Reviewerly/exHarmony huggingface dataset:
Adaptive Test Recommendation for Mastery Learning

We tackle the problem of recommending tests to learners to achieve upskilling. Our work is grounded in two learning theories: mastery learning, an instructional strategy that guides learners by providing them tests of increasing difficulty, reviewing their test results, and iterating until they reach a level of mastery; Flow Theory, which identifies different test zones, frustration, learnable, flow and boredom zones, to determine the best k tests to recommend to a learner. We formalize the AdUp Problem and develop a multi-objective optimization solution that adapts the difficulty of recommended tests to the learner's predicted performance, aptitude, and skill gap. We leverage existing models to simulate learner behavior and run experiments to demonstrate that our formalization is best to attain skill mastery. We discuss open research directions including the applicability of reinforcement learning and the recommendation of peers in collaborative projects.
Implementation of Rabin Karp Algorithm in E-Commerce Search Box Feature (Case Study: Sinar Baja Store)

Inside any e-commerce platform, search features are a key factor in an online business. In order to look for the desired item name, users need to type a pattern into the search feature. Inside the typing process users may make some mistypes. Based on the problem, the writer tried to create a search box as the search feature inside an e-commerce system with a capability to look for item names, even if there are some accidental mistypes inside the user's typing process. The main purpose is to reduce the possibility of empty results after a searching process due to the user’s accidental mistypes. This research utilizes Rabin Karp Algorithm in order to look for the item names. Result of this research is that the Rabin Karp Algorithm can be implemented in a website-based e-commerce, but this method is slower than SQL as a method to search item names inside the same system. It happens because Rabin Karp requires a longer process than SQL to get the similarity percentage from each comparison. This research shows that k-gram value inside the algorithm affects the overall results with a condition where greater the value.
Analisis Penerapan Sistem Informasi Akuntansi Pelelangan Atas Barang Jaminan Gadai Pada PT Pegadaian Area Denpasar 1

This study aims to determine the application of the accounting information system for the auction of pledged collateral at PT Pegadaian Area Denpasar 1 and which is appropriate to be applied to PT Pegadaian, the cause of the accounting information system for the auction of pledged collateral at PT Pegadaian Area Denpasar 1 is not able to prevent the increase in the balance of BJDPL, and the impact on company performance. Primary data obtained from interviews. Meanwhile, secondary data were obtained from company profiles, monthly outlet performance evaluation reports, OSL performance reports for the position of the Denpasar Regional Office, and an overview of the application of an auction accounting information system for collateralized goods at PT Pegadaian Area Denpasar 1. Data collection methods were carried out through observation, interviews, documentation, and literature study. The analytical method used is descriptive qualitative method. The results of this study indicate that the auction accounting information system for pledged collateral applied to PT Pegadaian Area Denpasar 1 has not been able to prevent the increase in BJDPL balances caused by internal company factors (system error and human error) and company external factors (customer's inability to pay off obligations and changes in customer personal data). The auction accounting information system for pledged collateral at PT Pegadaian Area Denpasar 1 has a positive effect on company performance, with system weaknesses that can still be prevented and corrected.
La investigación del comportamiento de la información en el siglo XXI: el viaje hasta ahora

Information behavior describes the many ways in which human beings interact with informationhow people seek and utilize information, but also includes other activities such as avoiding/stopping, distorting, encountering by chance, organizing, storing, creating, sharing, diffusing, and deciding to stop using information.Prior studies have attempted to review the history of information seeking and information behavior research in the past 50-60 years.While there have been recent studies looking at different aspects of information behavior, there is a need to bring the key conclusions from these together in one place.This paper seeks to answer the question, -What is the trajectory of information behavior research in the 21st century?What are some of the future directions?‖The unit of analysis is research articles published on information behavior between the years 2000 and 2023.These include papers published in Information Research, the Journal of the Association for Information Science and Technology, and those presented in Information Seeking in Context conferences and the Annual Meetings of the Association for Information Science and Technology, and research on information behavior models and context.While not meant to be exhaustive, this paper should bring new and existing researchers up to speed on some of the recent developments in the field during the past two decades.
Drafting of Online Meeting Minutes Based on Video Recording Using Topic Modelling

Meeting minutes are important because they can track decisions and agreements made during the meeting. Meeting minutes can also be used as a benchmark for whether the meeting objectives have been achieved or not. Minutes are taken during the meeting until the end of the meeting, which contains essential points from the meeting. Minutes in online meetings are currently still done manually, and generally, every meeting is recorded as documentation that requires more Human Resources to change the recording of the meeting file Based on the problems above, a solution to this problem is needed by creating an automatic note-taking system that can assist the note-takers in concluding the meeting, especially in the Information Technology Department. This study uses the Latent Dirichlet Allocation (LDA) method to determine topic modeling. Based on this research, the system calculation using the LDA method produces the results obtained on the coherence score and similarity score only get an average value of 64.56% and 57.91% where these values are still less than optimal if used in actual conditions.
Investigation into the reading culture of undergraduates in Nigerian universities: A qualitative approach

A strong reading culture is an essential tool for anybody who wants to achieve in life. It broadens students' knowledge, breadth of knowledge, and enjoyment. Reading culture is crucial for personal and academic success, and its significance cannot be over-emphasised. This study was designed to investigate the reading culture of undergraduates in federal universities in Southwestern Nigeria. Three objectives guided the study. Survey research design was adopted for the study while focus group discussion was used for data collection. A total of three sessions of focus group discussion were held with eighteen undergraduates across the three universities selected for the study which are, University of Ibadan, Obafemi Awolowo University and University of Lagos, Lagos. Six undergraduates were selected from each of the universities to form the sample for the study. Data collected were analysed using Atlas ti8. The findings revealed that the undergraduates read majorly for academic purposes and that they undergraduates have positive attitudes towards reading.. The study recommended that university management should make the curriculum more flexible to enable undergraduates to be involved in reading exercises other than academics. Undergraduates should also make determined efforts to engage more in pleasure/recreational reading to enhance their reading culture.
HEALTH RESORT DAN SPA DENGAN PENDEKATAN ARSITEKTUR TROPIS

Changes in lifestyle with increasingly crowded routines make humans pay less attention to physical conditions that can cause stress. So that from this potential, the idea of planning a design that prioritizes "health” in tourism and "health" can also be found in beautiful views around the area. The location is located in Singkawang City, which is in the waterfront area (Palm Beach) Jl. Pasir Panjang Beach, Sedau Village, Singkawang Selatan District which is a tourism area, and the soil type is soft soil. The selection of the structure used applies a concrete pile foundation. Health resorts and spas located in the tropics use a design approach using tropical architecture to provide visitors with comfort. This effort is made by implementing placement and orientation facing the main view and spreading so that each building gets wind circulation and is supported by mass compositions and the use of local materials, the use of natural ventilation, skylights, and sun shading. Visitors consist of individuals, couples, families, and other general groups who do not stay overnight by providing lodging facilities that are not only for resting but have health service facilities (health resort) like resort standards, resort suite, and resort family suite, treatment facility (health spa), restaurants, and wedding venues. The design method consists of the stages of an idea, information, analysis, synthesis, evaluation, and optimization.
Analisis Perbandingan Metode Dempster Shafer Dan Certainty Factor Dalam Mendeteksi Penyakit Anemia

&#x0D; &#x0D; &#x0D; &#x0D; Anemia is one of the nutritional disorders that are still often found in Indonesia. The Household Health Survey found that around 57% of Indonesian adolescent girls aged 10-14 years and 39.5% of women aged 15 years and over still suffer from anemia. From the high cases of anemia sufferers, researchers aim to help the community in preventing and treating patients by providing information related to the symptoms of anemia, so that researchers will create an expert system that can diagnose anemia. An expert system is a computer-based system that uses knowledge, facts, and reasoning techniques to solve problems that can usually only be solved by an expert in a particular field. The purpose of developing expert systems is actually not to replace the role of humans, but to transfer human knowledge into the form of systems, so that they can be used by many people and are not limited by time. In this study, researchers want to build an expert system that can detect anemia, where in this study the researcher will compare the Dempster-Shafer method and Certainty Factor, where the system is built using PHP programming language and MySQL database that can be accessed anytime and anywhere so as to make it easier for patients to diagnose anemia without the need to meet directly with experts.&#x0D; &#x0D; &#x0D; &#x0D;
Representing a Novel Expanded Version of Shor’s Algorithm and a Real-Time Experiment using IBM Q-Experience Platform

The data are stored on the memory of the classical computer in small units of classical bits, which could be either 0 or 1. However, on a Quantum Computer, The Quantum States of each Quantum Bit (Qbit), would be every possible number between 0 and 1, including themselves. By placing the photons on a special state, which is a spot located at the middle of the two-dimensional space vectors (█(1@0)) and (█(1@1)) on the Unit Circle, which is called Superposition and we can take advantage of properties of this state when we place lots of vectors of N-dimensional spaces in superposition and we can do a parallelization and factorization for getting significant speedup. In fact, in Quantum Computing we are taking advantage of Quantum Dynamic Principles to process the data, which Classical Computers lack on, by considering the limitations of logical concepts behind them. Through this paper, we expand a quantum algorithm for the number of n Qbits in a new way and by implementing circuits using IBM-Q Experience, we are going to have some practical results, which are more obvious to be demonstrable. By expanding the Quantum Algorithms and using Linear Algebra, we can manage to achieve the goals at a higher level, the ones that Classical Computers are unable to perform, as machine learning problems with complicated models and by expanding the subject we can mention majors in different sciences like Chemistry (predicting the Structure of proteins with higher percentage accuracy in less period), Astronomy and so on.
Hoax Identification of Indonesian Tweeters using Ensemble Classifier

Fake information, better known as hoaxes, is often found on social media.Currently, social media is not only used to make friends or socialize with friends online, but some use it to spread hate speech and false information.Hoaxes are very dangerous in social life, especially in countries with large populations and ethnically diverse cultures, such as Indonesia.Although there have been many studies on detecting false information, the accuracy and efficiency still need to be improved.To help prevent the spread of these hoaxes, we built a model to identify false information in Indonesian using an ensemble classifier that combines the n-gram method, term frequency-inverse document frequency, and passive-aggressive classifier method.The evaluation process was carried out using 5000 samples from Twitter social media accounts in this study.The testing process is carried out using four schemes by dividing the dataset into training and test data based on the ratios of 90:10, 80:20, 70:30, and 60:40.The inspection results show that our software can accurately detect hoaxes at 91.8%.We also found an increase in the accuracy and precision of hoax detection testing using the proposed method compared to several previous studies.The results show that our proposed method can be developed and used in detecting hoaxes in Indonesian on various social media platforms.
University students' cyclical self-assessment process mediated by artificial intelligence in academic writing

This study was designed to investigate the cyclical self-assessment process in the Academic Writing class mediated by Artificial Intelligence (AI). This narrative inquiry elicited data from three university students having different writing proficiency levels: high, middle, and low levels at one university in Indonesia. The data was collected through reflective notes and interviews. The data was then analysed using thematic analysis. The study revealed two significant findings. First, the three university students with different writing proficiency levels engaged in different stages of cyclical self-assessment caused by two main factors, namely learning motivation and level of trust in AI. The more motivated university student was to learn, the more likely they were to seek external feedback actively. Additionally, their level of trust in Automated Written Corrective Feedback (AWCF) and Automated Writing Evaluation (AWE) engaged them in an evaluation and revision process that engaged them in a cyclical self-assessment process that would improve their final results. Second, Artificial Intelligence (AI) could facilitate an effective cyclical self-assessment process with various features. The implication of the study is discussed.
ALP: Action-Aware Embodied Learning for Perception

Current methods in training and benchmarking vision models exhibit an over-reliance on passive, curated datasets. Although models trained on these datasets have shown strong performance in a wide variety of tasks such as classification, detection, and segmentation, they fundamentally are unable to generalize to an ever-evolving world due to constant out-of-distribution shifts of input data. Therefore, instead of training on fixed datasets, can we approach learning in a more human-centric and adaptive manner? In this paper, we introduce Action-Aware Embodied Learning for Perception (ALP), an embodied learning framework that incorporates action information into representation learning through a combination of optimizing a reinforcement learning policy and an inverse dynamics prediction objective. Our method actively explores in complex 3D environments to both learn generalizable task-agnostic visual representations as well as collect downstream training data. We show that ALP outperforms existing baselines in several downstream perception tasks. In addition, we show that by training on actively collected data more relevant to the environment and task, our method generalizes more robustly to downstream tasks compared to models pre-trained on fixed datasets such as ImageNet.
Genes in Intelligent Agents

The genes in nature give the lives on earth the current biological intelligence through transmission and accumulation over billions of years. Inspired by the biological intelligence, artificial intelligence (AI) has devoted to building the machine intelligence. Although it has achieved thriving successes, the machine intelligence still lags far behind the biological intelligence. The reason may lie in that animals are born with some intelligence encoded in their genes, but machines lack such intelligence and learn from scratch. Inspired by the genes of animals, we define the ``genes'' of machines named as the ``learngenes'' and propose the Genetic Reinforcement Learning (GRL). GRL is a computational framework that simulates the evolution of organisms in reinforcement learning (RL) and leverages the learngenes to learn and evolve the intelligence agents. Leveraging GRL, we first show that the learngenes take the form of the fragments of the agents' neural networks and can be inherited across generations. Second, we validate that the learngenes can transfer ancestral experience to the agents and bring them instincts and strong learning abilities. Third, we justify the Lamarckian inheritance of the intelligent agents and the continuous evolution of the learngenes. Overall, the learngenes have taken the machine intelligence one more step toward the biological intelligence.
Snowman: A Million-scale Chinese Commonsense Knowledge Graph Distilled from Foundation Model

Constructing commonsense knowledge graphs (CKGs) has attracted wide research attention due to its significant importance in cognitive intelligence. Nevertheless, existing CKGs are typically oriented to English, limiting the research in non-English languages. Meanwhile, the emergence of foundation models like ChatGPT and GPT-4 has shown promising intelligence with the help of reinforcement learning from human feedback. Under the background, in this paper, we utilize foundation models to construct a Chinese CKG, named Snowman. Specifically, we distill different types of commonsense head items from ChatGPT, and continue to use it to collect tail items with respect to the head items and pre-defined relations. Based on the preliminary analysis, we find the negative commonsense knowledge distilled by ChatGPT achieves lower human acceptance compared to other knowledge. Therefore, we design a simple yet effective self-instruct filtering strategy to filter out invalid negative commonsense. Overall, the constructed Snowman covers more than ten million Chinese commonsense triples, making it the largest Chinese CKG. Moreover, human studies show the acceptance of Snowman achieves 90.6\%, indicating the high-quality triples distilled by the cutting-edge foundation model. We also conduct experiments on commonsense knowledge models to show the usability and effectiveness of our Snowman.
Nonlinear improvement of measurement-device-independent quantum key distribution using multimode quantum memory

This paper proposes a quantum key distribution (QKD) scheme for measurement-device-independent QKD (MDI-QKD) utilizing quantum memory (QM), which is based on two distinct functions of QM: on-demand storage and multimode storage. We demonstrate a nonlinear increase in the secure key rate due to the utilization of QM. In the protocol incorporating on-demand storage, it is acknowledged that the secure key rate is scaled by $R=O(\sqrt{η_{ch}})$ as $η_{ch}$, while as an alternative approach, we reveal that the improvement is $O(m_s^2)$, with $m_s$ being the number of modes in frequency (spatial) multiplexing in the scheme incorporating multimode storage. We adopt an atomic frequency comb as a QM that incorporates the two functions and propose an architecture based on MDI-QKD to attain experimental feasibility. This scheme can be extended to quantum repeaters, and even for a single quantum-repeater node, there is a nonlinear enhancement and an experimental incentive to increase the number of modes.
Typo-Robust Representation Learning for Dense Retrieval

Dense retrieval is a basic building block of information retrieval applications. One of the main challenges of dense retrieval in real-world settings is the handling of queries containing misspelled words. A popular approach for handling misspelled queries is minimizing the representations discrepancy between misspelled queries and their pristine ones. Unlike the existing approaches, which only focus on the alignment between misspelled and pristine queries, our method also improves the contrast between each misspelled query and its surrounding queries. To assess the effectiveness of our proposed method, we compare it against the existing competitors using two benchmark datasets and two base encoders. Our method outperforms the competitors in all cases with misspelled queries. Our code and models are available at https://github. com/panuthept/DST-DenseRetrieval.
Graph-based Active Learning for Surface Water and Sediment Detection in Multispectral Images

We develop a graph active learning pipeline (GAP) to detect surface water and in-river sediment pixels in satellite images. The active learning approach is applied within the training process to optimally select specific pixels to generate a hand-labeled training set. Our method obtains higher accuracy with far fewer training pixels than both standard and deep learning models. According to our experiments, our GAP trained on a set of 3270 pixels reaches a better accuracy than the neural network method trained on 2.1 million pixels.
Blind Video Quality Assessment at the Edge

Owing to the proliferation of user-generated videos on the Internet, blind video quality assessment (BVQA) at the edge attracts growing attention. The usage of deep-learning-based methods is restricted to be applied at the edge due to their large model sizes and high computational complexity. In light of this, a novel lightweight BVQA method called GreenBVQA is proposed in this work. GreenBVQA features a small model size, low computational complexity, and high performance. Its processing pipeline includes: video data cropping, unsupervised representation generation, supervised feature selection, and mean-opinion-score (MOS) regression and ensembles. We conduct experimental evaluations on three BVQA datasets and show that GreenBVQA can offer state-of-the-art performance in PLCC and SROCC metrics while demanding significantly smaller model sizes and lower computational complexity. Thus, GreenBVQA is well-suited for edge devices.
Ternary extremal four-negacirculant self-dual codes

In this note, we give basic properties of ternary four-negacirculant self-dual codes. By exhaustive computer search based on the properties, we complete a classification of ternary extremal four-negacirculant self-dual codes of lengths 40, 44, 48, 52 and 60.