Reviewerly/exHarmony huggingface dataset:
Measuring the Developing of Competences with Collaborative Interdisciplinary Work

Assuring and measuring how a competence is developed in an academic ambience is not an easy task. This work presents an analysis of how a group of people acknowledges and evaluates how a collaborative and interdisciplinary approach around a specific problematic situation helps to develop a group of competences. The measuring of a competence has two actors: the first is the designer of the activity to develop such competence and the second is the performer of the activity. This work focuses on the second actor. In such case, the problematic situation was the design of a 3D interactive virtual environment to help people learn a simple task such as changing a flat tire. This work presents a qualitative analysis of how a sample of 37 students acknowledge the development of four competences according to four levels. These levels vary from basic to high achiever. The results indicate that the students acknowledge a high level of developing such competences, which proves that these type of activities are an important approach to help not only students but people in general to assure that they have achieved a certain level of a competence.
Wearable Bio-Signal(PPG)-Based Personal Authentication Method Using Random Forest and Period Setting Considering the Feature of PPG Signals

A study regarding personal authentication based on PPG has been conducted using the Random Forest algorithm.In order to ensure correct authentication, data features must be consistent.This consistency is provided through the normalization of the PPG signal using maximum-minimum normalization and spline interpolation.The threshold is set by using normalized data and the highest value among the points above the threshold is set as the peak of the period.After establishing candidates of valley with values below the threshold, a shifted period is calculated by designating the last valley next to the following peak to the valley of the period.In order to reduce errors in the one period PPG data, the data is averaged after overlapping.A discrete cosine transform (DCT) is used to extract features from the preprocessed data.Thus, the extracted features are used as the input variables for machine learning techniques such as Decision Tree, KNN, Random Forest.The accuracy of these algorithms is 93%, 98%, and 99% respectively.
Model Predictive Control for Uncalibrated and Constrained Image-Based Visual Servoing Without Joint Velocity Measurements

This paper presents a novel scheme for image-based visual servoing (IBVS) of a robot manipulator by considering robot dynamics without using joint velocity measurements in the presence of constraints, uncalibrated camera intrinsic and extrinsic parameters and unknown feature position parameters. An approach to design model predictive control (MPC) method based on identification algorithm and sliding mode observer has been proposed. Based on the MPC method, the IBVS tasks can be considered as a nonlinear optimization problem while the constraints due to the visibility constraint and the torque constraint can be explicitly taken into account. By using the depth-independent interaction matrix framework, the identification algorithm can be used to update the unknown parameters and the prediction model. In addition, many existing controllers require the joint velocity measurements which can be contaminated by noises, thus resulting in the IBVS performance degradation. To overcome the problem without joint velocity measurements, the sliding mode observer is designed to estimate the joint velocities of the IBVS system. The simulation results for both eye-in-hand and eye-to-hand camera configurations are presented to verify the effectiveness of the proposed control method.
About Low DFR for QC-MDPC Decoding

McEliece-like code-based key exchange mechanisms using QC-MDPC codes can reach IND-CPA security under hardness assumptions from coding theory, namely quasi-cyclic syndrome decoding and quasi-cyclic codeword finding. To reach higher security requirements, like IND-CCA security, it is necessary in addition to prove that the decoding failure rate (DFR) is negligible, for some decoding algorithm and a proper choice of parameters. Getting a formal proof of a low DFR is a difficult task. Instead, we propose to ensure this low DFR under some additional security assumption on the decoder. This assumption relates to the asymptotic behavior of the decoder and is supported by several other works. We define a new decoder, Backflip, which features a low DFR. We evaluate the Backflip decoder by simulation and extrapolate its DFR under the decoder security assumption. We also measure the accuracy of our simulation data, in the form of confidence intervals, using standard techniques from communication systems.
Key concepts for phase-to-coordinate conversion in fringe projection systems

Fringe projection systems encode the scanned object shape as a phase distribution according to the system parameters. However, to obtain the object shape in physical units of length, the demodulated phase must be converted to the coordinates of the observed points on the object surface. The design of a phase-to-coordinate conversion algorithm is straightforward when the following key concepts are considered: cameras and projectors as direction sensors, gratings as coordinate-encoding devices, absolute phase, and triangulation. In this paper, the theoretical principles of these concepts are formalized. Then, an efficient and generalized phase-to-coordinate conversion method, which supports systems with multiple cameras and projectors arranged arbitrarily, is proposed. The usefulness of this approach is illustrated by a 3D surface imaging experiment.
Path Planning approach with the extraction of Topological Maps from Occupancy Grid Maps in steep slope vineyards

Robotic platforms are being developed for precision agriculture, to execute repetitive and long term tasks. Autonomous monitoring, pruning, spraying and harvesting are some of these agricultural tasks, which requires an advanced path planning system aware of maximum robot capabilities (mobile platform and arms), terrain slopes and plant/fruits position. The state of the art path planning systems have two limitations: are not optimized for large regions and the path planning is not aware of agricultural tasks requirements. This work presents two solutions to overcome these limitations. It considers the VGR2TO (Vineyard Grid Map to Topological) approach to extract from a 2D grid map a topological map, to reduce the total amount of memory needed by the path planning algorithm and to reduce path search space. Besides, introduces an extension to the chosen algorithm, the Astar algorithm, to ensure a safe path and a maximum distance from the vine trees to enable robotic operations on the tree and its fruits.
CNNs found to jump around more skillfully than RNNs: Compositional Generalization in Seq2seq Convolutional Networks

Lake and Baroni (2018) introduced the SCAN dataset probing the ability of seq2seq models to capture compositional generalizations, such as inferring the meaning of "jump around" 0shot from the component words.Recurrent networks (RNNs) were found to completely fail the most challenging generalization cases.We test here a convolutional network (CNN) on these tasks, reporting hugely improved performance with respect to RNNs.Despite the big improvement, the CNN has however not induced systematic rules, suggesting that the difference between compositional and noncompositional behaviour is not clear-cut.
Vocabulary-Informed Zero-Shot and Open-Set Learning

Despite significant progress in object categorization, in recent years, a number of important challenges remain; mainly, the ability to learn from limited labeled data and to recognize object classes within large, potentially open, set of labels. Zero-shot learning is one way of addressing these challenges, but it has only been shown to work with limited sized class vocabularies and typically requires separation between supervised and unsupervised classes, allowing former to inform the latter but not vice versa. We propose the notion of vocabulary-informed learning to alleviate the above mentioned challenges and address problems of supervised, zero-shot, generalized zero-shot and open set recognition using a unified framework. Specifically, we propose a weighted maximum margin framework for semantic manifold-based recognition that incorporates distance constraints from (both supervised and unsupervised) vocabulary atoms. Distance constraints ensure that labeled samples are projected closer to their correct prototypes, in the embedding space, than to others. We illustrate that resulting model shows improvements in supervised, zero-shot, generalized zero-shot, and large open set recognition, with up to 310K class vocabulary on Animal with Attributes and ImageNet datasets.
Annotating and Analyzing Semantic Role of Elementary Units and Relations in Online Persuasive Arguments

For analyzing online persuasions, one of the important goals is to semantically understand how people construct comments to persuade others.However, analyzing the semantic role of arguments for online persuasion has been less emphasized.Therefore, in this study, we propose a novel annotation scheme that captures the semantic role of arguments in a popular online persuasion forum, so-called ChangeMyView.Through this study, we have made the following contributions: (i) proposing a scheme that includes five types of elementary units (EUs) and two types of relations; (ii) annotating ChangeMyView which results in 4612 EUs and 2713 relations in 345 posts; and (iii) analyzing the semantic role of persuasive arguments.Our analyses captured certain characteristic phenomena for online persuasion.
Dialogue-Act Prediction of Future Responses Based on Conversation History

Sequence-to-sequence models are a common approach to develop a chatbot.They can train a conversational model in an end-to-end manner.One significant drawback of such a neural network based approach is that the response generation process is a black-box, and how a specific response is generated is unclear.To tackle this problem, an interpretable response generation mechanism is desired.As a step toward this direction, we focus on dialogue-acts (DAs) that may provide insight to understand the response generation process.In particular, we propose a method to predict a DA of the next response based on the history of previous utterances and their DAs.Experiments using a Switch Board Dialogue Act corpus show that compared to the baseline considering only a single utterance, our model achieves 10.8% higher F1-score and 3.0% higher accuracy on DA prediction.
Empirical assessment of mobile device users’ information security behavior towards data breach

Purpose The purpose of this paper is to investigate the information security behavior of mobile device users in the context of data breach. Much of the previous research done in user information security behavior have been in broad contexts, therefore creating needs of research that focuses on specific emerging technologies and trends such as mobile technology. Design/methodology/approach This study was an empirical study that gathered survey data from 390 mobile users. Delphi study and pilot study were conducted prior to the main survey study. Partial Least Square Structural Equation Modeling was used to analyze the survey data after conducting pre-analysis data screening. Findings This study shows that information security training programs must be designed by practitioners to target the mobile self-efficacy (MSE) of device users. It also reveals that practitioners must design mobile device management systems along with processes and procedures that guides users to take practical steps at protecting their devices. This study shows the high impact of MSE on users’ protection motivation (PM) to protect their mobile devices. Additionally, this study reveals that the PM of users influences their usage of mobile device security. Originality/value This study makes theoretical contributions to the existing information security literature. It confirms PM theory’s power to predict user behavior within the context of mobile device security usage. Additionally, this study investigates mobile users’ actual security usage. Thus, it goes beyond users’ intention.
Action Research Implementation in Developing an Open Source and Low Cost Robotic Platform for STEM Education

The aim of this paper is double: (a) to record the latest theoretical considerations (literature review) in the field of STEM (acronym of Science, Technology, Engineering, Mathematics), Educational Robotics and the Educational Robotic Platforms used in their implementation, and (b) to validate the argumentation on the potential contribution of an Action Research implementation on STEM education with the ultimate goal of designing and developing an "open philosophy", low-cost, hardware and software educational platform for the implementation of STEM and Educational Robotics.This paper is divided into 7 sections: Introduction, STEM Education, Educational Robotics, Problem statement, Action Research, Methodology, and Conclusion.The Introduction introduces the concept and necessity of STEM education approach.STEM Education section reviews recently published scientific literature related to STEM education (literature review) and summarize the pros and barriers of its use in education.Educational Robotics introduces the robotics as an educational tool and presents empirical evidence on its effectiveness.Educational Robot Platforms subsection presents the most popular -along with their main specs-educational robots for STEM and Educational Robotics use.Problem statement section identifies the scientific gap and composes the necessity to implement research (specifically an Action Research) on designing and developing an "open philosophy", low-cost, hardware and software academic platform for the implementation of STEM and Educational Robotics.Action research section reviews recently published scientific literature related to action research.Research Methodology section presents research's proposal development phases and finally, Conclusion summarizes paper's findings.
A Semi-Markov Structured Support Vector Machine Model for High-Precision Named Entity Recognition

Named entity recognition (NER) is the backbone of many NLP solutions.F 1 score, the harmonic mean of precision and recall, is often used to select/evaluate the best models.However, when precision needs to be prioritized over recall, a state-of-the-art model might not be the best choice.There is little in the literature that directly addresses training-time modifications to achieve higher precision information extraction.In this paper, we propose a neural semi-Markov structured support vector machine model that controls the precisionrecall trade-off by assigning weights to different types of errors in the loss-augmented inference during training.The semi-Markov property provides more accurate phrase-level predictions, thereby improving performance.We empirically demonstrate the advantage of our model when high precision is required by comparing against strong baselines based on CRF.In our experiments with the CoNLL 2003 dataset, our model achieves a better precisionrecall trade-off at various precision levels.
Unsupervised Learning of Discourse-Aware Text Representation for Essay Scoring

Existing document embedding approaches mainly focus on capturing sequences of words in documents.However, some document classification and regression tasks such as essay scoring need to consider discourse structure of documents.Although some prior approaches consider this issue and utilize discourse structure of text for document classification, these approaches are dependent on computationally expensive parsers.In this paper, we propose an unsupervised approach to capture discourse structure in terms of coherence and cohesion for document embedding that does not require any expensive parser or annotation.Extrinsic evaluation results show that the document representation obtained from our approach improves the performance of essay Organization scoring and Argument Strength scoring.
Interpolated Adversarial Training: Achieving robust neural networks without sacrificing too much accuracy

Adversarial robustness has become a central goal in deep learning, both in the theory and the practice. However, successful methods to improve the adversarial robustness (such as adversarial training) greatly hurt generalization performance on the unperturbed data. This could have a major impact on how the adversarial robustness affects real world systems (i.e. many may opt to forego robustness if it can improve accuracy on the unperturbed data). We propose Interpolated Adversarial Training, which employs recently proposed interpolation based training methods in the framework of adversarial training. On CIFAR-10, adversarial training increases the standard test error ( when there is no adversary) from 4.43% to 12.32%, whereas with our Interpolated adversarial training we retain the adversarial robustness while achieving a standard test error of only 6.45%. With our technique, the relative increase in the standard error for the robust model is reduced from 178.1% to just 45.5%. Moreover, we provide mathematical analysis of Interpolated Adversarial Training to confirm its efficiencies and demonstrate its advantages in terms of robustness and generalization.
Unsupervised Grounding of Plannable First-Order Logic Representation from Images

Recently, there is an increasing interest in obtaining the relational structures of the environment in the Reinforcement Learning community. However, the resulting “relations” are not the discrete, logical predicates compatible with the symbolic reasoning such as classical planning or goal recognition. Meanwhile, Latplan (Asai and Fukunaga 2018) bridged the gap between deep-learning perceptual systems and symbolic classical planners. One key component of the system is a Neural Network called State AutoEncoder (SAE), which encodes an image-based input into a propositional representation compatible with classical planning. To get the best of both worlds, we propose First-Order State AutoEncoder, an unsupervised architecture for grounding the first-order logic predicates and facts. Each predicate models a relationship between objects by taking the interpretable arguments and returning a propositional value. In the experiment using 8Puzzle and a photo-realistic Blocksworld environment, we show that (1) the resulting predicates capture the interpretable relations (e.g., spatial), (2) they help to obtain the compact, abstract model of the environment, and finally, (3) the resulting model is compatible with symbolic classical planning.
MCF3D: Multi-Stage Complementary Fusion for Multi-Sensor 3D Object Detection

We present MCF3D, a multi-stage complementary fusion three-dimensional (3D) object detection network for autonomous driving, robot navigation, and virtual reality. This is an end-to-end learnable architecture, which takes both LIDAR point clouds and RGB images as inputs and utilizes a 3D region proposal subnet and second stage detector(s) subnet to achieve high-precision oriented 3D bounding box prediction. To fully exploit the strength of multimodal information, we design a series of fine and targeted fusion methods based on the attention mechanism and prior knowledge, including “pre-fusion,” “anchor-fusion,” and “proposal-fusion.” Our proposed RGB-Intensity form encodes the reflection intensity onto the input image to strengthen the representational power. Our designed proposal-element attention module allows the network to be guided to focus more on efficient and critical information with negligible overheads. In addition, we propose a cascade-enhanced detector for small classes, which is more selective against close false positives. The experiments on the challenging KITTI benchmark show that our MCF3D method produces state-of-the-art results while running in near real-time with a low memory footprint.
Time-rescaled quantum dynamics as a shortcut to adiabaticity

This paper presents a new method of controlling the time evolution of quantum systems that is applicable to both continuous and discrete systems. The realization of the protocol does not require knowledge about the spectrum of the system.
A privacy encryption algorithm based on an improved chaotic system

This paper proposes an encryption scheme based on an improved chaotic system that can hide key information. First, the CML chaotic map is improved, and the chaotic state can be entered from 0. The image of the person is divided into key information and non-key information and spread by different diffusion methods. The RSA algorithm is used to encrypt the size of the key information as part of the key to enhancing the resistance to the chosen plaintext attack. Scrambling is performed at the pixel level using a CML system with a sorting function. The experimental and security analysis results show that the new algorithm has a large key space, is sensitive to keys and plaintext images, has a good encryption effect, and can resist common attack methods.
Document Gated Reader for Open-Domain Question Answering

Open-domain question answering focuses on using diverse information resources to answer any types of question. Recent years, with the development of large-scale data set and various deep neural networks models, some recent advances in open domain question answering system first utilize the distantly supervised dataset as the knowledge resource, then apply deep learning based machine comprehension techniques to generate the right answers, which achieves impressive results compared with traditional feature-based pipeline methods.
nlpUP at SemEval-2019 Task 6: A Deep Neural Language Model for Offensive Language Detection

This paper presents our submission for the SemEval shared task 6, sub-task A on the identification of offensive language.Our proposed model, C-BiGRU, combines a Convolutional Neural Network (CNN) with a bidirectional Recurrent Neural Network (RNN).We utilize word2vec to capture the semantic similarities between words.This composition allows us to extract long term dependencies in tweets and distinguish between offensive and non-offensive tweets.In addition, we evaluate our approach on a different dataset and show that our model is capable of detecting online aggressiveness in both English and German tweets.Our model achieved a macro F1-score of 79.40% on the SemEval dataset.
MITRE at SemEval-2019 Task 5: Transfer Learning for Multilingual Hate Speech Detection

This paper describes MITRE's participation in SemEval-2019 Task 5, HatEval: Multilingual detection of hate speech against immigrants and women in Twitter.The techniques explored range from simple bag-of-ngrams classifiers to neural architectures with varied attention mechanisms.We describe several styles of transfer learning from auxiliary tasks, including a novel method for adapting pretrained BERT models to Twitter data.Logistic regression ties the systems together into an ensemble submitted for evaluation.The resulting system was used to produce predictions for all four HatEval subtasks, achieving the best mean rank of all teams that participated in all four conditions.