Reviewerly/exHarmony huggingface dataset:
OCR Evaluation Tools for the 21st Century

We introduce ocreval, a port of the ISRI OCR Evaluation Tools, now with Unicode support. We describe how we upgraded the ISRI OCR Evaluation Tools to support modern text processing tasks. ocreval supports producing character-level and word-level accuracy reports, supporting all characters representable in the UTF-8 character encoding scheme. In addition, we have implemented the Unicode default word boundary specification in order to support word-level accuracy reports for a broad range of writing systems. We argue that character-level and word-level accuracy reports produce confusion matrices that are useful for tasks beyond OCR evaluation—including tasks supporting the study and computational modeling of endangered languages.
Simultaneously learning affinity matrix and data representations for machine fault diagnosis

Recently, preserving geometry information of data while learning representations have attracted increasing attention in intelligent machine fault diagnosis. Existing geometry preserving methods require to predefine the similarities between data points in the original data space. The predefined affinity matrix, which is also known as the similarity matrix, is then used to preserve geometry information during the process of representations learning. Hence, the data representations are learned under the assumption of a fixed and known prior knowledge, i.e., similarities between data points. However, the assumed prior knowledge is difficult to precisely determine the real relationships between data points, especially in high dimensional space. Also, using two separated steps to learn affinity matrix and data representations may not be optimal and universal for data classification. In this paper, based on the extreme learning machine autoencoder (ELM-AE), we propose to learn the data representations and the affinity matrix simultaneously. The affinity matrix is treated as a variable and unified in the objective function of ELM-AE. Instead of predefining and fixing the affinity matrix, the proposed method adjusts the similarities by taking into account its capability of capturing the geometry information in both original data space and non-linearly mapped representation space. Meanwhile, the geometry information of original data can be preserved in the embedded representations with the help of the affinity matrix. Experimental results on several benchmark datasets demonstrate the effectiveness of the proposed method, and the empirical study also shows it is an efficient tool on machine fault diagnosis.
Consensus-Based Optimization for 3D Human Pose Estimation in Camera Coordinates

3D human pose estimation is frequently seen as the task of estimating 3D poses relative to the root body joint. Alternatively, we propose a 3D human pose estimation method in camera coordinates, which allows effective combination of 2D annotated data and 3D poses and a straightforward multi-view generalization. To that end, we cast the problem as a view frustum space pose estimation, where absolute depth prediction and joint relative depth estimations are disentangled. Final 3D predictions are obtained in camera coordinates by the inverse camera projection. Based on this, we also present a consensus-based optimization algorithm for multi-view predictions from uncalibrated images, which requires a single monocular training procedure. Although our method is indirectly tied to the training camera intrinsics, it still converges for cameras with different intrinsic parameters, resulting in coherent estimations up to a scale factor. Our method improves the state of the art on well known 3D human pose datasets, reducing the prediction error by 32% in the most common benchmark. We also reported our results in absolute pose position error, achieving 80 mm for monocular estimations and 51 mm for multi-view, on average. Source code is available at https://github.com/dluvizon/3d-pose-consensus .
Joint and individual analysis of breast cancer histologic images and genomic covariates

The two main approaches in the study of breast cancer are histopathology (analyzing visual characteristics of tumors) and genomics. While both histopathology and genomics are fundamental to cancer research, the connections between these fields have been relatively superficial. We bridge this gap by investigating the Carolina Breast Cancer Study through the development of an integrative, exploratory analysis framework. Our analysis gives insights—some known, some novel—that are engaging to both pathologists and geneticists. Our analysis framework is based on angle-based joint and individual variation explained (AJIVE) for statistical data integration and exploits convolutional neural networks (CNNs) as a powerful, automatic method for image feature extraction. CNNs raise interpretability issues that we address by developing novel methods to explore visual modes of variation captured by statistical algorithms (e.g., PCA or AJIVE) applied to CNN features.
How do latent print examiners perceive proficiency testing? An analysis of examiner perceptions, performance, and print quality

Proficiency testing has the potential to serve several important purposes for crime laboratories and forensic science disciplines. Scholars and other stakeholders, however, have criticized standard proficiency testing procedures since their implementation in laboratories across the United States. Specifically, many experts label current proficiency tests as non-representative of actual casework, at least in part because they are not sufficiently challenging (e.g., [1], [2], [3], [4]. In the current study, we surveyed latent print examiners (n = 322) after they completed a Collaborative Testing Services proficiency test about their perceptions of test items. We also evaluated respondents’ test performance and used a quality metric algorithm (LQMetrics) to obtain objective indicators of print quality on the test. Results were generally consistent with experts’ concerns about proficiency testing. The low observed error rate, examiner perceptions of relative ease, and high objective print quality metrics together suggest that latent print proficiency testing is not especially challenging. Further, examiners indicated that the test items that most closely resembled real-world casework were also the most difficult and contained prints of the lowest quality. Study findings suggest that including prints of lower quality may increase both the difficulty and representativeness of proficiency testing in latent print examination.
Integrating Construal-level Theory in Designing Fear Appeals in IS Security Research

Organizations increasingly use fear appeals to motivate users to engage in behaviors that protect information security. Though academic interest in the topic has burgeoned, prior research has mainly focused on providing process evidence on how low- and high-threat security messages influence protective behaviors. According to protection motivation theory, however, the threat-appraisal phase, in which the receiver evaluates whether a fear appeal is threatening or not, follows exposure to the fear appeal. One can indeed design fear appeals to manipulate different dimensions, including the threat depicted and the coping response provided. These dimensions, in turn, influence protection motivation. The general focus on low- and high-threat messages runs the risks of 1) foregoing key theoretical insights that can stem from specific message manipulations and 2) inadvertently introducing message confounds. To address this issue, we introduce construal-level theory as the theoretical lens to design and identify potential confounds in fear-appeal manipulations. We further discuss how researchers can seamlessly integrate construal-level theory into information security studies based on protection motivation theory. Our work has important theoretical and methodological implications for IS security researchers.
Coverage optimization in the Cooperative Surveillance Task using Multiple Micro Aerial Vehicles

In the task of cooperative surveillance using Micro Aerial Vehicles (MAVs), MAVs cooperatively observe a given set of Areas of Interest (AoI). The missions are usually prepared in a decoupled manner: first, the sensing locations are found, followed by computations of the trajectories assuming GPS-based localization. The precision of GPS may, however, be insufficient to keep the MAVs in compact groups, which may lead to mutual collisions. To avoid the collisions between MAVs, a camera-based on-board localization has to be used. This however requires to maintain positions of the team members in the given range to enable reliable on-board localization (each MAV has to be visible from other ones). The task of the mission planning is to find an appropriate distribution of MAVs above AoIs together with feasible trajectories from a depot to reach these locations. The on-board localization constraints and MAV motion constraints have to be satisfied during the entire mission. We propose a modification of RRT (Rapidly Exploring Random Tree) for this mission planning. The algorithm first explores the state space to find suitable sensing locations together with feasible trajectories towards them. Then, the sensing locations are optimized using Particle Swarm optimization (PSO). The proposed method has been verified in numerous simulations and outdoor experiments. The achieved results exhibit significantly better performance in terms of lower computational power and complexity of solved scenarios than the state-of-the-art solutions.
Offence in Dialogues: A Corpus-Based Study

In recent years an increasing number of analyses of offensive language has been published, however, dealing mainly with the automatic detection and classification of isolated instances.In this paper we aim to understand the impact of offensive messages in online conversations diachronically, and in particular the change in offensiveness of dialogue turns.In turn, we aim to measure the progression of offence level as well as its direction -For example, whether a conversation is escalating or declining in offence.We present our method of extracting linear dialogues from tree-structured conversations in social media data and make our code publicly available. 1Furthermore, we discuss methods to analyse this dataset through changes in discourse offensiveness.Our paper includes two main contributions; first, using a neural network to measure the level of offensiveness in conversations; and second, the analysis of conversations around offensive comments using decoupling functions.
A Qualitative Evaluation Framework for Paraphrase Identification

In this paper, we present a new approach for the evaluation, error analysis, and interpretation of supervised and unsupervised Paraphrase Identification (PI) systems.Our evaluation framework makes use of a PI corpus annotated with linguistic phenomena to provide a better understanding and interpretation of the performance of various PI systems.Our approach allows for a qualitative evaluation and comparison of the PI models using human interpretable categories.It does not require modification of the training objective of the systems and does not place additional burden on the developers.We replicate several popular supervised and unsupervised PI systems.Using our evaluation framework we show that: 1) Each system performs differently with respect to a set of linguistic phenomena and makes qualitatively different kinds of errors; 2) Some linguistic phenomena are more challenging than others across all systems.
Improved calibration method based on the RANSAC approach and an improved gray centroid method for a laser-line-based structured light system

This paper proposes an improved calibration method for a structured light system by using the random sample consensus (RANSAC) method with nonlinear optimization and an improved gray centroid method. The proposed method is composed of two steps: calibrating intrinsic and extrinsic parameters for the camera, exploiting the improved gray centroid method to extract the centerline, and fitting the structured light plane by the RANSAC approach with the three-dimensional (3D) points obtained from different positions. The error function caused by the extracted centerline is deduced based on the pixel error perturbation method. The error results of the 3D points are simulated and analyzed. An imaging system is built to realize the 3D imaging. The experimental results show that the calibration error is within 0.08 mm and the reconstruction error is less than 0.45 mm. Moreover, it performs better for the reconstruction of complex objects compared with traditional methods.
Memrise: Promoting Students’ Autonomous Learning Skill through Language Learning Application

To master English language, students cannot only learn English from what is learned in the classroom given by the teacher. They need to explore many things to support their understanding about language. Therefore, independent learning skill is crucially needed. The students are the one who knows what they need to study according to their priority. However, sometimes, it is hard for students to build their independent learning skill since there is no encouragement from the teacher to promote it, and also students’ lack of awareness related to what they are learning. The role of technology like online learning application nowadays really helps teachers and students overcome this problem. This study is aimed to explore the use of language learning application named &lt;em&gt;Memrise&lt;/em&gt; to enhance students’ autonomous learning skill outside the class, especially in studying English. A descriptive study was conducted to eighteen Management students taking English for Business II subject. The data gained from questioner and interview showed that &lt;em&gt;Memrise&lt;/em&gt; helps students learn English and especially improve their vocabulary. Moreover, because it can be accessed anywhere and anytime, students can always learn English whenever they want to. Thus, it really supports students’ independent learning skill and boosts their English skills.
Generalized Convolutional Forest Networks for Domain Generalization and Visual Recognition

When constructing random forests, it is of prime importance to ensure high accuracy and low correlation of individual tree classifiers for good performance. Nevertheless, it is typically difficult for existing random forest methods to strike a good balance between these conflicting factors. In this work, we propose a generalized convolutional forest networks to learn a feature space to maximize the strength of individual tree classifiers while minimizing the respective correlation. The feature space is iteratively constructed by a probabilistic triplet sampling method based on the distribution obtained from the splits of the random forest. The sampling process is designed to pull the data of the same label together for higher strength and push away the data frequently falling to the same leaf nodes. We perform extensive experiments on five image classification and two domain generalization datasets with ResNet-50 and DenseNet-161 backbone networks. Experimental results show that the proposed algorithm performs favorably against state-of-the-art methods.
Sensing Tree Automata as a Model of Syntactic Dependencies

Various aspects of syntax have recently been characterized in subregular terms.However, these characterizations operate over very different representations, including string encodings of c-command relations as well as tiers projected from derivation trees.We present a way to unify these approaches via sensing tree automata over Minimalist grammar dependency trees.Sensing tree automata are deterministic top-down tree automata that may inspect the labels of all daughter nodes before assigning them specific states.It is already known that these automata cannot correctly enforce all movement dependencies in Minimalist grammars, but we show that this result no longer holds if one takes into account several well-established empirical restrictions on movement.Sensing tree automata thus furnish a strong yet uniform upper bound on the complexity of syntactic dependencies.
Skin Diseases Classification Using Local Binary Pattern and Convolutional Neural Network

Skin disease is one of the diseases that are often found in tropical countries like Indonesia. Lack of knowledge about the types and prevention of skin diseases results a person suffering from acute skin diseases. Computer technology is expected to help detect disease early so that it can minimize the occurrence of more dangerous diseases. This paper proposes a method for introducing the shape, color, and texture of skin diseases in digital images and classifying the results of image analysis based on the type of disease in human skin. The method used is a combination of Local Binary Pattern (LBP) and Convolutional Neural Network (CNN) methods which can later be used as sensors or vision for skin diseases automatically. The results of this study can help in the early identification of skin diseases, helping parties who want to know the image value of skin diseases by using LBP and classifying it based on the type of disease using CNN. This study shows the level of accuracy of combining LBP with CNN is quite high with an average value of 92%. In addition, this research can also be used as reference material for the development of further research in image processing that uses LBP and classification using CNN.
Evaluating Lossy Compression Rates of Deep Generative Models

The field of deep generative modeling has succeeded in producing astonishingly realistic-seeming images and audio, but quantitative evaluation remains a challenge. Log-likelihood is an appealing metric due to its grounding in statistics and information theory, but it can be challenging to estimate for implicit generative models, and scalar-valued metrics give an incomplete picture of a model's quality. In this work, we propose to use rate distortion (RD) curves to evaluate and compare deep generative models. While estimating RD curves is seemingly even more computationally demanding than log-likelihood estimation, we show that we can approximate the entire RD curve using nearly the same computations as were previously used to achieve a single log-likelihood estimate. We evaluate lossy compression rates of VAEs, GANs, and adversarial autoencoders (AAEs) on the MNIST and CIFAR10 datasets. Measuring the entire RD curve gives a more complete picture than scalar-valued metrics, and we arrive at a number of insights not obtainable from log-likelihoods alone.
Design of an Autonomous Tracked Mower Robot using Vision-Based Remote Control

Nowadays, autonomous vehicles and automated guided vehicles (AGV) are one of the most up-to-date subjects because of the readiness of the sensors, actuators, and real-time controllers. Similar to the AGV, an autonomous track mower robot was designed in this research. The mobile robot, which had a National Instruments real-time controller, was remotely controlled from a computer. Unlike to common mowers, the mower has track geometry in order to climb up a steep path. The position of the robot was controlled by two DC geared motors and feedback by using vision system. A web camera was installed on the robot to capture the environment, make a decision, and change the robot trajectory. Experiments have shown that robot can cut tall grass and operate in a specific task.
JPEG XT Image Compression with Hue Compensation for Two-Layer HDR Coding

We propose a novel JPEG XT image compression with hue compensation for two-layer HDR coding. LDR images produced from JPEG XT bitstreams have some distortion in hue due to tone mapping operations. In order to suppress the color distortion, we apply a novel hue compensation method based on the maximally saturated colors. Moreover, the bitstreams generated by using the proposed method are fully compatible with the JPEG XT standard. In an experiment, the proposed method is demonstrated not only to produce images with small hue degradation but also to maintain well-mapped luminance, in terms of three kinds of criterion: TMQI, hue value in CIEDE2000, and the maximally saturated color on the constant-hue plane.
Latent Dirichlet Allocation (LDA) for improving the topic modeling of the official bulletin of the spanish state (BOE)

Since Internet was born most people can access fully free to a lot sources of information. Every day a lot of web pages are created and new content is uploaded and shared. Never in the history the humans has been more informed but also uninformed due the huge amount of information that can be access. When we are looking for something in any search engine the results are too many for reading and filtering one by one. Recommended Systems (RS) was created to help us to discriminate and filter these information according to ours preferences. This contribution analyses the RS of the official agency of publications in Spain (BOE), which is known as "Mi BOE". The way this RS works was analysed, and all the meta-data of the published documents were analysed in order to know the coverage of the system. The results of our analysis show that more than 89% of the documents cannot be recommended, because they are not well described at the documentary level, some of their key meta-data are empty. So, this contribution proposes a method to label documents automatically based on Latent Dirichlet Allocation (LDA). The results are that using this approach the system could recommend (at a theoretical point of view) more than twice of documents that it now does, 11% vs 23% after applied this approach.
Zero-Shot Text-to-SQL Learning with Auxiliary Task

Recent years have seen great success in the use of neural seq2seq models on the text-to-SQL task. However, little work has paid attention to how these models generalize to realistic unseen data, which naturally raises a question: does this impressive performance signify a perfect generalization model, or are there still some limitations?In this paper, we first diagnose the bottleneck of the text-to-SQL task by providing a new testbed, in which we observe that existing models present poor generalization ability on rarely-seen data. The above analysis encourages us to design a simple but effective auxiliary task, which serves as a supportive model as well as a regularization term to the generation task to increase the models' generalization. Experimentally, We evaluate our models on a large text-to-SQL dataset WikiSQL. Compared to a strong baseline coarse-to-fine model, our models improve over the baseline by more than 3% absolute in accuracy on the whole dataset. More interestingly, on a zero-shot subset test of WikiSQL, our models achieve 5% absolute accuracy gain over the baseline, clearly demonstrating its superior generalizability.