Reviewerly/exHarmony huggingface dataset:
Addressing Low Dimensionality Feature Subset Selection: ReliefF(-k) or Extended Correlation-Based Feature Selection(eCFS)?

This paper tackles problems where attribute selection is not only able to choose a few features but also to achieve a low performance classification in terms of accuracy compared to the full attribute set. Correlation-based feature selection (CFS) has been set as the baseline attribute subset selector due to its popularity and high performance. Around hundred data sets have been collected and submitted to CFS; then the problems fulfilling simultaneously the conditions: (a) a number of selected attributes lower than six and (b) a percentage of selected attributes lower than a forty per cent, have been tested onto two directions. Firstly, in the scope of data selection at the feature level, an advanced contemporary approach have been conducted as well as some options proposed in a prior work. Secondly, the pre-processed and initial problems have been tested with some sturdy classifiers. Moreover, this work introduces a new taxonomy of feature selection according to the solution type and the followed way to compute it. The test bed comprises seven problems featured by a low dimensionality after the CFS application, three out of them report a single selected attribute, another one with two extracted features and the three remaining data sets with four or five retained attributes; additionally, the initial feature set is between six and twenty-nine and the complexity of the problems, in terms of classes, fluctuates between two and twenty-one, throwing averages of sixteen and around five for both aforementioned properties. The contribution concluded that the advanced procedure (extended CFS) is suitable for problems where only one or two attributes are selected by CFS; for data sets with more than two selected features the baseline method is preferable to the advanced one, although the considered feature ranking method achieved intermediate results.
Retrieval Driven Classification for Mammographic Masses

Accurate diagnosis is pivotal for successful treatment for breast cancer. High chances of survival are possible, if malignancy is detected at an early stage. Mammography is the most efficient and widely accepted modality for screening breast cancer. In this paper we propose a decision support system based on image retrieval which retrieves similar pathology based mammographic images to serve the physician in the diagnosis of breast cancer. The work explores how to use the retrieved similar cases as references to improve the classification performance. The rationale is that by incorporating the closeness information for decision making improves classifier performance rather than making decision from whole database. Experiments were carried out on DDSM database utilizing 4300 images of breast cancer. The results demonstrated the effectiveness of proposed system and show the vitality for clinical applications.
Self-Guided Hash Coding for Large-Scale Person Re-identification

The laborious manual person ID annotation results in limited training data and increased difficulty in learning discriminative representations. Meanwhile, high dimensional deep features are not ready for fast indexing and matching. Those challenges hinder the application of person Re-Identification (ReID) in large-scale data. To conquer those challenges, we propose a novel training strategy to learn compact binary hash codes. To facilitate feature learning, person images are decomposed into body parts, which are then composed across images into new positive and negative training samples. Binary code quality restrictions are also applied the during training procedure. Requiring no extra annotation costs, our algorithm iteratively generates hard training samples by itself and makes discriminative hash code learning with a limited number of labeled data possible. We hence use "self-guided" to describe this training procedure. Extensive experiments are conducted on two large-scale person ReID datasets, i.e., Market1501 and MSMT17 with distractors, showing our method is competitive compared with recent works.
Natural Language Interactions in Autonomous Vehicles: Intent Detection and Slot Filling from Passenger Utterances

Understanding passenger intents and extracting relevant slots are crucial building blocks towards developing contextual dialogue systems for natural interactions in autonomous vehicles (AV). In this work, we explored AMIE (Automated-vehicle Multi-modal In-cabin Experience), the in-cabin agent responsible for handling certain passenger-vehicle interactions. When the passengers give instructions to AMIE, the agent should parse such commands properly and trigger the appropriate functionality of the AV system. In our current explorations, we focused on AMIE scenarios describing usages around setting or changing the destination and route, updating driving behavior or speed, finishing the trip, and other use-cases to support various natural commands. We collected a multi-modal in-cabin dataset with multi-turn dialogues between the passengers and AMIE using a Wizard-of-Oz scheme via a realistic scavenger hunt game activity. After exploring various recent Recurrent Neural Networks (RNN) based techniques, we introduced our hierarchical joint models to recognize passenger intents along with relevant slots associated with the action to be performed in AV scenarios. Our experimental results outperformed certain competitive baselines and achieved overall F1-scores of 0.91 for utterance-level intent detection and 0.96 for slot filling tasks. In addition, we conducted initial speech-to-text explorations by comparing intent/slot models trained and tested on human transcriptions versus noisy Automatic Speech Recognition (ASR) outputs. Finally, we evaluated the results with single passenger rides versus the rides with multiple passengers.
Polyhedral Optimization of TensorFlow Computation Graphs

We present $${\textsf {R}}\text {-}{\textsf {Stream}}{\cdot }{\textsf {TF}}$$ , a polyhedral optimization tool for neural network computations. $${\textsf {R}}\text {-}{\textsf {Stream}}{\cdot }{\textsf {TF}}$$ transforms computations performed in a neural network graph into C programs suited to the polyhedral representation and uses R-Stream, a polyhedral compiler, to parallelize and optimize the computations performed in the graph. $${\textsf {R}}\text {-}{\textsf {Stream}}{\cdot }{\textsf {TF}}$$ can exploit the optimizations available with R-Stream to generate a highly optimized version of the computation graph, specifically mapped to the targeted architecture. During our experiments, $${\textsf {R}}\text {-}{\textsf {Stream}}{\cdot }{\textsf {TF}}$$ was able to automatically reach performance levels close to the hand-optimized implementations, demonstrating its utility in porting neural network computations to parallel architectures.
Extracting Valuable Information from Big Data for Machine Learning Control: An Application for a Gas Lift Process

The present work investigated the use of an echo state network for a gas lift oil well. The main contribution is the evaluation of the network performance under conditions normally faced in a real production system: noisy measurements, unmeasurable disturbances, sluggish behavior and model mismatch. The main pursued objective was to verify if this tool is suitable to compose a predictive control scheme for the analyzed operation. A simpler model was used to train the neural network and a more accurate process model was used to generate time series for validation. The system performance was investigated with distinct sample sizes for training, test and validation procedures and prediction horizons. The performance of the designed ESN was characterized in terms of slugging, setpoint changes and unmeasurable disturbances. It was observed that the size and the dynamic content of the training set tightly affected the network performance. However, for data sets with reasonable information contents, the obtained ESN performance could be regarded as very good, even when longer prediction horizons were proposed.
Combining gene expression data and prior knowledge for inferring gene regulatory networks via Bayesian networks using structural restrictions

Abstract Gene Regulatory Networks (GRNs) are known as the most adequate instrument to provide a clear insight and understanding of the cellular systems. One of the most successful techniques to reconstruct GRNs using gene expression data is Bayesian networks (BN) which have proven to be an ideal approach for heterogeneous data integration in the learning process. Nevertheless, the incorporation of prior knowledge has been achieved by using prior beliefs or by using networks as a starting point in the search process. In this work, the utilization of different kinds of structural restrictions within algorithms for learning BNs from gene expression data is considered. These restrictions will codify prior knowledge, in such a way that a BN should satisfy them. Therefore, one aim of this work is to make a detailed review on the use of prior knowledge and gene expression data to inferring GRNs from BNs, but the major purpose in this paper is to research whether the structural learning algorithms for BNs from expression data can achieve better outcomes exploiting this prior knowledge with the use of structural restrictions. In the experimental study, it is shown that this new way to incorporate prior knowledge leads us to achieve better reverse-engineered networks.
Negative Emotions, Positive Experience

Since its idealization more than 20 years ago, much research has been carried out on the User eXperience (UX) field, with several evaluation methods being proposed. However, studies have been pointing out conflicting results when evaluating the UX. Users frequently evaluate their UX as positive, even when experiencing many negative emotions when interacting with a product. Moreover, variables such as the peak-end effect and the memory-experience gap may also have been influencing the results, leading to misinterpretations about the product's quality. In this context, this paper presents our work-in-progress research on the following research question: "What are we doing wrong when evaluating the UX?". We discuss about different variables that may have been influencing users' perceptions about their experiences in previous studies and highlight research opportunities. With this work, we expect to shed light on and bring reflection to current practices on UX evaluations in order to progress the research in the UX field.
Comparative Study of the Conventional Mathematical and Fuzzy Logic Controllers for Velocity Regulation

Currently, we are in the digital era, where robotics, with the help of the Internet of Things (IoT), is exponentially advancing, and in the technology market we can find multiple devices for achieving these systems, such as the Raspberry Pi, Arduino, and so on. The use of these devices makes our work easier regarding processing information or controlling physical mechanisms, as some of these devices have microcontrollers or microprocessors. One of the main challenges in speed control applications is to make the decision to use a fuzzy logic control (FLC) system instead of a conventional controller system, such as a proportional integral (PI) or a proportional integral-derivative (PID). The main contribution of this paper is the design, integration, and comparative study of the use of these three types of controllers—FLC, PI, and PID—for the speed control of a robot built using the Lego Mindstorms EV3 kit. The root mean square error (RMSE) and the settling time were used as metrics to validate the performance of the speed control obtained with the controllers proposed in this paper.
Goal modeling for collaborative groups of cyber-physical systems with GRL

In the future, cyber-physical systems (CPS) will increasingly need to have the capability to collaborate with other CPS during runtime to fulfill an overall purpose. This is, for instance, the case for autonomous driving where individual vehicles form a platoon in order to reduce energy consumption and improve the overall traffic flow. When engineering such CPS, it is important to understand the goals the individual systems should fulfill as well as the goals of collaborating groups the individual CPS are potentially part of during operation. In this context, the use of goal modeling techniques seems promising to systematically identify, document, and analyze the scope and context of individual CPS and groups of collaborating CPS in terms of goals and goal relationships. Even though many approaches for goal modeling have been proposed, only a few of them already found their way to industry - such as GRL (goal-oriented requirements language), which is also the essential part of the ITU Z.150/151 standard. In this paper, we report on empirical insights gained from applying the GRL for goal modeling of collaborative groups of CPS in the application fields 'autonomous driving' and 'collaborative transport robots'. We explore different challenges that originate from the very nature of dynamical collaboration of CPS and report on our findings from applying GRL in this context. In order to facilitate goal modeling for individual CPS and collaborative groups thereof, we also propose extensions to GRL addressing specific limitations of GRL identified in our studies.
MATY

Caregiving to a person with Alzheimer's can be a very demanding task, both from physical and psychological perspectives. Technological responses to support caregiving, and improve the quality of life of people with Alzheimer's and their caregivers are lacking. Using a research through design approach, we devised a robot focused on empowering people with Alzheimer and fostering their autonomy, from the initial sketch to a working prototype. MATY is a robot that encourages communication with relatives and promotes routines by eliciting the person to take action, using a multisensorial approach (e.g., projecting biographical images, playing suggestive sounds, or emitting soothing aromas). The paper reports the iterative, incremental design process performed together with stakeholders. We share first lessons learned in this process with HCI researchers and practitioners designing solutions, particularly robots, to assist people with dementia and their caregivers.
Pyrboxes: An efficient multi-scale scene text detector with feature pyramids

Scene text detection has attracted many researches due to its importance to various applications. However, current approaches could not keep a good balance between accuracy and speed, i.e., a high-performance accuracy but with a low processing speed, or vice-versa. In this paper, we propose a novel model, named PyrBoxes, for efficient and effective multi-scale scene text detection. PyrBoxes consists of an SSD-based backbone that utilizes deep layers with strong semantics to detect texts in various sizes, and a proposed grouped pyramid module that leverages basic layers to append detailed locations into detection. Most existing detectors discard features from the basic layers due to the efficiency issue. We argue these layers contain fine-grained information, which is complementary to high-level semantics. Based on this, the grouped pyramid module combines the basic layers recursively into a detection layer via a top-down partition and a bottom-up group. Extensive experiments on both horizontal and oriented benchmarks, including ICDAR2013 Focused Scene Text, ICDAR2015 Incidental Text and COCO-Text, demonstrate that PyrBoxes achieves state-of-the-art or highly competitive performance compared with baselines, while runs significantly faster at inference. Furthermore, by experimenting on another ChiTVText dataset, PyrBoxes shows great generality to Chinese and long text lines. By visualizing some qualitative results, as expected, PyrBoxes provides more accurate locations and reduces the rate of missed detections, especially for small-sized texts.
Toward RDB to NoSQL

The emergence of applications dealing with structured, semi structured, and non-structured data created demands on new data storage systems. The relational model, widely used to store data from diverse applications, does not meet all the imposed scenarios. In response, NoSQL databases have emerged as an option. As a consequence, new approaches for converting the relational model to NoSQL models have been created. However, most of them target a specific NoSQL model and provide little (or none) support for customization for transformations with different cardinalities. In this paper we present a novel approach to convert relational databases (RDB) to document and column family NoSQLs. Our approach receives as input a set of directed acyclic graphs (DAG) representing the target NoSQL model. The DAGs are used to generate commands to transform the input RDB data. The approach supports different cardinalities, and the commands can be customized. We have developed a tool to interpret the DAGs and that supports different transformation strategies. We performed experiments to validate our solution, with different configurations, where conversions were carried out to document and column-family storage.
Functional Digital Nudges

Digital nudges hold enormous potential to change behavior. Despite the appeal to consider timing as a critical factor responsible for the success of digital nudges, a comprehensive organizing framework to guide the design of digital nudges considering nudge moment is yet to be provided. In this paper, we advance the theoretical model to design digital nudges by incorporating three key components: (1) Identifying the optimal digital nudge moment (2) Inferring this optimal moment and (3) Delivering the digital nudge at that moment. We further discuss the existing work and open research avenues.
Does Regulating Hate Speech Undermine Democratic Legitimacy? A Cautious ‘No’

This paper critiques the version of the argument that the regulation of hateful speech by the state undermines its democratic legitimacy made by Ronald Dworkin and James Weinstein (hereafter the Legitimacy Argument). It argues that in some cases the harmful effects of hateful speech on the democratic process outweigh those of restriction. It does not challenge the central premise of the Legitimacy Argument, that a wide-ranging right to freedom of expression is an essential political right in a liberal democracy. Instead, it uses ideal and nonideal theory as a framework for judgements about the regulation of hate speech. The mistake underpinning the Legitimacy Argument is that it assumes that other conditions pervade that make an ideal democratic procedure possible when they do not. In reality the state can be put in a position where, whatever course of action it takes with regard to the regulation or non-regulation of hate speech, some citizens will not be able to participate fully in political deliberation. Under such conditions there remain strong pro tanto reasons not to regulate hate speech on democratic grounds, but they are not all-things-considered reasons, and there are also pro tanto reasons to regulate hate speech that might outweigh them in some cases. This leads to the cautious conclusion that while there might be a normative justification for the regulation of hate speech in individual instances, the debate is best understood as one between competing pro tanto reasons, and must be approached on a case-by-case basis.
The scientific events ontology of the OpenResearch.org curation platform

Scholarly events, such as conferences play a key role in scholarly communication from many research fields, such as computer science. We describe a systematic redesign of the OpenResearch Scientific Events Ontology (OR-SEO) that is used as a schema for the event pages on OpenResearch.org curation platform. OR-SEO is now in use in thousands of event pages on OpenResearch, which enables users to create events wiki-pages without going into the details of the implementation of the ontology. We syntactically and semantically validated OR-SEO to conform to the W3C standards. It has been published through a persistent URL following W3C best practices for publishing Linked data and has been registered at Linked Open Vocabularies.
Does Creating Programming Assignments with Tests Lead to Improved Performance in Writing Unit Tests?

We have constructed a tool, CrowdSorcerer, in which students create programming assignments, their model solutions and associated test cases using a simple input-output format. We have used the tool as a part of an introductory programming course with normal course activities such as programming assignments and a final exam. In our work, we focus on whether creating programming assignments and associated tests correlate with students' performance in a testing-related exam question. We study this through an analysis of the quality of student-written tests within the tool, measured using the number of test cases, line coverage and mutation coverage, and students' performance in testing related exam question, measured using exam points. Finally, we study whether previous programming experience correlates with how students act within the tool and within the testing related exam question.
Interactive AI for Linguistic Education Built on VR Environment Using User Generated Contents

Currently, the market for Korean language education is over 4 trillion won and 20 trillion yen in Japan and 50 trillion in China. Traditional markets used to learn languages through native speaker, such as offline academies, burns, English, etc. In addition, in the case of English, it has become just a subject for studying for tests, not only to communicate.This causes pressure on young people, resulting in poor learning efficiency. To learn language as a means of communication, the environment needs to be created in order to acquire language. There are the language learning programs in the virtual reality environment. such as `Learn Languages VR by Mondly' and `House of Language VR'. In the case of Learn Languages program, it supports many environment and teaches many different languages. However, depending on the stated scenario, the degree of freedom of speech is low.In the case of ` House of Language VR' program, the degree of freedom of speech is significantly reduced by matching words to words. Also, it is less realistic because it is in progress through animation programs produced for infants.In learning languages, learning to speaking in a real-life situation with native speakers is required for advanced skill.In this study, we intend to produce a learning program using VR devices and interacting with native speakers.
Protecting high-dimensional quantum entanglement from the amplitude-phase decoherence sources by weak measurement and reversal

Entangled states with high-dimensional systems have been investigated widely because of its various advantages. This paper investigates the effect of the weak measurement and reversal (WMR) on the protection of the entanglement for an arbitrarily entangled qutrit–qutrit state from the amplitude-phase decoherence sources. By solving the master equation of the qutrit–qutrit system, we numerically get the maximal amount of entanglement. The numerical results show explicitly that the WMR operation indeed helps for protecting entanglement from amplitude-phase decoherence. In particular, WMR can protect entanglement more efficiently in the case of strong decay rates than those with small decay rates. These results shed new light on the protection of high-dimensional quantum entanglement in a complex quantum environment.