Reviewerly/exHarmony huggingface dataset:
Functional agricultural monitoring data storage based on sustainable block chain technology

In agriculture, the impact of environment on crops is the most critical. How to accurately and real-time monitor the growth environment of crops is very important for the growth of crops. However, in the process of monitoring, how to store the monitoring data safely and reliably has become a research problem. Therefore, this paper proposes a storage method of agricultural monitoring data based on Sustainable blockchain technology. Among them, this paper mainly studies the monitoring data storage and privacy protection scheme. With the continuous storage of monitoring data, the amount of data is increasing, which leads to the shortage of local storage space. Therefore, in the improvement of the existing local storage space storage method, with the advantages of cloud storage, the data is encrypted and stored in the cloud, and the privacy side based on blockchain is adopted It can ensure the privacy and security of storage. In the simulation analysis, it is found that the data storage method designed in this paper can store more than 0.5 mb/s for three groups of data with the size of 0 MB, 100 MB and 200 MB. Considering the privacy protection scheme of the storage system, the 1024 bit key is adopted, which can ensure the security of agricultural monitoring data stored in the cloud.
Access control and classifier-based blockchain technology in e-healthcare applications

E-healthcare promises to be the next big wave in the healthcare industry, which makes security the most essential component in this framework. Security in sensor network communication is recommended for providing information secrecy and authentication. Many security algorithms are proposed by numerous creators, and among all the security models, Blockchain Technology (BT) with dispersed ledger provides the highest level of security for e-healthcare systems. To improve the security of the healthcare framework, a learning classifier is the support vector machine that is used to characterize sensitive and nonsensitive data from e-healthcare information. After this procedure comes the BT approach, which is connected to protect sensitive data using various layers of encryption, and decryption processes. When the healthcare data are verified, the access-control process considers the reason for authentication. Finally, the simulation results were attained for assessing the performance of the e-healthcare information security model regarding throughput, energy, and security-level measures.
Table extraction and understanding for scientific and enterprise applications

Valuable high-precision data are often published in the form of tables in both scientific and business documents. While humans can easily identify, interpret and contextualize tables, developing general-purpose automated techniques for extraction of information from tables is difficult due to the wide variety of table formats employed across corpora. To extract useful data from tables, data cells must be correctly extracted and linked to all relevant headers, units of measure and in-text references. Table extraction involves identifying the border and cell structure for each document table, while table understanding provides context by linking cells with semantic information inside and outside the table, such as row and column headers, footnotes, titles, and references in surrounding text. The objective of this tutorial is to provide a detailed synopsis of existing approaches for table extraction and understanding, highlight open research problems, and provide an overview of potential applications.
Hue-correction scheme considering CIEDE2000 for color-image enhancement including deep-learning-based algorithms

In this paper, we propose a novel hue-correction scheme for color-image-enhancement algorithms including deep-learning-based ones. Although hue-correction schemes for color-image enhancement have already been proposed, there are no schemes that can both perfectly remove perceptual hue-distortion on the basis of CIEDE2000 and be applicable to any image-enhancement algorithms. In contrast, the proposed scheme can perfectly remove hue distortion caused by any image-enhancement algorithm such as deep-learning-based ones on the basis of CIEDE2000. Furthermore, the use of a gamut-mapping method in the proposed scheme enables us to compress a color gamut into an output RGB color gamut, without hue changes. Experimental results show that the proposed scheme can completely correct hue distortion caused by image-enhancement algorithms while maintaining the performance of the algorithms and ensuring the color gamut of output images.
Spatio-temporal air pollution modelling using a compositional approach

Air pollutant data are compositional in character because they describe quantitatively the parts of a whole (atmospheric composition). However, it is common to use air pollutant concentrations in statistical models without considering this characteristic of the data and, therefore, without control of common statistical problems, such as spurious correlations and subcompositional incoherence. This paper now proposes a daily multivariate spatio-temporal model with a compositional approach. The air pollution spatio-temporal model is based on a dynamic linear modelling framework with Bayesian inference. The novel modelling methodology was applied in an urban area for carbon monoxide (CO, mg·m−3), sulfur dioxide (SO2, μg·m−3), ozone (O3, μg·m−3), nitrogen dioxide (NO2, μg·m−3), and particulate matter less than 2.5 μm in aerodynamic diameter (PM2.5, μg·m−3). The proposal complemented and improved the conventional approach in air pollution modelling. The main improvements come from a fast multivariate data description, high spatial-correlation, and adequate modelling of air pollutants with high variability.
Valuing Expertise During the Pandemic

This article addresses how social media platforms can better highlight expert voices through design choices. Misinformation, after all, has exploded during the Covid-19 pandemic, and platforms have struggled to address the issue. The authors examine this critical gap in validation mechanisms in the current social media platforms and suggest possible solutions for this urgent problem with third-party partnerships.
Ship Detection in Remote Sensing Image based on Faster R-CNN with Dilated Convolution

Ship detection based on remote sensing image can provide effective assistance for maritime reconnaissance, port and fisheries management. This paper mainly presents an improved Faster R-CNN algorithm for ship detection with dilated convolution. Firstly, the image of remote sensing ship is preprocessed. This paper uses dark channel prior method to dehaze image and enhance dehazed image quality by grey world algorithm. Second, The Faster R-CNN is improved in ship detection. The dilated convolutional is introduced into Faster R-CNN to increases its feature extraction capability. Finally, ship detection software platform is built. The remote ship image preprocessing and ship detection network is integrated into an application software. The ship detection network can be trained end to end. Experimental results on the HRSC2016 datasets illustrated that the proposed presents a good performance for ship detection task.
Sparse Variational Deterministic Policy Gradient for Continuous Real-Time Control

Recent advancements in deep reinforcement learning for real control tasks have received interest from many researchers and field engineers in a variety of industrial areas. However, in most cases, optimal policies obtained by deep reinforcement learning are difficult to implement on cost-effective and lightweight platforms such as mobile devices. This can be attributed to their computational complexity and excessive memory usage. For this reason, this article proposes an off-policy deep reinforcement learning algorithm called the sparse variational deterministic policy gradient (SVDPG). SVDPG provides highly efficient policy network compression under the standard reinforcement learning framework. The proposed SVDPG integrates Bayesian pruning, which is known as a state-of-the-art neural network compression technique, with the policy update in an actor–critic architecture for reinforcement learning. It is demonstrated that SVDPG achieves a high compression rate of policy networks for continuous control benchmark tasks while preserving a competitive performance. The superiority of SVDPG in low-computing power devices is proven by comparing the level of compression in terms of the memory requirements and computation time on a commercial microcontroller unit. Finally, it is confirmed that the proposed SVDPG is also reliable in real-world scenarios since it can be applied to the swing-up control of an inverted pendulum system.
WatchOver

Simultaneous alcohol and marijuana (SAM) use can significantly impact young adults' physical and mental well-being. While SAM use is becoming increasingly prevalent in this population, there has not been much work to monitor and understand related behaviors and contexts. We aim to address this gap by using smartwatches to collect ecological momentary assessments (EMAs) and sensor data. In this paper, we describe the design and development of the smartwatch framework focusing on SAM use. We also collected pilot data from an n=1 deployment over 7 days using the framework. Our findings indicate that EMAs on smartwatches can be completed with lower perceived burden, which is important for longitudinal SAM use data collection. We also provide design guidelines and rationale for future work aiming to use smartwatches.
Linguistic Variation and Change in 250 Years of English Scientific Writing: A Data-Driven Approach

We trace the evolution of Scientific English through the Late Modernperiod to modern time on the basis of a comprehensive corpus composedof the Transactions and Proceedings of the Royal Society of London, thefirst and longest-running English scientific journal established in 1665.Specifically, we explore the linguistic imprints of specialization and di-versification in the science domain which accumulate in the formation of"scientific language" and field-specific sublanguages/registers (chemistry,biology etc).We pursue an exploratory, data-driven approach using state-of-the-art computational language models and combine them with selectedinformation-theoretic measures (entropy, relative entropy) for comparingmodels along relevant dimensions of variation (time, register). Focusing onselected linguistic variables (lexis, grammar), we show how we deploy com-putational language models for capturing linguistic variation and changeand discuss benefits and limitations.
Pretrained Embeddings for Stance Detection with Hierarchical Capsule Network on Social Media

Stance detection on social media aims to identify the stance of social media users toward a topic or claim, which can provide powerful information for various downstream tasks. Many existing stance detection approaches neglect to model the deep semantic representation information in tweets and do not explore aggregating the hierarchical features among words, thus degrading performance. To address these issues, this article proposes a novel deep learning approach P retrained E mbeddings for Stance Detection with H ierarchical C apsule N etwork (PE-HCN) without complicated preprocessing. Specifically, PE-HCN first adopts a pretrained language model and then uses a related textual entailment task for fine-tuning to obtain the deep textual representations of tweets. The PE-HCN approach extends the dynamic routing scheme to cope with these deep textual representations by utilizing primary capsules for routing the information among words in each tweet and applying secondary capsules to transmit the aggregated features to each category capsule accordingly. Moreover, to improve the confidences of the category capsules, we design an adaptive feedback mechanism to dynamically strengthen the routing signals. Through experiments on three benchmark datasets, compared with the state-of-the-art baselines, the extensive results exhibit that PE-HCN achieves competitive improvements of up to 6.32%, 2.09%, and 1.8%, respectively.
A novel dynamic multi-criteria ensemble selection mechanism applied to drinking water quality anomaly detection

The provision of clean and safe drinking water is a crucial task for water supply companies from all over the world. To this end, automatic anomaly detection plays a critical role in drinking water quality monitoring. Recent anomaly detection studies use techniques that focus on a single global objective. Yet, companies need solutions that better balance the trade-off between false positives (FPs), which lead to financial losses to water companies, and false negatives (FNs), which severely impact public health and damage the environment. This work proposes a novel dynamic multi-criteria ensemble selection mechanism to cope with both problems simultaneously: the non-dominated local class-specific accuracy (NLCA). Moreover, experiments rely on recent time series related classification metrics to assess the predictive performance. Results on data from a real-world water distribution system show that NLCA outperforms other ensemble learning and dynamic ensemble selection techniques by more than 15% in terms of time series related F1 scores. As a conclusion, NLCA enables the development of stronger anomaly detection systems for drinking water quality monitoring. The proposed technique also offers a new perspective on dynamic ensemble selection, which can be applied to different classification tasks to balance conflicting criteria.
An Empirical Study of Quick Remedy Commits

Software systems are continuously modified to implement new features, to fix bugs, and to improve quality attributes. Most of these activities are not atomic changes, but rather the result of several related changes affecting different parts of the code. For this reason, it may happen that developers omit some of the needed changes and, as a consequence, leave a task partially unfinished, introduce technical debt or, in the worst case scenario, inject bugs. Knowing the changes that are mistakenly omitted by developers can help in designing recommender systems able to automatically identify risky situations in which, for example, the developer is likely to be pushing an incomplete change to the software repository.
Outlier Detection in Sensor Data using Ensemble Learning

Analyzing sensor data from a production environment is quite challenging because of the high-dimensional nature of the data. In addition, the generated data is in the form of time-series, where the sequence of registrations may be of utmost significance. One of the main goals of the paper is to determine if the given time-series of feature combinations is normal or rare. This goal could successfully be achieved by combining multiple machine learning models. In this paper, a sliding window based ensemble method is proposed to detect outliers in a streaming fashion. The proposed method uses a combination of clustering algorithms to construct subgroups (clusters) representing different data structures. These structures are later used in a one-class classification algorithm to identfy the outliers. Thus, if a pattern does not belong to any of the common structures or clusters, it is an outlier. Further, based on the rare pattern classification, machine failures could be predicted in advance.
Traffic Measurement and Congestion Detection Based on Real-Time Highway Video Data

Since global road traffic is steadily increasing, the need for intelligent traffic management and observation systems is becoming an important and critical aspect of modern traffic analysis. In this paper, we cover the development and evaluation of a traffic measurement system for tracking, counting and classifying different vehicle types based on real-time input data from ordinary highway cameras by using a hybrid approach including computer vision and machine learning techniques. Moreover, due to the relatively low framerate of such cameras, we also present a prediction model to estimate driving paths based on previous detections. We evaluate the proposed system with respect to different real-life road situations including highway-, toll station- and bridge-cameras and manage to keep the error rate of lost vehicles under 10%.
Single image rain removal via multi-module deep grid network

Rain streaks severely degenerate the performances of image/video processing tasks, therefore effective methods for removing rain streaks are required for a wide range of practical applications. In this paper, we introduce an end-to-end deep network, called GridDerainNet, to remove rain streaks within single image under different conditions. The architecture of GridDerainNet consists of three modules: pre-processing, multi-scale attentive module and post-processing. The pre-processing module can effectively generate several variants of the given rainy image, in order to extract more key features from the input. The multi-scale attentive module implements a novel attention mechanism, which allows more flexible information exchange and aggregation, taking full use of diversities of a given image. In the end, post-processing module furthers to reduce residual artifacts after previous two steps. Quantitative and qualitative experimental results demonstrate that the proposed algorithm outperforms several state-of-the-art methods on both synthetic and real-world images.
Asymmetric Deeply Fused Network for Detecting Salient Objects in RGB-D Images

Most RGB-D salient object detection (SOD) models use the same network to process RGB images and their corresponding depth maps. Subsequently, these models perform direct concatenation and summation at deep or shallow layers. However, these models ignore the complementarity of multi-level features extracted from RGB images and depth maps. This paper presents an asymmetric deeply fused network (ADFNet) for RGB-D SOD. Two different backbone networks, i.e., ResNet-50 and VGG-16, are utilized to process RGB images and related depth maps. We use an aggregation decoder and adaptive attention transformer module (AATM) to avoid information loss in the decoding process. Additionally, we use an attention early fusion module (AEFM) and deep fusion module (DFM) to deal with the deep features in various complex situations. Experiments validate the effectiveness of the proposed ADFNet, which outperforms thirteen recent RGB-D SOD models in the analysis of five public RGB-D SOD datasets.
Intelligent human hand gesture recognition by local–global fusing quality-aware features

Each normal human has pairwise hands, which can make sophisticated hand gestures delivering different semantic meanings. In practice, disabled persons can communicate with each other by their hand gestures. However, for normal people without specific training process, it is challenging for them to understand the meaning of the various hand gestures. In this work, we propose a novel quality-aware human–computer interaction (HCI) framework for understanding the sophisticated human hand gestures, wherein the key technique is a multi-view feature learning algorithm that optimally fuses hand silhouettes, different figure positions, and hand motions. More specifically, given each human hand, we first extract the multimodal visual features, such as the hand silhouettes based on background removal, different figure position localization using active learning, and hand motions by leveraging optical flow. Afterward, we propose a multi-view learning-based feature fusing scheme that optimizes the multimodal features both locally and globally. Based on this, the optimal weights of different features channels can be calculated. By leveraging the optimally fused feature, we train a multi-class kernel machine to classify the various human hand gestures into each category with a particular meaning. Comprehensive experimental results on a large-scale human hand gesture data set have demonstrated that our method can achieve a highly competitive recognition accuracy by spending less time consumption. Besides, our method can robustly support arbitrary number of semantic category characterizing human hand gestures. Last but not least, our proposed hand gesture understanding technique can be conveniently incorporated into many state-of-the-art HCI system.
Privacy-Preserving Identifiers for IoT: A Systematic Literature Review

The Internet of Things (IoT) paves the way for smart applications such as in E-health, E-homes, transportation, or energy production. However, IoT technologies also pose privacy challenges for their users, as they allow the tracking and monitoring of the users' behavior and context. The EU General Data Protection Regulation (GDPR) mandates data controller to follow a data protection by design and default approach by implementing for instance pseudonymity for achieving data minimisation. This paper provides a systematic literature review for answering the question of what types of privacy-preserving identifiers are proposed by the literature in IoT environments for implementing pseudonymity. It contributes with classifications and analyses of IoT environments for which privacy-preserving identifiers have been proposed and of the pseudonym types and underlying identity management architectures used. Moreover, it discusses trends and gaps in regard to addressing privacy trade-offs.