Reviewerly/exHarmony huggingface dataset:
Use of Graph Theory for the Representation of Scientific Collaboration

The objective of this work is to present a proposal for the use of graph theory for the representation of scientific collaboration in a Mexican educational institution based on the retrieval of data available in various repositories of scientific production considering the problem of the inconsistencies in the registry of the name of the authors. In the state of the art, the concepts involved are exposed, such as scientific production, digital repositories, interoperability, the Law of Open Science in Mexico and the theory of graphs. The proposed methodology uses elements for the extraction of knowledge for data mining, involving harvesting, processing and visualization. In the results section, the analysis of the state of scientific collaboration in the Universidad Autónoma de Yucatán is presented through concepts of graph theory, observing that there are 22 scientific collaboration groups and each researcher, on average, collaborates with 6 researchers from the same institution (obtained from the average grade of the graph) and have 18 collaborations in their publications (obtained the average grade of a weighted graph). Finally, conclusions and future work are exposed.
Deep Structured Prediction for Facial Landmark Detection

Existing deep learning based facial landmark detection methods have achieved excellent performance. These methods, however, do not explicitly embed the structural dependencies among landmark points. They hence cannot preserve the geometric relationships between landmark points or generalize well to challenging conditions or unseen data. This paper proposes a method for deep structured facial landmark detection based on combining a deep Convolutional Network with a Conditional Random Field. We demonstrate its superior performance to existing state-of-the-art techniques in facial landmark detection, especially a better generalization ability on challenging datasets that include large pose and occlusion.
Decision Support System for Breast Cancer Prediction

Cancer is considered to be one of the deadliest disease in the world. The second largest cancer disease leading to the death in women is Breast Cancer. Breast cancer is one such cancer which develops from the tissues of breast. Early diagnosis of breast cancer is very much necessary in order to prevent the growth of cancer cells. These cancer cells are detected and analyzed using various ML Techniques. In this paper, we have taken the data from Wisconsin Breast Cancer dataset. Five ML algorithms have been applied and a comparison is made between them. The best of five is identified and the accuracy level for the recurrence of cancer in affected patients is also compared. Different types of ML techniques which includes Support Vector Machines (SVMs) and Logistic Regression, Random Forest have been widely applied for research purposes in development of accuracy and better precision. In addition to this, we have also applied the KNN algorithm and Neural Network algorithm to produce better accuracy in analyzing the data.
Fast neural network training on a cluster of GPUs for action recognition with high accuracy

We propose algorithms and techniques to accelerate training of deep neural networks for action recognition on a cluster of GPUs. The convergence analysis of our algorithm shows it is possible to reduce communication cost and at the same time minimize the number of iterations needed for convergence. We customize the Adam optimizer for our distributed algorithm to improve efficiency. In addition, we employ transfer-learning to further reduce training time while improving validation accuracy. For the UCF101 and HMDB51 datasets, the validation accuracies achieved are 93.1% and 67.9% respectively. With an additional end-to-end trained temporal stream, the validation accuracies achieved for UCF101 and HMDB51 are 93.47% and 81.24% respectively. As far as we know, these are the highest accuracies achieved with the two-stream approach using ResNet that does not involve computationally expensive 3D convolutions or pretraining on much larger datasets.
Vote-for-It: Investigating Mobile Device-Based Interaction Techniques for Collocated Anonymous Voting and Rating

During discussions in collocated work it is necessary to vote for results or to rate them to reach an agreement and continue working. To ensure impartiality and to avoid social embarrassment, the assessment should then be performed anonymously in so far as other groups members should not see directly how a person votes or rates. With a growing number of digital devices in collaboration, this requirement also concerns such kinds of equipment. Our approach of ensuring anonymity of individual votes and ratings submitted on personal mobile phones is to avoid shoulder surfing activities. For this purpose, we designed four device-based interactions that aim at being easy to use and eyes-free to perform to stay in touch with the environment and potential shoulder surfers. We conducted a user study to investigate these interaction techniques and observed seven groups with four participants each while testing the interactions. Participants evaluated usability and User Experience (UX) aspects as well as unobtrusiveness of the four device-based interactions. Furthermore, participants gave valuable user feedback and stated that our proposed interactions help to avoid shoulder surfing.
Mean-Variance Loss for Monocular Depth Estimation

Monocular depth estimation is a widely studied computer vision problem with a vast variety of applications. In this paper, we formulate it as a pixel-wise classification task and use a mean-variance loss for robust depth estimation via distribution learning. More precisely, the mean-variance loss is composed of a mean loss that penalizes the difference between the mean of predicted depth distribution and the ground-truth depth, and a variance loss that penalizes the variance of predicted depth distribution to obtain a more focused distribution. The mean-variance loss is jointly trained with the soft-max loss to supervise a Deep Convolutional Neural Networks (DCNN) for depth estimation. Experimental results on the NYUDv2 dataset show that the proposed method outperforms previous state-of-the-art approaches.
Why go the extra mile? How different degrees of post-editing affect perceptions of texts, senders and products among end users

In recent decades, post-editing has received its fair share of attention in the industry as well as in academic circles. What has attracted by far the most attention is the question of quality: together, machine translation and post-editing defy long-standing and commonplace notions of quality. In this paper, we try to observe quality from the vantage point of end users, who are believed to have the final say on a text’s fitness for purpose. We will report on an experiment in which end users were asked to pass judgment on manipulated machine translations with different degrees of post-editing. Our findings demonstrate that the additional effort associated with higher degrees of post-editing does not necessarily lead to more positive judgments about text quality. The evidence suggests that text quality is context-dependent and is, therefore, subject to a somewhat opaque process of constant (re)negotiation.
Deep RGB-D Canonical Correlation Analysis For Sparse Depth Completion

In this paper, we propose our Correlation For Completion Network (CFCNet), an end-to-end deep learning model that uses the correlation between two data sources to perform sparse depth completion. CFCNet learns to capture, to the largest extent, the semantically correlated features between RGB and depth information. Through pairs of image pixels and the visible measurements in a sparse depth map, CFCNet facilitates feature-level mutual transformation of different data sources. Such a transformation enables CFCNet to predict features and reconstruct data of missing depth measurements according to their corresponding, transformed RGB features. We extend canonical correlation analysis to a 2D domain and formulate it as one of our training objectives (i.e. 2d deep canonical correlation, or “2D^2CCA loss). Extensive experiments validate the ability and flexibility of our CFCNet compared to the state-of-the-art methods on both indoor and outdoor scenes with different real-life sparse patterns. Codes are available at: https://github.com/choyingw/CFCNet.
CREATIVITY 4.0. EMPOWERING CREATIVITY IN THE DIGITAL ERA.

In the 21 st Century we are witnessing a radical transformation of society, primarily due to the introduction of a wealth of new devices, sensors, robots, and applications that are changing how people process information, behave, and socialise.The digital era and its technologies are having a profound impact especially on the digitally enhanced generation who are growing up with digital habits (TV, cell phones, video games, etc.) which are required to develop new competencies and skills among which human creativity is the most important one.The design education system is struggling to keep up with the demands of these new digital challenges.It has, therefore, become essential for design research to understand the impact of digital technologies on the creative process to develop an appropriate training model for the next digital generation of designers representing the actors of a near future.The objective of this paper is to briefly present the Creativity 4.0 model framed as part of the methodology used to identify the most crucial factors that influence creativity in the digital era.The model allows to explore some condition brought by the digital revolution that could enable or inhibit those factors and that could be of interest for the design educators' community.
Uncoupled Regression from Pairwise Comparison Data

Uncoupled regression is the problem to learn a model from unlabeled data and the set of target values while the correspondence between them is unknown. Such a situation arises in predicting anonymized targets that involve sensitive information, e.g., one's annual income. Since existing methods for uncoupled regression often require strong assumptions on the true target function, and thus, their range of applications is limited, we introduce a novel framework that does not require such assumptions in this paper. Our key idea is to utilize \emph{pairwise comparison data, which consists of pairs of unlabeled data that we know which one has a larger target value. Such pairwise comparison data is easy to collect, as typically discussed in the learning-to-rank scenario, and does not break the anonymity of data. We propose two practical methods for uncoupled regression from pairwise comparison data and show that the learned regression model converges to the optimal model with the optimal parametric convergence rate when the target variable distributes uniformly. Moreover, we empirically show that for linear models the proposed methods are comparable to ordinary supervised regression with labeled data.
Earthquakes and ghost towns in Sicily: from the Valle del Belìce in 1968 to the Val di Noto in 1693. The first stage of the virtual seismic itinerary through Italy

Destructive earthquakes leave traces not only on the physical landscape but also on human processes. Among the most devastating consequences is the abandoning of towns and villages, in favour of locations deemed safer. Abandoning a village and resettling in a new location, whether a gradual process or the result of a sudden traumatic situation, is an event of great historical, cultural and anthropological impact. It entails the loss of a piece of history or culture – local identity – and the more suddenly it happens, the more dramatic the after-effects. Italy has almost two hundred localities that have been abandoned for different causes (landslides, floods, volcanic eruptions, but also social or environmental reasons), some ninety of them owing to earthquakes. Sicily, a land of volcanoes and earthquakes, has several traces of former villages either in the form of fairly substantial ruins or even half-destroyed, which are usually located a few kilometres away from the new settlements. In this paper, we present the methodological approach developed in the framework of the EDURISK Project to enhance the awareness of seismic risk as an element of daily life; the case-study of the Valle del Belìce after the 1968 earthquakes represents the richness and complexity of this approach well. Following this rationale, in 2006, we published the first multimedia product devoted to Sicily based on the tools available at that time: a DVD-Rom hosting the interactive Quick TimeTM Virtual Reality format. Today, multimedia technology is much more advanced and web- oriented, but the methodological approach is still valid.
Building a Chinese AMR Bank with Concept and Relation Alignments

Abstract Meaning Representation (AMR) is a meaning representation framework in which the meaning of a full sentence is represented as a single-rooted, acyclic, directed graph. In this article, we describe an on-going project to build a Chinese AMR (CAMR) corpus, which currently includes 10,149 sentences from the newsgroup and weblog portion of the Chinese TreeBank (CTB). We describe the annotation specifications for the CAMR corpus, which follow the annotation principles of English AMR but make adaptations where needed to accommodate the linguistic facts of Chinese. The CAMR specifications also include a systematic treatment of sentence-internal discourse relations. One significant change we have made to the AMR annotation methodology is the inclusion of the alignment between word tokens in the sentence and the concepts/relations in the CAMR annotation to make it easier for automatic parsers to model the correspondence between a sentence and its meaning representation. We develop an annotation tool for CAMR, and the inter-agreement as measured by the Smatch score between the two annotators is 0.83, indicating reliable annotation. We also present some quantitative analysis of the CAMR corpus. 46.71% of the AMRs of the sentences are non-tree graphs. Moreover, the AMR of 88.95% of the sentences has concepts inferred from the context of the sentence but do not correspond to a specific word.
Connecting to the Ethereum Blockchain

Now that you have a solid understanding of how a blockchain works, you are ready to connect to the real Ethereum blockchain. To get connected to the Ethereum blockchain, you need an Ethereum client – an application that runs as an Ethereum node on the blockchain. Using an Ethereum client, you can perform tasks such as the following:
Online Algorithms on Antipowers and Antiperiods

The definition of antipower introduced by Fici et al. (ICALP 2016) captures the notion of being the opposite of a power: a sequence of k pairwise distinct blocks of the same length. Recently, Alamro et al. (CPM 2019) defined a string to have an antiperiod if it is a prefix of an antipower, and gave complexity bounds for the offline computation of the minimum antiperiod and all the antiperiods of a word. In this paper, we address the same problems in the online setting. Our solutions rely on new arrays that compactly and incrementally store antiperiods and antipowers as the word grows, obtaining in the process this information for all the word’s prefixes. We show how to compute those arrays online in $$O(n\log n)$$ space, $$O(n\log n)$$ time, and $$o(n^\epsilon )$$ delay per character, for any constant $$\epsilon >0$$ . Running times are worst-case and hold with high probability. We also discuss more space-efficient solutions returning the correct result with high probability, and small data structures to support random access to those arrays.
An improved relief feature selection algorithm based on Monte-Carlo tree search

The goal of feature selection methods is to find the optimal feature subset by eliminating irrelevant or redundant information from the original feature space according to some evaluation criteria. In the literature, the Relief algorithm is a typical feature selection method, which is simple and easy to execute. However, the classification accuracy of the Relief algorithm is usually affected by the noise. In recent years, the Monte Carlo Tree Search (MCTS) technique has achieved great success in strategy selections of large-scale systems by building a tree and quickly focusing on the most valuable part of the search space. In this paper, with the benefit of MCTS, an MCTS-based feature selection approach is proposed to deal with the feature selection problem of high dimensional data, where the Relief algorithm is used as the evaluation function of the MCTS approach. The effectiveness of the proposed approach is demonstrated by experiments on some benchmark problems.
Analisis Kesiapan Modernisasi Daerah Irigasi Kedung Putri Pada Tingkat Sekunder Menggunakan Metode K-Medoids Clustering

Preparation for the modernization of the Kedung Putri Irrigation System (DI Kedung Putri) required a comprehensive assessment of the irrigation pillars, one of which was at the secondary level. To facilitate the assessment and development plan, a clustering was carried out using the k-medoids method, that used a representative data (called medoid) as the cluster center. Then, the decision making was conducted by using the Analytic Hierarchy Process (AHP) method. Performance assessment of 21 secondary channels was stated as the readiness index of irrigation modernization (IKMI). The assessment result showed that 9,52% included in good criteria, 71,43% included in fair criteria, and 19,05% included in poor criteria. Based on these results that DI Kedung Putri was not ready yet to be modernized. For this reason, it was necessary to conduct the system improvement in groups, namely by grouping based on similarities (clustering). The used method was k-medoids clustering using Rapid Miner 9.0 software. The clustering result showed that the optimal cluster number were 4 clusters, with the Davies Bouldin Index (DBI) value -1,959. The members of the 0, 1, 2 and 3 cluster were 6, 6, 8 and 1 secondary channels, respectively. Furthermore, the priority scale in clusters development was needed based on the performance of irrigation pillars on secondary channels. The results of AHP analysis showed that the order of priority development starts from cluster 0, followed by cluster 2, 1, and 3. The recommendations for the development of secondary channels incorporated in cluster, such as increasing water supply, routine infrastructure maintenance, technical assistance, and public campaigns in irrigation management. The secondary channel incorporated in cluster 3 had good performance on all pillars, so it only needed to maintain the existing operation and maintenance patterns.
Automated Identification of Verbally Abusive Behaviors in Online Discussions

Discussion forum participation represents a crucial support for learning and often the only way of supporting social interactions in online settings.However, learner behavior varies considerably in these forums, including positive behaviors such as sharing new ideas or asking thoughtful questions, but also verbally abusive behaviors, which could have disproportionate detrimental effects.To provide means for mitigating potential negative effects on course participation and learning, we developed an automated classifier for identifying communication that show linguistic patterns associated with hostility in online forums.In so doing, we employ several wellestablished automated text analysis tools and build on common practices for handling highly imbalanced datasets and reducing sensitivity to overfitting.Although still in its infancy, our approach shows promising results (AUC ROC=0.74)towards establishing a robust detector of abusive behaviors.We provide an overview of the classification (linguistic and contextual) features most indicative of online aggression.
Service-Oriented Model Encapsulation and Selection Method for Complex System Simulation Based on Cloud Architecture

With the rise in cloud computing architecture, the development of service-oriented simulation models has gradually become a prominent topic in the field of complex system simulation. In order to support the distributed sharing of the simulation models with large computational requirements and to select the optimal service model to construct complex system simulation applications, this paper proposes a service-oriented model encapsulation and selection method. This method encapsulates models into shared simulation services, supports the distributed scheduling of model services in the network, and designs a semantic search framework which can support users in searching models according to model correlation. An optimization selection algorithm based on quality of service (QoS) is proposed to support users in customizing the weights of QoS indices and obtaining the ordered candidate model set by weighted comparison. The experimental results showed that the parallel operation of service models can effectively improve the execution efficiency of complex system simulation applications, and the performance was increased by 19.76% compared with that of scatter distribution strategy. The QoS weighted model selection method based on semantic search can support the effective search and selection of simulation models in the cloud environment according to the user’s preferences.
Iterative Learning Optimization for UAV Path Planning with Avoidance Zones

Effective path planning of unmanned aerial vehicles (UAVs) operating under avoidance zones is one of the critical capabilities to guarantee mission success. To obtain an optimized solution within reasonable computational time, an iterative learning optimization method (ILOM) is proposed to solve the UAV path planning problem with guaranteed computational performance in terms of convergence and objective value. First, the UAV path planning problem is formulated as a quadratically constrained quadratic programming (QCQP) problem. Next, a method combining matrix decomposition and iterative optimization is developed to solve QCQPs. However, computational performance is influenced by the algorithmic parameters involved in the iterative optimization method. Considering the implicit relationship between the algorithmic parameters and computational performance, convolutional neural network is applied to optimally select parameters in the iterative method instead of determining them from experience. Finally, the proposed ILOM is implemented in simulation examples to validate improved computational efficiency.
Optimized Algorithm for RFID-Based Activity Recognition of the Elderly

As the trend toward an increasingly aging global population accelerates, more attention is being focused on healthcare services for the elderly. Traditional healthcare services for the elderly require personal, meticulous care. With increasing numbers of the elderly and the “inverted pyramid” of family structure, more and more elderly people are often unable to obtain the healthcare they need in their final years. This paper introduces a Densely Convolutional Network for optimizing the algorithm model of an RFID-based activity recognition system, leading to a more accurate analysis of the basic daily behaviors of the elderly.During the experiment, We also optimized the original recognition process from combination of a series object-use recognition to treating multiple object-use processes as a union process.Which improves the efficiency and speed of activity recognition.