Reviewerly/exHarmony huggingface dataset:
Finding Harmful Comments on Social Networking Sites Using NLP and Machine Learning Methods

The usage of violent language has significantly increased due to social media and networking. A key component in this is the younger generation. More than half of young people who use social media are affected by cyberbullying. Harmful interactions occur as a result of insults expressed on social net-working websites. These comments foster an unprofessional tone on the internet, which is usually un-derstood and mitigated through passive mechanisms and techniques. Additionally, the recall rates of current systems that combine insult detection with machine learning and natural language processing are incredibly poor. To establish a viable classification scheme for such concepts, the research ana-lyzes how to identify bullying in writing by examining and testing various approaches. We propose an effective method to assess bullying, identify aggressive comments, and analyze their veracity. NLP and machine learning are employed to examine social perception and identify the aggressive impact on in-dividuals or groups. The ideal prototyping system for identifying cyber dangers in social media relies heavily on an efficient classifier. The goal of the paper is to emphasize the critical role that learning strategies play in enhancing natural language processing efficiency.
PEMODELAN PROFIL MUKA AIR BANJIR RENCANA BANGUNAN CULVERT PADA SUNGAI KRUENG JAMBO BALEE

Krueng Jambo Balee River, which is a tributary in Krueng Jambo Aye river basin, flows through the Pertagas pipeline installation (RoW, right of way), at segment of Sta. 8+500 to Sta. 8+600. Flooding that occurred at RoW Jambo Balee was caused by the draining capacity of the existing culverts which were unsufficient to flow discharge from the upstream river, thus inundating the surrounding area, and overtopping occurred on the RoW. This condition is a serious problem for the security of vital objects for the oil and gas industry, as well as disrupting the activities of the surrounding people who use the RoW as a village road. The analysis in this study aims to design alternative types and dimensions of flood control structures, i.e in this study is culverts. The alternatives were analyzed using HEC-RAS model. The modeling results show that the net width of the culvert needed to allow floods to pass through the 25-year return period is 8.5 meters, height of the culvert from the riverbed is 3,0 meters, and the width follows the width of the RoW, i.e. 6.0 meters. The water level profile for the magnitude of the 25-year flood gives a flood water level of 2.39 m from the river bed (elevation +36.39 m), where the inlet is not submerged. This study provides a technical plan for the design of culverts for river management, as well as flood management and control measures at the RoW Jambo Balee.
Managing Bibliometrics with Contributor Roles and Literature Provenance for NPDS Metadata Records

Tracking the provenance of information and knowledge with sources, concepts, ideas and the contributions of creators, inventors, and scholars remains essential to the reproducibility of scientific results, the reliability of engineering methods, the integrity of clinical trials, and the fair allocation of research and development resources.Since the first version, the PORTAL-DOORS Project (PDP) has provided schemas for the Nexus-PORTAL-DOORS-Scribe (NPDS) cyberinfrastructure which have always supported inclusion of provenance data as part of the metadata describing the entity corresponding to an NPDS record.However, PDP has not yet provided alternative templated formats for the provenance infosubset (aka, provenance facet) of the entire infoset that documents the entity for the identified NPDS record.Therefore, this report introduces a template for the provenance facet suitable for a generic entity type, along with alternatives appropriate for various specific entity types, including a template for authored publications that enables descriptions of contributor roles compatible with the CRediT taxonomy.By extending the PDP-DREAM ontology and creating a provenance subontology for the NPDS cyberinfrastructure with semantic classes and properties for these contributor roles, semantic search in NPDS repositories can be facilitated to support more effective discovery of resources with consideration of entities as same, similar, related, or different.
Research on Image Fusion in UAV Visual Navigation

Abstract The accuracy of multi-sensor image fusion is the key to improve the accuracy of UAV visual navigation. In this paper, a two-step registration method combining region registration and feature registration is proposed. First, rough image registration is carried out based on improved Hough transform and Fourier-Mellin transform, and image precise registration is implemented based on Harris corner feature and cross-correlation function. Finally, wavelet transform is applied to image fusion and image edge enhancement to realize the coarse to accurate registration of SAR images.
PRACTICAL SKILLS TRAINING AS A TOOL FOR THE DEVELOPMENT OF COGNITIVE FUNCTIONS

The problem of reducing the cognitive functions of students is considered. The necessity of involving students in educational processes through the collective is indicated. The necessity of developing practical skills of students is shown
Analisis Penerimaan Teknologi Virtualisasi Museum Pada Platform SIMVONI Menggunakan UTAUT

The COVID-19 pandemic has paralyzed various sectors both domestically and abroad. The rapid spread of the virus has led the government to issue restrictions on public activities and the closure of various public places, including museums which are part of the tourism sector. This has disrupted museum activities, requiring alternative ways for the public to enjoy museum collections. Therefore, the adoption of virtual tour technology can be implemented to solve this problem and an evaluation is needed to see the acceptance of this technology by the public. This research was conducted to explore the factors that influence the acceptance of a web-based virtual tour called SIMVONI using the Unified Theory of Acceptance and Use of Technology (UTAUT) method and to recommend improvements to the SIMVONI developers based on the research results. The data obtained will be analyzed using SEM-PLS) The results of this research are that the significantly influential factors are social influence and intention to use, and some other factors that are not significantly influential are performance expectancy, effort expectancy, facilitating condition, and interaction.The impact of this acceptance is that users tend to share their experiences using the new technology with people around them and it can increase their desire to visit the museum in person after experiencing the virtual tour service. This can be utilized by SIMVONI developers as a promotional strategy with User Generated Content (UGC) techniques such as asking users to share their experiences or testimonials in the form of photos, videos, or writing
CLOTH BAG OBJECT DETECTION USING THE YOLO ALGORITHM (YOU ONLY SEE ONCE) V5

The use of plastic in modern life is increasing rapidly, causing the number of people who use plastic to increase, one of which is when shopping. The function of plastic bags as packaging for luggage is not comparable to the impact caused by plastic waste in the years to come. Plastic bags take a long time, even hundreds to thousands of years, to completely decompose. In order to support the government's program to reduce the use of plastic bags, this study will discuss how to detect cloth bags as a substitute for plastic bags. In this research, a system will be implemented to detect the use of cloth bags with Roboflow and Yolo v5. After carrying out all stages of the research, it can be concluded that the goodie bag detection model has been successfully created. The detection model was created using the YOLOV5 algorithm. The dataset used consists of 102 goodie bag images. The process model uses 100 epochs with the training result mAP@0.5 is 89.8%. So, in other words, it can be said that YOLO v5 can detect goodie bags very well.
Features of Professionalization and Socialization of Youth in the Conditions of Innovative Transformations

The subject of the study is the professionalization and socialization of young people in new socio-economic conditions. The purpose of the work is to establish tools for professionalization of a cohort of the population aged 14–35 years in new socio-economic conditions. The research methodology is based on general and special methods of scientific cognition: theoretical analysis, synthesis and classification. The analysis of the state of the labor market, the most popular professions in 2022 was carried out. The number of labor force by age groups was analyzed, as well as graduates of educational institutions in 2018–2020, whose main job is not related to the received specialty in 2021. The essence and history of the development of professional orientation were considered. The current state of career guidance in the Russian Federation, the system of business incubators and accelerators is analyzed. As a result of the conducted research, it is concluded that career guidance of young people in the Russian Federation at the present stage is extremely relevant and requires measures to improve career guidance.
School Learning Action Cell (SLAC) Session Implementation and Teachers’ Competencies in Cluster 4 of Calamba, Division of Calamba City

This study aims to evaluate the extent of School Learning Action Cell (SLAC) Session Implementation and Teachers' Competencies in Cluster 4 of Calamba, Division of Calamba City.Specifically, it sought to answer the following questions: 1.What is the extent of the School Learning Action Cell (SLAC) Session Implementation in the Cluster 4 of the Division of Calamba; 2. What is the level of Teachers Competency; 3. Does the School Learning Action Cell(SLAC) Session Implementation have significant relationship to the Professional Development of Teachers in terms of Functional Competencies in the Cluster 4 of Calamba, Division of Calamba City?; 4. Does the School Learning Action Cell(SLAC) Session Implementation have significant relationship to the Professional Development of Teachers in terms of Behavioral Competencies in the Cluster 4 of Calamba, Division of Calamba City?In conducting this study, a descriptive study method was used to collect the data and information needed to test the hypothesis and to answer questions concerning the extent of the school learning action cell (SLAC) session implementation in the cluster 4 of the division of Calamba.The instrument used was questionnaire in the form of checklist and Five-Likert scale to gather information headed in the accomplishment of the study.The respondents of the study is composed of one hundred eight (108) teachers from Cluster 4 of the Division of Calamba, Calamba City.The statistical treatment used in this study included frequency counts, equivalent percentages, mean average, and standard deviation.The two way ANOVA was used to determine the whether there is a significant relationship between the extent of school learning action cell (SLAC) to the level of professional development of the teachers.Based on the prior findings of the study, the researcher found out that the extent of the extent school learning action cell (SLAC) session implementation and the professional development of teachers in terms of functional competencies and behavioral competencies are all very high.This means that teachers' professional development is significantly related by the extent of the SLAC sessions.
Monocular polarized three-dimensional absolute depth reconstruction technology for multi-target scenes

The traditional polarization three-dimensional (3D) imaging technology has limited applications in the field of vision because it can only obtain the relative depth information of the target. Based on the principle of polarization stereo vision, this study combines camera calibration with a monocular ranging model to achieve high-precision recovery of the target’s absolute depth information in multi-target scenes. Meanwhile, an adaptive camera intrinsic matrix prediction method is proposed to overcome changes in the camera intrinsic matrix caused by focusing on fuzzy targets outside the depth of field in multi-target scenes, thereby realizing monocular polarized 3D absolute depth reconstruction under dynamic focusing of targets at different depths. Experimental results indicate that the recovery error of monocular polarized 3D absolute depth information for the clear target is less than 10%, and the detail error is only 0.19 mm. Also, the precision of absolute depth reconstruction remains above 90% after dynamic focusing on the blurred target. The proposed monocular polarized 3D absolute depth reconstruction technology for multi-target scenes can broaden application scenarios of the polarization 3D imaging technology in the field of vision.
Validation of a Motion Sickness Prediction Model via Flight Tests on DLR's Bo-105 Helicopter

In previous work, a motion sickness prediction model aimed at vertical lift applications was developed. To validate this model, flight tests with a MBB Bo-105 Helicopter owned and operated by the DLR were conducted. In total, 32 test subjects were flown in 16 sorties on 30 minute sinusoidal flight paths of various frequencies. The test design and implementation included the development of a suitable measurement flight instrumentation, auditive cueing systems for accurate following of the test trajectory and questionnaires for recording motion sickness during flight. The results are analyzed and it is shown that the previously developed motion sickness prediction model agrees well with the motion sickness observed during flight in the case of medium motion sickness.
A Federated Learning Approach for Authentication and User Identification based on Behavioral Biometrics

A smartphone can collect behavioral data without requiring additional actions on the user’s part and without the need for additional hardware. In an active or continuous user authentication process, information from integrated sensors, such as touch, and gyroscope, is used to monitor the user continuously. These sensors can capture behavioral (touch patterns, accelerometer) or physiological (fingerprint, face) data of the user naturally interacting with the device. However, transferring data from multiple users’ mobile devices to a server is not recommended due to user data privacy concerns. This paper introduces an Federated Learning (FL) approach to define a user’s biometric behavior pattern for continuous user identification and authentication. We also evaluate whether FL can be helpful in behavioral biometrics. Evaluation results compare CNNs in different epochs using FL and a centralized method with low chances of wrong predictions in user identification by the gyroscope.
UniSyn: An End-to-End Unified Model for Text-to-Speech and Singing Voice Synthesis

Text-to-speech (TTS) and singing voice synthesis (SVS) aim at generating high-quality speaking and singing voice according to textual input and music scores, respectively. Unifying TTS and SVS into a single system is crucial to the applications requiring both of them. Existing methods usually suffer from some limitations, which rely on either both singing and speaking data from the same person or cascaded models of multiple tasks. To address these problems, a simplified elegant framework for TTS and SVS, named UniSyn, is proposed in this paper. It is an end-to-end unified model that can make a voice speak and sing with only singing or speaking data from this person. To be specific, a multi-conditional variational autoencoder (MC-VAE), which constructs two independent latent sub-spaces with the speaker- and style-related (i.e. speak or sing) conditions for flexible control, is proposed in UniSyn. Moreover, supervised guided-VAE and timbre perturbation with the Wasserstein distance constraint are leveraged to further disentangle the speaker timbre and style. Experiments conducted on two speakers and two singers demonstrate that UniSyn can generate natural speaking and singing voice without corresponding training data. The proposed approach outperforms the state-of-the-art end-to-end voice generation work, which proves the effectiveness and advantages of UniSyn.
SEAT: Stable and Explainable Attention

Attention mechanism has become a standard fixture in many state-of-the-art natural language processing (NLP) models, not only due to its outstanding performance, but also because it provides plausible innate explanations for neural architectures. However, recent studies show that attention is unstable against randomness and perturbations during training or testing, such as random seeds and slight perturbation of embeddings, which impedes it from being a faithful explanation tool. Thus, a natural question is whether we can find an alternative to vanilla attention, which is more stable and could keep the key characteristics of the explanation. In this paper, we provide a rigorous definition of such an attention method named SEAT (Stable and Explainable ATtention). Specifically, SEAT has the following three properties: (1) Its prediction distribution is close to the prediction of the vanilla attention; (2) Its top-k indices largely overlap with those of the vanilla attention; (3) It is robust w.r.t perturbations, i.e., any slight perturbation on SEAT will not change the attention and prediction distribution too much, which implicitly indicates that it is stable to randomness and perturbations. Furthermore, we propose an optimization method for obtaining SEAT, which could be considered as revising the vanilla attention. Finally, through intensive experiments on various datasets, we compare our SEAT with other baseline methods using RNN, BiLSTM and BERT architectures, with different evaluation metrics on model interpretation, stability and accuracy. Results show that, besides preserving the original explainability and model performance, SEAT is more stable against input perturbations and training randomness, which indicates it is a more faithful explanation.
Heuristic Search for Multi-Objective Probabilistic Planning

Heuristic search is a powerful approach that has successfully been applied to a broad class of planning problems, including classical planning, multi-objective planning, and probabilistic planning modelled as a stochastic shortest path (SSP) problem. Here, we extend the reach of heuristic search to a more expressive class of problems, namely multi-objective stochastic shortest paths (MOSSPs), which require computing a coverage set of non-dominated policies. We design new heuristic search algorithms MOLAO* and MOLRTDP, which extend well-known SSP algorithms to the multi-objective case. We further construct a spectrum of domain-independent heuristic functions differing in their ability to take into account the stochastic and multi-objective features of the problem to guide the search. Our experiments demonstrate the benefits of these algorithms and the relative merits of the heuristics.
Improving Interpretability via Explicit Word Interaction Graph Layer

Recent NLP literature has seen growing interest in improving model interpretability. Along this direction, we propose a trainable neural network layer that learns a global interaction graph between words and then selects more informative words using the learned word interactions. Our layer, we call WIGRAPH, can plug into any neural network-based NLP text classifiers right after its word embedding layer. Across multiple SOTA NLP models and various NLP datasets, we demonstrate that adding the WIGRAPH layer substantially improves NLP models' interpretability and enhances models' prediction performance at the same time.
Effective Integration of Weighted Cost-to-Go and Conflict Heuristic within Suboptimal CBS

Conflict-Based Search (CBS) is a popular multi-agent path finding (MAPF) solver that employs a low-level single agent planner and a high-level constraint tree to resolve conflicts. The vast majority of modern MAPF solvers focus on improving CBS by reducing the size of this tree through various strategies with few methods modifying the low level planner. Typically low level planners in existing CBS methods use an unweighted cost-to-go heuristic, with suboptimal CBS methods also using a conflict heuristic to help the high level search. In this paper, we show that, contrary to prevailing CBS beliefs, a weighted cost-to-go heuristic can be used effectively alongside the conflict heuristic in two possible variants. In particular, one of these variants can obtain large speedups, 2-100x, across several scenarios and suboptimal CBS methods. Importantly, we discover that performance is related not to the weighted cost-to-go heuristic but rather to the relative conflict heuristic weight's ability to effectively balance low-level and high-level work. Additionally, to the best of our knowledge, we show the first theoretical relation of prioritized planning and bounded suboptimal CBS and demonstrate that our methods are their natural generalization.
Examination of Vehicle Fraud Detection Possibilities with the Help of Fuzzy Inference System

Insurance fraud is when a person or legal entity seeks to gain an improper advantage by making an incorrect compensation claim. These cases can cause severe economic damage. As a result, the detection of fraudulent incidents is an important issue nowadays, specifically in the case of the liability motor insurance market. In the past decades, soft computing techniques have emerged to model and support the recognition of the problem. In this paper, a theoretical Mamdani-type Fuzzy inference system is introduced to predict the assumed probability of being an insurance fraud with the help of easily determinable parameters: the insurance payout, Ft; the age of innocent participant vehicle, years; and the payment period of the insurance contract. The output variable of the system generated was the assumed probability, %. The model aims to describe critical events and detect suspicious cases at an early stage.
Scalable Theory-Driven Regularization of Scene Graph Generation Models

Several techniques have recently aimed to improve the performance of deep learning models for Scene Graph Generation (SGG) by incorporating background knowledge. State-of-the-art techniques can be divided into two families: one where the background knowledge is incorporated into the model in a subsymbolic fashion, and another in which the background knowledge is maintained in symbolic form. Despite promising results, both families of techniques face several shortcomings: the first one requires ad-hoc, more complex neural architectures increasing the training or inference cost; the second one suffers from limited scalability w.r.t. the size of the background knowledge. Our work introduces a regularization technique for injecting symbolic background knowledge into neural SGG models that overcomes the limitations of prior art. Our technique is model-agnostic, does not incur any cost at inference time, and scales to previously unmanageable background knowledge sizes. We demonstrate that our technique can improve the accuracy of state-of-the-art SGG models, by up to 33%.