Miracle-dz/UniFAR-FTUs huggingface dataset:
VCP increases or decreases tau seeding using specific cofactors

Background Neurodegenerative tauopathies may progress based on seeding by pathological tau assemblies, whereby an aggregate is released from one cell, gains entry to an adjacent or connected cell, and serves as a specific template for its own replication in the cytoplasm. In vitro seeding reactions typically take days, yet seeding into the complex cytoplasmic milieu can happen within hours. A cellular machinery might regulate this process, but potential players are unknown. Methods We used proximity labeling to identify factors that control seed amplification. We fused split-APEX2 to the C-terminus of tau repeat domain (RD) to reconstitute peroxidase activity upon seeded intracellular tau aggregation. We identified valosin containing protein (VCP/p97) 5h after seeding. Mutations in VCP underlie two neurodegenerative diseases, multisystem proteinopathy and vacuolar tauopathy, but its mechanistic role is unclear. We utilized tau biosensors, a cellular model for tau aggregation, to study the effects of VCP on tau seeding. Results VCP knockdown reduced tau seeding. However, distinct chemical inhibitors of VCP and the proteasome had opposing effects on aggregation, but only when given <8h of seed exposure. ML-240 increased seeding efficiency ∼40x, whereas NMS-873 decreased seeding efficiency by 50%, and MG132 increased seeding ∼10x. We screened VCP co-factors in HEK293 biosensor cells by genetic knockout or knockdown. Reduction of ATXN3, NSFL1C, UBE4B, NGLY1, and OTUB1 decreased tau seeding, as did NPLOC4, which also uniquely increased soluble tau levels. Reduction of FAF2 and UBXN6 increased tau seeding. Conclusions VCP uses distinct cofactors to determine seed replication efficiency, consistent with a dedicated cytoplasmic processing complex that directs seeds towards dissolution vs. amplification.
"Is There Anything Else I Can Help You With?" Challenges in Deploying an On-Demand Crowd-Powered Conversational Agent

Intelligent conversational assistants, such as Apple's Siri, Microsoft's Cortana, and Amazon's Echo, have quickly become a part of our digital life. However, these assistants have major limitations, which prevents users from conversing with them as they would with human dialog partners. This limits our ability to observe how users really want to interact with the underlying system. To address this problem, we developed a crowd-powered conversational assistant, Chorus, and deployed it to see how users and workers would interact together when mediated by the system. Chorus sophisticatedly converses with end users over time by recruiting workers on demand, which in turn decide what might be the best response for each user sentence. Up to the first month of our deployment, 59 users have held conversations with Chorus during 320 conversational sessions. In this paper, we present an account of Chorus' deployment, with a focus on four challenges: (i) identifying when conversations are over, (ii) malicious users and workers, (iii) on-demand recruiting, and (iv) settings in which consensus is not enough. Our observations could assist the deployment of crowd-powered conversation systems and crowd-powered systems in general.
Formal Concept Analysis: Mathematical Foundations

From the Publisher: This is the first textbook on formal concept analysis. It gives a systematic presentation of the mathematical foundations and their relation to applications in computer science, especially in data analysis and knowledge processing. Above all, it presents graphical methods for representing conceptual systems that have proved themselves in communicating knowledge. Theory and graphical representation are thus closely coupled together. The mathematical foundations are treated thoroughly and illuminated by means of numerous examples.
Reduction algorithms for the multiband imaging photometer for Spitzer: 6 months of flight data

The first six months of flight data from the Multiband Imaging Photometer for Spitzer (MIPS) were used to test MIPS reduction algorithms based on extensive preflight laboratory data and modeling. The underlying approach for the preflight algorithms has been found to be sound, but some modifications have improved the performance. The main changes are scan mirror dependent flat fields at 24 μm, hand processing to remove the time dependent stim flash latents and fast/slow response variations at 70 μm, and the use of asteroids and other sources instead of stars for flux calibration at 160 μm due to a blue "leak. " The photometric accuracy of flux measurements is currently 5%, 10%, and 20% at 24, 70, and 160 μm, respectively. These numbers are expected to improve as more flight data are analyzed and data reduction algorithms refined.
HRV and perceived stress role in the persuasiveness of health communications

Health communications have the purpose to inform about health risks or more sustainable lifestyles. For that aim, persuasion is employed in the attempt to produce a change in a person’s attitudes or behavior. Literature on persuasion has focused on various individual differences, however, the study about how differences in perceived stress moderate the processing of health communication is scarce [1]. Perceived stress is a subjective measure of the degree to which situations in one’s life are considered stressful. People with high levels of stress seem to have difficulties processing threatening health information such as disease detection, contrary to communications that promote healthy behaviors [2]. In the last decades, the study of heart rate variability (HRV) has become of increasing interest because of its power to clarify the relationship between different psychological and physiological processes [3]. Previous research had reported the predictive value of HRV in aspects such as attentional control and emotional regulation. Higher levels of HRV are considered beneficial in mitigating problematic situations [4], while low HRV has been associated with impaired regulatory mechanisms, which in turn reduce the ability to cope with stressors. Based on the above and through a within-subject design, the present study aimed to examine which of three persuasive strategies is perceived as more persuasive: Ethos (relies on the source's authority), Pathos (appeals to emotions), or Logos (alludes to the argument's logic). Also, we aimed to evaluate the predictive role of HRV and perceived stress in the perceived persuasiveness of messages about the limit of meat consumption
LMU at HaSpeeDe3: Multi-Dataset Training for Cross-Domain Hate Speech Detection

We describe LMU Munich’s hate speech detection system for participating in the cross-domain track of the HaSpeeDe3 shared task at EVALITA 2023. The task focuses on the politics and religion domains, having no in-domain training data for the latter. Our submission combines multiple training sets from various domains in a multitask prompt-training system. We experimented with both Italian and English source datasets as well as monolingual Italian and multilingual pre-trained language models. We found that the Italian out-of-domain datasets are the most influential on the performance in the test domains and that combining both monolingual and multilingual language models using an ensemble gives the best results. Our system ranked second in both domains.
WiC-ITA at EVALITA2023: Overview of the EVALITA2023 Word-in-Context for ITAlian Task

WiC-ita is a shared task proposed at the EVALITA 2023 campaign. The task focuses on the meaning of words in specific contexts and has been modelled as both a binary classification and a ranking problem. Overall, 4 groups took part in both subtasks, with 9 different runs. In this report, we describe how the task was set up, we report the system results, and we discuss them.
A Novel Algorithm for Counting All Common Subsequences

A similarity measure is key to any distance-based data analysis task, e.g., clustering, classification and case-based reasoning. The choice of similarity measure depends on the type of data used in a data analysis task. For sequence data the well known similarity measures include sequence edit distance and longest common subsequence. A new measure of sequence similarity, all common subsequence (ACS), is recently proposed. ACS uses the number of common subsequences between a pair of sequences as a measure of similarity. The more common subsequences there are between them, the more similar they are to each other. A dynamic programming algorithm is presented in H. Wang (2007) to compute ACS, which we call calACS-DP. In this paper we analyze the complexities of this algorithm in time and space, which are both quadratic in the length of sequence. Based on this analysis we present a novel, more efficient algorithm to compute ACS, which we call calACS. This algorithm is derived from an algorithm presented in H. Wang and C. Liu, (2006). The space complexity of this algorithm is O(min{|A|, |B|} + 1) and the time complexity of this algorithm is O(|A| times |B|) for any two sequences A and B. We further illustrate this algorithm with an example.
Robotic Process Automation: Exploring the Technological and Organizational Dimensions for Successful Implementation in Organizations

This thesis proposes a case study focusing on Robotic Process Automation (RPA) in the context of digital transformation. The study aims to address RPA from both technological and organizational perspectives. From a technological viewpoint, task selection is a crucial first step in RPA. In that context, process mining (PM) is a key technique for identifying suitable tasks, and this research aims to study how PM can aid in systematically selecting tasks for RPA. From an organizational perspective, RPA projects require organizational support to overcome resistance and achieve employee commitment. However, there is limited literature addressing RPA from a strategic management perspective. This study seeks to explore the barriers and levers organizations encounter during RPA implementation from a strategic management viewpoint, using McKinsey’s 7S framework. The research methodology involves a mixed approach. For the technological perspective, a quantitative case study will be conducted to apply PM process discovery techniques. For the organizational perspective, qualitative research with semi-structured interviews based on the 7S framework will be employed. The case study will be carried out in collaboration with a Spanish regional bank experienced in RPA implementation. The proposed solution aims to bridge the gap in RPA literature by addressing the challenges in task selection using PM techniques and exploring RPA deployment from a strategic management perspective. The innovative mixed methodology may open new research directions linking strategic management and RPA deployment.
A New Evolutive Generator for Graphs with Communities and its Application to Abstract Argumentation

Graph generators are a powerful tool to provide benchmarks for various subfields of KR (e.g. abstract argumentation, description logics, etc.) as well as other domains of AI (e.g. resources allocation, gossip problem, etc.). In this paper, we describe a new approach for generating graphs based on the idea of communities, i.e. parts of the graph which are densely connected, but with fewer connections between different communities. We discuss the design of an application named crusti_g2io implementing this idea, and then focus on a use case related to abstract argumentation. We show how crusti_g2io can be used to generate structured hard argumentation instances which are challenging for the fourth International Competition on Computational Models of Argumentation (ICCMA’21) solvers.
EMit at EVALITA 2023: Overview of the Categorical Emotion Detection in Italian Social Media Task

The Emotions in Italian (EMit) task is the first edition of a shared task on emotion analysis and opinion mining in Italian messages at EVALITA 2023. EMit presents two subtasks: (i) Subtask A, that consists in an emotion detection challenge, and (ii) Subtask B, that introduces a novel problem of target detection of the expressed emotion. Additionally, EMit challenges systems with a thorough in-domain and out-of-domain evaluation, probing the generalization capabilities of the submitted solutions. In general, 4 teams have participated in Subtask A, achieving a macro-averaged f-score of 0.6028 and 0.4977 in the in-domain and out-of-domain sets, respectively. In Subtask B a team has participated, obtaining 0.6459 in the in-domain set and 0.3223 in the out-of-domain set as macro-averaged f-scores. The obtained results indicate that further work needs to be done to solve the task, opening new avenues of research.
Stable Semantics for Epistemic Abstract Argumentation Framework

Dung’s Abstract Argumentation Framework (AAF) has emerged as a central formalism in AI for modeling disputes among agents. A recent extension of the Dung’s framework is the so-called Epistemic Abstract Argumentation Framework (EAAF), which enhances AAF by allowing the representation of some pieces of epistemic knowledge [1]. EAAF generalizes the concept of attack in AAF, introducing strong and weak epistemic attacks , whose intuitive meaning is that an attacked argument is epistemically accepted only if the attacking argument is possibly or certainly rejected, respectively. The semantics of EAAF has been de � ned and studied for several argumentation semantics but not for the stable one, which is arguably one of the most investigated semantics in argumentation. Motivated by this, in this paper, we propose an intuitive stable semantics for EAAF that naturally extends that for AAF and coincides with the preferred semantics in the case of odd-cycle free EAAFs (analogously to what happens in the case of AAF). We analyze the complexity of two argumentation problems: existence , i.e. checking whether there is at least one epistemic extension; and acceptance , i.e. checking whether an argument is epistemically accepted.
ExtremITA at EVALITA 2023: Multi-Task Sustainable Scaling to Large Language Models at its Extreme

This paper explores the potential application of a monolithic neural model for all tasks in EVALITA 2023. We evaluated two models: extremIT5 , an encoder-decoder model, and extremITLLaMA an instruction-tuned Decoder-only Large Language Model, specifically designed for handling Italian instructions. Our approach revolves around representing tasks in natural language, where we provide instructions to the model using prompts that define the expected responses. Remarkably, our best-performing model achieved first place in 41% of the subtasks and showcased top-three performance in 64%. These subtasks encompass various semantic dimensions, including Affect Detection, Authorship Analysis, Computational Ethics, Named Entity Recognition, Information Extraction, and Discourse Coherence.
Formation of Na-containing complex molecules in the gas phase in dense molecular clouds: quantum study of the Na+ + H2 and Na+ + D2 radiative association step

The Na + � H 2 radiative association (RA) is the ﬁrst and limiting step in the models of formation of NaH in the gas phase. Also the accurate evaluation of the RA rate is required to be included in chemical models of dense molecular clouds. In this work a quantum study of the formation of the Na + � H 2 and Na + � D 2 complexes by radiative association is presented. We use a 3D potential energy surface published recently and a quantum dynamical method based on the driven equation formalism. The values obtained for the radiative association rates are compared with previous evaluations based on approximate methods and the possible role played by these two processes in the gas phase chemistry of dense molecular clouds is discussed.
O-Dang at HODI and HaSpeeDe3: A Knowledge-Enhanced Approach to Homotransphobia and Hate Speech Detection in Italian

This paper describes our methods implemented during the EVALITA 2023 campaign for homotransphobia (HODI task) and hate speech detection (HaSpeeDe3 task) in Italian. We present three knowledge-enhanced approaches, namely via triple verbalisation, via prompting and via a majority vote, and we compare them to the AlBERTo baseline. These systems leverage the knowledge graph O-Dang, which contains information about named entities in Italian dangerous speech. Our knowledge-enhanced systems outperformed all the competition’s baselines. Our best submissions achieved the macro-F1 score of 0.912 for HaSpeeDe3 and 0.795 for HODI, reaching the 1st and 3rd place, respectively. These results were achieved by using our baseline for HODI, and a majority voting approach for HaSpeeDe3.1
Augmented Reality Visual Effects for Mitigating Anxiety of In-person Communication for Individuals with Social Anxiety Disorder

Individuals with social anxiety disorder (SAD) experience heightened anxiety in in-person communication, particularly when they perceive negative evaluations from their conversation partner based on eye gaze and facial expressions. To address this issue, this study developed an augmented reality (AR) system for in-person conversations, capable of concealing the presence and masking the facial emotions of the conversation partner. An online survey was conducted using Amazon Mechanical Turk (n = 130) to examine the visual effects that individuals with SAD find anxiety-inducing during in-person conversations. 91 out of them have a tendency for SAD. For the aspect of "ease to talk", individuals with SAD achieved significantly higher scores for the visual effects involving a smiley face or anime avatar overlaid onto the conversation partner, as opposed to the remaining visual effects (p < .05). Based on these findings, we developed two prototypes wherein an AR head-mounted display (HMD) overlays either a smiley face or an anime avatar onto the conversation partner during an in-person conversation. A small-scale user study was conducted to investigate participant’s ability to interact with a conversation partner, using these visual effects, as well as their subjective experiences. The results were further analyzed through user interviews. The social anxiety levels of participants were measured using the Liebowitz Social Anxiety Scale. Participants with SAD exhibited a preference for communicating with an anime avatar rather than real individuals, while participants without SAD preferred real-person interactions. These findings inform future research, wherein the objective (heart rate, eye tracking, self-disclosure) and quantitative data (questionnaires) will be utilized to evaluate anxiety levels during in-person conversations.
Collaborate and Explain on the Fly: Nonmonotonic Logical Reasoning and Incremental Learning for Ad Hoc Teamwork

This paper describes an architecture for ad hoc teamwork, i.e., to enable an agent to collaborate with other agents “on the fly". State of the art frameworks for ad hoc teamwork often pursue a data-driven approach, using a large labeled dataset of prior observations to model the behavior of other agents and to determine the ad hoc agent’s behavior. These models are computationally expensive to learn, lack transparency, and make it difficult to recognize and adapt to previously unseen changes. In a departure from existing work, we introduce an architecture for ad hoc teamwork that performs non-monotonic logical reasoning with prior commonsense domain knowledge and models that are learned and revised rapidly from limited examples to predict the behavior of other agents. In addition, the architecture enables the agent to provide relational descriptions as on-demand explanations of its decisions and beliefs in response to different types of questions. We evaluate the architecture’s capabilities in two benchmark multiagent collaboration domains: Fort Attack and Half field Offense, in comparison with data-driven and knowledge-driven baselines.
DisCoTex at EVALITA 2023: Overview of the Assessing DIScourse COherence in Italian TEXts task

The Assessing DIScourse COherence in Italian TEXts (DisCoTEX) task is the first shared task focused on modelling discourse coherence for Italian real-word texts, which has been proposed for the first time at EVALITA 2023. Providing two different datasets from different textual genres, we arranged the task into two independent tasks: a more traditional one, aimed at evaluating whether models are able to distinguish well-organized documents from corrupted ones and a less explored one, which assesses the models’ performance on texts evaluated for coherence by human raters. In this paper, we describe the datasets released, we discuss the different approaches tackled by the participating systems and provide a first analysis of the obtained results.
MULTI-Fake-DetectiVE at EVALITA 2023: Overview of the MULTImodal Fake News Detection and VErification Task

This paper introduces the MULTI-Fake-DetectiVE shared task for the EVALITA 2023 campaign. The task was aimed at exploringmultimodalitywithintherealmoffakenewsandintendedtoaddresstheproblemfromtwoperspectives, represented by the two sub-tasks. In sub-task 1, we aimed to evaluate the effectiveness of multimodal fake news detection systems. In sub-task 2, we sought to gain insights into the interplay between text and images, specifically how they mutually influence the interpretation of content in the context of distinguishing between fake and real news. Both perspectives were framed as classification problems. The paper presents an overview of the task. In particular, we detail the key aspects of the task, including the creation of a new dataset for fake news detection in Italian, the evaluation methodology and criteria, the participant systems, and their results. In light of the obtained results, we argue that the problem is still open and propose some future directions.
UMUTeam at PoliticIT-EVALITA2023: Evaluating Transformer Model for Detecting Political Ideology in Italian Texts (short paper)

This paper describes the participation of the UMUTeam in the PoliticIT shared task organized at EVALITA 2023. It is an automatic document classification task on clusters of texts, which consists of extracting self-assigned gender as a demographic trait, and ideology as a psychographic trait through a set of texts written in Italian by several authors sharing these traits. For this task, we used the fine-tuning approach of a pre-trained transformer-based masked language model for Italian called dbmdz/bert-base-italian-cased to carry out the identification of different features. After several submissions for these tasks, our team ranked sixth out of 7 participants, with an average F1 score of 70.426% of all classification models. However, our binary political ideology classification model obtained the fourth-best result with an F1 score of 86.63%.
Mobile terrestrial laser scanning in urban tree inventory.

In this study we evaluated an accuracy of mobile terrestrial laser scanning (MLS) measurements in urban tree inventory. The MLS data were collected in August 2010 with the FGI Roamer mobile mapping system, consisting of a Faro LS 880 laser scanner and a NovAtel HG1700 SPAN58 INS system mounted in a car. Study areas were divided to park and urban forest located in Seurasaari, Helsinki, Finland. Studied inventory characteristics were tree location and –diameter-at-breast-height (dbh). Reference measurements consisted altogether from 201 trees, which locations were measured using RTK GPS and static terrestrial laser scanning while tree dbhs were measured using steel callipers. Tree mapping and dbh measurements were done from the MLS point clouds manually. Tree location accuracy of MLS measurements were 0.72 m in park and 0.47 cm in forest conditions as root mean squared error in dbh were 7.0% and 12.5%, respectively. MLS can be used in urban tree inventory in targets with a good visibility and relatively dense network of roads or paths.