Preliminary Notions of Arguments from Commonsense Knowledge

Highlights High rates of streambank erosion are detrimental to the stability and function of streams. This collection brings together six studies that represent key advances in streambank erosion research. Current research directions on streambank erosion, erodibility characterization, and sediment loading are presented. Future research directions and challenges related to high-frequency data collection and modeling are discussed. Abstract. Streams are in dynamic equilibrium with their environments, and as that environment is altered by human development and changing climate, streambank erosion is a common, but little understood, result. This article highlights the contributions of the special collection “Streambank Erosion, Sediment Dynamics, and Restoration (SER),” which assembled six studies that represent key advances in streambank erosion research, highlight current research in the field, and identify directions for future research. The studies in this special collection were grouped into three central themes: (1) streambank erosion monitoring, (2) streambank erodibility characterization, and (3) streambank erosion loading. In this article, key findings within each of these central themes are summarized, emphasizing the significant contributions of each study. Likewise, perspectives on future research directions are discussed, outlining important challenges that remain to be addressed. Overall, the studies in this special collection are unified in their overarching goal of improving quantitative and predictive understanding of streambank erosion phenomena. Keywords: Keywords., Erodibility Parameters, Jet Erosion Test, Monitoring, Sediment, Soil Erosion, Stabilization Practices, Streambank, Stream Restoration.

In order to be fully robust and responsive to a dynamically changing real-world environment, intelligent robots will need to engage in a variety of simultaneous reasoning modalities. In particular, in this paper we consider their needs to i) reason with commonsense knowledge, ii) model their nondeterministic action outcomes and partial observability, and iii) plan toward maximizing long-term rewards. On one hand, Answer Set Programming (ASP) is good at representing and reasoning with commonsense and default knowledge, but is ill-equipped to plan under probabilistic uncertainty. On the other hand, Partially Observable Markov Decision Processes(POMDPs) are strong at planning under uncertainty toward maximizing long-term rewards, but are not designed to incorporate commonsense knowledge and inference. This paper introduces the CORPP algorithm which combines P-log,a probabilistic extension of ASP, with POMDPs to integrate commonsense reasoning with planning under uncertainty.Our approach is fully implemented and tested on a shopping request identification problem both in simulation and on a real robot. Compared with existing approaches using P-log or POMDPs individually, we observe significant improvements in both efficiency and accuracy.

Computational argumentation is primed to strengthen the current hot research field of Explainable Artificial Intelligence (XAI), e.g., by dialectical approaches. In this paper, we extend and discuss a recently proposed approach of so-called strong acceptance on abstract argumentation that aims to support explaining argumentative acceptance. Our goal is to push these results into the realm of structured argumentation. In this setting, a knowledge base induces an abstract argumentation framework (AF) via instantiation. We investigate how and under which conditions it is possible to transfer results regarding strong acceptance between the given knowledge base and the induced AF. To this end we consider generic functions formalizing the interaction of the AF and the knowledge base. This approach helps us to infer rather general results making basic assumptions rather than dealing with the technical details of several structured argumentation formalisms. Along the way, we apply our techniques to the concrete approach of assumption-based argumentation (ABA) which constitutes one of the primal structured argumentation formalisms.

Making human milk matter: the need for EU regulation

A key ingredient of computational argumentation in AI is the generation of arguments in favor of or against claims under scrutiny. In this paper we look at the complexity of argument construction and reasoning in the prominent structured formalism of assumption-based argumentation (ABA). We point out that reasoning in ABA by means of constructing an abstract argumentation framework (AF) gives rise to two main sources of complexity: (i) constructing the AF and (ii) reasoning within the constructed graph. Since both steps are intractable in general, it is no surprise that the best performing state-of-the-art ABA reasoners skip the instantiation procedure entirely and perform tasks directly on the input knowledge base. Driven by this observation, we identify and study atomic and symmetric ABA, two ABA fragments that preserve the expressive power of general ABA, and that can be utilized to have milder complexity in the first or second step. We show that using atomic ABA allows for an instantiation procedure for general ABA leading to polynomially-bounded AFs and that symmetric ABA can be used to create AFs that have mild complexity to reason on. By an experimental evaluation, we show that using the former approach with modern AF solvers can be competitive with state-of-the-art ABA solvers, improving on previous AF instantiation approaches that are hindered by intractable argument construction.

In this paper, we present our submission for SemEval 2020 Task 4 - Commonsense Validation and Explanation (ComVE). The objective of this task was to develop a system that can differentiate statements that make sense from the ones that don’t. ComVE comprises of three subtasks to challenge and test a system’s capability in understanding commonsense knowledge from various dimensions. Commonsense reasoning is a challenging task in the domain of natural language understanding and systems augmented with it can improve performance in various other tasks such as reading comprehension, and inferencing. We have developed a system that leverages commonsense knowledge from pretrained language models trained on huge corpus such as RoBERTa, GPT2, etc. Our proposed system validates the reasonability of a given statement against the backdrop of commonsense knowledge acquired by these models and generates a logical reason to support its decision. Our system ranked 2nd in subtask C with a BLEU score of 19.3, which by far is the most challenging subtask as it required systems to generate the rationale behind the choice of an unreasonable statement. In subtask A and B, we achieved 96% and 94% accuracy respectively standing at 4th position in both the subtasks.

Humans use countless basic, shared facts about the world to efficiently navigate in their environment. This commonsense knowledge is rarely communicated explicitly, however, understanding how commonsense knowledge is represented in different paradigms is important for both deeper understanding of human cognition and for augmenting automatic reasoning systems. This paper presents an in-depth comparison of two large-scale resources of general knowledge: ConceptNet, an engineered relational database, and SWOW a knowledge graph derived from crowd-sourced word associations. We examine the structure, overlap and differences between the two graphs, as well as the extent to which they encode situational commonsense knowledge. We finally show empirically that both resources improve downstream task performance on commonsense reasoning benchmarks over text-only baselines, suggesting that large-scale word association data, which have been obtained for several languages through crowd-sourcing, can be a valuable complement to curated knowledge graphs

Current and future research directions in network optimization

This mapping review aims to identify and discuss current research directions on intracavitary temporomandibular joints (TMJs) injections. The inclusion criteria allowed studies published in the last full six years, based on patients diagnosed with temporomandibular joint disorders (TMDs), treated by TMJ intra-articular injections. Medical databases covered by the Association for Computing Machinery, Bielefeld Academic Search Engine, PubMed, and Elsevier Scopus engines were searched. The results were visualized with tables, charts, and diagrams. Of the 2712 records identified following the selection process, 152 reports were qualified for review. From January 2017, viscosupplementation with hyaluronic acid (HA) was the best-documented injectable administered into TMJ cavities. However, a significant growing trend was observed in the number of primary studies on centrifuged blood preparations administrations that surpassed the previously leading HA from 2021.

Research Question/IssueFrom its inception in the late 1980s and early 1990s, founder‐chief executive officer (CEO) research has garnered significant scholarly attention in the strategy and entrepreneurship disciplines, although other fields—such as economics, finance, and family business—have also generated substantial research insight on this topic. Despite this progress, a limited consensus exists on the influence of the founder CEO owing to the fragmented nature of extant research. In this review, we address this fragmentation by reviewing current literature, synthesizing the discipline‐specific findings into an integrated framework, and highlighting promising directions for future founder‐CEO research.Research Findings/InsightsUsing a cross‐disciplinary review of founder‐CEO research based on 221 articles from 24 academic journals, we synthesize current research and provide directions for future research. Our framework organizes current research on the founder CEO into three broad themes. First, we review and synthesize scholarly work on factors related to the founder CEO during the nascent years of the firm. In the second theme, we review research that explores the impact of the founder CEO on strategic choices and firm configurations. In the third theme, we review the performance consequences of the founder CEO.Theoretical/Academic ImplicationsThis review advances research on the founder CEO by taking stock of insights from various disciplines, highlighting advancements made, and suggesting promising directions for future research on founder CEOs. Additionally, a critical evaluation of current methodological approaches provides opportunities for strengthening the rigor of scholarly inquiry in this area.Practitioner/Policy ImplicationsThis review provides corporate governance scholars and policymakers with a current, multidisciplinary summary of research findings on founder CEOs and their impact on the firm across various stages of the organizational life cycle.

Learning commonsense knowledge and conducting commonsense reasoning are basic human ability to make presumptions about the type and essence of ordinary situation in daily life, which serve as very important goals in human-centric Artificial Intelligence (AI). With the increasing number of media types and quantities provided by various Internet services, commonsense learning and reasoning with no doubt are playing key roles in making progresses for human-centric multimedia analysis. Therefore, this paper first introduces the basic concept of commonsense knowledge and commonsense reasoning, then summarizes commonsense resources and benchmarks, gives an overview on recent commonsense learning and reasoning methods, and discusses several popular applications of commonsense knowledge in real-world scenarios. This work distinguishes itself from existing literature that merely pays attention to natural language processing in focusing more on multimedia which include both natural language processing and computer vision. Furthermore, we also present our insights and thinking on future research directions for commonsense.

Recent developments of deep eutectic solvent as absorbent for CO2 removal from syngas produced from gasification: Current status, challenges, and further research

Memorization of piano music for performance has often been shrouded in mystery. A body of neuro-music research has emerged, mostly in the last twenty years, that addresses how musicians perceive, learn, and memorize music, and how these processes operate in music performance. This paper is a compendium of current research on music perception, learning, memorization, and performance, specifically focused on piano music and piano performance. It is intended to benefit both pianists (performers, teachers, students), and neuro-music researchers. This research explains the operation of human memory systems and, from this platform, addresses aspects of music memorization such as multi-modal approaches and individual differences. A discussion of motor learning precedes current research comparing it with perceptual learning. Further, this work discusses memory acquisition, stabilization, and sleep consolidation, supported by current piano-specific research. It considers the role of the original modality of learning, and reviews the significance of metacognition and attention. Additionally, this paper presents information on auditory imagery in practice and performance, and on methods for developing musical expressiveness. A final section summarizes the most significant implications for musicians and important directions for future research.

Reasoning over commonsense knowledge bases (CSKBs) whose elements are in the form of free-text is an important yet hard task in NLP.While CSKB completion only fills the missing links within the domain of the CSKB, CSKB population is alternatively proposed with the goal of reasoning unseen assertions from external resources.In this task, CSKBs are grounded to a large-scale eventuality (activity, state, and event) graph to discriminate whether novel triples from the eventuality graph are plausible or not.However, existing evaluations on the population task are either not accurate (automatic evaluation with randomly sampled negative examples) or of small scale (human annotation).In this paper, we benchmark the CSKB population task with a new large-scale dataset by first aligning four popular CSKBs, and then presenting a highquality human-annotated evaluation set to probe neural models' commonsense reasoning ability.We also propose a novel inductive commonsense reasoning model that reasons over graphs.Experimental results show that generalizing commonsense reasoning on unseen assertions is inherently a hard task.Models achieving high accuracy during training perform poorly on the evaluation set, with a large gap between human performance.Codes and

Review of Night Vision: Current Research and Future Directions: Symposium Proceedings.