Reviewerly/exHarmony huggingface dataset:
Opening the Black Box: Analyzing Attention Weights and Hidden States in Pre-trained Language Models for Non-language Tasks

Investigating deep learning language models has always been a significant research area due to the “black box” nature of most advanced models. With the recent advancements in pre-trained language models based on transformers and their increasing integration into daily life, addressing this issue has become more pressing. In order to achieve an explainable AI model, it is essential to comprehend the procedural steps involved and compare them with human thought processes. Thus, in this paper, we use simple, well-understood non-language tasks to explore these models’ inner workings. Specifically, we apply a pre-trained language model to constraithmetic problems with hierarchical structure, to analyze their attention weight scores and hidden states. The investigation reveals promising results, with the model addressing hierarchical problems in a moderately structured manner, similar to human problem-solving strategies. Additionally, by inspecting the attention weights layer by layer, we uncover an unconventional finding that layer 10, rather than the model’s final layer, is the optimal layer to unfreeze for the least parameter-intensive approach to fine-tune the model. We support these findings with entropy analysis and token embeddings similarity analysis. The attention analysis allows us to hypothesize that the model can generalize to longer sequences in ListOps dataset, a conclusion later confirmed through testing on sequences longer than those in the training set. Lastly, by utilizing a straightforward task in which the model predicts the winner of a Tic Tac Toe game, we identify limitations in attention analysis, particularly its inability to capture 2D patterns.
The Importance of Distrust in AI

In recent years the use of Artificial Intelligence (AI) has become increasingly prevalent in a growing number of fields. As AI systems are being adopted in more high-stakes areas such as medicine and finance, ensuring that they are trustworthy is of increasing importance. A concern that is prominently addressed by the development and application of explainability methods, which are purported to increase trust from its users and wider society. While an increase in trust may be desirable, an analysis of literature from different research fields shows that an exclusive focus on increasing trust may not be warranted. Something which is well exemplified by the recent development in AI chatbots, which while highly coherent tend to make up facts. In this contribution, we investigate the concepts of trust, trustworthiness, and user reliance. In order to foster appropriate reliance on AI we need to prevent both disuse of these systems as well as overtrust. From our analysis of research on interpersonal trust, trust in automation, and trust in (X)AI, we identify the potential merit of the distinction between trust and distrust (in AI). We propose that alongside trust a healthy amount of distrust is of additional value for mitigating disuse and overtrust. We argue that by considering and evaluating both trust and distrust, we can ensure that users can rely appropriately on trustworthy AI, which can both be useful as well as fallible.
From Black Boxes to Conversations: Incorporating XAI in a Conversational Agent

The goal of Explainable AI (XAI) is to design methods to provide insights into the reasoning process of black-box models, such as deep neural networks, in order to explain them to humans. Social science research states that such explanations should be conversational, similar to human-to-human explanations. In this work, we show how to incorporate XAI in a conversational agent, using a standard design for the agent comprising natural language understanding and generation components. We build upon an XAI question bank, which we extend by quality-controlled paraphrases, to understand the user’s information needs. We further systematically survey the literature for suitable explanation methods that provide the information to answer those questions, and present a comprehensive list of suggestions. Our work is the first step towards truly natural conversations about machine learning models with an explanation agent. The comprehensive list of XAI questions and the corresponding explanation methods may support other researchers in providing the necessary information to address users’ demands. To facilitate future work, we release our source code and data https://github.com/bach1292/XAGENT/ .
Development of a Human-Centred Psychometric Test for the Evaluation of Explanations Produced by XAI Methods

One goal of Explainable Artificial Intelligence (XAI) is to interpret and explain the inferential process of data-driven machine-learned models to make it comprehensible for humans. To reach it, it is necessary to have a reliable tool to collect the opinions of human users about the explanations generated by XAI methods of trained complex models. Psychometrics can be defined as the science behind psychological assessment. It studies the theory and techniques for measuring latent constructs such as intelligence, introversion, and conscientiousness. The knowledge developed in psychometrics was exploited to develop and evaluate a novel questionnaire for reliably evaluating the explanations produced by XAI methods. Explainability is a multi-faceted concept. Thus, it was necessary to create a set of questions to assess various facets and return a comprehensive, reliable measurement of explainability. The questionnaire development process was divided into two phases. First, a pilot study was designed and carried out to test the first version of the questionnaire. The results of this study were exploited to create a second, refined version of the questionnaire. The questionnaire was evaluated by assessing 1) its internal structure with the Exploratory Factor Analysis to analyse the interrelationships between the questionnaire’s items, 2) its reliability with the Cronbach alpha tests, and 3) its construct validity by comparing the distribution of the questionnaire’s answers with a set of quantitative metrics. Results showed that the questionnaire is promising as it was deemed a valid and reliable tool for evaluating XAI methods.
Toward Inclusive Online Environments: Counterfactual-Inspired XAI for Detecting and Interpreting Hateful and Offensive Tweets

The prevalence of hate speech and offensive language on social media platforms such as Twitter has significant consequences, ranging from psychological harm to the polarization of societies. Consequently, social media companies have implemented content moderation measures to curb harmful or discriminatory language. However, a lack of consistency and transparency hinders their ability to achieve desired outcomes. This article evaluates various ML models, including an ensemble, Explainable Boosting Machine (EBM), and Linear Support Vector Classifier (SVC), on a public dataset of 24,792 tweets by T. Davidson, categorizing tweets into three classes: hate, offensive, and neither. The top-performing model achieves a weighted F1-Score of 0.90. Furthermore, this article interprets the output of the best-performing model using LIME and SHAP, elucidating how specific words and phrases within a tweet contextually impact its classification. This analysis helps to shed light on the linguistic aspects of hate and offense. Additionally, we employ LIME to present a suggestive counterfactual approach, proposing no-hate alternatives for a tweet to further explain the influence of word choices in context. Limitations of the study include the potential for biased results due to dataset imbalance, which future research may address by exploring more balanced datasets or leveraging additional features. Ultimately, through these explanations, this work aims to promote digital literacy and foster an inclusive online environment that encourages informed and responsible use of digital technologies (A GitHub repository containing code, data, and pre-trained models is available at: https://github.com/DeedahwarMazhar/XAI-Counterfactual-Hate-Speech ).
A Hybrid Scheme for Efficient Deformable Image Registration

Deformable image registration (DIR) is one of the important processing steps for image analysis in the compute vision field. Although various methods have been proposed to deal with the DIR problem, it is still challenging to achieve a good balance between the registration speed and accuracy. This paper introduces a novel hybrid registration model which is capable of fast and accurately warping a target image to a reference image. This achievement owns to a coarse-to-fine registration scheme, where an automatic thin plate splines-based (A-Tps) method is proposed for preliminary registration, then an improved inertia Demons (I-Demons) algorithm is developed for fine warping the result of previous output. Experimental results demonstrate the outstanding performance of the proposed model in terms of registration speed and accuracy.
Relational Alignment and Distance Optimization for Cross-Modality Person Re-identification

Cross-modality person re-identification (VI-ReID) is a challenging pedestrian retrieval problem, where the two main challenges are intra-class differences and cross-modality differences between visible and infrared images. To address these issues, many state-of-the-art methods attempt to learn coarse image alignment or part-level person features, however, it is often limited by the effects of intra-identity variation and image alignment is not always good. In this paper, to overcome these two shortcomings, a relational alignment and distance optimization network (RADONet) is constructed. Firstly, we design a cross-modal relational alignment (CM-RA) that exploits the correspondence between cross-modal images to handle cross-modal differences at the pixel level. Secondly, we propose a cross-modal Wasserstein Distance (CM-WD) to mitigate the effects of intra-identity variation in modal alignment. In this way, our network is able to overcome the effects of identity variations by focusing on reducing inter-modal differences and performing more effective feature alignment. Extensive experiments show that our method outperforms state-of-the-art methods on two challenging datasets, with improvements of 3.39% and 2.06% on the SYSU-MM01 dataset for Rank-1 and mAP, respectively.
Full Resolution Repetition Counting

Given an untrimmed video, repetitive actions counting aims to estimate the number of repetitions of class-agnostic actions. To handle the various length of videos and repetitive actions, also optimization challenges in end-to-end video model training, down-sampling is commonly utilized in recent state-of-the-art methods, leading to ignorance of several repetitive samples. In this paper, we attempt to understand repetitive actions from a full temporal resolution view, by combining offline feature extraction and temporal convolution networks. The former step enables us to train repetition counting network without down-sampling while preserving all repetitions regardless of the video length and action frequency, and the later network models all frames in a flexible and dynamically expanding temporal receptive field to retrieve all repetitions with a global aspect. Besides, temporal self-similarity matrix is used in our model to represent the correlation of action, which contains much cycle information in time series. We experimentally demonstrate that our method achieves better or comparable performance in three public datasets, i.e., TransRAC, UCFRep and QUVA. We expect this work will encourage our community to think about the importance of full temporal resolution.
A Case Study on Learning Experience through Living Lab Participation of Adults with Disabilities

Objectives The purpose of this study is to explore the learning experiences of adults with disabilities through their participation in living lab activities.&#x0D; Methods The site that this study paid attention to is a welfare center for the disabled in Daejeon Metropolitan City, which conducted the citizen-participating social problem-solving living lab project in 2020. The study was con-ducted over a year from March 2020 to February 2021, with a total of 14 participants, including 2 people with phys-ical disabilities, 3 people with developmental disabilities, 5 social workers, 2 parents with disabilities, and 2 assis-tants with disabilities. Based on the participants' experiences, the qualitative case study method was based on an in-depth understanding of the research topic. The retrospective records of participants in the collected Living Lab reports and results reports were referenced, and line-by-line analysis was conducted by repeatedly reading individual in-depth interview data for each person with physical disabilities, developmental disabilities, and social workers. Various efforts were made to comply with research ethics, such as completing research ethics educa-tion, preparing prior consent forms for research participants, reviewing research results, and discussing experts.&#x0D; Results As a result of the analysis, the learning experience that occurred in the process of the disabled becoming the main body and solving their problems on their own through living lab activities was found, which contributed to finding meaning and reward, improving self-directedness, and improving efficacy.&#x0D; Conclusions According to the results of these studies, it was found that people with disabilities, such as Living Lab activities, need to make continuous efforts to speak out and expand opportunities to participate in social activities. The study's findings show that learning experiences through living lab activities contribute to the ac-quisition of subjectivity and initiative of the disabled, and that the participation of the disabled in living lab activities needs to be actively utilized in a new way of lifelong education. Based on this, discussions and follow-up studies were proposed.
Towards the Deployment of Knowledge Based Systems in Safety-Critical Systems

At Thales, we studied the use of Knowledge-Based System (KBS) to create a crew-assistant, inserted inside the safety-critical cockpit systems. Developing a KBS as a safety-critical system induced new needs such as a high amount of verification activities or a bounded reasoning time. This paper aims at presenting our needs and related new challenges to the scientific community.
Entity Typing with Triples Using Language Models

Entity Typing is the task of assigning a type to an entity in a knowledge graph. In this paper, we propose ETwT (Entity Typing with Triples), which leverages the triples of an entity, namely its label, description and the property labels used on it. We analyse which language models and classifiers are best suited to this input and compare ETwT’s performance on coarse-grained and fine-grained entity typing. Our evaluation demonstrates that ETwT is able to predict coarse-grained entity types with an F $$_1$$ score of 0.994, outperforming three baselines.
VRKG-CollaborativeExploration - Data-Driven Discussions in the Metaverse

The metaverse is an immersive, multi-user, virtual world where humans, represented by avatars, can entertain, socialize, and collaborate. This demo proposes VRKG-CollaborativeExploration, a Virtual Reality application that enables users to join thematic and collaborative data-driven discussions in the social metaverse where data are retrieved from Knowledge Graphs via SPARQL queries.
A Distributed and Parallel Processing Framework for Knowledge Graph OLAP

Business intelligence and analytics refers to the ensemble of tools and techniques that allow organizations to obtain insights from big data for better decision making. Knowledge graphs are increasingly being established as a central data hub and prime source for BI and analytics. In the context of BI and analytics, KGs may be used for various analytical tasks; the integration of data and metadata in a KG potentially facilitates interpretation of analysis results. Knowledge Graph OLAP (KG-OLAP) adapts the concept of online analytical processing (OLAP) from multidimensional data analysis for the processing of KGs for analytical purposes. The current KG-OLAP implementation is a monolithic system, which greatly inhibits scalability. We propose a research plan for the development of a framework for distributed and parallel data processing for KG-OLAP over big data. In particular, we propose a framework for KG-OLAP over big data based on the data lakehouse architecture, which leverages existing frameworks for parallel and distributed data processing. We are currently at an early stage of our research.
Modeling Grammars with Knowledge Representation Methods: Subcategorization as a Test Case

An OWL ontology is used to model a grammar that accounts for subcategorization, showing that ontologies are able to generate (mildly) context-sensitive languages. Semantic Web knowledge representation methods offer a useful way to model the implicit knowledge that defines human linguistic abilities. When a grammar is modeled as a set of ontological constraints (i.e. classes with restrictions on their properties), ungrammatical sentences are defined as facts that lead to inconsistencies which can be discovered by a reasoner. Property chains are used to “pass on” the category of a syntactic complement as the value of a head’s subcategorization feature, modeling the concept of structure sharing that is central to constraint-based theories of syntax like HPSG. By treating utterances as instances and syntactic constraints as axioms, this approach offers points of contact with efforts to model grammars as Linguistic Linked Open Data in the Semantic Web.
Optimisation of Link Traversal Query Processing over Distributed Linked Data through Adaptive Techniques

An increasing amount of distributed Linked Data is being made available at different locations, with varying formats, structures, interfaces and availability. Making use of that data through declarative query languages such as SPARQL requires query engines capable of executing queries over it. Efficiently executing queries over the data requires efficient query plans, yet prior access to the information for producing such plans may not be possible due to the distributed and dynamic nature of the data. Furthermore, the inability to be aware of all data sources at a given time, following links to discover data in the form of link traversal may be needed. Consequently, query planning and optimisation may need to be performed with limited information, and the initial plan may no longer be optimal. Discovering additional information and data sources during query execution and adjusting the execution based on such discoveries using adaptive query processing techniques therefore could help perform queries more efficiently. The aim of this work is to explore a variety of existing or potential new techniques and their combinations for query-relevant information acquisition and query plan adaptation within the context of distributed Linked Data. Already prior results from multiple studies have demonstrated the benefits of various such techniques within or beyond Linked Data and Link Traversal Query Processing, and this work seeks to build upon such results to realise the benefits of various techniques in practice to tackle performance-related challenges.
Automating Benchmark Generation for Named Entity Recognition and Entity Linking

Named Entity Recognition (NER) and Linking (NEL) have seen great advances lately, especially with the development of language models pre-trained on large document corpora, typically written in the most popular languages (e.g., English). This makes NER and NEL tools for other languages, with fewer resources available, fall behind the latest advances in AI. In this work, we propose an automated benchmark data generation process for the tasks of NER and NEL, based on Wikipedia events. Although our process is applied and evaluated on Greek texts, the only requirement for its applicability to other languages is the availability of Wikipedia events pages in that language. The generated Greek datasets, comprising around 19k events and 41k entity mentions, as well as the code to generate such datasets, are publicly available.
Study-Buddy: A Knowledge Graph-Powered Learning Companion for School Students

Large Language Models (LLMs) have the potential to substantially improve educational tools for students. However, they face limitations, including factual accuracy, personalization, and the lack of control over the sources of information. This paper presents Study-Buddy, a prototype of a conversational AI assistant for school students to address the above-mentioned limitations. Study-Buddy embodies an AI assistant based on a knowledge graph, LLMs models, and computational persuasion. It is designed to support educational campaigns as a hybrid AI solution. The demonstrator showcases interactions with Study-Buddy and the crucial role of the Knowledge Graph for the bot to present the appropriate activities to the students. A video demonstrating the main features of Study-Buddy is available at: https://youtu.be/DHPTsN1RI9o .
A Source-Agnostic Platform for Finding and Exploring Ontologies at Bosch

The usage of semantic technologies for purposes like data integration, information retrieval, search, and decision-making is steadily on the rise.
On the Problem of Automatically Aligning Indicators to SDGs

In this paper we present a first approach to the application of transformer-based language models to the automatic alignment to sustainable development goals (SDGs). This task is quite relevant for the development of new tools that aim at measuring the engagement degree of the organization’s indicators to the SGDs. Our first experiments show that this task is hard, and that even powerful large language models do not achieve a high accuracy as in other NLP tasks.
Wisdom of the Sellers: Mining Seller Data for eCommerce Knowledge Graph Generation

Query understanding is a fundamental part of an e-commerce search engine, and it is crucial for correctly identifying the buyer intent. To perform a semantic query understanding, in this work we introduce a relationship-rich product Knowledge Graph (KG), mined from seller provided data, which captures entities and relationships to model the whole product inventory, allowing us to identify the buyer intent more accurately.
Semantic Parsing for Knowledge Graph Question Answering with Large Language Models

This thesis explores the topic of Knowledge Graph Question Answering with a special emphasis on semantic parsing approaches, incorporating pre-trained text-to-text language models. We use the text generation ability of these models to convert natural language questions to logical forms. We test whether correct logical forms are being generated, and if not, how to mitigate the failure cases. As a second step, we try to make the same models generate additional information to aid the process of grounding of the logical forms to entities, relations and literals in the Knowledge Graph. In experiments conducted so far, we see encouraging results on both generation of base logical forms, and grounding them to the KG elements. At the same time, we discover failure cases prompting directions in future work (The author considers himself a ‘middle-stage’ Ph.D. candidate).