Reviewerly/exHarmony huggingface dataset:
Identification of Malignancies from Free-Text Histopathology Reports Using a Multi-Model Supervised Machine Learning Approach

We explored various Machine Learning (ML) models to evaluate how each model performs in the task of classifying histopathology reports. We trained, optimized, and performed classification with Stochastic Gradient Descent (SGD), Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), Adaptive Boosting (AB), Decision Trees (DT), Gaussian Naïve Bayes (GNB), Logistic Regression (LR), and Dummy classifier. We started with 60,083 histopathology reports, which reduced to 60,069 after pre-processing. The F1-scores for SVM, SGD KNN, RF, DT, LR, AB, and GNB were 97%, 96%, 96%, 96%, 92%, 96%, 84%, and 88%, respectively, while the misclassification rates were 3.31%, 5.25%, 4.39%, 1.75%, 3.5%, 4.26%, 23.9%, and 19.94%, respectively. The approximate run times were 2 h, 20 min, 40 min, 8 h, 40 min, 10 min, 50 min, and 4 min, respectively. RF had the longest run time but the lowest misclassification rate on the labeled data. Our study demonstrated the possibility of applying ML techniques in the processing of free-text pathology reports for cancer registries for cancer incidence reporting in a Sub-Saharan Africa setting. This is an important consideration for the resource-constrained environments to leverage ML techniques to reduce workloads and improve the timeliness of reporting of cancer statistics.
Multi-Task Learning using AraBert for Offensive Language Detection

The use of social media platforms has become more prevalent, which has provided tremendous opportunities for people to connect but has also opened the door for misuse with the spread of hate speech and offensive language. This phenomenon has been driving more and more people to more extreme reactions and online aggression, sometimes causing physical harm to individuals or groups of people. There is a need to control and prevent such misuse of online social media through automatic detection of profane language. The shared task on Offensive Language Detection at the OSACT4 has aimed at achieving state of art profane language detection methods for Arabic social media. Our team “BERTologists” tackled this problem by leveraging state of the art pretrained Arabic language model, AraBERT, that we augment with the addition of Multi-task learning to enable our model to learn efficiently from little data. Our Multitask AraBERT approach achieved the second place in both subtasks A & B, which shows that the model performs consistently across different tasks.
A command to estimate and interpret models of dynamic compositional dependent variables: New features for dynsimpie

Philips, Rutherford, and Whitten (2016, Stata Journal 16: 662–677) introduced dynsimpie, a command to examine dynamic compositional dependent variables. In this article, we present an update to dynsimpie and three new adofiles: cfbplot, effectsplot, and dynsimpiecoef. These updates greatly enhance the range of models that can be estimated and the ways in which model results can now be presented. The command dynsimpie has been updated so that users can obtain both prediction plots and change-from-baseline plots using postestimation commands. With the new command dynsimpiecoef, various types of coefficient plots can also be obtained. We illustrate these improvements using monthly data on support for political parties in the United Kingdom.
On Free Description Logics with Definite Descriptions

Definite descriptions are phrases of the form ‘the x such that φ’, used to refer to single entities in a context. They are often more meaningful to users than individual names alone, in particular when modelling or querying data over ontologies. We investigate free description logics with both individual names and definite descriptions as terms of the language, while also accounting for their possible lack of denotation. We focus on the extensions of ALC and, respectively, EL with nominals, the universal role, and definite descriptions. We show that standard reasoning in these extensions is not harder than in the original languages, and we characterise the expressive power of concepts relative to first-order formulas using a suitable notion of bisimulation. Moreover, we lay the foundations for automated support for definite descriptions generation by studying the complexity of deciding the existence of definite descriptions for an individual under an ontology. Finally, we provide a polynomial-time reduction of reasoning in other free description logic languages based on dual-domain semantics to the case of partial interpretations.
Reversible Data Hiding with Selected Directional Context Based Prediction Using 8-Neighborhood

In this paper, a novel reversible data hiding technique using a selected directional context based prediction has been suggested. In the proposed prediction strategy, the predicted values are obtained across vertical, horizontal, diagonal and anti-diagonal directions using pixel values in 8-neighborhood of a pixel. The final predicted value is obtained as the average of predicted values for a set of selected directional contexts. These directions are selected using the diversities in the neighborhood pixel values in the respective directions. Finally, the prediction error in an image pixel is used to insert the secret data in the pixel using a prediction error histogram bin shifting method. An adaptive histogram bin shifting method is introduced in this paper to adaptively embed one or two bits of data in a pixel. The proposed selected directional context based prediction with adaptive histogram bin shifting has outperformed several state- of-the-art reversible data hiding techniques.
Learning Joint Shape and Appearance Representations with Metamorphic Auto-Encoders

Transformation-based methods for shape analysis offer a consistent framework to model the geometrical content of images. Most often relying on diffeomorphic transforms, they lack however the ability to properly handle texture and differing topological content. Conversely, modern deep learning methods offer a very efficient way to analyze image textures. Building on the theory of metamorphoses, which models images as combined intensity-domain and spatial-domain transforms of a prototype, we introduce the “metamorphic” auto-encoding architecture. This class of neural networks is interpreted as a Bayesian generative and hierarchical model, allowing the joint estimation of the network parameters, a representative prototype of the training images, as well as the relative importance between the geometrical and texture contents.We give arguments for the practical relevance of the learned prototype and Euclidean latent-space metric, achieved thanks to an explicit normalization layer. Finally, the ability of the proposed architecture to learn joint and relevant shape and appearance representations from image collections is illustrated on BraTs 2018 datasets, showing in particular an encouraging step towards personalized numerical simulation of tumors with data-driven models.
Learning to learn generative programs with Memoised Wake-Sleep

We study a class of neuro-symbolic generative models in which neural networks are used both for inference and as priors over symbolic, data-generating programs. As generative models, these programs capture compositional structures in a naturally explainable form. To tackle the challenge of performing program induction as an 'inner-loop' to learning, we propose the Memoised Wake-Sleep (MWS) algorithm, which extends Wake Sleep by explicitly storing and reusing the best programs discovered by the inference network throughout training. We use MWS to learn accurate, explainable models in three challenging domains: stroke-based character modelling, cellular automata, and few-shot learning in a novel dataset of real-world string concepts.
Active Depth Estimation: Stability Analysis and its Applications

Recovering the 3D structure of the surrounding environment is an essential task in any vision-controlled Structure-from-Motion (SfM) scheme. This paper focuses on the theoretical properties of the SfM, known as the incremental active depth estimation. The term incremental stands for estimating the 3D structure of the scene over a chronological sequence of image frames. Active means that the camera actuation is such that it improves estimation performance. Starting from a known depth estimation filter, this paper presents the stability analysis of the filter in terms of the control inputs of the camera. By analyzing the convergence of the estimator using the Lyapunov theory, we relax the constraints on the projection of the 3D point in the image plane when compared to previous results. Nonetheless, our method is capable of dealing with the cameras’ limited field-of-view constraints. The main results are validated through experiments with simulated data.
How Do Modelers Code Artificial Societies? Investigating Practices and Quality of Netlogo Codes from Large Repositories

Many guidelines have been developed for simulations in general or for agent-based models which support artificial societies. When applying such guidelines to examine existing practices, assessment studies are limited by the artifacts released by modelers. Although code is the final product defining an artificial society, 90% of the code produced is not released hence previous assessments necessarily focused on higher-level items such as conceptual design or validation. We address this gap by collecting 338 artificial societies from two hosting platforms, CoMSES/OpenABM and GitHub. An innovation of our approach is the use of software engineering techniques to automatically examine the models with respect to items such as commenting the code, using libraries, or dividing code into functions. We found that developers of artificial societies code the decision-making of their agents from scratch in every model, despite the existence of several libraries that could be used as building blocks.
Fusing Hand Postures and Speech Recognition for Tasks Performed by an Integrated Leg–Arm Hexapod Robot

Hand postures and speech are convenient means of communication for humans and can be used in human–robot interaction. Based on structural and functional characteristics of our integrated leg-arm hexapod robot, to perform reconnaissance and rescue tasks in public security application, a method of linkage of movement and manipulation of robots is proposed based on the visual and auditory channels, and a system based on hand postures and speech recognition is described. The developed system contains: a speech module, hand posture module, fusion module, mechanical structure module, control module, path planning module and a 3D SLAM (Simultaneous Localization and Mapping) module. In this system, three modes, i.e., the hand posture mode, speech mode, and a combination of the hand posture and speech modes, are used in different situations. The hand posture mode is used for reconnaissance tasks, and the speech mode is used to query the path and control the movement and manipulation of the robot. The combination of the two modes can be used to avoid ambiguity during interaction. A semantic understanding-based task slot structure is developed by using the visual and auditory channels. In addition, a method of task planning based on answer-set programming is developed, and a system of network-based data interaction is designed to control movements of the robot using Chinese instructions remotely based on a wide area network. Experiments were carried out to verify the performance of the proposed system.
The Utility of Context When Extracting Entities From Legal Documents

When reviewing documents for legal tasks such as Mergers and Acquisitions, granular information (such as start dates and exit clauses) need to be identified and extracted. Inspired by previous work in Named Entity Recognition (NER), we investigate how NER techniques can be leveraged to aid lawyers in this review process. Due to the extremely low prevalence of target information in legal documents, we find that the traditional approach of tagging all sentences in a document is inferior, in both effectiveness and data required to train and predict, to using a first-pass layer to identify sentences that are likely to contain the relevant information and then running the more traditional sentence-level sequence tagging. Moreover, we find that such entity-level models can be improved by training on a balanced sample of relevant and non-relevant sentences. We additionally describe the use of our system in production and how its usage by clients means that deep learning architectures tend to be cost inefficient, especially with respect to the necessary time to train models.
Discrete Total Variation-Based Non-Local Means Filter for Denoising Magnetic Resonance Images

Magnetic resonance (MR) images suffer from noise introduced by various sources. Due to this noise, diagnosis remains inaccurate. Thus, removal of noise becomes a very important task when dealing with MR images. In this paper, a denoising method has been discussed that makes use of non-local means filter and discrete total variation method. The proposed approach has been compared with other noise removal techniques like non-local means filter, anisotropic diffusion, total variation, and discrete total variation method, and it proves to be effective in reducing noise. The performance of various denoising methods is compared on basis of metrics such as peak signal-to-noise ratio (PSNR), mean square error (MSE), universal image quality index (UQI), and structure similarity index (SSIM) values. This method has been tested for various noise levels, and it outperformed other existing noise removal techniques, without blurring the image.
Methodological Aspects of Transition to a New Educational Process Organization

In this paper we explained the selection of methodological foundations of transition to a new educational process organization based on the understanding of the post-non-classical methodology features of pedagogical research. They do not lead to the elimination of methods and cognitive attitudes of classical and non-classical research methodology, but more clearly define their scope. The selected methodological foundations determined the direction of the scientific search for a solution to the problem as follows: understanding the goals of modern education as contributing to the formation of identity (personal and professional) of a person at different stages of his/her life, considering the growing uncertainty of the modern world, which implies focus on the formation of universal human competences; understanding competence as a multifunctional knowledge “pack”, abilities (skills) and relationships that each person needs for a full personal life and professional activity; recognition of multivariance as an essential feature of modern educational process. We found that the new educational process design reflects the emerging trend of the “anthropological transition” in science, which is associated with the emergence of free access to knowledge and responsible attitude to the knowledge application. It is shown that the content design of university educational (and training) programs should be oriented toward the independent design of personalized learning by a student. This idea involves the appeal to personal experiences of teachers and students as a core of educational content affecting all its components by designing an individual student model of educational content expressed in the formed individual set of key competencies of a particular student. We established that the proposed approach to the design of the new educational process is founded on the content design based on the ideas of humanitarian design, determining the probabilistic nature of dynamic learning models design. The term “probabilistic” reflects the uncertainty of pedagogical systems, the dependence of ongoing pedagogical processes on a specific context, and the term “dynamic modeling” is understood as planning (selection) of active teaching methods allowing students to be creatively involved in the educative process.
A Supplementary Explanation for Experimental Environment of “Implementation Methodologies of Deep Learning-Based Signal Detection for Conventional MIMO Transmitters”

To solve practical challenges for implementing deep learning-based algorithm in MIMO signal detector such as handling complex number or designing proper neural network for a specific communication system, (Baek et al., 2019) has proposed candidate implementation methodologies with simple verification experiments. According to (Baek et al., 2019), it was shown that the proposed algorithms can achieve the optimal ML performance. However, due to the lack of explanation on the experimental environment, it is difficult for readers to reproduce the presented experiments and obtain the same results. This document precisely explains on the experimental environments of (Baek et al., 2019) including the exact channel profiles.
RETRACTED: Research on Network Teaching of Music Major Based on Artificial Intelligence Technology

Abstract With the penetration of artificial intelligence technology in the field of music, the demand and training of music professionals have changed. The mode of network teaching also occupies an important position in the training of music major. Based on the background of artificial intelligence technology, this paper explores the teaching mode of music major, and puts forward the reform and innovation strategy of music professional network teaching mode under the background of artificial intelligence technology. This paper starts with the introduction of artificial intelligence and its application in the field of music teaching, analyzes the influence of network teaching mode of Music Major under the background of artificial intelligence, and puts forward the Talent Cultivation Countermeasures of Music Major under the network teaching mode. This paper studies the status quo of music teaching in conservatory of music, characteristics of network teaching mode and platform construction. With the continuous enrichment of music professional network teaching resources, this new teaching mode can reflect its advantages and efficiency advantages. Starting from the influence of artificial intelligence technology on Music Majors at present, this paper analyzes that the best teaching effect can be obtained by using multimedia network teaching in teaching, and puts forward the practical application method of artificial intelligence technology in music professional network teaching under modern network conditions. The experimental results show that we should constantly improve the function research of the network teaching platform of Music Major Based on artificial intelligence technology, and carry out more optimized settings combined with music professional courses, so that teachers and students can obtain the best use effect, and ensure the smooth progress of music professional network teaching mode.
Action2Motion

Action recognition is a relatively established task, where given an input sequence of human motion, the goal is to predict its action category. This paper, on the other hand, considers a relatively new problem, which could be thought of as an inverse of action recognition: given a prescribed action type, we aim to generate plausible human motion sequences in 3D. Importantly, the set of generated motions are expected to maintain its diversity to be able to explore the entire action-conditioned motion space; meanwhile, each sampled sequence faithfully resembles a natural human body articulation dynamics. Motivated by these objectives, we follow the physics law of human kinematics by adopting the Lie Algebra theory to represent the natural human motions; we also propose a temporal Variational Auto-Encoder (VAE) that encourages a diverse sampling of the motion space. A new 3D human motion dataset, HumanAct12, is also constructed. Empirical experiments over three distinct human motion datasets (including ours) demonstrate the effectiveness of our approach.
Quantification of Transducer Misalignment in Ultrasound Tongue Imaging

In speech production research, different imaging modalities have been employed to obtain accurate information about the movement and shaping of the vocal tract. Ultrasound is an affordable and non-invasive imaging modality with relatively high temporal and spatial resolution to study the dynamic behavior of tongue during speech production. However, a long-standing problem for ultrasound tongue imaging is the transducer misalignment during longer data recording sessions. In this paper, we propose a simple, yet effective, misalignment quantification approach. The analysis employs MSE distance and two similarity measurement metrics to identify the relative displacement between the chin and the transducer. We visualize these measures as a function of the timestamp of the utterances. Extensive experiments are conducted on a Hungarian and Scottish English child dataset. The results suggest that large values of Mean Square Error (MSE) and small values of Structural Similarity Index (SSIM) and Complex Wavelet SSIM indicate corruptions or issues during the data recordings, which can either be caused by transducer misalignment or lack of gel.
Mapping Bit to Symbol Unpredictability in Convolutionally Encoded Messages with Checksums, with Application to Galileo OSNMA

This paper investigates the distribution of unpredictable symbols in the OSNMA scheme, which introduces cryptographic elements into the “Reserved 1” field of the odd page of the Galileo I/NAV message. The primary purpose of this cryptographic data is to provide authentication of the contents of the I/NAV (and other) messages, through a delayed release symmetric key scheme based on the TESLA protocol. A secondary benefit of this approach is that the navigation messages are not predictable, preventing a spoofer from, for example, generating a spoofed navigation message today and broadcasting it tomorrow. Prior work has described an attack, the Forward Estimation Attack (FEA) [1], that takes advantage of the forward error correction (FEC) employed by the Galileo E1 OS in order to ensure that a spoofed receiver correctly decodes the I/NAV message, even if it has been generated with some errors in some symbols. FEA exploits the fact that the FEC can correct symbol errors on those symbols that are not known a priori by the spoofer, or “unpredictable” symbols, thereby ensuring the correct decoding of the navigation message. At the same time, this attack does not break the NMA scheme, in that it does not make the receiver vulnerable to spoofed navigation messages, but rather makes it more likely that the receiver will decode the correct message, even if a spoofed message, arriving in delay or even in advance, is broadcast. In order to defend against such an attack and some other attacks altering the signal time of arrival, the receiver can re-encode the navigation message into symbols once it has been successfully decoded and compare the symbol error rates for those symbols that are predictable and those that were not. In order to perform this comparison in a meaningful way, it is first necessary to know which symbols are predictable and which are not. This paper presents in full detail, for the first time to the knowledge of the authors, how this can be achieved, and proposes a simple implementation in the receiver. This method takes also into account the impact of the CRC in the symbol unpredictability. Given this information it is then straightforward to design and implement a FEA detection mechanism.