Reviewerly/exHarmony huggingface dataset:
Multimodal Fusion Interactions: A Study of Human and Automatic Quantification

In order to perform multimodal fusion of heterogeneous signals, we need to understand their interactions: how each modality individually provides information useful for a task and how this information changes in the presence of other modalities. In this paper, we perform a comparative study of how humans annotate two categorizations of multimodal interactions: (1) partial labels, where different annotators annotate the label given the first, second, and both modalities, and (2) counterfactual labels, where the same annotator annotates the label given the first modality before asking them to explicitly reason about how their answer changes when given the second. We further propose an alternative taxonomy based on (3) information decomposition, where annotators annotate the degrees of redundancy: the extent to which modalities individually and together give the same predictions, uniqueness: the extent to which one modality enables a prediction that the other does not, and synergy: the extent to which both modalities enable one to make a prediction that one would not otherwise make using individual modalities. Through experiments and annotations, we highlight several opportunities and limitations of each approach and propose a method to automatically convert annotations of partial and counterfactual labels to information decomposition, yielding an accurate and efficient method for quantifying multimodal interactions.
PET-3DFlow: A Normalizing Flow Based Method for 3D PET Anomaly Detection

Anomaly detection of Positron Emission Tomography (PET) are important tasks for clinical diagnosis and treatments. For 3D PET images, it is arduous for the annotations of lesions and the number of positive samples are generally very limited. Although there have been many work on deep learning based anomaly detection for medical images, most of them cannot be directly applied on 3D PET data. In this paper, from a set of normal PET data, we propose a tractable and efficient 3D normalizing flow based model, namely PET-3DFlow for anomaly detection and provide lesion localization for the reference of clinicians. The loss of In PET-3DFlow consists of the log-likelihood originated from the normalizing flow model and the reconstruction error of the autoencoder learned from the normal images. Experimental evaluation on 3D PET data showed that the proposed model outperformed the other approaches, which validated the effectiveness of our proposed PET anomaly detection method.
A Visual-Based Aircraft Pose Estimation Method During Take-Off

In this paper, we propose a visual-based aircraft pose estimation method, and this method can provide positioning and navigation for the aircraft during the autonomous take-off phase. Using images obtained by a camera installed in the aircraft cockpit, the pose estimation model can estimate the horizontal position deviation and heading deviation between the aircraft and the runway centerline. Then, the flight control system uses these two deviations to control the aircraft for autonomous take-off. The pose estimation model is designed by deep learning algorithm, with a simple and efficient structure. We performed 1000 experiments using a Boeing 737 model in a flight simulation environment, and the results show that the maximum horizontal position error and maximum heading error outputted from the pose estimation model during take-off were 1.89 m and 0.95 $$^\circ $$ , respectively. In all experiments, the aircraft was able to complete a well take-off navigated by the pose estimation model.
Fuzzy Neighborhood Systems in Fuzzy Topological Ring

In this paper, we have established a necessary and sufficient condition for a family of fuzzy sets in the form {r+r.E} in a ring R that dependent of two binary operations {+ ,.} to be the family of neighborhoods of an element r of a fuzzy topological ring.
A Bi-directional Multi-hop Inference Model for Joint Dialog Sentiment Classification and Act Recognition

The joint task of Dialog Sentiment Classification (DSC) and Act Recognition (DAR) aims to predict the sentiment label and act label for each utterance in a dialog simultaneously. However, current methods encode the dialog context in only one direction, which limits their ability to thoroughly comprehend the context. Moreover, these methods overlook the explicit correlations between sentiment and act labels, which leads to an insufficient ability to capture rich sentiment and act clues and hinders effective and accurate reasoning. To address these issues, we propose a Bi-directional Multi-hop Inference Model (BMIM) that leverages a feature selection network and a bi-directional multi-hop inference network to iteratively extract and integrate rich sentiment and act clues in a bi-directional manner. We also employ contrastive learning and dual learning to explicitly model the correlations of sentiment and act labels. Our experiments on two widely-used datasets show that BMIM outperforms state-of-the-art baselines by at least 2.6% on F1 score in DAR and 1.4% on F1 score in DSC. Additionally, Our proposed model not only improves the performance but also enhances the interpretability of the joint sentiment and act prediction task.
DialogRE$$^{C+}$$: An Extension of DialogRE to Investigate How Much Coreference Helps Relation Extraction in Dialogs

Dialogue relation extraction (DRE) that identifies the relations between argument pairs in dialogue text, suffers much from the frequent occurrence of personal pronouns, or entity and speaker coreference. This work introduces a new benchmark dataset DialogRE $$^{C+}$$ , introducing coreference resolution into the DRE scenario. With the aid of high-quality coreference knowledge, the reasoning of argument relations is expected to be enhanced. In DialogRE $$^{C+}$$ dataset, we manually annotate total 5,068 coreference chains over 36,369 argument mentions based on the existing DialogRE data, where four different coreference chain types namely speaker chain, person chain, location chain and organization chain are explicitly marked. We further develop 4 coreference-enhanced graph-based DRE models, which learn effective coreference representations for improving the DRE task. We also train a coreference resolution model based on our annotations and evaluate the effect of automatically extracted coreference chains demonstrating the practicality of our dataset and its potential to other domains and tasks.
Early detection of the risk of depressive episodes using a proprietary diagnostic test by new epistemic similarity measures

The first primary motivation of this paper is to propose new methods for measuring similarity between interval-valued fuzzy sets based on an order relation of the intervals, which are a possible and necessary similarity measure. To capture the basic idea of the suggested approach, one has to realize that an interval-valued fuzzy set (IVFS) can be considered as a family of closed intervals describing imprecise information, or uncertainty, on the possible belongingness of each element of the universe of discourse and the dependence between them. Consequently, any relation between two interval-valued fuzzy sets could be analyzed using the links between the corresponding families of intervals. The core of the proposed methodology is based on the description of possible and necessary inclusion and the width of the corresponding intervals in epistemic issues, i.e. optimistic (possible) and pessimistic (necessary) measures. Our second motivation is to use our concept of measures to early detect the risk of depressive episodes among students tested for this research. We propose an effective algorithm to estimate the risk of depressive episodes. By using machine learning methods concerning uncertainty, we obtained more sensitivity to diagnosis compared to the currently used tests, e.g., CESD-R. We aim to identify the risk of depressive states before they are indicated in other tests such as CESD-R, which can prevent the appearance of symptoms after early medical consultation. In the initial study, we were able to achieve a 4-week faster diagnosis of at-risk depression in 20% of subjects who had CESD-R at risk, so we are talking about a more sensitive test, in this sense, as a result of this work. The proposed methodology, in the form of a system, will be used in medical centers by general practitioners or entrepreneurs for their subordinates to eliminate depression-related breaks at an early prevention stage.
Counterfactuals Explanations for Outliers via Subspaces Density Contrastive Loss

Explainable AI refers to techniques by which the reasons underlying decisions taken by intelligent artifacts are single out and provided to users. Outlier detection is the task of individuating anomalous objects within a given data population they belong to. In this paper we propose a new technique to explain why a given data object has been singled out as anomalous. The explanation our technique returns also includes counterfactuals, each of which denotes a possible way to "repair" the outlier to make it an inlier. Thus, given in input a reference data population and an object deemed to be anomalous, the aim is to provide possible explanations for the anomaly of the input object, where an explanation consists of a subset of the features, called choice, and an associated set of changes to be applied, called mask, in order to make the object "behave normally". The paper presents a deep learning architecture exploiting a features choice module and mask generation module in order to learn both components of explanations. The learning procedure is guided by an ad-hoc loss function that simultaneously maximizes (minimizes, resp.) the isolation of the input outlier before applying the mask (resp., after the application of the mask returned by the mask generation module) within the subspace singled out by the features choice module, all that while also minimizing the number of features involved in the selected choice. We present experiments on both artificial and real data sets and a comparison with competitors validating the effectiveness of the proposed approach.
iSOUP-SymRF: Symbolic Feature Ranking with Random Forests in Online Multi-target Regression

Abstract The task of feature ranking has received considerable attention across various prediction tasks in the batch learning scenario, but not in the online learning setting. Available methods that estimate feature importances on data streams have thus far focused on ranking the features for the tasks of classification and occasionally multi-label classification. We propose a novel online feature ranking method for online multi-target regression, iSOUP-SymRF, which estimates feature importance scores based on the positions at which a feature appears in the trees of a random forest of iSOUP-Trees. By utilizing iSOUP-Trees, which can address multiple structured output prediction tasks on data streams, iSOUP-SymRF promises feature ranking across a variety of online structured output prediction tasks. We examine the robustness of iSOUP-SymRF and the feature rankings it produces in terms of the methods’ parameters: the size of the ensemble and the number of selected features. Furthermore, to show the utility of iSOUP-SymRF and its rankings we use them in conjunction with two state-of-the-art online multi-target regression methods, iSOUP-Tree and AMRules, and analyze the impact of adding features according to the rankings.
Exploring the Reduction of Configuration Spaces of Workflows

Many current AutoML platforms include a very large space of alternatives (the configuration space) that make it difficult to identify the best alternative for a given dataset. In this paper we explore a method that can reduce a large configuration space to a significantly smaller one and so help to reduce the search time for the potentially best workflow. We empirically validate the method on a set of workflows that include four ML algorithms (SVM, RF, LogR and LD) with different sets of hyperparameters. Our results show that it is possible to reduce the given space by more than one order of magnitude, from a few thousands to tens of workflows, while the risk that the best workflow is eliminated is nearly zero. The system after reduction is about one order of magnitude faster than the original one, but still maintains the same predictive accuracy and loss.
Chinese Event Causality Identification Based on Retrieval Enhancement

Event causality identification (ECI) is a critical and challenging information extraction task, which aims to identify whether there is a causal relationship between the two events. To address the current problem of insufficient annotated data in Chinese event causality identification, and the rich semantics of the event itself is not exploited. We proposed a prompt-learning based retrieval enhancement Chinese events causality identification framework, named RE-CECI, which improves the few-shot learning capability of the model. Moreover, it enables the pretrained language model to better learn event information by adding retrieved examples to the input text, after which the model can learn more event information. In addition, we construct a retrieval store of retrieved examples to serve as clues for reasoning about causality. Our experimental results on the only available Chinese causality dataset, show that our proposed method significantly improves the performance of Chinese event causality identification.
UZNER: A Benchmark for Named Entity Recognition in Uzbek

Named entity recognition (NER) is a key task in natural language processing, and entity recognition can provide necessary semantic information for many downstream tasks. However, the performance of NER is often limited by the richness of language resources. For low-resource languages, NER usually performs poorly due to the lack of sufficient labeled data and pre-trained models. To address this issue, we manually constructed a large-scale, high-quality Uzbek NER corpus of Uzbek, and experimented with various NER methods. We improved state-of-the-art baseline models by introducing additional features and data translations. Data translation enables the model to learn richer syntactic structure and semantic information. Affix features provide knowledge at the morphological level and play an important role in identifying oversimplified low-frequency entity labels. Our data and models will be available to facilitate low-resource NER.
PERBEDAAN INDEKS PLAK ANTARA SIKAT GIGI LURUS DAN SIKAT GIGI MELENGKUNG PADA ANAK TK ALIF

Dental plaque is the trigger effect of dental caries. The plaque index and children's dental caries in children are related. Dental and oral hygiene care is carried out to stop the growth of plaque. Plaque can be removed from all tooth surfaces by brushing your teeth thoroughly and frequently. This is a simple and practical technique for managing and preventing plaque buildup. The equipment most commonly used to clean teeth and mouth is a toothbrush. Brush your teeth properly, whether in frequency, time, or technique, using manual and electric toothbrushes of various sizes and shapes. Research Objectives to determine the difference in plaque index between straight toothbrushes and curved toothbrushes at Alif Kindergarten. Materials and methods findings using a quasi-experimental method and a cross-sectional study design and the Mann-Whitney U Test. Results on the results of the Mann-Whitney U Test, the p-value is 0.000 &lt;0.05. Thus, there are differences in the values before and after the plaque index in the treatment of straight and curved toothbrushes. Conclusion based on the findings there is a difference in the plaque index score in children at Alif Kindergarten after brushing their teeth using a straight toothbrush and a curved toothbrush.
Stroke Community Rehabilitation Centre (SCORE): A community transformation program

&#x0D; &#x0D; &#x0D; Stroke is the second most prevalent disease in Malaysia, so promoting awareness of stroke is essential as it is preventable and treatable if action is prompt. Long-term rehabilitation at the community level is also crucial to reducing congestion in acute care hospitals. Hence, establishing an evidence-based community rehabilitation centre would help educate the community and support the welfare of stroke survivors.&#x0D; This article discusses a community-engagement initiative launched by experts from University Malaya Rehabilitation Medicine in partnership with Pusat Pemulihan Kesihatan (PERKIM), a religious and social welfare organisation in Malaysia, to transform an existing non-functional community centre run by PERKIM into the Stroke Community Rehabilitation Centre (SCORE). This was achieved through the provision of expert input into how to improve service provision, knowledge transfer to the community, and implementation of more thematic and creative components to the model of care currently offered. Importantly, under this new model, stroke survivors and the wider community would be considered learners and active participants in their own care, not mere passive recipients of charity.&#x0D; Since its inception in 2016, the number of patients has almost doubled, increasing to over 100. Thus, the Stroke Community Rehabilitation Centre benefits the stroke community by providing resources and education to facilitate recovery at a reduced cost to hospital-based care. With its adherence to the recommended features of the community-based rehabilitation model, as outlined by WHO, the success of SCORE is an exemplary model for future stroke community rehabilitation centres in Malaysia.&#x0D; &#x0D; &#x0D;
PERANCANGAN SISTEM APLIKASI PENJUALAN SPAREPART MOTOR BERBASIS ANDROID

Currently the ongoing sales process in Bengkel Top 1 Motor requires the buyer to come and meet in person to purchase the required spareparts. This thus affects the sales process where buyers who come to look for spareparts equipment only reach buyers who are around the workshop, and conventional purchases require in-depth knowledge of employees about prices. System design is the best solution for solving problems and with a computerized system an activity can be achieved that effectively and efficiently supports workshop activities (Abdurrahman &amp; Maspirah, 2017). The Agile development method has the stages used, namely the process of making a design based on mutual agreement in the form of interviews and observation (planning), coding and design (implementation), testing (testing), documentation, disseminating information (deployment), maintenance (Handayani et al., 2023) Apart from being used as an Android communication tool, Android can also be used as a platform to be accessed and developed easily because it supports several devices in making applications, one of which is Android games (Agis et al, 2021).From the problems experienced by the Bengkel Top 1 Motor, it is considered necessary to design a sales application so that the sales process is faster and more efficient.
PERANCANGAN APLIKASI JASA LAUNDRY DENGAN METODE EXTREME PROGRAMMING (XP) BERBASIS ANDROID

One of the businesses that has currently penetrated the market is the laundry business. With a sizable income, the laundry business is increasingly in demand by many people as a main or side business. Viky Laundry is one of the laundry places that has customers every day considering its strategic location, which is near residential areas. Unfortunately, in the process, Viky Laundry still uses manual methods without using a computer system as a tool. Therefore, research was conducted to design an Android-based laundry service application that is more effective and provides time efficiency for the laundry shuttle process, records incoming and outgoing clothes, and provides information about Viky Laundry itself. The research methods used include literature study, interviews, design and testing. The software development model used in this research is the Extreme Programming (XP) method. The technology used, then produced an application where customers easily and practically do the laundry process and laundry owners will more easily receive orders from customers. And for laundry entrepreneurs also with the existence of this application helps in recording incoming and outgoing clothes. Based on the results of interviews, and black box testing, the application made is quite helpful for laundry entrepreneurs and laundry customers.
ANALISIS DAN DETEKSI MALWARE PADA PROTOKOL JARINGAN MENGGUNAKAN METODE MALWARE ANALISIS DINAMIS DAN MALWARE ANALISIS STATIS

Network security is a crucial issue in the rapidly developing era of information technology. One of the main threats is malware, which is malicious software that harms systems and data. To solve this problem, analysis and detection of malware on network protocols need to be improved using effective methods. This research explores the application of two main methods, namely static and dynamic malware analysis. First, static malware analysis involves examining malware files without running them. This method includes analysis of signatures, heuristics, and malicious code to identify typical malware patterns in files on the network.Second, dynamic malware analysis executes malware in an isolated environment (sandbox) to monitor its behavior and impact on the system. This process allows detection of malicious changes and attempts by malware to propagate itself. By combining these two methods, a holistic and efficient approach in malware detection on network protocols can be achieved. It is hoped that the results of this research can improve network security, protect infrastructure from ever-evolving malware threats. The use of static and dynamic analysis methods will help deal with increasingly complex security challenges, enabling networks to operate more securely and reliably.
ERROR ANALYSIS ON STUDENTS WRITING TASK OF PERSONAL LETTERS

This study tries to classify and explain the many types of writing errors that were discovered in students' personal letters. The outcomes are ascertained by a qualitative data analysis employing the writing assignments of the students as the source of data. The Surface Strategy Taxonomy is used to pinpoint the errors. The researcher employs comparative analysis to divide the students into several skill levels when gathering data. The goal of this study is to identify mistakes committed by science-majoring eleventh-graders at SMAN 6 Kota Semarang, who number 36 students, in their personal letter assignment. The total of errors are 274 errors were classified in this study, with omission errors (f=131, 47.81%) as the most common. The second commonplace fault category was mis-formation errors (f=127, 46.35%). These categories were followed by addition error (f=9, 3.28%) and mis-order error (f=7, 2.55%). From the above result, we can conclude that the most prevalent mistakes among the learners is omission where they frequently eliminate sort of parts which needed in the full sentences due to the fact lack of English grammar knowledge.