Reviewerly/exHarmony huggingface dataset:
Solving Partial Differential Equations using a Quantum Computer

The simulation of quantum systems currently constitutes one of the most promising applications of quantum computers. However, the integration of more general partial differential equations (PDEs) for models of classical systems that are not governed by the laws of quantum mechanics remains a fundamental challenge. Current approaches such as the Variational Quantum Linear Solver (VQLS) method can accumulate large errors and the associated quantum circuits are difficult to optimize. A recent method based on the Feynmann-Kitaev formalism of quantum dynamics has been put forth, where the full evolution of the system can be retrieved after a single optimization of an appropriate cost function. This spacetime formulation alleviates the accumulation of errors, but its application is restricted to quantum systems only. In this work, we introduce an extension of this formalism applicable to the non-unitary dynamics of classical systems including for example, the modeling of diffusive transport or heat transfer. In addition, we demonstrate how PDEs with non-linear elements can also be integrated to incorporate turbulent phenomena.
EFFORTS TO IMPROVE CHILDREN'S INTEREST IN READING THROUGH COMMUNITY READING PARK IN UJAN MAS DISTRICT, KEPAHIANG REGENCY

The condition of Indonesian people's interest in reading is still apprehensive. The love of reading has not been well entrenched in society. The purpose of this study was to analyze how to increase children's interest in reading through community reading gardens in Ujan Mas District, Kepahiang Regency. The approach used in this research is a qualitative approach and the research method used is a qualitative descriptive research method. Data were collected by observation, interviews and documentation. The results of the study show that 1) The formation of reading habits should start as early as possible in life, namely from childhood. Cultivating a love of reading should be a shared responsibility. The benefits of reading for the community are very important to add and enrich thinking and broaden knowledge. The public can use the library as a suggestion to get various information and knowledge needed. Libraries as information resource centers and learning resource centers can take various practical and applicable steps to cultivate people's love of reading. In this way, the hope of realizing an intelligent and quality society will be realized; 2) Efforts by the Management of Community Reading Parks in fostering interest in reading are carried out by planning programs by identifying needs, implementing programs, integrating learning with reading activities in Community Reading Parks, and improving the facilities and infrastructure owned by Community Reading Parks so that they can be utilized effectively. directly by the community.
Characterizing the Ideal Audio-Visual Learning Content of Writing Course Learned in Distance

The research aimed to discuss the need for a learning tool that was capable of presenting audio and visual aids to accommodate students in learning writing. It was urgently needed as a logical consequence of the COVID-19 pandemic, which disallowed face-to-face learning to be conducted. The research was conducted at the Department of English Education, Faculty of Teachers Training and Education, Universitas Muhammadiyah Parepare, by utilizing a questionnaire to explore the characteristics of ideal writing material restricted to visual aids. The research was participated by the second and fourth-semester learners as the population and recruited the samples based on simple random sampling. Every individual in the population had an equal chance to be chosen as a sample. There were 30 undergraduate students who enrolled in basic writing and advanced writing courses who became the subject of the research. Based on the research findings, eight characteristics of good audio visuals are recommended by the students. Those characteristics include the visual and audio elements/items, creative and innovative content, language and expression, operations and mechanisms (various learning opportunities and preferences), and eye-catching form/shape/design, which deliver brief and clear learning material under several considerations. Furthermore, following the findings, it also indicates that the effectiveness of delivering audiovisual aids materials in the learning process depends on how clear the audiovisual item (i.e. the text and audio, how efficient the material and audiovisual durations provided, how interesting and well-organized the design to attract students’ learning intention, how good the visual quality and how the audiovisual) can be produced into a small size to minimize the use of internet data. The research concludes that audiovisual learning materials need to be selectively chosen and periodically and systematically evaluated to best facilitate students in dealing with writing.
Automated navigation of a mobile robot using ultrasonic range sensor based on modified E-Bug algorithm

Mobile robotics is the discipline of robotics that deals with moveable robot systems that can work in an unfamiliar environment or terrain. It is mostly utilized in navigation and exploration research, with applications in autonomous guided vehicles. A mobile robot's path planning is critical for determining the optimal path from source to destination while avoiding collisions with obstacles if present in the path. This paper employs the modified E-Bug algorithm to find out an approximate optimized path for a mobile robot using a simple range sensor in terms of certain parameters. The near optimum path for navigating a mobile robot from a start point to a destination point in the presence of obstacles is determined by detecting ‘sudden points’ using an ultrasonic range sensor fixed on the Activity-Bot mobile robot to avoid any barriers in its path. Furthermore, the efficiency of the modified E-Bug algorithm in a real-world situation is verified using basic integrated development environment (IDE) software by considering the dimensions of the robot rather than treating it as a point. The developed program in C language has been successfully tested for a variety of workspace layouts, and it will aid process engineers in path planning for industrial applications.
Lite-MDETR: A Lightweight Multi-Modal Detector

Recent multi-modal detectors based on transformers and modality encoders have successfully achieved impressive results on end-to-end visual object detection conditioned on a raw text query. However, they require a large model size and an enormous amount of computations to achieve high performance, which makes it difficult to deploy mobile applications that are limited by tight hardware resources. In this paper, we present a Lightweight modulated detector, Lite-MDETR, to facilitate efficient end-to-end multi-modal understanding on mobile devices. The key primitive is that Dictionary-Lookup-Transformormations (DLT) is proposed to replace Linear Transformation (LT) in multi-modal detectors where each weight in Linear Transformation (LT) is approximately factorized into a smaller dictionary, index, and coefficient. This way, the enormous linear projection with weights is converted into efficient linear projection with dictionaries, a few lookups and scalings with indices and coefficients. DLT can be applied to any pretrained multi-modal detectors, removing the need to perform expensive training from scratch. To tackle the challenging training of DLT due to non-differentiable index, we convert the index and coefficient into a sparse matrix, train this sparse matrix during the fine-tuning phase, and recover it back to index and coefficient during the inference phase. Our experiments on phrase grounding, referring expression comprehension and segmentation, and VQA show that our Lite-MDETR achieves similar accuracy as the prior multi-modal detectors with up to ~ 4.1 × model size reduction.
Enhancement of White Blood Cells Images using Shock Filtering Equation for Classification Problem

Medical image processing has developed rapidly in the last decade. The autodetection and classification of white blood cells (WBC) is one of the medical image processing applications. The analysis of WBC images has engaged researchers from medical also technology fields. Since WBC detection plays an essential role in the medical field, this paper presents a system for distinguishing and classifying WBC types: eosinophils, neutrophils, lymphocytes, and monocytes, using K-Nearest Neighbor (K-NN) and Logistic Regression (LR). This study aims to find the best accuracy of pre-processing images using original grayscale, shock filtering, and thresholding grayscale. The highest average accuracy in classifying WBC images in the conducting research is 43.54% using the LR algorithm from 2103 images. It is obtained from the combination of thresholding grayscale image and shock filtering equation to enhance the quality of an image. Overall, using two algorithms, KNN and LR, the classification accuracy can increase up to 12%.
Effect of Advertisement on Consumers’ Buying Behaviour of Real Estates in Tanzania: Insight from the National Housing Corporation

The article examines the influence of advertisement elements—namely television, radio, print media and online advertisements—on consumers’ buying behaviour of real estate products in Tanzania, using evidence of the National Housing Corporation (NHC). Informed by the positivist and deductive paradigms, as well as theoretical lens of the AIDA model, the study used structured questionnaires to gather quantitative data from 103 conveniently sampled NHC customers based in the Tanzania’s sprawling city of Dar es Salaam. These were complemented by in-depth information from 3 key informants. Data were subjected to multiple regression analysis. The findings indicate that of all the four constructs, only two—television, and radio— advertisements were the major shakers as they emerged as significant predictors with 53% and 27% contribution to changing the consumers’ buying behaviour, respectively. Print and online media, on the other hand, insignificantly contributed 13% and 7% to influencing consumers’ buying behaviour respectively. These findings have implications for policy makers and real estate companies seeking to make the most from advertising of real estate products through the media.
PODCAST UTILIZATION TO ENHANCE STUDENT'S LISTENING SKILL

Listening skill is one of language skills which is crucial to promote language users’ communicative competences. It is essential for the teachers to utilize appropriate media to create a supportive learning atmosphere.Podcast as one of ICT tools can be optimized to provide the students with periodical listening sources to help practicing the student’s listening skill. This research explored Podcast utilization in listening class and aimed at finding out whether or not Podcast utilization could enhance students’ listening comprehension and compare the students’ listening comprehension between the students who were taught using Podcast and the students who were not taught using Podcast. A quasi experimental research was applied as the research design. This research involved eighty-eight students selected using purposive sampling technique. Listening comprehension test was administered to obtain the data. The obtained data were analyzed by using Paired Sample T-test and Independent Sample T-test. The result of Paired Sample T-test showed that there was a significant progress in student listening comprehension after they got treatment by using Podcast. The result of Independent Sample T-test revealed that the progress of experimental group exceeded the progress of control group in their listening comprehension. It proved that utilizing Podcast could enhance students’ listening skills. Keywords : Podcast, listening skills, ICT utilization
Study on the Digital Intelligent Diagnosis of Miniature Machine Tools

Several industries are currently focusing on smart technologies, high customization, and the integration of solutions.This study focuses on the intelligent diagnosis of digital small machine tools.Furthermore, the main technology processes and cases for smart manufacturing for machine tool applications are introduced.Owing to the requirements of automated processing to determine the quality of a process in advance, the health status of a machine should be monitored in real time, and machine abnormalities should be detected periodically.In this study, we captured the real-time signals of temperature, spindle current, and the vibration of three small five-axis machine tools.Moreover, we used a principal component analysis to diagnose and compare the health status of the spindles and machines.We developed a miniature machine tool health monitoring application to avoid time delays and loss from damage, and used the application to monitor the machine health online under an actual application.Therefore, the technology can also be used in an online diagnosis of machine tools through modeling technology, allowing the user to monitor trends in the machine health.This research provides a feasible method for monitoring machine health.We believe that the intelligent functions of machine tools will continue to increase in the future.
Modelling Logic Gates in Python

In this paper, an attempt has been made to develop a Python module for different Logic Gates. The correctness of the codes has been checked against three numerical problems, and it has been observed that the program results match exactly with the results in the literature. As a result, the developed functions have shown high accuracy with the least effort and error in all the cases.
A Tool For Software Requirement Allocation Using Artificial Intelligence Planning

This paper presents the AI Task Allocation tool (ATA’) for allocating software requirements into versions using the artificial intelligence (AI) planning technique. A model using AI planning language is proposed for a software project representation containing a set of requirements and one or more development teams. The generated plan indicates the allocation of requirements considering as criteria: requirement development time, priority levels, and dependency relationships. A case study was carried out to assess the use of the ATA’ tool to provide a plan that organizes the software requirements. The preliminary results indicated that plans allocate requirements according to the assigned criteria. Thus, the results suggested that ATA’ can contribute to the planning of incremental development projects, allowing the requirement allocation among teams.
To study the challenges faced in application of artificial intelligence in automobile industry

Artificial Intelligence (AI), as the name suggests and described by the Oxford dictionary, is "the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages". In today’s technology era, there is a growing trend of tasking machines to perform what humans are expected to do. With the development of "Sofiya", the robot activated on February 14, 2016, our world of technology has seen a pragmatic change. May it be voice recognition, decision making, or anything which needs a human mind, it is now expected to be done by machines that are programmed by none other than humans. One of the focus areas where AI has been growing by leaps and bounds is that of the manufacturing industry. May it be manufacturing cars, FMCGs, consumer durables, etc., AI is now making its presence felt. With every sector managing processes and trying to ensure zero error, AI is possibly the answer.
Gaussian Process Modeling of Approximate Inference Errors for Variational Autoencoders

Variational autoencoder (VAE) is a very successful generative model whose key element is the so-called amortized inference network, which can perform test time inference using a single feed forward pass. Unfortunately, this comes at the cost of degraded accuracy in posterior approximation, often underperforming the instance-wise variational optimization. Although the latest semi-amortized approaches mitigate the issue by performing a few variational optimization updates starting from the VAE's amortized inference output, they inherently suffer from computational overhead for inference at test time. In this paper, we address the problem in a completely different way by considering a random inference model, where we model the mean and variance functions of the variational posterior as random Gaussian processes (GP). The motivation is that the deviation of the VAE's amortized posterior distribution from the true posterior can be regarded as random noise, which allows us to view the approximation error as uncertainty in posterior approximation that can be dealt with in a principled GP manner. In particular, our model can quantify the difficulty in posterior approximation by a Gaussian variational density. Inference in our GP model is done by a single feed forward pass through the network, significantly faster than semi-amortized methods. We show that our approach attains higher test data likelihood than the state-of-the-arts on several benchmark datasets.
Signal Restoration and Prediction for End-to-End Learning of Practical Wireless Communication System

Autoencoders (AEs) have been proposed to learn the physical layer of wireless communication systems in an end-to-end fashion. However, it is challenging to jointly train the transmitter part and the receiver part of an end-to-end AE, since closed-form transfer functions of wireless channels are unknown in practice. In this paper, two solutions, namely signal restoration and signal prediction, are proposed to address this challenge. Deep neural networks (DNNs) along with deep AEs are employed to implement these solutions. And these solutions are further evaluated in a practical wireless communication system with superheterodyne architecture, bandpass channel noises, and quantization noises. Evaluation results show that with the aid of the proposed solutions, end-to-end AEs can be jointly trained and perform better. The proposed signal restoration solution also helps improve the performance of conventional modulation schemes, e.g., quadrature amplitude modulation (QAM).
Anxiety Towards the English Language in Relation to Academic Performance of Grade 11 Students of University of Bohol

Language anxiety is a complex psychological construct described as an apprehension that occurs when a language student performs a second or foreign language. Academic performance is referred to as the grades of the students in the English subject. The study aimed to determine English Anxiety in relation to the academic performance of the respondents. This study used a descriptive survey method of assessing the level of English language anxiety and the academic performance of the students in English. Documentary analysis was used for academic performance in the English subject of the School Year, 2017-2018. The result revealed that students at the University of Bohol- Senior High School have a low level of English Anxiety. This result showed that language anxiety significantly affected the academic performance of the students in which the higher the English language Level of Anxiety, the lesser the academic performance of the students. Teachers should provide a healthy environment and a conducive learning atmosphere. The language instructor should have a stringent-free approach and a friendly personality so that the students won’t be frightened in making a mistake in the language class. It is also vital that students should be encouraged to speak up and let them practice both esteem and English language proficiency. Above all, there should be an integration between the English curriculum and extra-curricular activities to motivate students to use it for further competitions. Extra-curricular activities should be a vehicle for the enhancement and development of students’ skills.
Open Science, Publications and Code

This conference on open science is covering many aspects of strategic relevance to build the future of Europe, a future based on knowledge leading to more opportunities for younger generations to express their talent in attractive research careers, be it in universities, research organisations or industry.Some sessions will be dedicated to discussing the assessment of research and researchers' performance, which affects their behaviours.This debate is timely and justified.Most researchers are used to measuring in their labs when collecting and processing data.Scientists have to measure with accuracy to produce reliable and verifiable results.The Nobel laureate Richard Feynman started one of his famous lectures challenging his students about the complexity introduced over the centuries by using different measurement units.Measuring research and researchers' performance is of a different nature and more complex in many ways.It has direct impact on people and a far-reaching policy dimension for the European Research Area.We therefore agree on the need to ensure objectiveness and fairness as guiding principles and we can use assessment methodologies to support broader policy objectives, considering that we live in times of change, including in the scientific methods and the public perception of the role of science.Research processes are in constant evolution, accelerated by the current pandemic and the transition to a resilient, green and digital economy and an inclusive society.The variety of activities making increased use of advanced technologies puts us in a situation where research deliverables are richer and not limited to delivering and counting publications.Modern science delivers other outputs, mostly digital, like datasets, software, algorithms or protocols, and it delivers highly skilled people through specialised education and mentoring.Multidisciplinary collaborations are required to address new scientific questions arising from increased complexity and that is another aspect of this evolution.More and more researchers from different domains, from biology and physics to economics, anthropology or humanities, work together in the boundaries of areas of knowledge.One of the actions included in the first Policy Agenda for the European Research Area, for the next three years 2022-2024, is to advance towards the reform of the assessment system for research, researchers and institutions to improve their quality, performance and impact.We consulted a variety of stakeholders in 2021, including organisations representing researchers, public funders, private funders, universities, research centres, academies, national evaluation agencies and policymakers, and we received strong support for the objective of ensuring a combination of qualitative and quantitative indicators to evaluate research and researchers without compromising on objectivity and peer review.