Design and Construction of Zana Robot for Modeling Human Player in Rock-paper-scissors Game using Multilayer Perceptron, Radial basis Functions and Markov Algorithms

In this paper, the implementation of artificial neural networks (multilayer perceptron [MLP] and radial base functions [RBF]) and the upgraded Markov chain model have been studied and performed to identify the human behavior patterns during rock, paper, and scissors game. The main motivation of this research is the design and construction of an intelligent robot with the ability to defeat a human opponent. MATLAB software has been used to implement intelligent algorithms. After implementing the algorithms, their effectiveness in detecting human behavior pattern has been investigated. To ensure the ideal performance of the implemented model, each player played with the desired algorithms in three different stages. The results showed that the percentage of winning computer with MLP and RBF neural networks and upgraded Markov model, on average in men and women is 59%, 76.66%, and 75%, respectively. Obtained results clearly indicate a very good performance of the RBF neural network and the upgraded Markov model in the mental modeling of the human opponent in the game of rock, paper, and scissors. In the end, the designed game has been employed in both hardware and software which include the Zana intelligent robot and a digital version with a graphical user interface design on the stand. To the best knowledge of the authors, the precision of novel presented method for determining human behavior patterns was the highest precision among all of the previous studies

ReviewerGPT? An Exploratory Study on Using Large Language Models for Paper Reviewing

Given the rapid ascent of large language models (LLMs), we study the question: (How) can large language models help in reviewing of scientific papers or proposals? We first conduct some pilot studies where we find that (i) GPT-4 outperforms other LLMs (Bard, Vicuna, Koala, Alpaca, LLaMa, Dolly, OpenAssistant, StableLM), and (ii) prompting with a specific question (e.g., to identify errors) outperforms prompting to simply write a review. With these insights, we study the use of LLMs (specifically, GPT-4) for three tasks: 1. Identifying errors: We construct 13 short computer science papers each with a deliberately inserted error, and ask the LLM to check for the correctness of these papers. We observe that the LLM finds errors in 7 of them, spanning both mathematical and conceptual errors. 2. Verifying checklists: We task the LLM to verify 16 closed-ended checklist questions in the respective sections of 15 NeurIPS 2022 papers. We find that across 119 {checklist question, paper} pairs, the LLM had an 86.6% accuracy. 3. Choosing the "better" paper: We generate 10 pairs of abstracts, deliberately designing each pair in such a way that one abstract was clearly superior than the other. The LLM, however, struggled to discern these relatively straightforward distinctions accurately, committing errors in its evaluations for 6 out of the 10 pairs. Based on these experiments, we think that LLMs have a promising use as reviewing assistants for specific reviewing tasks, but not (yet) for complete evaluations of papers or proposals

Modelling intransitivity in pairwise comparisons with application to baseball data

In most commonly used ranking systems, some level of underlying transitivity is assumed. If transitivity exists in a system then information about pairwise comparisons can be translated to other linked pairs. For example, if typically A beats B and B beats C, this could inform us about the expected outcome between A and C. We show that in the seminal Bradley-Terry model knowing the probabilities of A beating B and B beating C completely defines the probability of A beating C, with these probabilities determined by individual skill levels of A, B and C. Users of this model tend not to investigate the validity of this transitive assumption, nor that some skill levels may not be statistically significantly different from each other; the latter leading to false conclusions about rankings. We provide a novel extension to the Bradley-Terry model, which accounts for both of these features: the intransitive relationships between pairs of objects are dealt with through interaction terms that are specific to each pair; and by partitioning the $n$ skills into $A+1\leq n$ distinct clusters, any differences in the objects' skills become significant, given appropriate $A$. With $n$ competitors there are $n(n-1)/2$ interactions, so even in multiple round robin competitions this gives too many parameters to efficiently estimate. Therefore we separately cluster the $n(n-1)/2$ values of intransitivity into $K$ clusters, giving $(A,K)$ estimatable values respectively, typically with $A+K<n$. Using a Bayesian hierarchical model, $(A,K)$ are treated as unknown, and inference is conducted via a reversible jump Markov chain Monte Carlo (RJMCMC) algorithm. The model is shown to have an improved fit out of sample in both simulated data and when applied to American League baseball data.Comment: 26 pages, 7 figures, 2 tables in the main text. 17 pages in the supplementary materia

A Ubiquitous Framework for Statistical Ranking Systems

Ranking systems are everywhere. The thesis will often select sports as its motivating applications, given their accessibility; however, schools and universities, harms of drugs, quality of wines, are all ranked, and all with arguably far greater importance. As such, the methodology is kept necessarily general throughout. In this thesis, a novel conceptual framework for statistical ranking systems is proposed, which separates ranking methodology into two distinct classes: absolute systems, and relative systems. Part I of the thesis deals with absolute systems, with a large portion of the methodology centred on extreme value theory. The methodology is applied to elite swimming, and a statistical ranking system is developed which ranks swimmers, based initially on their personal best times, across different swimming events. A challenge when using extreme value theory in practice is the small number of extreme data, which are by definition rare. By introducing a continuous data-driven covariate, the swim-time can be adjusted for the distance, gender category, or stroke, accordingly, and so allowing all data across all 34 individual events to be pooled into a single model. This results in more efficient inference, and therefore more precise estimates of physical quantities, such as the fastest time possible to swim a particular event. Further increasing inference efficiency, the model is then expanded to include data comprising all the performances of each swimmer, rather than just personal bests. The data therefore have a longitudinal structure, also known as panel data, containing repeated measurements from multiple independent subjects. This work serves as the first attempt at statistical modelling of the extremes of longitudinal data in general and the unique forms of dependence that naturally arise due to the structure of the data. The model can capture a range of extremal dependence structures (asymptotic dependence and asymptotic independence), with this characteristic determined by the data. With this longitudinal model, inference can be made about the careers of individual swimmers - such as the probability an individual will break the world record or swim the fastest time next year. In Part II, the thesis then addresses relative systems. Here, the focus is on incorporating intransitivity into statistical ranking systems. In transitive systems, an object A ranked higher than B implies that A is expected to exhibit preference over B. This is not true in intransitive systems, where pairwise relationships can differ from that which is expected from the underlying rankings alone. In some intransitive systems, a single underlying and unambiguous ranking may not even exist. The seminal Bradley-Terry model is expanded on to allow for intransitivity, and then applied to baseball data as a motivating example. It is found that baseball does indeed contain intransitive elements, and those pairs of teams exhibiting the largest degree of intransitivity are identified. Including intransitivity improves prediction performance for future pairwise comparisons. The thesis ultimately concludes by harmonising the two parts - acknowledging that in reality, there is always some relative element to an absolute system. Forging the armistice between these system types could enflame research into the areas connecting them, which until now remains barren

Determining the effect of human cognitive biases in social robots for human-robotm interactions

The research presented in this thesis describes a model for aiding human-robot interactions based on the principle of showing behaviours which are created based on 'human' cognitive biases by a robot in human-robot interactions. The aim of this work is to study how cognitive biases can affect human-robot interactions in the long term. Currently, most human-robot interactions are based on a set of well-ordered and structured rules, which repeat regardless of the person or social situation. This trend tends to provide an unrealistic interaction, which can make difficult for humans to relate ‘naturally’ with the social robot after a number of relations. The main focus of these interactions is that the social robot shows a very structured set of behaviours and, as such, acts unnaturally and mechanical in terms of social interactions. On the other hand, fallible behaviours (e.g. forgetfulness, inability to understand other’ emotions, bragging, blaming others) are common behaviours in humans and can be seen in regular social interactions. Some of these fallible behaviours are caused by the various cognitive biases. Researchers studied and developed various humanlike skills (e.g. personality, emotions expressions, traits) in social robots to make their behaviours more humanlike, and as a result, social robots can perform various humanlike actions, such as walking, talking, gazing or emotional expression. But common human behaviours such as forgetfulness, inability to understand other emotions, bragging or blaming are not present in the current social robots; such behaviours which exist and influence people have not been explored in social robots. The study presented in this thesis developed five cognitive biases in three different robots in four separate experiments to understand the influences of such cognitive biases in human–robot interactions. The results show that participants initially liked to interact with the robot with cognitive biased behaviours more than the robot without such behaviours. In my first two experiments, the robots (e.g., ERWIN, MyKeepon) interacted with the participants using a single bias (i.e., misattribution and empathy gap) cognitive biases accordingly, and participants enjoyed the interactions using such bias effects: for example, forgetfulness, source confusions, always showing exaggerated happiness or sadness and so on in the robots. In my later experiments, participants interacted with the robot (e.g., MARC) three times, with a time interval between two interactions, and results show that the likeness the interactions where the robot shows biased behaviours decreases less than the interactions where the robot did not show any biased behaviours. In the current thesis, I describe the investigations of these traits of forgetfulness, the inability to understand others’ emotions, and bragging and blaming behaviours, which are influenced by cognitive biases, and I also analyse people’s responses to robots displaying such biased behaviours in human–robot interactions

Philosophical Issues in Sport Science

The role and value of science within sport increases with ever greater professionalization and commercialization. Scientific and technological innovations are devised to increase performance, ensure greater accuracy of measurement and officiating, reduce risks of harm, enhance spectatorship, and raise revenues. However, such innovations inevitably come up against epistemological and metaphysical problems related to the nature of sport and physical competition. This Special Issue identifies and explores key and contemporary philosophical issues in relation to the science of sport and exercise. It is divided into three sections: 1. Scientific evidence, causation, and sport; 2. Science technology and sport officiating; and 3. Scientific influences on the construction of sport. It brings together scholars working on philosophical problems in sport to examine issues related to the values and assumptions behind sport and exercise science and key problems resulting from these and to provide recommendations for improving its practice

Functional Magnetic Resonance Imaging

"Functional Magnetic Resonance Imaging - Advanced Neuroimaging Applications" is a concise book on applied methods of fMRI used in assessment of cognitive functions in brain and neuropsychological evaluation using motor-sensory activities, language, orthographic disabilities in children. The book will serve the purpose of applied neuropsychological evaluation methods in neuropsychological research projects, as well as relatively experienced psychologists and neuroscientists. Chapters are arranged in the order of basic concepts of fMRI and physiological basis of fMRI after event-related stimulus in first two chapters followed by new concepts of fMRI applied in constraint-induced movement therapy; reliability analysis; refractory SMA epilepsy; consciousness states; rule-guided behavioral analysis; orthographic frequency neighbor analysis for phonological activation; and quantitative multimodal spectroscopic fMRI to evaluate different neuropsychological states

Shared mechanisms support controlled retrieval from semantic and episodic memory: Evidence from semantic aphasia

Semantic cognition is supported by at least two interactive components: semantic representations and control mechanisms that shape retrieval to suit the circumstances. Semantic and episodic memory draw on largely distinguishable stores, yet it is unclear whether controlled retrieval from these representational systems is supported by shared mechanisms. Patients with semantic aphasia (SA) show heteromodal semantic control deficits following stroke to left inferior frontal gyrus (LIFG), an area implicated in semantic processing plus the control of memory and language. However, episodic memory has not been examined in these patients and although the role of LIFG in semantics is well-established, neuroimaging cannot ascertain whether this area is directly implicated in episodic control or if its activation reflects semantic processing elicited by the stimuli. Neuropsychology can address this question, revealing whether this area is necessary for both domains. We found that: (i) SA patients showed difficulty discarding dominant yet irrelevant semantic links during semantic and episodic decisions. Similarly, recently encoded events promoted interference during retrieval from both domains. (ii) Deficits were multimodal (i.e. equivalent using words and pictures) in both domains and, in the episodic domain, memory was compromised even when semantic processing required by the stimuli was minimal. (iii) In both domains, deficits were ameliorated when cues reduced the need to internally constrain retrieval. These cues could involve semantic information, self-reference or spatial location, representations all thought to be unaffected by IFG lesions. (iv) Training focussed on promoting flexible retrieval of conceptual knowledge showed generalization to untrained semantic and episodic tasks in some individuals; in others repetition of specific associations gave rise to inflexible retrieval and overgeneralization of trained associations during episodic tasks. Although the neuroanatomical specificity of neuropsychology is limited, this thesis provides evidence that shared mechanisms support the controlled retrieval of episodic and semantic memory

Computational Intelligence in Electromyography Analysis

Electromyography (EMG) is a technique for evaluating and recording the electrical activity produced by skeletal muscles. EMG may be used clinically for the diagnosis of neuromuscular problems and for assessing biomechanical and motor control deficits and other functional disorders. Furthermore, it can be used as a control signal for interfacing with orthotic and/or prosthetic devices or other rehabilitation assists. This book presents an updated overview of signal processing applications and recent developments in EMG from a number of diverse aspects and various applications in clinical and experimental research. It will provide readers with a detailed introduction to EMG signal processing techniques and applications, while presenting several new results and explanation of existing algorithms. This book is organized into 18 chapters, covering the current theoretical and practical approaches of EMG research