Reviewerly/exHarmony huggingface dataset:
Weather Parameters Forecasting as Variables for Rainfall Prediction using Adaptive Neuro Fuzzy Inference System (ANFIS) and Support Vector Regression (SVR)

Abstract The weather anomaly phenomenon that occurs can have some negative impact such as flooding, floods will paralyze the economic activities of the community, transportation activities, damage public infrastructure. In this research forecasting weather parameters as a variable for predicting the amount of rainfall using the ANFIS method and Support Vector Regression (SVR) with the aim to provide information on future weather conditions quickly and accurately. The people can prepare themselves and prepare the equipment needed to deal with it. Rainfall predicted based on synop data such us relative humidity, wind, and temperature. Each parameters must forcasted by using ANFIS and the result used for predict rainfall. Accurate prediction calculated using MSE and RMSE. Predictions of parameters that affect rainfall using the ANFIS method shown that for wind speed predictions having RMSE of 1.975004, temperature predictions have RMSE of 0.742332, and predictions of relative humidity have RMSE of 3.871590. Predicted rainfall based on the data results of the nearest method pre-processing using the Support Vector Regression (SVR) method produces an MSE error value of 0.0928.
Learning Relational Representations by Analogy using Hierarchical

Gaetano Rossiello, Alfio Gliozzo, Robert Farrell, Nicolas Fauceglia, Michael Glass. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Materiales didácticos digitales y coronavirus en tiempos de confinamiento en el contexto español

In this paper, a description and analysis of the main proposals of didactic materials that have been elaborated on COVID-19 (coronavirus) directed to a child and youth public available on the network in the Spanish context was made. In the first part of the paper we reflect on the reasons and needs that have justified the appearance and development of these resources. Subsequently, we present the search process carried out mainly through the Google search engine and the final selection of the materials considered. Next, a descriptive analysis of the materials was carried out using an elaborated ad hoc matrix that integrates 5 dimensions. As a general conclusion, we highlight the diversity of resources produced and the need for the coronavirus to be part of future proposals for materials, textbooks and educational work at school. Keywords: Digital Teaching Materials. Coronavirus. COVID-19. Children-youths.
Stochastic Fairness and Language-Theoretic Fairness in Planning in Nondeterministic Domains

We address two central notions of fairness in the literature of nondeterministic fully observable domains. The first, which we call stochastic fairness, is classical, and assumes an environment which operates probabilistically using possibly unknown probabilities. The second, which is language-theoretic, assumes that if an action is taken from a given state infinitely often then all its possible outcomes should appear infinitely often; we call this state-action fairness. While the two notions coincide for standard reachability goals, they differ for temporally extended goals. This important difference has been overlooked in the planning literature and has led to the use of a product-based reduction in a number of published algorithms which were stated for state-action fairness, for which they are incorrect, while being correct for stochastic fairness. We remedy this and provide a correct optimal algorithm for solving state-action fair planning for ltl/ltlf goals, as well as a correct proof of the lower bound of the goal-complexity. Our proof is general enough that it also provides, for the no-fairness and stochastic-fairness cases, multiple missing lower bounds and new proofs of known lower bounds. Overall, we show that stochastic fairness is better behaved than state-action fairness.
SSD-TSEFFM: New SSD Using Trident Feature and Squeeze and Extraction Feature Fusion

The single shot multi-box detector (SSD) exhibits low accuracy in small-object detection; this is because it does not consider the scale contextual information between its layers, and the shallow layers lack adequate semantic information. To improve the accuracy of the original SSD, this paper proposes a new single shot multi-box detector using trident feature and squeeze and extraction feature fusion (SSD-TSEFFM); this detector employs the trident network and the squeeze and excitation feature fusion module. Furthermore, a trident feature module (TFM) is developed, inspired by the trident network, to consider the scale contextual information. The use of this module makes the proposed model robust to scale changes owing to the application of dilated convolution. Further, the squeeze and excitation block feature fusion module (SEFFM) is used to provide more semantic information to the model. The SSD-TSEFFM is compared with the faster regions with convolution neural network features (RCNN) (2015), SSD (2016), and DF-SSD (2020) on the PASCAL VOC 2007 and 2012 datasets. The experimental results demonstrate the high accuracy of the proposed model in small-object detection, in addition to a good overall accuracy. The SSD-TSEFFM achieved 80.4% mAP and 80.2% mAP on the 2007 and 2012 datasets, respectively. This indicates an average improvement of approximately 2% over other models.
Feature-Based and Adaptive Rule Adaptation in Dynamic Environments

Abstract Rule-based systems have been used increasingly to augment learning algorithms for annotating data. Rules alleviate many of the shortcomings inherent in pure algorithmic approaches, in cases algorithms are not working well or lack from enough training data. However, in dynamic curation environments where data are constantly changing, there is a need to craft and adapt rules to keep them applicable and precise. Rule adaptation has been proven to be painstakingly difficult and error-prone, as an analyst is needed for examining the precision of rules and applying different modifications to adapt the imprecise ones. In this paper, we present an autonomic and conceptual approach to adapt data annotation rules. Our approach offloads analysts from adapting rules; it boosts rules to annotate a larger number of items using a set of high-level conceptual features, e.g. topic. We utilize a Bayesian multi-armed-bandit algorithm, an online learning algorithm that adapts rules based on the feedback collects from the curation environment over time. We propose a summarization technique, which offers a set of high-level conceptual features for annotating items by identifying the semantical relationships among them. We conduct experiments on different curation domains and compare the performance of our approach with systems relying on analysts for adapting rules. The experimental results show that our approach has a comparative performance to analysts in adapting rules.
Multidimensional mining of public opinion in emergency events

Purpose The suddenness, urgency and social publicity of emergency events lead to great impacts on public life. The deep analysis of emergency events can provide detailed and comprehensive information for the public to get trends of events timely. With the development of social media, users prefer to express opinions on emergency events online. Thus, massive public opinion information of emergencies has been generated. Hence, this paper aims to conduct multidimensional mining on emergency events based on user-generated contents, so as to obtain finer-grained results. Design/methodology/approach This paper conducted public opinion analysis via fine-grained mining. Specifically, public opinion about an emergency event was collected as experimental data. Secondly, opinion mining was conducted to get users’ opinion polarities. Meanwhile, users’ information was analysed to identify impacts of users’ characteristics on public opinion. Findings The experimental results indicate that public opinion is mainly negative in emergencies. Meanwhile, users in developed regions are more active in expressing opinions. In addition, male users, especially male users with high influence, are more rational in public opinion expression. Originality/value To the best of the authors’ knowledge, this is the first research to identify public opinion in emergency events from multiple dimensions, which can get in-detail differences of users’ online expression.
FELiX: Fixation-based Eye Fatigue Load Index A Multi-factor Measure for Gaze-based Interactions

Eye fatigue is a common challenge in eye tracking applications caused by physical and/or mental triggers. Its impact should be analyzed in eye tracking applications, especially for the dwell-time method. As emerging interaction techniques become more sophisticated, their impacts should be analyzed based on various aspects. We propose a novel compound measure for gaze-based interaction techniques that integrates subjective NASA TLX scores with objective measurements of eye movement fixation points. The measure includes two variations depending on the importance of (a) performance, and (b) accuracy, for measuring potential eye fatigue for eye tracking interactions. These variations enable researchers to compare eye tracking techniques on different criteria. We evaluated our measure in two user studies with 33 participants and report on the results of comparing dwell-time and gaze-based selection using voice recognition techniques.
Scalable and Secure Architecture for Distributed IoT Systems

Internet-of-things (IoT) is perpetually revolutionizing our daily life and rapidly transforming physical objects into an ubiquitous connected ecosystem. Due to their massive deployment and moderate security levels, those devices face a lot of security, management, and control challenges. Their classical centralized architecture is still cloaking vulnerabilities and anomalies that can be exploited by hackers for spying, eavesdropping, and taking control of the network. In this paper, we propose to improve the IoT architecture with additional security features using Artificial Intelligence (AI) and blockchain technology. We propose a novel architecture based on permissioned blockchain technology in order to build a scalable and decentralized end-to-end secure IoT system. Furthermore, we enhance the IoT system security with an AI-component at the gateway level to detect and classify suspected activities, malware, and cyber-attacks using machine learning techniques. Simulations and practical implementation show that the proposed architecture delivers high performance against cyber-attacks.
Disentangling knowledge production and data production

Abstract With today's increasing attention to open science and open data, knowledge production and data production have significant impacts on both research and policy arenas. We scrutinize the concept of data production in relation to knowledge production in the earth and environmental sciences. We present three empirical cases that illustrate issues arising when knowledge production and data production as interdependent but distinct processes are not clear in the mind of researchers and policymakers. A two‐stream model is developed that highlights their interplay yet avoids the conflation of knowledge and data production. This approach highlights knowledge and data in terms of processes rather than stable objects or assets. Further, we suggest that considering knowledge and data production in relation to their development by communities rather than as commodities helps in understanding the debates and issues that arise in contemporary research practice.
Error Detection for Arabic Text Using Neural Sequence Labeling

The English language has, thus far, received the most attention in research concerning automatic grammar error correction and detection. However, these tasks have been less investigated for other languages. In this paper, we present the first experiments using neural network models for the task of error detection for Modern Standard Arabic (MSA) text. We investigate several neural network architectures and report the evaluation results acquired by applying cross-validation on the data. All experiments involve a corpus we created and augmented. The corpus has 494 sentences and 620 sentences after augmentation. Our models achieved a maximum precision of 78.09%, recall of 83.95%, and F0.5 score of 79.62% in the error detection task using SimpleRNN. Using an LSTM, we achieved a maximum precision of 79.21%, recall of 93.8%, and F0.5 score of 79.16%. Finally, the best results were achieved using a BiLSTM with a maximum precision of 80.74%, recall of 85.73%, and F0.5 score of 81.55%. We compared the results of the three models to a baseline, which is a commercially available Arabic grammar checker (Microsoft Word 2007). LSTM, BiLSTM, and SimpleRNN all outperformed the baseline in precision and F0.5. Our work shows preliminary results, demonstrating that neural network architectures for error detection through sequence labeling can successfully be applied to Arabic text.
Accountabilities of posthuman research

Abstract What constitutes ‘good’ posthuman research? This article offers three dynamics to help assess the value of posthuman-inspired inquiry. We propose that a good posthuman research account should show evidence that the researcher: (1) attended to their own more-than-humanness and made explicit how they interviewed and attuned to the nonhuman things of their inquiry; (2) reassembled resemblings of the posthuman world by inventively weaving and fusing human and nonhuman storylines; and (3) offered analytic insights into the liveliness of posthuman research work as the performativity of difference.
Investigating the vulnerability fixing process in OSS projects: Peculiarities and challenges

Although vulnerabilities can be considered and treated as bugs, they present numerous peculiarities compared to other types of bugs (canonical bugs in the remainder of the paper). A vulnerability adds functionality to a system, as it allows an adversary to misuse or abuse the system, while a canonical bug is an incomplete or incorrect implementation of a requirement, and thus degrades the functionality of the system. This difference can affect the fixing process of vulnerabilities. By mining the repositories of 6 open source projects, we characterize the differences in the fixing process between vulnerabilities and canonical bugs, highlighting critical issues which could represent challenges for future research. Results of our study demonstrate that: (i) more re-assignments (than the ones observed in canonical bugs) are required for finding the developers able to handle vulnerability-related bugs, (ii) developers’ security-related skills should be profiled, to improve the efficiency of the security bug assignment tasks, and, consequently, reduce the re-assignments, and (iii) vulnerabilities require more effort, contributors and time to define the fixing strategy but smaller time to fix than canonical bugs.
Learning UI Functional Design Principles Through Simulation With Feedback

The user interface (UI) is a key component of an interactive software application; therefore, it is important to provide software developers with basic UI design skills. However, teaching UI design is challenging, even at a basic level, and there is little teaching support. In this article, we investigate the benefits of the feedback-enriched simulation environment (FENIkS) for learning fundamental UI design principles toward designing the functional aspects of a UI. FENIkS is a model-driven educational environment making use of simulation as learning support by generating a UI and the underlying application starting from conceptual domain and presentation models. The generated application and UI contain feedback elements for the learners. This feedback shows if the generated prototype is compliant with certain key design principles and why the principles are considered to be well applied or not. We conducted an experiment using bachelor-level courses to observe the effects of FENIkS on the learning of UI design principles by novice UI designers. The effectiveness of FENIkS was measured by comparing the results of students on a test performed without and with the use of FENIkS, using statistical methods. The findings show an improvement in student's learning of UI design principles when using the FENIkS approach.
Cost-Effective Placement of Recharging Stations in Drone Path Planning for Surveillance Missions on Large Farms

The energy limitation remains one of the biggest constraints in drone path planning, since it prevents drones from performing long surveillance missions. To assist drones in such missions, recharging stations have recently been introduced. They are platforms where the drone can autonomously land to recharge its battery before continuing the mission. However, the cost of those platforms remains a significant obstacle to their adoption. Consequently, it is important to reduce their number while planning the path of the drone. This work introduces the Single Drone Multiple Recharging Stations on Large Farm problem (SD-MRS-LF). A large farm is considered as an area of interest to cover with a set of candidate locations where recharging stations can be installed. The aim is to determine the path of the drone that minimizes the number of locations for recharging stations as well as the completion time of the surveillance mission. This path planning problem falls within the realm of computational geometry and is related to similar problems that are encountered in the field of robotics. The problem is complicated due to environmental constraints on farms such as wind speed and direction, which produce asymmetries in the optimal solution. A back-and-forth-k-opt simulated annealing (BFKSA) approach is proposed to solve the defined problem. The new approach is compared to the basic back-and-forth (BF) and a K-opt variant of the well-known simulated annealing (KSA) approach over a set of 20 random topologies in different environmental conditions. The results from computational experiments show that the BFKSA approach outperforms the others, in terms of providing feasible solutions and minimizing the number of recharges.
Breast Cancer Classification from Histopathological Images Using Transfer Learning and Deep Neural Networks

Early diagnosis of breast cancer is the most reliable and practical approach to mitigate cancer. Computer-aided detection or computer-aided diagnosis is one of the software technologies designed to assist doctors in detecting or diagnosing cancer and to reduce mortality using medical image analysis. Recently, Convolution Neural Networks became very popular in medical image analysis helping to process vast amount of data to detect and classify cancer in a fast and efficient manner. In this paper, we implemented deep neural networks ResNet18, InceptionV3 and ShuffleNet for binary classification of breast cancer in histopathological images. We have used networks pre-trained by the transfer learning on the ImageNet database and with fine-tuned output layers trained on histopathological images from the public dataset BreakHis. The highest average accuracy achieved for binary classification of benign or malignant cases was 98.73% for ResNet18, followed by 97.65% for ShuffleNet and 97.44% for Inception-V3Net.
Artistic Style in Robotic Painting; a Machine Learning Approach to Learning Brushstroke from Human Artists

Robotic painting has been a subject of interest among both artists and roboticists since the 1970s. Researchers and interdisciplinary artists have employed various painting techniques and human-robot collaboration models to create visual mediums on canvas. One of the challenges of robotic painting is to apply a desired artistic style to the painting. Style transfer techniques with machine learning models have helped us address this challenge with the visual style of a specific painting. However, other manual elements of style, i.e., painting techniques and brushstrokes of an artist, have not been fully addressed.We propose a method to integrate an artistic style to the brushstrokes and the painting process through collaboration with a human artist. In this paper, we describe our approach to 1) collect brushstrokes and hand-brush motion samples from an artist, and 2) train a generative model to generate brushstrokes that pertains to the artist's style, and 3) fine tune a stroke-based rendering model to work with our robotic painting setup. We will report on the integration of these three steps in a separate publication. In a preliminary study, 71% of human evaluators find our reconstructed brushstrokes are pertaining to the characteristics of the artist's style. Moreover, 58% of participants could not distinguish a painting made by our method from a visually similar painting created by a human artist.
On novelty detection for multi-class classification using non-linear metric learning

Novelty detection is a binary task aimed at identifying whether a test sample is novel or unusual compared to a previously observed training set. A typical approach is to consider distance as a criterion to detect such novelties. However, most previous work does not focus on finding an optimum distance for each particular problem. In this paper, we propose to detect novelties by exploiting non-linear distances learned from multi-class training data. For this purpose, we adopt a kernelization technique jointly with the Large Margin Nearest Neighbor (LMNN) metric learning algorithm. The optimum distance tries to keep each known class’ instances together while pushing instances from different known classes to remain reasonably distant. We propose a variant of the K-Nearest Neighbors (KNN) classifier that employs the learned distance to detect novelties. Besides, we use the learned distance to perform multi-class classification. We show quantitative and qualitative experiments conducted on synthetic and real data sets, revealing that the learned metrics are effective in improving novelty detection compared to other metrics. Our method also outperforms previous work regularly used for novelty detection.
Ensemble Approaches for Uncertainty in Spoken Language Assessment

Deep learning has dramatically improved the performance of automated systems on a range of tasks including spoken language assessment. One of the issues with these deep learning approaches is that they tend to be overconfident in the decisions that they make, with potentially serious implications for deployment of systems for high-stakes examinations. This paper examines the use of ensemble approaches to improve both the reliability of the scores that are generated, and the ability to detect where the system has made predictions beyond acceptable errors. In this work assessment is treated as a regression problem. Deep density networks, and ensembles of these models, are used as the predictive models. Given an ensemble of models measures of uncertainty, for example the variance of the predicted distributions, can be obtained and used for detecting outlier predictions. However, these ensemble approaches increase the computational and memory requirements of the system. To address this problem the ensemble is distilled into a single mixture density network. The performance of the systems is evaluated on a free speaking prompt-response style spoken language assessment test. Experiments show that the ensembles and the distilled model yield performance gains over a single model, and have the ability to detect outliers.