Reviewerly/exHarmony huggingface dataset:
MoBoost

The linear model is commonly utilized in hashing methods owing to its efficiency. To obtain better accuracy, linear-based hashing methods focus on designing a generalized linear objective function with different constraints or penalty terms that consider neighborhood information. In this study, we propose a novel generalized framework called Model Boost (MoBoost), which can achieve the self-improvement of the linear-based hashing. The proposed MoBoost is used to improve model parameter optimization for linear-based hashing methods without adding new constraints or penalty terms. In the proposed MoBoost, given a linear-based hashing method, we first execute the method several times to get several different hash codes for training samples, and then combine these different hash codes into one set utilizing one novel fusion strategy. Based on this set of hash codes, we learn some new parameters for the linear hash function that can significantly improve accuracy. The proposed MoBoost can be generally adopted in existing linear-based hashing methods, achieving more precise and stable performance compared to the original methods while imposing negligible added expenditure in terms of time and space. Extensive experiments are performed based on three benchmark datasets, and the results demonstrate the superior performance of the proposed framework.
How do Moral Values Differ in Tweets on Social Movements?

In this paper, we analyze and compare the representation of social movements on social media from the perspective of morality. Following previous research, which found associations between morality, collective action, and social decision-making, we postulate that moral values are distinct across different movements since these movements represent moral values of those who support or oppose them. The result of our analysis of four movements as represented on Twitter (#BlackLivesMatter, #WhiteLivesMatter, #AllLivesMatter, and #BlueLivesMatter) reveal that \#BlueLivesMatter represents values such as Care, Harm, Loyalty, and Authority, while \#WhiteLivesMatter features Harm and Fairness. Moreover, we find that Harm is the most prominent moral value in all of our datasets. Our analysis provides a robust understanding of authors' moral stances, which contextualizes the influence of movements on people, and how these movements are perceived in society.
Creativity in Digital Reality / <i>Creatividad en la realidad digital</i>

Digital environments — simulations, games and realities — have been gaining more attention in behaviour research as the relevant technology has become more advanced and affordable. This increased attention has been paralleled by the growing research literature, a corner of which belongs to studies of creativity in digital environments. This mini review highlights the main threads of this corner, focusing on the utilization of digital environments in education and industry.
Margin-Based Random Forest for Imbalanced Land Cover Classification

The problem of class imbalance is often encountered in remote sensing data and has a negative effect on the classification performance of supervised classifiers even in ensemble models. The ensemble margin is a fundamental concept in ensemble learning with potential effectiveness in improving the classification of remote sensing data. This paper proposes a novel margin based extended random forest algorithm to address the class imbalance issues in the difficult context of remote sensing classification. This algorithm combines ensemble learning with data sampling. A comparative analysis is conducted with respect to standard random forest, undersampling and over-sampling combined ensembles.
Achieve Revocable Access Control for Fog-Based Smart Grid System

Due to its prodigious advantages, smart grid technology has received considerable attention in recent years. However, security issues are still currently challenging in smart grid. In this paper, aiming at tackle the security issue of power consumption data, we propose a new Ciphertext Policy Attribute-based Encryption (CP-ABE) scheme with revocation for the fog- based smart grid system. Specifically, in order to achieve attribute revocation without requiring users to be always online, we divide users' attributes into attribute groups, assign an attribute group key to each group, and selectively distribute group key update messages. In addition, our scheme uses the DH (Diffie- Hellman) tree to distribute the group key statelessly, which solves the problem of collusion attack. The combination of attribute revocation and user revocation has been used to improve the efficiency of the revocation mechanism. Furthermore, the proposed scheme outsources unnecessary computing operations to fog nodes, so that the computing overhead of users is independent of the number of attributes. Both security analysis and experimental results demonstrate that our proposed scheme can balance the security objectives with the actual efficiency.
Supporting Privacy Control and Personalized Data Usage Explanations in a Context-Based Adaptive Collaboration Environment

The General Data Protection Regulation, e.g., provides the “right of access by the data subject” and demands explanations of data usages, i.e. explanations where and for what purpose personal data is being processed. Supporting this kind of privacy control and related personalized explanations of data usage in context-based adaptive collaboration environments are big challenges. Currently, users cannot retrace the usage and the storage of their personal data in context-based adaptive collaboration environments. We address the aforementioned challenges by developing a context-based adaptive collaboration platform, the CONTact platform, that can be linked to or integrated into different kinds of collaboration environments (e.g., meinDorf55+, a novel community support system for elderly). The CONTact platform supports users with privacy control and personalized explanations of data usages. In this paper we present an excerpt of our extended domain model and two sample situations when privacy control and personalized explanations get relevant. We use a sample ontology that is based on our domain model to illustrate the related processes and rules. Using our approach users can control their data usage and are able to get personalized explanations of their data usage in a context-based adaptive collaboration environment. This helps us observing legal regulations, e.g. privacy laws like the GDPR.
Stage-by-Stage Based Design Paradigm of Two-Pathway Model for Gaze Following

Gaze, which is an important non-verbal cue of interactions between human beings, can be used to estimate a person’s point of regard as well as deduce his intention. And gaze following is an task to estimate the visual attention of people in a single image. To tackle this challenging problem, earlier state-of-the-art work try to combine the information from image saliency as well as the gaze directions of people, thus demonstrate a deep-learning based two-pathway model. However, previous work do not focus much on why such a two-pathway model works well. Thus, in this paper, we divide the two-pathway model into three stages, compare different mechanisms in those stages to better understand how each stage may influence the model performance. Finally, we find out the best combinations of the mechanism in three stages and evaluate the model on the benchmark GazeFollow.
Automatic Bug Priority Prediction Using DNN Based Regression

Bugs are inevitable during software development. It is important to prioritize bugs and fix them based on their priorities. The priority assignment is usually done manually. Besides the cost of human effort, this process may also introduce bias since different people might have different opinions on the same issue. In this paper, we propose an approach to automate the process. It builds features from bug reports using Natural Language Processing, then trains a predictive model based on a deep Neural Network. The proposed approach was tested using a comprehensive data set containing more than 82 thousand bug reports. It runs in near real-time and its performance is significantly better than the previously reported results.
Frequency-Resolved Optical Gating Recovery via Smoothing Gradient

Frequency-resolved optical gating (FROG) is a popular technique for complete characterization of ultrashort laser pulses. The acquired data in FROG, called FROG trace, is the Fourier magnitude of the product of the unknown pulse with a time-shifted version of itself, for several different shifts. To estimate the pulse from the FROG trace, we propose an algorithm that minimizes a smoothed non-convex least-squares objective function. The method consists of two steps. First, we approximate the pulse by an iterative spectral algorithm. Then, the attained initialization is refined based upon a sequence of block stochastic gradient iterations. The algorithm is theoretically simple, numerically scalable, and easy-to-implement. Empirically, our approach outperforms the state-of-the-art when the FROG trace is incomplete, that is, when only few shifts are recorded. Simulations also suggest that the proposed algorithm exhibits similar computational cost compared to a state-of-the-art technique for both complete and incomplete data. In addition, we prove that in the vicinity of the true solution, the algorithm converges to a critical point. A Matlab implementation is publicly available at https://github.com/samuelpinilla/FROG.
Blockchain Technology Application in Improving of Energy Efficiency and Power Quality

This article presents one of the many uses of blockchain technology in the power grid, especially for improving energy efficiency and power quality. As a key element, it will show you how to use the blockchain technology to store in a decentralized way the data collected by existing intelligent sensors or which can be mounted in the power system. The purpose of this article is to present the point of view of the need to improve the energy system generated by the many problems arising from its age and its incompatibility with new production and consumption technologies. Blockchain implementation will be low and medium voltage, where the most affected are final consumers (whether household or industrial) that have started to have new energy-efficient technologies but have an unpredictable consumption that cannot be anticipated, thus increasing network losses. The article also contains data on energy efficiency in a decentralized energy system through the use of new IT technologies and, in particular, by involving consumers who will have a double role as part of the blockchain.
Kernelized evolutionary distance metric learning for semi-supervised clustering

This study proposes a novel distance metric learning method called evolutionary distance metric learning (EDML) to improve clustering quality that simultaneously evaluates inter- and intra-clusters. While we also provide an extension which integrates kernelization technique to the proposed method n amely kernelized evolutionary distance metric learning (K-EDML). Hence, the non-linear transformation of distance metric can be performed while maintaining all properties of EDML. The proposed methods are able to handle either class label or pairwise constraints and directly improve any clustering index as an objective function. Both can be viewed as utilization of cluster-level soft constraints, unlike other instance-level hard constraints which sometimes collapse the clustering. Also, maintaining neighbor relation of clusters can lead to better visualization of the clustering result. For multimodality problem of the objective function, an evolutionary algorithm (EA), differential evolution with self-adapting control parameters and generalized opposition-based learning (GOjDE), is employed to optimize a metric transform matrix based on the Mahalanobis distance. We empirically demonstrate the drawback of EDML in non-linearly separable input space and illustrate the benefit of kernel function to extension K-EDML method by showing its superior result benefits to other clustering algorithms in the semi-supervised clustering on various real-world datasets.
A New Recommendation Approach Based on Probabilistic Soft Clustering Methods: A Scientific Documentation Case Study

Recommender system (RS) clustering is an important issue, both for the improvement of the collaborative filtering (CF) accuracy and to obtain analytical information from their high sparse datasets.RS items and users usually share features belonging to different clusters, e.g., a musical-comedy movie.Soft clustering, therefore, is the CF clustering's most natural approach.In this paper, we propose a new prediction approach for probabilistic soft clustering methods.In addition, we put to test a not traditional scientific documentation CF dataset: SD4AI, and we compare results with the MovieLens baseline.Not traditional CF datasets have challenging features, such as not regular rating frequency distributions, broad range of rating values, and a particularly high sparsity.The results show the suitability of using soft-clustering approaches, where their probabilistic overlapping parameters find optimum values when balanced hard/soft clustering is used.This paper opens some promising lines of research, such as RSs' use in the scientific documentation field, the Internet of Things-based datasets processing, and design of new model-based soft clustering methods.
Putting network verification to good use

The past decade has witnessed remarkable progress in the field of network verification, and interest from academia and industry has spurred the development of increasingly sophisticated verification tools and algorithms. However, outside of a handful of large cloud computing providers, the use of network verification is still sparse. We argue that the next frontier for network verification is to enable easy and effective use by "average" network engineers. Whereas in software development, practitioners frequently use testing frameworks to describe the expected behavior of their systems and to measure the effectiveness of their tests through metrics such as code coverage, no such frameworks exist for the equally challenging task of designing and maintaining networks. To address this gap, we outline the design of a network verification framework. In doing so, we propose 1) a method to compute test coverage for networks, which tells engineers how well their invariants are testing the network; and 2) a new declarative invariant language that makes it easy to express network invariants and enables computation of coverage metrics.
Entity Disambiguation Based on Parse Tree Neighbours on Graph Attention Network

Entity disambiguation (ED) aims to link textual mentions in a document to the correct named entities in a knowledge base (KB). Although global ED model usually outperforms local model by collectively linking mentions based on the topical coherence assumption, it may still incur incorrect entity assignment when a document contains multiple topics. Therefore, we propose to extract global features locally, i.e., among a limited number of neighbouring mentions, to combine the respective superiority of both models. In particular, we derive mention neighbours according to the syntactic distance on a dependency parse tree, and propose a tree connection method CoSimTC to measure the cross-tree distance between mentions. Besides, we extend the Graph Attention Network (GAT) to integrate both local and global features to produce a discriminative representation for each candidate entity. Our experimental results on five widely-adopted public datasets demonstrate better performance compared with state-of-the-art approaches.
Human-Computer text conversation through NLP in Tamil using Intent Recognition

Chatbots which are also known as conversational interfaces, present a new way for individuals to interact with computer systems. A Chabot allows a user to ask questions in the same manner that they would address a human. Chatbots are currently being adopted in almost all fields. Our Chatbot aims at bridging the gap between parents and educational Institutions. Proportions of parent contribution ordinarily incorporate the quality and recurrence of correspondence with educators just as investment in organization capacities and exercises. Studies show that Parent's involvement and knowledge in their ward's performance, mainly in colleges is scanty. The main reason could be a communication i.e. either communication gap between teachers and parents, or even communication with their child could be an issue. Most parents are unaware of the happenings at college. This paper focuses on developing a Chatbot that leverages NLP by using the Dialog Flow API to answers parent's queries in the native language (Tamil) thus assisting parents in keeping track of their students' performance at college. The significant components of our Chabot incorporate intent detection, routines for conversation management, dialogue design and slot fulfilment. It also uses Flask micro framework and Pusher Channels.
Hypozonal orogenic gold mineralization in the Giyani Goldfield, Northern Kaapvaal Craton/Limpopo Complex

Abstract The paper reviews published and unpublished geological data pertaining to the structural and metamorphic controls, rock types, characteristic features, source, and timing of hypozonal orogenic gold mineralization in the Giyani Goldfield. The Giyani Goldfield includes the NW domain of the &amp;gt;3.0 Ga Giyani greenstone belt (GGB) at the northern edge of the Kaapvaal Craton and the southern retrograde hydrated domain of the juxtaposed Southern Marginal Zone (SMZ) of the ca. 2.72 Ga Limpopo Complex (LC). Mineralization at all gold mines and gold prospects of the Giyani Goldfield is structurally controlled and closely associated with the Hout River shear zone (HRSZ) and associated smaller shear zones suggesting a specific tectonic setting. This tectonic setting is the direct consequence of thrusting the SMZ of the LC against and over the adjacent GGB at the position of the steeply north-dipping (south verging) HRSZ during the exhumation stage of the ca. 2.72 to 2.69 Ga Limpopo orogeny followed by regional retrograde hydration of the southern part of the SMZ at ca. 2.68 to 2.62 Ga. This tectonic setting offers an explanation for a deep-seated crustal source for gold and for the concentration of orogenic gold mineralization within specific structural features located within the Giyani Goldfield. This tectonic setting also explains the lithological, structural and metamorphic complexity, metasomatic alteration and post-peak metamorphic timing of gold mineralization. Finally, it provides important clues with regards to a crustal source for gold mineralizing fluids and the identification of new potential targets for gold exploration in the Giyani Goldfield.
Deep Restoration of Vintage Photographs From Scanned Halftone Prints

A great number of invaluable historical photographs unfortunately only exist in the form of halftone prints in old publications such as newspapers or books. Their original continuous-tone films have long been lost or irreparably damaged. There have been attempts to digitally restore these vintage halftone prints to the original film quality or higher. However, even using powerful deep convolutional neural networks, it is still difficult to obtain satisfactory results. The main challenge is that the degradation process is complex and compounded while little to no real data is available for properly training a data-driven method. In this research, we adopt a novel strategy of two-stage deep learning, in which the restoration task is divided into two stages: the removal of printing artifacts and the inverse of halftoning. The advantage of our technique is that only the simple first stage requires unsupervised training in order to make the combined network generalize on real halftone prints, while the more complex second stage of inverse halftoning can be easily trained with synthetic data. Extensive experimental results demonstrate the efficacy of the proposed technique for real halftone prints; the new technique significantly outperforms the existing ones in visual quality.
Poster

Pre-trained Deep Neural Network (DNN) models are increasingly used in smartphones and other user devices to enable prediction services, leading to potential disclosures of (sensitive) information from training data captured inside these models. Based on the concept of generalization error, we propose a framework to measure the amount of sensitive information memorized in each layer of a DNN. Our results show that, when considered individually, the last layers encode a larger amount of information from the training data compared to the first layers. We find that the same DNN architecture trained with different datasets has similar exposure per layer. We evaluate an architecture to protect the most sensitive layers within an on-device Trusted Execution Environment (TEE) against potential white-box membership inference attacks without the significant computational overhead.
Recommendation for Multi-stakeholders and through Neural Review Mining

Recommender systems are able to produce a list of recommended items tailored to user preferences, while the end user is the only stakeholder in these traditional system. However, there could be multiple stakeholders in several applications domains (e.g., e-commerce, movies, music). Recommendations are necessary to be produced by balancing the needs of different stakeholders. First session of this tutorial introduces multi-stakeholder recommender systems (MSRS) with several case studies, and discusses the corresponding methods and challenges in MSRS. Reviews in an e-commerce platform may be mined to address cold-start problem and to generate explanations. Our earlier tutorial covered aspect-based sentiment analysis of products and topic models/distributed representations that bridge vocabulary gap between user reviews and product descriptions. Focus in the second session of this tutorial instead is on recent neural methods for review text mining - covering hands-on code for its use to enhance product recommendation. Each section will introduce topics from various mechanism (e.g., attention) and task (e.g., review ranking) perspectives, present cutting-edge research and a walk-through of programs executed on Jupyter notebook using real-world data sets.
Co-purchaser Recommendation Based on Network Embedding

Although recommending co-purchasers for a target buyer on the group buying is an interesting problem, existing studies haven't paid attention to this topic. Different from the collaborator recommendation that only considers users with high similarity to the target user, co-purchaser recommendation takes both users with high and weak similarity into account, and the recommendation results can achieve high recall and diversity. However, the task turns out to be a challenging problem since it is hard to make a precise recommendation for buyers with weak similarity. To address the problem, we propose the following two methods. In the first one, we directly impose a penalty to the weakly similar co-purchasers in the embedding space. To further improve the recommendation performance, in the second one, we smoothly increase the co-occurrence probability of the weakly similar co-purchasers by truncated bias walk. Our experimental results on real datasets show that the proposed methods, particularly the latter, can effectively complete the co-purchaser recommendation and has a high recommendation performance.