Reviewerly/exHarmony huggingface dataset:
Online Convex Optimization of a Multi-task Fuzzy Rule-based Evolving System

This paper extends the recently conceived learning mechanism called EVeP (Extreme Value evolving Predictor), an evolving fuzzy-rule-based predictor characterized by innovative procedures to define the antecedent and consequent parts of the existing fuzzy rules. In EVeP, information granules are recursively updated and associated with Weibull distributions, a generalization of Gaussian distributions which incorporates more robust statistics to establish the region of influence of each fuzzy rule. Shared information from all the rules, in a multitask formulation, is adopted to set the consequent parameters in EVeP. Given that the multi-task formulation is solved using batch learning and gradient descent, the computational cost per iteration tends to be high, being a concern in practical applications. Therefore, here the multi-task framework at the consequent part of the rules was revised to incorporate online convex optimization, given rise to EVeP_OCO. Now, antecedent and consequent parts of the rules are updated in a fully recursive way, with a clear reduction in the computational burden per iteration, particularly when the worst case scenarios are considered: the cost per iteration depends on the current number of rules to be updated. The case studies are composed of a variety of benchmark time series prediction problems. They demonstrate the significant gain in terms of computational cost per iteration, with an admissible reduction in performance by replacing a batch multi-task learning procedure by an online counterpart.
Cutting down high dimensional data with Fuzzy weighted forests (FWF)

Takagi-Sugeno-Kang (TSK) rule-based fuzzy systems struggle to deal with high dimensional data and suffer from the curse of dimensionality. As the number of input features increases, the number of rules increases exponentially, which reduces the model's interpretability rapidly. This paper presents a novel fuzzy weighted forest aggregation method to effectively model high dimensional data by reducing the number of fuzzy rules, without sacrificing accuracy. The fuzzy weighted forest is comprised of several fuzzy weighted trees. Each tree is created based on a subset of features captured across different parts of the input space. Given n input features and N samples, every fuzzy tree is randomly assigned n′ features and N′ samples, where n′ and N′ are significantly smaller than n and N respectively. Each path within a tree, from root to leaf, forms a fuzzy rule. The non-leaf nodes represent the antecedents of rules, and the leaf node represents the consequents. This study shows how the proposed method utilizes pruning to significantly reduce the number of fuzzy rules. This method therefore creates a less complex model whilst achieving high accuracy comparable with, and sometimes better than, existing state-of-the-art TSK-based fuzzy models.
Controlling Membership Spread in Type-2 Fuzzy Clustering

Fuzzy C-means (FCM) has been a prominent clustering algorithm for a long time. It was extended to a type-2 framework by the Linguistic Fuzzy C-means (LFCM) that operates on vectors of fuzzy numbers utilizing the extension principle, the decomposition theorem, and interval analyses. The purpose of this paper is to study the effects of the iterative type-2 fuzzy clustering algorithm. The LFCM incorporates uncertainty through type-2 fuzzy sets, but it is prone to membership spread, i.e., the uncertainty in a membership function can become too large or broad during the iterative alternating optimization procedure. We devise three dampening approaches to mitigate the problem. Vertical cut dampening, linear dampening, and reflection dampening are defined along with the experiments conducted on a synthetic dataset named the butterfly dataset. We also illustrate the updated memberships (fuzzy numbers) and the resulting cluster prototypes (fuzzy vectors) from visual standpoints. Applying any of these dampening approaches will result in thinner membership functions and helps us control the uncertainty not to grow rapidly, and in fact, aid in convergence.
FuzzyTM: a Software Package for Fuzzy Topic Modeling

Unstructured text data is collected daily in large amounts by many organizations. Analyzing all this data is time intensive and too costly in many cases. One technique to systematically analyze large corpora of texts is topic modeling, which returns the latent topics present in a corpus. Recently, several fuzzy topic modeling algorithms have been proposed and have shown superior results over the existing algorithms. Although various Python libraries offer topic modeling algorithms, none includes fuzzy topic models. Therefore, we present FuzzyTM, a Python library for training fuzzy topic models and creating topic embeddings for downstream tasks. The user-friendly pipelines with default values allow practitioners to train a topic model with minimal effort. Meanwhile, its modular design allows researchers to modify each software element and for future methods to be added.
Enhanced Tree-Based Anomaly Detection

Anomaly detection in data sets is one of the most important challenges for modern analysts and data administrators. It is usually based on algorithms that use raw data. In this study, we analyze the possibilities of improving the well-known Isolation Forest algorithm based on binary search trees for data preprocessing using the grouping of both attributes first, and then records within attribute groups. Attribute clustering is based on hierarchical grouping, while record grouping uses K-Means and Fuzzy C-Means. To describe the relationships between records, data membership functions are also used, built on the basis of record distances from centroids. This approach gives a new look at the possibilities of the Isolation Forest method and leads to a significant improvement in the results for selected public databases.
On the Detection of Anomalies with the Use of Choquet Integral and Their Interpretability in Motion Capture Data

Modern information technologies allow for the collection, processing, and data analysis. A very important role of these systems can be observed in the analysis of medical records, particularly in the analysis of motion capture data. Detection and interpretation of data collected from movement recording devices enables for a fast diagnosis, e.g. disease or misfunction. Moreover, it can be assistive in the introduction of appropriate treatment or rehabilitation. Anomaly detection methods play a key role in the evaluation of this type of medical research results. In this study, we introduce an innovative approach based on the aggregation of the results of eleven anomaly detection classifiers outcomes with the fuzzy Choquet integral. Furthermore, the results of numerical experiments are confronted with the assessments of the experts in medical field. The results show the great potential of our method in supporting the decision-making process based on the motion capture data analysis. Moreover, we have caught the differences between the expert's understanding of anomaly and the anomalies in data found by the modern machine learning methods.
First-Order Autonomous Learning Multi-Model Systems for Multiclass Classification tasks

The First-Order Autonomous Learning Multi-Model (ALMMo) system was initially introduced as a regressor which could be easily adapted to a binary classifier. In this paper, an extension of the ALMMo algorithm is proposed, enabling it to tackle multi-class classification tasks, without escalating the computational demand substantially. Thus, this paper highlights the flexibility of the method by increasing its range of capabilities. The proposed extension is tested in 3 benchmark datasets, and the obtained results are presented as a proof of the concept. Furthermore, these results are compared to 2 benchmark methods, those being shallow neural-networks and support vector machines; as well as to the ALMMo-0 classifier.
Alpha-cut based compositional representation of fuzzy sets and exploration of associated fuzzy set regression

The compositional representation of data and associated statistical approaches is a powerful framework for modelling and reasoning about quantities which reflect proportions of a whole. Recently, an increasing body of work has started exploring the adoption of a compositional representation for modelling interval-valued data reflecting uncertainty or vagueness, for example interval-valued questionnaire responses. Results have flagged the intriguing potential of this approach, such as the elegant handling of traditional inference challenges, including implicitly ensuring coherence in linear regression for interval data, i.e. ensuring the estimated left bound of intervals is smaller than the right one. Building on these insights, extending the compositional representation via alpha-cut decomposition to fuzzy sets is an intuitive next step. In this paper, we discuss this compositional representation of fuzzy sets, building on prior interval work. We proceed to explore the adoption of compositional regression approaches to conduct linear regression on fuzzy set valued data sets. We demonstrate the approach, discuss results and in particular flag shortcomings and the challenges for next steps.
Semantic Correlation Graph Embedding

Many data sets include categorical features in the form of nominal and ordinal features. However, most machine learning algorithms cannot deal with categorical features directly because they require numerical input features. Categorical embeddings are an effective approach to converting categorical features into numerical vectors. This work proposes a novel embedding approach, called Semantic Correlation Graph Embedding, to create embeddings from knowledge graphs. The approach constructs a semantic correlation graph of triplets among the categorical features to learn numerical embeddings. Our approach aims to uncover relationships taking place in categorical data in terms of low-level knowledge and semantics that may help group the features of the data sets under semantic entities. Three distinct embedding models are proposed according to how the graph is constructed. The results are evaluated with two public data sets. They show that the learned embeddings produce a statistically significant improvement in the performance of the classification tasks in terms of AUC, F1 score, precision, and recall.
An Integrated Fuzzy Logic System under Microsoft Azure using Simpful

Mobile applications in the area of human-centered applications are based on fuzzy logic have exhibited their effectiveness in managing intelligent environments, however the deployment of mobile fuzzy logic systems has been usually associated with dedicated hardware and software packages. Introducing openness for fuzzy logic systems offers exciting features such as system independence, simplicity, load balancing, and controlled resource allocation. On the other hand, while major cloud service providers support readymade commercial services for AI techniques such as for deep neural networks, there is no similar services for fuzzy logic systems. This study aims to develop a cloud-based fuzzy logic system under Microsoft Azure, employing Simpful as the cloud-side Python library and FML as data exchange standard. The developed cloud service is shown to effectively serve mobile phone applications for human monitoring purposes. Also in the present study, two types of fuzzy inference systems namely Mamdani and TSK have been utilized wherein both these systems have been compared on the basis of their processing time and accuracy of result. Results indicated that Mamdani fuzzy inference system outperformed TSK fuzzy inference system in terms of processing time by 0.456 seconds. Moreover, the detection accuracy of Mamdani system was found to be higher than that of TSK system by 6.82%.
A Study on the Best Way to Compress Natural Language Processing Models

Current research in Natural Language Processing shows a growing number of models extensively trained with large computational budgets. However, these models present computationally demanding requirements, preventing them from being deployed in devices with strict resource and response latency limitations. In this paper, we apply state-of-the-art model compression techniques to create compact versions of several of these models. In order to evaluate whether the trade-off between model performance and budget is worthwhile, we evaluate them in terms of efficiency, model simplicity and environmental foot-print. We also present a brief comparison between uncompressed and compressed models when running in low-end hardware.
A new multi-rules approach to improve the performance of the Chi fuzzy rule classification algorithm

The Chi algorithm has been used in many applications to obtain a set of fuzzy rules from the data. Its main advantage is that it requires low learning time in relation to others learning algorithms and that makes it a good candidate for tackling Big Data problems. However, this algorithm has some shortcomings, among them, its limited generalization capability, which leads to obtaining a high number of uncovered examples over new unseen examples. An improved version of this algorithm is presented in this paper. The main difference lies in the process of assigning a candidate rule to each example, the original algorithm assigns only one, and in the new proposal, several rules are assigned. The inclusion of more rules improves the generalization capacity of the acquired knowledge, which in turn leads to an improvement in accuracy. The paper includes an extensive experimental study to show the performance of the proposal.
Extract attribute types and values based on sentiment analysis and preference disaggregation

By harnessing evaluation information like attribute types and attribute values hidden in online reviews, managers can acquire consumer preference and satisfaction over products. Most existing studies only considered how to extract attribute types or attribute values of products from textual reviews, but ignored the role of attribute-level ratings in reflecting consumer preference and satisfaction. Based on the techniques of sentiment analysis and preference disaggregation, this paper unifies the quantitative and qualitative information extracted from textual reviews and attribute-level ratings to obtain attribute types and values of products. A real case on TripAdvisor.com is given to verify the proposed method.
Security considerations for the procurement and acquisition of Artificial Intelligence (AI) systems

Procurement is a critical step in the setup of systems, as reverting decisions made at this point is typically time-consuming and costly. Especially Artificial Intelligence (AI) based systems face many challenges, starting with unclear and unknown side parameters at design time of the systems, changing ecosystems and regulations, as well as problems of overselling capabilities of systems by vendors. Furthermore, the AI Act puts forth a great deal of additional requirements for operators of critical AI systems, like risk management and transparency measures, thus making procurement even more complex. In addition, the number of providers of AI systems is drastically increasing. In this paper we provide guidelines for the procurement of AI based systems that support the decision maker in identifying the key elements for the procurement of secure AI systems, depending on the respective technical and regulatory environment. Furthermore, we provide additional resources for utilizing these guidelines in practical procurement.
Blockchain Technology Adoption in Outbound Logistics for the Fourth Industrial Revolution

There are opportunities for disruptive applications of blockchain technology in outbound logistics management, especially with peer-to-peer networking support to keep records of verified transactions in distributed ledgers without control by an intermediate party. The impact of implementing a blockchain solution in logistics processes and the effect on process parameters is not well understood or quantified in the literature. The objective of this study is to investigate whether there are opportunities in outbound logistics operations to benefit from the application of blockchain technology and to evaluate the impact of implementing a blockchain technology solution in a specific industry use case. A design science research process, which combines qualitative and quantitative research methods, guided this study. A stochastic discrete event simulation model was developed to evaluate the impact of a blockchain solution for an industry use case in an outbound logistics process. The time to reach visibility is quantified for a Hyperledger Fabric implementation. The results indicated that it would have a significant effect on the time it takes to gain insight into the process and transparency. This study provides evidence that a blockchain solution can have a notable impact on information availability and transparency in an outbound logistics process.
Automation of Frontline Service Encounters: A Script Theory Perspective

Technological developments have allowed more frontline service encounters to be automated, but there are still failed attempts, suggesting a gap between technology’s ability and customer readiness. This paper discusses factors that affect the substitution of human employees by technology in the frontline from the perspective of script theory. Originating from cognitive psychology, script theory (Abelson, 1976; Schank & Abelson, 1977) states that human minds consist of structures that help make sense of situations and guide behaviors. Technological developments such as improving humanness illustrate attempts to mimic human-to-human interactions, i.e. try to get close to the traditional service scripts. However, customers may still reject these efforts if they cannot adjust their scripts to suit the new service style. Based on a literature review of script theory in the service context, propositions will be developed with the aim to include both technology and customers in the discussion of automated service encounters.
Fast Track Innovating Process : A Maker-Movement-Based Approach to Connect Digital Talents with Companies

when companies start an innovation projects for smart products, they often face several obstacles: e.g. a lack of tools to select the right ideas, the difficulty to make ideas easy to explain to management board, and the “culture clash” within the organization. In this paper, we present the Fast Track Innovating approach to link traditional innovation processes with lean and fast development approaches, inspired by the maker mindset “Just do it”.
A New Theory on Temporary Systems that Explains Project Behavior and Performance

Although adaptive approaches to managing complex projects have shown performance improvement, projects are still not controlling the scope of work and exceed schedule and budget. Therefore, these performance problems must be caused by other factors affecting the project’s behavior. This paper proposes a new system’s theory that explains project behavior and performance. A project is a temporary system of resources that possess the knowledge and skills to fulfill the output scope (i.e., product, service, or result). The natural variation in project behavior and performance is the uncertainty within the project resources to fulfill the complexity of the project output. The theory is based on the following principles. First, there is natural variance is in all systems. Second, the more natural variance in a system, the more chaotic a system will behave. Third, improving the system’s performance can only be achieved by reducing the natural variance in the system. The theory was developed from studying the behavior and performance of ten longitudinal IT projects that ranged in complexity over a period of seven years.
The Role of the Project Manager in the Agile Methodology

The role of the Project Manager was well defined by the PMBOK [1] before the introduction of the Agile approach to R & D organizations. The basic assumption of this study is that the development methodology in the organization, being the traditional Waterfall or Agile, impacts the role of the PM. Therefore, this research is looking to prove how the functioning of a PM in Agile has changed from the functioning of the traditional PM is functioning. In the literature survey, we encountered new management roles in the agile world, that didn't exist before: The Scrum Master (team lead in Agile) and the Agile coach (a function that is meant to help the organization define new agile processes and assimilate the agile methodology). Based on the literature review, we formulated our research hypotheses about an overlap between these functions and the PM role in agile. It was found that agile reduces the importance of the PM role, causing a reduction in control and quality and less commitment to him, since he is not a scrum master.
The Analysis of Global Joint Research Activities in Future Prospective Field : Focusing on the Field of Bio-pharmaceuticals

Although R&D international cooperation is becoming more active in the era of the Fourth Industrial Revolution, science and technology convergence and technology change, South Korea lacks international joint study activities due to inferior research capabilities, closed R&D ecosystems, and lack of an international cooperation system. In the 2020 National Science and Technology Innovation Competency Assessment, South Korea's international cooperation category index ranks 16th among 35 OECD countries, but is lower than average, requiring strategies to enhance international cooperation activities. In this study, the analytical data selected SCI papers consisting of a joint study in the field of bio-pharmaceuticals. The KDD/KM Co-author methodology, a big data analysis methodology, was utilized to conduct international joint research status and network analysis among key researchers. Through this, the development plan for vitalizing the international cooperation network was derived, and the results of this study will contribute to the development of government support policies for vitalizing international cooperation centered on key researchers in South Korea.
Extracting and Quantifying Actionable Knowledge Using Twitter Data

Knowledge management is essential for businesses to gain competitive advantage. Social platforms like Twitter, Facebook, and others hold important information external to the organization. These platforms are enablers to creating, sharing and forming opinions about topics of interest. External knowledge management of social data possess two types of challenges to knowledge workers and managers: 1) unfamiliarity of methods to obtain data and distill actionable knowledge; and 2) inability to quantify the acquired knowledge. Without familiarity in methods and quantifiable knowledge, justifying return on investments on the knowledge-related programs is inefficient. This research studies existing methods-topic mining and sentiment analysis-to obtain actionable knowledge, and it formulates a novel framework to mine external knowledge from Twitter. It also identifies the nature of actionable knowledge metrics that can be applied to a broad knowledge discovery process. The framework is applied on a dataset and the results are discussed. This study benefits knowledge workers and managers by identifying and quantifying actionable knowledge from social platforms. Thus, it provides ROI justifications and contribute toward achieving measurable knowledge value for businesses.