Reviewerly/exHarmony huggingface dataset:
Viric Learning - A Novel Transfer Learning Method

Increasingly, information systems include intelligent components to make decisions or perform actions based on data, owing to advances in data science and artificial intelligence. Although advances have been made at the machine level to enable the execution of more complex models, due to the changing context they can quickly become outdated and require a high number of resources to adapt to new conditions, thus transfer learning is being investigated. This discipline seeks to optimize learning tasks of an intelligent model based on the learning from another model. Therefore, a method is proposed that focuses on what all models receive in a generic way: data. The objective is to elaborate a design data register that, in controlled training sequences, transfers only the quality knowledge of the model that from it was created to a new one by means of optimization algorithms. Likewise, it will also obtain clarity on the criteria used by the models to perform their functions, the development of objective mechanisms to obtain traceability of the training data and an optimal method of adaptation to the changing context to which the information systems are subjected.
KAJIAN INFOGRAFIS KEBUDAYAAN PROVINSI DAERAH ISTIMEWA YOGYAKARTA

This research aims to find out about the presentation of cultural infographic data in the Special Region of Yogyakarta. The discussion in this research will focus on cultural infographic data in the Special Region of Yogyakarta. The method used in this research is a qualitative approach with descriptive method. Data collection was done by interview, observation and documentation. The results show that the presentation using infographics will be very easy to understand in reading secondary data. The results of this study indicate that infographics as promotional media play a role in knowledge utilization. With research indicators, namely attractiveness, clarity of infographics, ease of understanding. So from the results of the study it can be concluded that infographics as a promotional media has a role in the utilization of data presentation: seen in the aspect of attractiveness, it can be concluded that the presentation of the content in infographics attracts the attention of infographic readers through the display of colors and images so as to attract readers' interest; seen from the aspect of clarity, the information presented in infographic content provides clarity to readers through the display of letters and the suitability of the images used; from the aspect of ease, infographic readers can understand the information in infographics easily so that readers can understand the meaning of the message and can interpret it. Keywords: Infographic, Culture, Tourism
Towards Optimizing Performance of Machine Learning Algorithms on Unbalanced Dataset

Imbalanced data, a common occurrence in real-world datasets, presents a challenge for machine learning classification models. These models are typically designed with the assumption of balanced class distributions, leading to lower predictive performance when faced with imbalanced data. To address this issue, this paper employs data preprocessing techniques, including Synthetic Minority Oversampling Technique (SMOTE) for oversampling and random undersampling, on unbalanced datasets. Additionally, genetic programming is utilized for feature selection to enhance both performance and efficiency. In our experiment, we leverage an imbalanced bank marketing dataset sourced from the UCI Machine Learning Repository. To evaluate the effectiveness of our techniques, we implement it on four different classification algorithms: Decision Tree, Logistic Regression, K-Nearest Neighbors (KNN), and Support Vector Machines (SVM). We compare various evaluation metrics, such as accuracy, balanced accuracy, recall, F-score, Receiver Operating Characteristics (ROC) curve, and Precision-Recall (PR) curve, across different scenarios: unbalanced data, oversampled data, undersampled data, and data cleaned with Tomek-Links. Our findings reveal that all four algorithms demonstrate improved performance when the minority class is oversampled to half the size of the majority class and the majority class is undersampled to match the minority class. Subsequently, applying Tomek-Links on the balanced dataset further enhances performance.
Improving the Performance of Hybrid Models Using Machine Learning and Optimization Techniques

Hybrid models, which combine multiple machine learning algorithms or optimization techniques, have shown great promise in tackling complex real-world problems. The integration of diverse approaches can lead to enhanced performance, increased accuracy, and more robust predictions. In this paper, we explore various methods to improve the performance of hybrid models using machine learning and optimization techniques. We discuss the advantages of hybrid models, the challenges associated with their design and implementation, and present case studies to demonstrate their effectiveness in different domains. Hybrid models, which combine diverse machine learning techniques and optimization strategies, have emerged as a powerful approach for tackling complex problems and enhancing performance across various domains. This paper delves into the realm of improving hybrid model performance through the synergistic integration of machine learning and optimization techniques. By seamlessly amalgamating different models and leveraging optimization methodologies, hybrid models can achieve superior predictive accuracy, robustness, and generalization. The paper presents a comprehensive framework for enhancing hybrid model performance, encompassing key stages such as problem formulation, data collection, preprocessing, model selection, integration, and feature engineering. Additionally, it highlights the pivotal role of optimization techniques, ranging from hyperparameter tuning and gradient-based optimization to constraint optimization and multi-objective optimization. The paper emphasizes the importance of ensemble methods, elucidating their potential to further elevate hybrid model efficacy. Furthermore, the concept of interpretability and explain ability is explored, ensuring that the developed hybrid models remain intelligible and transparent, especially in critical decision-making scenarios. The iterative nature of refining hybrid models is discussed, stressing the significance of continuous experimentation and adaptation to achieve optimal outcomes. Through a cohesive synthesis of machine learning and optimization, this paper offers insights into how hybrid models can be harnessed to address intricate challenges in various domains. The presented framework serves as a guiding beacon for researchers and practitioners, facilitating the design, development, and deployment of hybrid models that push the boundaries of performance and innovation.
Zero-shot Cross-Linguistic Learning of Event Semantics

Malihe Alikhani, Thomas Kober, Bashar Alhafni, Yue Chen, Mert Inan, Elizabeth Nielsen, Shahab Raji, Mark Steedman, Matthew Stone. Proceedings of the 15th International Conference on Natural Language Generation. 2022.
EDU-AP: Elementary Discourse Unit based Argument Parser

Neural approaches to end-to-end argument mining (AM) are often formulated as dependency parsing (DP), which relies on token-level sequence labeling and intricate post-processing for extracting argumentative structures from text. Although such methods yield reasonable results, operating solely with tokens increases the possibility of discontinuous and overly segmented structures due to minor inconsistencies in token level predictions. In this paper, we propose EDU-AP, an end-to-end argument parser, that alleviates such problems in dependency-based methods by exploiting the intrinsic relationship between elementary discourse units (EDUs) and argumentative discourse units (ADUs) and operates at both token and EDU level granularity. Further, appropriately using contextual information, along with optimizing a novel objective function during training, EDU-AP achieves significant improvements across all four tasks of AM compared to existing dependency-based methods.
Inferring Ranked Dialog Flows from Human-to-Human Conversations

We present a novel technique to infer ranked dialog flows from human-to-human conversations that can be used as an initial conversation design or to analyze the complexities of the conversations in a call center. This technique aims to identify, for a given service, the most common sequences of questions and responses from the human agent. Multiple dialog flows for different ranges of top paths can be produced so they can be reviewed in rank order and be refined in successive iterations until additional flows have the desired level of detail. The system ingests historical conversations and efficiently condenses them into a weighted deterministic finite-state automaton, which is then used to export dialog flow designs that can be readily used by conversational agents. A proof-of-concept experiment was conducted with the MultiWoz data set, a sample output is presented and future directions are outlined.
Dialogue Evaluation with Offline Reinforcement Learning

Nurul Lubis, Christian Geishauser, Hsien-chin Lin, Carel van Niekerk, Michael Heck, Shutong Feng, Milica Gasic. Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue. 2022.
Generating Meaningful Topic Descriptions with Sentence Embeddings and LDA

A major part of business operations is interacting with customers. Traditionally this was done by human agents, face to face or over telephone calls within customer support centers. There is now a move towards automation in this field using chatbots and virtual assistants, as well as an increased focus on analyzing recorded conversations to gather insights. Determining the different services that a human agent provides and estimating the incurred call handling costs per service are key to prioritizing service automation. We propose a new technique, ELDA (Embedding based LDA), based on a combination of LDA topic modeling and sentence embeddings, that can take a dataset of customer-agent dialogs and extract key utterances instead of key words. The aim is to provide more meaningful and contextual topic descriptions required for interpreting and labeling the topics, reducing the need for manually reviewing dialog transcripts.
Guidance in Radiology Report Summarization: An Empirical Evaluation and Error Analysis

Automatically summarizing radiology reports into a concise impression can reduce the manual burden of clinicians and improve the consistency of reporting. Previous work aimed to enhance content selection and factuality through guided abstractive summarization. However, two key issues persist. First, current methods heavily rely on domain-specific resources to extract the guidance signal, limiting their transferability to domains and languages where those resources are unavailable. Second, while automatic metrics like ROUGE show progress, we lack a good understanding of the errors and failure modes in this task. To bridge these gaps, we first propose a domain-agnostic guidance signal in form of variable-length extractive summaries. Our empirical results on two English benchmarks demonstrate that this guidance signal improves upon unguided summarization while being competitive with domain-specific methods. Additionally, we run an expert evaluation of four systems according to a taxonomy of 11 fine-grained errors. We find that the most pressing differences between automatic summaries and those of radiologists relate to content selection including omissions (up to 52%) and additions (up to 57%). We hypothesize that latent reporting factors and corpus-level inconsistencies may limit models to reliably learn content selection from the available data, presenting promising directions for future work.
GenTUS: Simulating User Behaviour and Language in Task-oriented Dialogues with Generative Transformers

Hsien-chin Lin, Christian Geishauser, Shutong Feng, Nurul Lubis, Carel van Niekerk, Michael Heck, Milica Gasic. Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue. 2022.
ANALISIS MINAT BACA BUKU PADA PESERTA DIDIK KELAS IV DI MIN 1 SIDOARJO

Reading activities are part of education, but the reality in the field shows that students tend to be lazy when asked to read books. This is because there are internal factors that come from oneself and external factors that come from outside. This research aims to analyze the level of interest in reading books, factors that influence interest in reading books and teachers' efforts to increase students' interest in reading books. This research is a survey with descriptive analysis using a combination approach, namely qualitative and quantitative. Data collection techniques using interview and questionnaire methods are given to students. The subjects of this research were class IV students at MIN 1 Sidoarjo with a total of 29 students. The research results show that the reading interest of class IV students at Min 1 Sidoarjo is in the good category with a percentage of 77.9. There are internal and external factors that influence students' reading interest. Teachers have tried to increase students' interest in reading by getting used to reading activities before learning takes place and motivating students about the importance of reading books
UD_Japanese-CEJC: Dependency Relation Annotation on Corpus of Everyday Japanese Conversation

In this study, we have developed Universal Dependencies (UD) resources for spoken Japanese in the Corpus of Everyday Japanese Conversation (CEJC). The CEJC is a large corpus of spoken language that encompasses various everyday conversations in Japanese, and includes word delimitation and part-of-speech annotation. We have newly annotated Long Word Unit delimitation and Bunsetsu (Japanese phrase)-based dependencies, including Bunsetsu boundaries, for CEJC. The UD of Japanese resources was constructed in accordance with hand-maintained conversion rules from the CEJC with two types of word delimitation, part-of-speech tags and Bunsetsu-based syntactic dependency relations. Furthermore, we examined various issues pertaining to the construction of UD in the CEJC by comparing it with the written Japanese corpus and evaluating UD parsing accuracy.
OpinionConv: Conversational Product Search with Grounded Opinions

When searching for products, the opinions of others play an important role in making informed decisions. Subjective experiences about a product can be a valuable source of information. This is also true in sales conversations, where a customer and a sales assistant exchange facts and opinions about products. However, training an AI for such conversations is complicated by the fact that language models do not possess authentic opinions for their lack of real-world experience. We address this problem by leveraging product reviews as a rich source of product opinions to ground conversational AI in true subjective narratives. With OpinionConv, we develop the first conversational AI for simulating sales conversations. To validate the generated conversations, we conduct several user studies showing that the generated opinions are perceived as realistic. Our assessors also confirm the importance of opinions as an informative basis for decision making.
When to generate hedges in peer-tutoring interactions

This paper explores the application of machine learning techniques to predict where hedging occurs in peer-tutoring interactions. The study uses a naturalistic face-to-face dataset annotated for natural language turns, conversational strategies, tutoring strategies, and nonverbal behaviors. These elements are processed into a vector representation of the previous turns, which serves as input to several machine learning models, including MLP and LSTM. The results show that embedding layers, capturing the semantic information of the previous turns, significantly improves the model’s performance. Additionally, the study provides insights into the importance of various features, such as interpersonal rapport and nonverbal behaviors, in predicting hedges by using Shapley values for feature explanation. We discover that the eye gaze of both the tutor and the tutee has a significant impact on hedge prediction. We further validate this observation through a follow-up ablation study.
From Chatter to Matter: Addressing Critical Steps of Emotion Recognition Learning in Task-oriented Dialogue

Shutong Feng, Nurul Lubis, Benjamin Ruppik, Christian Geishauser, Michael Heck, Hsien-chin Lin, Carel van Niekerk, Renato Vukovic, Milica Gasic. Proceedings of the 24th Meeting of the Special Interest Group on Discourse and Dialogue. 2023.
Empathetic Response Generation for Distress Support

AI-driven chatbots are seen as an attractive solution to support people undergoing emotional distress. One of the main components of such a chatbot is the ability to empathize with the user. But a significant limitation in achieving this goal is the lack of a large dialogue dataset containing empathetic support for those undergoing distress. In this work, we curate a large-scale dialogue dataset that contains ≈1.3M peer support dialogues spanning across more than 4K distress-related topics. We analyze the empathetic characteristics of this dataset using statistical and visual means. To demonstrate the utility of this dataset, we train four baseline neural dialogue models that can respond empathetically to distress prompts. Two of the baselines adapt existing architecture and the other two incorporate a framework identifying levels of cognitive and emotional empathy in responses. Automatic and human evaluation of these models validate the utility of the dataset in generating empathetic responses for distress support and show that identifying levels of empathy in peer-support responses facilitates generating responses that are lengthier, richer in empathy, and closer to the ground truth.
Analysis and Impact of Training Set Size in Cross-Subject Human Activity Recognition

The ubiquity of consumer devices with sensing and computational capabilities, such as smartphones and smartwatches, has increased interest in their use in human activity recognition for healthcare monitoring applications, among others. When developing such a system, researchers rely on input data to train recognition models. In the absence of openly available datasets that meet the model requirements, researchers face a hard and time-consuming process to decide which sensing device to use or how much data needs to be collected. In this paper, we explore the effect of the amount of training data on the performance (i.e., classification accuracy and activity-wise F1-scores) of a CNN model by performing an incremental cross-subject evaluation using data collected from a consumer smartphone and smartwatch. Systematically studying the incremental inclusion of subject data from a set of 22 training subjects, the results show that the model’s performance initially improves significantly with each addition, yet this improvement slows down the larger the number of included subjects. We compare the performance of models based on smartphone and smartwatch data. The latter option is significantly better with smaller sizes of training data, while the former outperforms with larger amounts of training data. In addition, gait-related activities show significantly better results with smartphone-collected data, while non-gait-related activities, such as standing up or sitting down, were better recognized with smartwatch-collected data.
KNOWLEDGE MANAGEMENT PRACTICES AND EMPLOYEE PERFORMANCE OF SIREET OUTGROWERS EMPOWERMENT AND PRODUCER COMPANY LIMITED IN NANDI COUNTY, KENYA

This study examined the influence of practices of knowledge management on employee performance at Sireet Outgrowers Empowerment and Producer Company Limited in Nandi County. The specific objectives included knowledge sharing, knowledge culture, knowledge IT tools and knowledge creation. The theory anchoring the study included knowledge based view and supported by the resource-based view theory. The study took an explanatory and cross-sectional survey designs. Census survey of all 60 employees formed the sample size and was involved in answering the structured questionnaire. A pilot test was done using 6 respondents who checked for validity and reliability of the research instrument. The researcher collected the data and conducted analysis that included descriptive, correlation and regression. The findings were presented in tables and prose form for deliberations. The study found that knowledge management practices of knowledge sharing, knowledge culture, knowledge IT tools and knowledge creation influenced the performance of employees at the of Sireet Outgrowers Empowerment &amp; Producer Company Limited in Nandi County. Knowledge IT tools had the largest influence on employee performance, followed by knowledge creation, knowledge culture and knowledge sharing. The study concluded that knowledge management practices had positive and significant influence on employee performance. The study recommends that the management of Sireet Company in Nandi County to embrace knowledge management practices as it seeks to improve performance of its employees. The management should invest in training programs for creation and sharing of information, develop firm structures and practices that create, share and utilize knowledge for improvement in performance of employees. Other organizations seeking to improve performance of its employees in embrace practices under knowledge management. This can be done through formal and informal trainings, investing in technological systems and tools, embrace cultures and practices for creating, sharing and using knowledge. Key Words: Knowledge Sharing, Knowledge Culture, Knowledge IT Tools, Knowledge Creation CITATION : Rotich, J. J., &amp; Mutuku, M. K. (2023). Knowledge management practices and employee performance of Sireet Outgrowers Empowerment and producer company limited in Nandi County, Kenya. The Strategic Journal of Business &amp; Change Management, 10 (4), 1114 – 1134. http://dx.doi.org/10.61426/sjbcm.v10i4.2808
Comparison of Evaluation Metrics for Short Story Generation

The aim of this study was to analyze the correlation among different automatic evaluation metrics for text generation. In the study, texts were generated from short stories using different language models: N-gram model, Continuous Bag-of-Word (CBOW) model, Gated recurrent unit (GRU) model, and Generative Pre-trained Transformer 2 (GPT-2) model. All models were trained on short Aesop’s fables. The quality of the generated text was measured with various metrics: Perplexity, BLEU score, the number of grammatical errors, Self-BLEU score, ROUGE score, BERTScore, andWord Mover’s Distance (WMD). The resulting correlation analysis of the evaluation metrics showed four groups of correlated metrics. Firstly, perplexity and grammatical errors were moderately correlated. Secondly, BLEU, ROUGE and BERTScore were highly correlated. Next, WMD was negatively correlated with BLEU, ROUGE and BERTScore. On the other hand, Self-BLEU, which measures text diversity within the model, did not correlate with the other metrics. In conclusion, to evaluate text generation, a combination of various metrics should be used to measure different aspects of the generated text.