Reviewerly/exHarmony huggingface dataset:
Critical Quality Factors for Rapid, Scalable, Agile Development

Agile methods frequently have difficulties with qualities, often specifying quality requirements as stories, e.g., "As a user, I need a safe and secure system." Such projects will generally schedule some capability releases followed by safety and security releases, only to discover user-developer misunderstandings and unsecurable agile code, leading to project failure. Very large agile projects also have further difficulties with project velocity and scalability. Examples are trying to use daily standup meetings, 2-week sprints, shared tacit knowledge vs. documents, and dealing with user-developer misunderstandings. At USC, our Parallel Agile, Executable Architecture research project shows some success at mid-scale (50 developers). We also examined several large (hundreds of developers) TRW projects that had succeeded with rapid, high-quality development. The paper elaborates on their common Critical Quality Factors: a concurrent 3-team approach, an empowered Keeper of the Project Vision, and a management approach emphasizing qualities.
A Brief Review of Receptive Fields in Graph Convolutional Networks

Convolutional neural networks have been shown successful in extracting features from images and texts. However, it is difficult to apply convolutional neural networks directly on ubiquitous graph data since the graph data lies in an irregular structure. A significant number of researchers engrossed themselves in studying graph convolutional networks transformed from Euclidean domain. Previous graph convolutional networks overviews mainly focus on reviewing recent methods in a comprehensive ways. In this survey, we review the convolutional networks from the perspective of receptive fields. Roughly, the convolutional networks fall into three main categories: spectral based methods, sampling based methods and attention based methods. We analysis the differences of these methods and propose three potential directions for future research of graph convolutional networks.
Multi-feature and Multi-instance Learning with Anti-overfitting Strategy for Engagement Intensity Prediction

This paper proposes a novel engagement intensity prediction approach, which is also applied in the EmotiW Challenge 2019 and resulted in good performance. The task is to predict the engagement level when a subject student is watching an educational video in diverse conditions and various environments. Assuming that the engagement intensity has a strong correlation with facial movements, upper-body posture movements and overall environmental movements in a time interval, we extract and incorporate these motion features into a deep regression model consisting of layers with a combination of LSTM, Gated Recurrent Unit (GRU) and a Fully Connected Layer. In order to precisely and robustly predict the engagement level in a long video with various situations such as darkness and complex background, a multi-features engineering method is used to extract synchronized multi-model features in a period of time by considering both the short-term dependencies and long-term dependencies. Based on the well-processed features, we propose a strategy for maximizing validation accuracy to generate the best models covering all the model configurations. Furthermore, to avoid the overfitting problem ascribed to the extremely small database, we propose another strategy applying a single Bi-LSTM layer with only 16 units to minimize the overfitting, and splitting the engagement dataset (train + validation) with 5-fold cross validation (stratified k-fold) to train the conservative model. By ensembling the above models, our methods finally win the second place in the challenge with MSE of 0.06174 on the testing set.
Gaussian Processes for Data Fulfilling Linear Differential Equations

A method to reconstruct fields, source strengths and physical parameters based on Gaussian process regression is presented for the case where data are known to fulfill a given linear differential equation with localized sources. The approach is applicable to a wide range of data from physical measurements and numerical simulations. It is based on the well-known invariance of the Gaussian under linear operators, in particular differentiation. Instead of using a generic covariance function to represent data from an unknown field, the space of possible covariance functions is restricted to allow only Gaussian random fields that fulfill the homogeneous differential equation. The resulting tailored kernel functions lead to more reliable regression compared to using a generic kernel and makes some hyperparameters directly interpretable. For differential equations representing laws of physics such a choice limits realizations of random fields to physically possible solutions. Source terms are added by superposition and their strength estimated in a probabilistic fashion, together with possibly unknown hyperparameters with physical meaning in the differential operator.
Online Bayesian max-margin subspace learning for multi-view classification and regression

Multi-view data have become increasingly popular in many real-world applications where data are generated from different information channels or different views such as image + text, audio + video, and webpage + link data. Last decades have witnessed a number of studies devoted to multi-view learning algorithms, especially the predictive latent subspace learning approaches which aim at obtaining a subspace shared by multiple views and then learning models in the shared subspace. However, few efforts have been made to handle online multi-view learning scenarios. In this paper, we propose an online Bayesian multi-view learning algorithm which learns predictive subspace with the max-margin principle. Specifically, we first define the latent margin loss for classification or regression in the subspace, and then cast the learning problem into a variational Bayesian framework by exploiting the pseudo-likelihood and data augmentation idea. With the variational approximate posterior inferred from the past samples, we can naturally combine historical knowledge with new arrival data, in a Bayesian passive-aggressive style. Finally, we extensively evaluate our model on several real-world data sets and the experimental results show that our models can achieve superior performance, compared with a number of state-of-the-art competitors.
Development of a Machine Learning Model Predicting an ICU Admission for Patients with Elective Surgery and Its Prospective Validation in Clinical Practice.

Frequent utilization of the Intensive Care Unit (ICU) is associated with higher costs and decreased availability for patients who urgently need it. Common risk assessment tool, like the ASA score, lack objectivity and do account only for some influencing parameters. The aim of our study was (1) to develop a reliable machine learning model predicting ICU admission risk after elective surgery, and (2) to implement it in a clinical workflow. We used electronic medical records from more than 61,000 patients for modelling. A random forest model outperformed other methods with an area under the curve of 0.91 in the retrospective test set. In the prospective implementation, the model achieved a sensitivity of 73.3% and a specificity of 80.8%. Further research is essential to determine physicians' attitudes to machine learning models and assess the long term improvement of ICU management.
Continuous Data-driven Software Engineering - Towards a Research Agenda

The rapid pace with which software needs to be built, together with the increasing need to evaluate changes for end users both quantitatively and qualitatively calls for novel software engineering approaches that focus on short release cycles, continuous deployment and delivery, experiment-driven feature development, feedback from users, and rapid tool-assisted feedback to developers. To realize these approaches there is a need for research and innovation with respect to automation and tooling, and furthermore for research into the organizational changes that support flexible data-driven decision-making in the development lifecycle. Most importantly, deep synergies are needed between software engineers, managers, and data scientists. This paper reports on the results of the joint 5th International Workshop on Rapid Continuous Software Engineering (RCoSE 2019) and the 1st International Workshop on Data-Driven Decisions, Experimentation and Evolution (DDrEE 2019), which focuses on the challenges and potential solutions in the area of continuous data-driven software engineering.
Technological Determinism, New Literacies And Learning Process And The Impact Towards Future Learning

The interest of this research is to investigate technological change, new literacies and learning processes which impact future learning. With technological determinism, the members of the university need to have technological knowledge, belief of change and adopt the technology. The problem in this research is the habits in the individual who works within our campus that would not change, because with the new literacies used and introduced in communication, besides learning in using online systems will continue to be implemented.Â The research sampling is conducted of 76 respondents, consisting of students, lecturers, employees, staff and technicians. And this research method uses is quantitative methods and semi-structured qualitative methods. The result obtained that there are members of campus who are willing to change in communication and the ways of thinking and acting to follow the change in future learning 4.0 industrial revolutions
IMPROVING STUDENTS’ SPEAKING SKILL THROUGH ENGLISH MOVIE IN SCOPE OF SPEAKING FOR GENERAL COMMUNICATION

Speaking for general communication is compulsory subject that must be taken by students during second semester. In reality, most of the students at IKIP Siliwangi face some problems related to speaking skill. To improve the students’ speaking skill, the lecturers need to know what media is the most suitable for the students. This research used Collaborative Classroom Action Research (CAR). The objectives of the study are: 1) finding out whether or not English Movie can improve the students’ speaking skill 2) finding out classroom situation when English Movie implemented in speaking class. To collect data, the researcher used instruments of observations, tests and questionnaires. The finding showed that the use of English Movie was effective in improving the speaking abilities of the student in class. This can be seen from the improvements after each cycle. The average score of pre test was 70.5. After the implementation of English Movie in every cycle, the students’ speaking score were getting better. It can be seen from the result of students’ average score 76.5 in cycle 1 and 81 in cycle 2. Therefore, it can be concluded that English Movie can improve students’ speaking skill and improve speaking classroom into a better situation. Keywords : Improving, Speaking, English Movie
Teaching Robots to Act and Converse in Physical Spaces: Participatory Design Fictions with Museum Guides

This paper reflects on the expectations of museum guides regarding companion AI-powered robots in a science museum space. We employed Design Fiction as a technique to explore machine teaching of future technologies in public spaces. The fiction is illustrated by an open-ended “imaginary abstract” which showcases the dilemma of buying AI robots to work as floor guides in a science museum. Forty-seven museum guides participated in a study in which they were asked to write the end of a fictional story. Participants described their impressions and implications of teaching robots who would do their jobs. This design fiction activity is expected to help grounding the debate on machine teaching paradigms, values, and social dilemmas which new technologies bring to physical spaces.
Variable neighborhood search for graphical model energy minimization

Graphical models factorize a global probability distribution/energy function as the product/ sum of local functions. A major inference task, known as MAP in Markov Random Fields and MPE in Bayesian Networks, is to find a global assignment of all the variables with maximum a posteriori probability/minimum energy. A usual distinction on MAP solving methods is complete/incomplete, i.e. the ability to prove optimality or not. Most complete methods rely on tree search, while incomplete methods rely on local search. Among them, we study Variable Neighborhood Search (VNS) for graphical models. In this paper, we propose an iterative approach above VNS that uses (partial) tree search inside its local neighborhood exploration. The proposed approach performs several neighborhood explorations of increasing search complexity, by controlling two parameters, the discrepancy limit and the neighborhood size. Thus, optimality of the obtained solutions can be proven when the neighborhood size is maximal and with unbounded tree search. We further propose a parallel version of our method improving its anytime behavior on difficult instances coming from a large graphical model benchmark. Last we experiment on the challenging minimum energy problem found in Computational Protein Design, showing the practical benefit of our parallel version. A solver is available at https://github.com/toulbar2/toulbar2.
The Impact of Rule-Based Text Generation on the Quality of Abstractive Summaries

In this paper we describe how an abstractive text summarization method improved the informativeness of automatic summaries by integrating syntactic text simplification, subject-verb-object concept frequency scoring and a set of rules that transform text into its semantic representation.We analyzed the impact of each component of our approach on the quality of generated summaries and tested it on DUC 2002 dataset.Our experiments showed that our approach outperformed other state-of-the-art abstractive methods while maintaining acceptable linguistic quality and redundancy rate.
Using Enriched Samples for Semi-Automated Vocabulary Expansion to Identify Rare Events in Clinical Text: Sexual Orientation as a Use Case.

We demonstrate the utility of concept lexicon expansion and evaluation using enriched samples of patients and documents with sexual orientation as a use case for rare event detection in electronic medical records. Using this approach, we found 7 additional words and 21 misspellings beyond our initial set of five seed words. We can use the expanded vocabulary to further develop a full natural language processing system to identify instances where sexual orientation is documented.
Models to evaluate service Provisioning over Cloud Computing Environments - A Blockchain-As-A-Service case study

ThestrictnessoftheServiceLevelAgreements(SLAs)ismainlyduetoasetofconstraintsrelated to performance and dependability attributes, such as availability. This paper shows that system’s availability values may be improved by deploying services over a private environment, which may obtain better availability values with improved management, security, and control. However, how much a company needs to afford to keep this improved availability? As an additional activity, this paper compares the obtained availability values with the infrastructure deployment expenses and establishes a cost × benefit relationship. As for the system’s evaluation technique, we choose modeling; while for the service used to demonstrate the models’ feasibility, the blockchain-as-a-service was the selected one. This paper proposes and evaluate four different infrastructures hosting blockchains: (i) baseline; (ii) double redundant; (iii) triple redundant, and (iv) hyper-converged. The obtained results pointed out that the hyper-converged architecture had an advantage over a full triple redundant environment regarding availability and deployment cost.
A Three Word-Level Approach Used in Machine Learning for Romanian Sentiment Analysis

In this paper, we propose a new approach to evaluate online feelings, emotions or opinions that allows text classification with some polarity and produce a solution for some relevant sentiment analysis challenges that improves the reliability of sentiment analysis performed. Thus, we propose a semi-supervised machine learning system, based on a taxonomy of emotionally charged words with three classes, taking into account the neutral polarity class, but also a comparison of results by applying several classification algorithms, such as Naïve Bayes, Decision Trees, Support Vector Machines and our proposed approach. We present aspects about natural language complexity from the sentiment perspective and some language resources, considering a Romanian corpus based on 25,841 news and a Romanian language dictionary containing 42,497 words, which can be used by sentiment analysis systems. The scope of the paper is to identify the utility and usability of multiple data sets in a real-world application, to get relevant results about online comments perception that can provide valuable future perspectives for a nation's social and even political issues. The best result of our implemented system, after having processed the corpus, is over 82%, compared to classical machine learning classification methods with a best score of 70%.
Mitigating Demographic Biases in Social Media-Based Recommender Systems

In KDD ’19: Social Impact Track, August 04–08, 2019, Anchorage, Alaska.ACM, New York, NY, USA,
Identification of Opinion Spammers using Reviewer Reputation and Clustering Analysis

Online reviews have increasingly become a very important resource before making a purchasing decisions. Unfortunately, malicious sellers try to game the system by hiring a person or team (which is called spammers) to fabricate fake reviews to improve their reputation.Existing methods mainly take the problem as a general binary classification or focus on some heuristic rules. However, supervised learning methods relies heavily on a large number of labeled examples of deceptive and truthful opinions by domain experts, and most of features mentioned in the heuristic strategy ignore the characteristic of the group organization among spammers. In this paper, an effective method of identifying opinion spammers is proposed. Firstly, suspected spammers are detected by means of unsupervised learning based on reviewer’s reputation. We believe that the reviewer’s reputation has a direct relation with the quality of reviews. Generally, review written by user with lower reputation, shows lower quality and higher possibility to be fake. Therefore, the model assigns reputation score to each reviewer wherein the content based factors and activeness of reviewers are employed efficiently. On basis of all suspected spammers, k-center clustering algorithm is performed to further spot the spammers based on the observation of burst of review release time. Experimental results on Amazon’s dataset are encouraging and indicate that our approach poses high accuracy and recall, and good performance is achieved.
A Probabilistic Morphology Model for German Lemmatization

Lemmatization is a central task in many NLP applications. Despite this importance, the number of (freely) available and easy to use tools for German is very limited. To fill this gap, we developed a simple lemmatizer that can be trained on any lemmatized corpus. For a full form word the tagger tries to find the sequence of morphemes that is most likely to generate that word. From this sequence of tags we can easily derive the stem, the lemma and the part of speech (PoS) of the word. We show (i) that the quality of this approach is comparable to state of the art methods and (ii) that we can improve the results of Part-of-Speech (PoS) tagging when we include the morphological analysis of each word.
Simple and Effective Load Volume Estimation in Moving Trucks using LiDARs

Industries need to track the amount of materials and goods transported through processing units in order to optimize production. In large-scale industries, trucks and trains are commonly used for transportation. The manual evaluation of the volume of material being transported by these vehicles can be imprecise, inefficient, and even unsafe for employees. Therefore, this work presents an automated system for estimating the volume of load in moving trucks using a pair of multi-layer light detection and ranging (LiDAR) sensors. The sensors are mounted in a structure so that trucks can pass through without stopping. The proposed system can be used with any type of compact load such as grains, and powders. A mesh of the load is built and used for estimating the volume. A simple, efficient, and effective heuristic is proposed for tracking the truck's positions. The system was deployed and evaluated in a mining company in real conditions of operation. Experimental results indicate that the system produces accurate estimates of the volume of ore powder transported by trucks. The reconstruction of the loads and the estimative of their volumes are performed once the data is acquired and lasts less than 1.5 minutes on average.
Exploring EFL Graduate Students’ Attitudes toward, and Use of, Mobile Phones in Language Learning

Mobile phones offer unique opportunities for learners of English as a foreign language (EFL) to boost their language skills.Many studies have investigated the use of mobile applications in language learning, but little research has dealt with EFL graduate students.This study is an attempt to explore graduate EFL students' attitudes toward the use of mobile phones in language learning and to shed light on the problems students face when using their mobile phones for language learning.For data collection, a 25-item questionnaire was developed and circulated online to 70 graduate EFL students at King Khalid University, Saudi Arabia.The results showed that students' attitudes were positive, and there was a significant difference in attitudes in favour of female students.Also, the results revealed that most of the informants frequently use mobile phones, and also, there was a significant difference in favour of females.Additionally, the results showed that some students use smartphones effectively without facing any problems, whereas the majority of students face various challenges such as technical difficulties, the small size of the screen, distraction, becoming less patient, and getting tired.The findings indicated that further research is needed to scrutinize all these problems, with particular emphasis on health problems and distraction.
Water quality monitoring at a virtual watershed monitoring station using a modified deep extreme learning machine

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On Bisimilarity in Lambda Calculi with Continuous Probabilistic Choice

Applicative bisimiliarity is a coinductively-defined program equivalence in which programs are tested as argument-passing processes. Starting with the seminal work by Abramsky, applicative bisimiliarity has been proved to be a powerful technique for higher-order program equivalence. Recently, applicative bisimiliarity has also been generalised to lambda calculi with algebraic effects, and with discrete probabilistic choice in particular. In this paper, we show that applicative bisimiliarity behaves well in a lambda-calculus in which probabilistic choice is available in a more general form, namely through an operator for sampling of values from continuous distributions. Our main result shows that applicative bisimilarity is sound for contextual equivalence, hence providing a new reasoning principle for higher-order probabilistic languages.
A survey on hyper basis function neural networks

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