Reviewerly/exHarmony huggingface dataset:
Exploring Expressed Emotions for Neural News Recommendation

Due to domain-specific challenges such as short item lifetimes and continuous cold-start issues, news recommender systems rely more on content-based methods to deduce reliable user models and make personalized recommendations. Research has shown that alongside the content of an item, the way it is presented to the users also plays a critical role. In this work, we focus on the effect of incorporating expressed emotions within news articles on recommendation performance. We propose a neural news recommendation model that disentangles semantic and emotional modeling of news articles and users. While we exploit the textual content for the semantic representation, we extract and combine emotions of different information levels for the emotional representation. Offline experiments on a real-world dataset show that our approach outperforms non-emotion-aware solutions significantly. Finally, we provide a future outline, where we plan to investigate a) the online performance and b) the explainability/explorability of our approach.
Survey2Persona: Rendering Survey Responses as Personas

Data-driven persona generation can benefit from stakeholder inputs while offloading the complexities of high-dimensional datasets. To this end, we present Survey2Persona (S2P), an interactive web interface for real-time persona generation from survey data. The users of the web interface—the designers—can upload survey data and have the interface automatically generate personas. Researchers and practitioners can use S2P to explore different respondent types in their survey datasets in a privacy-preserving manner, which is akin to making the analytical journey more productive, enjoyable, and human-centered. We make the system publicly available and provide argumentation about its novelty and value for user modeling and human-computer interaction communities.
Blind-area Elimination in Video Surveillance Systems by WiFi Sensing with Minimum QoS Loss

Video surveillance systems have demonstrated their great importance in security protection these years. However, due to limited budget, installing cameras in every place of surveillance region is not practical and then blind areas become inevitable. As a result, eliminating blind areas with the lowest cost has become a tough challenge. To the best of our knowledge, this is the first work that fixes the blind spots of video surveillance systems based on WiFi sensing technique. By taking existing WiFi infrastructure as the sensing device, this paper attempts to eliminate blind spots with tiny hardware cost. Moreover, in order to completely fix blind area with minimum loss of video communication QoS, the WiFi sensing device’s location boundary that satisfies the above objective is modeled and estimated. To that end, a visitor-disturbed channel model is first derived for precisely describing the inherent relation between the appearance of visitor and the change of wireless channel. Then a location boundary model satisfying both blind-area elimination and QoS maximization is further derived. Based on the derived model, a practical system is designed to estimate the real location boundary. The simulation and experiment results have not only verified the correctness of our derived location boundary model, but also showed its good performance on both blind-area elimination and communication QoS optimization.
Evaluation of Natural Language Processing for Requirements Traceability

Requirements traceability remains a challenge, especially in multi-level system of systems being developed by many different organizations. This paper develops and tests automated tracing methods based on Natural Language Processing (NLP) techniques to help ensure links between parent and child requirements are correct while preventing common requirements traceability issues. Using publicly available requirements documentation from the National Aeronautics and Space Administration (NASA), the developed software tool analyzed 215 requirements, generated a Term Frequency – Inverse Document Frequency (TF-IDF) matrix of the document collection, and classified parent-child requirement pairs using the histogram distance and cosine similarity measures under eighteen different similarity measure thresholds. Precision, recall, and F-scores were calculated, yielding maximum F-scores for each similarity measure with the objective of understanding the performance and utility of histogram distance for automated requirements tracing. The results indicate natural language processing is likely not a practical approach to requirements traceability.
Towards language-independent brown build detection

In principle, continuous integration (CI) practices allow modern software organizations to build and test their products after each code change to detect quality issues as soon as possible. In reality, issues with the build scripts (e.g., missing dependencies) and/or the presence of "flaky tests" lead to build failures that essentially are false positives, not indicative of actual quality problems of the source code. For our industrial partner, which is active in the video game industry, such "brown builds" not only require multidisciplinary teams to spend more effort interpreting or even re-running the build, leading to substantial redundant build activity, but also slows down the integration pipeline. Hence, this paper aims to prototype and evaluate approaches for early detection of brown build results based on textual similarity to build logs of prior brown builds. The approach is tested on 7 projects (6 closed-source from our industrial collaborators and 1 open-source, Graphviz). We find that our model manages to detect brown builds with a mean F1-score of 53% on the studied projects, which is three times more than the best baseline considered, and at least as good as human experts (but with less effort). Furthermore, we found that cross-project prediction can be used for a project's onboarding phase, that a training set of 30-weeks works best, and that our retraining heuristics keep the F1-score higher than the baseline, while retraining only every 4--5 weeks.
An Efficient Fusion Mechanism for Multimodal Low-resource Setting

The effective fusion of multiple modalities (i.e., text, acoustic, and visual) is a non-trivial task, as these modalities often carry specific and diverse information and do not contribute equally. The fusion of different modalities could even be more challenging under the low-resource setting, where we have fewer samples for training. This paper proposes a multi-representative fusion mechanism that generates diverse fusions with multiple modalities and then chooses the best fusion among them. To achieve this, we first apply convolution filters on multimodal inputs to generate different and diverse representations of modalities. We then fuse pairwise modalities with multiple representations to get the multiple fusions. Finally, we propose an attention mechanism that only selects the most appropriate fusion, which eventually helps resolve the noise problem by ignoring the noisy fusions. We evaluate our proposed approach on three low-resource multimodal sentiment analysis datasets, i.e., YouTube, MOUD, and ICT-MMMO. Experimental results show the effectiveness of our proposed approach with the accuracies of 59.3%, 83.0%, and 84.1% for the YouTube, MOUD, and ICT-MMMO datasets, respectively.
MuchSUM: Multi-channel Graph Neural Network for Extractive Summarization

Recent studies of extractive text summarization have leveraged BERT for document encoding with breakthrough performance. However, when using a pre-trained BERT-based encoder, existing approaches for selecting representative sentences for text summarization are inadequate since the encoder is not explicitly trained for representing sentences. Simply providing the BERT-initialized sentences to cross-sentential graph-based neural networks (GNNs) to encode semantic features of the sentences is not ideal because doing so fail to integrate other summary-worthy features like sentence importance and positions. This paper presents MuchSUM, a better approach for extractive text summarization. MuchSUM is a multi-channel graph convolutional network designed to explicitly incorporate multiple salient summary-worthy features. Specifically, we introduce three specific graph channels to encode the node textual features, node centrality features, and node position features, respectively, under bipartite word-sentence heterogeneous graphs. Then, a cross-channel convolution operation is designed to distill the common graph representations shared by different channels. Finally, the sentence representations of each channel are fused for extractive summarization. We also investigate three weighted graphs in each channel to infuse edge features for graph-based summarization modeling. Experimental results demonstrate our model can achieve considerable performance compared with some BERT-initialized graph-based extractive summarization systems.
Bilateral Self-unbiased Learning from Biased Implicit Feedback

Implicit feedback has been widely used to build commercial recommender systems. Because observed feedback represents users' click logs, there is a semantic gap between true relevance and observed feedback. More importantly, observed feedback is usually biased towards popular items, thereby overestimating the actual relevance of popular items. Although existing studies have developed unbiased learning methods using inverse propensity weighting (IPW) or causal reasoning, they solely focus on eliminating the popularity bias of items. In this paper, we propose a novel unbiased recommender learning model, namely BIlateral SElf-unbiased Recommender (BISER), to eliminate the exposure bias of items caused by recommender models. Specifically, BISER consists of two key components: (i) self-inverse propensity weighting (SIPW) to gradually mitigate the bias of items without incurring high computational costs; and (ii) bilateral unbiased learning (BU) to bridge the gap between two complementary models in model predictions, i.e., user- and item-based autoencoders, alleviating the high variance of SIPW. Extensive experiments show that BISER consistently outperforms state-of-the-art unbiased recommender models over several datasets, including Coat, Yahoo! R3, MovieLens, and CiteULike.
Ontologies and Open Data for Enriching Personalized Social Moments in Human Robot Interaction

This paper describes our proposal for enriching personalized social moments and dialogues between human and robot in the context of the Sugar, Salt & Pepper laboratory. The lab focused on the use of the Pepper robot in a therapeutic context to promote autonomies and functional acquisitions in highly functioning (Asperger) children with autism. This paper is focused on a post-hoc work aimed at improving the robot's autonomous dialogue strategies. In particular we are integrating the robot's dialogue with a knowledge base to have the robot able to move and reason on an ontology, and thus enriching its dialogue's strategies. For instance, the taxonomic structure of the ontology could allow Pepper to drive the focus of the conversation to related topics or to more general or specific topics, and, in general, it could improve its capability to manage the conversation and disambiguate the input from the user.
End-to-end Distantly Supervised Information Extraction with Retrieval Augmentation

Distant supervision (DS) has been a prevalent approach to generating labeled data for information extraction (IE) tasks. However, DS often suffers from noisy label problems, where the labels are extracted from the knowledge base (KB), regardless of the input context. Many efforts have been devoted to designing denoising mechanisms. However, most strategies are only designed for one specific task and cannot be directly adapted to other tasks. We propose a general paradigm (Dasiera) to resolve issues in KB-based DS. Labels from KB can be viewed as universal labels of a target entity or an entity pair. While the given context for an IE task may only contain partial/zero information about the target entities, or the entailed information may be vague. Hence the mismatch between the given context and KB labels, i.e., the given context has insufficient information to infer DS labels, can happen in IE training datasets. To solve the problem, during training, Dasiera leverages a retrieval-augmentation mechanism to complete missing information of the given context, where we seamlessly integrate a neural retriever and a general predictor in an end-to-end framework. During inference, we can keep/remove the retrieval component based on whether we want to predict solely on the given context. We have evaluated Dasiera on two IE tasks under the DS setting: named entity typing and relation extraction. Experimental results show Dasiera's superiority to other baselines in both tasks.
Full-text retrievals and EBSCO Discovery Service: Assessing usage of e-journals across multiple platforms

This study utilizes COUNTER 5 data from the University of Dayton (UD) to measure full-text retrievals of e-journal articles from five major academic journal publishers (Taylor & Francis, SAGE, Oxford, Wiley, and Springer). Usage data from these publishers’ e-journals within EBSCO is compared to the same content when accessed from publisher platforms such as Wiley Online Library or SpringerLINK. Building on previous studies that have largely focused on links (or referrals) from the library discovery layer to publisher platforms, this study analyzes usage of full text-articles stored within EBSCO Discovery Service and EBSCOhost subject databases to consider how these full-text holdings within EBSCO might affect referrals to publisher platforms. The findings indicate that full-text article holdings within EBSCO are used more often than the same content in publisher platforms, suggesting that UD students and researchers rely heavily on—and likely often start with—EBSCO for their learning and research.
A Family of Neural Contextual Matrix Factorization Models for Context-Aware Recommendations

Recommender systems can produce item recommendations tailored to user preferences and assist user decision making in several real-world applications. Context-aware recommender systems can be built and developed to adapt the recommendations to different contextual situations, since user preferences may vary from contexts to contexts (e.g., time, location, companion, etc.). Recently, the deep learning and neural network techniques have been applied to help build better recommendation models. In this paper, we extend and propose a general neural contextual matrix factorization framework, evaluate and compare a family of these neural contextual matrix factorization models for context-aware recommendations. Particularly, we exploit and analyze the impact on the performance of context-aware recommendations by considering two factors – the component(s) where contexts can be fused into, and the embedding mode utilized to represent context situations.
Neural Pseudo-Relevance Feedback Models for Sparse and Dense Retrieval

Pseudo-relevance feedback mechanisms have long served as an effective technique to improve the retrieval effectiveness in information retrieval. Recently, large pre-trained language models, such as T5 and BERT, have shown a strong capacity to capture the latent traits of texts. Given the success of these models, we seek to study the capacity of these models for query reformulation. In addition, the BERT models have demonstrated further promise for dense retrieval, where the query and documents are encoded into the contextualised embeddings and relevant documents are retrieved by conducting the semantic matching operation. Although the success of pseudo-relevance feedback for sparse retrieval is well documented, effective pseudo-relevance feedback approaches for dense retrieval paradigm are still in their infancy. Thus, we are concerned with excavating the potential of the pseudo-relevance feedback information combined with the large pre-trained models to conduct effective query reformulation operating on both sparse retrieval and dense retrieval.
Improving Efficiency and Robustness of Transformer-based Information Retrieval Systems

This tutorial focuses on both theoretical and practical aspects of improving the efficiency and robustness of transformer-based approaches, so that these can be effectively used in practical, high-scale, and high-volume information retrieval (IR) scenarios. The tutorial is inspired and informed by our work and experience while working with massive narrative datasets (8.5 billion medical notes), and by our basic research and academic experience with transformer-based IR tasks. Additionally, the tutorial focuses on techniques for making transformer-based IR robust against adversarial (AI) exploitation. This is a recent concern in the IR domain that we needed to take into concern, and we want to want to share some of the lessons learned and applicable principles with our audience. Finally, an important, if not critical, element of this tutorial is its focus on didacticism -- delivering tutorial content in a clear, intuitive, plain-speak fashion. Transformers are a challenging subject, and, through our teaching experience, we observed a great value and a great need to explain all relevant aspects of this architecture and related principles in the most straightforward, precise, and intuitive manner. That is the defining style of our proposed tutorial.
Bias Mitigation for Toxicity Detection via Sequential Decisions

Increased social media use has contributed to the greater prevalence of abusive, rude, and offensive textual comments. Machine learning models have been developed to detect toxic comments online, yet these models tend to show biases against users with marginalized or minority identities (e.g., females and African Americans). Established research in debiasing toxicity classifiers often (1) takes a static or batch approach, assuming that all information is available and then making a one-time decision; and (2) uses a generic strategy to mitigate different biases (e.g., gender and racial biases) that assumes the biases are independent of one another. However, in real scenarios, the input typically arrives as a sequence of comments/words over time instead of all at once. Thus, decisions based on partial information must be made while additional input is arriving. Moreover, social bias is complex by nature. Each type of bias is defined within its unique context, which, consistent with intersectionality theory within the social sciences, might be correlated with the contexts of other forms of bias. In this work, we consider debiasing toxicity detection as a sequential decision-making process where different biases can be interdependent. In particular, we study debiasing toxicity detection with two aims: (1) to examine whether different biases tend to correlate with each other; and (2) to investigate how to jointly mitigate these correlated biases in an interactive manner to minimize the total amount of bias. At the core of our approach is a framework built upon theories of sequential Markov Decision Processes that seeks to maximize the prediction accuracy and minimize the bias measures tailored to individual biases. Evaluations on two benchmark datasets empirically validate the hypothesis that biases tend to be correlated and corroborate the effectiveness of the proposed sequential debiasing strategy.
An efficient deep equilibrium model for medical image segmentation

In this paper, we propose an effective method that takes the advantages of classical methods and deep learning technology for medical image segmentation through modeling the neural network as a fixed point iteration seeking for system equilibrium by adding a feedback loop. In particular, the nuclear segmentation of medical image is used as an example to demonstrate the proposed method where it can successfully complete the challenge of segmenting nuclei from cells in different histopathological images. Specifically, the nuclei segmentation is formulated as a dynamic process to search for the system equilibrium. Starting from an initial segmentation generated either by a classic algorithm or pre-trained deep learning model, a sequence of segmentation output is created and combined with the original image to dynamically drive the segmentation towards the expected value. This dynamical extension to neural networks requires little extra change on the backbone deep neural network while it significantly increased model accuracy, generalizability, and stability as demonstrated by intensive experimental results from pathological images of different tissue types across different open datasets.
Rethinking Correlation-based Item-Item Similarities for Recommender Systems

This paper studies correlation-based item-item similarity measures for recommendation systems. While current research on recommender systems is directed toward deep learning-based approaches, nearest neighbor methods have been still used extensively in commercial recommender systems due to their simplicity. A crucial step in item-based nearest neighbor methods is to compute similarities between items, which are generally estimated through correlation measures like Pearson. The purpose of this paper is to re-investigate the effectiveness of correlation-based nearest neighbor methods on several benchmark datasets that have been used for recommendation evaluation in recent years. This paper also provides a more effective estimation method for correlation measures than the classical Pearson correlation coefficient and shows that this leads to significant improvements in recommendation performance.
Automatic segmentation of thoracic CT images using three deep learning models

Deep learning (DL) techniques are widely used in medical imaging and in particular for segmentation. Indeed, manual segmentation of organs at risk (OARs) is time-consuming and suffers from inter- and intra-observer segmentation variability. Image segmentation using DL has given very promising results. In this work, we present and compare the results of segmentation of OARs and a clinical target volume (CTV) in thoracic CT images using three DL models.We used CT images of 52 patients with breast cancer from a public dataset. Automatic segmentation of the lungs, the heart and a CTV was performed using three models based on the U-Net architecture. Three metrics were used to quantify and compare the segmentation results obtained with these models: the Dice similarity coefficient (DSC), the Jaccard coefficient (J) and the Hausdorff distance (HD).The obtained values of DSC, J and HD were presented for each segmented organ and for the three models. Examples of automatic segmentation were presented and compared to the corresponding ground truth delineations. Our values were also compared to recent results obtained by other authors.The performance of three DL models was evaluated for the delineation of the lungs, the heart and a CTV. This study showed clearly that these 2D models based on the U-Net architecture can be used to delineate organs in CT images with a good performance compared to other models. Generally, the three models present similar performances. Using a dataset with more CT images, the three models should give better results.
What the fork?

This work presents findings and mitigations on an understudied issue, which we term shrinkwrapped clones, that is endemic to the npm software package ecosystem. A shrink-wrapped clone is a package which duplicates, or near-duplicates, the code of another package without any indication or reference to the original package. This phenomenon represents a challenge to the hygiene of package ecosystems, as a clone package may siphon interest from the package being cloned, or create hidden duplicates of vulnerable, insecure code which can fly under the radar of audit processes.