Reviewerly/exHarmony huggingface dataset:
RECO-FSCA: Reconfigurable Low-Power Implementation of Fprime-Software for CubeSats Applications

The Python Productivity for Zynq (PYNQ) project, developed by Xilinx, is a member of the Zynq class of devices. NASA's Jet Propulsion Laboratory has created the F′ (FPrime) software framework, specifically designed for rapid deployment and development in space applications. FPrime adopts a component-driven approach, enabling the reuse of universal components across multiple projects, thereby reducing development time and costs. In our study, we focused on developing components for FPrime and deploying them on the PYNQ-Z1 and PYNQ-Z2 boards. These components were designed to assess memory performance, critical for flight software, as well as computationally intensive tasks. To test memory-intensive tasks, we designed and implemented an array copying component. For computationally intensive tasks, a modular exponentiation task was developed. Additionally, we integrated the GAGE hash function into FPrime, providing data for computationally intensive operations. Intra-component security features were also incorporated to mitigate the risk of Man-in-the-Middle attacks. Comprehensive measurements were recorded for each aspect of the project. Future work will involve further benchmarking and exploring the feasibility of implementing FPrime on embedded devices within Cube-Sats.
Employee Information System Design Of KAI Property Management Limited Liability Company Medan Based On Android

Indonesian Railways Limited Liability Company Property Management or KAI Property Management Limited Liability Company Medan is one of the subsidiaries of KAI Limited Liability Company which was established in 2009 with core businesses in the fields of construction, property, trade and maintenance of railway infrastructure and its supporting facilities. In the process of importing employee data at KAI Property Management Limited Liability Company Medan still uses manuals, so to make it easier to collect data, it makes an employee information system that is easy to use and quite safe. This study used waterfall, uml, and blackbox methods. The waterfall method consists of analysis, design, development, testing, and deployment, and uml used usecase diagrams, activity diagrams, and sequence diagrams. The results of this study are in the form of an information system design that uses codular as an android application maker that can help in terms of processing office employee data.
Pengaruh Kemampuan Komunikasi Interpersonal dan Kecerdasan Sosial Terhadap Prestasi Belajar PKN Siswa SD Kelas V

This study aims to determine the effect of interpersonal communication skills and social intelligence on the PKn learning achievement of elementary school students in class V of the Cluster IX Lubuklinggau. This type of research is quantitative research using multiple linear regression models. The population in this study were all elementary school students in Group IX of Lubuklinggau accredited A as many as 2 elementary schools with 115 students. The sample in this study were 58 students taken proportionally. Data collection techniques and instruments in this study using a questionnaire. Data analysis techniques using multiple linear regression formula and t test. The results of this study indicate that Fcount (27.495) is greater than Ftable (4.02) at a significant level ? = 0.05. This shows that interpersonal communication (X1) and social intelligence (X2) together influence the learning achievement. Thus it can be concluded that there is an influence of interpersonal communication skills and social intelligence on the learning achievement of PKn students in the fifth grade of Class V Cluster IX Lubuklinggau. This type of research is quantitative research using linear regression models.
Server-side Rescoring of Spoken Entity-centric Knowledge Queries for Virtual Assistants

On-device Virtual Assistants (VAs) powered by Automatic Speech Recognition (ASR) require effective knowledge integration for the challenging entity-rich query recognition. In this paper, we conduct an empirical study of modeling strategies for server-side rescoring of spoken information domain queries using various categories of Language Models (LMs) (N-gram word LMs, sub-word neural LMs). We investigate the combination of on-device and server-side signals, and demonstrate significant WER improvements of 23%-35% on various entity-centric query subpopulations by integrating various server-side LMs compared to performing ASR on-device only. We also perform a comparison between LMs trained on domain data and a GPT-3 variant offered by OpenAI as a baseline. Furthermore, we also show that model fusion of multiple server-side LMs trained from scratch most effectively combines complementary strengths of each model and integrates knowledge learned from domain-specific data to a VA ASR system.
Towards Explainable Computer Vision Methods via Uncertainty Activation Map

This paper focuses on the problem of highlighting the input image regions that result in increasing predictive uncertainty. In particular, we focus on two types of uncertainty, epistemic and aleatoric, and present an uncertainty activation mapping method that can incorporate both types of uncertainty. To this end, we first utilize a mixture-of-experts model combined with class-activation mapping (CAM). The proposed method is extensively evaluated in two different scenarios: multi-label and artificial noise injection scenarios, where we show that our proposed method can effectively capture uncertain regions.
Assessing the strengths and limitations of quantitative micromorphometry for the forensic examination of small arms propellant towards brand identification.

Smalls arms propellants (SAP) also known as canister powders are readily accessible and cost-effective materials that firearms enthusiasts can acquire for the legitimate assembly of ammunition. These attributes also make SAPs advantageous for the construction of improvised explosive devices (IEDs). Thus, there is a need to develop robust metrics for the characterization of propellants to provide investigative leads as well as for comparisons between known and recovered residues. The goal of this research was to investigate the utility of a high-throughput, non-destructive, and low-cost quantitative automated image analysis routine for the characterization and discrimination of SAP. For this project, 204 one-pound canisters of smokeless propellant (powder) were acquired from local and online sources. These samples represent nine manufacturers and 154 unique brands. From this set, five brands were selected to assess the intra- and inter-lot variability. Eight parameters, which encompass size- and shape-dependent metrics were measured for each sample. A total of ∼85,000 granules (∼680,000 measurements) were analyzed using linear discriminant analysis. A detailed assessment of the variables shows that the size-dependent metrics provide the greatest amount of sample discrimination. Overall accuracy of the method to correctly classify a test subset of data to the brand level is ∼84.72%. The results from this study provide a framework in which to interpret smokeless propellant micromorphometry in the context of intelligence purposes for initial stages of criminal investigations, and for traditional comparisons between known and unknown samples.
A joint Multi-decoder Dual-attention U-Net framework for tumor segmentation in Whole Slide Images

Despite the recent increasing interest in digital pathology, efficient analysis of Whole Slide Images (WSIs) faces many challenges that hinder performance, such as their massive size, high heterogeneity, and imbalance distribution. This paper proposes a Joint Multi-decoder Dual-attention U-Net (JMDU-Net) framework for tumor segmentation in WSIs to address these issues. JMDU-Net is an end-to-end framework that consists of a single encoder with a main and four supportive decoder branches. JMDU-Net incorporates the design of multiple novel blocks that adaptively acquire diversified contextual channel-wise features and progressively utilize knowledge from multi-perspective hierarchical feature fusion. Moreover, a multi-magnification majority-voting ensemble is proposed to jointly consider the multi-scale representations of different magnifications in the WSIs. The experimental results and statistical tests performed on the public PAIP 2019 dataset demonstrate the efficiency of JMDU-Net by obtaining an average of 88.0% dice score with 77.4% clipped Jaccard index value for 5-fold cross-validation on training data, while the proposed ensemble succeeded in obtaining a 86.4% dice score with 79.8% clipped Jaccard index value for the official validation set. Furthermore, the generalizability is assessed against DigestPath 2019 and PAIP 2023 datasets. JMDU-Net achieved average dice of 84.9% and 75.3% and Jaccard values of 74.4% and 63.0% on these datasets, respectively. The main aim of JMDU-Net is to provide a complementary assessment to medical professionals to enhance the level of care introduced to the patients by harnessing the powerfulness of deep learning in the cancer diagnosis task. The code will be available publicly at https://github.com/Heba-AbdeNabi/JMDU-Net.
Mixed reality in surgical telepresence: a novel extended mean value cloning with automatic trimap generation and accurate alpha matting for visualization

Abstract The aim of this research is to propose an extended mean value cloning algorithm with automatic trimap generation and accurate alpha matting. This implementation improves the visualization accuracy of the merged video by reducing the discolored and smudging artefacts of the remote surgeon’s boundary. It also makes the merge robust for the illumination changes by taking less processing time in real time surgery. The proposed system uses automatic trimap generation from the source video for accurate foreground extraction. Extended mean value cloning with gradient mixing is then applied for the cloning with optimized alpha matting for accurate and realistic video composition. The proposed system improved the visualization accuracy by providing almost 99.7% visibility of the pixels compared to the state-of-the-art solution, which provides 99.1% visibility of pixels. The overlay error was reduced from 0.93 mm to 0.63 mm. The processing time was also reduced. The proposed solution processed 8 frames per second, which is less time than the state-of-the-art solution, which processed 5 frames per second. The extended mean value cloning smooths the differences that presented in the target and source frames for seamless and realistic blending of pixels. The automatic trimap generation reduced the risk of false foreground selection and the generated optimal trimaps improved the alpha matte quality, which is optimized to reduce the smudging artefacts completely and to produce accurate visualization of the final merged image.
READING COMPREHENSION OF REPORT TEXT THROUGH SCIENTIFIC APPROACH

Reading is one of the language skills. That should be mastered by students, especially for senior High School. Objectives and development of knowledge, technology, art, and culture, and relationship with the others country. It is true that language is really necessary for education. The problem of this research is How to improve the students’ reading comprehension through scientific approach. The study aimed to improve the students’ reading comprehension through scientific approach. This research design of this study was classroom action research (CAR). This research is conducted at XI Agama 2 of MA Al-Amiriyyah Blokagung, it was known that the students had problem in reading comprehension. Based on the result of analysis of the data, can be concluded that the application of scientific approach in learning report text can improve the students’ reading comprehension. It can be shown from the result of mean score and test score have increased. The mean score in pre-cycle was 65,78 and the percentage of completeness of 37%, in cycle 1 was 69,47 and percentage of completeness of 63%, the mean score in cycle 2 was 81,05 and percentage of completeness of 100%.
Two Birds with One Stone: A Link Prediction Model for Knowledge Hypergraph Based on Fully-Connected Tensor Decomposition

Knowledge hypergraph link prediction aims to predict missing relationships in knowledge hypergraphs and is one of the effective methods for graph completion. The existing optimal knowledge hypergraph link method based on tensor decomposition, i.e., GETD (Generalized Model based on Tucker Decomposition and Tensor Ring Decomposition), has achieved good performance by extending Tucker decomposition, but there are still two main problems: (1)GETD does not establish operations or connections between any two tensor factors, resulting in limited representation of tensor correlation (referred to as finiteness); (2)The tensor decomposed by GETD is highly sensitive to the arrangement of tensor patterns (referred to as sensitivity). In response to the above issues, we propose a knowledge hypergraph link prediction model, called GETD $$^+$$ , based on fully-connected tensor decomposition(FCTN). By combining Tucker decomposition and FCTN, a multi-linear operation/connection is established for any two factor tensors obtained from tensor decomposition. This not only enhances the representation ability of tensors, but also eliminates sensitivity to tensor pattern arrangement. Finally, the superiority of the GETD $$^+$$ model was verified through a large number of experiments on real knowledge hypergraph datasets and knowledge graph datasets.
The Implementation of Cooperative Learning Through Teams Games Tournament (TGT) Model to Improve Students’ Speaking Ability

The objective of this research is to know the implementation of cooperative learning through (TGT) model in teaching English to improve students’ speaking ability. This was classroom action research (CAR) it was objectives to repair teaching-learning. The methods of collecting primary data are observation and test. The methods of supporting data were obtained from interviews and documentation. Based on the result and data analysis it can be concluded that the teacher already applied the team's games tournament (TGT) and good enough preparation. The students get significant scores. It can be seen that before using teams games tournament students’ mean score was 59.8, in Cycle 1 students’ mean score was 72 and students’ mean score in Cycle 2 was 79. Finally, it is a great number that the implementation of cooperative learning through teams games tournament (TGT) in teaching speaking is success categories and the teacher should give more exercise in English to the students to develop their skill in teaching speaking, give motivation student’s continually, the teacher should make a good relationship with students to get result of speaking material maximally.
Low-Cost Optimized U-Net Model with GMM Automatic Labeling Used in Forest Semantic Segmentation

Currently, Convolutional Neural Networks (CNN) are widely used for processing and analyzing image or video data, and an essential part of state-of-the-art studies rely on training different CNN architectures. They have broad applications, such as image classification, semantic segmentation, or face recognition. Regardless of the application, one of the important factors influencing network performance is the use of a reliable, well-labeled dataset in the training stage. Most of the time, especially if we talk about semantic classification, labeling is time and resource-consuming and must be done manually by a human operator. This article proposes an automatic label generation method based on the Gaussian mixture model (GMM) unsupervised clustering technique. The other main contribution of this paper is the optimization of the hyperparameters of the traditional U-Net model to achieve a balance between high performance and the least complex structure for implementing a low-cost system. The results showed that the proposed method decreased the resources needed, computation time, and model complexity while maintaining accuracy. Our methods have been tested in a deforestation monitoring application by successfully identifying forests in aerial imagery.
Assessing Mediating Effect of National Culture on the Relationship of Leadership Style with UAE Smart Government Organizational Performance

This study presents persuasive evidence that national culture acts as a mediator in the relationship between leadership style and organisational performance in the context of UAE Smart Government. By investigating these links, the study emphasises the importance of taking cultural aspects into account when evaluating the impact of leadership on organisational outcomes in the context of smart governance. Data collected through questionnaire survey was used to develop a PLS-SEM mediation model in SmartPLS software. The modelling analysis found that national culture partially mediates the leadership styles with the organisational performance. It further found that Abu Dhabi's smart government had low worker productivity requirements and lacked a strong national culture. Leadership has a major and positive impact on the organization's national culture. As a result, it is acceptable to claim that leadership style has a substantial impact on organisational procedures. According to studies, there is a link between national culture and organisational performance. The findings indicate a connection between leadership styles and the nation's commitment, cohesion, and mission. This implies that an organisational culture in which employees participate in important organisational decisions, that is consistent in its workplace practises, and that is motivated by a clear mission, ultimately affects the organisational performances of employees in such organisations.
Visualizing emotions with linearly parameterized facial expressions

The key challenge of affective computing is to translate subjective emotional experiences intomeasurable data. Most recent advances in that field have relied on facial expressions asindicators for inner emotional states.Our current understanding of these expressions is categorical, i.e.there are some subjective feelings that are understood to be indicatedby specific faces. There is no agreed measure for their distance and noclear rule for how emotions can combine or do exclude.The computational processing of emotions therefore mostly relies oncompositional approaches using intependent dimensions, like valence and arousal. However, these methods lacka consistent mapping to a facial expression that turned out so crucial for theidentification of emotional states.This paper seeks to solve this problem by introducing a consistent facial visualizationfor a five dimensional model. A graphical representation as a comic style facial expression is provided inexamples, explanations and code. It visualizes emotions in a comically exaggerated style,similar to the successful emojis used in electronic communication.All graphical parameters depend linearly onthe input dimensions (valence, arousal, dominance, contempt and control).This might be a rough and crude method. It might not even be a good one,but it is at least something linear in the space of emotions.Empirical results from crowd workers confirm that the encoded emotionalinformation can be recorgnized intuitively and without further training.
Ranking of Leadership Styles and National Culture Factors Affecting Smart Government Organizational Performance

Investigating leadership style, organisational performance, and national culture indicators in Abu Dhabi, the smart-government capital of the UAE was carried out through quantitative method of research. Total 274 valid data samples were gathered and analysed with SPSS software to perform descriptive assessment. Reliability test revealed that all the indicators have Cronbach Alpha value above 0.7 confirming the validity of the data. Skeweness and Kutosis values of the parameters showed that the data follows normal distribution. Employee morale and satisfaction were cited as the most critical parameters for gauging organisational performance, while the study found that personal steadiness and stability is the most desired parameters of national culture. All four of the leadership styles that were studied were deemed significant. Based on responses, "my supervisor makes others feel good to be around him/her" is the most important aspect of a transformational leader, while "my supervisor tells others what to do if they want to be rewarded for their work" is the most important aspect of a transactional leader. Among the parameters studied, "As a rule, my supervisor allows me to appraise my own work" ranked highest in accordance with Laissez Faire Leadership, while "Do you agree that the Authoritative leadership style employed by your supervisor contributes to your feelings of insecurity in your work and the need for clear direction?" ranked highest in accordance with Authoritative Leadership. These finding pointed out that the designed parameters can be used for further study related to national culture, organizational performance and leadership styles adopted in UAE in relation with Smart government.
Proposing A Predictive Model for Assessing the Influence of Employee Empowerment on Organizational Performance

Embracing employee empowerment in the UAE Federal National Council is an absolute necessity for achieving outstanding organizational performance. By granting employees freedom to make decisions, take initiative, and contribute their unique perspectives, it can unleash untapped potential and drive the council towards unparalleled success. Hence, the use of a novel multi-linear regression model in this study is an important step forward in understanding the relationship between employee empowerment and organisational performance. Unlike prior research, which may have ignored essential components, this model incorporates the independent variables meaningfulness, self-determination, impact, and competence. It examines how employee empowerment affects overall organisational performance by taking these essential factors into account. This novel methodology paves the way for new insights and potential breakthroughs in optimising workforce potential and attaining outstanding organisational success. The study used 115 respondents from the employees of UAE Federal National Council Organization to develop the model using multi-linear regression approach. The establish model can be used to predict the performance of UAE Federal National Council Organization by inserting the parameters of the Employee Empowerment which are Meaning; Competence; Impact; and Self-determination. Hopefully, the outcomes of this research contribute to the community of UAE Federal National Council Organization.