OpenRaiser/Intern-Atlas huggingface dataset:
A General Framework for the Representation of Function and Affordance: A Cognitive, Causal, and Grounded Approach, and a Step Toward AGI
Venue: arXiv cs.AI 2022
Year: 2022

In AI research, so far, the attention paid to the characterization and representation of function and affordance has been sporadic and sparse, even though this aspect features prominently in an intelligent system's functioning. In the sporadic and sparse, though commendable efforts so far devoted to the characterization and understanding of function and affordance, there has also been no general framework that could unify all the different use domains and situations related to the representation and application of functional concepts. This paper develops just such a general framework, with an approach that emphasizes the fact that the representations involved must be explicitly cognitive and conceptual, and they must also contain causal characterizations of the events and processes involved, as well as employ conceptual constructs that are grounded in the referents to which they refer, in order to achieve maximal generality. The basic general framework is described, along with a set of basic guiding principles with regards to the representation of functionality. To properly and adequately characterize and represent functionality, a descriptive representation language is needed. This language is defined and developed, and many examples of its use are described. The general framework is developed based on an extension of the general language meaning representational framework called conceptual dependency. To support the general characterization and representation of functionality, the basic conceptual dependency framework is enhanced with representational devices called structure anchor and conceptual dependency elaboration, together with the definition of a set of ground level concepts. These novel representational constructs are defined, developed, and described. A general framework dealing with functionality would represent a major step toward achieving Artificial General Intelligence.
Characterizing Properties and Trade-offs of Centralized Delegation Mechanisms in Liquid Democracy
Venue: arXiv cs.MA 2022
Year: 2022

Liquid democracy is a form of transitive delegative democracy that has received a flurry of scholarly attention from the computer science community in recent years. In its simplest form, every agent starts with one vote and may have other votes assigned to them via delegation from other agents. They can choose to delegate all votes assigned to them to another agent or vote directly with all votes assigned to them. However, many proposed realizations of liquid democracy allow for agents to express their delegation/voting preferences in more complex ways (e.g., a ranked list of potential delegates) and employ a centralized delegation mechanism to compute the final vote tally. In doing so, centralized delegation mechanisms can make decisions that affect the outcome of a vote and where/whether agents are able to delegate their votes. Much of the analysis thus far has focused on the ability of these mechanisms to make a correct choice. We extend this analysis by introducing and formalizing other important properties of a centralized delegation mechanism in liquid democracy with respect to crucial features such as accountability, transparency, explainability, fairness, and user agency. In addition, we evaluate existing methods in terms of these properties, show how some prior work can be augmented to achieve desirable properties, prove impossibility results for achieving certain sets of properties simultaneously, and highlight directions for future work.
Social Practices for Social Driven Conversations in Serious Games
Venue: arXiv cs.AI 2022
Year: 2022

This paper describes the model of social practice as a theoretical framework to manage conversation with the specific goal of training physicians in communicative skills. To this aim, the domain reasoner that manages the conversation in the Communicate! \cite{jeuring} serious game is taken as a basis. Because the choice of a specific Social Practice to follow in a situation is non-trivial we use a probabilistic model for the selection of social practices as a step toward the implementation of an agent architecture compliant with the social practice model.
A Speedup Theorem for Asynchronous Computation with Applications to Consensus and Approximate Agreement
Venue: arXiv cs.DC 2022
Year: 2022

We study two fundamental problems of distributed computing, consensus and approximate agreement, through a novel approach for proving lower bounds and impossibility results, that we call the asynchronous speedup theorem. For a given $n$-process task $Π$ and a given computational model $M$, we define a new task, called the closure of $Π$ with respect to $M$. The asynchronous speedup theorem states that if a task $Π$ is solvable in $t\geq 1$ rounds in $M$, then its closure w.r.t. $M$ is solvable in $t-1$ rounds in $M$. We prove this theorem for iterated models, as long as the model allows solo executions. We illustrate the power of our asynchronous speedup theorem by providing a new proof of the wait-free impossibility of consensus using read/write registers, and a new proof of the wait-free impossibility of solving consensus using registers and test\&set objects for $n>2$. The proof is merely by showing that, in each case, the closure of consensus (w.r.t. the corresponding model) is consensus itself. Our main application is the study of the power of additional objects, namely test\&set and binary consensus, for wait-free solving approximate agreement faster. By analyzing the closure of approximate agreement w.r.t. each of the two models, we show that while these objects are more powerful than read/write registers from the computability perspective, they are not more powerful as far as helping solving approximate agreement faster is concerned.
Applications of Deep Learning in Fish Habitat Monitoring: A Tutorial and Survey
Venue: arXiv cs.CV 2022
Year: 2022

Marine ecosystems and their fish habitats are becoming increasingly important due to their integral role in providing a valuable food source and conservation outcomes. Due to their remote and difficult to access nature, marine environments and fish habitats are often monitored using underwater cameras. These cameras generate a massive volume of digital data, which cannot be efficiently analysed by current manual processing methods, which involve a human observer. DL is a cutting-edge AI technology that has demonstrated unprecedented performance in analysing visual data. Despite its application to a myriad of domains, its use in underwater fish habitat monitoring remains under explored. In this paper, we provide a tutorial that covers the key concepts of DL, which help the reader grasp a high-level understanding of how DL works. The tutorial also explains a step-by-step procedure on how DL algorithms should be developed for challenging applications such as underwater fish monitoring. In addition, we provide a comprehensive survey of key deep learning techniques for fish habitat monitoring including classification, counting, localization, and segmentation. Furthermore, we survey publicly available underwater fish datasets, and compare various DL techniques in the underwater fish monitoring domains. We also discuss some challenges and opportunities in the emerging field of deep learning for fish habitat processing. This paper is written to serve as a tutorial for marine scientists who would like to grasp a high-level understanding of DL, develop it for their applications by following our step-by-step tutorial, and see how it is evolving to facilitate their research efforts. At the same time, it is suitable for computer scientists who would like to survey state-of-the-art DL-based methodologies for fish habitat monitoring.
Software Engineering in Australasia
Venue: arXiv cs.SE 2022
Year: 2022

Six months ago an important call was made for researchers globally to provide insights into the way Software Engineering is done in their region. Heeding this call we hereby outline the position Software Engineering in Australasia (New Zealand and Australia). This article first considers the software development methods practices and tools that are popular in the Australasian software engineering community. We then briefly review the particular strengths of software engineering researchers in Australasia. Finally we make an open call for collaborators by reflecting on our current position and identifying future opportunities
Learned reconstruction methods with convergence guarantees
Venue: arXiv cs.CV 2022
Year: 2022

In recent years, deep learning has achieved remarkable empirical success for image reconstruction. This has catalyzed an ongoing quest for precise characterization of correctness and reliability of data-driven methods in critical use-cases, for instance in medical imaging. Notwithstanding the excellent performance and efficacy of deep learning-based methods, concerns have been raised regarding their stability, or lack thereof, with serious practical implications. Significant advances have been made in recent years to unravel the inner workings of data-driven image recovery methods, challenging their widely perceived black-box nature. In this article, we will specify relevant notions of convergence for data-driven image reconstruction, which will form the basis of a survey of learned methods with mathematically rigorous reconstruction guarantees. An example that is highlighted is the role of ICNN, offering the possibility to combine the power of deep learning with classical convex regularization theory for devising methods that are provably convergent. This survey article is aimed at both methodological researchers seeking to advance the frontiers of our understanding of data-driven image reconstruction methods as well as practitioners, by providing an accessible description of useful convergence concepts and by placing some of the existing empirical practices on a solid mathematical foundation.
Guaranteeing Timed Opacity using Parametric Timed Model Checking
Venue: arXiv cs.CR 2022
Year: 2022

Information leakage can have dramatic consequences on systems security. Among harmful information leaks, the timing information leakage occurs whenever an attacker successfully deduces confidential internal information. In this work, we consider that the attacker has access (only) to the system execution time. We address the following timed opacity problem: given a timed system, a private location and a final location, synthesize the execution times from the initial location to the final location for which one cannot deduce whether the system went through the private location. We also consider the full timed opacity problem, asking whether the system is opaque for all execution times. We show that these problems are decidable for timed automata (TAs) but become undecidable when one adds parameters, yielding parametric timed automata (PTAs). We identify a subclass with some decidability results. We then devise an algorithm for synthesizing PTAs parameter valuations guaranteeing that the resulting TA is opaque. We finally show that our method can also apply to program analysis.
Comparative Snippet Generation
Venue: arXiv cs.CL 2022
Year: 2022

We model product reviews to generate comparative responses consisting of positive and negative experiences regarding the product. Specifically, we generate a single-sentence, comparative response from a given positive and a negative opinion. We contribute the first dataset for this task of Comparative Snippet Generation from contrasting opinions regarding a product, and a performance analysis of a pre-trained BERT model to generate such snippets.
The Effects of Spatial Configuration on Relative Translation Gain Thresholds in Redirected Walking
Venue: arXiv cs.HC 2022
Year: 2022

In this study, we explore how spatial configurations can be reflected in determining the threshold range of Relative Translation Gains (RTGs), a translation gain-based Redirected Walking (RDW) technique that scales the user's movement in Virtual Reality (VR) in different ratios for width and depth. While previous works have shown that various cognitive factors or individual differences influence the RDW threshold, constructive studies investigating the impact of the environmental composition on the RDW threshold with regard to the user's visual perception were lacking. Therefore, we examined the effect of spatial configurations on the RTG threshold by analyzing the participant's responses and gaze distribution data in two user studies. The first study concerned the size of the virtual room and the existence of objects within it, and the second study focused on the combined impact of room size and the spatial layout. Our results show that three compositions of spatial configuration (size, object existence, spatial layout) significantly affect the RTG threshold range. Based on our findings, we proposed virtual space rescaling guidelines to increase the range of adjustable movable space with RTGs for developers: placing distractors in the room, setting the perceived movable space to be larger than the adjusted movable space if it's an empty room, and avoid placing objects together as centered layout. Our findings can be used to adaptively rescale VR users' space according to the target virtual space's configuration with a unified coordinate system that enables the utilization of physical objects in a virtual scene.
Web-Based Platform for Evaluation of Resilient and Transactive Smart-Grids
Venue: arXiv cs.ET 2022
Year: 2022

Today's smart-grids have seen a clear rise in new ways of energy generation, transmission, and storage. This has not only introduced a huge degree of variability, but also a continual shift away from traditionally centralized generation and storage to distributed energy resources (DERs). In addition, the distributed sensors, energy generators and storage devices, and networking have led to a huge increase in attack vectors that make the grid vulnerable to a variety of attacks. The interconnection between computational and physical components through a largely open, IP-based communication network enables an attacker to cause physical damage through remote cyber-attacks or attack on software-controlled grid operations via physical- or cyber-attacks. Transactive Energy (TE) is an emerging approach for managing increasing DERs in the smart-grids through economic and control techniques. Transactive Smart-Grids use the TE approach to improve grid reliability and efficiency. However, skepticism remains in their full-scale viability for ensuring grid reliability. In addition, different TE approaches, in specific situations, can lead to very different outcomes in grid operations. In this paper, we present a comprehensive web-based platform for evaluating resilience of smart-grids against a variety of cyber- and physical-attacks and evaluating impact of various TE approaches on grid performance. We also provide several case-studies demonstrating evaluation of TE approaches as well as grid resilience against cyber and physical attacks.
Incremental Information Gain Mining Of Temporal Relational Streams
Venue: arXiv cs.IR 2022
Year: 2022

This paper studies the problem of mining for data values with high information gain in relational tables. High information gain can help data analysts and secondary data mining algorithms gain insights into strong statistical dependencies and causality relationship between key metrics. In this paper, we will study the problem of high information gain identification for scenarios involving temporal relations where new records are added continuously to the relations. We show that information gain can be efficiently maintained in an incremental fashion, making it possible to monitor continuously high information gain values.
A Unified Continuous Learning Framework for Multi-modal Knowledge Discovery and Pre-training
Venue: arXiv cs.CL 2022
Year: 2022

Multi-modal pre-training and knowledge discovery are two important research topics in multi-modal machine learning. Nevertheless, none of existing works make attempts to link knowledge discovery with knowledge guided multi-modal pre-training. In this paper, we propose to unify them into a continuous learning framework for mutual improvement. Taking the open-domain uni-modal datasets of images and texts as input, we maintain a knowledge graph as the foundation to support these two tasks. For knowledge discovery, a pre-trained model is used to identify cross-modal links on the graph. For model pre-training, the knowledge graph is used as the external knowledge to guide the model updating. These two steps are iteratively performed in our framework for continuous learning. The experimental results on MS-COCO and Flickr30K with respect to both knowledge discovery and the pre-trained model validate the effectiveness of our framework.
Bounded strategic reasoning explains crisis emergence in multi-agent market games
Venue: arXiv cs.MA 2022
Year: 2022

The efficient market hypothesis (EMH), based on rational expectations and market equilibrium, is the dominant perspective for modelling economic markets. However, the most notable critique of the EMH is the inability to model periods of out-of-equilibrium behaviour in the absence of any significant external news. When such dynamics emerge endogenously, the traditional economic frameworks provide no explanation for such behaviour and the deviation from equilibrium. This work offers an alternate perspective explaining the endogenous emergence of punctuated out-of-equilibrium dynamics based on bounded rational agents. In a concise market entrance game, we show how boundedly rational strategic reasoning can lead to endogenously emerging crises, exhibiting fat tails in "returns". We also show how other common stylised facts of economic markets, such as clustered volatility, can be explained due to agent diversity (or lack thereof) and the varying learning updates across the agents. This work explains various stylised facts and crisis emergence in economic markets, in the absence of any external news, based purely on agent interactions and bounded rational reasoning.
Random Access-based Multiuser Computation Offloading for Devices in IoT Applications
Venue: arXiv cs.IT 2022
Year: 2022

In various Internet-of-Things (IoT) applications, a number of devices and sensors are used to collect data sets. As devices become more capable and smarter, they can not only collect data sets, but also process them locally. However, since most devices would be limited in terms of computing power and energy, they can take advantage of offloading so that their tasks can be carried out at mobile edge computing (MEC) servers. In this paper, we discuss computation offloading for devices in IoT applications. In particular, we consider users or devices with sporadic tasks, where optimizing resource allocation between offloading devices and coordinating for multiuser offloading becomes inefficient. Thus, we propose a two-stage offloading approach that is friendly to devices with sporadic tasks as it employs multichannel random access for offloading requests with low signaling overhead. The stability of the two-stage offloading approach is considered with methods to stabilize the system. We also analyze the latency outage probability as a performance index from a device perspective.
Density Regression and Uncertainty Quantification with Bayesian Deep Noise Neural Networks
Venue: arXiv cs.LG 2022
Year: 2022

Deep neural network (DNN) models have achieved state-of-the-art predictive accuracy in a wide range of supervised learning applications. However, accurately quantifying the uncertainty in DNN predictions remains a challenging task. For continuous outcome variables, an even more difficult problem is to estimate the predictive density function, which not only provides a natural quantification of the predictive uncertainty, but also fully captures the random variation in the outcome. In this work, we propose the Bayesian Deep Noise Neural Network (B-DeepNoise), which generalizes standard Bayesian DNNs by extending the random noise variable from the output layer to all hidden layers. The latent random noise equips B-DeepNoise with the flexibility to approximate highly complex predictive distributions and accurately quantify predictive uncertainty. For posterior computation, the unique structure of B-DeepNoise leads to a closed-form Gibbs sampling algorithm that iteratively simulates from the posterior full conditional distributions of the model parameters, circumventing computationally intensive Metropolis-Hastings methods. A theoretical analysis of B-DeepNoise establishes a recursive representation of the predictive distribution and decomposes the predictive variance with respect to the latent parameters. We evaluate B-DeepNoise against existing methods on benchmark regression datasets, demonstrating its superior performance in terms of prediction accuracy, uncertainty quantification accuracy, and uncertainty quantification efficiency. To illustrate our method's usefulness in scientific studies, we apply B-DeepNoise to predict general intelligence from neuroimaging features in the Adolescent Brain Cognitive Development (ABCD) project.
LUEM : Local User Engagement Maximization in Networks
Venue: arXiv cs.SI 2022
Year: 2022

Understanding a social network is a fundamental problem in social network analysis because of its numerous applications. Recently, user engagement in networks has received extensive attention from many research groups. However, most user engagement models focus on global user engagement to maximize (or minimize) the number of engaged users. In this study, we formulate the so-called Local User Engagement Maximization (LUEM) problem. We prove that the LUEM problem is NP-hard. To obtain high-quality results, we propose an approximation algorithm that incorporates a traditional hill-climbing method. To improve efficiency, we propose an efficient pruning strategy while maintaining effectiveness. In addition, by observing the relationship between the degree and user engagement, we propose an efficient heuristic algorithm that preserves effectiveness. Finally, we conducted extensive experiments on ten real-world networks to demonstrate the superiority of the proposed algorithms. We observed that the proposed algorithm achieved up to 605% more engaged users compared to the best baseline algorithms.
A Survey on Uncertainty Reasoning and Quantification for Decision Making: Belief Theory Meets Deep Learning
Venue: arXiv cs.AI 2022
Year: 2022

An in-depth understanding of uncertainty is the first step to making effective decisions under uncertainty. Deep/machine learning (ML/DL) has been hugely leveraged to solve complex problems involved with processing high-dimensional data. However, reasoning and quantifying different types of uncertainties to achieve effective decision-making have been much less explored in ML/DL than in other Artificial Intelligence (AI) domains. In particular, belief/evidence theories have been studied in KRR since the 1960s to reason and measure uncertainties to enhance decision-making effectiveness. We found that only a few studies have leveraged the mature uncertainty research in belief/evidence theories in ML/DL to tackle complex problems under different types of uncertainty. In this survey paper, we discuss several popular belief theories and their core ideas dealing with uncertainty causes and types and quantifying them, along with the discussions of their applicability in ML/DL. In addition, we discuss three main approaches that leverage belief theories in Deep Neural Networks (DNNs), including Evidential DNNs, Fuzzy DNNs, and Rough DNNs, in terms of their uncertainty causes, types, and quantification methods along with their applicability in diverse problem domains. Based on our in-depth survey, we discuss insights, lessons learned, limitations of the current state-of-the-art bridging belief theories and ML/DL, and finally, future research directions.