Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of VINCI 2009-2017 Proceedings

Both the metadata and the textual contents of scientific publications can provide us with insights about the development and the current state of the corresponding scientific community. In this short paper, we take a look at the proceedings of VINCI from the previous years and conduct several types of analyses. We summarize the yearly statistics about different types of publications, identify the overall authorship statistics and the most prominent contributors, and analyze the current community structure with a co-authorship network. We also apply topic modeling to identify the most prominent topics discussed in the publications. We hope that the results of our work will provide insights for the visualization community and will also be used as an overview for researchers previously unfamiliar with VINCI.
Higher-order Relation Schema Induction using Tensor Factorization with Back-off and Aggregation

Relation Schema Induction (RSI) is the problem of identifying type signatures of arguments of relations from unlabeled text. Most of the previous work in this area have focused only on binary RSI, i.e., inducing only the subject and object type signatures per relation. However, in practice, many relations are high-order, i.e., they have more than two arguments and inducing type signatures of all arguments is necessary. For example, in the sports domain, inducing a schema win(WinningPlayer, OpponentPlayer, Tournament, Location) is more informative than inducing just win(WinningPlayer, OpponentPlayer). We refer to this problem as Higher-order Relation Schema Induction (HRSI). In this paper, we propose Tensor Factorization with Back-off and Aggregation (TFBA), a novel framework for the HRSI problem. To the best of our knowledge, this is the first attempt at inducing higher-order relation schemata from unlabeled text. Using the experimental analysis on three real world datasets we show how TFBA helps in dealing with sparsity and induce higher-order schemata.
Overexpression of Lilium formosanum MADS-box (LFMADS) Causing Floral Defects While Promoting Flowering in Arabidopsis thaliana, Whereas Only Affecting Floral Transition Time in Nicotiana tabacum

The Formosa lily (Lilium formosanum) is one of the most common horticultural species in Taiwan. To explore gene regulation involved in this species, we used transcriptome analysis to generate PH-FB (mixed floral buds) and PH-LF (mature leaves) datasets. Combination of the PH-FB and PH-LF constructed a de novo assembly of the ALL dataset, including 18,041 contigs and 23,807 unigenes by Nr, GO, COG, and KEGG databases. The differential gene expression (DGE) analysis revealed 9937 genes were upregulated while 10,383 genes were downregulated in the developing floral buds compared to mature leaves. Seven putative genes (LFMADS1 to 7) encoding floral organ identity proteins were selected for further analysis. LFMADS1-6 genes were specifically expressed in the floral organ, while LFMADS7 in the floral buds and mature leaves. Phylogenetic analysis revealed that LFMADS1-3 is classified into B-class, LFMADS4 into C-class, LFMADS5 into D-class, and LFMADS6-7 into E-class, respectively. LFMADS-GFP fusion proteins appeared to localize in the nucleus, supporting their roles as transcription factors (TFs). Overexpression of the LFMADS2, LFMADS4, and LFMADS6 genes in Arabidopsis resulted in early flowering and floral defect, however, only early flowering in transgenic tobacco was observed. Highly expressed floral integrator genes, including AtFT, AtLFY, and AtFUL in transgenic Arabidopsis and NtFUL and NtSOC1 in transgenic tobacco, resulted in early flowering phenotype through qRT-PCR analysis. Yeast two-hybrid analysis suggested that LFMADSs may form higher order complexes with the B-, C-, D, and/or E-class proteins to determine the floral organ identity. Furthermore, E-class LFMADS proteins may function as a glue to mediate and strengthen the protein-protein interactions. Therefore, our de novo datasets would provide information for investigating other differentially expressed candidate transcripts. In addition, functional conservation of LFMADSs appears to be vital in floral transition and floral organ identity.
Exploring Word Sense Disambiguation Abilities of Neural Machine Translation Systems (Non-archival Extended Abstract)

Neural machine translation systems have been shown to achieve state-of-the-art translation performance for many language pairs. In order to produce a correct translation, MT systems must learn how to disambiguate words with multiple senses and pick the correct translation. We explore the extent to which the word embeddings for ambiguous words are able to disambiguate senses at deeper layers of the NMT encoder, which are thought to represent words with surrounding context. Consistent with previous research, we ﬁnd that the NMT system fails to translate many ambiguous words correctly. We provide an evaluation framework to use for proposed improvements to word sense disambiguation abilities of NMT systems.
Modification in the SAR Super-Resolution Model Using the Fractal Descriptor LMME in the Term Regularizer

This paper presents a modified simultaneous auto regressive (SAR) super-resolution model, by adding a fractal operator as a new regularized term. We propose the local morphologic multifractal exponent (LMME) descriptor to determine the hyperparameters estimation of the probability distribution function representing the high resolution (HR) image. Due to the LMME’s texture sensibility characteristic, the estimated HR images showed good enhancements in their edge elements and detail regions when compared with the images generated by the original SAR model, at the same time that mitigates the unwanted noise. The proposed modification was also extended to the model that combines the SAR method with $\ell _{1}$ -norm. A comparison using peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) metrics is presented in order to measure the quality of the HR images estimated by the proposed methods in relation to other available. The use of the LMME descriptor presented better adjustments of the restored image estimation distributions parameters, reflected on the values of PSNR and SSIM obtained in the experiments using low resolution images with moderate noise levels (SNR of 30 dB and 40 dB). In addition, our proposal converges in fewer iterations.
Capacity Based Efficient Beam-Selection Methods for mmWave 5G MIMO Communication Systems

In this work, capacity based optimal best beam set selection methods in MIMO channel condition are presented. Proposed methods are suitable for existing Verizon 5G specifications \cite{VzW} and also scalable to upcoming 3GPP NR\cite{NR} mmWave communications systems. Proposed methods are aimed at improving the performance, robustness and computational efficiency of the user equipment (UE) system where beam measurements are intended. Two methods namely, a two stage capacity maximization (CM) based method and a hybrid power and capacity maximization based method are proposed herein. Capacity based solutions are preferred over received signal strength (RSS) based methods in view of its superior performance in presence of unknown interference and noise. Hybrid method exploits the sparse channels inherent in beamformed mmWave systems. Also, it is preferable as it complies with the evolving 5G communication requirements which need estimation of additional alternative MIMO transmit and receive beam pairs apart from the serving beams to achieve higher throughput by overcoming random beam blockage and misalignment. Efficacy and performance of the proposed beam-selection methods is compared with existing RSS based method. Also computational complexity of the proposed methods are analyzed and numerical results comparing them are presented. Advantages of the capacity based methods proposed are demonstrated through analysis and simulation results.
Understanding Software Cohesion Metrics: Experimental Assessment of Conceptual Cohesion

Cohesion has been recognized as an important quality attribute of software design across decades. It is defined as the degree to which a module is focused on a single concern. However, measuring cohesion is not trivial as it is difficult to capture the concerns addressed by software modules. Several structural cohesion metrics have been proposed in the last decades and incorporated in industry-scale software measurement tools. Structural cohesion metrics rely on the degree to which the internal elements of a module are structurally related to each other. Cohesion, however, is not always well represented by module internal structure. For instance, in object-oriented programs, we can found classes that implement just one concern but have methods that seldom call each other or do not access attributes in common. Knowing this limitation, researchers have recently proposed an alternative way of measuring cohesion: the so-called conceptual cohesion metrics. Conceptual cohesion metrics are based on the explicit identification of the concerns realized by each module. However, there is a lack of understanding on how conceptual cohesion metrics fit among a number of conventional structural cohesion metrics. In this context, we performed a series of empirical studies in order to evaluate how and in which degree conceptual cohesion metrics differ from conventional structural cohesion metrics. Overall, our research was based on data obtained from: (i) a web-survey with eighty software developers from nine countries and (ii) the repository of six open source, long-lived, industry-scale, medium to large-sized systems with a change set of around thirty thousand source code revisions. As a result, we could explain that conceptual cohesion is orthogonal to structural cohesion and represents an additional dimension of cohesion measurement. We also found that such additional dimension is closer to how developers reason about module cohesion. In addition, we found that conceptual cohesion is a potential indicator of module changeproneness. Finally, we analyzed in which degree different strategies for mapping concerns to source code impact on conceptual cohesion measurement results. The results showed that, when feasible, manual mapping should be chosen. However, as it requires much effort, one of the investigated automatic mapping strategies can be also considered when the manual strategy is not feasible. We made all of our study datasets and materials public available on the web, including the concern mapping that we manually produced for a real system, and three tool extensions for computing a conceptual cohesion metric. These materials can be used or extended by researchers in future studies. In summary, our results can be directly used by software engineers when planning and executing cohesion measurement in their tasks and when building metric tools for development environments. Overall, this justifies additional effort to keep improving cohesion measurement body of knowledge and technology.
On Regularization and Active-set Methods with Complexity for Constrained Optimization

The main objective of this research is to introduce a practical method for smooth bound-constrained optimization that possesses worst-case evaluation complexity $O(\varepsilon^{-3/2})$ for finding an $\varepsilon$-approximate first-order stationary point when the Hessian of the objective function is Lipschitz continuous. As other well-established algorithms for optimization with box constraints, the algorithm proceeds visiting the different faces of the domain aiming to reduce the norm of an internal projected gradient and abandoning active constraints when no additional progress is expected in the current face. The introduced method emerges as a particular case of a method for minimization with linear constraints. Moreover, the linearly constrained minimization algorithm is an instance of a minimization algorithm with general constraints whose implementation may be unaffordable when the constraints are complicated. As a procedure for leaving faces, a different method is employed that may be regarded as a.
Head Movement Based Interaction in Mobility

ABSTRACT The touchless techniques in human computer interaction (HCI) can effectively expand communication capabilities. In the paper we present the innovative touchless computer control method based on head movement analysis. The aim of our work was to replace the standard mouse with the movements of the user’s head. In contrast to the known solutions, our proposition does not require image recording of the user’s head and complex image analysis. The analysis of position in our solution is made using the camera worn by the user on the head. A project of such a solution has been developed and the research of it has been carried out. It has been shown that in this way it is possible to effectively move the screen cursor to the position which is identified by the user’s face orientation. Additionally, in this solution, the eye image analysis has been performed. Interpretation of blinking allowed executing system commands. Using the built prototype the experiments have been carried out in a group of 30 people. Studies have shown high efficiency and ergonomics of the proposed solution.
Joint Linear Regression and Nonnegative Matrix Factorization Based on Self-Organized Graph for Image Clustering and Classification

Nonnegative matrix factorization (NMF) technique has been developed successfully to represent the intuitively meaningful feature of data. A suitable representation can faithfully preserve the intrinsic structure of data. Due to the fact that it introduces the label information, semi-supervised NMF has been demonstrated more advantageous in image representation than original NMF. However, previous semi-supervised NMF variants construct a label indicator matrix only for tagging the labeled data and not being optimized together with the matrix factorization. It is short of label propagation and fails to work for predicting the attribution of data. Moreover, the transductive semi-supervised NMF variants cannot dispose the prediction of unseen data, restricting the application of NMF. In this paper, a joint optimization framework of linear regression and NMF (LR-NMF) based on the self-organized graph is proposed for a completed task which simultaneously takes into account image representation and attribution prediction. By minimizing the proposed objective, three interactive threads are running: decomposing the data into nonnegative basis matrix and the corresponding representation, linear regression using the nonnegative representation, and label propagation based on the self-organized graph which is defined in the feature space. The products of LR-NMF can be viewed as extracting nonnegative feature for clustering, meanwhile, they can be used to solve the out-of-sample problem for classification. Extensive clustering and classification experiments on the digit, face, and object challenging data sets are presented to show the efficacy of the proposed LR-NMF algorithm.
The System Usability Scale: Past, Present, and Future

ABSTRACT The System Usability Scale (SUS) is the most widely used standardized questionnaire for the assessment of perceived usability. This review of the SUS covers its early history from inception in the 1980s through recent research and its future prospects. From relatively inauspicious beginnings, when its originator described it as a “quick and dirty usability scale,” it has proven to be quick but not “dirty. ” It is likely that the SUS will continue to be a popular measurement of perceived usability for the foreseeable future. When researchers and practitioners need a measure of perceived usability, they should strongly consider using the SUS.
BehaviorTracker: Visual Analytics of Customer Switching Behavior in O2O Market

Visualization of customer behavior is urgently needed for an increasing number of customer orders on O2O (online to offline) platform. Although many works have been done on visualizing customer opinion or customer click events of one store, visualizing customer switching behavior among stores is still challenging. The challenge is to show customer order records over time and structure the inter-connection among different stores when customer switching behavior happens. In this work, we focus on Takeout O2O service to present a novel visual analysis system for retailers focusing on customer switching behavior patterns. Firstly we define five customer segments based on switching behavior. Then this system enables temporal-spatial driver exploration for different segments through several interactive views. Moreover, in order to visualize inter-connection sequences, augmented streamgraph with the bundled parallel coordinates is proposed as one alternative technique to visualize temporal event sequences. Case studies through collaboration with domain experts also demonstrate the usefulness and effectiveness of this system in helping customer relationship management.
Dynamic UAV Swarm Deployment for Non-Uniform Coverage

In many monitoring and mapping applications, high-resolution data are required only in certain areas while others can receive lower attention. To this end, unmanned aerial vehicles (UAVs) can adjust the flight altitude to increase the resolution only where needed, making non-uniform coverage strategies efficient both in time and energy expenditure. In a multi-UAV monitoring context, it is necessary to deploy UAVs to inspect in parallel those areas where a higher resolution is required. To address this problem, we propose a decentralised deployment strategy inspired by the collective behaviour of honeybees. This strategy dynamically assigns UAVs to different areas to be monitored, and suitably re-assigns them to other areas when needed. We introduce an analytical macroscopic model of area monitoring from UAVs, and we propose a parameterisation that leads to an efficient allocation of UAVs to the areas to be monitored. We exploit abstract multi-agent simulations to study the dynamics of the deployment of UAVs to multiple areas, and we present results with simulations of a UAV swarm engaged in a weed monitoring and mapping task.
Which Melbourne? Augmenting Geocoding with Maps

The purpose of text geolocation is to associate geographic information contained in a document with a set (or sets) of coordinates, either implicitly by using linguistic features and/or explicitly by using geographic metadata combined with heuristics. We introduce a geocoder (location mention disambiguator) that achieves state-of-the-art (SOTA) results on three diverse datasets by exploiting the implicit lexical clues. Moreover, we propose a new method for systematic encoding of geographic metadata to generate two distinct views of the same text. To that end, we introduce the Map Vector (MapVec), a sparse representation obtained by plotting prior geographic probabilities, derived from population figures, on a World Map. We then integrate the implicit (language) and explicit (map) features to significantly improve a range of metrics. We also introduce an open-source dataset for geoparsing of news events covering global disease outbreaks and epidemics to help future evaluation in geoparsing.
Question Condensing Networks for Answer Selection in Community Question Answering

Answer selection is an important subtask of community question answering (CQA). In a real-world CQA forum, a question is often represented as two parts: a subject that summarizes the main points of the question, and a body that elaborates on the subject in detail. Previous researches on answer selection usually ignored the difference between these two parts and concatenated them as the question representation. In this paper, we propose the Question Condensing Networks (QCN) to make use of the subject-body relationship of community questions. In our model, the question subject is the primary part of the question representation, and the question body information is aggregated based on similarity and disparity with the question subject. Experimental results show that QCN outperforms all existing models on two CQA datasets.
Accelerated Spectral Ranking

The problem of rank aggregation from pairwise and multiway comparisons has a wide range of implications, ranging from recommendation systems to sports rankings to social choice. Some of the most popular algorithms for this problem come from the class of spectral ranking algorithms; these include the rank centrality algorithm for pairwise comparisons, which returns consistent estimates under the Bradley-Terry-Luce (BTL) model for pairwise comparisons (Negahban et al., 2017), and its generalization, the Luce spectral ranking algorithm, which returns consistent estimates under the more general multinomial logit (MNL) model for multiway comparisons (Maystre & Grossglauser, 2015). In this paper, we design a provably faster spectral ranking algorithm, which we call accelerated spectral ranking (ASR), that is also consistent under the MNL/BTL models. Our accelerated algorithm is achieved by designing a random walk that has a faster mixing time than the random walks associated with previous algorithms. In addition to a faster algorithm, our results yield improved sample complexity bounds for recovery of the MNL/BTL parameters: to the best of our knowledge, we give the first general sample complexity bounds for recovering the parameters of the MNL model from multiway comparisons under any (connected) comparison graph (and improve significantly over previous bounds for the BTL model for pairwise comparisons). We also give a message-passing interpretation of our algorithm, which suggests a decentralized distributed implementation. Our experiments on several real world and synthetic datasets confirm that our new ASR algorithm is indeed orders of magnitude faster than existing algorithms. Department of Computer and Information Science, University of Pennsylvania, Philadelphia, USA. Correspondence to: Arpit Agarwal , Shivani Agarwal . Proceedings of the 35 th International Conference on Machine Learning, Stockholm, Sweden, PMLR 80, 2018. Copyright 2018 by the author(s).
H∞ Controller Design for a Robotic Research and Educational Mining Loader

A robotic platform consisting of a skid-steer compact loader is employed as a setup to design and study strategies for reducing material spillage by robotic mining mobile manipulators. The present work describes the design methodology for a controller approach to reduce disturbances on the end-effector induced by the terrain and propagated through the wheels and arm links of the machine. The proposed approach H∞ control strategy that includes a feedforward action, computed using the pitch rate of the mobile base, and considers the hydraulic arm dynamics, as well as the reaction forces in the contact points of the mobile base. Alternative control schemes based on the classic proportional-derivative (PD) H∞ strategy without feedforward action, were also implemented and evaluated on simulations and experiments using a semi-autonomous Cat® 262C compact skid-steer loader equipped with inclination and inertial sensors. The best results were obtained using the proposed approach. The control scheme proposed reduces ground disturbances around 89% and 68% when traversing a 4 m long ramp at 12.5% and 25% of maximum vehicle speed, respectively.
EmotionX-JTML: Detecting emotions with Attention

This paper addresses the problem of automatic recognition of emotions in conversational text datasets for the EmotionX challenge. Emotion is a human characteristic expressed through several modalities (e.g., auditory, visual, tactile). Trying to detect emotions only from the text becomes a difficult task even for humans. This paper evaluates several neural architectures based on Attention Models, which allow extracting relevant parts of the context within a conversation to identify the emotion associated with each utterance. Empirical results in the validation datasets demonstrate the effectiveness of the approach compared to the reference models for some instances, and other cases show better results with simpler models.