Reviewerly/exHarmony huggingface dataset:
CapDet: Unifying Dense Captioning and Open-World Detection Pretraining

Benefiting from large-scale vision-language pre-training on image-text pairs, open-world detection methods have shown superior generalization ability under the zero-shot or few-shot detection settings. However, a pre-defined category space is still required during the inference stage of existing methods and only the objects belonging to that space will be predicted. To introduce a “real” open-world detector, in this paper, we propose a novel method named CapDet to either predict under a given category list or directly generate the category of predicted bounding boxes. Specifically, we unify the open-world detection and dense caption tasks into a single yet effective framework by introducing an additional dense captioning head to generate the region-grounded captions. Besides, adding the captioning task will in turn benefit the generalization of detection performance since the captioning dataset covers more concepts. Experiment results show that by unifying the dense caption task, our CapDet has obtained significant performance improvements (e.g., +2.1% mAP on LVIS rare classes) over the baseline method on LVIS (1203 classes). Besides, our CapDet also achieves state-of-the-art performance on dense captioning tasks, e.g., 15.44% mAP on VG V1.2 and 13.98% on the VG-COCO dataset.
OCELOT: Overlapped Cell on Tissue Dataset for Histopathology

Cell detection is a fundamental task in computational pathology that can be used for extracting high-level medical information from whole-slide images. For accurate cell detection, pathologists often zoom out to understand the tissue-level structures and zoom in to classify cells based on their morphology and the surrounding context. However, there is a lack of efforts to reflect such behaviors by pathologists in the cell detection models, mainly due to the lack of datasets containing both cell and tissue annotations with overlapping regions. To overcome this limitation, we propose and publicly release OCELOT, a dataset purposely dedicated to the study of cell-tissue relationships for cell detection in histopathology. OCELOT provides overlapping cell and tissue annotations on images acquired from multiple organs. Within this setting, we also propose multi-task learning approaches that benefit from learning both cell and tissue tasks simultaneously. When compared against a model trained only for the cell detection task, our proposed approaches improve cell detection performance on 3 datasets: proposed OCELOT, public TIGER, and internal CARP datasets. On the OCELOT test set in particular, we show up to 6.79 improvement in F1-score. We believe the contributions of this paper, including the release of the OCELOT dataset at https://lunit-io.github.io/research/publications/OCELOT are a crucial starting point toward the important research direction of incorporating cell-tissue relationships in computation pathology.
On Justice, Pedagogy, and Decolonial(Izing) Praxis

Abstract This paper goes beyond — transcends — “pedagogy as justice,” recognizing that justice, particularly in these present times, may not be enough. Its wager is with pedagogies of and for life ; pedagogies that plant and cultivate, that push and enable other modes of living, despite the capitalist‐modern‐colonial‐racist system, beyond the system, and in the system's margins, borders, fissures, and cracks. These pedagogies, as Catherine Walsh argues here, are necessarily tied to and constitutive of decolonial(izing) praxis, a praxis that, while not negating justice, takes us beyond it. The argument here is for the pedagogical‐praxistical work being done now, and yet to be done; the actional thought, thoughtful action, and ongoing creation and construction of an otherwise for existence, for dignity, and for life in these times of multiple entwined pandemic‐viruses, including capitalist greed, systemic racism, heteropatriarchy, and existential‐territorial dispossession in which COVID is/was one more thread of the entwine.
Polling the Blogosphere: A Rule-Based Approach to Belief Classification

The research described here is part of a larger project with the objective of determining if a writer believes a proposition to be true or false. This task requires a deep understanding of a proposition's semantic context, which is far beyond NLP's state of the art. In light of this difficulty, this paper presents a shallow semantic framework that addresses the sub-problem of finding a proposition's truth-value at the sentence level. The framework consists of several classes of linguistic elements that, when linked to a proposition through specific lexico-syntactic connectors, change its truth-value. A pilot evaluation of a system implementing this framework yields promising results.
Improved Visual Inspection of Advanced Gas-Cooled Reactor Fuel Channels

Visual inspection of fuel channels is important for assessing the health of the UK’s fleet of Advanced Gas-Cooled Reactor (AGR) power plants. For each fuel channel inspected, any defects found must be classified and assessed by a panel of experts and documented before the plant can return to service. Part of the current inspection process involves extracting relevant frames from visual inspection videos and manually assembling them to form a “crack montage” image. As the plants age, there is increasing pressure to inspect more fuel channels. Dealing with this increase in inspection demand requires new techniques to support the analysis of an increased volume of gathered video data so that crack montages can be made within the tight timescales of the outages. Recent work by the authors has created a technique for automatically processing inspection videos to extract the relevant frames and produce so called chanoramas from which any required defect montages can be cropped. Chanoramas are 360° panoramic images, which show the entire inside surface of the fuel channel inspected, and this provides completely a new way for plant operators to view their visual inspection data and analyse the condition of AGR fuel channels. In this paper we present an industrial case study which first introduces the concept of a chanorama and summarises some initial findings of testing the techniques used to create them. Then, based on the initial testing results, new and advanced image processing techniques which have been designed to improve the quality of the final chanoramas are presented. The paper then expands upon the use of the raw data and describes techniques for rendering it to allow 3D visualisations of the fuel channels which allow inspection engineers to view features of interest from a range of different angles.
Computing Applicability Conditions for Plans with Loops

The utility of including loops in plans has been long recognized by the planning community. Loops in a plan help increase both its applicability and the compactness of representation. However, progress in finding such plans has been limited largely due to lack of methods for reasoning about the correctness and safety properties of loops of actions. We present novel algorithms for determining the applicability and progress made by a general class of loops of actions. These methods can be used for directing the search for plans with loops towards greater applicability while guaranteeing termination, as well as in post-processing of computed plans to precisely characterize their applicability. Experimental results demonstrate the efficiency of these algorithms.
Player Autonomy versus Designer Intent: A Case Study of Interactive Tour Guides

We explore the tradeoff between player autonomy and designer intent by simulating a system of autonomous museum tour guides. Visitors may have different art preferences or may wish to visit different exhibits on multiple visits. Often, these desires conflict. For example, visitors may wish to see the museum's most popular work, but that could cause congestion, ruining the experience. Thus, our task is to build a set of guides that can satisfy their visitors' goals while also providing quality experiences for all. We present the results of a case study indicating that there is a space in the design spectrum between fully author-controlled narrative and complete player autonomy that reduces the frequency of bad experiences while allowing visitors to realize their own goals.
Diversity Measurement of Recommender Systems under Different User Choice Models

Recommender systems are increasingly used for personalised navigation through large amounts of information, especially in the e-commerce domain for product purchase advice. Whilst much research effort is spent on developing recommenders further, there is little to no effort spent on analysing the impact of them - neither on the supply (company) nor demand (consumer) side. In this paper, we investigate the diversity impact of a movie recommender. We define diversity for different parts of the domain and measure it in different ways. The novelty of our work is the usage of real rating data (from Netflix) and a recommender system for investigating the (hypothetical) effects of various configurations of the system and users' choice models.We consider a number of different scenarios (which differ in the agent's choice model), run very extensive simulations, analyse various measurements regarding experimental validation and diversity, and report on selected findings. The choice models are an essential part of our work, since these can be influenced by the owner of the recommender once deployed.
Modeling Autobiographical Memory for Believable Agents

We present a multi-layer hierarchical connectionist network model for simulating human autobiographical memory in believable agents. Grounded in psychological theory, this model improves on previous attempts to model agents’ event knowledge by providing a more dynamic and non-deterministic representation of autobiographical memories. From this model, a Java-based proof-of-concept prototype system was created for use as an enabling technology in video games. This prototype was leveraged in the creation of a Minecraft modification (mod) implementation of the model that is able to demonstrate context-dependent recall and the effects of recency on memory recall. Wider implications of the model in agent and game design are discussed.
Game-Based Data Capture for Player Metrics

Player metrics are an invaluable resource for game designers and QA analysts who wish to understand players, monitor and improve game play, and test design hypotheses. Usually such metrics are collected in a straightforward manner by passively recording players; however, such an approach has several potential drawbacks. First, passive recording might fail to record metrics which correspond to an infrequent player behavior. Secondly, passive recording can be a costly, laborious, and memory intensive process, even with the aid of tools. In this paper, we explore the potential for an active approach to player metric collection which strives to collect data more efficiently, and thus with less cost. We use an online, iterative approach which models the relationship between player metrics and in-game situations probabilistically using a Markov Decision Process (MDP) and solves it for the best game configurations to run. To analyze the benefits and limitations of this approach, we implemented a system, called GAMELAB, for recording player metrics in Second Life.
Agile Methodologies for Hardware/Software Teams for a Capstone Design Course: Lessons Learned

Agile methodologies aid software engineering teams by defining the tools and processes necessary to succeed.These methodologies can be tailored to support engineers from other disciplines such as computer engineering.This paper discusses a four year effort at Embry-Riddle Aeronautical University to integrate agile methodologies into our capstone design course for computer engineering and software engineering.This paper discusses the positive and negative aspects of using agile methodologies, and how the faculty has adapted agile processes/tools to better support larger and more multi-disciplinary teams.It concludes with some lessons learned for faculty considering using agile for their design projects.
Ontologies: a revamped cross-disciplinary buzzword or a truly promising interdisciplinary research topic?

The following article provides an introductory overview of the different research domains (computational linguistics, termino graphy, artificial intelligence (AI), philosophy and database semantics) for which ontologies and the emerging field of the Semantic Web have become a main point of interest. It will be pointed out that each of these domains uses a different definition for an ontology. A specific ontology engineering methodology (VUB STAR Lab DOGMA) will be presented and emphasis will be put on the specific role and contribution of (multilingual) terminography in this ontology. In addition, we will explain what ontologies might offer to advance the state of the art of linguistics and terminography.
Analyzing the Political Sentiment of Tweets in Farsi

We examine the question of whether we can automatically classify the sentiment of individual tweets in Farsi, to determine their changing sentiments over time toward a number of trending political topics. Examining tweets in Farsi adds challenges such as the lack of a sentiment lexicon and part-of-speech taggers, frequent use of colloquial words, and unique orthography and morphology characteristics. We have collected over 1 million Tweets on political topics in the Farsi language, with an annotated data set of over 3,000 tweets. We find that an SVM classifier with Brown clustering for feature selection yields a median accuracy of 56% and accuracy as high as 70%. We use this classifier to track dynamic sentiment during a key period of Irans negotiations over its nuclear program.
A random block-coordinate Douglas–Rachford splitting method with low computational complexity for binary logistic regression

In this paper, we propose a new optimization algorithm for sparse logistic regression based on a stochastic version of the Douglas-Rachford splitting method. Our algorithm sweeps the training set by randomly selecting a mini-batch of data at each iteration, and it allows us to update the variables in a block coordinate manner. Our approach leverages the proximity operator of the logistic loss, which is expressed with the generalized Lambert W function. Experiments carried out on standard datasets demonstrate the efficiency of our approach w.r.t. stochastic gradient-like methods.
Performance evaluation of DNN with other machine learning techniques in a cluster using Apache Spark and MLlib

Sentiment analysis on large data has become challenging due to the diversity, and nature of data. Advancements in the internet, along with large data availability have obviated the traditional limitations to distributed computing. The objective of this work is to carry out sentiment analysis on Apache Spark distributed Framework to speed up computations and enhance machine performance in diverse environments. The analysis, such as polarity identification, subjective analysis and email spam etc., are carried on various text datasets. After pre-processing, Term Frequency-Inverse Document Frequency (TF-IDF) and unsupervised Spark-Latent Dirichlet Allocation (LDA) clustering algorithms are used for feature extraction and selection to improve the accuracy. Deep Neural Networks (DNN), Support Vector Machines (SVM), Tree ensemble classifiers are used to evaluate the performance of the framework on single node and cluster environments. Finally, the proposed work aims at building an approach for enhancing machine performance, more in terms of runtime over accuracy.
Hyperspectral image denoising via minimizing the partial sum of singular values and superpixel segmentation

Hyperspectral images (HSIs) are often corrupted by noise during the acquisition process, thus degrading the HSI’s discriminative capability significantly. Therefore, HSI denoising becomes an essential preprocess step before application. This paper proposes a new HSI denoising approach connecting Partial Sum of Singular Values (PSSV) and superpixels segmentation named as SS-PSSV, which can remove the noise effectively. Based on the fact that there is a high correlation between different bands of the same signal, it is easy to know the property of low rank between distinct bands. To this end, PSSV is utilized, and in order to better tap the low-rank attribute of pixels, we introduce the superpixels segmentation method, which allows pixels in HSI with high similarity to be grouped in the same sub-block as much as possible. Extensive experiments display that the proposed algorithm outperforms the state-of-the-art.
An artificial neural network approach for detecting skin cancer

This study aims to present diagnose of melanoma skin cancer at an early stage. It applies feature extraction method of the first order for feature extraction based on texture in order to get high degree of accuracy with method of classification using artificial neural network (ANN). The method used is training and testing phases with classification of Multilayer Perceptron (MLP) neural network. The results showed that the accuracy of test image with 4 sets of training for image not suspected of melanoma and melanoma with the lowest accuracy of 80% and the highest accuracy of 88.88%, respectively. The 4 sets of training used consisted of 23 images. Of the 23 images used as a training consisted of 6 as not suspected of melanoma images and 17 as suspected melanoma images.
Human-Like Sketch Object Recognition via Analogical Learning

Deep learning systems can perform well on some image recognition tasks. However, they have serious limitations, including requiring far more training data than humans do and being fooled by adversarial examples. By contrast, analogical learning over relational representations tends to be far more data-efficient, requiring only human-like amounts of training data. This paper introduces an approach that combines automatically constructed qualitative visual representations with analogical learning to tackle a hard computer vision problem, object recognition from sketches. Results from the MNIST dataset and a novel dataset, the Coloring Book Objects dataset, are provided. Comparison to existing approaches indicates that analogical generalization can be used to identify sketched objects from these datasets with several orders of magnitude fewer examples than deep learning systems require.
Thermal-difference states of light: Quantum states of heralded photons

We introduce thermal-difference states (TDSs), a three-parameter family of single-mode non-Gaussian bosonic states whose density operator is a weighted difference of two thermal states. We show that the states of ``heralded photons'' generated via parametric down-conversion (PDC) are precisely those among the TDSs that are nonclassical, meaning they have a negative $P$ function. The three parameters correspond in that context to the initial brightness of PDC and the transmittances, characterizing the linear loss in the signal and the idler channels. At low initial brightness and unit transmittances, the heralded photon state is known to be a single-photon state. We explore the influence of brightness and linear loss on the heralded state of the signal mode. In particular, we analyze the influence of the initial brightness and the loss on the state nonclassicality by computing several measures of nonclassicality, such as the negative volume of the Wigner function, the sum of quantum Fisher information for two quadratures, and the ordering sensitivity, introduced recently by us [S. De Bi\`evre, D. B. Horoshko, G. Patera, and M. I. Kolobov, Phys. Rev. Lett. 122, 080402 (2019)]. We finally argue that the TDSs provide benchmark states for the analysis of a variety of properties of single-mode bosonic states.
Hierarchical dynamic depth projected difference images–based action recognition in videos with convolutional neural networks

Temporal information plays a significant role in video-based human action recognition. How to effectively extract the spatial–temporal characteristics of actions in videos has always been a challenging problem. Most existing methods acquire spatial and temporal cues in videos individually. In this article, we propose a new effective representation for depth video sequences, called hierarchical dynamic depth projected difference images that can aggregate the action spatial and temporal information simultaneously at different temporal scales. We firstly project depth video sequences onto three orthogonal Cartesian views to capture the 3D shape and motion information of human actions. Hierarchical dynamic depth projected difference images are constructed with the rank pooling in each projected view to hierarchically encode the spatial–temporal motion dynamics in depth videos. Convolutional neural networks can automatically learn discriminative features from images and have been extended to video classification because of their superior performance. To verify the effectiveness of hierarchical dynamic depth projected difference images representation, we construct a hierarchical dynamic depth projected difference images–based action recognition framework where hierarchical dynamic depth projected difference images in three views are fed into three identical pretrained convolutional neural networks independently for finely retuning. We design three classification schemes in the framework and different schemes utilize different convolutional neural network layers to compare their effects on action recognition. Three views are combined to describe the actions more comprehensively in each classification scheme. The proposed framework is evaluated on three challenging public human action data sets. Experiments indicate that our method has better performance and can provide discriminative spatial–temporal information for human action recognition in depth videos.
Experimenting with academic subjectivity: collective writing, peer production and collective intelligence

Following involvement in several academic collectively written articles, the authors question traditional notions of the ‘lone’ individualist author model as the expected standard in the humanities as opposed to large research teams in physical sciences. They use Barthes and Foucault to question the function and the concept of the author and assumed notions of subjectivity. Recent collective writing as a form of peer production and publishing is an attempt to reinvent the concepts of authorship, the author subject and author subjectivity. These bring to the fore the processes of peer review, questions of ownership (for example, of what remains in a revision, whose contribution becomes revised and by whom), and blurr the boundaries around author/collective voice and are discussed in this paper. Its transversality is proving as complex as the term suggests, in terms of developing new ways of connecting, thinking, examining and working, in ways that have not been the norm at least in the field of philosophy of education. Contemporary questions of the potential social, philosophical, legal, epistemological and ethical implications for and of authorship and subjectivity have barely been touched on to date, but this article begins to broach this gap.