Reviewerly/exHarmony huggingface dataset:
Software Effort Estimation with Use Case Point Approach on Website Builder Project: A Systematic Performance Evaluation

Website development is the primary concern to restore the positive image of a tourist village that had sunk due to the pandemic. The problem is the need for more competence of village operators related to website templating, the hassle of managing a website content management system, and knowledge of coding and styling. Therefore, a website builder was created to make a website profile easier for anyone, be it a tourism village manager or a small medium enterprise owner. In software development, it is necessary to analyze effort estimation to determine the productivity and effort required (in hours) to complete the project. The purpose of this study is to execute the software effort estimation with a use case point approach. The UCP (use case point) will convert the use case diagram into the work effort needed in the project in several stages. The results show that this project has 111,322 UCP and 20 productivities. The project is estimated to be finished in 111,322 hours, with 20 hours per man per use case. This project also requires experienced resources for applications, lead analysts, and object-oriented with a relatively high level of importance. This research impacts the successful development of website builder projects by identifying the estimated effort required to develop a project.
Innovative Emerging Ontology-driven Frameworks: A Systematic Literature Review

Previous research has shown that ontologies and related semantic web technologies have positioned themselves as good solutions for data integration and resource reusability. Pitfalls and traps in modelling domains can be avoided if researchers and scholars adopt and use ontology-driven frameworks for their research. This research work aims to review currently developed or proposed ontology-driven frameworks, and clearly illustrate their development, application, and practicality. The review then ultimately addresses three main research questions driving the literature review through a synthesis of information that exists about ontology-driven frameworks. Search strings were used to obtain articles from online electronic databases. The PRISMA chart was used for the final selection of the 60 articles for review. A method of scoring called the Assessment of Multiple System Reviews (AMSTAR) was used on the included studies for quality assessment. The AMSTAR mean overall result was 9, the median 10, and the standard deviation 0.99. The results reveal a downward trend of ontologies in 2010, with Web Ontology Language (OWL) being the most used language for ontology-driven frameworks and systems, with over 70% usage.
Sistem Pengaduan Masyarakat Berbasis Web menggunakan Metode Waterfall

&#x0D; &#x0D; &#x0D; &#x0D; Kragilan Village is one of the villages in Mojosongo District, Boyolali Regency, Central Java. The obstacle experienced by Kragilan Village is in community complaints which are still carried out conventionally. In this way, the community must come directly to the Village office to submit complaints and in this case it is less effective because the complaint file can be lost and damaged. So to accommodate all complaints from the public, it is necessary to have a system that is made to make it easier for the community to express their aspirations in an easy way and can be used whenever the community needs it. The design of this system uses the UML (Unified Modeling Language) model, namely use case diagrams, activity diagrams, sequence diagrams, and class diagrams. At the development stage of this system using the Codeigniter framework, and a MySQL database with the Waterfall system development method. . This system has been tested by testing Black Box testing and beta testing through questionnaires to website users. The results of the Black Box test show that the function on this website system as a whole has been running well without any errors.&#x0D; &#x0D; &#x0D; &#x0D;
ESL Writing Anxiety and its Effects on Writing Performance of College Students in an Academically Disadvantaged Context of Pakistan: A Case Study

This study investigated the levels of ESL writing anxiety and its effects on the writing performance of 161 (85 male and 76 female) college students of Balochistan with a non-probability sampling. For data collection, the Second Language Writing Anxiety Inventory (SLWAI) of Cheng (2004) was used in addition to an essay writing task completed by the participants. The SLWAI results indicated that overall ESLWA was significantly high (M= 2.99, SD = .67). Somatic anxiety (SA) was the most prevailing one (M=3.39, SD = .90). The second most significant type was cognitive anxiety (CA) (M=3.11, SD = .82), whereas, the avoidance behaviour (AB) was the third in level (M= 2.45, SD =.87). The statistical analysis of writing tasks revealed an adverse association between the WA and WP of the college students of Balochistan.
Pembudayaan Literasi Bahasa Dalam Meningkatkan Minat Baca Siswa Kelas III SDIT Baiturrahman Sepat

The importance of reading for students in carrying out their learning and acquiring knowledge encourages schools to create a literate school environment. Schools are obligated to provide facilities that support a reading literacy culture for students by enhancing their reading willingness. İt is crucial to develop school programs that boost reading interest and cultivate a literacy culture within the school. This research aims to provide an overview of the literacy program at SDIT Baiturrahman Sepat and an explanation of how this program enhances reading interest among third-grade students. The research utilizes a descriptive qualitative approach, with the school principal, third-grade teachers, and library personnel as the subjects. İnterviews, observations, and documentation are the methods employed by the researcher to collect data. The research findings reveal that the literacy activities at SDIT Baiturrahman Sepat have been effectively carried out with guidance from the Ministry of Education and Culture (Kemendikbud). This is evidenced by the school’s program that divides literacy activities into three stages: familiarization, development, and learning. The school endeavors to implement this program to enhance reading interest among the students of SDIT Baiturrahman Sepat. The impacts include students developing a fondness for reading, improved reading abilities, increased self-confidence, and creativity, as well as enhanced student academic achievements.
Development of Platformer Game “Adventure Drake” Using Finite State Machine Method

Video game is one of the most popular hobbies out there, someone who have been played games always ended with fun experience and adrenaline rush. The people who enjoy this type of hobby so diverse starring with kids, teenager, and adult. With increasing technology playing game is not exclusive on desktop pc or game console, but now can be play on smartphone with android and IOS system operations, so they can play game wherever they want, and whenever they want. For that reason, the author with interested in designing game with the platformer genre using 2D technology and using finite state machine for controlling character animation Therefore author make research platformer game adventure drake using finite state machine, the itself game can be played by anytime at anywhere. The main reason to building this game therefore adding onto the collection Indonesia game’s and analyses the game with implementing finite state machine method. The result from building this game is great, the game success implementing both 2D technology and finite state machine.
Analisis Kemunculan Indikator Literasi Membaca pada Lembar Kerja Peserta Didik (LKPD) Berbasis Model RADEC di SD Laboratorium Percontohan UPI Kampus Purwakarta

This study aims to analyze the occurrence of reading literacy indicators in a set of Student Worksheets (LKPD) for elementary school students (grades 2, 3, 4, and 5) based on the Read, Answer, Discuss, Explain, Create (RADEC) model at UPI Campus Purwakarta Laboratory Elementary School. A case study methodology was employed in this research. The analysis results demonstrate the dominance of Informational Texts in almost all cases, emphasizing the importance of mastering factual information in reading literacy. Personal and cultural contextual texts also play a central role in developing reading skills. On the other hand, in the cognitive process, the elements of evaluating and reflecting on information play a key role in advancing reading literacy. The research findings provide in-depth insights into the dynamics of learning in various classes and subjects. The implications of this analysis indicate the need to develop more focused and effective learning strategies, taking into account the unique characteristics of each class and subject. By expanding the scope of reading literacy understanding through diverse texts, including fiction, and integrating personal, social, and scientific dimensions, reading education at the elementary level can continue to evolve. This research serves as a starting point in the journey towards more empowered and integrated reading literacy education at UPI Campus Purwakarta Laboratory Elementary School.
Integrated Science Interactive E-Book Local Potential of Kulon Progo: An Overview of Teacher and Student Needs

This research aims to analyze the needs of students and teachers for the development of interactive e-books that are integrated with the local potential of Kulon Progo. This research is qualitative research with a case study approach. Data collection uses needs analysis questionnaires, interviews and documentation. A needs analysis questionnaire was given to 156 students of SMPN 1 Nanggulan Kulon Progo. Interviews were conducted with two science teachers at SMPN 1 Nanggulan Kulon Progo. Interview and questionnaire data analysis techniques use qualitative descriptive. The results of this research are 1) aspects of the student learning process tend to be passive with varying student learning motivation; 2) aspects of using interactive learning media have never used interactive e-books for learning; 3) aspects of student characteristics in understanding concepts are still lacking, preferring to use smartphone media and practicums when learning; 4) the aspect of integrating science with the local potential of Kulon Progo has never been used in learning. Based on data analysis, it can be concluded that teachers and students need interactive e-books that are integrated with the local potential of Kulon Progo to support the learning process.
Development of Cybersecurity Competency and Professional Talent for Cyber Ummah

The world is facing with threats in digital transformation. Cyber threats become trending as reported by my countries. Developed countries like Britain, America, Europe and Japan already prepared countermeasures for various incidents on computer threats since Internet was introduced. They formulated and developed a successful model to produce computer security expert and highly skilled talent at various level diploma, bachelor and professional. University and College established academic program in computer and internet security at bachelor and postgraduate level. Industries at those countries introduced certification program in computer and internet security. Throughout our studies, limited initiatives related to talent development in combating computer security issues including cyber threats. Previous studies showed development of cybersecurity talent in Muslim countries is critical. Malaysia needs 20000 cybersecurity professional in 2025 and only achieved 2500 at present. This study presents our experience in developing cybersecurity competencies and professional talent for OIC-Country. We collaborated virtually with OIC-CERT (OIC Centre for Emergency and Response Team) in knowledge exchange, proposed appropriate competency model and participate in professional certification development. We presented the eight years active involvement with OIC-CERT activities. All initiatives established by OIC-CERT has produced outstanding impact to OIC Countries. One of the impactful initiatives known GlobalAce, it getting serious attention by many muslim countries. We also get benefit of other programs such as training for risks analysis, incident management and policy development. Our students be able to participate with Virtual Lecture on Combating Insider Threats, Cyber Threats Drill, and Security Audit. OIC-CERT also introduce the first Industry Journal in Cybersecurity known as OIC-CERT Journal of Cybersecurity.
A Study of the Half-Cauchy-Exponential Prior in Quantile Regression

A modification of the familiar half-Cauchy prior is considered. The modification consists of writing the half-Cauchy prior as the product of the gamma and inverse gamma distributions plus adding an exponential distribution on the scale parameter. Furthermore, we consider this model in the setting of the quantile regression structure. Additionally, The Gibbs sampler is calculated for this model. Finally, the properties of this model are demonstrated using simulated data and it is shown that this method performs very well compared to other distributions.
Studies on new approaches for sound wave generated energy

This article aims to consolidate information pertinent to the possibilities of generating energy from sound waves, using materials derived from research for a future academic dissertation by the same author. The combination of this data from a speculative design perspective allows us to develop conceptual principles for products and services that promote the use of an abundant and clean energy source, which still presents unique challenges to be integrated into a significant capacity. Sound production, in general, is found in natural environments and also manufactured objects, presenting the possibility of obtaining this energy through means not even imagined until then. As inspiration, the human auditory system and some of its functions are discussed here, such as the conversion of mechanical signals to electrical ones, helping in an elaborate understanding of listening and providing creative opportunities for formulating solutions in terms of energy generation. Also investigating the phenomenon of piezoelectricity, Wireless Sensor Networks, and the production of electrical charge through synthetic materials, opens up space for new ways of manipulating biological properties. The conclusions presented here are approaches, in this context, to the conditions for implementing frequencies and sound waves as energy sources.
Roman Urdu Sentiment Analysis of Songs‘ Reviews

To process Natural Language reviews using Machine Learning techniques is known as Sentiment Analysis. It is a way to categorize people's opinions, sentiments, and attitudes towards a specific entity. Due to easy access to the internet and smart devices, people are becoming habitual in posting reviews about any specific entity/product, they use. These reviews are very helpful for all types of users in decision-making. In the past, most of the work in Sentiment Analysis was carried out on resource-rich language but very little literature is witnessed on resource-poor languages. Very few efforts have been made to build language resources to process the Roman Urdu language. This research targets to perform Sentiment Analysis on Urdu (i.e. source-poor language) in Roman script. For this purpose, the dataset is generated from the comments on songs. Three songs from the Sub-continent music industry opt from YouTube. After pre-processing the reviews, Roman Urdu reviews are analysed using Naïve Bayes, KNN, Decision Tree (ID3) and ANN. Naïve Bayes outperforms the other classifiers and achieved 82.41% results in terms of accuracy.
TRM-UAP: Enhancing the Transferability of Data-Free Universal Adversarial Perturbation via Truncated Ratio Maximization

Aiming at crafting a single universal adversarial perturbation (UAP) to fool CNN models for various data samples, universal attack enables a more efficient and accurate evaluation for the robustness of CNN models. Early universal attacks craft UAPs depending on data priors. For more practical applications, the data-free universal attacks that make UAPs from random noises have aroused much attention recently. However, existing data-free UAP methods perturb all the CNN feature layers equally via the maximization of the CNN activation, leading to poor transferability. In this paper, we propose a novel datafree universal attack without depending on any real data samples through truncated ratio maximization, which we term as TRM-UAP. Specifically, different from the maximization of the positive activation in convolution layers, we propose to optimize the UAP generation from the ratio of positive and negative activations. To further enhance the transferability of universal attack, TRM-UAP not only performs the ratio maximization merely on low-level generic features via the truncation strategy, but also incorporates a curriculum optimization algorithm that can effectively learn the diversity of artificial images. Extensive experiments on the ImageNet dataset verify that TRMUAP achieves a state-of-the-art average fooling rate and excellent transferability on different CNN models as compared to other data-free UAP methods. Code is available at https://github.com/RandolphCarter0/TRMUAP.
Vanishing Point Estimation in Uncalibrated Images with Prior Gravity Direction

We tackle the problem of estimating a Manhattan frame, i.e. three orthogonal vanishing points, and the unknown focal length of the camera, leveraging a prior vertical direction. The direction can come from an Inertial Measurement Unit that is a standard component of recent consumer devices, e.g., smartphones. We provide an exhaustive analysis of minimal line configurations and derive two new 2-line solvers, one of which does not suffer from singularities affecting existing solvers. Additionally, we design a new non-minimal method, running on an arbitrary number of lines, to boost the performance in local optimization. Combining all solvers in a hybrid robust estimator, our method achieves increased accuracy even with a rough prior. Experiments on synthetic and real-world datasets demonstrate the superior accuracy of our method compared to the state of the art, while having comparable runtimes. We further demonstrate the applicability of our solvers for relative rotation estimation. The code is available at https://github.com/cvg/VP-Estimation-with-Prior-Gravity.
Progressive Spatio-Temporal Prototype Matching for Text-Video Retrieval

The performance of text-video retrieval has been significantly improved by vision-language cross-modal learning schemes. The typical solution is to directly align the global video-level and sentence-level features during learning, which would ignore the intrinsic video-text relations, i.e., a text description only corresponds to a spatio-temporal part of videos. Hence, the matching process should consider both fine-grained spatial content and various temporal semantic events. To this end, we propose a text-video learning framework with progressive spatio-temporal prototype matching. Specifically, the matching process is decomposed into two complementary phases: object-phrase prototype matching and event-sentence prototype matching. In the object-phrase prototype matching phase, the spatial prototype generation mechanism predicts key patches or words, which are aggregated into object or phrase prototypes. Importantly, optimizing the local alignment between object-phrase prototypes helps the model perceive spatial details. In the event-sentence prototype matching phase, we design a temporal prototype generation mechanism to associate intra-frame objects and interact inter-frame temporal relations. Such progressively generated event prototypes can reveal semantic diversity in videos for dynamic matching. Validated by comprehensive experiments, our method consistently outperforms the state-of-the-art methods on four video retrieval benchmark. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Differentiable Transportation Pruning

Deep learning algorithms are increasingly employed at the edge. However, edge devices are resource constrained and thus require efficient deployment of deep neural networks. Pruning methods are a key tool for edge deployment as they can improve storage, compute, memory bandwidth, and energy usage. In this paper we propose a novel accurate pruning technique that allows precise control over the output network size. Our method uses an efficient optimal transportation scheme which we make end-to-end differentiable and which automatically tunes the exploration-exploitation behavior of the algorithm to find accurate sparse sub-networks. We show that our method achieves state-of-the-art performance compared to previous pruning methods on 3 different datasets, using 5 different models, across a wide range of pruning ratios, and with two types of sparsity budgets and pruning granularities.
Rethinking Amodal Video Segmentation from Learning Supervised Signals with Object-centric Representation

Video amodal segmentation is a particularly challenging task in computer vision, which requires to deduce the full shape of an object from the visible parts of it. Recently, some studies have achieved promising performance by using motion flow to integrate information across frames under a self-supervised setting. However, motion flow has a clear limitation by the two factors of moving cameras and object deformation. This paper presents a rethinking to previous works. We particularly leverage the supervised signals with object-centric representation in real-world scenarios. The underlying idea is the supervision signal of the specific object and the features from different views can mutually benefit the deduction of the full mask in any specific frame. We thus propose an Efficient object-centric Representation amodal Segmentation (EoRaS). Specially, beyond solely relying on supervision signals, we design a translation module to project image features into the Bird’s-Eye View (BEV), which introduces 3D information to improve current feature quality. Furthermore, we propose a multi-view fusion layer based temporal module which is equipped with a set of object slots and interacts with features from different views by attention mechanism to fulfill sufficient object representation completion. As a result, the full mask of the object can be decoded from image features updated by object slots. Extensive experiments on both real-world and synthetic benchmarks demonstrate the superiority of our proposed method, achieving state-of-the-art performance. Our code will be released at https://github.com/kfan21/EoRaS.
Template-guided Hierarchical Feature Restoration for Anomaly Detection

Targeting for detecting anomalies of various sizes for complicated normal patterns, we propose a Template-guided Hierarchical Feature Restoration method, which introduces two key techniques, bottleneck compression and template-guided compensation, for anomaly-free feature restoration. Specially, our framework compresses hierarchical features of an image by bottleneck structure to preserve the most crucial features shared among normal samples. We design template-guided compensation to restore the distorted features towards anomaly-free features. Particularly, we choose the most similar normal sample as the template, and leverage hierarchical features from the template to compensate the distorted features. The bottleneck could partially filter out anomaly features, while the compensation further converts the reminding anomaly features towards normal with template guidance. Finally, anomalies are detected in terms of the cosine distance between the pre-trained features of an inference image and the corresponding restored anomaly-free features. Experimental results demonstrate the effectiveness of our approach, which achieves the state-of-the-art performance on the MVTec LOCO AD dataset.