Reviewerly/exHarmony huggingface dataset:
Multi-Disks Networks Autonomous Decisions Maker Artificial Intelligence Mapping (MNADMAI-Mapping) for Hydrones and Drones

The rationale of the Multi-Disks Networks Autonomous Decisions Maker Artificial Intelligence Mapping (MNADMAI-Mapping) for Hydrones and Drones revolves around the efficacy of a multi-level mega-data analysis algorithm with the uses of a new mathematical and graphical approach as the most effective analytical control system to programming any UAV (Zenko, 2013) such as Hydrone (drones for water or under water) and Drone (air) missions. The main motivation behind the creation of the MNADMAI-Mapping for Hydrones and Drones is to offer a new artificial intelligence machine tool to evaluate and take auto-decisions in a possible war (Ashkenazi, 2016), warlike, domestic conflicts, or border problem militarily under a large list of possible military actions (defense or attack) efficiently. Thereby, the mission of the MNADMAI-Mapping for Hydrones and Drones is to offer to any army, navy, and air force an alternative artificial intelligence system (Leys, 2018).
2D GAME DEVELOPMENT

This abstract presents an overview of a 2D game designed to provide players with an engaging and challenging experience. Drawing inspiration from the classic combines simple yet addictive gameplay mechanics with modern design elements, aiming to captivate players of all ages. The gameplay centers around guiding a charming bird through a series of obstacles, each presented in a pixel-art inspired environment. Players must use navigation keys to control the bird's flight, navigating tight gaps between each obstacle and collecting valuable rewards along the way. With every successful maneuver, the bird earns points by collecting gem on their way, fostering a competitive atmosphere as players strive to achieve higher scores.
Penyusunan dan Publikasi Karya Ilmiah: Bimtek terhadap Guru-Guru di Wilayah Perbatasan NKRI-Timor Leste

Writing and carrying out the process of publishing scientific papers for teachers is not an easy job. Therefore, this community service activity is carried out to equip teachers with an understanding of the systematics of an article, tricks for finding quality references, the use of grammar in writing scientific papers, strategies to avoid plagiarism traps, the style of journal templates, and the process of submitting articles. The methods of this community service are lectures and discussions. This activity was carried out online for 3 days, with 163 participants. Through the methods of activity above, participants can add insight into writing and submitting scientific articles, but participants have not yet produced scientific articles. Therefore, it is recommended that similar activities in the next period focus more on assisting participants to write articles, as well as assisting in the submission process and revision to the publication of articles in journals.
The financial performance of Islamic boarding school in the domain of social networking, trust, and morality of financial accountability

Purpose - The purpose of this study was to examine the effect of social capital and Morality of Financial Accountability (MoFA) on the financial performance of modern Islamic boarding schools.Method - The population of this research is Islamic boarding schools in Central Java with 40 samples. Measurements were made using a questionnaire with indicators contained in the variables of social networks, trust, MoFA, and financial performance. Data processing was carried out using WarpPLS software with small sample .Result - The results of this study are social network and trust variables have a significant and positive effect on financial performance with an influence value of 23% and 33% respectively. Meanwhile, if it is moderated by spiritual capital, it has an influence on the relationship between social network variables and trust on financial performance, respectively 28% and 21%. Thus spiritual capital will strengthen the relationship between social networks and trust.Implication - The relationship between MoFA in social capital can strengthen the financial performance of Islamic boarding schools due to additional information about plans to use donated funds for good.Originality - MoFA is the development of additional information on the basis of compilation on the legality of kindness, disclosure of consistent reports, honesty in transactions.
Design and Fabrication of Quadcopter Drone for Delivery

IEOM Society Publication Database: A dedicated hub for knowledge and insights in the field of Industrial Engineering and Operations Management. Explore research, articles, and resources that exemplify our commitment to advancing IE & OM globally, fostering critical thinking, and serving humanity.
Diagramming as a Tool for Measuring and Comparing Spatial Relationships in Handwriting

Diagramming is a technique document examiners can use to highlight graphic relationships within handwriting. It provides the forensic document examiner with an additional tool for discovery and measurement and can be part of the process of determining whether or not handwriting is authentic. The focus here will be on signatures and how mapping the proportions within signatures gives insights that might otherwise be missed. Diagramming is particularly helpful in discovering spatial relationships within signatures. It is a means of graphically mapping out habitual movement, providing another perspective when comparing questioned signatures with known exemplars. The technical aspect of diagramming enhances observation while adding precision and reducing bias in the examination process. A handwriting examination requires an understanding of the intention behind the written gesture. That includes evaluating the expressive aspects of the signatures as a whole and not relying solely on linear measures that do not relate to other features or the writing. It requires the ability to holistically integrate written cues and examine how the parts fit together. Handwriting identification is both art (perception and experience) and science (methodology and systematic examination). This graphic mapping process enhances objectivity and helps reduce errors. The concept of diagramming was developed by Werner Wolff in Diagrams of the Unconscious. His research and exploration of habitual configurations in signatures revealed repetitive patterns that are highlighted through diagramming. As with most techniques, diagramming has advantages and limitations. Only by working with diagramming can a handwriting examiner learn where and when it can be used effectively and accurately. Overall, diagramming can potentially add a valuable dimension to the handwriting identification process.
Distances in the face-centered cubic crystalline structure applying operational research

The f.c.c. (face-centered cubic) grid is the structure of many crystals and minerals. It consists of four cubic lattices. It is supposed that there are two types of steps between two grid points. It is possible to step to one of the nearest neighbors of the same cubic lattice (type 1) or to step to one of the nearest neighbors of another cubic lattice (type 2). Steps belonging to the same type have the same length (weight). However, the two types have different lengths and thus may have different weights. This paper discusses the minimal path between any two points of the f.c.c. grid. The minimal paths are explicitly given, i.e. to obtain a minimal path one is required to perform only O(1) computations. The mathematical problem can be the model of different spreading phenomena in crystals having the f.c.c. structure.
THE EFFORT TO PROVIDE PROFESSIONAL NEEDS OF INFORMATION AND COMMUNICATION TECHNOLOGY (ICT) IN THE EDUCATIONAL ENVIRONMENT

Education is a process to influence students to be able to adapt as well as possible to the environment so that it can cause a change in social life. While the teaching participate for directing the purpose of educational process and changes can be achieved. To create an educational atmosphere and standard qualifications, it is necessary to provide effective and efficient technology by utilizing Information and Communication Technology (ICT) based digital-devices by using computer equipment and its supporting facilities. The development of concept of education management is designed to increase the ability to manage changes and developments in education related to the achievement of purposes, policies, strategies, planning, curriculum that have been determined by the government and education authorities so that it is expected to realize the effort of professional employe that compatible with the era of development
PYDOP

Delta-Oriented Programming (DOP) is a modular and flexible approach to implement Software Product Lines (SPLs). In DOP, an SPL is structured in four parts: the feature model describes the configuration space of the SPL; the base artifact is an initial artifact; the deltas are sequences of transformation operations on an artifact; and the configuration knowledge indicates for which configuration and in which order the deltas are applied on the base artifact to generate the variant corresponding to the input configuration. In principle, DOP can thus be used to construct SPLs over any artifact that can be transformed. In this paper, we introduce the pydop library which implements DOP in python3. This library provides all the necessary structures to build SPLs over any transformable python artifact. Moreover, it provides transformation operations over python classes and modules, thus enabling the possibility to build SPLs over python code as well. Finally, this library also provides a simple Multi-SPL mechanism that enable SPL interactions.
SUBJECTIVE ANSWER ASSEMENT USING MACHINE LEARNING

Subjective response assessment is important and time-consuming.This study proposes a machine learning and NLP-based subjective response evaluation method.The suggested method classifies subjective replies using natural language processing and Multinomial Naive Bayes (MNB).The system pre-processes, extracts, and classifies data.This study improves subjective response assessment accuracy and efficiency.
CasTensoRF: Cascaded Tensorial Radiance Fields for Novel View Synthesis

Novel views synthesized from Neural Radiance Fields (NeRF) have reached remarkable rendering quality. However, a 5D radiance field volume is too large to be stored or directly rendered. In order to efficiently reconstruct and manipulate such a high-order tensor, we leverage inspirations from previous tensor decomposition methods, e.g. Tensorial Radiance Fields (TensoRF) and Hierarchical Tucker decomposition. And we propose a Hierarchical Vector-Matrix decomposition (HVMD) framework to learn a sparse approximation of high-order tensors. The proposed HVMD takes advantage of tensor separation and factorization properties and builds a hierarchical scheme that enables a better approximation of the high-order tensor with a very limited number of parameters. Our method achieves better-rendering quality than TensoRF in the NeRF-synthetic dataset given the same model size. The advantage gets more significant when the network parameter number becomes extremely small.
MIM: Lightweight Multi-Modal Interaction Model for Joint Video Moment Retrieval and Highlight Detection

Joint video moment retrieval and highlight detection aims to find the relevant moments and highlight clips in a video with natural language. It is an emerging task though its individual problems have been studied for a while. The current methods utilize transformer to interact between modals, which leads to a huge cost of parameters and computation in spite of great performance. To address this problem, we present a cross-modal attention mechanism to capture related features from different modalities in a few-parameter way. Furthermore, a lightweight multi-modal interaction model (MIM) is proposed to solve video moment retrieval and highlight detection jointly. In the case of greatly reducing the number of parameters, we achieve competitive performance and faster convergence speed compared to previous method. Extensive experiments on four datasets demonstrate the effectiveness of our method.
The Multi-Strategy based Ant Colony Algorithm for the Path Planning of Mobile Robots

To address the issues of slow convergence speed in the early stage, rapid decrease of diversity, and a tendency to get stuck in local optima in traditional Ant Colony Optimization algorithms for mobile robot path planning, a composite Multi-strategy improved Ant Colony Optimization algorithm is proposed. In a two-dimensional plane, the mobile robot working environment is created using a grid method. The L-M trust region strategy is used to plan the initial information weight of the path map to reduce the blindness of ant colony path search in the early stage. Next, a new heuristic function is constructed based on multi-factor induction strategy to reduce the probability of the ant colony getting stuck in local optima. Finally, the “lion king rule” strategy is used to improve the updating method of information weight. Experimental results show that the improved Ant Colony Optimization algorithm effectively improves the early stage convergence speed, has good global optimization performance, and verifies the feasibility and superiority of the improved Ant Colony Optimization algorithm in two-dimensional space path planning.
LPI Radar Signals Intra-Pulse Modulation Recognition Based on CA-ShuffleNet

Recently, radar signal recognition methods based on deep learning have achieved great success. However, these approaches still need to be improved due to the limitation of the number of parameters and computation complexity. This paper proposes an intra-pulse modulation recognition approach for low probability of intercept (LPI) radar signal based on time-frequency analysis (TFA) and lightweight convolutional neural network. Through TFA and image preprocessing, the time-frequency images (TFIs) of radar signals are extracted, noise interference is reduced and redundant frequency band is removed. In order to maintain resolution of images and enhance the ability of the network to extract channel and location information, we introduce dilated convolution and coordinate attention (CA) to ShuffleNetV2. And we proposed a joint loss function consisting of center loss and label smoothing loss to alleviate the overfitting problem and make samples cluster better. Simulation results show that the proposed approach can achieve an overall accuracy of recognition of 98.14% when the SNR is -8 dB.
Frequency-Assisted Adaptive Sharpening Scheme Considering Bitrate and Quality Tradeoff

Sharpening is a widely adopted technique to improve video quality, which can effectively emphasize textures and alleviate blurring. However, increasing the sharpening level comes with a higher video bitrate, resulting in degraded Quality of Service (QoS). Furthermore, the video quality does not necessarily improve with increasing sharpening levels, leading to issues such as over-sharpening. Clearly, it is essential to figure out how to boost video quality with a proper sharpening level while also controlling bandwidth costs effectively. This paper thus proposes a novel Frequency-assisted Sharpening level Prediction model (FreqSP). We first label each video with the sharpening level correlating to the optimal bitrate and quality tradeoff as ground truth. Then taking uncompressed source videos as inputs, the proposed FreqSP leverages intricate CNN features and high-frequency components to estimate the optimal sharpening level. Extensive experiments demonstrate the effectiveness of our method.
Peer Upsampled Transform Domain Prediction for G-PCC

To meet the growing demand for point cloud compression, MPEG is developing a point cloud compression standard called as G-PCC. In G-PCC, upsampled transform domain prediction (UTDP) is used to improve attribute coding performance. However, only the attributes in the previous level can be used to predict the attributes of transform sub-blocks in UTDP, which limits the efficiency of UTDP. To address this limitation, we propose a method called peer-UTDP to improve UTDP by using peer neighbors in this paper. With peer-UTDP, attributes of co-plane or co-line peer neighbors in the level same as that of the transform sub-block can be used as prediction in the upsampling process. Experimental results show that our method outperforms G-PCC with an average coding gain of -5.1%, -5.4%, -5.1% and -1.4% under C1 condition, and -5.1%, -5.6%, -5.6% and -1.7% under C2 condition for Y, Cb, Cr and reflectance, respectively. The proposed peer-UTDP has been adopted by G-PCC.
Variable-Rate Neural Image Compression with Joint Content-Channel Features and Accurate R-λ Model

Recently, neural image compression has received increasing attention as it has achieved superior rate-distortion performance than conventional image compression methods. However, most neural image compression models are fixed rate models, they need to train multiple models for different bit rates, which greatly increases the burden of computation and storage. While some variable-rate models scale the latent features only in the channels, which do not consider the differences in the content of each image. To address these issues, firstly we design a Content Gain Attention Module (CGAM) to learn the relationship between the content of each image and the bit rates. Then we propose a variable-rate neural image compression framework, which utilizes CGAM modules to achieve variable rate in one single model. The framework takes a source image and a Lagrange multiplier λ as inputs and outputs a compressed image with variable rates. Moreover, to achieve more precise rate control, an accurate m, R-λ model is further proposed. Besides, the Channel Gain Unit is utilized to assist variable rate, which scales latent features in a channel-wise manner. Extensive experimental results show that our method is effective and outperforms existing models.
AGE and GENDER PREDICTON from HAND WRITTING TEXT using ATTENTION-BASED CNN

The goal is to predict the age range and gender of a person based on their handwriting. Two feature extraction methods, Histogram of Oriented Gradients and Linear Discriminant Analysis, are employed, along with two machine learning techniques, Support Vector Machine and Attention Based Convolutional Neural Network. For each feature extraction method, the system predicts the age range and gender using SVM and ABCNN. The system provides accuracy metrics for each prediction to decide which technique is best, indicating the reliability of the age and gender estimations. This is designed to work in real-time, enabling quick predictions for incoming handwriting samples. Key Words : Histogram of Oriented Gradients (HOG), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Attention Based Convolutional Neural Network (ABCNN) , Convolutional Neural Network (CNN)
Mitigasi Banjir di Kelurahan Wawombalata Kota Kendari

The problem of flooding is still a problem that occurs in urban communities. High water intensity during the rainy season results in the potential for flooding in residential areas around the river flow. This study aims to determine flood mitigation in the Wawombalata sub-district, Kendari City. The method used in this research is survey and map overlay. The results showed that: On the flood hazard map, the area that has the highest hazard class is in the RT 5 section. Meanwhile, the flood hazard in the high class covers parts of the RT 1, RT 2, RT 3, RT 4, RT 5, RT 6 and RT 7. Overall the potential for flooding in the Wawombalata sub-district is classified as a high hazard class up to the highest class around residential areas and community housing. Flood mitigation strategies that can be carried out include: Conducting learning about disaster mitigation and outreach as well as disaster emergency simulations, maintaining the functions of vital objects/public facilities. Coordinating disaster management at the District/City level and and the Central Government. Keywords: Mitigation, Hazard, Flood, Community
T-FIM: Transparency in Federated Identity Management for Decentralized Trust and Forensics Investigation

Federated Identity Management (FIM) has gained significant adoption as a means to simplify user authentication and service authorization across diverse domains. It serves as a centralized authentication and authorization method, enabling users to access various applications or resources using credentials issued by a universally trusted identity provider (IdP). However, recent security incidents indicate that the reliability of credentials issued by IdP is not absolute in practice. If the IdP fails, it can persistently access any application that trusts it as any user. This poses a significant security threat to the entire system. Furthermore, with the increasing adoption of FIM across diverse scenarios, there is a growing demand for the development of an identity management system that can effectively support digital forensics investigations into malicious user behavior. In this work, we introduce transparency to federated identity management, proposing T-FIM to supervise unconditional trust. T-FIM employs privacy-preserving logs to record all IdP-issued tokens, ensuring that only the true owner can access the exact token. We utilize identity-based encryption (IBE), but not just as a black box, encrypting tokens before they are publicly recorded. In addition, we propose a decentralized private key generator (DPKG) to provide IBE private keys for users, avoiding the introduction of a new centralized trust node. T-FIM also presents a novel approach to digital forensics that enables forensic investigators to collect evidence in a privacy-preserving manner with the cooperation of the DPKG. We conduct a comprehensive analysis of the correctness, security, and privacy aspects of T-FIM. To demonstrate the practical feasibility of T-FIM, we evaluated the additional overhead through experimental evaluations. Additionally, we compared its performance with other similar schemes to provide a comprehensive understanding of its capabilities and advantages.
Assessing Knowledge Quality Using Fuzzy MCDM Model

The purpose of knowledge management is to excavate the tacit knowledge accumulated by each enterprise member through the knowledge proposal system. Each knowledge proposal must be assessed, and after passing the quality assessment, the knowledge proposal will be stored in the knowledge repository and shared with other employees who need the knowledge at work. In the long run, the capabilities of all employees will gradually enhance and the competitiveness of enterprises will naturally increase. The correct assessment of knowledge quality is the key to the success of knowledge management. Some scholars propose to use the AHP (analytical hierarchical process) to determine the quality of knowledge. The problem with this approach is that the AHP can only obtain the relative quality of all knowledge, not the actual quality of knowledge. Therefore, this study proposes a fuzzy assessment model to measure knowledge quality, which includes a knowledge quality fuzziness index (KQFI) and a checking gate. First, experts conduct linguistic evaluation on the weight of criteria and knowledge quality. All linguistic evaluations are then integrated into a knowledge quality fuzziness index (KQFI), which is compared with a fuzzy threshold (FT); then, the level of goodness of KQFI to FT is obtained. If it is greater than 0.5, it means that the quality of the knowledge proposal is qualified; otherwise, it means that the quality of the knowledge proposal is unqualified. This study uses a case including five experts and nine knowledge proposals to demonstrate the applicability of the method. The results show that the method finally judges six knowledge instances as qualified and three as unqualified. The results show that the proposed method can indeed assist enterprises to effectively screen knowledge proposals.