Miracle-dz/UniFAR-FTUs huggingface dataset:
Design Metaphors for Understanding User Expectations of Socially Interactive Robot Embodiments

The physical design of a robot suggests expectations of that robot’s functionality for human users and collaborators. When those expectations align with the robot’s true capabilities, users are more likely to adopt the technologies for their intended use. However, the relationship between expectations and socially interactive robot design is not well understood. This article applies the concept of design metaphors to robot design and contributes the Metaphors for Understanding Functional and Social Anticipated Affordances dataset of 165 extant robots and the expectations users place on them. We used Mechanical Turk to crowd-source user expectation over three user studies. The first study (N = 382) associated crowd-sourced design metaphors to different robot embodiments. The second study (N = 803) assessed initial social expectations of robot embodiments. The final study (N = 805) addressed the degree of abstraction of the design metaphors and the functional expectations projected on robot embodiments. We performed analyses to gain insights into how design metaphors can be used to understand social and functional expectations of robots and how these data can be visualized to be useful for study designers and robot designers. Together, these results can serve to guide robot designers toward aligning user expectations with true robot capabilities, facilitating positive human–robot interaction.
AccHashtag: Accelerated Hashing for Detecting Fault-Injection Attacks on Embedded Neural Networks

We propose AccHashtag, the first framework for high-accuracy detection of fault-injection attacks on Deep Neural Networks (DNNs) with provable bounds on detection performance. Recent literature in fault-injection attacks shows the severe DNN accuracy degradation caused by bit flips. In this scenario, the attacker changes a few DNN weight bits during execution by injecting faults to the dynamic random-access memory (DRAM). To detect bit flips, AccHashtag extracts a unique signature from the benign DNN prior to deployment. The signature is used to validate the model’s integrity and verify the inference output on the fly. We propose a novel sensitivity analysis that identifies the most vulnerable DNN layers to the fault-injection attack. The DNN signature is constructed by encoding the weights in vulnerable layers using a low-collision hash function. During DNN inference, new hashes are extracted from the target layers and compared against the ground-truth signatures. AccHashtag incorporates a lightweight methodology that allows for real-time fault detection on embedded platforms. We devise a specialized compute core for AccHashtag on field-programmable gate arrays (FPGAs) to facilitate online hash generation in parallel to DNN execution. Extensive evaluations with the state-of-the-art bit-flip attack on various DNNs demonstrate the competitive advantage of AccHashtag in terms of both attack detection and execution overhead.
Cut Them Loose? Firms’ Response Strategies to Environmental Misconduct by Supplying Firms

Firms are increasingly held accountable for their suppliers’ environmental misconduct and often face significant stakeholder pressure to respond or even end their affiliations with misbehaving suppliers. Despite the importance of the subject, we know little about antecedents to supplier exclusion. In this article, we build on the assumption of stakeholder expectations being a core mechanism—and adopt an expectancy-violation theory lens. Drawing also on complementary insights into signaling theory, we argue that the severity of the misconduct affects the decision to cut ties. Furthermore, we argue that the engagement in environmental partnerships of client firms as well as suppliers moderates this relationship. To test our hypotheses, we use a unique sample of 434 client firm–supplier dyads, including 27 focal firms pressured by Greenpeace to remove “dirty” palm oil suppliers, accused of illegal deforestation. The study’s findings provide contributions to several literature streams related to inter-organizational dynamics and environmental sustainability.
Managing diverse workforce: How to safeguard skilled migrants’ self-efficacy and commitment

This study aims to uncover the organizational and contextual factors undermining skilled migrants’ professional development and their contributions to host economies. Drawing from the Conservation of Resources Theory, this research developed an integrated model to investigate the effects of most prevalent internal and external factors of burnout and limited career alternatives on skilled migrants’ organizational commitment. The model incorporates mediating roles of professional self-efficacy and career turnover intention to demonstrate how depletion of resources through burnout leads to further resource losses by negatively impacting employees' intentions about their career continuity and professional self-efficacy and, ultimately, undermines their organizational commitment. This research provides managers of cross-cultural workplaces with insights into the critical role of supportive resources in leveraging the untapped intellectual capital brought to host organizations by skilled migrants. This study suggests that the availability of career options can maximize skilled migrants’ contributions to organizations.
Opinion Leader Detection in Asian Social Networks using Modified Spider Monkey Optimization

The Asian social networks are dominated by the society’s collectivist culture, and this interestingly introduces an influence mechanism aided by word-of-mouth and opinion leaders. An opinion leader can help to generate and shape other people’s opinion and achieve a high information spread on any topic. In this work, a modified spider monkey optimization based opinion leader detection approach is proposed. Firstly, we employ the modified node2vec graph embedding to generate the lower dimensional vectors which act as the initial features for the nodes in a typical Asian social network. Next, the entire population is broken down into several groups using the k-means++ algorithm where the number of clusters is equal to the number of opinion leaders to be selected. The local and global leaders are chosen by using the coordinates of the cluster centres of these clusters. The coordinates of the centroids of the clusters are then used to detect the local and global leaders in the network. The local leaders then form the seed set of opinion leaders for the network. The positions of the nodes in the network, including the local and global leaders, are updated over a number of iterations. At the end of these iterations, the seed set generating the maximum influence forms the set of opinion leaders in the network. We test our proposed approach using the popular information diffusion and cognitive opinion dynamics (COD) models. We perform intensive experiments on several real-life social networks based on various performance metrics. The results obtained reveal that the proposed approach outperforms several existing techniques of opinion leader detection.
Place-making and the London estates: land ownership and the built environment

ABSTRACT In inner London many real estate companies own and manage ownership clusters ‒ spatially concentrated property portfolios or landed estates. The literature has framed this feature of the London property market as the renewed interest in the estate model of development. Drawing on empirical research in London, this research examines ownership clusters, exploring their investment and management strategies. Research findings suggest that cluster managers employ a curatorial form of place-making in their relevant neighbourhoods, selecting the mix of uses and users, unit placement, and investing in public space in order to increase footfall, consumer circulation, dwell time and spending to maximize real estate returns.
A Intelligent Detection Method for Irony and Stereotype Based on Hybird Neural Networks

For the task of Profiling Irony and Stereotype Spreaders on Twitter[1,2], a deep learning model based on a combination of RNN and CNN is proposed in this paper. A special RNN is used to solve the context’s long-term dependency, and CNN is used to further extract relational features. The task involves classifying authors as Ironic or non-Ironic based on the number of their tweets, and the task is a judgment for those authors who use irony to spread a stereotype (ISS), that the task does as a binary classification task. After training and predicting on the task datasets given by PAN 22, the accuracy of the model announced by the organizer is about 0.9056.
Dual Convolutional Neural Networks and Regression model based Coral Reef Annotation and Localization

There has been an enormous interest in using deep learning to classify underwater substrate images to identify various objects like ﬁshes, plankton, coral reefs, seagrass, submarines, and gestures of sea divers. This classiﬁcation is essential for measuring water bodies’ health and quality and protecting endangered species. In the previous study, we examined the effectiveness and flexibility of the CNN model using online individual coral and grouped datasets. Our observations from the previous study showed that our model predicted the substrates correctly. However, certain miscalculations led to lower recall scores. This year, we introduced a new approach and changed the platform to build a system that will improve the scores as well as predictions in an efficient manner. For the current study, we trained our model using high-level APIs of Keras and TensorFlow python libraries and produced improved predictions and accuracy.
ARC-NLP at CheckThat!-2022: Contradiction for Harmful Tweet Detection

The target task of our team in CLEF2022 CheckThat! Lab challenge is Task-1C, harmful tweet detection. We propose a novel approach, called ARC-NLP-contra, which is a contradiction check approach by using the idea that harmful tweets contradict with the real-life facts in the scope of COVID-19 pandemic. Besides, we propose and examine two other models. The first model, called ARC-NLP-hc, is a traditional approach that utilizes hand-crafted tweet and user features. The second model, called ARC-NLP-pretrain, pretrains a Transformer-based language model by using COVID-related Turkish tweets. We compare the performances of these three models, and submit the highest performing model in the preliminary experiments to the challenge. We make submissions for Task-1A, 1B, 1C in Turkish and Task-1C in English. We have the winning solution for Task-1C, harmful tweet detection in Turkish, using ARC-NLP-contra that is our contradiction check approach. © 2022 Copyright for this paper by its authors.
Style Change Detection Based On Bi-LSTM And Bert

This article is an overview of PAN 2022's approach to the Style Change Detection task. The goal of this competition task is to identify the text positions for author switching in a given multi-author document. PAN helps us segment the tasks, respectively task1 finds paragraphs with two authors and cuts them into different texts; tesk2 finds paragraphs with two or more authors, finds the writing style happens changing location. tesk3 does a more fine-grained lookup on the task requirements of tesk2 to find where in the sentence the writing style changes. This paper designs a method to handle the task based on a model composed of convolutional neural networks, Bert and bidirectional long-short-term memory networks, using binary classification to judge style changes and author label problems. The f1 scores are obtained, which are 0.67 for task1 and 0.40 for task2 and 0.65 for tesk3.
Overview of ImageCLEFmedical 2022 - Caption Prediction and Concept Detection

The 2022 ImageCLEFmedical caption prediction and concept detection tasks follow similar challenges that were already run from 2017–2021. The objective is to extract Unified Medical Language System (UMLS) concept annotations and/or captions from the image data that are then compared against the original text captions of the images. The images used for both tasks are a subset of the extended Radiology Objects in COntext (ROCO) data set which was used in ImageCLEFmedical 2020. In the caption prediction task, lexical similarity with the original image captions is evaluated with the BiLingual Evaluation Understudy (BLEU) score. In the concept detection task, UMLS terms are extracted from the original text captions, combined with manually curated concepts for image modality and anatomy, and compared against the predicted concepts in a multi-label way. The F1-score was used to assess the performance. The task attracted a strong participation with 20 registered teams. In the end, 12 teams submitted 157 graded runs for the two subtasks. Results show that there is a variety of techniques that can lead to good prediction results for the two tasks. Participants used image retrieval systems for both tasks, while multi-label classification systems were used mainly for the concept detection, and Transformer-based architectures primarily for the caption prediction subtask.
Early risk detection of mental illnesses using various types of textual features

This paper documents the participation of our team, BioInfo@UAVR, in the first and second task of the 2022 edition of CLEF eRisk. With the goal of achieving an early detection of subjects at risk of specific mental illnesses, pathological gambling and depression for tasks 1 and 2 respectively, using data sourced from social networks, more specifically, Reddit. To this end, we trained several machine learning models, dividing our experiments into three feature engineering approaches of increasing complexity, corresponding to very commonly used vectorization methods in natural language processing, those of bag-of-words, distributional semantics word embeddings and contextualized language models. Additionally, we evaluated the impact of the inclusion of sentiment analysis features. Despite having subpar results on the official evaluation, we managed to improve them considerably post submission with a few tweaks, showing that these solutions can work if properly fine-tuned.
Expanding Spatial Regions and Incorporating IDF for PHOC-Based Math Formula Retrieval at ARQMath-3

For ARQMath 2022, the XY-PHOC Team has built upon their previous work from ARQMath 2021. We submitted multiple runs for the Formula Retrieval task (Task 2). Pyramidal Histogram of Character (PHOC) formula encodings capture the two-dimensional layout of symbols, using binary vectors to indicate the spatial regions where symbols appear. Our updated PHOC models add additional X/Y partitions, and introduce ellipsoidal regions to capture symmetric layouts (e.g., ‘ 𝑥 + 𝑦 ’ and ‘ 𝑦 + 𝑥 ’). Our PHOC models were reimplemented in OpenSearch, which has decreased Mean Response Times (MRTs) by orders of magnitude. We also explored incorporating Inverse Document Frequency (IDF) weights in our PHOC similarity function. Despite their simplicity, our PHOC models were competitive at ARQMath 2022 in P ′ @10 measures. Our IDF models did not perform as expected, and we will be exploring ways to improve them in the future. We also introduce a new tool for visually comparing ARQMath Task 2 runs ( ARQMathCompare ). This along with Python libraries for PHOC embedding and retrieval ( AnyPHOC and PHOCindexing ) are publicly available at https://gitlab.com/dprl.
KULeuven at LeQua 2022: Model Calibration in Quantification Learning

In many decision-making applications it is of particular interest to obtain aggregated properties about the data sample. Quantification learning (or prevalence estimation ) is the task of training predictors via supervised learning to estimate the class distribution of an unlabelled test set. In this paper, we describe our perspective on the "LeQua 2022: Learning to Quantify" challenge, and our entry in "the vector task - T1A," which achieved 1st place on the leaderboard in terms of relative absolute error, and 2nd place in terms of absolute error. We explored the relationship between probabilistic calibration and quantification, concluding that calibration can help with quantification, but this typically requires an i.i. d. assumption or known distribution shift.
Semantic Approaches Survey for Job Recommender Systems

Newly, the e-recruitment phenomenon has been widely spread, which led to the increase of job descriptions online and caused a remarkable growth in the number of jobs seekers sending their resumes searching for new opportunities. This enormous amount of information makes finding the appropriate job/candidate a difficult process. The recommender system technology is designed to help users find items that match their interests. Therefore, several recommendation approaches have been proposed to improve the e-recruitment process. Semantic technologies have been proposed to guide document processing and automatic matching, improving the job recommendation results. The research reported in this paper is a literature review of the semantic e-recruitment approaches; we spotlight the benefits of exploiting the semantic technologies in different aspects (information extraction and matching) in this context. Furthermore, we classified those approaches by comparing the semantic technologies used the advantages and disadvantages of each approach.
Automatic acquisition of lexical knowledge about multiword predicates

Automatic Acquisition of Lexical Knowledge about Multiwor d Predicates Afsaneh Fazly Doctor of Philosophy Graduate Department of Computer Science University of Toronto 2007 A multiword predicate is the combination of a predicate (oft en a verb) with one or more of its arguments, that together form a single unit of predicative m eaning. We focus on a broad class of multiword predicates, in which a verb combines with a noun in the direct object position (e.g.,give a groanandshoot the breeze ). The semantic interpretation of such multiword predicates involves a certain degree of idiosyncrasy; moreover, th y are crosslinguistically frequent and appear in all text genres. Hence, they pose a great challe nge to the current models of natural language processing. Most existing computational mod els treat multiword predicates as syntactically-dependent word sequences or collocations. Such a treatment ignores other important characteristics of these constructions, reflected in their distinct lexical and syntactic behaviour. Nonetheless, cues from the lexicosyntactic pro perties of multiword predicates have often been used in linguistic and psycholinguistic studies to explain their peculiar semantic behaviour. On the one hand, simple statistical approaches t hat only draw on the frequency of multiword predicates fail to account for much of the synta ctic and semantic behaviour of these constructions. On the other hand, linguistic theorie s provide generalizations about the behaviour of multiword predicates that can be augmented wit h probabilistic knowledge about language in use. The main goal of the present study is to propo se ways of combining the predictive power of linguistic theories with the coverage and r obustness of statistical techniques to acquire linguistically-plausible and reliable corpus-dr awn knowledge about multiword predicates.
Ensemble-Based Clustering for Writing Style Change Detection in Multi-Authored Textual Documents

Style change detection aims at detecting writing style breaches: the positions at which the writing style changes and authors switch within a multi-authored document. This task has a significant role in forensic linguistics, cybercrime investigation, and intrinsic plagiarism detection. Detecting authors’ switch positions require decomposing the text into its authorial components. One of the feasible solutions to achieve this goal is to cluster the textual document into stylistically homogeneous clusters where each cluster includes all text fragments that are similar in writing style and hence are written by the same author. In this paper, we propose a within-document authorship clustering method based on ensemble learning to tackle style change detection in multi-authored documents. The proposed authorship clustering is an unsupervised learning method that does not require training or parameter tuning. The only parameter needed is the number of authors which is estimated by using an ensemble paragraph clustering to accurately capture author distribution at the paragraph level and precisely predict the number of authors. The experimental results obtained on PAN 2022 test dataset show that the proposed method achieved an F1 score of 0.52 to detect style changes between paragraphs and an F1 score of 0.49 to detect authors’ switches between sentences. To attribute authors within a document, the method achieved an F1 score of 0.22, a Diarization error rate of 0.57, and a Jaccard error rate of 0.35.
Profiling Irony and Stereotype Spreaders on Twitter: PAN Shared Task (IROSTEREO) 2022

We present our solution to the problem proposed by IROSTEREO’s PAN Shared Task in 2022. It proposes the detection of irony and stereotype spreaders on Twitter. Throughout the memory, we will show how through: the technique based on neural networks for the pre-training of natural language processing such as BERT, the use of sentence embeddings, and two alternatives to put them together; it will be achieved from a set of tweets associated with a set of authors, predict whether an author is ironic or not. Lastly, it will be presented a model that achieves high accuracy and with which finally participated in the competition.
BUM at CheckThat!-2022: A Composite Deep Learning Approach to Fake News Detection using Evidence Retrieval

We detail a deep learning approach based on the transformer architecture for performing fake news detection. The proposed approach is composed of a deep learning network which receives as input the claim to be verified, a series of predictions made by other models, and supporting evidence in the form of ranked passages. We validate our approach participating as the Brisbane–Udine–Melbourne (BUM) Team in the CLEF2022-CheckThat! Lab (Task 3: Fake News Detection), where we achieve an F1-score of 0.275 , ranking 10 th out of 25 participants. 1
LEVIRANK: Limited Query Expansion with Voting Integration for Document Retrieval and Ranking

In order to make informed decisions in personal life, the information available on the internet is often overwhelming, and thus comparative decision-making is particularly challenging. Given the plethora of online resources, it often results in wastage of resources for finding relevant and correct responses. The Touché 2022 Shared Task 2 for Comparative Questions focuses on solving this problem by retrieving corresponding documents given a comparative question. The importance of the retrieved documents is determined both by their relevance and quality. In this paper, we present a three-stage retrieval, ranking, and stance prediction system called the LeviRANK. It uses bidirectional self-attention-based language models for argumentation detection in documents. In the first stage, it incorporates an empirically novel retrieval approach that produces the highest recall values for small comparative queries. The retrieval module uses voting -based BM25 retrieval for merging multiple BM25 retrievals from a pool of relevant expanded queries. We then use monoT5 and duoT5 document rankers based on the "Expando-Mono-Duo" design pattern. Finally, we identify object stance by building a two-step stance prediction approach which first separates out documents that are specifically related to objects and further identifies the given relevant object in them. With the proposed approach, we observe that bidirectional self-attention-based document ranking models successfully identify argumentation structure better than the probabilistic ranking models. The LeviRANK system ranks the highest mean nDCG@5 score of 0.758 for document relevance task, second-highest nDCG@5 score of 0.744 for document quality task, and second-highest Macro-F1 score of 0.301 for the stance prediction task.