Reviewerly/exHarmony huggingface dataset:
Mapping lithology, hydrothermal alteration, and Fe mineralization using satellite data in the Champeh salt dome, South of Iran

Champeh salt dome is located 30 km northwest of Bandar Lengeh, east of Hormozgan province, Iran. Iron ores and several alterations can be found in this salt dome, which consists of evaporitic and volcanic sequences of the Hormoz series and Pabdeh-Jahrom, Asmari, Gachsaran, and Mishan formations. The existing geological maps do not provide comprehensive details of mineralization, alteration, and various lithologies in this salt dome. Therefore, the separation and detection of these phenomena, by using ASTER (advanced spaceborne thermal emission and reflection radiometer) and OLI (operational land imager) satellite images and utilizing different image processing methods can be helpful to locate them and produce a lithological map of this area. The processing methods used in this study are band ratio, false-color combination, principal component analysis, independent component analysis, minimum noise fraction, spectral angle mapping, and linear spectral unmixing. Due to a vast number of salt domes in the south and southwest of Iran, and the similarity of these salt domes in terms of mineralogy and lithology, the results of this study can be useful in future geological and exploratory studies for this salt dome and similar salt domes of the world.
A Reinforcement Learning Based Approach of Context-driven Adaptive User Interfaces

Adaptive User Interfaces (AUI) have to exploit Artificial Intelligence powerful to adapt User Interfaces (UI)s to People With Disabilities (PWD). Thus, Machine Learning methods could master disabilities problems and barriers. In this paper, we propose a Reinforcement Learning (RL) based approach for resolving and surmounting PWD-UI interactions barriers since RL is good for learning good behavior. Hence, we have called the approach as RL-AUIAC as Reinforcement Learning of Adaptive User Interfaces for Accessibility Context. RL-AUIAC is based on three Knowledge Layers (KL)s depending on the kind of adaptation and the resolved problem at each layer. KL uses the Exploration-Exploitation dilemma to respond to the question: what to learn from each planned-adaptation sequences? In fact, Disability Knowledge Layer (DKL)learns UI structure behavior depending on the disability profile. Modality Knowledge Layer(MKL) learns facilities of adaptation on the basis of the couple <modality, disability profile>. Platform Knowledge Layer (PKL) explores-exploits platform-knowledge to learn adaptation facilities on the basis of <modality, disability profile, platform>.
Fruit images Visibility enhancement using Type-II Fuzzy

We suggested a Fuzzy image enhancement technique using Type-II to improvise visibly weather-degraded fruit images. The acquired fruit picture is divided into dark and light regions with Fuzzy partitioned. Fuzzy-sure entropy was used to choose the optimum visibility enhancement threshold values. Compared to the ATCE, BPDHE, GC and CLAHE image enhancement frameworks, the proposed scheme offers improved image quality. Experimental findings show that the approach in terms of visual and quantitative analysis have a lower percentage of saturated pixels, better output of visible edges and color gradients.
CACnet: Cube Attentional CNN for Automatic Speech Recognition

End-to-end models have been widely used in Automatic Speech Recognition (ASR). Convolutional Neural Networks (CNNs) can effectively use spectrum information to model acoustic models. However, the convolution layers have limitations on the receptive field leading to restrictions for long speech signals. Inspired by this, we propose a Cube Attention CNN network(CACnet) that uses two different attention blocks to integrate the feature information of different dimensions for extending context information. Thereinto, the Global Deep Attention Block utilizes non-local operations to compute interactions between any two positions on feature maps and enables the acquirement of global feature representations while the Cross-Channel Attention Block adaptively recalibrates channel-wise feature responses. Then, outputs of the above two attention modules will be added up to further improve the feature representation which contributes to enrich contextual information. Finally, the performance of our proposed architecture will be explored under ASR tasks in English circumstances. Experiments on LibriSpeech indicate that CACnet achieves a word error rate (WER) of 3.78%/9.56% without language model (LM), and 2.84%/6.97% with LM, which is near state-of-the-art accuracy. CACnet on WSJ with 4.4% WER obtains better performance, compared to CTC-based CNN models, such as QuartzNet and Jasper, with the same language model. The proposed network achieves competitive accuracy while having fewer parameters. Moreover, CACnet can be easily incorporated into any existed network since it has the same input and output dimensions.
An end‐to‐end trusted architecture for network slicing in <scp>5G</scp> and beyond networks

Abstract 5G opens the door not only for new network services but also for a new business model involving new business actors. Particularly, the introduction of Network Slicing encourages the appearance of new stakeholders, namely Verticals or network slice tenants, Network Slice Providers, and Resource Providers. The Network Slice Provider sells end‐to‐end network slices (virtual end‐to‐end mobile network) to the Vertical, while leasing virtual and physical resources from Resource Providers to deploy and instantiate these end‐to‐end network slices. Consequently, a trusted relationship among these actors should be established and maintained. To fill this gap, in this paper, we propose a Blockchain‐based trust architecture that aims to: (1) create and negotiate the deployment of an end‐to‐end slice network; (2) manage automatically the service level agreements (SLA) established between the involved actors; while guaranteeing security and anonymous transactions.
An Improved Method of Low Light Image Enhancement Based on Retinex

Low light image enhancement is a highly challenging task, which has received major attention over the decades. Inspired by Retinex Net [1], this paper improves low light image enhancement by mimicking the mechanism of Retinex. The model consists of a Decom-RNet (Decomposition Residual Net) for image decomposition and an Enhance-RNet (Enhancement Residual Net) for illumination enhancement and adjustment. Decom-RNet decomposes the image into illumination and reflection, and Enhance-RNet enhances the illumination. In particular, we introduce an attention module into residual learning, which significantly improves the accuracy of residual learning. Experimental results demonstrate that our method outperforms the state-of-the-art low light image enhancement methods.
A Comparison of Anomaly Detection Methods for Industrial Screw Tightening

Within the context of Industry 4.0, quality assessment procedures using data-driven techniques are becoming more critical due to the generation of massive amounts of production data. In this paper, we address the detection of abnormal screw tightening processes, which is a relevant industrial task. Since labeling is costly, requiring a manual effort, we focus on unsupervised approaches. In particular, we assume a low-dimensional input screw fastening approach that is based only on angle-torque pairs. Using such pairs, we explore three main unsupervised Machine Learning (ML) algorithms: Local Outlier Factor (LOF), Isolation Forest (iForest) and a deep learning Autoencoder (AE). For benchmarking purposes, we also explore a supervised Random Forest (RF) algorithm. Several computational experiments were held by using recent industrial data with 2.8 million angle-torque pair records and a realistic and robust rolling window evaluation. Overall, high quality anomaly discrimination results were achieved by the iForest (99%) and AE (95% and 96%) unsupervised methods, which compared well against the supervised RF (99% and 91%). When compared with iForest, the AE requires less computation effort and provides faster anomaly detection response times.
A general auto-shift minimal-step phase-shifting algorithm for arbitrary cavity length

Phase demodulation by wavelength-tuning phase-shifting interferometry is increasingly significant for precision metrology of engineering surfaces. However, the existing multi-surface measurement algorithms can only be applied to the case that the cavity length of the measured plate is a certain multiple of its optical thickness. For flexible muti-surface interferometry, we present a general auto-shift minimal-step phase-shifting algorithm (AMPA) for arbitrary cavity lengths. To achieve this, as a basis, the in-depth analysis of the residual error of the phase demodulation algorithm under each combination of the cavity coefficient M and the phase division number N is performed. By this method, the residual error of the phase demodulation algorithm can be controlled by adjusting N for each M. And due to a wise selection of the Blackman-Harris window function, the satisfactory abilities of harmonic error suppression and phase extraction can be provided. The performance of the developed algorithm is studied under various factors, and its superiority over traditional algorithms is verified. Based on the Zernike polynomials, the numerical simulation indicates the maximum error of reconstructed wavefronts is less than 2 × 10−5λ0. Experimental studies on a rectangular plate and a circular plate using a Fizeau wavelength-tuning interferometer further imply that our algorithm is valid and reliable. Meanwhile, a comparative analysis of the reconstructed surface shapes using the developed AMPA and the classical 36-step and 6N-5 algorithms is performed based on the introduced height evaluation parameters. The comparative results show that the maximum errors of the arithmetic mean height Sa and the root mean square height Sq are 6.8 nm and 6.5 nm, respectively. And other height parameters also support the validity of the proposed algorithm.
Impact of Hybrid Neural Network Structure on Performance of Multiclass Classification

This article describes hybrid convolutional neural network that uses one quantum circuit for image classification. The different configurations of the hybrid neural network with the quantum circuit are considered. Several different quantum circuits with different number of qubits are compared. These hybrid neural network configurations are evaluated on MNIST and MNIST Fashion datasets, which is radically different from MNIST dataset. Performance of hybrid neural network is compared for multiclass classification on MNIST and MNIST Fashion datasets for 4, 6, 8, 10 classes using quantum circuits with 2, 3, 4 qubits. The results of the experiments indicate the feasibility of using hybrid neural networks for multiclass classification.
DEN-DQL: Quick Convergent Deep Q-Learning with Double Exploration Networks for News Recommendation

Due to the dynamic characteristics of news and user preferences, personalized recommendation is a challenging problem. Traditional recommendation methods simply focus on current reward, which just recommend items to maximize the number of current clicks. And this may reduce users' interest in similar items. Although the news recommendation framework based on deep reinforcement learning preciously proposed (i.e, DRL, based on deep Q-learning) has the advantages of focusing on future total rewards and dynamic interactive recommendation, it has two issues. First, its exploration method is slow to converge, which may bring new users a bad experience. Second, it is hard to train on off-line data set because the reward is difficult to be determined. In order to address the aforementioned issues, we propose a framework named DEN-DQL for news recommendation based on deep Q-learning with double exploration networks. Also, we develop a new method to calculate rewards and use an off-line data set to simulate the online news clicking environment to train DEN-DQL. Then, the well trained DEN-DQL is tested in the online environment of the same data set, which demonstrates at least 10% improvement of the proposed DEN-DQL.
Image Shape Manipulation from a Single Augmented Training Sample

In this paper, we present DeepSIM, a generative model for conditional image manipulation based on a single image. We find that extensive augmentation is key for enabling single image training, and incorporate the use of thin-plate-spline (TPS) as an effective augmentation. Our network learns to map between a primitive representation of the image to the image itself. The choice of a primitive representation has an impact on the ease and expressiveness of the manipulations and can be automatic (e.g. edges), manual (e.g. segmentation) or hybrid such as edges on top of segmentations. At manipulation time, our generator allows for making complex image changes by modifying the primitive input representation and mapping it through the network. Our method is shown to achieve remarkable performance on image manipulation tasks.
Challenges in Detecting Emergent Behavior in System Testing

Abstract System integration testing in the defense and aerospace industry is becoming increasingly complex. The long lifetime of the system drives the need for sub‐system modifications throughout the system life cycle. The manufacturer must verify that these modifications do not negatively affect the system's behavior. Hence, an extensive test regime is required to ensure reliability and robustness of the system. System behaviors that emerge from the interaction of sub‐systems can be difficult to pre‐define and capture in a test setup using acceptance criteria. Typical challenges with current test practice include late detection of unwanted system behavior, high cost of repetitive manual processes, and risk of release delays because of late error detection. This paper reviews the state of practice at a case company in the defense and aerospace industry. We use an industry‐as‐laboratory approach to explore the situation in the company. The research identifies the challenges and attempts to quantify the potential gain from improving the current practice. We find that the current dependency on manual analysis generates resources ‐and scheduling constraints and communication issues that hinder efficient detection of system emergent behavior. We explore two approaches to automate anomaly detection of system behavior from test data. The first approach looks at anomaly detection in a top‐down approach to give an indication of the system integrity. The second approach uses anomaly detection on system parts, resulting in the ability to localize the root causes. The work lays the foundation for further research of automated anomaly detection in system testing.
MAT-DQN: Toward Interpretable Multi-agent Deep Reinforcement Learning for Coordinated Activities

We propose an interpretable neural network architecture for multi-agent deep reinforcement learning to understand the rationale for learned cooperative behavior of the agents. Although the deep learning technology has contributed significantly to multi-agent systems to build coordination among agents, it is still unclear what information the agents depend on to behave cooperatively. Removing this ambiguity may further improve the efficiency and productivity of multi-agent systems. The main idea of our proposal is to adopt the transformer to deep Q-network for addressing the above-mentioned issue. By extracting multi-head attention weights from the transformer encoder, we propose a multi-agent transformer deep Q-network (MAT-DQN) and show that agents using attention mechanisms possess better coordination capability with other agents despite being trained individually for a cooperative patrolling task problem; thus, they can exhibit better performance results compared with the agents with vanilla DQN (which is a baseline method). Furthermore, we indicate that it is possible to visualize heatmaps of attentions, which indicate the influential input-information in agents’ decision-making process for their cooperative behaviors.
Spatio-Temporal Fusion Network for Video Super-Resolution

The video super-resolution (VSR) is a traditional computer vision task, and in recent years, the methods based on deep learning have achieved promising performance. Most of the previous VSR methods utilize optical flow to perform alignment. However, in the case of occlusions and large motions, it is difficult to obtain accurate optical flow, which will affect the final recovery results. In addition, how to effectively fuse spatio-temporal information in different frames is also a key problem in VSR. However, most of the existing approaches ignore the value of local and/or global spatial-temporal information. In this paper, we propose a novel Spatio-Temporal Fusion Network (STFN) that contains a feature extraction module, an alignment module, a fusion module and a reconstruction module, to address these challenges. Specifically, we propose an enhanced cascaded feature fusion block (ECFFB) in alignment module, which can predict offsets of deformable convolution (DC) more precisely to effectively handle large motions. Furthermore, we design a fusion module, including local temporal fusion block (LTFB) and pyramidal spatio-temporal fusion block (PSTF) in a single-branch manner. These two blocks utilize local and global spatiotemporal information respectively to achieve better fusion result. Experimental results demonstrate that our STFN significantly outperforms the state-of-the-arts methods on several datasets.
Continual knowledge infusion into pre-trained biomedical language models

Biomedical language models produce meaningful concept representations that are useful for a variety of biomedical natural language processing (bioNLP) applications such as named entity recognition, relationship extraction and question answering. Recent research trends have shown that the contextualized language models (e.g. BioBERT, BioELMo) possess tremendous representational power and are able to achieve impressive accuracy gains. However, these models are still unable to learn high-quality representations for concepts with low context information (i.e. rare words). Infusing the complementary information from knowledge-bases (KBs) is likely to be helpful when the corpus-specific information is insufficient to learn robust representations. Moreover, as the biomedical domain contains numerous KBs, it is imperative to develop approaches that can integrate the KBs in a continual fashion.We propose a new representation learning approach that progressively fuses the semantic information from multiple KBs into the pretrained biomedical language models. Since most of the KBs in the biomedical domain are expressed as parent-child hierarchies, we choose to model the hierarchical KBs and propose a new knowledge modeling strategy that encodes their topological properties at a granular level. Moreover, the proposed continual learning technique efficiently updates the concepts representations to accommodate the new knowledge while preserving the memory efficiency of contextualized language models. Altogether, the proposed approach generates knowledge-powered embeddings with high fidelity and learning efficiency. Extensive experiments conducted on bioNLP tasks validate the efficacy of the proposed approach and demonstrates its capability in generating robust concept representations.
Perceived benefits of Continuous Deployment in Software-Intensive Embedded Systems

Continuous Deployment (CD) advocates for quick and frequent deployments of software to production. The goal is to bring new functionality as early as possible to users while learning from their usage. CD has emerged from web-based applications where it has been gaining traction over the past years. While CD is appealing for many software development organizations, empirical evidence on perceived benefits in software-intensive embedded systems is scarce. The objective of this paper is to identify perceived benefits after transitioning to continuous deployment from a long-cycle release and deployment process. To do that, a case study at a multinational telecommunication company was conducted focusing on large and complex embedded software; the Third Generation (3G) Radio Access Network (RAN) software.
Are Coverage Criteria Meaningful Metrics for DNNs?

The wide deployment of Deep Neural Networks (DNNs), though achieving great success in many domains, has severe safety concerns. Inspired by testing criteria from traditional software engineering, various coverage criteria have been proposed to ensure the safety of DNNs. However, the validity of coverage criteria was questioned in related researches. In this paper, we evaluate the performance of dominating coverage criteria in two aspects: 1) distinguishing different qualities of test sets, 2) improving the safety and robustness of DNNs. The evaluation result confirms that coverage criteria are meaningful metrics for DNNs. Specifically, the way for improving robustness is contrary to the previous assumption: the higher coverage criterion score, the better. In addition, we propose a new coverage criterion called Independence Neuron Coverage (INC) which is finer grained to capture DNNs' subtle behaviour. Experiments show that INC is efficient and performs better than other evaluated coverage criteria in both aspects.
Utility-preserving differentially private skyline query

Skyline query has been widely applied in multi-criterion decision making, whose potential privacy risk is practically concerned on some specific occasions. Differential Privacy (DP) is a rigorous privacy-preserving method for its robustness and reliability. Considering DP will deteriorate the data utility, this paper proposes the Individual Differential Privacy via Spectral Clustering (iDP-SC) to address the privacy leakage in skyline query. It shifts the calculation of the local sensitivity from the original dataset to the one processed by spectral clustering, and through this, the sensitivity is reduced as well as the calibrated noise. As a result, it maintain higher utility without sacrificing the privacy preservation provided by differential privacy. Furthermore, compared with existing work for privacy-preserving skyline query, the proposed iDP-SC avoids the disclosure of key information, while providing the quantitative analysis of privacy protection level. The performance of the proposed iDP-SC was examined through comparison with the DP and Individual Differential Privacy (iDP) on both real and synthetic datasets. The experimental results demonstrates the capability and effectiveness of the proposed approach. Along this line of research, future work could be extended to the situation where the curator is un-trusted, or the personalized privacy requirements of retailers etc.