Reviewerly/exHarmony huggingface dataset:
PENGEMBANGAN SISTEM PENCATATAN DAN PELAPORAN INSIDEN KESELAMATAN PASIEN (IKP) BERBASIS WEB DI RUMAH SAKIT

Background: In an effffort to improve patient safety in Hj. Anna&#x0D; Lasmanah Hospital Banjarnegara Regency, this study&#x0D; developed a web-based incident reporting system. This is an&#x0D; effffective tool with advantages suitable for promoting an open&#x0D; culture for patient safety.&#x0D; Objectives: Improve incident reporting mechanisms in hospital&#x0D; considering the comfort, convenience and confifidentiality, so&#x0D; that it can support the efffforts of improve patient safety.&#x0D; Methods:This was action research design, conducted at the Hj.&#x0D; Anna Lasmanah Hospital Banjarnegara Regency. Research&#x0D; participants amounted to 7 (seven) people. The research began&#x0D; with the identifification of needs through interviews, observation&#x0D; and study of documentation. The process of making the&#x0D; prototype systems was based on the results of the identifification&#x0D; of needs. The evaluation process was carried out using a&#x0D; questionnaire, distributed to 40 participants to assess usability.&#x0D; While for evaluating importance, scientififically acceptable,&#x0D; transparency, and feasibility, we conducted observation and&#x0D; interviews with participants. Interview data were analyzed&#x0D; qualitatively, through the process of data transcription, coding&#x0D; and categorization. Data from questionnaires done by&#x0D; calculating the average.&#x0D; Results: This research resulted a prototype incident reporting&#x0D; system that accommodated to the basic needs of the user. Most&#x0D; participants (91,96%) were satisfified with the application&#x0D; developed. The prototype system is considered appropriate to&#x0D; be applied in the hospital, with some suggestions for the need&#x0D; for improvement regarding the application program, network&#x0D; infrastructure and human resource capacity.&#x0D; Conclusion: A prototype system developed was considered&#x0D; can improve incident reporting mechanisms in hospital&#x0D; considering the comfort, convenience and confifidentiality, so&#x0D; that it can support the efffforts of improve patient safety.
Application of ALMM Technology to Intelligent Control System for a Fleet of Unmanned Aerial Vehicles

The article is related to an intelligent information system for managing a fleet of Unmanned Aerial Vehicles (UAVs) while taking into account various dynamically changing constraints. Variability of the time intervals that are available for flights over certain areas located close to the airports is one of the essential constraints. The system must be developed very flexibly to easily accommodate changing both flight destinations and performance conditions. The authors propose application of Algebraic Logic Meta Modelling (ALMM) technology to design and implement the models and algorithms used in this system. The article presents part of the research carried out by the authors during the design of the aforementioned system. An Algebraic Logic (AL) model of UAV flights optimization scheduling problem is given. The execution of overflights in the circumpolar zone with the criterion of minimizing the total completion time of all tasks $$C_{max}$$ is described. Then a hybrid algorithm for solving this problem and the results of the experiments carried out are presented. The component nature of the proposed approach allows easy transposition of the models and algorithms in case of more complex, additional assumption and restrictions referring to manage flights in real conditions.
Leveraging Hierarchical Similarities for Contrastive Clustering

Recently, contrastive clustering has demonstrated high performance in the field of deep clustering due to its powerful feature extraction capabilities. However, existing contrastive clustering methods suffer from inter-class conflicts and often produce suboptimal clustering outcomes due to the disregard of latent class information. To address this issue, we propose a novel method called Contrastive learning using Hierarchical data similarities for Deep Clustering (CHDC), consisting of three modules, namely the inter-class separation enhancer, the intra-class compactness enhancer, and the clustering module. Specifically, to induct the latent class information by utilizing the sample pairs with data similarities, the inter-class separation enhancer and the intra-class compactness enhancer handle negative and positive sample pairs, respectively, with distinct hierarchical similarities. Additionally, the clustering module aims to ensure the alignment of cluster assignments between samples and their neighboring samples. By designing these three modules that work collaboratively, inter-class conflicts are alleviated, allowing CHDC to learn more discriminative features. Lastly, we design a novel update method for positive sample pairs to reduce the likelihood of introducing erroneous information. To evaluate the performance of CHDC, we conduct extensive experiments on five widely adopted image classification datasets. The experimental results demonstrate the superiority of CHDC compared to state-of-the-art methods. Moreover, ablation studies demonstrate the effectiveness of the proposed modules.
SDPSAT: Syntactic Dependency Parsing Structure-Guided Semi-Autoregressive Machine Translation

The advent of non-autoregressive machine translation (NAT) accelerates the decoding superior to autoregressive machine translation (AT) significantly, while bringing about a performance decrease. Semi-autoregressive neural machine translation (SAT), as a compromise, enjoys the merits of both autoregressive and non-autoregressive decoding. However, current SAT methods face the challenges of information-limited initialization and rigorous termination. This paper develops a layer-and-length-based syntactic labeling method and introduces a syntactic dependency parsing structure-guided two-stage semi-autoregressive translation (SDPSAT) structure, which addresses the above challenges with a syntax-based initialization and termination. Additionally, we also present a Mixed Training strategy to shrink exposure bias. Experiments on six widely-used datasets reveal that our SDPSAT surpasses traditional SAT models with reduced word repetition and achieves competitive results with the AT baseline at a $$2\times \sim 3 \times $$ speedup.
Improving Out-of-Distribution Detection with Margin-Based Prototype Learning

Deep Neural Networks often make overconfident predictions when encountering out-of-distribution (OOD) data. Previous prototype-based methods significantly improved OOD detection performance by optimizing the representation space. However, practical scenarios present a challenge where OOD samples near class boundaries may overlap with in-distribution samples in the feature space, resulting in misclassification, and few methods have considered the challenge. In this work, we propose a margin-based method that introduces a margin into the common instance-prototype contrastive loss. The margin leads to broader decision boundaries, resulting in better distinguishability of OOD samples. In addition, we leverage learnable prototypes and explicitly maximize prototype dispersion to obtain an improved representation space. We validate the proposed method on several common benchmarks with different scoring functions and architectures. Experiments results show that the proposed method achieves state-of-the-art performance. Code is available at https://github.com/liujunzhuo/MarginOOD .
Joint Regularization Knowledge Distillation

Knowledge distillation is devoted to increasing the similarity between a small student network and an advanced teacher network in order to improve the performance of the student network. However, these methods focus on teacher and student networks that receive supervision from each other independently and do not consider the network as a whole. In this paper, we propose a new knowledge distillation framework called Joint Regularization Knowledge Distillation (JRKD), which aims to reduce network differences through joint training. Specifically, we train teacher and student networks through joint regularization loss to maximize consistency between the two networks. Meanwhile, we develop a confidence-based continuous scheduler method (CBCS), which divides examples into center examples and edge examples based on the example confidence distribution of network output. Prediction differences between networks are reduced when training with a central example. Teacher and student networks will become more similar as a result of joint training. Extensive experimental results on benchmark datasets such as CIFAR-10, CIFAR-100, and Tiny-ImagNet show that JRKD outperforms many advanced distillation methods.
Towards Forward Responsibility in BDI Agents

In this paper, we discuss forward responsibilities in Belief-Desire-Intention agents, that is, responsibilities that can drive future decision-making. We focus on individual rather than global notions of responsibility. Our contributions include: (a) extended operational semantics for responsibility-aware rational agents; (b) hierarchical responsibilities for improving intention selection based on the priorities (i.e., hierarchical level) of a responsibility; and (c) shared responsibilities which allow agents with the same responsibility to update their priority levels (and consequently commit or not to the responsibility) depending on the lack (or surplus) of agents that are currently engaged with it.
On Searching for Minimal Integer Representation of Undirected Graphs

Minimal and efficient graph representations are key to store, communicate, and sample the search space of graphs and networks while meeting user-defined criteria. In this paper, we investigate the feasibility of gradient-free optimization heuristics based on Differential Evolution to search for minimal integer representations of undirected graphs. The class of Differential Evolution algorithms are population-based gradient-free optimization heuristics having found a relevant attention in the nonconvex and nonlinear optimization communities. Our computational experiments using eight classes of Differential Evolution schemes and graph instances with varying degrees of sparsity have shown the merit of attaining minimal numbers for graph encoding/representation rendered by exploration-oriented strategies within few function evaluations. Our results have the potential to elucidate new number-based encoding and sample-based algorithms for graph representation, network design and optimization.
The English Students’ Problems in Facing TOEFL Test at STIE Madani Balikpapan During the Academic Year 2022/2023

TOEFL is a language testing tool widely used in universities to check students’ capability in English mastery. The purpose of this study, titled "The English Students’ Problems in Facing the TOEFL Test at STIE Madani Balikpapan during the Academic Year 2022-2023," is to: (1) determine the issues experienced by Accounting Department students when performing the TOEFL test; and (2) determine ways to analyze among English students at STIE Madani Balikpapan with high TOEFL grades and low TOEFL grades. This study's sampling consists of 20 accounting students from the Economic Faculty at STIE Madani Balikpapan who took the TOEFL test. This study employed descriptive qualitative research. (1) Semi-structured interviews were used to gather information regarding the challenges that English students confront when completing the TOEFL test. (2) A closed-ended questionnaire was used to gather information about the ways in which students’ study, including study style, taking notes, ascertaining along the way, reading use, conning, and time direction. The consequences of this consultation bring out that, at that point, there are some issues that may arise while attempting the TOEFL test. The issues that have been identified are connected to a deficiency in English language achievement and cognition, inadequate time instruction, suffering absorption, and bearing issuing with the final retentions. The questionnaire consequences establish a few divergences in the field of study use between English language educators with eminent TOEFL grades and educators who have poor TOEFL grades.
DeepCon: An End-to-End Multilingual Toolkit for Automatic Minuting of Multi-Party Dialogues

In this paper, we present our minuting tool DeepCon, an end-to-end toolkit for minuting the multiparty dialogues of meetings. It provides technological support for (multilingual) communication and collaboration, with a specific focus on Natural Language Processing (NLP) technologies: Automatic Speech Recognition (ASR), Machine Translation (MT), Automatic Minuting (AM), Topic Modelling (TM) and Named Entity Recognition (NER). To the best of our knowledge, there is no such tool available. Further, this tool follows a microservice architecture, and we release the tool as open-source, deployed on Amazon Web Services (AWS). We release our tool open-source here http://www.deepcon.in.
AARGH! End-to-end Retrieval-Generation for Task-Oriented Dialog

We introduce AARGH, an end-to-end task-oriented dialog system combining retrieval and generative approaches in a single model, aiming at improving dialog management and lexical diversity of outputs. The model features a new response selection method based on an action-aware training objective and a simplified single-encoder retrieval architecture which allow us to build an end-to-end retrieval-enhanced generation model where retrieval and generation share most of the parameters. On the MultiWOZ dataset, we show that our approach produces more diverse outputs while maintaining or improving state tracking and context-to-response generation performance, compared to state-of-the-art baselines.
Validating Predictive Models Of Evaluative Language For Controllable Data2Text Generation

In data2text generation, tabular data is transformed into a text that expresses information from that source domain. While some text types, such as instructions, demand objective and neutral language without any expressive and evaluative content, many other text types are expected to provide expressions for these kinds of subjective meanings. In controllable, pipelined neural NLG separate learning models, notably regression models, can be used to predict whether some feature deviates sufficiently strongly from an expected value, so that evaluative language would be appropriate for verbalizing this finding. In this paper, we present an empirical study on the comprehension of evaluative adverbs and adjectival modifiers in car reviews, a text type that is characterized by a mixture of factual information with evaluations expressing positive or negative surprise. We show to what extend regression-based decision boundaries for producing evaluative content in controllable data2text NLG match the reader’s expectations that are raised by those evaluative markers. Finally we show that regression values in combination with standard deviation of the technical input data constitute reasonable Boolean thresholds for both positive and negative surprise, which provide the basis for the development of more complex models that also include the scalar base of adverbs and modifiers.
QualityAdapt: an Automatic Dialogue Quality Estimation Framework

Despite considerable advances in open-domain neural dialogue systems, their evaluation remains a bottleneck. Several automated metrics have been proposed to evaluate these systems, however, they mostly focus on a single notion of quality, or, when they do combine several sub-metrics, they are computationally expensive. This paper attempts to solve the latter: QualityAdapt leverages the Adapter framework for the task of Dialogue Quality Estimation. Using well defined semi-supervised tasks, we train adapters for different subqualities and score generated responses with AdapterFusion. This compositionality provides an easy to adapt metric to the task at hand that incorporates multiple subqualities. It also reduces computational costs as individual predictions of all subqualities are obtained in a single forward pass. This approach achieves comparable results to state-of-the-art metrics on several datasets, whilst keeping the previously mentioned advantages.
The DialPort tools

The DialPort project (http://dialport.org/), funded by the National Science Foundation (NSF), covers a group of tools and services that aim at fulfilling the needs of the dialog research community. Over the course of six years, several offerings have been created, including the DialPort Portal and DialCrowd. This paper describes these contributions, which will be demoed at SIGDIAL, including implementation, prior studies, corresponding discoveries, and the locations at which the tools will remain freely available to the community going forward.
A System For Robot Concept Learning Through Situated Dialogue

Robots operating in unexplored environments with human teammates will need to learn unknown concepts on the fly. To this end, we demonstrate a novel system that combines a computational model of question generation with a cognitive robotic architecture. The model supports dynamic production of back-and-forth dialogue for concept learning given observations of an environment, while the architecture supports symbolic reasoning, action representation, one-shot learning and other capabilities for situated interaction. The system is able to learn about new concepts including objects, locations, and actions, using an underlying approach that is generalizable and scalable. We evaluate the system by comparing learning efficiency to a human baseline in a collaborative reference resolution task and show that the system is effective and efficient in learning new concepts, and that it can informatively generate explanations about its behavior.
LAD: Language Models as Data for Zero-Shot Dialog

To facilitate zero-shot generalization in task-oriented dialog, this paper proposes Language Models as Data (LAD). LAD is a paradigm for creating diverse and accurate synthetic data which conveys the necessary structural constraints and can be used to train a downstream neural dialog model. LAD leverages GPT-3 to induce linguistic diversity. LAD achieves significant performance gains in zero-shot settings on intent prediction (+15%), slot filling (+31.4 F-1) and next action prediction (+10 F-1). Furthermore, an interactive human evaluation shows that training with LAD is competitive with training on human dialogs.
Reverse-Engineering Decoding Strategies Given Blackbox Access to a Language Generation System

Neural language models are increasingly deployed into APIs and websites that allow a user to pass in a prompt and receive generated text. Many of these systems do not reveal generation parameters. In this paper, we present methods to reverse-engineer the decoding method used to generate text (i.e., top-_k_ or nucleus sampling). Our ability to discover which decoding strategy was used has implications for detecting generated text. Additionally, the process of discovering the decoding strategy can reveal biases caused by selecting decoding settings which severely truncate a model’s predicted distributions. We perform our attack on several families of open-source language models, as well as on production systems (e.g., ChatGPT).
MODEL PEMBELAJARAN PROBLEM BASED LEARNING (PBL) DALAM MENINGKATKAN KEMAMPUAN BERPIKIR KREATIF DAN RASA INGIN TAHU PESERTA DIDIK DALAM PEMBELAJARAN PAI

In the PAI learning process, a high level of curiosity is needed to support successful learning. Apart from that, creative thinking skills are also needed to solve problems. An attitude of curiosity and creative thinking abilities can be developed in the application of a learning model. This research aims to examine whether the learning modelproblem-based learning can increase students' curiosity and creative thinking abilities in PAI learning. This research uses a qualitative approach with methodsthe study of literature. In this research, researchers look for data or information related to the learning modelproblem-based learning, creative thinking abilities, and students' curiosity. The results of this research concluded that the use of learning modelsproblem-based learning can increase students' curiosity and creative thinking abilities in PAI learning. Apart from that, researchers also concluded several indicators to measure students' curiosity in learning with modelsproblem-based learning, including: (1) exploring related material, (2) having a desire to know more deeply, (3) trying to dig up information from other sources, and (4) asking questions both to teachers and friends.
Nexus of Cryptocurrency and Output gap in Nigeria: A Decision Tree Regression in Machine Learning using Python Programming Language.

This study examines how the output gap would react to daily bitcoin series, this is achieved through the use of decision tree regression in machine learning which is estimated in a Python programming language. The gap in literature is in three strands which are basically empirical literature, variable, and methodology. High-powered (daily) data were used in the analysis covering the scope of 2010 to 2022, where Bitcoin was used to proxy cryptocurrency. The Garch model was used to control the volatility in the bitcoin series before carrying out the decision tree regression analysis. The work of Jia et al (2023) was used to lay the assumption of Bitcoin and the prediction of output gap in Nigeria. The result of our analysis shows that bitcoin is predicted to reduce the output gap in Nigeria, which was linked to the fact that macro variables such as inflation can be controlled based on the result. Policy implications from the result were used to conclude the paper.
C3: Compositional Counterfactual Contrastive Learning for Video-grounded Dialogues

Video-grounded dialogue systems aim to integrate video understanding and dialogue understanding to generate responses that are relevant to both the dialogue and video context. Most existing approaches employ deep learning models and have achieved remarkable performance, given the relatively small datasets available. However, the results are partially accomplished by exploiting biases in the datasets rather than developing multimodal reasoning, resulting in limited generalization. In this paper, we propose a novel approach of Compositional Counterfactual Contrastive Learning (C3) to develop contrastive training between factual and counterfactual samples in video-grounded dialogues. Specifically, we design factual/counterfactual samples based on the temporal steps in videos and tokens in dialogues and propose contrastive loss functions that exploit object-level or action-level variance. Different from prior approaches, we focus on contrastive hidden state representations among compositional output tokens to optimize the representation space in a generation setting. We achieved promising performance gains on the Audio-Visual Scene-Aware Dialogues (AVSD) benchmark and showed the benefits of our approach in grounding video and dialogue context.
Speaker Role Identification in Call Centre Dialogues: Leveraging Opening Sentences and Large Language Models

This paper addresses the task of speaker role identification in call centre dialogues, focusing on distinguishing between the customer and the agent. We propose a text-based approach that utilises the identification of the agent’s opening sentence as a key feature for role classification. The opening sentence is identified using a model trained through active learning. By combining this information with a large language model, we accurately classify the speaker roles. The proposed approach is evaluated on a dataset of call centre dialogues and achieves 93.61% accuracy. This work contributes to the field by providing an effective solution for speaker role identification in call centre settings, with potential applications in interaction analysis and information retrieval.