Reviewerly/exHarmony huggingface dataset:
Learning from the Target: Dual Prototype Network for Few Shot Semantic Segmentation

Due to the scarcity of annotated samples, the diversity between support set and query set becomes the main obstacle for few shot semantic segmentation. Most existing prototype-based approaches only exploit the prototype from the support feature and ignore the information from the query sample, failing to remove this obstacle.In this paper, we proposes a dual prototype network (DPNet) to dispose of few shot semantic segmentation from a new perspective. Along with the prototype extracted from the support set, we propose to build the pseudo-prototype based on foreground features in the query image. To achieve this goal, the cycle comparison module is developed to select reliable foreground features and generate the pseudo-prototype with them. Then, a prototype interaction module is utilized to integrate the information of the prototype and the pseudo-prototype based on their underlying correlation. Finally, a multi-scale fusion module is introduced to capture contextual information during the dense comparison between prototype (pseudo-prototype) and query feature. Extensive experiments conducted on two benchmarks demonstrate that our method exceeds previous state-of-the-arts with a sizable margin, verifying the effectiveness of the proposed method.
TiGAN: Text-Based Interactive Image Generation and Manipulation

Using natural-language feedback to guide image generation and manipulation can greatly lower the required efforts and skills. This topic has received increased attention in recent years through refinement of Generative Adversarial Networks (GANs); however, most existing works are limited to single-round interaction, which is not reflective of real world interactive image editing workflows. Furthermore, previous works dealing with multi-round scenarios are limited to predefined feedback sequences, which is also impractical. In this paper, we propose a novel framework for Text-based Interactive image generation and manipulation (TiGAN) that responds to users' natural-language feedback. TiGAN utilizes the powerful pre-trained CLIP model to understand users' natural-language feedback and exploits contrastive learning for a better text-to-image mapping. To maintain the image consistency during interactions, TiGAN generates intermediate feature vectors aligned with the feedback and selectively feeds these vectors to our proposed generative model. Empirical results on several datasets show that TiGAN improves both interaction efficiency and image quality while better avoids undesirable image manipulation during interactions.
Complete Physical Characterization of Quantum Nondemolition Measurements via Tomography

We introduce a self-consistent tomography for arbitrary quantum nondemolition (QND) detectors. Based on this, we build a complete physical characterization of the detector, including the measurement processes and a quantification of the fidelity, ideality, and backaction of the measurement. This framework is a diagnostic tool for the dynamics of QND detectors, allowing us to identify errors, and to improve their calibration and design. We illustrate this on a realistic Jaynes-Cummings simulation of a superconducting qubit readout. We characterize nondispersive errors, quantify the backaction introduced by the readout cavity, and calibrate the optimal measurement point.
Explainable Survival Analysis with Convolution-Involved Vision Transformer

Image-based survival prediction models can facilitate doctors in diagnosing and treating cancer patients. With the advance of digital pathology technologies, the big whole slide images (WSIs) provide increasing resolution and more details for diagnosis. However, the gigabyte-size WSIs would make most models computationally infeasible. To this end, instead of using the complete WSIs, most of existing models only use a pre-selected subset of key patches or patch clusters as input, which might fail to completely capture the patient's tumor morphology. In this work, we aim to develop a novel survival analysis model to fully utilize the complete WSI information. We show that the use of a Vision Transformer (ViT) backbone, together with convolution operations involved in it, is an effective framework to improve the prediction performance. Additionally, we present a post-hoc explainable method to identify the most salient patches and distinct morphology features, making the model more faithful and the results easier to comprehend by human users. Evaluations on two large cancer datasets show that our proposed model is more effective and has better interpretability for survival prediction.
Reservoir Computing for Early Stage Alzheimer’s Disease Detection

Artificial Neural Networks (ANNs) have amassed unprecedented success in information processing ranging from image recognition to time series prediction. The success can largely be attributed to the availability of large datasets for training and the increased complexity of the models. Unfortunately, for some applications only a limited amount of samples is available for training. Fewer training samples increases the risk of over-fitting and poor generalization especially in high complexity models. Moreover, complex models with a large number of trainable parameters require more energy to train and optimize compared to simpler ones. In this paper, to the best of our knowledge, we propose the first use of ANNs for Early Stage Alzheimer Disease classification (ES-AD) from the handwriting (HW). We propose using a framework for building Recurrent Neural Networks (RNNs) known as Reservoir Computing (RC), both numerically and experimentally, that simplifies training by optimizing the output layer only. We also propose the Bidirectional Long Term Short Term (BiLSTM) and Convolutional Neural Network (CNN) methods for the sake of comparison. For a fairer comparison, we not only consider the accuracies but also the energy costs incurred to obtain the respective accuracies in order to assess the accuracy-efficiency trade-off. Our numerical and experimental results show that RC yields a classification accuracy of 85%, which is 3% worse than that of BiLSTM and 2% better than that of CNN, at a relatively lower training and significantly lower inference costs. We hope that our findings highlight the importance of examining the accuracy-efficiency trade-off of various models in the community in order to reduce the overall impact of ANNs training on the environment.
Universally Composable Subversion-Resilient Cryptography

Subversion attacks undermine security of cryptographic protocols by replacing a legitimate honest party’s implementation with one that leaks information in an undetectable manner. An important limitation of all currently known techniques for designing cryptographic protocols with security against subversion attacks is that they do not automatically guarantee security in the realistic setting where a protocol session may run concurrently with other protocols. We remedy this situation by providing a foundation of reverse firewalls (Mironov and Stephens-Davidowitz, EUROCRYPT’15) in the universal composability (UC) framework (Canetti, FOCS’01 and J. ACM’20). More in details, our contributions are threefold: Additionally, all our sanitized protocols are transparent, in the sense that communicating with a sanitized core looks indistinguishable from communicating with an honest core. Thanks to the composition theorem, our methodology allows, for the first time, to design subversion-resilient protocols by sanitizing different sub-components in a modular way.
Morphological Processing of Low-Resource Languages: Where We Are and What’s Next

Automatic morphological processing can aid downstream natural language processing applications, especially for low-resource languages, and assist language documentation efforts for endangered languages.Having long been multilingual, the field of computational morphology is increasingly moving towards approaches suitable for languages with minimal or no annotated resources.First, we survey recent developments in computational morphology with a focus on low-resource languages.Second, we argue that the field is ready to tackle the logical next challenge: understanding a language's morphology from raw text alone.We perform an empirical study on a truly unsupervised version of the paradigm completion task and show that, while existing state-of-the-art models bridged by two newly proposed models we devise perform reasonably, there is still much room for improvement.The stakes are high: solving this task will increase the language coverage of morphological resources by a number of magnitudes.
The Efficient Provision of Information Resources in Academic Libraries: Theory and Practice

Due to the increasing availability of digital content and systems improvements that have accelerated physical delivery, academic libraries are afforded opportunities to more efficiently provide users access to library materials. A theory for efficient provision of information resources, and thus more efficient use of financial resources, was proposed by one of the coauthors in "The Future of Academic Library Materials Expenditures: A Thought Experiment." The strategies on which the theory is based include purchase on demand, library publishing, and improved open access discovery. This article details the theory and then examines IUPUI University Library's experience of implementing strategies based on this theory. The authors analyze the effectiveness of the theory and offer guidance for libraries considering a similar path.
AD-CGAN: Contrastive Generative Adversarial Network for Anomaly Detection

Anomaly detection (AD), a fundamental challenge in machine learning, aims to find samples that do not belong to the distribution of the training data. Among unsupervised anomaly detection models, models based on generative adversarial networks show promising results. These models mainly rely on the rich representations learned from the normal training data to find anomalies. However, their performance is bounded by the limitations of GANs, known as mode collapse, in learning complex training distribution. This work presents a new GAN-based anomaly detection model with a combination of contrastive learning to mitigate the negative effect of mode collapse in more complex distributions. Our unsupervised Anomaly Detection model based on Contrastive Generative Adversarial Network, AD-CGAN, contrasts a sample with local feature maps of itself instead of only contrasting the given sample with other instances as in conventional contrastive learning approaches. Contrastive loss in AD-CGAN helps the model learn more discriminative representations of normal samples. Furthermore, we consider a new normality score to target anomalous samples. The normality score is defined on the encoded representations of samples obtained from the model. Extensive experiments showed AD-CGAN outperforms its counterparts on multiple benchmarks with a significant improvement in ROC-AUC over the previously proposed reconstruction-based approaches.
Exploring the Use of Efficient Projection Kernels for Motion Saliency Estimation

In this paper we investigate the potential of a family of efficient filters – the Gray-Code Kernels – for addressing visual saliency estimation guided by motion. Our implementation relies on the use of 3D kernels applied to overlapping blocks of frames and is able to gather meaningful spatio-temporal information with a very light computation. We introduce an attention module that reasons on the use of pooling strategies, combined in an unsupervised way to derive a saliency map highlighting the presence of motion in the scene. In the experiments we show that our method is able to effectively and efficiently identify the portion of the image where the motion is occurring, providing tolerance to a variety of scene conditions.
Juvenile Crime Categorization with EM Clustering

In India, the juvenile delinquency or child crimes is seen extensively in all states. The primary reasons for criminal behavior in the youth are the education levels of child, family background and others like money problems, friends, internet, drugs. In this research paper, We use expectation–maximization cluster technique of machine learning, to categorize juvenile crimes in India with two major risk elements, family status and education levels of children. This research will help to visualize the major areas in India where children are involving in crimes and helps authorities to take early preventive steps to mitigate juvenile delinquency.
Visualization Methods for Exploratory Subgroup Discovery on Time Series Data

This paper presents visualization methods for exploratory subgroup discovery, focusing on numeric time series data. We provide four novel visualizations for the inspection and understanding of subgroups. These visualizations facilitate interpretation in order to get insights into the data and the respective subgroups, while also supporting statistical interpretation and assessment of the subgroups and their respective parameters. Furthermore, we illustrate the approach in the context of complex time series data – specifically on team interactions in the affective computing context.
Feature Ranking for Feature Sorting and Feature Selection: FR4(FS)$$^2$$

This paper proposes a methodology to feature sorting as well as feature selection in the context of supervised machine learning algorithms. Feature sorting has been revealed as a step which may play a paramount role in machine learning. Nonetheless, the scalability is an important drawback. This paper proposes to add a further stage in order to only retain attributes with a positive influence (att+) and limiting them in a predefined percentage of att+ set. This contribution aims at introducing a new methodology where all attributes are not included in the data mining task but also the positive influence ones till a certain limit. We have followed two different types of sorting by means of different feature ranking methods. The approach has been assessed in three binary problems with a number of features between 1000 and 10000, and a number of instances from 200 to 7000; the test-bed includes challenging data sets from NIPS 2003. According to the experimental results for InfoGain and GainRatio the 90% of the attributes with positive influence are enough to get results in most of the cases comparable to the results with raw data taking into account that the required time to train the classifiers is shorter and hence in the non-required time we may be able to process more instances.
GRS: Combining Generation and Revision in Unsupervised Sentence Simplification

We propose GRS: an unsupervised approach to sentence simplification that combines text generation and text revision.We start with an iterative framework in which an input sentence is revised using explicit edit operations, and add paraphrasing as a new edit operation.This allows us to combine the advantages of generative and revision-based approaches: paraphrasing captures complex edit operations, and the use of explicit edit operations in an iterative manner provides controllability and interpretability.We demonstrate these advantages of GRS compared to existing methods on the Newsela and ASSET datasets.
LeaningTower@LT-EDI-ACL2022: When Hope and Hate Collide

The 2022 edition of LT-EDI proposed two tasks in various languages.Task hope required models for the automatic identification of hopeful comments for equality, diversity, and inclusion.Task antiLGBT focused on the identification of homophobic and transphobic comments.We targeted both tasks in English by using reinforced BERT-based approaches.Our core strategy aimed at exploiting the data available for each given task to augment the amount of supervised instances in the other.On the basis of an active learning process, we trained a model on the dataset for Task i and applied it to the dataset for Task j to iteratively integrate new silver data for Task i.Our official submissions to the shared task obtained a macro-averaged F 1 score of 0.53 for Task hope and 0.46 for Task antiLGBT , placing our team in the third and fourth positions out of 11 and 12 participating teams respectively.
MetaBlog: A Metadata Driven Semantics Aware Approach for Blog Tagging

Blogs on the internet are very popular and due to their popularity and knowledge filled contents on various topic, today internet has a lot of blogs on various sites, hence if a person wants to look for a particular blog it becomes quite a task for him/her to get the relevant blog out of internet searches. To solve this problem several approaches such as TextRank and TF-IDF, User Selection combined with knowledge flow, labels and PageRank, clustering etc. were proposed in the past. The proposed MetaBlog approach involves knowledge centric approach infused with machine learning paradigm such as random forest where a semantic similarity is computed between the RDF generated values and Random Forest top 25% selection and a final tag is finalized. The proposed MetaBlog yields an overall Accuracy of 93.20%, with a low False Discovery Rate of 0.09.
A Synchronous Measurement System for the First Derivative of Out-of-Plane Deformation of Double Ends

In current practical applications, synchronously obtaining the information about out-of-plane deformation of double ends is often necessary. However, traditional measurement systems cannot achieve the synchronous measurement of double ends, as two sets of measurement systems are often needed. This study solves the problem by designing a synchronous measurement system for the first derivative of the double-end deformation based on shearography. The first derivative distribution of the double-end deformation is measured synchronically by imaging the two end surfaces in different areas of the camera’s target plane through different shear structures. In addition, by placing apertures in each Michelson shear structure, the synchronous dynamic measurement of both ends and the independent adjustment of the shear and spectral distribution in the Michelson shearography system are realized. The principle of the measurement system is introduced in detail, and the feasibility and practicability of the proposed system are verified by experiments.
A Big Data Deep Learning Approach for Credit Card Fraud Detection

Credit card usage, both online and in-person, has increased tremendously in the past few years leading to more and credit card frauds and thefts especially during online transactions. For instance, CPA Canada reports that a 34% of respondents have personally experienced fraud, with 18% reporting that a credit card fraud occurred. As these thefts are often anomalous in nature, artificial intelligence techniques, in particular deep learning and neural networks, can be used to detect these fraudulent transactions. This paper studies convolutional neural network (CNN) to analyze the usage data and to predict the occurrence of fraudulent transactions. In addition, SMOTE, random undersampling, and random oversampling technique are used to reduce the false-positive rate. Performance of the proposed model was evaluated using three different sampling methods, as well as without sampling techniques. The results demonstrate the advantage of using sampling methods.
Data Management in Healthcare Research as a Guarantee of its Quality

Today, in times of outbreaks of epidemics such as the Zika virus and COVID-19, health systems around the globe face an urgent need to respond quickly to overcome their spread and prevent the re-infection of humanity. Among the solutions was the World Health Organization’s call for immediate, rapid, and, most importantly, open dissemination of medical research data. Although data sharing benefits science and society, there is a range of ethical, legal, cultural, financial, and technical barriers to the dissemination and reuse of medical research data today. Therefore, the quality of data, namely the possibility of their easy search, availability, compatibility, and reusability, is considered relevant in developing data infrastructure in healthcare. Consequently, there is an urgent need to develop an appropriate research data management system in the healthcare system. The aim is to study the efficiency of data management in the healthcare system. This goal is proposed to be achieved in the following sequence: to consider the essence of the concept of research data management; to conduct a bibliometric study of the concept of data management in the medical fields of knowledge, to analyze the frequency of publications on the issue of data management of medical research, indexed by the Scopus database. The object of research is the healthcare sector. The subject is the determinants of the impact of proper management of medical research data on the healthcare sector. The terminological maps of term relationships were constructed using the VOSviewer visualization tool. The study found that 8% of all data management research was conducted in the medical fields of knowledge. The study found an upward trend in the number of health data management publications with the highest growth rate in 2019 and 2020. Analysis of terminology clusters revealed that the keywords «Big Data», «Machine Learning», «Data Collection», «Data Quality», «Data Sharing», «Data Reuse», «COVID-19», and «Blockchain» have the highest number of mentions in publications and strong connection with other publications. Thus, we justified the importance of developing a coherent program and strategic plans for managing research data in the health care system. The results of the study can be used to support decision-making on future opportunities to effectively influence the development of access to medical research data, as well as to ensure the improvement of the quality and confidentiality of research data in the health system.
Supervised Knowledge Aggregation for Knowledge Graph Completion

We explore data-driven rule aggregation based on latent feature representations in the context of knowledge graph completion. For a given query and a collection of rules obtained by a symbolic rule learning system, we propose end-to-end trainable aggregation functions for combining the rules into a confidence score answering the query. Despite using latent feature representations for rules, the proposed models remain fully interpretable in terms of the underlying symbolic approach. While our models improve the base learner constantly and achieve competitive results on various benchmark knowledge graphs, we outperform current state-of-the-art with respect to a biomedical knowledge graph by a significant margin. We argue that our approach is in particular well suited for link prediction tasks dealing with a large multi-relational knowledge graph with several million triples, while the queries of interest focus on only one specific target relation.
Improving Multiple Documents Grounded Goal-Oriented Dialog Systems via Diverse Knowledge Enhanced Pretrained Language Model

In this paper, we mainly discuss about our submission to MultiDoc2Dial task, which aims to model the goal-oriented dialogues grounded in multiple documents.The proposed task is split into grounding span prediction and agent response generation.The baseline for the task is the retrieval augmented generation model, which consists of a dense passage retrieval model for the retrieval part and the BART model for the generation part.The main challenge of this task is that the system requires a great amount of pre-trained knowledge to generate answers grounded in multiple documents.To overcome this challenge, we adopt multitask learning, data augmentation, model pretraining and contrastive learning to enhance our model's coverage of pretrained knowledge.We experiment with various settings of our method to show the effectiveness of our approaches.Our final model achieved 37.78 F1 score, 22.94 SacreBLEU, 36.97Meteor, 35.46 RougeL, a total of 133.15 on DialDoc Shared Task at ACL 2022 released test set.