Reviewerly/exHarmony huggingface dataset:
A Bayesian Interpretation of Fuzzy C-Means

In Explainable Artificial Intelligence, the interpretation of the decisions provided by a model is of primary importance. In this context, we consider Fuzzy C-Means (FCM), which is a clustering algorithm that induces a model from data by assigning, to each data-point, a degree of membership to each cluster such that the sum of memberships is one. A fuzzification parameter is also used to tune the degree of fuzziness of clusters. The distribution of membership degrees suggests an interpretation of membership degrees within the Probability Theory. This paper shows that the membership degrees resulting from FCM can be interpreted as posterior probabilities derived from a Bayesian model, which assumes that data are generated through a specific probability density function. The results give a clear interpretation of the membership degrees of FCM, as well as its fuzzification parameter, within a sound theoretical framework, and shed light on possible extensions of the algorithm.
Interpretable Neuro-Fuzzy Models for Stress Prediction

Machine learning algorithms are useful in assisting medical judgments, but the resulting models are frequently challenging for doctors to comprehend. Conversely, IF-THEN rules articulated in natural language are successful at describing interpretable models that are returned by Fuzzy Inference Systems. However, the construction of the fuzzy rule basis in conventional fuzzy systems requires human expertise. Neuro-fuzzy systems can infer fuzzy rule-based models from data, saving time by doing away with the need to manually build the rules. Using neuro-fuzzy systems to develop prediction models from data in the form of fuzzy rules that are suitable to enhance decision-making for stress assessment is what we propose in this paper. Our results highlight how well neuro-fuzzy models perform in providing precise predictions while maintaining interpretability.
Social Learning and Sentiment Contagion in the Bitcoin Market

Using novel data on investors' social interactions, we document sentiment contagion as a direct consequence of social learning in the Bitcoin market. Our findings suggest that the social learning process is inefficient, as investors respond positively to social sentiment, but social sentiment does not positively predict returns. Echo chamber effect and selective interpretation of information may simultaneously contribute to such inefficiency. We further confirm that sentiment contagion is not a sideshow: at the individual level, sentiment contagion predicts the direction of trading conditional on its occurrence; at the Bitcoin market level, the aggregated sentiment contagion index positively predicts market trading volume and volatility. Moreover, we highlight the error-prone nature of social learning, wherein the socially constructed optimism predicts Bitcoin crashes. Finally, we establish a link between social learning and bubbles: the elevated propagation of optimism is highly correlated with the trading volume.
The Inevitability of Vagueness in Fuzzy Logic

Fuzzy Logic, in its fuzzy control incarnation, can be as well seen as an answer to the belated question on how can an algorithm take in account the subtle variations in a complex system – be it the simple and paradigmatic thermostat, the description of a traffic jam or the inner workings of a spaceship. Contrary to the best whodunit, the answer is given at the very start, and the history of the following years of fuzziness is a long demonstration of how, thanks to its explanatory power, a simple idea can be implemented in countless devices, and become the manifest for a technological society in which the logic discourse is based more on the human approach and on embracing the permanent state of flux and uncertainty that is the human experience than on the futile search for absolute truths and endless precision. If the control answer is somehow a given, so many other answers that concern foundations of Fuzzy Logic still beget proper questions. In this paper some of such foundational questions pertaining vagueness, its role in the definition of fuzziness and its many incarnations are set in their historical perspective, and some of the dots outlining the path from a rigid search for truth typical of the end of the nineteenth century to the more nuanced approach that has swept the twentieth century are connected.
Why Fractional Fuzzy

In many practical situation, control experts can only formulate their experience by using imprecise (“fuzzy”) words from natural language. To incorporate this knowledge in automatic controllers, Lotfi Zadeh came up with a methodology that translates the informal expert statements into a precise control strategy. This methodology – and its following modifications – is known as fuzzy control. Fuzzy control often leads to a reasonable control – and we can get an even better control result by tuning the resulting control strategy on the actual system. There are many parameters that can be changed during tuning, so tuning usually is rather time-consuming. Recently, it was empirically shown that in many cases, quite good results can be attained by using a special 1-parametric tuning procedure called fractional fuzzy inference – we get up to 40% improvements just by selecting the proper value of a single parameter. In this paper, we provide a theoretical explanation of why fractional fuzzy inference works so well.
Measuring Overhead Costs of Federated Learning Systems by Eavesdropping

This paper addresses the issue of communication overhead costs of federated learning including transmission bandwidth and synchronisation efforts. These costs typically consist of locally observable costs on executing components, but there are also hidden costs that can only be measured from a system-wide perspective. The goal is to provide insight into these hidden costs, measure them and identify strategies for reducing them. We propose an approach to tackle the hidden costs by establishing a methodology consisting of an eavesdropping concept and an evaluation strategy. This way we obtain a refined analysis of directly observable costs contrasting hidden costs, which is underpinned by experiments based on a 40-client-spanning federated learning system and the FEMNIST dataset.
Contextual Boosting to Explainable SVM Classification

Finding suitable mechanisms whereby rationale behind support vector machine (SVM) predictions can be known and understood without substantial difficulties is an ongoing challenge. Aiming to find such a mechanism, we look into the contextualization of SVM models. Hence, we propose a novel explainable SVM classifier that makes use of a parallel arrangement of contextualized SVM models for offering predictions that depend on a particular event, situation or idea. The proposed classifier allows decision makers to state in a clear manner the context of the predictions they would like to be offered. This aspect is deemed to be important since decision makers can take advantage of the improvement in the interpretability of such contextualized predictions for making more informed decisions. The improvement in interpretability is illustrated through an example in which digitized handwritten vowels are contextually identified. Another example where hand gestures are recognized by means of electromyography (EMG) signals shows how the proposed classifier can also improve the accuracy of the resulting models.
The Impact Level of Using Games and Electronic Activities in Improving the Visual Perception among Students with Learning Disabilities from their Teachers Perspective in Jordan

The study aimed to identify the impact level of using games and electronic activities in improving the visual perception among students with learning. The study used the descriptive survey method, and the study members were from the entire community of (162) teachers for learning disabilities students belonging to the education directorates of Amman Governorate. A questionnaire was developed by the researchers which included (35) items to investigate the views of learning disabilities teachers, which was applied after extracting the validity and reliability of the tool in the second semester of the academic year 2018-2019. The results showed that the effect level of using games and electronic activities in improving the visual perception among students with learning disabilities was medium, where the mean of the total score was (3.66). The result also showed that there were statistically significant differences in the effect of the sex variable in favor of females, and the impact of the scientific qualification variable in favor of bachelor, and the impact of the variable of years of experience came in favor of (5 to 10 years), as for the teaching method variable came in favor of the electronic method. The researchers recommended the necessity of moving towards computerizing education in special education through educational electronic games and activities, and working on preparing training programs to train teachers of students with learning disabilities to produce and design electronic games and activities in light of technological development, and conduct research, studies and conferences, which highlights the electronic games and educational activities in the educational e-learning process, among the categories of special education students who are able to learn and train.
Character Queries: A Transformer-Based Approach to On-line Handwritten Character Segmentation

On-line handwritten character segmentation is often associated with handwriting recognition and even though recognition models include mechanisms to locate relevant positions during the recognition process, it is typically insufficient to produce a precise segmentation. Decoupling the segmentation from the recognition unlocks the potential to further utilize the result of the recognition. We specifically focus on the scenario where the transcription is known beforehand, in which case the character segmentation becomes an assignment problem between sampling points of the stylus trajectory and characters in the text. Inspired by the $k$-means clustering algorithm, we view it from the perspective of cluster assignment and present a Transformer-based architecture where each cluster is formed based on a learned character query in the Transformer decoder block. In order to assess the quality of our approach, we create character segmentation ground truths for two popular on-line handwriting datasets, IAM-OnDB and HANDS-VNOnDB, and evaluate multiple methods on them, demonstrating that our approach achieves the overall best results.
A study of English listening support teaching supported by mobile smart terminal APP

Abstract With the development of communication tools such as streaming media technology, education methods and concepts have changed accordingly. In this context, English listening teaching concepts and teaching methods have also changed. In this paper, by analyzing the advantages of the smart terminal APP, we propose a multiple-assisted English teaching model, construct a vector machine evaluation model based on the least squares method, and screen the most suitable mobile English listening assistance system for students through the meritocracy of particle swarm algorithm. Finally, the guidelines of teaching methods adapted to English listening assistance education for high school students were developed by combining support vector machines. The experiments show that the multi-source terminal teaching system in the context of mobile teaching transforms cell phones that interfere with classroom teaching into teaching assistants, improves the original listening teaching environment, breaks through the time and space limitations of the traditional listening teaching mode, and provides a reference for teaching English listening courses.
Action class relation detection and classification across multiple video datasets

The Meta Video Dataset (MetaVD) provides annotated relations between action classes in major datasets for human action recognition in videos. Although these annotated relations enable dataset augmentation, it is only applicable to those covered by MetaVD. For an external dataset to enjoy the same benefit, the relations between its action classes and those in MetaVD need to be determined. To address this issue, we consider two new machine learning tasks: action class relation detection and classification. We propose a unified model to predict relations between action classes, using language and visual information associated with classes. Experimental results show that (i) pre-trained recent neural network models for texts and videos contribute to high predictive performance, (ii) the relation prediction based on action label texts is more accurate than based on videos, and (iii) a blending approach that combines predictions by both modalities can further improve the predictive performance in some cases.
Infusing a Convolutional Neural Network with Encoded Joint Node Image Data to Recognize 25 Daily Human Activities

Human activity recognition (HAR) has gained popularity in the field of computer vision such as video surveillance, security, and virtual reality. However, traditional methods are limited in terms of computations and holistic learning of human skeletal sequences. In this article, a new time‐series skeleton joint data imaging method is infused into an improved convolutional neural network to handle these problems. First, the raw time‐series data of 33 body nodes are transformed to red–green–blue images by encoding the 3D positional information to one pixel. Second, the LeNet‐5 network is enhanced by expanding the receptive field, introducing coordinate attention and the smooth maximum unit to improve smoothness and feature extraction. Third, the ability of coded images to express human activities is studied in various environments. It is shown in the experimental results that the method achieves an impressive accuracy of 98.02% in recognizing 25 daily human activities, such as running, writing, and walking. In addition, it is shown that the number of floating point operations, parameters, and inference time of the method are 0.08%, 0.47%, and 3.05%, respectively, of the average values for six other networks (including AlexNet, GoogLeNet, and MobileNet). The proposed method is thus a novel, lightweight, and high‐precision solution for HAR.
Deixis Analysis on the Ellen Show YouTube Channel

Deixis refers to the study of language usage, whether spoken or written, in order to indicate or point to something specific. The objective of this research is to analyze the deixis employed in The Ellen Show's YouTube channel and comprehend the meaning conveyed through this deixis. The study utilized a qualitative approach with content analysis. The analysis was conducted by applying deixis theory proposed by Yule. The findings reveal three categories of deixis found in the data: person deixis, spatial deixis, and temporal deixis. Among these, temporal deixis is the most prevalent, followed by person deixis, and spatial deixis is the least frequently used. In addition, the analysis of the data shows that person deixis is used to clarify by whom a sentence was uttered and to whom it refers. Spatial deixis is used to clarify a sentence regarding the place and reference of a word. Meanwhile, temporal deixis is used to give a description of the time and description of an event occur. To conclude, the use of deixis in communication may convey clear information about who is talking, where it takes pace, and when it happens.
Event and Entity Extraction from Generated Video Captions

Annotation of multimedia data by humans is time-consuming and costly, while reliable automatic generation of semantic metadata is a major challenge. We propose a framework to extract semantic metadata solely from automatically generated video captions. As metadata, we consider entities, the entities' properties, relations between entities, and the video category. Our framework combines automatic video captioning models with natural language processing (NLP) methods. We use state-of-the-art dense video captioning models with masked transformer (MT) and parallel decoding (PVDC) to generate captions for videos of the ActivityNet Captions dataset. We analyze the output of the video captioning models using NLP methods. We evaluate the performance of our framework for each metadata type, while varying the amount of information the video captioning model provides. Our experiments show that it is possible to extract high-quality entities, their properties, and relations between entities. In terms of categorizing a video based on generated captions, the results can be improved. We observe that the quality of the extracted information is mainly influenced by the dense video captioning model's capability to locate events in the video and to generate the event captions. An earlier version of this paper has been published on arXiv [20]. We provide the source code here: https://github.com/josch14/semantic-metadata-extraction-from-videos .
The Similarity of Virtual Meal of a Co-eating Agent Affects Human Participant

In co-eating with real people, similar food consumption was found to benefit food intake and some subjective feelings. While co-eating agents have the potential to be caregivers or companions, the discussion of meal similarity between participants and agents is lacking. In this study, to achieve better social facilitation and the sense of eating together by a co-eating agent, we focused on the effects of meal similarity on eating amount and subjective feelings. We developed co-eating agents which can eat three types of food and conducted a laboratory-based artificial co-eating experiment. The results showed that participants perceived the meal similarity and the sense of eating together to be higher when the co-eating agent eats similar virtual food. In addition, a relationship was found between food tastiness and the difference of eating amount between the conditions. We propose that creating similar foods for co-eating agents can improve the feeling of togetherness in artificial co-eating and have the potential to facilitate eating when the food is preferred.
Exploring the Reciprocal Emotional Interaction Between Humans and Affective Technology Through Design Fictions and “Speculative Entanglement”

This paper explores how emotion recognition technology is perceived, understood, felt, and reimagined through a set of design fiction processes by making and re-making arrangements of complex relationships between technologies, practices, emotions, and everyday lives. Emotion plays a crucial part in how we interact with the world: it is ephemeral, contingent, and contextual. Technologies capturing human emotions trace how we feel from almost all perspectives of our lives. To start with our explorations, we first write design fiction and take them to workshops as a probe into complex relationships between emotion recognition and our everyday lives. Participants work in teams to create design fiction prototypes that expand and respond to the fictional worldbuilding. By analyzing how the entanglements of emotion recognition technology in human lives are carried through and transformed among multiple design fiction processes, we contribute to a set of design processes that uses design fiction as a probe for the next speculation, in this case, reconfigure emotional recognition among humans and technology in design fiction workshops.
Hyper-Stacked: Scalable and Distributed Approach to AutoML for Big Data

The emergence of Machine Learning (ML) has altered how researchers and business professionals value data. Applicable to almost every industry, considerable amounts of time are wasted creating bespoke applications and repetitively hand-tuning models to reach optimal performance. For some, the outcome may be desired; however, the complexity and lack of knowledge in the field of ML become a hindrance. This, in turn, has seen an increasing demand for the automation of the complete ML workflow (from data preprocessing to model selection), known as Automated Machine Learning (AutoML). Although AutoML solutions have been developed, Big Data is now seen as an impediment for large organisations with massive data outputs. Current methods cannot extract value from large volumes of data due to tight coupling with centralised ML libraries, leading to limited scaling potential. This paper introduces Hyper-Stacked, a novel AutoML component built natively on Apache Spark. Hyper-Stacked combines multi-fidelity hyperparameter optimisation with the Super Learner stacking technique to produce a strong and diverse ensemble. Integration with Spark allows for a parallelised and distributed approach, capable of handling the volume and complexity associated with Big Data. Scalability is demonstrated through an in-depth analysis of speedup, sizeup and scaleup.
Towards a Design Toolkit for Designing AR Interface with Head-Mounted Display for Close-Proximity Human-Robot Collaboration in Fabrication

Industry 5.0 puts forward clear requirements for improving the interactive experience of human-machine collaboration, but few design tools currently exist to assist designers to think through how to design human-robot interactions, with a focus on turn-taking, coordination and communication of the interactive intention flow. We present a design toolkit for designers to explore, specify, analyse and document the unfolding interaction between human(s) and robot(s) through a model of interaction where the roles of leader and follower shape the forms of collaborative behaviour. We illustrate our preliminary design toolkit in action and demonstrate how it can be used to aid in exploring new augmented reality applications in robotic fabrication.
Enhancing Trust in Machine Learning Systems by Formal Methods

With the deployment of applications based on machine learning techniques the need for understandable explanations of these systems' results becomes evident. This paper clarifies the concept of an "explanation": the main goal of an explanation is to build trust in the recipient of the explanation. This can only be achieved by creating an understanding of the results of the AI systems in terms of the users' domain knowledge. In contrast to most of the approaches found in the literature, which base the explanation of the AI system's results on the model provided by the machine learning algorithm, this paper tries to find an explanation in the specific expert knowledge of the system's users. The domain knowledge is defined as a formal model derived from a set of if-then-rules provided by experts. The result from the AI system is represented as a proposition in a temporal logic. Now we attempt to formally prove this proposition within the domain model. We use model checking algorithms and tools for this purpose. If the proof is successful, the result of the AI system is consistent with the model of the domain knowledge. The model contains the rules it is based on and hence the path representing the proof can be translated back to the rules: this explains, why the proposition is consistent with the domain knowledge. The paper describes the application of this approach to a real world example from meteorology, the short-term forecasting of cloud coverage for particular locations.