Reviewerly/exHarmony huggingface dataset:
Evaluation and analysis of irrigation water use efficiency based on an extreme learning machine model optimized by the spider monkey optimization algorithm

To promote the efficient use of water resources and the sustainable development of the agricultural economy, the main evaluation index that links economic production and the effective use of water resources, irrigation water use efficiency (IWUE), is scientifically evaluated and analyzed. To obtain accurate evaluation results, an evaluation model for IWUE based on an extreme learning machine (ELM) model optimized by the spider monkey optimization (SMO) algorithm (i.e., the SMO-ELM model) was proposed. The model was applied to the evaluation and analysis of IWUE in 24 large and medium irrigation districts in Heilongjiang Province. On this basis, the main driving factors of IWUE differentiation in the irrigation districts were identified, and adaptive control strategies were explored. In addition, to thoroughly test the performance of the constructed SMO-ELM evaluation model, a comparative analysis was performed for a BP model, an ELM model, an ELM model with crow search algorithm optimization (CSA-ELM) and the SMO-ELM model. The results show that there are regional differences in the IWUE of the irrigation districts and that the IWUE of the districts in the eastern part of the study area is better than that of the districts in the central and western parts. The irrigation districts with relatively high evaluation grades (IV to V) are distributed mostly in the eastern part of the study area, and most of the irrigation districts with low evaluation grades (I to II) are located near the main branch of the Songhuajiang River. Differences in indexes, such as the soak field quota and proportion of rice sowing area, are the most important factors related to variations in IWUE grades among the irrigation districts, and targeted control strategies such as planting structure optimization are proposed. The rationality and superiority of the SMO-ELM model are verified by comparing the coefficient of determination (R2), mean square error (MSE), mean absolute percentage error (MAPE), and accuracy of classification (accuracy) of the BP, ELM, CSA-ELM and SMO-ELM models. The R2, MSE, MAPE, and accuracy of the SMO-ELM model are 0.9999, 4.33 × 10−10, 0.00039, and 99.8%, respectively, which are much better than those of the other models. The results of complexity analysis verify the stability of the IWUE evaluation results for the irrigation districts. This article provides a new model for the evaluation of IWUE and scientific and technological support for ensuring the safety of water resources and maintaining the sustainable green development of agriculture.
Transductive Learning for Real-Time Twitter Search

Recency is an important dimension of relevance for real-time Twitter search as users tend to be interested in fresh news and events. By incorporating various sources of evidence, the application of learning to rank (LTR) algorithms to real-time Twitter search has shown beneficial in finding not only relevant, but also recent tweets in response to given queries. However, the potential effectiveness brought by LTR may not have been fully exploited due to the lack of labeled data available for properly learning a ranking model, since human labels are expensive in real-world applications. To this end, this paper proposes a transductive algorithm that incrementally aggregate the labeled tweets through an iterative process. Experimental results on the standard Tweets11 dataset show that our approach is able to outperform strong baselines without the use of human labels.
A Feature-driven Method for Automating the Assessment of OSINT Cyber Threat Sources

Global malware campaigns and large-scale data breaches show how everyday life can be impacted when the defensive measures fail to protect computer systems from cyber threats. Understanding the threat landscape and the adversaries' attack tactics to perform it represent key factors for enabling an efficient defense against threats over the time. Of particular importance is the acquisition of timely and accurate information from threats intelligence sources available on the web which can provide additional intelligence on emerging threats even before they can be observed as actual attacks. Currently, specific indicators of compromise (e.g. IP addresses, domains, hashsums of malicious files) are shared in a semi-automated and structured way via so-called threat feeds. Unfortunately, current systems have to deal with the trade-off between the timeliness of such an alert (i.e. warning at the first mention of a threat) and the need to wait for verification by other sources (i.e. warning after multiple sources have verified the threat). In addition, due to the increasing number of open sources, it is challenging to find the right balance between feasibility and costs in order to identify a relatively small subset of valuable sources. In this paper, a method to automate the assessment of cyber threat intelligence sources and predict a relevance score for each source is proposed. Specifically, a model based on meta-data and word embedding is defined and experimented by training regression models to predict the relevance score of sources on Twitter. The results evaluation show that the assigned score allows to reduce the waiting time for intelligence verification, on the basis of its relevance, thus improving the time advantage of early threat detection.
EIGAT: Incorporating global information in local attention for knowledge representation learning

Graph Attention Networks (GATs) have proven a promising model that takes advantage of localized attention mechanism to perform knowledge representation learning (KRL) on graph-structure data, e.g., Knowledge Graphs (KGs). While such approaches model entities’ local pairwise importance, they lack the capability to model global importance relative to other entities of KGs. This causes such models to miss critical information in tasks where global information is also a significant component for the task, such as in knowledge representation learning. To address the issue, we allow the proper incorporation of global information into the GAT family of models through the use of scaled entity importance, which is calculated by an attention-based global random walk algorithm. In the context of KRL, incorporating global information boosts performance significantly. Experimental results on KG entity prediction against the state-of-the-arts sufficiently demonstrate the effectiveness of our proposed model.
<scp>Real‐time</scp> automated segmentation of breast lesions using <scp>CNN‐based</scp> deep learning paradigm: Investigation on mammogram and ultrasound

Abstract The existing studies involving single imaging modalities (i.e., mammogram (MG) or ultrasound (US)) to detect breast lesions have demonstrated limited clinical application because radiologists rarely interpret an MG without a corresponding US and vice‐versa. Thus, this article aims to develop a Computer Aided Segmentation (CAS) system for detecting breast lesions in both MG and US. A customized convolutional neural network (CNN) is adopted for this purpose. A new real‐time bi‐modal database of MG and US is used for dual‐modality evaluation. Twelve performance measures, five shape measurements, area under receiver operating characteristics (ROC), and paired T‐test are used to assess the performance of proposed CAS system. A Dice Similarity Coefficient (DSC) of 0.64 (for MG) and 0.77 (for US) and Jaccard Index (JI) of 0.53 (for MG) and 0.64 (for US) indicate that the US can be used as an adjunct technique to MG in the segmenting breast lesions.
Using Jupyter notebooks as didactic instruments in translation technology teaching

This paper intends to illustrate the didactic potential of Python-based Jupyter notebooks in teaching translation technology, machine translation in particular, to translation students. It discusses the basic makeup of Jupyter notebooks and shows how these notebooks can be set up for students who have had little to no prior exposure to the Python programming language. Then, the paper discusses the general didactic benefits of Jupyter notebooks for both students and lecturers in a translation studies context. It shows how students can interact with these notebooks, which translation technological and translation-oriented natural language processing (NLP) concepts can be taught using them and to what extent interaction with these notebooks can help students understand, in a very general way, some basic principles of (NLP-oriented) Python programming. Finally, the paper presents the results of a pilot study on the use of Jupyter notebooks in a machine translation course in an MA programme in specialised translation.
Dual guidance enhanced network for light field salient object detection

Saliency detection models using light field data as input have not been thoroughly explored. Existing deep saliency models usually treat multi-focus images as independent information and extract their features separately, which may be cumbersome and over-rely on well-designed network structure. Besides, they do not fully explore the cross-modal complementarity and cross-level continuity of information, and rarely consider edge cues. Based on the above observations, in this paper, we investigate a novel Dual Guidance Enhanced Network (DGENet), which considers both spatial content and explicit boundary cues. Specifically, DGENet contains two key modules, i.e., the recurrent global-guided focus module (RGFM) and the boundary-guided semantic accumulation module (BSAM). These two modules are composed of multiple units, and the units in each module are not independent of each other. RGFM is used to distill out effective squeezed information of focal slices and RGB images between different levels. The learned global context features guide the network to focus on the salient region via a progressive reverse attention-driven strategy. Furthermore, BSAM introduces salient edge features to guide the accumulation of salient object features to generate salient maps with sharp boundaries. Extensive experiments on three challenging light field datasets demonstrate that our DGENet is superior to cutting-edge 2D, 3D and 4D methods.
Dual Encoder Attention U-net for Nuclei Segmentation

Nuclei segmentation in whole slide images (WSIs) stained with Hematoxylin and Eosin (H&E) dye, is a key step in computational pathology which aims to automate the laborious process of manual counting and segmentation. Nuclei segmentation is a challenging problem that involves challenges such as touching nuclei resolution, small-sized nuclei, size, and shape variations. With the advent of deep learning, convolution neural networks (CNNs) have shown a powerful ability to extract effective representations from microscopic H&E images. We propose a novel dual encoder Attention U-net (DEAU) deep learning architecture and pseudo hard attention gating mechanism, to enhance the attention to target instances. We added a new secondary encoder to the attention U-net to capture the best attention for a given input. Since H captures nuclei information, we propose a stain-separated H channel as input to the secondary encoder. The role of the secondary encoder is to transform attention prior to different spatial resolutions while learning significant attention information. The proposed DEAU performance was evaluated on three publicly available H&E data sets for nuclei segmentation from different research groups. Experimental results show that our approach outperforms other attention-based approaches for nuclei segmentation.
iMon

Gaze tracking is a key building block used in many mobile applications including entertainment, personal productivity, accessibility, medical diagnosis, and visual attention monitoring. In this paper, we present iMon, an appearance-based gaze tracking system that is both designed for use on mobile phones and has significantly greater accuracy compared to prior state-of-the-art solutions. iMon achieves this by comprehensively considering the gaze estimation pipeline and then overcoming three different sources of errors. First, instead of assuming that the user's gaze is fixed to a single 2D coordinate, we construct each gaze label using a probabilistic 2D heatmap gaze representation input to overcome errors caused by microsaccade eye motions that cause the exact gaze point to be uncertain. Second, we design an image enhancement model to refine visual details and remove motion blur effects of input eye images. Finally, we apply a calibration scheme to correct for differences between the perceived and actual gaze points caused by individual Kappa angle differences. With all these improvements, iMon achieves a person-independent per-frame tracking error of 1.49 cm (on smartphones) and 1.94 cm (on tablets) when tested with the GazeCapture dataset and 2.01 cm with the TabletGaze dataset. This outperforms the previous state-of-the-art solutions by ~22% to 28%. By averaging multiple per-frame estimations that belong to the same fixation point and applying personal calibration, the tracking error is further reduced to 1.11 cm (smartphones) and 1.59 cm (tablets). Finally, we built implementations that run on an iPhone 12 Pro and show that our mobile implementation of iMon can run at up to 60 frames per second - thus making gaze-based control of applications possible.
Syntactic Complexity of Different Text Types: From the Perspective of Dependency Distance Both Linearly and Hierarchically

Dependency distance (DD) is a well-established measure of syntactic complexity. Previous studies largely focused on the linear dimension, mostly by mean of dependency distance (MDD). In the present study, a new quantitative indicator –mean hierarchical dependency distance (MHDD), is proposed to discuss DD-related issues. Combining MHDD and MDD, the study investigates syntactic complexity of different texts, using strictly length-controlled sentences of 12 text types from the Freiburg-Brown corpus of American English. Correlations of MHDD and MDD have been identified, and possible reasons are discussed from the mathematical and theoretical perspectives. Mathematically, one is that the numerator of MHDD overlaps with the denominator of MDD, both being (n-1) where n is the number of words in the sentence. The other is that the denominator of MHDD (maximum hierarchical layer: MAXHL) and the numerator of MDD (sum of DD: SOD), are positively correlated. We believe that it is the positive correlation of SOD and MAXHL that ensures the change of MDD and MHDD in the same direction. It is also worth noting that both MAXHL and SOD seem to be minimized at their respective data spectrum, which foreshadows the dependency distance minimization (DDM) tendency on the hierarchical dimension.
Visibility and Ceiling Nowcasting Using Artificial Intelligence Techniques for Aviation Applications

This work presents a novel approach for simulating visibility (Vis) and ceiling base height (Hc) in up to 1 h using several machine learning (ML) algorithms. Ten years of meteorological data at 15 min intervals for Santos Dumont airport (SDA), Rio de Janeiro, Brazil were used in the ML method training and testing process. In the investigation, several categorical and regressive algorithms were trained and tested, and the results were verified with observations. The forecast results reveal that the categorical methods produced satisfactory results only up to 15 min for visibility prediction with the probability of detection greater than 85%. On the other hand, the regressive methods were found to be more capable of generating an accurate prediction of Vis and Hc compared to categorical method up to 60 min. The forecast evaluation metrics for Vis and Hc had correlation coefficients of 0.99 ± 0.00 and 0.96 ± 0.00, with mean absolute errors of 324 ± 77 m, and 167 ± 21 m, respectively. Results suggested that ML methods can improve the prediction of Vis and Hc up to 1 h when accurate observations are used for the analysis.
Object Search Using Edge-AI Based Mobile Robot

This study proposes and develops an edge-AI-based autonomous mobile robot based on open-source software. The robot is capable of voice and object recognition; it can detect and approach an object specified by a user&#x0027;s voice. Because the robot is controlled by voice commands, the user can control the robot intuitively. In the present study, we used a robot operating system to facilitate the development. All functions, including voice recognition, object recognition, and motor control, were implemented in the edge AI computer based on open-source software. We conducted preliminary experiments to verify the performance of the developed system.
Blockchain based choreographies: The construction industry case study

Abstract BPMN choreography is a modeling language capable to describe scenarios where several independent participants have to collaborate in a climate of opposing interests and therefore are forced to trust each other. For this reason, in many contexts, a strong need for transparency, responsibility, and choreography compliance arise by the various participants. Blockchains and smart contracts, thanks to their characteristic of providing a decentralized and consensus‐based validation mechanism, seem to be able to meet these needs in an untrusted scenario. Nevertheless, most of the related work focused either on transparency, accountability, or compliance, but none on all three of them. Furthermore, such works do not take into account the nondeterministc nature of choreographies. This work aims at using blockchains and smart contracts in this scenario providing a formally well‐defined set of tools to match all three the aforementioned requirements. This work applies the proposed techniques to a case study from the construction industry, an economical relevant application domain where the demand for transparency, accountability, and compliance with procurement contracts (that can be modeled as choreographies) is very strong.
Implementation and Practice of Deep Learning-Based Instance Segmentation Algorithm for Quantification of Hepatic Fibrosis at Whole Slide Level in Sprague-Dawley Rats

Exponential development in artificial intelligence or deep learning technology has resulted in more trials to systematically determine the pathological diagnoses using whole slide images (WSIs) in clinical and nonclinical studies. In this study, we applied Mask Regions with Convolution Neural Network (Mask R-CNN), a deep learning model that uses instance segmentation, to detect hepatic fibrosis induced by N-nitrosodimethylamine (NDMA) in Sprague-Dawley rats. From 51 WSIs, we collected 2011 cropped images with hepatic fibrosis annotations. Training and detection of hepatic fibrosis via artificial intelligence methods was performed using Tensorflow 2.1.0, powered by an NVIDIA 2080 Ti GPU. From the test process using tile images, 95% of model accuracy was verified. In addition, we validated the model to determine whether the predictions by the trained model can reflect the scoring system by the pathologists at the WSI level. The validation was conducted by comparing the model predictions in 18 WSIs at 20× and 10× magnifications with ground truth annotations and board-certified pathologists. Predictions at 20× showed a high correlation with ground truth (R2 = 0.9660) and a good correlation with the average fibrosis rank by pathologists (R2 = 0.8887). Therefore, the Mask R-CNN algorithm is a useful tool for detecting and quantifying pathological findings in nonclinical studies.
Questions in argumentative dialogue

Despite questions having a long-standing history in theoretical linguistics, the interface between empirically grounded corpus linguistics of questioning behaviour and analytically driven pragmatic theory of question structure and context has received significantly less attention. This paper aims to contribute to this field of research by showing that a four-way categorisation into question types, namely, pure questioning, assertive questioning, rhetorical questioning and challenge questioning, allows us to capture and represent questions in over two million words in natural language argumentative dialogue. In this type of dialogue questioning has been claimed to serve as the engine that drives the shape and development of a discourse. Our investigation covers three genres of argumentative discourse in which questions play a key role, namely political debates, moral dilemmas and sessions of participatory deliberative democracy. Through deep algorithmic analysis of the data, we test a variety of hypotheses from argumentation and linguistic theory, clearly demonstrating for the first time that (particular types of) questions directly catalyse argument structure and that the illocutionary consequences of non-canonical questions are much more varied than previously thought.
MLoF: Machine Learning Accelerators for the Low-Cost FPGA Platforms

In Internet of Things (IoT) scenarios, it is challenging to deploy Machine Learning (ML) algorithms on low-cost Field Programmable Gate Arrays (FPGAs) in a real-time, cost-efficient, and high-performance way. This paper introduces Machine Learning on FPGA (MLoF), a series of ML IP cores implemented on the low-cost FPGA platforms, aiming at helping more IoT developers to achieve comprehensive performance in various tasks. With Verilog, we deploy and accelerate Artificial Neural Networks (ANNs), Decision Trees (DTs), K-Nearest Neighbors (k-NNs), and Support Vector Machines (SVMs) on 10 different FPGA development boards from seven producers. Additionally, we analyze and evaluate our design with six datasets, and compare the best-performing FPGAs with traditional SoC-based systems including NVIDIA Jetson Nano, Raspberry Pi 3B+, and STM32L476 Nucle. The results show that Lattice’s ICE40UP5 achieves the best overall performance with low power consumption, on which MLoF averagely reduces power by 891% and increases performance by 9 times. Moreover, its cost, power, Latency Production (CPLP) outperforms SoC-based systems by 25 times, which demonstrates the significance of MLoF in endpoint deployment of ML algorithms. Furthermore, we make all of the code open-source in order to promote future research.
Using Microsoft PowerApps, Mendix and OutSystems in Two Development Scenarios: An Experience Report

Digitalisation, rapid and low-cost development, and a new communication base between business and IT are driving the rise of low-code development platforms (LCDPs). Picking up on this promise, this paper reports on our experiences with the development capabilities of three leading LCDPs: Microsoft PowerApps, Mendix and OutSystems. We present how to develop the same example application on each of these platforms. Two relevant development scenarios are considered. The first scenario describes the development of an independent application entirely implemented on an LCDP. Whereas the second scenario focuses on developing a dependent application using an existing local backend. The results of this paper may contribute to future research on the development potentials of LCDPs and the improvement needs of existing ones.