Reviewerly/exHarmony huggingface dataset:
AUTOMATED TESTING OF WEB PROJECT FUNCTIONALITY WITH USING OF ERROR PROPAGATION ANALYSIS

Automated testing is indispensable in the area of software engineering, particularly for web project functionality, as the complexity of software systems continues to surge. This paper delves into the pivotal role of automated testing and how the integration of error propagation analysis, grounded in chaos theory, can elevate its efficacy. The objective is to elucidate the significance of this methodology and its application in bolstering the reliability and performance of web projects. Automated testing automates the execution of predefined test cases, offering efficiency gains, reduced human error, and swift defect detection in software development. Various testing approaches, including unit testing, integration testing, and regression testing, cater to distinct facets of software functionality, ensuring seamless operation of all components. Web project functionality is integral to the user experience, encompassing navigation menus, forms, and search features. Testing this functionality is imperative to unearth inconsistencies or errors that could compromise user satisfaction and task completion. This paper proposes a methodology for automated testing coupled with error propagation analysis, which involves scrutinizing how errors evolve through a system over time. Chaos theory, a branch of mathematics examining complex systems' behavior, is employed to understand how minor variations in initial conditions can precipitate substantial system behavior shifts. Traditional error propagation analysis hinges on linear, deterministic models, but real-world systems often exhibit non-linear, chaotic characteristics, rendering such models inadequate. Chaos theory's non-linear dynamics model the intricate interactions between input variables and their effects on outputs, capturing the sensitivity of chaotic systems to initial conditions. This approach appreciates system complexity and intricate feedback loops, enhancing error analysis's robustness and accuracy. However, the application of chaos theory introduces complexity and computational demands, necessitating a balance between model intricacy and practicality. The proposed methodology unveil valuable insights into error propagation within web projects' functionality, pinpointing vulnerable components and areas ripe for improvement. The methodology's advantages include the ability to identify potential issues and vulnerabilities, ultimately enhancing web project reliability.
Synchronization of the Internship Registration System using a Study Plan and Acceptance Letter Based on Tesseract-OCR and Template Matching

SIMPI is an apprentice monitoring system in the Department of Electrical Engineering and Informatics that is un-connected between the academic system (SIAKAD) and the submission system of the internship letter. It often triggers verification errors for interns who have not registered internship in their study plan or do not have an internship acceptance letter. For this reason, we propose a modification of the registration that employs Tesseract-OCR (Optical Character Recognition) to generate text from an image of an acceptance letter while Template-Matching verifies Template Matching verifies student status in SIAKAD. Both methods prevent students' practices in the industry before finishing their registration on SIAKAD and obtaining an industrial acceptance letter. Method performance is measured based on error rate. The error rate based on the eligibility of interns reaches 0.09, while the error rate based on the legality of the letters reaches 0.16. Both results indicate that the proposed method can reject the registration of ineligible students and illegal document.
Teaching Information Literacy in University Libraries in Kenya during the COVID-19 Pandemic

Kenya detected its first COVID-19 case on March 19th, 2020, which led to educational institutions being shut down and learning activities shifting online. This change greatly impacted university operations, including university libraries. Before the pandemic, university libraries were teaching information literacy (IL) through conventional methods, which were mostly physical. Universities transitioning to online education necessitated libraries to rethink IL instruction. The purpose of this study is to assess the suitability of the modes of teaching, librarian skills, and resources required for IL delivery in university libraries in Kenya during the COVID-19 period. The objectives are to establish the modes of teaching IL used by university libraries, the skills, and resources required in the teaching of IL in an online environment, identify the challenges faced to offer IL during the COVID-19 pandemic period and recommend the best practices in IL delivery in an online environment. The study used systematic review of existing literature. Multi-platforms, synchronous and asynchronous, were employed at varied degrees; the success of the online modes of teaching was affected by the inaccessibility of the requisite resources, unpreparedness by the librarians and students as well as the demand for new skills. The study recommends contextualization of teaching initiatives, provision of professional development to librarians, development of contingency plans, and evaluation of the online modes of teaching for continual improvement.
Quality Teacher Education in Punjab with the vision of the International Network of Teacher Education Institutes

Punjab is assisting the nation in achieving Sustainable Development Goal 4: Quality Education by putting the Asia-Pacific ESD Teacher Competency Framework into practice. A comparison of Punjab's public and private universities that provide teacher education is another goal of this research, as is determining how many of the different elements of ESD have been covered in the courses that make up the B.Ed. honors program. Along with a content study of the latter, the integration of the three domains of the Asia-Pacific ESD Teacher Competency Framework into the curriculum has also been sought. Several Punjabi institutions, both public and private, have incorporated the SDG curriculum into their teacher preparation programs.
Enhancing Neural Style Transfer with Gradient Loss for High-Quality Artistic Image Synthesis

Art is an incredible skill of humans and it is a great achievement of artificial intelligence that there exists an algorithm for artistic images, which was firstly developed by Leon A. Gatys. Gatys proposed an artificial system of fill up an image with desired styles. Although a lot of corrections have been made in this algorithm and some are effective, but cannot say that it has reached its full form and the research is still going on. This paper introduces optimal modifications to make the result of combined artistic style images with high perceptual quality. Here introduces a new loss term called the gradient loss, which identifies the edge features and makes it more precise for the high visual experience. As well as for getting the optimum loss value of style image, bring the smooth l1 loss instead of computing the mean squared error. The effectiveness of our model illustrates experiments.
Facebook vs. Face-to-Face Discourse in English: A Gender-Based Study of Conversational Styles

The study highlights the major differences between Face-to-face and Facebook conversations while considering the gender-based patterns in the respective conversational styles.The authors have used an open-ended questionnaire to conduct qualitative research to make it possible. The sample consisted of 12 people aged 18-25 from different universities in Pakistan who used Facebook daily. The study finds out that face-to-face conversations are the preferred mode of conversation mainly by women as being on Facebook gives them a degree of lenience which face-to-face conversations lack. The comfortable environment on Facebook makes it more comfortable for them to be more expressive. Facebook has broken the cross-gender barriers and has made people aware that having cross-gender conversations while staying within a limit is possible, and society is slowly accepting it. The study has far-reaching implications for social media researchers as they can have further research probes into socio-cognitive dimensions of the issue from a behavioural science perspective.
Accessibility and utilization of information resources in the post COVID-19 era

University libraries play a pivotal role in fostering teaching, research, and innovation by offering essential information resources. However, patrons often encounter challenges in using these resources. The research aimed to examine the factors influencing the utilization of information resources in university libraries at Kenyatta University Meru University of Science and Technology and post-COVID-19. The objectives were to: determine the skills needed by librarians in supporting utilization of information, analyse the innovative methodologies used to facilitate access to library resources/services, examine the challenges faced by librarians and patrons and determine the importance of Information Communication Technology (ICT) in improving information resources usability and consumption. The study adopted a descriptive survey research design with quantitative research methods. The study was conducted at Kenyatta University and Meru University of Science and Technology libraries. Simple random and purposeful sampling techniques were used to distribute questionnaires to patrons and librarians, respectively. Data was collected from 20 librarians and 309 patrons, out of whom, 26 librarians and 357 patrons were sampled. The questionnaires underwent expert review to ensure their validity and reliability. The study found that 81% of librarians prefer online search resources, while 41% prefer physical textbooks. 16.5% of patrons preferred e-books and e-journals, with inadequate computers (32%) and unreliable networks (25.7%) as challenges affecting access and utilization of resources. The librarians identified lack of cooperation, inadequate digital library skills, understaffing, and poor network as major obstacles to facilitating access and utilization of library resources. The study recommends that university management should enhance librarian capacity building, training, and reengineer ICT infrastructure to enhance access and utilization of resources in the post-COVID-19 era. The study's findings will aid librarians in redesigning library functions to enhance the efficacy of library services in meeting user information needs.
Classification of Potential Blood Donors Using Artificial Neural Networks and Alternating Least Squares

Blood has an important role in human life. Lack of blood can cause illness and even death. To meet the needs of human blood, it requires blood donors from other people. The problem that occurs is that everyone can only donate blood once every three months. The problem is that not all blood donors routinely donate blood every three months. Therefore, it is important for the Red Cross to be able to classify active and passive donors, especially when there is an urgent need for blood. A method is needed that can be used to provide recommendations for active donors who have a high probability of donating blood so that they can do so immediately. So that the Red Cross can conduct the selection of potential donors more effectively and so that there are not many expired blood stocks. In this study, the classification of potential donors was carried out using an Artificial Neural Network and Alternating Least Squares. The results from the list of potential donor recommendations are then tested using the Mean Reciprocal Rank. The test results show a result of 0.0052186103057361, with an average time of 127. With the Artificial Neural Network model that uses two dense layers of 1024 nodes, 10 epochs, and a sigmoid as the activation function, the training accuracy is 98.90% and the testing accuracy is 99.20%.
Improve Quality Of Public Opinion In Website Using Blockchain Technology

The unemployment rate in Indonesia is quite high, where the average value in Indonesia is 18%, the largest among Cambodia, Nigeria, and lower-middle-class countries, which show an average of 12%. The high unemployment rate is caused by the level of motivation of students to continue working, studying or participating in competency training with a lack of interest. To increase students' interest, it is necessary to have a large number of critical communications to arouse students' enthusiasm and motivation. The method used is qualitative and quantitative where to design the system design and system validation, the technique used is the System Development Life Cycle Waterfall. The results obtained by the Heuristic Evaluation stated that not many things needed to be improved for the system that was created and the SUS (System Usability Score) stated that it was good with a minimum score of 68 given by 5 experts. The Blockchain system can already be run or applied to the wider community. Keywords: Blockchain, Heuristic Evaluation, System Usability Scale, Siswa, System Development Cycle
Validation of a Questionnaire to Evaluate the Changes Produced in the Teaching/Learning Process During the Covid-19 Pandemic: Dimensions of Teaching Methodologies, Technologies and Evaluation

This article describes the validation process of a questionnaire designed to know the opinion of students about the changes produced in the teaching/learning process in higher education because of the COVID-19 pandemic. The study aims to search what has changed in university teaching after the Confinement, how face-to-face teaching has adapted to online teaching during the Confinement and what has remained of the experience lived in the new normality. For this validation, a group of experts, researchers and teachers from universities in Spain and Portugal, specializing in Didactics, Technology, Engineering and Health Sciences, have been selected to answer three questions for each of the questions in our questionnaire to be validated, to find out from the experts whether each of the questions asked is appropriate for our study, an adequacy score and finally a comment that provides us with information to improve the questionnaire to be validated. The data collected in this validation process have been qualitatively analyzed with the support of the webQDA software and quantitatively with the SPSS software, allowing the addition or the modification of some questions, with the purpose of obtaining a validated questionnaire to be sent to the students at the University of Extremadura.
Semantic interoperability model for learning object repositories

Interoperability among repositories requires syntactic and semantic compatibility, achieved through the adoption of metadata standards. However, different learning object repositories currently use diverse metadata standards to describe their resources, so multiple metadata standards describe the same term, and vice versa. This paper proposes an ontology-based interoperability model, featuring a shared vocabulary (SV) and a set of matching rules. The SV establishes a common terminology for learning objects, while the matching rules enable translation between the SV and any metadata standard. As a result, both deposit and search for learning objects can be conducted using any metadata standard, thanks to the rules that ensure seamless translations where needed. To evaluate the proposed model, a prototype has been developed, which implements the SV and matching rules. This experience has shown that using ontologies and matching rules to provide an interoperability model for learning objects repositories is a valid, flexible and user-friendly solution.
Virtual traditional craft simulation system in mixed reality environment

In a previous study, we implemented a high presence virtual traditional craft system using a head mounted display (HMD) and a mobile traditional craft presentation system using augmented reality (AR). However, the high presence virtual traditional craft system had to construct the different cultural architectures in advance, and the amount of work was enormous. Moreover, the mobile traditional craft presentation system lacks a sense of presence because users experience traditional crafts on the mobile terminal's flat display. Therefore, in this study, we developed a mixed reality (MR) virtual traditional craft simulation system using an HMD. With MR technology, we have overcome the work cost and low presence issues relative to the construction of a virtual reality (VR) space. We conducted a comparative evaluation experiment with 30 subjects to evaluate our proposed system. We obtained high evaluations of the system's presence and applicability; however, several operability issues were identified.
Sample Weighting with Hierarchical Equalization Loss for Dense Object Detection

Label assignment (LA) is one of the essential phases in the object detection paradigm and aims to classify samples as foreground or background. Current LA strategies generally discriminate samples by explicit thresholds and then calculate weighted losses based on their significances. However, existing methods mostly neglect to consider the importance of samples comprehensively due to the uneven distribution of objects and the limitations of detector structures. In this paper, we propose a hierarchical equalization loss (HEL) by reconsidering the underlying factors affecting sample weights. First, we mitigate sample imbalance at three progressive levels. (1) Task level. We propose task-reconciled weights (TRW) to overcome the effects caused by inter-task inconsistencies (i.e., the inherent differences of classification and localization). (2) Instance level. We propose instance-aware normalization (IAN) for reconstructing the distribution of sample weights within an instance to suppress environmental noise. (3) Pyramid level. We propose hierarchical modulation (HM) to alleviate the unbalanced distribution of multi-scale objects on feature pyramids. Then, we stack the above three mechanisms and formulate the effective weighted loss. Moreover, we propose a staggered candidate bag construction (SCBC) mechanism to further improve the robustness of our method. Without adding any extra overhead, HEL can improve the performance of representative detectors by an impressive margin. Equipped with HEL, a single “ResNet-50+FPN+Head” detector can achieve a performance of 41.9 AP on COCO under 1× schedule, outperforming other existing LA methods. Extensive experiments conducted on multiple backbones and datasets demonstrate the effectiveness of our method.
RCFL: Redundancy-Aware Collaborative Federated Learning in Vehicular Networks

In vehicular networks (VNets), vehicular federated learning (VFL) is a new learning paradigm that can protect data privacy of vehicle nodes (VNs) while training models. In VFL, the importance of data (IoD) is a key factor that affects model training accuracy. However, due to the heterogeneity of data in the VFL, it is a challenge to evaluate the quality of data owned by different VNs and design an efficient federated learning scheme to enable the VNs to complete learning tasks collaboratively. In this paper, we consider the IoD and propose a redundancy-aware collaborative federated learning (RCFL) scheme for the VFL. In the scheme, by jointly considering the data quality and the cooperation among VNs, we first design a redundancy-aware federated learning architecture to efficiently provide learning services in VNets. Then, we develop a data importance model that integrates the non-independent and identically distributed (non-IID) degree and the redundancy of data (RoD) to evaluate the data quality and formulate the cooperation of the VNs as a coalition game to improve their data importance, where the equilibrium of the coalition game is obtained by designing a coalition formation algorithm. After that, by considering the diversified characteristics of data and the available resources of different VNs in each coalition, a coalition-based federated learning algorithm is designed to enable the distributed coalitions to complete the learning task cooperatively with the target of improving the learning accuracy. The simulation results show that the proposed scheme outperforms the benchmark schemes in terms of the IoD obtained by the VNs and the training accuracy.
Efficient and Verifiable General Quantum Secret Sharing Based on Special Entangled State

Quantum secret sharing plays a crucial role in quantum cryptography. The two main trends in quantum secret sharing are to address the problem of scheme failure due to participant spoofing and to improve the efficiency of quantum secret sharing. This paper focuses on the quantum secret sharing scheme with general access structure due to its flexibility. We design a special entangled state by resorting to Monotone Span Program (MSP). Based on the special 2-dimensional entangled state, an efficient and verifiable general quantum secret sharing (GQSS) scheme is proposed. In the GQSS scheme, the authorized participants only provide the X-basis measurement results of particles to recover and verify the shared secret, which makes that our scheme have lower communication consumption and quantum computational complexity. The analysis shows that the proposed scheme is simpler and more practical compared with related quantum secret sharing schemes.
Basis and Dimension

In this chapter, we learn the notions of “subspace generation” and “linear independence”, which play essential roles in linear algebra. These yield the notions of “basis” and “dimension”, and they turn out to be useful tools to analyze a linear space. Some important theorems concerning them will appear. The notion of linear independence is very important, and the readers are encouraged to understand its meaning to proceed to the next step.
Minimal Exposure Escape Path Problem

This article introduces the minimal exposure escape path (MEEP) problem, in which a mobile robot must determine a path from a start position to a safe region that less exposes it to any sort of industrial environmental risks. It is a generalization of the classic minimal exposure path (MEP) problem, where the exposure is associated with the detection by an industrial wireless sensor network (IWSN). The MEEP problem is applicable in many situations when it is necessary to escape from or penetrate predefined zones avoiding threats along the way, a common task in military operations, or search and rescue missions. We model it as an optimal control problem and use a semi-Lagrangian numerical scheme to solve it. We show that our approach asymptotically converges to the optimal solution while still allowing us to incorporate different characteristics and typical real-world constraints. Simulated experiments show the applicability of our method in 2-D and 3-D spaces, considering obstacle-free or cluttered environments, with nonconvex and multiple goal regions, and heterogeneous networks.