Reviewerly/exHarmony huggingface dataset:
Rethinking the Set of Concepts—Applying Expert Analysis by the Workshop Method

The contradictions of the historical data with the six-component model necessarily led to a refinement of the FCM model. The parts of the system under discussion are systems in themselves, and to investigate their deeper structure is essential to understanding the operation of the system. The subsystems form this way a system of systems". Up-to-date engineering systems are often extremely complex, and they are usually composed of a multitude of interacting units, which are called subsystems. In areas where expert assessment is crucial, the process of expert participation in workshop form may ensure that the various opinions and interests are integrated into synergies and this way, sustainable solutions sought for can be found as a result of joint decision-making. Regional Waste Management Systems (RWMS) are also such complex systems where expert domain knowledge plays an important role, and which can always be subdivided into interconnected or separate subsystems and elements, where a number of factors has to be reconsidered and determined by an expert panel. These factors are usually complementary, in some cases conflicting or competing; but essentially the subsystems and subsystem elements influence each other and determine the overall operation of the system. In this Chapter, it will be shown how the workshop technique may result in a much more refined model, which may be the start of a model without contradictions and fully adequately simulating the real life system. The aim of the workshop designed and realised in the case study, described in the Chapter as a suitable method was to analyse the starting model and to construct an efficient new model of RWMSs, with more factors. The workshop method was applied in group sessions and plenary discussions. Expert teams were encouraged to develop a common, unified model of the system under investigation. This approach could hopefully help resolve the conflicts and disagreements in the previous model, and also, among certain experts' opinions. In the case study, as a result, a more refined model was developed indeed, however, then the problem emerged, how to build up, how to deal with, and how to evaluate this rather complex model.
MOBILE AUGMENTED REALITY APPLICATION (ARFABICS) OF HIJAIYAH LETTER INTRODUCTION AS LEARNING MEDIA FOR CHILDREN

In Al-Qur'an, which is the holy book for Muslims, hijaiyah letters function as a letter to form a word, just like in Indonesia, where alphabet letters are used to form a word. Therefore, many Muslims from adults to children learn how to read hijaiyah letters properly and correctly so that they can properly perform their prayers. Current technological developments can provide a variety of innovations in the world of education, one of which is a learning method that uses augmented reality technology that can bring up images of three-dimensional objects that are considered more interactive and effective in the learning process of children. This can help children understand learning materials more easily and be fun. In this study, the design of the Arfabics application for the introduction of hijaiyah letters using augmented reality technology will provide a fun new experience for children in their learning process of recognizing hijaiyah letters. This application is designed using the main software Unity 3D, Vuforia SDK, and using the Marker Based Tracking method. This research aims to improve children's visual understanding of hijaiyah letters by using three-dimensional augmented reality technology and make children more interested and happy in learning hijaiyah letters.
A comprehensive survey to study the utilities of image segmentation methods in clinical routine

Abstract The clinicians usually desire to know the shape of the liver during treatment planning to minimize the damage to the surrounding healthy tissues and hepatic vessels, thus, building the geometric model of the liver becomes paramount. There have been several liver image segmentation methods to build the model over the years. Considering the advantages of conventional image segmentation methods, this paper reviews them that spans over last 2 decades. The review examines about twenty-five automated and eleven semi-automatic approaches that include Probabilistic atlas, K -means, Model and knowledge-based (such as active appearance model, live wire), Graph cut, Region growing, Active contour-based, Expectation Maximization-based, Level sets, Laplacian network optimization, etc. The main contribution of this paper is to highlight their clinical suitability by providing their advantages and possible limitations. It is nearly impossible to assess the methodologies on a single scale because a common patient database is usually not used, rather, diverse datasets such as MICCAI 2007 Grand Challenge (Sliver), 3DIRCADb, Zhu Jiang Hospital of Southern Medical University (China) and others have been used. As a result, this study depends on the popular metrics such as FPR, FNR, AER, JCS, ASSD, DSC, VOE, and RMSD. offering a sense of efficacy of each approach. It is found that while automatic segmentation methods perform better technically, they are usually less preferred by the clinicians. Since the objective of this paper is to provide a holistic view of all the conventional methods from clinicians’ stand point, we have suggested a conventional framework based on the findings in this paper. We have also included a few research challenges that the readers could find them interesting.
Diversification of the Private Security Sector for the Expansion of the Private Security Industry: An Analysis of Hindering Factors and Development Strategies through Service Provision

This study attempted a comprehensive analysis of the expansion strategy and obstacles of the modern private security industry. The private security industry has grown by overcoming the limitations of public security services and meeting individual and private needs, and to this end, it has been analyzed that it has been expanded through various strategies, including providing customized security services to individual and private areas, utilizing technology-oriented security services, and organically integrating manpower and machines to provide safety and status information.&#x0D; However, the modern private security industry is facing a number of obstacles. Examples of these factors were self-accidents and crimes, lack of marketing, problems related to the manpower-oriented industrial structure, and the acidity of non-integrated organizations. These factors were found to undermine the perception and trust of private security services.&#x0D; Accordingly, it was suggested that in order to strengthen the private security industry, ethics education, marketing strategies, improvement of manpower education and evaluation, and efforts to strengthen organizational centralization and cooperation should be needed.
Experimental Results

In order to validate the modelling, control and design work presented in the previous chapters, and to evaluate the tilt-rotor aerial robot as an effective omnidirectional aerial manipulation platform according to our problem definition, we perform flight experiments that touch on many of these presented concepts.
Model Reduction Methods

Fuzzy Cognitive Maps are very useful tools primarily in decision making and management tasks. They represent the main factors, variables of a complex system and the internal causal relationships among them in a straightforward way. Simulations can be started with an initial state, and the future states of the system under investigation can be predicted. This way, what-if questions can be answered. If the model of a system is created by experts they are often tempted to include too many components, because they are not sure in the importance of them. An oversized model is excruciating to use in practice, however. Model reduction methods help to decrease model size but unavoidably cause information loss as well. This effect does not cause a problem in practical decision making applications if the model suggests the same decisions. This chapter covers three FCM model reduction methods, their theoretical background and behavioral properties.
Movie Recommender System: Addressing Scalability and Cold Start Problems

A recommender system is a tool used to utilized to forecast the inclination of a user for a specific item. Matrix Factorization is one of the most extensively employed and well-known techniques for constructing a Recommender System. This approach has been proven to be efficacious in numerous practical applications and has been employed in a variety of domains, including films, literature, songs, and electronic commerce. In this paper, we attempted to conduct a bias-minimized analysis of latent feature factorization and achieved successful results in small datasets. Our findings suggest that when studying a smaller segment of users, it is not necessary to incorporate all biases, resulting in lower computational costs. We also discussed the issue caused by cold start i.e., whenever a new user is available in the system, and possible solutions to address the issue of cold start.
SEAMLINE OPTIMIZATION FOR UAV IMAGE MOSAICKING USING GEOMETRY OF TRIANGULATED IRREGULAR NETWORK

Abstract. For efficient UAV (Unmanned Aerial Vehicle) image monitoring, it is essential to mosaic multiple UAV images into one seamless image. In a mosaicked image, relief displacement of terrain is a major source of error. It is difficult to form seamlines that avoid all areas of relief displacements. A seamline determination method alone is limited in reducing the mismatch error on mosaicked image. In this study, we constructed a TIN (Triangulated Irregular Network) using tiepoints generated by rigorous bundle adjustments. We detected the regions where relief displacement occurred using the slope of TIN facets. It was found that the error of mosaicked image were mostly on TIN facets with high slopes. Our method generated a mosaicked image after elimination of error-prone region and showed that the distortions were effectively removed. This study showed that the proposed method could produce mosaicked images with stable quality using geometric clues of TIN. We expected that our method can be used for UAV image mosaicking robust to mismatching factors.
Systemic Relationships Between Regional Sustainability and Waste Management

An assessment of local needs and conditions is essential to support an efficient waste management system, which can be used to select the components and waste management options that best meet these circumstances and their resource requirements. Each of these activities requires careful planning, but the model of sustainable municipal waste management offers a comprehensive interdisciplinary framework. The aim of the research is to develop and present a methodology as an environmental engineering problem that can be applied in practice to evaluate the sustainability of regional municipal waste management systems. As a result of the research, an integrated and interdisciplinary approach will be developed that can be effectively applied at regional level and give an accurate picture of the actual evolution of the role and impact of the waste management planning process and its implementation.
Pelatihan Penulisan Artikel Jurnal bagi Mahasiswa Program Studi Sastra Inggris Universitas Trunojoyo Madura

Writing articles of a scientific nature is sometimes still an obstacle for final year English Literature students at Trunojoyo Madura University who want to publish the results of their research. This service activity is carried out by providing training on how to write scientific articles so that students are ready for publication. The method used is an approach based on Asset Based Community-Driven Development (ABCD). This activity is carried out through zoom meeting media during student holidays so that this activity is expected to fill student holiday time. The activity was carried out on July 26 2022. The results of this activity show that students have an interest in writing scientific articles but are still confused about starting and are still afraid to write. It is hoped that from this training students will become more confident and interested in writing scientific articles well and correctly
Dynamics of Classical Fuzzy Cognitive Maps

In Fuzzy Cognitive Map based modelling, the final conclusion is based on the assumption that the system reaches an equilibrium point (fixed point) after a certain number of iteration. It is not straightforward that this leads to a fixed point, since the iteration may show various dynamical behaviours: multiple fixed points, limit cycle and unpredictable patterns may also occur. In this chapter, a gentle insight into the dynamics of FCMs' is provided based on solid mathematical tools. We prove sufficient conditions for the existence and uniqueness of fixed points for sigmoid, hyperbolic tangent and rescaled sigmoid FCMs, which are also necessary in the case of the latter two. Additionally, symmetrical properties of fixed points and basins of attraction of hyperbolic tangent and rescaled sigmoid FCMs are also introduced with mathematical proof.
Empirical Evaluations of Machine Learning Effectiveness in Detecting Web Application Attacks

Web applications remain a significant attack vector for cybercriminals seeking to exploit application vulnerabilities and gain unauthorized access to privileged data. In this research, we evaluate the efficacy of eight supervised machine learning algorithms - Naive Bayes, Decision Tree, AdaBoost, Random Forest, Logistic Regression, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Artificial Neural Network (ANN) - in detecting and countering web application attacks. Our results indicate that KNN and Random Forest classifiers achieve an accuracy rate of 89% and an area under the curve of 94% on the CSIC HTTP dataset, a commonly used benchmark in the field. Meanwhile, the Naive Bayes classifier proves the most efficient, taking the least computational time when differentiating between malicious and benign HTTP requests. These findings may help direct future efforts towards more efficient, machine learning-driven defenses against web application attacks.
PELATIHAN DAN PEMBUATAN WEBSITE MENGGUNAKAN HTML DAN CSS

Web Design Making Training Using HTML And CSS at Nusantara Plus is a community service activity that aims to increase students' knowledge and understanding of creating a simple web. The training adopts a practical approach, wherein participants learn the fundamental steps of creating web pages with HTML and CSS. The learning methods include theoretical sessions, demonstrations, and hands-on practice to ensure optimal comprehension. The service activities that have been carried out are training on creating a simpleweb using VSCode, which can increase the knowledge and skills of Nusantara Pluss Middle School students. This training is carried out for one day by informatics engineering students at Pamulang University. The students directly practice on their respectivelab computers. Students reported increased knowledge of basic HTML structure, styling with CSS, and consistent design implementation
Error analysis and comparison of wavelength tuning phase shifting interferometry of transparent optical flats

Wavelength-tuning phase-shifting interferometry has advantages over conventional hardware phase-shifting for the simultaneous detection of all surfaces of the transparent parallel flats. The Fourier transform and its derivative methods can be used to characterize the signal based on the optical path difference and the phase-shifting value of the corresponding interferometric harmonic signals on each measured surface and to solve for the phase accordingly. Therefore, considering the influence of the sampling length and the choice of the window function in the Fourier transform on the measurement results, based on the established multi-surface interference model, an adequate simulation analysis is carried out, and the corresponding explanations and discussions are given subsequently. In practical measurements, the appropriate window function and sampling length should be selected according to the corresponding measurement conditions, so that reliable measurements can be guaranteed.
A Sparse Sinkhorn Transformer for Multi-Behaviour Recommendation

Modelling dynamic user-item interactions using GNN in Recommender Systems has attracted significant research interest recently. Existing research has focused on single type user-item interactions such as “purchases or “user ratings in ecommerce domain, whereas real-world scenarios involve multi-type interactions such as “view”, “add-to-favourites”, “add-to-cart” and “product purchase” that are not considered by these approaches. To address this problem, Vanilla transformer-based self-attention mechanism have been adopted in literature for multi-behaviour recommendation. These methods suffer from high computational cost which results in scalability issues especially on large datasets. To overcome this limitation, we propose the Sinkhorn Transformer for Multi-Behaviour Recommendation (STMBR) model. Our proposed method uses a differentiable algorithm to sort and re-arrange elements of the input sequence based on Sparse Sinkhorn Attention. Experimental results on three real-world-datasets show our model’s competitive recommendation performance against the state-of-the-art baselines at lower computational cost.
Identifikasi Kesulitan Belajar Akademik

This article discusses the identification of academic learning difficulties that are often experienced by students. Academic learning difficulties include difficulties in reading, writing and arithmetic. The causes of academic learning difficulties can come from students' internal factors such as low cognitive abilities or external factors such as lack of support from parents or teachers. In overcoming academic learning difficulties, collaboration is needed between teachers, students and parents. Teachers must provide teaching that not only refers to the curriculum, but also looks at students' individual needs. Students must admit the difficulties they face and ask for help from teachers and parents. Parents must provide support and motivation in the learning process and teach skills needed for learning such as reading together and discussing.
Lexical Knowledge Representation and Semantic Composition of E-HowNet

Extended HowNet (E-HowNet) is a frame-based entity-relation model extended from HowNet that defines lexical sense to achieve natural language understanding. The major extension features include the following: (a) word senses are defined by either primitives or any well-defined concepts and conceptual relations; (b) semantic relations are explicitly expressed; (c) uniform representation of content words, function words, and phrases; (d) semantic composition and decomposition capacities; and (e) near-canonical representations for lexical senses and phrasal senses. Adapted from the HowNet ontology, the framework of the E-HowNet ontology has demonstrated lexical representation and semantic composition. Strategies for semantic role labeling were also addressed since this is a crucial task for automatic semantic composition. The goal of achieving natural language understanding should be accomplished after future improvement and evolution of the current E-HowNet model.
Stardist Segmentation to Determine the Printout Characteristics of Different Printers

The simplicity papers may be printed has increased illegal activities such as document forgery. Better ways to distinguish between fake and real documents are necessary to overcome this issue. This paper proposes a unique approach for reliably identifying printer sources. This study applied the Arabic letter from three different types of printers such as HP Laserjet P2010, HP LaserJet 2200dn, and LaserJet CP3525dn. The proposed method to identify the difference is by implementing Stardist technique. The result shows that each printer's threshold, probability, distance, and label image has a different pattern. From the result of mean and area in the image using Stardist can be seen that some printers as LJ-3525 have a lower value than others.
Exploring Robust Pose Invariant Face Recognition with Vision Transformers: A Multi-Modal Study

This paper investigates the application of vision transformers (ViT) to face recognition tasks, focusing on their robustness against pose variations in multi-modal settings. Leveraging the advantages of ViT, such as global context modeling and attention mechanisms, we evaluate their performance using RGB and depth face modalities. To facilitate this investigation, we propose a new multi-view and multi-modal dataset, featuring various pose variations, ranging from slight to extreme, with incorporated facial expressions. Four robust pose invariant evaluation protocols are established for comprehensive analysis. Three variants of ViT models (Base, Small, and Tiny) are trained and evaluated. Our findings indicate that ViT-Base achieves 97.11 % accuracy for RGB faces, while ViT-Small outperforms all RGB Face Transformers in depth modality, achieving 99.02% accuracy across all pose protocols. Importantly, our empirical study highlights the enhanced robustness of 3D depth maps for pose invariant face recognition tasks, irrespective of the deep learning techniques employed. These insights contribute to the understanding of ViT models in face recognition and open avenues for future research in this domain.
Effects of virtual reality-based cue exposure therapy on craving and physiological responses in alcohol-dependent patients-a randomised controlled trial

Cue exposure therapy is used to treat alcohol dependence. However, its effectiveness is controversial due to the limitations of the clinical treatment setting. Virtual reality technology may improve the therapeutic effect. The aim of this study is to explore whether virtual reality-based cue exposure therapy can reduce the psychological craving and physiological responses of patients with alcohol dependence.Forty-four male alcohol-dependent patients were recruited and divided into the study group (n = 23) and the control group (n = 21) according to a random number table. The control group received only conventional clinical treatment for alcohol dependence. The study group received conventional clinical treatment with the addition of VR cue exposure (treatment). The primary outcome was to assess psychological craving and physiological responses to cues of patients before and after treatment.After virtual reality-based cue exposure therapy, the changes in VAS and heart rate before and after cue exposure in the study group were significantly lower than those in the control group (P < 0.05), while the changes in skin conductance and respiration between the study group and the control group were not significantly different (P > 0.05). The changes in VAS and heart rate before and after cue exposure in the study group were significantly lower than those before treatment (P < 0.05), while the changes in skin conductance and respiration were not significantly different from those before treatment (P > 0.05). The changes in VAS, heart rate, skin conductance and respiration before and after cue exposure in the control group were not significantly different from those before treatment (P > 0.05).Virtual reality-based cue exposure therapy can reduce the psychological craving and part of the physiological responses of alcohol-dependent patients during cue exposure in the short term and may be helpful in the treatment of alcohol dependence.The study protocol was registered at the China Clinical Trial Registry on 26/02/2021 ( www.chictr.org.cn ; ChiCTR ID: ChiCTR2100043680).