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The use of social media has become a regular habit for many and has changed the way people interact with each other. In this article, we focus on analyzing whether news headlines support tweets and whether reviews are deceptive by analyzing the interaction or the influence that these texts have on the others, thus exploiting contextual information. Concretely, we define a deep learning method for relation—based argument mining to extract argumentative relations of attack and support. We then use this method for determining whether news articles support tweets, a useful task in fact-checking settings, where determining agreement towards a statement is a useful step towards determining its truthfulness. Furthermore, we use our method for extracting bipolar argumentation frameworks from reviews to help detect whether they are deceptive. We show experimentally that our method performs well in both settings. In particular, in the case of deception detection, our method contributes a novel argumentative feature that, when used in combination with other features in standard supervised classifiers, outperforms the latter even on small data sets. [ABSTRACT FROM AUTHOR]

Different semantics of abstract Argumentation Frameworks (AFs) provide different levels of decisiveness for reasoning about the acceptability of conflicting arguments. The stable semantics is useful for applications requiring a high level of decisiveness, as it assigns to each argument the label “accepted” or the label “rejected”. Unfortunately, stable labellings are not guaranteed to exist, thus raising the question as to which parts of AFs are responsible for the non-existence. In this paper, we address this question by investigating a more general question concerning preferred labellings (which may be less decisive than stable labellings but are always guaranteed to exist), namely why a given preferred labelling may not be stable and thus undecided on some arguments. In particular, (1) we give various characterisations of parts of an AF, based on the given preferred labelling, and (2) we show that these parts are indeed responsible for the undecisiveness if the preferred labelling is not stable. We then use these characterisations to explain the non-existence of stable labellings. We present two types of characterisations, based on labellings that are more (or equally) committed than the given preferred labelling on the one hand, and based on the structure of the given AF on the other, and compare the respective AF parts deemed responsible. To prove that our characterisations indeed yield responsible parts, we use a notion of enforcement of labels through structural revision, by means of which the preferred labelling of the given AF can be turned into a stable labelling of the structurally revised AF. Rather than prescribing how this structural revision is carried out, we focus on the enforcement of labels and leave the engineering of the revision open to fulfil differing requirements of applications and information available to users. [ABSTRACT FROM AUTHOR]

Arguments in structured argumentation are usually defined as trees, and extensions as sets of such tree-based arguments with various properties depending on the particular argumentation semantics. However, these arguments and extensions may have redundancies as well as circularities, which are conceptually and computationally undesirable. Focusing on the specific case of Assumption-Based Argumentation (ABA), we propose novel notions of arguments and admissible/grounded extensions, both defined in terms of graphs. We show that this avoids the redundancies and circularities of standard accounts, and set out the relationship to standard tree-based arguments and admissible/grounded extensions (as sets of arguments). We also define new notions of graph-based admissible/grounded dispute derivations for ABA, for determining whether specific sentences hold under the admissible/grounded semantics. We show that these new derivations are superior with respect to standard dispute derivations in that they are complete in general, rather than solely for restricted classes of ABA frameworks. Finally, we present several experiments comparing the implementation of graph-based admissible/grounded dispute derivations with implementations of standard dispute derivations, suggesting that the graph-based approach is computationally advantageous. [ABSTRACT FROM AUTHOR]

We propose a formal model for argumentation-based dialogues between agents, using assumption-based argumentation (ABA) as the underlying argumentation framework. Thus, the dialogues amount to conducting an argumentation process in ABA. The model is given in terms of ABA-specific utterances, debate trees and forests implicitly built during and drawn from dialogues, legal-move functions (amounting to protocols) and outcome functions. The model is generic in that it is not restricted to any specific dialogue types and can be used to support a wide range thereof. We prove a formal connection between dialogues and three well-known argumentation semantics (i.e. grounded, admissible and ideal extensions), by giving soundness results for our dialogue models with respect to these semantics. Thus, our dialogues can be seen as a distributed mechanism for successfully determining acceptability of claims (with respect to the semantics considered), while constructing argumentation frameworks and arguments for these claims. [ABSTRACT FROM AUTHOR]

Abstract: Assumption-based argumentation is a general-purpose argumentation framework with well-understood theoretical foundations and viable computational mechanisms (in the form of dispute derivations), as well as several applications. However, the existing computational mechanisms have several limitations, hindering their deployment in practice: (i) they are defined in terms of implicit parameters, that nonetheless need to be instantiated at implementation time; (ii) they are variations (for computing different semantics) of one another, but still require different implementation efforts; (iii) they reduce the problem of computing arguments to the problem of computing assumptions supporting these arguments, even though applications of argumentation require a justification of claims in terms of explicit arguments and attacks between them. In this context, the contribution of this paper is two-fold. Firstly, we provide a unified view of the existing (GB-, AB- and IB-)dispute derivations (for computation under the grounded, admissible and ideal semantics, respectively), by obtaining them as special instances of a single notion of X-dispute derivations that, in addition, renders explicit the implicit parameters in the original dispute derivations. Thus, X-dispute derivations address issues (i) and (ii). Secondly, we define structured X-dispute derivations, extending X-dispute derivations by computing explicitly the underlying arguments and attacks, in addition to assumptions. Thus, structured X-dispute derivations also address issue (iii). We prove soundness and completeness results for appropriate instances of (structured) X-dispute derivations, w.r.t. the grounded, admissible and ideal semantics, thus laying the necessary theoretical foundations for deployability thereof. [Copyright &y& Elsevier]

In symptomatic buttock arteriovenous malformations (AVM), embolization techniques and surgical resection are suggested treatment options. Our aim was to evaluate the feasibility and the long term results after single surgical resection. Twelve patients underwent surgical resection without pre-operative embolization. Eleven had incomplete procedures, including nine arterial embolizations (1 to 3 years ago). All were symptomatic, and CT scans showed AVM located in the cellular spaces with preservation of gluteal muscles. Median follow-up time was 80 months. On the last follow-up CT scan, 67% had no residual AVM. Pre-operative embolization, particularly with non-resorbable embolic material(Onyx), makes AVM resection and imaging follow-up more difficult (artifact) and should be avoided. [ABSTRACT FROM AUTHOR]

Introduction: Clinical decision support (CDS) systems (CDSSs) that integrate clinical guidelines need to reflect real-world co-morbidity. In patient-specific clinical contexts, transparent recommendations that allow for contraindications and other conflicts arising from co-morbidity are a requirement. In this work, we develop and evaluate a non-proprietary, standards-based approach to the deployment of computable guidelines with explainable argumentation, integrated with a commercial electronic health record (EHR) system in Serbia, a middle-income country in West Balkans. Methods: We used an ontological framework, the Transition-based Medical Recommendation (TMR) model, to represent, and reason about, guideline concepts, and chose the 2017 International global initiative for chronic obstructive lung disease (GOLD) guideline and a Serbian hospital as the deployment and evaluation site, respectively. To mitigate potential guideline conflicts, we used a TMR-based implementation of the Assumptions-Based Argumentation framework extended with preferences and Goals (ABA+G). Remote EHR integration of computable guidelines was via a microservice architecture based on HL7 FHIR and CDS Hooks. A prototype integration was developed to manage chronic obstructive pulmonary disease (COPD) with comorbid cardiovascular or chronic kidney diseases, and a mixed-methods evaluation was conducted with 20 simulated cases and five pulmonologists. Results: Pulmonologists agreed 97% of the time with the GOLD-based COPD symptom severity assessment assigned to each patient by the CDSS, and 98% of the time with one of the proposed COPD care plans. Comments were favourable on the principles of explainable argumentation; inclusion of additional co-morbidities was suggested in the future along with customisation of the level of explanation with expertise. Conclusion: An ontological model provided a flexible means of providing argumentation and explainable artificial intelligence for a long-term condition. Extension to other guidelines and multiple co-morbidities is needed to test the approach further. [ABSTRACT FROM AUTHOR]

Background. Our aim was to evaluate the benefit of surgical resection of the venous malformation (VM) of the external female genitalia. Methods. Over the period of 2009-2019, 18 consecutive females underwent surgical resection for vulvar VM. Evaluations included preoperative Doppler ultrasound, MRI, and pre-and postoperative photographic imaging. The main outcomes were: residual pain, cosmetic distortion, residual VM, and quality of life. Results. Over a 10 year periods, 18 females, mean age 35 years (range 9-71) were included in this study. All patients were symptomatic: 16 had intermittent pain or discomfort, 1 had bleeding and 2 requested cosmetic treatment. Of these cases, there were 5 isolated vulvar VM, 12 associated VM: 3 of the clitoral hood, 3 troncular pelvic vein insufficiency and 12 of the lower limb. Eight patients had undergone previous procedures: 2 sclerotherapy treatments (1 to 3 sessions), 4 partial surgical resections. There were 18 single resections in the vulva (7 focal, 11 complete), 2 partial resections in clitoral hood and 2 had resection of a VM in the peritoneo-vaginal canal at the same time. The mean follow-up was 42. 9 months (range 6-120). Two patients were lost to follow-up at 6 months. For all patients, elimination of pain and soft tissue redundancy was achieved. Two patients had persistent discomfort and 2 requested cosmetic treatment. Conclusion. Surgical resection of vulvar VM can be the best approach with few postoperative complications, good functional and cosmetic results. Appropriate preoperative evaluation is required to identify isolated VM or VM associated with ovarian vein or internal iliac vein insufficiency requiring to be treated before surgery. [ABSTRACT FROM AUTHOR]

Purpose: The migration percentage is a widely used criterion for surgery in displaced hips. Literature suggests that no hip can spontaneously improve if the migration percentage exceeds 45%, in a mixed population of cerebral palsy children. The aim of the present article was to identify the point of no return of the migration percentage in a selected sample of non-ambulatory cerebral palsy children, being the most exposed to hip luxation. Methods: This single-center retrospective cohort study included patients with spastic or dyskinetic cerebral palsy, Gross Motor Function Classification System level IV or V, age 0-18, having at least three pelvic radiographies, excluding radiographies relative to hips having previously undergone surgery. The following information was collected: sex, cerebral palsy subtype, Gross Motor Function Classification System level, presence of drug-resistant epilepsy, migration percentage, age at assessment, use of walking or standing assistive devices, previous botulinum injection, oral or intrathecal baclofen, and hip pain. Data were analyzed at the level of the individual hips. Descriptive statistics were presented. Receiver operating characteristic curve analysis was conducted to investigate which value of the migration percentage could be adopted as the "point of no return": that is, the cutoff value beyond which no migration percentage reduction, by more than 5%, could be expected. Results: The optimal cutoff value was identified as migration percentage =50%, with a sensitivity of 84.5% and a specificity of 100% (p-value <0.001). Conclusion: Based on the present study, migration percentage =50% is the "point of no return" for Gross Motor Function Classification System IV-V cerebral palsy patients, representing the cutoff value beyond which no spontaneous cerebral palsy reduction may be expected, unless addressing surgery. [ABSTRACT FROM AUTHOR]

Linear Datalog programs are programs whose clauses have at most one intensional atom in their bodies. We explore syntactic classes of Datalog programs (syntactically non-linear) which turn out to express no more than the queries expressed by linear Datalog programs. In particular, we investigate linearisability of (database queries corresponding to) piecewise linear Datalog programs and chain queries:(a) We prove that piecewise linear Datalog programs can always be transformed into linear Datalog programs, by virtue of a procedure which performs the transformation automatically. The procedure relies upon conventional logic program transformation techniques.(b) We identify a new class of linearisable chain queries, referred to as pseudo-regular, and prove their linearisability constructively, by generating, for any given pseudo-regular chain query, the Datalog program corresponding to it. [Copyright &y& Elsevier]

Bondarenko et al. have recently proposed an abstract framework for default reasoning. Besides capturing most existing formalisms and proving that their standard semantics all coincide, the framework extends these formalisms by generalising the semantics of admissible and preferred arguments, originally proposed for logic programming only.In this paper we analyse the computational complexity of credulous and sceptical reasoning under the semantics of admissible and preferred arguments for (the propositional variant of) the instances of the abstract framework capturing theorist, circumscription, logic programming, default logic, and autoepistemic logic. Although the new semantics have been tacitly assumed to mitigate the computational hardness of default reasoning under the standard semantics of stable extensions, we show that in many cases reasoning under the admissibility and preferability semantics is computationally harder than under the standard semantics. In particular, in the case of autoepistemic logic, sceptical reasoning under preferred arguments is located at the fourth level of the polynomial hierarchy, whereas the same form of reasoning under stable extensions is located at the second level. [Copyright &y& Elsevier]

• We study the computational complexity of Assumption-Based Argumentation (ABA) for classical argumentation decision problems. • We establish the computational complexity of function problems in Probabilistic ABA (PABA) to be in the class FP # P. • We provide a comprehensive picture of the computational complexity of ABA and PABA under several argumentation semantics. Reasoning with probabilistic information has recently attracted considerable attention in argumentation, and formalisms of Probabilistic Abstract Argumentation (PAA), Probabilistic Bipolar Argumentation (PBA) and Probabilistic Structured Argumentation (PSA) have been proposed. These foundational advances have been complemented with investigations on the complexity of some approaches to PAA and PBA, but not to PSA. We study the complexity of an existing form of PSA, namely Probabilistic Assumption-Based Argumentation (PABA), a powerful, implemented formalism which subsumes several forms of PAA and other forms of PSA. Specifically, we establish membership (general upper bounds) and completeness (instantiated lower bounds) of reasoning in PABA for the class FP # P (of functions with a # P -oracle for counting the solutions of an NP problem) with respect to newly introduced probabilistic verification, credulous and sceptical acceptance function problems under several ABA semantics. As a by-product necessary to establish PABA complexity results, we provide a comprehensive picture of the ABA complexity landscape (for both flat and generic, possibly non-flat ABA) for the classical decision problems of verification, existence, credulous and sceptical acceptance under those ABA semantics. [ABSTRACT FROM AUTHOR]

In this paper we show how re-interpreting PageRank as an argumentation semantics for a bipolar argumentation framework empowers its explainability. After showing that PageRank, naively re-interpreted as an argumentation semantics for support frameworks, fails to satisfy some generally desirable properties, we propose a novel approach able to reconstruct PageRank as a gradual semantics of a suitably defined bipolar argumentation framework, while satisfying these properties. We then show how the theoretical advantages afforded by this approach also enjoy an enhanced explanatory power: we propose several types of argument-based explanations for PageRank, each of which focuses on different aspects of the algorithm and uncovers information useful for the comprehension of its results. [ABSTRACT FROM AUTHOR]

Highlights • Arbitrated argumentative disputes explain outcomes of cases characterised by features and stages. • Excess features help to win the dispute and complement explanations. • High-level explanations with content-agnostic features explain passage of legislative bills. • Large-scale visualisations provide a collaborative environment for explanations. Abstract Explaining outputs determined algorithmically by machines is one of the most pressing and studied problems in Artificial Intelligence (AI) nowadays, but the equally pressing problem of using AI to explain outputs determined by humans is less studied. In this paper we advance a novel methodology integrating case-based reasoning and computational argumentation from AI to explain outcomes , determined by humans or by machines, indifferently, for cases characterised by discrete (static) features and/or (dynamic) stages. At the heart of our methodology lies the concept of arbitrated argumentative disputes between two fictitious disputants arguing, respectively, for or against a case's output in need of explanation, and where this case acts as an arbiter. Specifically, in explaining the outcome of a case in question, the disputants put forward as arguments relevant cases favouring their respective positions, with arguments/cases conflicting due to their features, stages and outcomes, and the applicability of arguments/cases arbitrated by the features and stages of the case in question. We in addition use arbitrated dispute trees to identify the excess features that help the winning disputant to win the dispute and thus complement the explanation. We evaluate our novel methodology theoretically, proving desirable properties thereof, and empirically, in the context of primary legislation in the United Kingdom (UK), concerning the passage of Bills that may or may not become laws. High-level factors underpinning a Bill's passage are its content-agnostic features such as type, number of sponsors, ballot order, as well as the UK Parliament's rules of conduct. Given high numbers of proposed legislation (hundreds of Bills a year), it is hard even for legal experts to explain on a large scale why certain Bills pass or not. We show how our methodology can address this problem by automatically providing high-level explanations of why Bills pass or not, based on the given Bills and their content-agnostic features. [ABSTRACT FROM AUTHOR]

Highlights • Selecting decisions that achieve most goals but with fewest redundant attributes. • An argumentative process to identify best decisions with explanation. • Application in literature search in law shows promising results. • Natural language explanations are useful for supporting human decision makers. Abstract We present a multi-attribute decision model and a method for explaining the decisions it recommends based on an argumentative reformulation of the model. Specifically, (i) we define a notion of best (i.e. , minimally redundant) decisions amounting to achieving as many goals as possible and exhibiting as few redundant attributes as possible, and (ii) we generate explanations for why a decision is best or better than or as good as another, using a mapping between the given decision model and an argumentation framework, such that best decisions correspond to admissible sets of arguments. Concretely, natural language explanations are generated automatically from dispute trees sanctioning the admissibility of arguments. Throughout, we illustrate the power of our approach within a legal reasoning setting, where best decisions amount to past cases that are most similar to a given new, open case. Finally, we conduct an empirical evaluation of our method with legal practitioners, confirming that our method is effective for the choice of most similar past cases and helpful to understand automatically generated recommendations. [ABSTRACT FROM AUTHOR]

Background The place of open surgery for treatment of the venous malformations (VMs) of knee joint is still discussed. The aim of this study was to evaluate the benefits of surgery in terms of pain, function, and quality of life. Design Retrospective, observational, monocentric study. Materials and methods Consecutive patients undergoing surgery for VMs of the knee between 2011 and 2014 were included. Data collection was performed using a prospective database by reviewing patient records. Pain, mobility of the joint, residual VMs as seen by resonance magnetic imaging (MRI), and quality of life were the main outcome endpoints for this study. Results Forty-three patients (27 females and 16 males, with a median age of 20 years, range 5 to 35 years) were included. Thirty-four VMs were localized in and around the knee joint, of which nine were extensive. Indication for surgery was intermittent or permanent pain. Details of the surgical excisions of the VM are as follows: suprapatellar area in 61%, suprapatellar and infrapatellar area in 23%, infrapatellar area in 7%, limited in the femoro-patellar area in 9%. Partial resection of a vastus muscle or patellar retinaculum was necessary for 21 patients. The mean duration of the surgery was 4 hours (range 2-7 hours). The median hospital stay was five days; full-time physiotherapy was systematic for 2-3 weeks. At 6 months, 79% no longer had pain, the mobility of the joint was normal for 58%. Out of the 39 patients who had an MRI at 6 months, 80% (31 Pts) had no residual intra-articular VMs, and 20% had limited residual VMs. At the last follow-up, 74% no longer had pain, 26% experienced intermittent pain with unusual activities, and 91% had normal mobility of the joint. Quality of life was increased significantly due to the pronounced impact on pain. Conclusion Extensive surgical excision of knee VMs is a safe procedure with good outcomes in terms of pain, function, and quality of life. [ABSTRACT FROM AUTHOR]

A significant challenge for recommender systems (RSs), and in fact for AI systems in general, is the systematic definition of explanations for outputs in such a way that both the explanations and the systems themselves are able to adapt to their human users' needs. In this paper we propose an RS hosting a vast repertoire of explanations, which are customisable to users in their content and format, and thus able to adapt to users' explanatory requirements, while being reasonably effective (proven empirically). Our RS is built on a graphical chassis, allowing the extraction of argumentation scaffolding, from which diverse and varied argumentative explanations for recommendations can be obtained. These recommendations are interactive because they can be questioned by users and they support adaptive feedback mechanisms designed to allow the RS to self-improve (proven theoretically). Finally, we undertake user studies in which we vary the characteristics of the argumentative explanations, showing users' general preferences for more information, but also that their tastes are diverse, thus highlighting the need for our adaptable RS. [ABSTRACT FROM AUTHOR]