Reviewerly/exHarmony huggingface dataset:
Dynamic Network Embedding by Time-Relaxed Temporal Random Walk

Network embedding, also called network representation learning, aims at mapping high dimensional network information into low dimensional vectors. Previous studies mainly focus on static networks. In recent years, dynamic network embedding attracts much attention and methods specific to dynamic network are emerging. However, previous dynamic network embedding methods, such as CTDNE, still have drawbacks when using random walk to generate node sequences. Temporal random walk strictly requires that the time value of next edge be larger (i.e. later visiting) than that of the previous visited edge, which often leads to insufficient information obtained by random walk. In this article, a novel model named Time-Relaxed Temporal Random Walk(TxTWalk) for dynamic network embedding is proposed. Firstly, a time-relaxed function is designed, which enables random walk to select the next edge in a time interval, not strictly larger than the time of previously visited edge. It can make the walking sequences obtained by TxTWalk contain a wider range of temporal information. Then the node sequences are put into the skip-gram model for training to generate embedding of nodes on dynamic networks. Experimental validations on various networks demonstrate that TxTWalk is more effective than other state-of-the-art methods in link prediction.
Formation Control and Collision Avoidance of Nonholonomic Mobile Robots

In this paper, the formation control and collision avoidance problem is investigated for nonholonomic mobile robots. To avoid collisions, an improved adaptive artificial potential field (IAAPF) is designed for obstacles and robots. Firstly, an repulsive potential function is considered to the formation kinematic modeling for collision avoidance. To handle the uncertain nonlinearities of the closed-loop error system, an error-based extended state observer is designed. Secondly, based on estimations and repulsive forces, a dis-tributed formation controller with an adaptive repulsive potential gain is proposed for collision avoidance. Finally, stability analysis of the closed-loop system is provided by using the Lyapunov stability theory and effectiveness of the proposed method is demonstrated by experiments.
A Joint Representation Learning Approach for Social Media Tag Recommendation

In this paper, we analyze the mutual relationship between social media text and tags and explore how to integrate sematic information for tag recommendation. Our key motivation is to jointly map all words, tags and posts to vectors in a same hidden sematic space by modeling the syntagmatic and paradigmatic information simultaneously. We propose two novel distributed representation learning models for tagged documents: Tag Representation Learning (TRL) and Tag and Word Representation Learning (TWRL). The first models the immediate relationship between tags and words. The second one adds a skip-gram output layer to the first model, in order to enhance the semantic relationship among words. Extensive experiments are conducted on large scale datasets crawled from Twitter and Sina Weibo. By simulating two typical recommendation tasks, we discover that both models mentioned above outperform other competitive baselines remarkably.
NVSS: High-quality Novel View Selfie Synthesis

We present a novel method to synthesize novel view selfies from a mobile phone captured video. This is challenging due to the inconsistent geometry that is caused by the person’s unavoidable movement. Recent methods reconstruct the whole deformable scene implicitly with a deformation field. We argue that they are inefficient and hard to fit diverse real-world videos. In contrast, we use an explicit reconstruction for generalization and efficiency, where we separately track, reconstruct, and synthesize the foreground and background to overcome the geometry inconsistency. Several novel and effective modules are proposed for better performance and visual results. We demonstrate the advantage of the proposed method against the existing alternatives in a collection of our captured selfie videos with the support of quantitative and qualitative results.
Data Practices Ten Years Later: A New Review of Selected Publications by Crop Sciences Faculty

This study explores data reuse and data sharing based on a review of the two most recent publications of each faculty member in crop sciences at the University of Illinois at Urbana-Champaign. Following the methodology of a study conducted in 2011, this 2021 study reveals current practices and compares present-day findings with those of the original study and other literature on data practices. In particular, this work addresses the variety of data sources used by scientists, data citation practices, common data sharing methods, and the challenges of determining the effects of funder policies on data sharing.
Distributed Decoding for Coded Distributed Computing

In distributed computing, when a large number of helper nodes assist a master node to finish a large task, the main challenge is when some of these helpers straggle. Several coded distributed computing schemes exist that resolve the straggling problem. In these solutions, however, the decoding remains a responsibility of the master node. The complexity of decoding can be significant and increase the execution time considerably. In this work, we propose a multilayer coding strategy that allows some helpers to assist with the decoding. With this multilayer structure, the original decoding is performed by some decoding helpers, and the master only minimally participates in decoding to make every layer reliable. The optimization problem to minimize the overall completion time is also studied and shown to have a simple solution in almost all practical scenarios.
Stochastic Schemata Exploiter-Based AutoML

Machine Learning (ML) models are often determined by relying on knowledge and experience. In recent years, many automatic building methods are proposed, but there are some problems related to accuracy, computation cost, and explainability. We propose the Stochastic Schemata Exploiter-Based AutoML. The Stochastic Schemata Exploiter (SSE) is one of the Evolutionary Algorithms. In each generation, SSE calculates fitness of the individuals, defines the best subsets according to their average fitness, generates individuals based on the subsets, and applies a mutation operator. Since the original SSE uses a binary string representation, we have to modify the SSE algorithm in the following points for parameter optimization: the initialization method, the schema extraction method, the new individual generation method, the mutation method, and the generation update method. In this paper, we propose a genetic representation of the stacking model and optimize the stacking model using SSE. Compared with the Genetic Algorithm, the Tree-structured Parzen Estimator, the Covariance Matrix Adaptation - Evolution Strategy, and Random Search, SSE shows an interesting feature: a better accuracy for combinatorial optimization problems with categorical, discrete, and continuous variables, such as hyper-parameter optimization. In addition, we propose the visualization of the process of SSE. The visualization helps us to understand the process, which is another advantage of the SSE-based optimization (SSEopt).
Neural Networks for Face Recognition

This chapter focuses on a neural network approach for pattern recognition. It gives an overview of the different attempts to apply ANN techniques to a variety of face recognition tasks. The chapter also describes critical elements of a typical face recognition system. Neural networks have been applied as pattern classifiers in a variety of fields including signal processing, speech recognition, image recognition, character recognition and dynamic systems. Face recognition has been studied for many years and has practical applications in areas such as security systems, identification of criminals and assistance with speech recognition systems. In face recognition an input image must be recognized regardless of its position, size, and angular orientation. Therefore, pattern recognition requires the nonlinear subdivision of the pattern space into subsets representing the objects to be identified. The transformation of images to their gradient equivalents prior to network training results in superior performance.
Interactive Lexical and Semantic Graphs for Semisupervised Relation Extraction

The performance of relation extraction (RE) is hindered by the lack of sufficient labeled data. Semisupervised methods can offer to help hands with this problem by augmenting high-quality unlabeled samples into the training data. However, existing semisupervised RE methods either need a set of manually defined rules or rely on the classifier trained on the small labeled data, i.e., the former requires the heavy intervention of human knowledge, and the latter is bound to the number and the quality of the labeled data. In this article, we present a novel semisupervised RE method that involves small human efforts and is robust to the size of the initial set of labeled data. Specifically, we adopt only two simple rules to build the lexical and semantic graphs which connect the labeled samples with the unlabeled ones. In this way, the graphs are much easier to construct yet keep the ability to transfer knowledge from labeled samples to unlabeled ones. We then develop a graph interaction module to fully exploit the reference information in lexical and semantic graphs, which is used to jointly recognize the high-quality unlabeled samples with the classifier. We conduct extensive experimental results on two public datasets. The results demonstrate that our proposed method significantly outperforms the state-of-the-art baselines.
In Quest of a New AR Technology Application to Enhance the Sustainability of Cultural Tourism: The Olive Heritage in Madeira Through the Looking Glass of a ‘Sandbox’ Approach

Abstract This article presents the results of an exploratory investigation looking for new applications of augmented reality (AR) technology to enhance the sustainability of cultural tourism. The ‘sandbox’ approach was initially developed by programmers to experiment innovative solutions in safe conditions, being adopted later in other research areas for the same reason. In this case, the olive heritage at Madeira had been chosen as a background not because it is harmless, but on the contrary, for offering some challenging characteristics. The purpose is to stimulate creativity, forcing thought outside the box. This methodological path will lead to a new concept—the hyperplace of augmented visit (HAV)—conciliating several kinds of attractions in a sustainable way. The general idea is to create a hybrid tour. The user will have the possibility of interacting with a mixture of real objects and digital avatars, virtually reachable by the mediation of AR. The former will be found at the location where the user stands, and the others will be distributed in different areas of the same destination. The study leads to the conclusion that this concept will encourage travellers to spend more time in each spot and to include new points of interest, less popular or even unexplored, in their itinerary, and is also resilient in a context of public health crisis. At the close, HAV's potential will be evaluated and relevant issues mapped to design a suitable working plan to implement a pilot experience.
Bias Quantification for Protected Features in Pattern Classification Problems

The need to measure and mitigate bias in machine learning data sets has gained wide recognition in the field of Artificial Intelligence (AI) during the past decade. The academic and business communities call for new general-purpose measures to quantify bias. In this paper, we propose a new measure that relies on the fuzzy-rough set theory. The intuition of our measure is that protected features should not change the fuzzy-rough set boundary regions significantly. The extent to which this happens can be understood as a proxy for bias quantification. Our measure can be categorized as an individual fairness measure since the fuzzy-rough regions are computed using instance-based information pieces. The main advantage of our measure is that it does not depend on any prediction model but on a distance function. At the same time, our measure offers an intuitive rationale for the bias concept. The results using a proof-of-concept show that our measure can capture the bias issues better than other state-of-the-art measures.
Identifying Keyword Predictors in Lecture Video Screen Text

Automatic discovery of keywords for lecture video segments is an important component of advanced navigation systems for lecture videos. The suitability of a word or a short phrase to be a keyword depends on various factors, including the frequency in a segment, relative frequency in reference to the full video, font size, time on screen, and the existence in domain and language dictionaries. The research presented in this paper provides a refined understanding of how various factors contribute to predicting keywords based on logistic regression analysis. The analysis employs a real-world dataset consisting of lecture videos from Biology, Computer Science, and Chemistry, hosted on Videopoints, a lecture video management portal. Term frequency, maximum font size, and presence in a domain dictionary were identified as the most important predictors of keywords. The results provide a scientific foundation and valuable insights into the design of future keyword prediction systems.
Large Scale Multimedia Management: Recent Challenges

In recent years, we have witnessed an incredible and rapid growth of multimedia content in its different forms (2D and 3D images, text, sound, video, etc [...]
Results of Application of SPOC on English for Tourist Guides Course

Nowadays, teaching media is important in learning because it is a medium for transferring content from the teacher to the learner or as a medium that the learners can use to learn on their own in order to help them learn effectively. Thailand is a country that earns its main income from tourism. However, there was a problem in using English to communicate because of the lack of learning materials. Therefore, the researcher realized the importance of developing English language skills by using advances in technological innovation to increase the ability of learning, Small Private Online Course (SPOC). The study was to study English for tourist guide skill and the students' satisfaction after studying with the material. A sample was selected by purposive sampling method from 16 English major students who have enrolled in a course. A study of Application of SPOC on English for Tourist Guides Course for English major students, Faculty of Humanities and Social Sciences, Prince of Songkla University found that 1) the results of English Language for Tour Guides Skills with Small Private Online Course (SPOC) were found that learners have skills at a high level and learners have a high level of satisfaction. However, the researcher found that the correctness of the language used by the students was not at a very good level. But on the other hand, most of the learners' performance skills and gestures, and tone of speech were at a very good level. This shows that learners who have learned with the SPOC can develop their skills very well. However, there must be more development in terms of grammar in order to use the language more correctly.
State Space Approximation of Gaussian Processes for Time Series Forecasting

Gaussian Processes (GPs), with a complex enough additive kernel, provide competitive results in time series forecasting compared to state-of-the-art approaches (arima, ETS) provided that: (i) during training the unnecessary components of the kernel are made irrelevant by automatic relevance determination; (ii) priors are assigned to each hyperparameter. However, GPs computational complexity grows cubically in time and quadratically in memory with the number of observations. The state space (SS) approximation of GPs allows to compute GPs based inferences with linear complexity. In this paper, we apply the SS representation to time series forecasting showing that SS models provide a performance comparable with that of full GP and better than state-of-the-art models (arima, ETS). Moreover, the SS representation allows us to derive new models by, for instance, combining ETS with kernels.
Forming Real-World Human-Robot Cooperation for Tasks With General Goal

In human-robot cooperation, the robot cooperates with humans to accomplish the task together. Existing approaches assume the human has a specific goal during the cooperation, and the robot infers and acts toward it. However, in real-world environments, a human usually only has a general goal (e.g., general direction or area in motion planning) at the beginning of the cooperation, which needs to be clarified to a specific goal (i.e., an exact position) during cooperation. The specification process is interactive and dynamic, which depends on the environment and the partner's behavior. The robot that does not consider the goal specification process may cause frustration to the human partner, elongate the time to come to an agreement, and compromise team performance. This work presents the Evolutionary Value Learning approach to model the dynamics of the goal specification process with State-based Multivariate Bayesian Inference and goal specificity-related features. This model enables the robot to enhance the process of the human's goal specification actively and find a cooperative policy in a Deep Reinforcement Learning manner. Our method outperforms existing methods with faster goal specification processes and better team performance in a dynamic ball balancing task with real human subjects.
Online Streaming Features Selection via Markov Blanket

Streaming feature selection has always been an excellent method for selecting the relevant subset of features from high-dimensional data and overcoming learning complexity. However, little attention is paid to online feature selection through the Markov Blanket (MB). Several studies based on traditional MB learning presented low prediction accuracy and used fewer datasets as the number of conditional independence tests is high and consumes more time. This paper presents a novel algorithm called Online Feature Selection Via Markov Blanket (OFSVMB) based on a statistical conditional independence test offering high accuracy and less computation time. It reduces the number of conditional independence tests and incorporates the online relevance and redundant analysis to check the relevancy between the upcoming feature and target variable T, discard the redundant features from Parents-Child (PC) and Spouses (SP) online, and find PC and SP simultaneously. The performance OFSVMB is compared with traditional MB learning algorithms including IAMB, STMB, HITON-MB, BAMB, and EEMB, and Streaming feature selection algorithms including OSFS, Alpha-investing, and SAOLA on 9 benchmark Bayesian Network (BN) datasets and 14 real-world datasets. For the performance evaluation, F1, precision, and recall measures are used with a significant level of 0.01 and 0.05 on benchmark BN and real-world datasets, including 12 classifiers keeping a significant level of 0.01. On benchmark BN datasets with 500 and 5000 sample sizes, OFSVMB achieved significant accuracy than IAMB, STMB, HITON-MB, BAMB, and EEMB in terms of F1, precision, recall, and running faster. It finds more accurate MB regardless of the size of the features set. In contrast, OFSVMB offers substantial improvements based on mean prediction accuracy regarding 12 classifiers with small and large sample sizes on real-world datasets than OSFS, Alpha-investing, and SAOLA but slower than OSFS, Alpha-investing, and SAOLA because these algorithms only find the PC set but not SP. Furthermore, the sensitivity analysis shows that OFSVMB is more accurate in selecting the optimal features.
Digital Data Narrative: Literature, ‘Daffodil Poets’

Unfortunately literature does not seem to be welcomed in the digital era. Ironically, however, digital literature works are popular in the digital world. Now, we need to think of the role of literature in this understandable or ununderstandable situation. A big question is ‘Why do We study literature?’We ask ourselves whether there is any way to improve digital data literacy tracking data around an author, circumstances, and the then literature. Important is it should be fun and easy. Narrative in education can reach this goal, when applied to literature with digital data literacy. This paper presents how a birth of literal works could be digitally tracked and shows how data literacy with a game factor digitally could be made possible. It could be named Digital Data Narrative-Literature (DDNL). Comparative literature should be repositioned in that one compares literal works of East and West in a theme and digitally finds out the results as a reader and contents producer inevitably using domain knowledge as to those works. A Korean poet - Jeonghui Kim - and an English poet - William Wordsworth - used ‘Daffodils’ in their poem. The difference between two - especially in their nature of presence and placeness – was examined by a means of semantic data curation, through which one can educationally approach digital data narrative and a new angle of interpreting literal works can be provided.
Context-Based Deep Learning Architecture with Optimal Integration Layer for Image Parsing

Deep learning models have been proved to be promising and efficient lately on image parsing tasks. However, deep learning models are not fully capable of incorporating visual and contextual information simultaneously. We propose a new three-layer context-based deep architecture to integrate context explicitly with visual information. The novel idea here is to have a visual layer to learn visual characteristics from binary class-based learners, a contextual layer to learn context, and then an integration layer to learn from both via genetic algorithm-based optimal fusion to produce a final decision. The experimental outcomes when evaluated on benchmark datasets show our approach outperforms existing baseline approaches. Further analysis shows that optimized network weights can improve performance and make stable predictions.
Inner spike and slab Bayesian nonparametric models

Discrete Bayesian nonparametric models whose expectation is a convex linear combination of a point mass at some point of the support and a diffuse probability distribution allow to incorporate strong prior information, while still being extremely flexible. Recent contributions in the statistical literature have successfully implemented such a modelling strategy in a variety of applications, including density estimation, nonparametric regression and model-based clustering. A thorough study is presented on a large class of nonparametric models, named inner spike and slab hNRMI models and obtained by considering homogeneous normalized random measures with independent increments (hNRMI) with base measure given by a convex linear combination of a point mass and a diffuse probability distribution. In turn, the distributional properties of these models are investigated, with focus on: i) the exchangeable partition probability function they induce, ii) the distribution of the number of distinct values in an exchangeable sample, iii) the posterior predictive distribution, and iv) the distribution of the number of elements that coincide with the only point of the support with positive probability. These theoretical findings represent the main building block for an actual implementation of Bayesian inner spike and slab hNRMI models by means of a generalized Pólya urn scheme.