Reviewerly/exHarmony huggingface dataset:
Desde las periferias de Santiago de Cali: una aproximación metodológica a la construcción de representaciones socioculturales y mediáticas por parte de grupos sociales subalternos en los medios digitales

This article describes the experiences related to the creation of sociocultural and media representations in digital social media (Facebook, Instagram, YouTube) during the development of the digital literacy pedagogical process called Alto Nápoles en Red. Said process is part of the methodological techniques used in a doctoral thesis in communication at present in construction. These qualitative techniques were developed with an involuntary-migrant community displaced by force from rural areas of southwestern Colombia (department of Cauca) and settled in one of the peripheries of Santiago de Cali. The paper aims to give an account of the possibilities, resistances and historical obstacles that these subordinate social groups have experienced in the framework of the internal armed conflict in Colombia. That is, its objective is to know about the uses and appropriations of digital ICT based on cultural differences, socioeconomic inequalities and media disconnections.
Phase unwrapping method based on improved quadratic pseudo-Boolean optimization

Maximum a posteriori estimation of Markov random fields (MRFs) is a popular research area in computer vision, and many algorithms have been proposed to deal with these types of problems. The phase-unwrapping problem is modeled as the optimization of MRFs in this research, and the binary algorithm, improved quadratic pseudo-Boolean optimization, is utilized to solve the phase-unwrapping problem. Both the interferometric phases generated from the commonly used computer simulated surfaces and also real terrains are researched in the experimental section, and the unwrapping results are compared. The proposed algorithm achieves unwrapping results comparable to the state-of-the-art unwrapping method but costs less time for large-scale phase images.
The Relationship Between Cultural Types According to Handy’s Model and Organizational Structure in Jordanian Public Universities

The aim of this paper is to examine the relationship between organizational culture and organizational structure.Therefore, 354 academic and administrative members from 3 public universities in Jordan have been chosen in order to highlight the relationship between their organizational culture and organizational structure.Organizational culture scale developed by Harrison & Stokes (1992) and organizational structure scale developed by Gougui (2017) have been used during the preparation of the study questions.Factor analysis, reliability analysis and Pearson correlation analysis have been the major statistical indicators of data analysis.Correlation analysis results indicate that there is a medium level (r=0,530), positive and significant (p<0,01) relationship between organizational culture and organizational structure and there is not a significant statistical relationship between power culture (sub-dimension of organizational culture) and internal communication (sub-dimension of organizational structure).On the other hand, there is a significant, but weak relationship, between power culture (sub-dimension of organizational culture) and other sub-dimensions of organizational structure.There is a medium level statistical relationship between person culture (sub-dimension of organizational culture) and organizational complexity (subdimension of organizational structure).Moreover, it is observed that, there is a statistical significant relationship between sub-dimensions of organizational culture and organizational structure except the power culture (sub-dimension of organizational culture) and managerial attitude toward change (sub-dimension of organizational structure).Finally, it is also observed that, there is a significant statistical relationship between role culture, task culture and the rest of the organizational structure sub-dimensions except organizational complexity.
Symmetry Configuration Mapping for Compact Representation of Quantum Chemistry on Quantum Computers

Near-term quantum computers may be able to significantly speed up complex computational tasks, but algorithms that make efficient use of quantum resources are needed. Quantum chemistry is widely regarded as a candidate for the first demonstration of quantum advantage with near-term quantum computers. In the present work, we demonstrate how taking advantage of the symmetries of a molecule leads to a reduction in the number of qubits required. This reduction in qubits also leads to a reduction in the number of variational parameters needed to reach chemical accuracy. Furthermore, we show how a simple modification of the hardware-efficient ansatz for the variational quantum eigensolver yields a factor of 3 reduction in the number of parameters with no loss in accuracy for most problems in quantum chemistry.
Searching for Interaction Functions in Collaborative Filtering

Interaction function (IFC), which captures interactions among items and users, is of great importance in collaborative filtering (CF). The inner product is the most popular IFC due to its success in low-rank matrix factorization. However, interactions in real-world applications can be highly complex. Many other operations (such as plus and concatenation) have also been proposed, and can possibly offer better performance than the inner product. In this paper, motivated by the success of automated machine learning, we propose to search for proper interaction functions (SIF) for CF tasks. We first design an expressive search space for SIF by reviewing and generalizing existing CF approaches. We then propose to represent the search space as a structured multi-layer perceptron, and design a stochastic gradient descent algorithm which can simultaneously update both architectures and learning parameters. Experimental results demonstrate that the proposed method can be much more efficient than popular AutoML approaches, and also obtain much better prediction performance than state-of-the-art CF approaches.
Research on Recognition of Multi-user Haptic Gestures

Screen touch gestures are widely used for touch-based active authentication by mobile device users. In this paper, we briefly introduce the basic principles of Extreme Learning Machine (ELM), and then use various multi-classification methods for tactile gesture recognition. Experiments using publicly available screen touch datasets such as Touchalytics dataset and UMDAA dataset show that ELM compares favorably to other classification methods. The recognition accuracy is good and the testing time is reasonable.
Augmented Reality in Logistics: qualitative analysis for a managerial perspective

Immersive technologies, namely virtual and augmented reality, are witnessing a growth in popularity, especially in creative fields, like videogames and film businesses. These technologies can also be effectively applied in other industrial sectors and their use can foster value creation and enhance efficiency and effectiveness, in a managerial perspective. The applications of augmented reality are analysed within the industrial field of logistics, according to three directives: training, operations and transport. A qualitative analysis is carried on to identify potential uses in this (and related) fields and a managerial perspective are proposed, identifying specific KPIs and heuristics for the three aforementioned areas. Results from the literature, field analysis and classification through KPIs point to effective utilisations of AR in logistics, both for training new (and often temporary) resources and in specific and core operations, like picking in warehouses and transport of goods.
Measuring Job Search Effectiveness

Users of online job search websites interact with ranked lists of job summaries generated in response to queries, hoping to identify one or more jobs of interest. Hence, the quality of job search rankings becomes a primary factor that affects user satisfaction. In this work, we propose methodologies and measures for evaluating the quality of job search rankings from a user modeling perspective. We start by investigating job seekers' behavior when they are interacting with the generated rankings, leveraging job search interaction logs from Seek.com, a well-known Australasian job search website. The output of this investigation will be an accurate model of job seekers that will be incorporated into an effectiveness metric. Recent proposals for job search ranking models used using two types of metrics to evaluate the quality of the ranking generated by the models: (1) offline metrics, such as [email protected] (k is set to the number of job summaries shown in the first page), [email protected], or Mean Reciprocal Rank (MRR); and (2) online metrics, such as click-through rate and job application rate [3, 6].
New Definition of Quality-Scale Robustness for Image Processing Algorithms, with Generalized Uncertainty Modeling, Applied to Denoising and Segmentation

Robustness is an important concern in machine learning and pattern recognition, and has attracted a lot of attention from technical and scientific viewpoints. Actually, the robustness models the capacity of a computerized approach to resist to perturbing phenomena and data uncertainties, and generate common artefact while designing algorithms. However, this question has not been dealt in depth in such a way for image processing tasks. In this article, we propose a novel definition of robustness dedicated to image processing algorithms. By considering a generalized model of image data uncertainty, we encompass the classic additive Gaussian noise alteration that we study through the evaluation of image denoising algorithms, but also more complex phenomena such as shape variability, which is considered for liver volume segmentation from medical images. Furthermore, we refine our evaluation of robustness wrt. our previous work by introducing a novel quality-scale definition. To do so, we calculate the worst loss of quality for a given algorithm over a set of uncertainty scales, together with the scale where this drop appears. This new approach permits to reveal any algorithm’s weakness, and for which kind of corrupted data it may happen.
THE ACQUISITION OF PARAPHRASING AND ITS IMPACT ON TEACHING AND LEARNING QUOTATIONS TO AVOID PLAGIARISM

Paraphrase is the one way to avoid plagiarism beside quotation. Paraphrase means using someone idea or thought as the references but in our own meaning without lost the meaning while quotation means using someone idea or thought without change anything. Paraphrase or quotation is an important things to teach to the students, especially for university students who learning about writing and have to mastered the writing skill. Unfortunately, not all of students know the correct way to do paraphrase so they can be labeled as plagiarist. From this problem, this study wants to provide several analysis of literature review about the acquisition of paraphrasing, teaching and learning quotations to avoid plagiarism, and also its impact on teaching and learning. The result of this study are the concepts of paraphrasing acquisition, impacts on teaching and learning quotations, and some suggestion or ways in avoiding plagiarism. Keywords: the acquisition of paraphrase, teaching and learning quotation, avoiding plagiarism
Machine-learning-based channel estimation

Wireless communication has been a highly active research field. Channel estimation technology plays a vital role in wireless communication systems. Channel estimates are required by wireless nodes to perform essential tasks such as precoding, beamforming, and data detection. A wireless network would have good performance with well-designed channel estimates. In this chapter, we first review the channel model for wireless communication systems and then describe two traditional channel estimation methods, and finally introduce two newly designed channel estimators based on deep learning and one expectation-maximization-based channel estimator.
Generating and Validating DSA Private Keys from Online Face Images for Digital Signatures

Signing digital documents is attracting more attention in recent years, according to the rapidly growing number of digital documents being exchanged online. The digital signature proves the authenticity of the document and the sender’s approval on the contents of the document. However, storing the private keys of users for digital signing imposes threats toward gaining unauthorized access, which can result in producing false signatures. Thus, in this paper, a novel approach is proposed to extract the private component of the key used to produce the digital signature from online face image. Hence, this private component is never stored in any database, so that, false signatures cannot be produced and the sender’s approval cannot be denied. The proposed method uses a convolutional neural network that is trained using a semi-supervised approach, so that, the values used for the training are extracted based on the predictions of the neural network. To avoid the need for training a complex neural network, the proposed neural network makes use of existing pretrained neural networks, that already have the knowledge about the distinctive features in the faces. The use of the MTCNN for face detection and Facenet for face recognition, in addition to the proposed neural network, to achieved the best performance. The performance of the proposed method is evaluated using the Colored FERET Faces Database Version 2 and has achieved robustness rate of 13.48% and uniqueness of 100%.
Manga character clustering with DBSCAN using fine-tuned CNN model

Manga (Japanese comic) is popular content worldwide. In Japan, e-comic accounts for about 80% of e-book market. In recent years, metadata extraction from manga image has been studied for providing e-comic service. Manga character is one of the important contents for story understanding. In conventional research, some character identification methods are proposed those classify characters’ face images using k-means clustering. However, there are two problems. First, kmeans method needs to specify the number of clusters, however the number of characters in target manga images is commonly unknown. Second, manga includes characters with few appearing, so it is difficult to classify characters with high purity. To solve these problems, we propose clustering method using DBSCAN which decides number of clusters automatically and is robust to noise data. In our prior research, it is experimented that character face clustering using DBSCAN and general CNN features. However, general CNN model is difficult to capture detailed features of manga characters. In this paper, we apply DBSCAN to fine-tuned CNN with manga character faces to improve the clustering accuracy. We also compare the optimal parameter determination method of DBSCAN. Experimental results showed that the dimensional reduction using Kernel PCA and UMAP is effective. In addition, we confirmed the validity of proposed method that determining the parameters of DBSCAN based on the slope changing of k-distance graph.
Blind Image Quality Evaluation of Stitched Image using Novel Hybrid Warping Technique

Image stitching is collection of sequential images captured at fixed camera center having considerable amount of overlap and produces aesthetically pleasing seamless panoramic view.But, practically it is very difficult to obtain clean and pristine stitched panoramic image of particular scene as such images are apparently distorted.In this paper, a novel Hybrid Warping technique is used that combine two global warps and one local warp and helps to refine image alignment stage.Our proposed method optimizes Homography Screening to rectify problem of perspective distortion and Edge Strength Similarity approach to quantify structural irregularities.The Blind Image Quality Evaluation models such as Blind Image Quality Index (BIQI), Blind/Reference-less Image Spatial QUality Evaluator (BRISQUE) and BLind Image Integrity Notator using DCT Statistics (BLIINDS-II) are employed to measure objective quality of stitched image.The experimental results showed that blind image quality score of proposed method is significantly better than latest existing methods.
Webometrics Ranking (WR) of World Universities and National Institutional Ranking Framework (NIRF): A Comparative Study

Ranking institutions of a country, continent or the entire world is not an easy task. However, there are certain agencies that regularly undertake such an exercise. In this paper we have considered Webometrics Ranking (WR) and National Institutional Ranking Framework (NIRF) specific to India for our study. Various parameters have been compared citing their features. The methodology used by these rankings have been discussed such as WR depends on web presence and categorises institutions on the basis of Presence rank, Impact rank, Openness rank and Excellence rank whereas NIRF categorises on the basis of Graduation Outcomes, Learning Teaching &amp; Resources, Outreach of students, Research and Professional Practice and Awareness. Indian Institute of Technology Bombay, Maharashtra tops the list in Webometrics Rankings whereas Indian Institute of Technology Madras, Chennai tops the list in NIRF rankings. An interpretation of these rankings along with their limitations have been given finally some conclusions have been drawn on the basis of the data.
Deciding Memory Safety for Forest Datastructures

Memory safety is the problem of determining if a heap manipulating program that allocates/frees memory locations and manipulates heap pointers, does not dereference a memory location that is not allocated. Memory safety errors are serious security vulnerabilities that can be exploited systematically to attack systems. In this paper we consider the problem of checking if a program, whose initial allocated heap forms a forest structure (i.e., a disjoint set of trees and lists), is memory safe. While the problem of checking memory safety of programs whose initial heap is a forest structure is undecidable, we identify a class of caching programs for which the problem of checking memory safety is decidable. Our experimental evaluation demonstrates that common library routines that manipulate forest data-structures using a single pass are almost always caching. We show that our decision procedure for such programs is effective in both proving memory safety and in identifying memory safety vulnerabilities.
Never Stop Creating

Participation in activities of personal interest and passion facilitates older adults' health and well-being. In this study, we investigated older adults' experiences related to their engagement and development of personal interests. We found out that older adults actively generated creative concepts and turned those ideas into innovative activities and artifacts, which positively revitalized a community, sustained an organization, and supported other people. Although any activity may be beneficial for older adults' health and well-being, we argue that everyday innovations play a particularly important role, and present an important opportunity to enhance their quality of life. We conclude by proposing design strategies to support everyday innovations to promote healthy aging.
ProcessExplorer: Interactive Visual Exploration of Event Logs with Analysis Guidance

Process analysts use process mining techniques to obtain fact-based knowledge from event logs about how business processes are actually executed in organizations. Often process discovery is the first step in their analytical workflow. However, when working with large amount of data and complex processes, exploring as-is process models to obtain interesting and insightful knowledge can be challenging. We propose ProcessExplorer, an interactive visual recommendation system for process discovery to ease event log exploration. ProcessExplorer automatically analyzes the event log to obtain promising subsets of cases, evaluates interesting process performance indicators, and recommends those that are most interesting and insightful. Our system uses multi-perspective trace clustering to identify candidate cases of interest and a deviation-based approach to assess the interestingness of process performance indicators. We implemented ProcessExplorer as a standalone desktop application that allows to explore any process and any event log. Our demo shows how the workflow of analysts is supported by the system through suggesting subset and insights recommendations.
Report of 14<sup>th</sup> International Conference on Webometrics, Informetrics and Scientometrics (WIS) &amp; 19th COLLNET Meeting 05 to 08 December 2018, University of Macau, Macau

(2019). Report of 14th International Conference on Webometrics, Informetrics and Scientometrics (WIS) & 19th COLLNET Meeting 05 to 08 December 2018, University of Macau, Macau. COLLNET Journal of Scientometrics and Information Management: Vol. 13, No. 1, pp. 3-6.
Effective Self Attention Modeling for Aspect Based Sentiment Analysis

Aspect Based Sentiment Analysis is a type of fine-grained sentiment analysis. It is popular in both industry and academic communities, since it provides more detailed information on the user generated text in product reviews or social network. Therefore, we propose a novel framework based on neural network to determine the polarity of a review given a specific target. Not only the words close to the target but also the words far from the target determine the polarity of the review given a certain target, so we use self attention to solve the problem of long distance dependence. Briefly, we do multiple linear mapping on the review, do multiple attention and combine them to attend to the information from different representation sub-spaces. Besides, we use domain embedding to get close to the real word embedding in a certain domain, since the meaning of the same word may be different in different situation. Moreover, we use position embedding to underline the target and pay more attention to the words that are close to the target to get better performance on the task. We validate our model on four benchmarks, they are SemEval 2014 restaurant dataset, SemEval 2014 laptop dataset, SemEval 2015 restaurant dataset and SemEval 2016 restaurant dataset. The final results show that our model is effective and strong, which brings a 0.74% boost averagely based on the previous state-of-the-art work.
Structuring Theories with Implicit Morphisms

We introduce implicit morphisms as a concept in formal systems based on theories and theory morphisms. The idea is that there may be at most one implicit morphism from a theory S to a theory T, and if S-expressions are used in T their semantics is obtained by automatically inserting the implicit morphism. The practical appeal of implicit morphisms is that they hit the sweet-spot of being extremely simple to understand and implement while significantly helping with structuring large collections of theories. Concrete applications include elegantly identifying isomorphic theories and extending theories with definitions and theorems as well as efficiently building and maintaining large, fine-granular, and heterogeneous hierarchies of theories. Our results are formulated and implemented in the language and system, and we expect they can be transferred to other morphism-based formalisms relatively easily.