Reviewerly/exHarmony huggingface dataset:
RATS: Robust Automated Tracking and Segmentation of Similar Instances

Continuous identification of objects with identical appearance is crucial to analyze the behavior of laboratory animals. Most existing methods attempt to avoid this problem by excluding direct social interactions or facilitating it by implants or artificial markers. Unfortunately, these techniques may distort the results as they can affect the behavior of the observed animals. In this paper, we present a simple, deep learning-based approach that can overcome these problems by providing reliable segmentation and tracking of similar instances. Recognition of frames where the system could not reliably locate the objects and mark them suggests human supervision is central to the system since there should be no mistake in instance tracking. Manual annotation of these data improves tracking and decreases annotation needs quickly. The proposed method achieves higher segmentation accuracy and more stable tracking than previous methods despite requiring only a small set of manually annotated data.
Fast Interactive Video Object Segmentation with Graph Neural Networks

Pixelwise annotation of image sequences can be very tedious for humans. Interactive video object segmentation aims to utilize automatic methods to speed up the process and reduce the workload of the annotators. Most contemporary approaches rely on deep convolutional networks to collect and process information from human annotations throughout the video. However, such networks contain millions of parameters and need huge amounts of labeled training data to avoid overfitting. Beyond that, label propagation is usually executed as a series of frame-by-frame inference steps, which is difficult to be parallelized and is thus time consuming. In this paper we present a graph neural network based approach for tackling the problem of interactive video object segmentation. Our network operates on superpixel-graphs which allow us to reduce the dimensionality of the problem by several magnitudes. We show, that our network possessing only a few thousand parameters is able to achieve state-of-the-art performance, while inference remains fast and can be trained quickly with very little data.
Sentiment Analysis of Student Textual Feedback to Improve Teaching

Instructor evaluation based on student feedback is essential in education, it allows instructors to see if their teaching has been effective. But, it is very challenging for an instructor who teaches numerous students to analyse feedback provided by all the students. To solve this problem, this paper develops a sentiment analysis model to analyse students’ feedback to assess the effectiveness of teaching and learning. In this paper, machine learning models like, Support Vector Machines, Multinomial Naive Bayes, Random Forests, K-Nearest Neighbours and Neural Networks are trained on feature engineering and re-sampling techniques to classify student feedback into three sentiment classes: negative, positive or neutral, using student dataset collected from Kaggle. From the analysis before the resampling of the data, K-Nearest Neighbours model is found to be more efficient in predicting student sentiment towards teaching practices than the other models with good accuracy of 81%. After the resampling of the data, Neural Networks performed better than the other models with good accuracy of 84%. The model will help institutions make effective decisions towards teaching and learning strategies.
Handling Heavily Abbreviated Manuscripts: HTR Engines vs Text Normalisation Approaches

Although abbreviations are fairly common in handwritten sources, particularly in medieval and modern Western manuscripts, previous research dealing with computational approaches to their expansion is scarce. Yet abbreviations present particular challenges to computational approaches such as handwritten text recognition and natural language processing tasks. Often, pre-processing ultimately aims to lead from a digitised image of the source to a normalised text, which includes expansion of the abbreviations. We explore different setups to obtain such a normalised text, either directly, by training HTR engines on normalised (i.e., expanded, disabbreviated) text, or by decomposing the process into discrete steps, each making use of specialist models for recognition, word segmentation and normalisation. The case studies considered here are drawn from the medieval Latin tradition.
A Three-Dimensional Path Planning Method of Autonomous Burrowing Robot for Lunar Subsurface Exploration

The subsurface exploration with autonomous burrowing robot might be a low-cost and high-efficient solution for a future extraterrestrial mission on the Moon. To enable a locomotive mechanism drilling into an uncertain lunar formation composed of soils and rocks, the design of trajectory planning scheme is a very challenging task. In this work, a trajectory planning method in a three-dimensional (3-D) geological domain with distributed obstacles is proposed. An improved pruning version of Rapid-exploration Random Tree algorithm was first developed, then a set of candidate paths was generated. By introducing the evaluation functions, the optimal path was selected among a group of recommended paths. At last, Bezier parametric curve was utilized to enhance the smoothness of robotic trajectory. The method was examined and discussed through numerical experiments. The simulation results show that this method may adapt to a variety of underground environments and different task requirements. Overall, the proposed method provides a powerful multi-objective optimization strategy to operate an autonomous burrowing robot in lunar subsurface. It can be further generalized to consider more factors in an intelligent decision-making manner.
Topic Modeling as a Tool for Analyzing Library Chat Transcripts

Library chat services are an increasingly important communication channel to connect patrons to library resources and services. Analysis of chat transcripts could provide librarians with insights into improving services. Unfortunately, chat transcripts consist of unstructured text data, making it impractical for librarians to go beyond simple quantitative analysis (e.g., chat duration, message count, word frequencies) with existing tools. As a stepping-stone toward a more sophisticated chat transcript analysis tool, this study investigated the application of different types of topic modeling techniques to analyze one academic library’s chat reference data collected from April 10, 2015, to May 31, 2019, with the goal of extracting the most accurate and easily interpretable topics. In this study, topic accuracy and interpretability—the quality of topic outcomes—were quantitatively measured with topic coherence metrics. Additionally, qualitative accuracy and interpretability were measured by the librarian author of this paper depending on the subjective judgment on whether topics are aligned with frequently asked questions or easily inferable themes in academic library contexts. This study found that from a human’s qualitative evaluation, Probabilistic Latent Semantic Analysis (pLSA) produced more accurate and interpretable topics, which is not necessarily aligned with the findings of the quantitative evaluation with all three types of topic coherence metrics. Interestingly, the commonly used technique Latent Dirichlet Allocation (LDA) did not necessarily perform better than pLSA. Also, semi-supervised techniques with human-curated anchor words of Correlation Explanation (CorEx) or guided LDA (GuidedLDA) did not necessarily perform better than an unsupervised technique of Dirichlet Multinomial Mixture (DMM). Last, the study found that using the entire transcript, including both sides of the interaction between the library patron and the librarian, performed better than using only the initial question asked by the library patron across different techniques in increasing the quality of topic outcomes.
VirtualGAN: Reducing Mode Collapse in Generative Adversarial Networks Using Virtual Mapping

This paper introduces a new framework for reducing mode collapse in Generative adversarial networks (GANs). The problem occurs when the generator learns to map several various input values (z) to the same output value, which makes the generator fail to capture all modes of the true data distribution. As a result, the diversity of synthetically produced data is lower than that of the real data. To address this problem, we propose a new and simple framework for training GANs based on the concept of virtual mapping. Our framework integrates two processes into GANs: merge and split. The merge process merges multiple data points (samples) into one before training the discriminator. In this way, the generator would be trained to capture the merged-data distribution rather than the (unmerged) data distribution. After the training, the split process is applied to the generator's output in order to split its contents and produce diverse modes. The proposed framework increases the chance of capturing diverse modes through enabling an indirect or virtual mapping between an input z value and multiple data points. This, in turn, enhances the chance of generating more diverse modes. Our results show the effectiveness of our framework compared to the existing approaches in terms of reducing the mode collapse problem.
Candidate word generation for OCR errors using optimization algorithm

OCR post-processing is an important step to improve OCR text accuracy. It includes two main tasks, error detection and error correction. Hill climbing algorithm is a heuristic search method used for solving optimization problems. In this paper, we present a novel OCR error correction approach using an adapted version of the Hill climbing algorithm. Correction candidates of OCR errors are explored by random character edits and evolved with the Hill climbing. The character edit patterns are obtained from the training data. The proposed model is evaluated on the benchmark dataset in the OCR post-correction competition of the International Conference on Document Analysis and Recognition 2017. It is shown that our model outperforms various baseline approaches in the competition. In addition, the randomness of the proposed algorithm is analyzed to verify its stability under parameter configurations.
Human Fall Detection Using Video Surveillance

Fall is one of the biggest challenge in elderly people, pregnant and small children’s, who stays alone in home. Sometimes this fall leads to severe injuries and even to death. Detecting the fall is very much important for elderly people. Convolutional Neural Network (CNN) is an deep learning algorithm used for image processing. In this paper, we present a video-based fall detection using CNN, this CNN will perform background subtraction and captures only foreground objects to detect the human movements and detect if fall happens. Firstly, camera will be capturing all the movements of the person. Our proposed model will detect the fall and finally an alarm is raised and email is sent to a given particular caretaker and family member. Our experimental results show the best performance of the proposed model.
Feature Focus: Towards Explainable and Transparent Deep Face Morphing Attack Detectors

Detecting morphed face images has become an important task to maintain the trust in automated verification systems based on facial images, e.g., at automated border control gates. Deep Neural Network (DNN)-based detectors have shown remarkable results, but without further investigations their decision-making process is not transparent. In contrast to approaches based on hand-crafted features, DNNs have to be analyzed in complex experiments to know which characteristics or structures are generally used to distinguish between morphed and genuine face images or considered for an individual morphed face image. In this paper, we present Feature Focus, a new transparent face morphing detector based on a modified VGG-A architecture and an additional feature shaping loss function, as well as Focused Layer-wise Relevance Propagation (FLRP), an extension of LRP. FLRP in combination with the Feature Focus detector forms a reliable and accurate explainability component. We study the advantages of the new detector compared to other DNN-based approaches and evaluate LRP and FLRP regarding their suitability for highlighting traces of image manipulation from face morphing. To this end, we use partial morphs which contain morphing artifacts in predefined areas only and analyze how much of the overall relevance each method assigns to these areas.
Combining blockchain and BPMN coreographies for construction management

Blockchain is considered a key technology of the current digital revolution and its application is spreading from cryptocurrencies to disparate processes requiring notarization. The well-known independence and contraposition of stakeholders in the AECO sector made DLT applications the hoped-for means to trust each other. Construction site management due to its complexity and the pluralism of the actors involved can be modelled as a BPMN coreography of intra-organizational processes. This aims of the work here presented is the exploitation of blockchains and smart contracts as tools to notarize the state of each intra-organizational process and to enforce compliance with the choreography
Renal Cell Carcinoma Classification from Vascular Morphology

Renal Cell Carcinoma (RCC) is one of the most common malignancies, and pathological diagnosis is the gold standard for RCC diagnostic method. Recognizing the type of RCC tumor and the possibility of cell migration highly depends on the geometric and topological properties of the vascular network. Motivated by the diagnosis pipeline, we explore whether the vascular network visible in RCC histopathological images is sufficient to characterize the RCC subtype. To achieve this, we firstly build a new vascular network-based RCC histopathological image dataset of 7 patients, namely VRCC200, with 200 well-labeled vascular network annotations. Based on these vascular networks of RCC histopathological images, we propose new hand-crafted features, namely skeleton features and lattice features. These features well represent the geometric and topological properties of the vascular networks of RCC histopathological images. Then we build strong benchmark results with various algorithms (both traditional and deep learning models) on the VRCC200 dataset. The result of skeleton and lattice features can outperform popular deep learning models. Finally, we further prove the robustness and advantage of proposed features on an additional database VRCC60 of 20 patients, with 60 vascular annotated images. All of the results of our experiments prove that the vascular network structure of RCC is one of the most important biomarkers for RCC diagnosis.
RuleBERT: Teaching Soft Rules to Pre-Trained Language Models

While pre-trained language models (PLMs) are the go-to solution to tackle many natural language processing problems, they are still very limited in their ability to capture and to use common-sense knowledge.In fact, even if information is available in the form of approximate (soft) logical rules, it is not clear how to transfer it to a PLM in order to improve its performance for deductive reasoning tasks.Here, we aim to bridge this gap by teaching PLMs how to reason with soft Horn rules.We introduce a classification task where, given facts and soft rules, the PLM should return a prediction with a probability for a given hypothesis.We release the first dataset for this task, and we propose a revised loss function that enables the PLM to learn how to predict precise probabilities for the task.Our evaluation results show that the resulting fine-tuned models achieve very high performance, even on logical rules that were unseen at training.Moreover, we demonstrate that logical notions expressed by the rules are transferred to the finetuned model, yielding state-of-the-art results on external datasets.
Sentiment-oriented query-focused text summarization addressed with a multi-objective optimization approach

Nowadays, the automatic text summarization is a highly relevant task in many contexts. In particular, query-focused summarization consists of generating a summary from one or multiple documents according to a query given by the user. Additionally, sentiment analysis and opinion mining analyze the polarity of the opinions contained in texts. These two issues are integrated in an approach to produce an opinionated summary according to the user’s query. Thereby, the query-focused sentiment-oriented extractive multi-document text summarization problem entails the optimization of different criteria, specifically, query relevance, redundancy reduction, and sentiment relevance. An adaptation of the metaheuristic population-based crow search algorithm has been designed, implemented, and tested to solve this multi-objective problem. Experiments have been carried out by using datasets from the Text Analysis Conference (TAC) datasets. Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics and the Pearson correlation coefficient have been used for the performance assessment. The results have reported that the proposed approach outperforms the existing methods in the scientific literature, with a percentage improvement of 75.5% for ROUGE-1 score and 441.3% for ROUGE-2 score. It also has been obtained a Pearson correlation coefficient of +0.841, reporting a strong linear positive correlation between the sentiment scores of the generated summaries and the sentiment scores of the queries of the topics.
Correlates of Compliance: Examining Consumer Fraud Risk Factors by Scam Type

Consumer fraud reports in the United States have increased each year along with median losses. Using survey data from 1,175 American and Canadian consumers who reported a scam to a North American consumer complaint organization, this study examines the correlates of engaging with (i.e., responding to) and complying with three types of consumer fraud: opportunity-based scams, threat-based scams, and consumer purchase scams. Consumers were less likely to engage with and lose money in threat-based scam solicitations relative to opportunity-based and consumer purchase scams. Risk factors, including household income, loneliness, financial fragility, and financial literacy, varied across scam categories. Different risk factors were associated with engaging in the scam than were associated with actually losing money. Having advance knowledge of fraud prior to being targeted was protective across scam types. Results derived from this unique data set that combined fraud reports from a consumer complaint organization with survey responses suggest that education about specific scams is effective at protecting against victimization. Additional research is needed on how to effectively deliver fraud awareness messages to those who are most susceptible.
School Culture and Digital Technologies: Educational Practices at Universities within the Context of the COVID-19 Pandemic

The pandemic caused by COVID-19 led schools to continue their work by relying on digital technologies. Changes in this matter are observed within three dimensions in the theoretical and conceptual background: (a) the influence of ITC in education, (b) the macrosocial changes in the educational systems and public policy derived from the COVID-19 pandemic, and (c) the impact of the COVID-19 pandemic in higher education and its role for the future. The general objective of this research was to characterize the educational practices executed by the university community (students, professors, and managers) during the emerging remote classes derived from the pandemic at the Universidad Autonoma Metropolitana, Cuajimalpa Campus, a public educational institution in Mexico (through an explanation for each educational actor profile). As specific research objectives, this paper: (a) examines whether the professors and students had enough digital technology to continue with the classes, (b) defines the obstacles they had in the use of said digital technology, and (c) recognizes the existence of innovative educational practices and determines whether stated learning was achieved in educational programs. For this purpose, a mixed methodology was chosen, comprising the application of surveys to students and professors and semi-structured interviews with managers, professors, and students. It was found that there was innovation in the area of resources (material–economic dimension) and in the area of relationships (socio-political dimension), while the discursive dimension (cultural-discursive dimension) was negatively impacted. Based on the above, we conclude that the school culture of the UAM-C is solid and that it has the necessary technological resources to continue with the teaching–learning process. The educational practice was transformed, which resulted in advantages and disadvantages, but despite these situations, most students developed their learning.
Dynamics of an artificial recurrent neural network for the problem of modeling a cognitive function

The purpose of this work is to build an artificial recurrent neural network whose activity models a cognitive function relating to the comparison of two vibrotactile stimuli coming with a delay and to analyze dynamic mechanisms underlying its work. Methods of the work are machine learning, analysis of spatiotemporal dynamics and phase space. Results. Activity of the trained recurrent neural network models a cognitive function of the comparison of two stimuli with a delay. Model neurons exhibit mixed selectivity during the course of the task. In the multidimensional activity, the components are found each of which depends on a certain task parameter. Conclusion. The training of the artificial neural network to perform the funciton analogous to the experimentally observed process is accompanied by the emergence of dynamic properties of model neurons which are similar to those found in the experiment.
Chinese Semantic Matching with Multi-granularity Alignment and Feature Fusion

Chinese semantic matching is a fundamental task in natural language processing, which is critical and yet challenging for a series of downstream tasks. Although recent work on text representation learning has shown its potential in improving the performance on semantic matching, relatively limited work has been done on exploring the relevant interactive information between two granularity of Chinese text, i.e., character and word. Existing methods usually focus on capturing the interactive features from single granularity, which lead to inefficient text representation. Also, they typically fail to consider the fusion of features from different granularity. As a result, they only achieve limited performance improvement. This paper proposes a novel Chinese semantic matching model based on multi-granularity alignment and feature fusion (MAFFo). To be specific, we first encode the texts from different granularity, which are further handled with soft-alignment attention mechanism to extract relevant interactive information between texts on different granularity. In addition, we devise a feature fusion structure to merge the features from different granularity to generate an ideal representation for the pair of input text sequences, followed by a sigmoid function to judge the semantic matching degree. Extensive experiments on the publicly available dataset BQ demonstrate that our model can effectively improve the performance of semantic matching task and achieve comparable performance with BERT-based methods.