summarization from different subtopics as a combinatorial optimization problem of minimizing sentence distance and maximizing topic diversity. We prove the submodularity of the above optimization problem, which allows us to propose a new multidocument summarization algorithm based on the greedy mechanism. Finally, we experimentally validate our new algorithms on BBC news summary and wikiHow data. The results show our new algorithms outperform the state-of-the-art methods.
Theoretical foundations of forward feature selection methods based on mutual information

Feature selection problems arise in a variety of applications, such as microarray analysis, clinical prediction, text categorization, image classification and face recognition, multi-label learning, and classification of internet traffic. Among the various classes of methods, forward feature selection methods based on mutual information have become very popular and are widely used in practice. However, comparative evaluations of these methods have been limited by being based on specific datasets and classifiers. In this paper, we develop a theoretical framework that allows evaluating the methods based on their theoretical properties. Our framework is grounded on the properties of the target objective function that the methods try to approximate, and on a novel categorization of features, according to their contribution to the explanation of the class; we derive upper and lower bounds for the target objective function and relate these bounds with the feature types. Then, we characterize the types of approximations taken by the methods, and analyze how these approximations cope with the good properties of the target objective function. Additionally, we develop a distributional setting designed to illustrate the various deficiencies of the methods, and provide several examples of wrong feature selections. Based on our work, we identify clearly the methods that should be avoided, and the methods that currently have the best performance.
Ontology and context based recommendation system using Neuro-Fuzzy Classification

Recommendation Systems (RS) identify the products of likely interest to the user. In earlier studies, the recommendation systems classify the reviews of products as positive or negative, verbatim on the features contained in the reviews, without reference to their context. In this paper, we develop an Ontology and Context Based Recommendation System (OCBRS) to assess the context of and determine the opinion of the review. We propose a Neuro-Fuzzy Classification approach using fuzzy rules to extract the context of the review. This approach automatically classifies the reviews under the respective fuzzy rule. Ontology facilitates a hierarchical and systematic methodology to group the context and acts as repository of context. The proposed approach appears to improve the accuracy of the RS.
A new approach to distributed fusion filtering for networked systems with random parameter matrices and correlated noises

This paper is concerned with the distributed filtering problem for a class of discrete-time stochastic systems over a sensor network with a given topology. The system presents the following main features: (i) random parameter matrices in both the state and observation equations are considered; and (ii) the process and measurement noises are one-step autocorrelated and two-step cross-correlated. The state estimation is performed in two stages. At the first stage, through an innovation approach, intermediate distributed least-squares linear filtering estimators are obtained at each sensor node by processing available output measurements not only from the sensor itself but also from its neighboring sensors according to the network topology. At the second stage, noting that at each sampling time not only the measurement but also an intermediate estimator is available at each sensor, attention is focused on the design of distributed filtering estimators as the least-squares matrix-weighted linear combination of the intermediate estimators within its neighborhood. The accuracy of both intermediate and distributed estimators, which is measured by the error covariance matrices, is examined by a numerical simulation example where a four-sensor network is considered. The example illustrates the applicability of the proposed results to a linear networked system with state-dependent multiplicative noise and different network-induced stochastic uncertainties in the measurements; more specifically, sensor gain degradation, missing measurements and multiplicative observation noises are considered as particular cases of the proposed observation model.
Convolutional Neural Network-Based Robust Denoising of Low-Dose Computed Tomography Perfusion Maps

The low-dose computed tomography (CT) perfusion data has low signal-to-noise ratio resulting in derived perfusion maps being noisy. These low-quality maps typically requires a denoising step to improve their utility in real-time. The existing methods, including state-of-the-art online sparse perfusion deconvolution (SPD), largely relies on the convolutional model that may not be applicable in all cases of brain perfusion. In this paper, a denoising convolutional neural network (DCNN) was proposed that relies only on computed perfusion maps for performing the denoising step. The network was trained with a large number of low-dose digital brain phantom perfusion maps to provide an approximation to the corresponding high-dose perfusion maps. The batch normalization coupled with residual learning makes the trained model invariant to the dynamic range of the input low-dose perfusion maps. The denoising of the raw-data using the convolutional neural network was also attempted here and shown to have limited applicability in the low-dose CT perfusion cases. The digital perfusion phantom as well as in-vivo results indicate that the proposed DCNN applied in the derived map domain provides superior improvement compared to the online SPD with an added advantage of being computationally efficient.
Solving Nonlinear Equations System With Dynamic Repulsion-Based Evolutionary Algorithms

Nonlinear equations system (NES) arises commonly in science and engineering. Repulsion techniques are considered to be the effective methods to locate different roots of NES. In general, the repulsive radius needs to be given by the user before the run. However, its optimal parameter setting is difficult and problem-dependent. To alleviate this drawback, in this paper, we first propose a dynamic repulsion technique, and then a general framework based on the dynamic repulsion technique and evolutionary algorithms (EAs) is presented to effectively solve NES. The major advantages of our framework are: 1) the repulsive radius is controlled dynamically during the evolutionary process; 2) multiple roots of NES can be simultaneously located in a single run; 3) the diversity of the population is preserved due to the population reinitialization; and 4) different repulsion techniques and different EAs can be readily integrated into this framework. To extensively evaluate the performance of our framework, we choose 42 problems with diverse features as the test suite. In addition, some representative differential evolution and particle swarm optimization variants are incorporated into the framework. Our method is also compared with other state-of-the-art methods. Experimental results indicate that the dynamic repulsion technique can improve the performance of the original repulsion technique with static repulsive radius. Moreover, the proposed method is able to yield better results compared with other methods.
A Dynamic Decision-Making Approach for Intrusion Response in Industrial Control Systems

Industrial control systems (ICSs) are facing more and more cybersecurity issues, leading to increasingly severe risks in critical infrastructure. To mitigate risks, developing an appropriate security strategy is of paramount importance. However, existing efforts on decision making in ICSs inherit some limitations, such as the lack of consideration of the strategy for securing both cyber and physical domains and a tradeoff between security and system requirements. To overcome these limitations, a decision-making approach is presented in this paper for intrusion response in ICSs. Aiming to determine the optimal security strategy against attacks promptly, it tries to secure the most “dangerous” attack paths and respond to functional failures. In this approach, measures that cover both cyber and physical domains are designed with in-depth analysis of attack propagation. They ensure the completeness of candidate security strategy space. A number of Pareto optimal solutions are determined from the strategy space through multiobjective optimization. The objective is to maximize the objective vector composed of security benefit, system benefit, and state benefit. Then, these solutions are prioritized by using a distance-based evaluation method, which pursues the optimal protection ability by making the objective vector of the selected strategy closest to the ideal one. The effectiveness of the proposed approach is demonstrated with a case study on a simulated process control system.
Phase-Modulated Entangling Gates Robust to Static and Time-Varying Errors

In quantum computing systems where entangling logic gates are mediated via bosonic oscillator modes ($e.g.$ trapped ions), residual coupling between qubits and oscillator is a dominant source of gate infidelity. This work shows how discrete phase modulation of the field mediating the entangling operation ensures that the system of qubits is decoupled from multiple oscillator modes, even in the presence of common time-varying sources of noise and hardware instability. The results demonstrate the capabilities of quantum control to drive major performance advances in quantum computing.
Ensuring privacy with constrained additive noise by minimizing Fisher information

The problem of preserving the privacy of individual entries of a database when responding to linear or nonlinear queries with constrained additive noise is considered. For privacy protection, the response to the query is systematically corrupted with an additive random noise whose support is a subset or equal to a pre-defined constraint set. A measure of privacy using the inverse of the trace of the Fisher information matrix is developed. The Cramér–Rao bound relates the variance of any estimator of the database entries to the introduced privacy measure. The probability density that minimizes the trace of the Fisher information (as a proxy for maximizing the measure of privacy) is computed. An extension to dynamic problems is also presented. Finally, the results are compared to the differential privacy methodology.