Reviewerly/exHarmony huggingface dataset:
Gating Mechanisms for Combining Character and Word-level Word Representations: an Empirical Study

In this paper we study how different ways of combining character and word-level representations affect the quality of both final word and sentence representations. We provide strong empirical evidence that modeling characters improves the learned representations at the word and sentence levels, and that doing so is particularly useful when representing less frequent words. We further show that a feature-wise sigmoid gating mechanism is a robust method for creating representations that encode semantic similarity, as it performed reasonably well in several word similarity datasets. Finally, our findings suggest that properly capturing semantic similarity at the word level does not consistently yield improved performance in downstream sentence-level tasks.
Organization, Bot, or Human: Towards an Efficient Twitter User Classification

Today, through Twitter, researchers propose approaches for classifying user accounts. However, they have to face confidence challenges owing to the diversity of the types of data propagated throughout Twitter. In addition, the messages from Twitter are imprecise, very short and even written in many dialects and languages. Moreover, the majority of the related works focus on the overall user’s activity, which makes them not suitable at the post-level classification. This paper presents an alternative approach for classifying user accounts as being accounts of bots, humans or organizations. The suggested approach consists in accurately classifying user accounts from one single post by leveraging a minimal number of language-independent features. We performed several experiments over a Twitter datasets and supervised learn-based algorithms. Our results demonstrated that simply using a minimal number of language-independent features extracted from one single post is sufficient to classify user accounts accurately and quickly. Our proposed approach yielded high F1-measure (>95%) and high AUC (>99%) using Random Forest.
Greedy active learning algorithm for logistic regression models

Abstract We study a logistic model-based active learning procedure for binary classification problems, in which we adopt a batch subject selection strategy with a modified sequential experimental design method. Moreover, accompanying the proposed subject selection scheme, we simultaneously conduct a greedy variable selection procedure such that we can update the classification model with all labeled training subjects. The proposed algorithm repeatedly performs both subject and variable selection steps until a prefixed stopping criterion is reached. Our numerical results show that the proposed procedure has competitive performance, with smaller training size and a more compact model compared with that of the classifier trained with all variables and a full data set. We also apply the proposed procedure to a well-known wave data set (Breiman et al., 1984) and a MAGIC gamma telescope data set to confirm the performance of our method.
Helicity amplitudes, polarization of EM waves and Stokes parameters: classical versus quantum theory

It is shown that the helicity amplitudes can be used to describe and analyze the properties of the electromagnetic field in classical and in quantum theory. On the one hand they embody the relativistic content of electromagnetic theory. On the other hand they give a concise description of such experimentally important notions as polarization, the Stokes parameters and the Poincaré sphere.
Annotating and Characterizing Clinical Sentences with Explicit Why-

Many clinical information needs can be stated as why-questions. The answers to them represent important clinical reasoning and justification. Clinical notes are a rich source for such why-question answering (why-QA). However, there are few dedicated corpora, and little is known about the characteristics of clinical why-QA narratives. To address this gap, the study performed manual annotation of 277 sentences containing explicit why-QA cues and summarized their quantitative and qualitative properties. The contributions are: 1) sharing a seed corpus that can be used for various QA-related training purposes, 2) adding to our knowledge about the diversity and distribution of clinical why-QA contents.
Worst-Case Efficient Sorting with QuickMergesort

The two most prominent solutions for the sorting problem are Quicksort and Mergesort.While Quicksort is very fast on average, Mergesort additionally gives worst-case guarantees, but needs extra space for a linear number of elements.Worst-case efficient in-place sorting, however, remains a challenge: the standard solution, Heapsort, suffers from a bad cache behavior and is also not overly fast for in-cache instances.In this work we present median-of-medians Quick-Mergesort (MoMQuickMergesort), a new variant of QuickMergesort, which combines Quicksort with Mergesort allowing the latter to be implemented in place.Our new variant applies the median-of-medians algorithm for selecting pivots in order to circumvent the quadratic worst case.Indeed, we show that it uses at most n log n + 1.6n comparisons for n large enough.We experimentally confirm the theoretical estimates and show that the new algorithm outperforms Heapsort by far and is only around 10% slower than Introsort (std::sort implementation of stdlibc++), which has a rather poor guarantee for the worst case.We also simulate the worst case, which is only around 10% slower than the average case.In particular, the new algorithm is a natural candidate to replace Heapsort as a worst-case stopper in Introsort.
G2C: A Generator-to-Classifier Framework Integrating Multi-Stained Visual Cues for Pathological Glomerulus Classification

Pathological glomerulus classification plays a key role in the diagnosis of nephropathy. As the difference between different subcategories is subtle, doctors often refer to slides from different staining methods to make decisions. However, creating correspondence across various stains is labor-intensive, bringing major difficulties in collecting data and training a vision-based algorithm to assist nephropathy diagnosis.This paper provides an alternative solution for integrating multi-stained visual cues for glomerulus classification. Our approach, named generator-to-classifier (G2C), is a twostage framework. Given an input image from a specified stain, several generators are first applied to estimate its appearances in other staining methods, and a classifier follows to combine visual cues from different stains for prediction (whether it is pathological, or which type of pathology it has). We optimize these two stages in a joint manner. To provide a reasonable initialization, we pre-train the generators in an unlabeled reference set under an unpaired image-to-image translation task, and then fine-tune them together with the classifier.We conduct experiments on a glomerulus type classification dataset collected by ourselves (there are no publicly available datasets for this purpose). Although joint optimization slightly harms the authenticity of the generated patches, it boosts classification performance, suggesting more effective visual cues are extracted in an automatic way. We also transfer our model to a public dataset for breast cancer classification, and outperform the state-of-the-arts significantly.
Mutual Guidance-Based Saliency Propagation for Infrared Pedestrian Images

Saliency detection is important in computer vision. However, most of the existing saliency models are designed for visible images. It is still a challenging problem to apply saliency detection algorithms on infrared images. In this paper, an effective propagation based saliency detection method for infrared pedestrian images is proposed. Firstly, based on the thermal characteristics of infrared images and thermal radiation models, a thermal analysis based saliency (TAS) is introduced. TAS measures the stableness of pedestrians based on maximally stable extremal regions, which is further improved by an intensity filter. Then, by taking into account the appearance characteristic of pedestrians, an appearance analysis weighted saliency (AAS) is proposed which combines the intensity and shape features of pedestrians to improve the intensity contrast. Finally, besides the commonly used intra-scale neighborhood, an inter-scale neighborhood is introduced to jointly construct a mutual guidance-based saliency propagation model. This model could simultaneously integrate the saliency features and improve the saliency performance. Two datasets DIP and IMS with 600 infrared pedestrian images are published. Then, extensive experiments and comparisons with state-of-the-art methods demonstrate the effectiveness of the proposed saliency method for infrared pedestrian images.
Human-in-the-loop Active Covariance Learning for Improving Prediction in Small Data Sets

Learning predictive models from small high-dimensional data sets is a key problem in high-dimensional statistics. Expert knowledge elicitation can help, and a strong line of work focuses on directly eliciting informative prior distributions for parameters. This either requires considerable statistical expertise or is laborious, as the emphasis has been on accuracy and not on efficiency of the process. Another line of work queries about importance of features one at a time, assuming them to be independent and hence missing covariance information. In contrast, we propose eliciting expert knowledge about pairwise feature similarities, to borrow statistical strength in the predictions, and using sequential decision making techniques to minimize the effort of the expert. Empirical results demonstrate improvement in predictive performance on both simulated and real data, in high-dimensional linear regression tasks, where we learn the covariance structure with a Gaussian process, based on sequential elicitation.
Academic Reader: An Interactive Question Answering System on Academic Literatures

We present Academic Reader, a system which can read academic literatures and answer the relevant questions for researchers. Academic Reader leverages machine reading comprehension technique, which has been successfully applied in many fields but has not been involved in academic literature reading. An interactive platform is established to demonstrate the functions of Academic Reader. Pieces of academic literature and relevant questions are input to our system, which then outputs answers. The system can also gather users’ revised answers and perform active learning to continuously improve its performance. A case study is provided presenting the performance of our system on all papers accepted in KDD 2018, which demonstrates how our system facilitates massive academic literature reading.
Labor Division with Movable Walls: Composing Executable Specifications with Machine Learning and Search (Blue Sky Idea)

Artificial intelligence (AI) techniques, including, e.g., machine learning, multi-agent collaboration, planning, and heuristic search, are emerging as ever-stronger tools for solving hard problems in real-world applications. Executable specification techniques (ES), including, e.g., Statecharts and scenario-based programming, is a promising development approach, offering intuitiveness, ease of enhancement, compositionality, and amenability to formal analysis. We propose an approach for integrating AI and ES techniques in developing complex intelligent systems, which can greatly simplify agile/spiral development and maintenance processes. The approach calls for automated detection of whether certain goals and sub-goals are met; a clear division between sub-goals solved with AI and those solved with ES; compositional and incremental addition of AI-based or ES-based components, each focusing on a particular gap between a current capability and a well-stated goal; and, iterative refinement of sub-goals solved with AI into smaller sub-sub-goals where some are solved with ES, and some with AI. We describe the principles of the approach and its advantages, as well as key challenges and suggestions for how to tackle them.
Faktor-Faktor yang Mempengaruhi Kriminalitas di Indonesia Tahun 2011-2016 dengan Regresi Data Panel

&lt;p&gt;Criminality in Indonesia is increasing every year, therefore an effort is needed to reduce criminality in Indonesia, one of which can be used by knowing which factors influence the increase of criminality. This paper discusses the factors that influence criminality by using panel data regression analysis. Unemployment, domestic violence cases, narcotics cases, embezzlement cases, and fraud cases have positive effect on the amount of criminality with &lt;em&gt;R&lt;/em&gt;&lt;em&gt;&lt;sup&gt;2&lt;/sup&gt;&lt;/em&gt; of 0,85823 or 85,823%.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Keywords&lt;/strong&gt;&lt;strong&gt; : &lt;/strong&gt;panel regression analysis, crime, Indonesia&lt;/p&gt;
Architecture and Building Enginnering Educational Data Mining. Learning Analytics for detecting academic dropout

The present work is part of a broader research related to the improvement of the teaching methodology, in the undergraduates degree of Architecture and Building Engineering through the application of new technologies. The aim of the proposal is to confirm that changes in the teaching methodology improve the learning experience, in our case using a learning analytics approach. In this case study, we focused in one First Year subject: Descriptive Geometry, which has a high rate of repeating students, as well as an early dropout. We have implemented an educational data mining mixed approach related to the mid-semester exams (midterm), and we stablished a relation with the final marks of the subject in two periods with differentiated methodologies, Pre-Bologna (2006-09), and Post-Bologna (2015- 18). The objective of this analysis is to predict what students are closer to leave the course after the midterm results based on the topics examined, so that we can influence and implement new methodologies, technologies and systems to improve these topics.
GAN-EM: GAN Based EM Learning Framework

Expectation maximization (EM) algorithm is to find maximum likelihood solution for models having latent variables. A typical example is Gaussian Mixture Model (GMM) which requires Gaussian assumption, however, natural images are highly non-Gaussian so that GMM cannot be applied to perform image clustering task on pixel space. To overcome such limitation, we propose a GAN based EM learning framework that can maximize the likelihood of images and estimate the latent variables. We call this model GAN-EM, which is a framework for image clustering, semi-supervised classification and dimensionality reduction. In M-step, we design a novel loss function for discriminator of GAN to perform maximum likelihood estimation (MLE) on data with soft class label assignments. Specifically, a conditional generator captures data distribution for K classes, and a discriminator tells whether a sample is real or fake for each class. Since our model is unsupervised, the class label of real data is regarded as latent variable, which is estimated by an additional network (E-net) in E-step. The proposed GAN-EM achieves state-of-the-art clustering and semi-supervised classification results on MNIST, SVHN and CelebA, as well as comparable quality of generated images to other recently developed generative models.
THE IMPACT OF TRANSFORMATIONAL LEADERSHIP STYLE ON EMPLOYEE ENGAGEMENT: USING STRUCTURAL EQUATION MODELING (SEM)

Employee engagement helps businesses in attaining their goals. Employee engagement has been at the center of attention for human resource personnel and researchers who stated that employee engagement can be a key driver of an organization's business success. In Palestine, only 11% of employees are engaged in work. Many researchers recently have found that transformational leadership style has an impact on the work engagement of employees. The population involved in this study includes 5281 full-time employees of which a sample of 328 employees was selected. Moreover, this study is considered to be the first local method to find and analyze the current causal effect of perceived transformational leadership style on employee engagement within the context of Palestine. The data was analyzed using Confirmatory Factor Analysis (CFA), using Structural Equation Modeling (SEM). Analysis of Moments Structure (AMOS) was employed to answer the research questions and to test the hypotheses in this study. Results found that transformational leadership style has a significant effect on employee engagement. Results show that transformational leadership is perceived to be more effective leadership in Palestine context. In conclusion, these study findings supported the hypothesis, answered the research question, and achieved the objectives of this study. Additionally; this study has successfully addressed the gaps found in previous literature review has contributed to the body of knowledge, as well as in providing useful implications to the industry in the context of Palestine and Middle Eastern Region.
Building a New Sentiment Analysis Dataset for Uzbek Language and Creating Baseline Models

Making natural language processing technologies available for low-resource languages is an important goal to improve the access to technology in their communities of speakers. In this paper, we provide the first annotated corpora for polarity classification for Uzbek language. Our methodology considers collecting a medium-size manually annotated dataset and a larger-size dataset automatically translated from existing resources. Then, we use these datasets to train sentiment analysis models on the Uzbek language, using both traditional machine learning techniques and recent deep learning models.
Motion Invariance in Visual Environments

The puzzle of computer vision might find new challenging solutions when we realize that most successful methods are working at image level, which is remarkably more difficult than processing directly visual streams, just as it happens in nature. In this paper, we claim that the processing of a stream of frames naturally leads to formulate the motion invariance principle, which enables the construction of a new theory of visual learning based on convolutional features. The theory addresses a number of intriguing questions that arise in natural vision, and offers a well-posed computational scheme for the discovery of convolutional filters over the retina. They are driven by the Euler- Lagrange differential equations derived from the principle of least cognitive action, that parallels the laws of mechanics. Unlike traditional convolutional networks, which need massive supervision, the proposed theory offers a truly new scenario in which feature learning takes place by unsupervised processing of video signals. An experimental report of the theory is presented where we show that features extracted under motion invariance yield an improvement that can be assessed by measuring information-based indexes.
K3S: Knowledge-Driven Solution Support System

As the volume of scientific papers grows rapidly in size, knowledge management for scientific publications is greatly needed. Information extraction and knowledge fusion techniques have been proposed to obtain information from scholarly publications and build knowledge repositories. However, retrieving the knowledge of problem/solution from academic papers to support users on solving specific research problems is rarely seen in the state of the art. Therefore, to remedy this gap, a knowledge-driven solution support system (K3S) is proposed in this paper to extract the information of research problems and proposed solutions from academic papers, and integrate them into knowledge maps. With the bibliometric information of the papers, K3S is capable of providing recommended solutions for any extracted problems. The subject of intrusion detection is chosen for demonstration in which required information is extracted with high accuracy, a knowledge map is constructed properly, and solutions to address intrusion problems are recommended.
HorNet: A Hierarchical Offshoot Recurrent Network for Improving Person Re-ID via Image Captioning

Person re-identification (re-ID) aims to recognize a person-of-interest across different cameras with notable appearance variance. Existing research works focused on the capability and robustness of visual representation. In this paper, instead, we propose a novel hierarchical offshoot recurrent network (HorNet) for improving person re-ID via image captioning. Image captions are semantically richer and more consistent than visual attributes, which could significantly alleviate the variance. We use the similarity preserving generative adversarial network (SPGAN) and an image captioner to fulfill domain transfer and language descriptions generation. Then the proposed HorNet can learn the visual and language representation from both the images and captions jointly, and thus enhance the performance of person re-ID. Extensive experiments are conducted on several benchmark datasets with or without image captions, i.e., CUHK03, Market-1501, and Duke-MTMC, demonstrating the superiority of the proposed method. Our method can generate and extract meaningful image captions while achieving state-of-the-art performance.
Predicting dominance in multi-person videos

We consider the problems of predicting (i) the most dominant person in a group of people, and (ii) the more dominant of a pair of people, from videos depicting group interactions. We introduce a novel family of variables called Dominance Rank. We combine features not previously used for dominance prediction (e.g., facial action units, emotions), with a novel ensemble-based approach to solve these two problems. We test our models against four competing algorithms in the literature on two datasets and show that our results improve past performance. We show 2.4% to 16.7% improvement in AUC compared to baselines on one dataset, and a gain of 0.6% to 8.8% in accuracy on the other. Ablation testing shows that Dominance Rank features play a key role.
Natural image segmentation with non-extensive mixture models

Finite mixture models have been widely used for image segmentation in many computer vision and pattern recognition problems. While images of natural scenes are difficult to model, we can employ emerging concepts from statistical physics to achieve better representations. This paper introduces a new class of finite mixture models for solving such problems. The proposed non-extensive mixture models have real-valued power-law exponents that characterize the degree of correlations. The exponents are used to capture rare or frequent occurring patterns in the image. They can describe complex features found with a hierarchy of sizes in natural images: from small objects with a few dozen pixels to large ones that occupy the entire image. We also present a method to determine the parameters based on maximum likelihood estimation. Our numerical experiments indicate more robust and accurate capabilities of non-extensive mixture models for natural image segmentation than conventional mixture models.