Reviewerly/exHarmony huggingface dataset:
Using Machine Learning and Deep Learning Methods to Predict the Complexity of Breast Cancer Cases

In many countries, the management of cancer patients must be discussed in multidisciplinary tumor boards (MTBs). These meetings have been introduced to provide a collaborative and multidisciplinary approach to cancer care. However, the benefits of MTBs are now being challenged because there are a lot of cases and not enough time to discuss all the of them. During the evaluation of the guideline-based clinical decision support system (CDSS) of the DESIREE project, we found that for some clinical cases, the system did not produce recommendations. We assumed that these cases were complex clinical cases and needed deeper MTB discussions. In this work, we trained and tested several machine learning and deep learning algorithms on a labelled sample of 298 breast cancer patient summaries, to predict the complexity of a breast cancer clinical case. XGboost and multi-layer perceptron were the models with the best result, with an F1 score of 83%.
Dark Channel Based Visibility Measuring from Daytime Scene Videos

&lt;p&gt;Visibility is one of the most important factors of meteorological observation. Low visibility caused by fog often has a great impact on human production and daily life. Therefore, visibility measurements and warnings are given as early as possible to avoid accidents. This paper proposes a new visibility measuring method based on dark channel characteristics of digital images in natural scenes. It uses the means of dark channel values obtained from foggy images and high visibility reference images as inputs of a deep fully connected neural network, and then the trained model is applied to estimate visibility. Experiments conducted on a dataset contained two foggy scenes. The results demonstrate the effectiveness of our proposed method for daytime visibility measurement. At the same time, this paper proposes and designs an automatic detection system based on our algorithm. It implements the real time visibility measuring through edge computing.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Diversity Over Size: On the Effect of Sample and Topic Sizes for Argument Mining Datasets

The task of Argument Mining, that is extracting argumentative sentences for a specific topic from large document sources, is an inherently difficult task for machine learning models and humans alike, as large Argument Mining datasets are rare and recognition of argumentative sentences requires expert knowledge. The task becomes even more difficult if it also involves stance detection of retrieved arguments. Given the cost and complexity of creating suitably large Argument Mining datasets, we ask whether it is necessary for acceptable performance to have datasets growing in size. Our findings show that, when using carefully composed training samples and a model pretrained on related tasks, we can reach 95% of the maximum performance while reducing the training sample size by at least 85%. This gain is consistent across three Argument Mining tasks on three different datasets. We also publish a new dataset for future benchmarking.
VQA-GNN: Reasoning with Multimodal Knowledge via Graph Neural Networks for Visual Question Answering

Visual question answering (VQA) requires systems to perform concept-level reasoning by unifying unstructured (e.g., the context in question and answer; "QA context") and structured (e.g., knowledge graph for the QA context and scene; "concept graph") multimodal knowledge. Existing works typically combine a scene graph and a concept graph of the scene by connecting corresponding visual nodes and concept nodes, then incorporate the QA context representation to perform question answering. However, these methods only perform a unidirectional fusion from unstructured knowledge to structured knowledge, limiting their potential to capture joint reasoning over the heterogeneous modalities of knowledge. To perform more expressive reasoning, we propose VQA-GNN, a new VQA model that performs bidirectional fusion between unstructured and structured multimodal knowledge to obtain unified knowledge representations. Specifically, we inter-connect the scene graph and the concept graph through a super node that represents the QA context, and introduce a new multimodal GNN technique to perform inter-modal message passing for reasoning that mitigates representational gaps between modalities. On two challenging VQA tasks (VCR and GQA), our method outperforms strong baseline VQA methods by 3.2% on VCR (Q-AR) and 4.6% on GQA, suggesting its strength in performing concept-level reasoning. Ablation studies further demonstrate the efficacy of the bidirectional fusion and multimodal GNN method in unifying unstructured and structured multimodal knowledge.
Are Piagetian Scales Just Intelligence Tests?

Previous research has noted the relationships between Piagetian and Psychometric tests. The literature on this topic has revealed consistent and moderate-to-high magnitude associations and largely unidimensional tests. However, most of this literature has not explicitly assessed whether Psychometric general intelligence corresponds to a similar factor measurable with Piagetian tests. This study did just that by fitting structural equation models to available data from administrations of Piagetian and Psychometric tests given to the same samples. The tests were modeled separately, and then in models where they were featured together so their general factors could be correlated. The results of those analyses were then subjected to a meta-analysis (K = 8, N = 862) and the meta-analytic relationship between Piagetian and Psychometric general factors was found to be exceptionally high (r = .847). Piagetian scales thus test intelligence, and intelligence tests assess Piagetian development.
Four algorithms for propositional forgetting

Abstract Four algorithms for propositional forgetting are compared. The first performs all possible resolutions and deletes the clauses containing a variable to forget. The second forgets a variable at time by resolving and then deleting all clauses that resolve on that variable. The third outputs the result of all possible linear resolutions on the variables to forget. The fourth generates a clause from the points of contradiction of a backtracking search. The latter emerges as the winner, with the second and first performing well in some cases. The linear resolution algorithm is inadequate, at least in the current implementation.
German Learning Media Application in Class X Senior High School

The purpose of this study was to describe the use of learning media in learning German in class X SMA Nommensen Pematangsiantar Campus. The data collection was carried out by the methods of observation, interviews, and documentation. The research data will be analyzed using a qualitative descriptive method. The results showed that (1) the use of German language learning media in class X SMA Nommensen Pematangsiantar Campus that had been implemented such as visual media ( PowerPoint, laptop ), silent projection media ( LCD projector), simple visual media (real objects in the classroom ). , whiteboards, picture cards, letter cards, textbooks), audio media ( speakers ). (2) The obstacles faced by teachers in the use of media are derived from media selection techniques and student characteristics. The teacher's solution to overcoming obstacles to the use of learning media is to adjust the learning media to the material and adjust the learning media used to the conditions of the participants educate.
Potential of case-technology in the formation of students’ research competence in practice-oriented learning

The article presents the results of research on the formation of students’ research competence. The authors conduct an analytical review of Russian and foreign scientific sources covering this pedagogical phenomenon. It is demonstrated that an essential role in the formation of research competence today is played by active learning technologies, which include case technologies. The potential of practice-oriented cases for classroom work used when working with students in pedagogical education and, as well as the potential of educational-research cases for extracurricular club work with school students, is revealed. The forms of cases as a means of developing research competence, requirements for them, and the implementation stages are described. The article also provides the results of practice-oriented learning carried out as part of experimental work with the use of case technologies. The authors’ materials can be used in the process of developing students’ research competence in both higher and secondary education institutions.
BanglaNLG and BanglaT5: Benchmarks and Resources for Evaluating Low-Resource Natural Language Generation in Bangla

This work presents BanglaNLG, a comprehensive benchmark for evaluating natural language generation (NLG) models in Bangla, a widely spoken yet low-resource language. We aggregate six challenging conditional text generation tasks under the BanglaNLG benchmark, introducing a new dataset on dialogue generation in the process. Furthermore, using a clean corpus of 27.5 GB of Bangla data, we pretrain BanglaT5, a sequence-to-sequence Transformer language model for Bangla. BanglaT5 achieves state-of-the-art performance in all of these tasks, outperforming several multilingual models by up to 9% absolute gain and 32% relative gain. We are making the new dialogue dataset and the BanglaT5 model publicly available at https://github.com/csebuetnlp/BanglaNLG in the hope of advancing future research on Bangla NLG.
Comparative Study of K-Means Clustering Algorithm and K-Medoids Clustering in Student Data Clustering

Universities as educational institutions have very large amounts of academic data which may not be used properly. The data needs to be analyzed to produce information that can map the distribution of students. Student academic data processing utilizes data mining processes using clustering techniques, K-Means and K-Medoids. This study aims to implement and analyze the comparison of which algorithm is more optimal based on the cluster validation test with the Davies Bouldin Index. The data used are academic data of UIN Sunan Kalijaga students in the 2013-2015 batch. In the k-Means process, the best number of clusters is 5 with a DBI value of 0.781. In the k-Medoids process, the best number of clusters is 3 with a DBI value of 0.929. Based on the value of the DBI validation test, the k-Means algorithm is more optimal than the k-Medoids. So that the cluster of students with the highest average GPA of 3,325 is 401 students.
Characterisation of state-preparation uncertainty in quantum key distribution

To achieve secure quantum key distribution, all imperfections in the source unit must be incorporated in a security proof and measured in the lab. Here we perform a proof-of-principle demonstration of the experimental techniques for characterising the source phase and intensity fluctuation in commercial quantum key distribution systems. When we apply the measured phase fluctuation intervals to the security proof that takes into account fluctuations in the state preparation, it predicts a key distribution distance of over 100 km of fiber. The measured intensity fluctuation intervals are however so large that the proof predicts zero key, indicating a source improvement may be needed. Our characterisation methods pave the way for a future certification standard.
Shedding Light on the Management of School Operational Assistance (SOA) Fund Effectiveness: a Case Study of State Basic Education Level in Demak Regency

School Operational Assistance (SOA) Fund is one of the educational funds in Indonesia. Based on the APBN Financial Note, the management of the SOA fund is still not optimal, and there are still many misuses of the SOA fund by various parties. Therefore, it is necessary to apply the good governance principles in the management of the SOA fund. This study aims to analyze the effectiveness of the management of the SOA fund, which is influenced by good governance, namely transparency, accountability, participation, and responsibility. The object of this study is the management of the SOA fund at the level of state basic education in Demak Regency, which consists of State Elementary Schools and State Junior High Schools. The data source of this research is primary data using a questionnaire. There are 494 respondents in this research. The respondents are headmasters, school treasurers, teachers, and other parties in school. The study results show that the SOA fund at the level of state basic education in Demak Regency has been managed effectively. The study results also show that all independent variables simultaneously have a positive effect on the effectiveness of the management of the SOA fund. Partially, transparency does not affect the effectiveness of SOA fund management, while accountability, participation, and responsibility have a positive effect on the effectiveness of the management of SOA fund.
HiVLP: Hierarchical Vision-Language Pre-Training for Fast Image-Text Retrieval

In the past few years, the emergence of vision-language pre-training (VLP) has brought cross-modal retrieval to a new era. However, due to the latency and computation demand, it is commonly challenging to apply VLP in a real-time online retrieval system. To alleviate the defect, this paper proposes a \textbf{Hi}erarchical \textbf{V}ision-\textbf{}Language \textbf{P}re-Training (\textbf{HiVLP}) for fast Image-Text Retrieval (ITR). Specifically, we design a novel hierarchical retrieval objective, which uses the representation of different dimensions for coarse-to-fine ITR, i.e., using low-dimensional representation for large-scale coarse retrieval and high-dimensional representation for small-scale fine retrieval. We evaluate our proposed HiVLP on two popular image-text retrieval benchmarks, i.e., Flickr30k and COCO. Extensive experiments demonstrate that our HiVLP not only has fast inference speed but also can be easily scaled to large-scale ITR scenarios. The detailed results show that HiVLP is $1,427$$\sim$$120,649\times$ faster than the fusion-based model UNITER and 2$\sim$5 faster than the fastest embedding-based model LightingDot in different candidate scenarios. It also achieves about +4.9 AR on COCO and +3.8 AR on Flickr30K than LightingDot and achieves comparable performance with the state-of-the-art (SOTA) fusion-based model METER.
FORMATION OF PERFORMANCE SKILLS IN THE CLASSROOM MAIN INSTRUMENT (PIANO)

In clause conditions of development of pianistic performing skills of the future teachers of music are considered. The typology of performing skills and ways of their formation is offered.
Robot-mediated Culture Education: Users’ Reception

Culture education can use a myriad of media, and this research explores how a robot can be used for culture education. To conduct the research, a questionnaire-based survey was conducted on adult and non-adult users. The findings showed that the two groups of respondents when calculated together had a higher level of satisfaction with educational effectiveness and the significant value of the system (89%, 917/1030) than operational experience (84%, 862/1030) and the robot’s sensory appeal (77%, 797/1030). The findings suggested that respondents had attached greater importance to the significant value and educational function of the robot-mediated system. Gaps existed between the two groups regarding their responses to the robot’s sensory appeal (84% vs. 64%), interaction with the robot (85% vs.77%), and reflective assessments on the value of the system (91% vs. 85%). Despite the gaps, the robot-mediated culture educational system has proved to be effective as it is in line with Norman’s (2004) visceral, behavioral and reflective needs of emotional design by creating a sensory appeal, giving users delightful operational experiences, and yielding positive assessments. The implications derived from the findings shed light on the educational value of using the robot as an agent of culture education.
Discovering Rules from a National Exam Repository: A Use Case for Data Analysis from Iranian Medical Schools Entry Exam

Many methods have been studied to analyze and interpret patterns and relationships that are embedded in the database to discover new knowledge in educational systems. Association rule mining is a type of data mining that identifies relationships among elements of the dataset. However, because these methods often generate various rules including non-significant ones, it is important to identify the most useful rules. Therefore, evaluating and ranking rules has become a topic of interest in the decision-making process in order to represent the level of usefulness of rules. We incorporated Apriori and Eclat algorithms on an educational dataset of a national medical exam in Iran. The aim of this study is to identify the usefulness of the extracted rules. This method can reliably discover new knowledge by interpreting the prioritized rules. The results show that those who have Scored in the highest category, i.e. [407,493], are accepted and who have scored in the lowest category, i.e. [150,236), are not accepted in the exam regardless of others features. Although, the rules that implication Accept=0 occurs, find out with high confidence, due to a large number of samples in this case. The ranking rules show this method is effective in the identification of insignificant rules that have no effect on decision making.
Interpretable Proof Generation via Iterative Backward Reasoning

We present IBR, an Iterative Backward Reasoning model to solve the proof generation tasks on rule-based Question Answering (QA), where models are required to reason over a series of textual rules and facts to find out the related proof path and derive the final answer.We handle the limitations of existed works in two folds: 1) enhance the interpretability of reasoning procedures with detailed tracking, by predicting nodes and edges in the proof path iteratively backward from the question; 2) promote the efficiency and accuracy via reasoning on the elaborate representations of nodes and history paths, without any intermediate texts that may introduce external noise during proof generation.There are three main modules in IBR, QA and proof strategy prediction to obtain the answer and offer guidance for the following procedure; parent node prediction to determine a node in the existing proof that a new child node will link to; child node prediction to find out which new node will be added to the proof.Experiments on both synthetic and paraphrased datasets demonstrate that IBR has better in-domain performance as well as cross-domain transferability than several strong baselines.Our code and models are available at https://github.com/find-knowledge/IBR.
Analysis a Maturity of Case Search Information Systems using Cobit 5 Framework

The application of information and communication technology has now become a necessity and demand on every public service organizing agency.Not least even judicial institutions or institutions that are one of the public service organizing agencies and to realize good service to the community, requires good management of information systems.The success of the governance of an organization is influenced by the extent to which the governance of information technology has been done.Information technology governance is also part of organizational governance in this case the maturity level of the information system that has been run now.This research uses the COBIT 5 framework with the domain DSS03 (manage problem) which aims to analyze the maturity level of information systems in the DSS 03 domain, this study was conducted with three stages of analysis, namely determining the current maturity level and the level of maturity that is expected, conducting gap analysis, and providing recommendations for advice.The process of collecting data for this study uses aids in the form of questionnaires and in-person interviews.The results of the research in the form of a current maturity level for domain DSS03 (manage problem) with an average value of 3.84 which means it is at level 4 (managed and measurable) means that the maturity level of SIPP problem management is well managed, while the expected maturity level is at level 5 (optimized.)The results of the calculation of the gap value for the domain DSS03 (manage problem) obtained a value of 1.This research has been carried out well following the expected research objectives.
Solving Larger Maximum Clique Problems Using Parallel Quantum Annealing

Quantum annealing has the potential to find low energy solutions of NP-hard problems that can be expressed as quadratic unconstrained binary optimization problems. However, the hardware of the quantum annealer manufactured by D-Wave Systems, which we consider in this work, is sparsely connected and moderately sized (on the order of thousands of qubits), thus necessitating a minor-embedding of a logical problem onto the physical qubit hardware. The combination of relatively small hardware sizes and the necessity of a minor-embedding can mean that solving large optimization problems is not possible on current quantum annealers. In this research, we show that a hybrid approach combining parallel quantum annealing with graph decomposition allows one to solve larger optimization problem accurately. We apply the approach on the Maximum Clique problem on graphs with up to 120 nodes and 6395 edges.
Implementasi Algoritma K-Means Clustering Seleksi Siswa Berprestasi Berdasarkan Keaktifan dalam Proses Pembelajaran

The learning process through various interactions and learning experiences has a considerable influence on developing student activity to improve the quality of education. The teacher is the most important factor in determining the success of students in the implementation of the process. The development of the quality and activeness of students in learning is a basic element as a form of success in the learning process which of course not all students have a level of speed in understanding material. This is a concern for schools in improving the quality of education. The purpose of this study was to classify the level of activity of students at SMP ABC using the correlation between grades and the level of student activity who would be recommended to take part in competitions or prospective scholarship recipients. The data source that we used in this study came from the State Junior High School ABC which consists of several variables, including student attendance data, academic scores, psychomotor scores, and affective values. The method used in this research is the Clustering method with the K-means Algorithm. The results of this study can be grouped into 3 clusters including cluster 0 indicating active students as many as 30 students, cluster 1 showing inactive students as many as 8 students, and cluster 2 indicating less active students as many as 21 students.
Safe Dynamic Reconfiguration of Concurrent Component-based Applications

Cloud computing and cyber-physical systems involve software capable of adapting at run time to remain compliant with user demands and environmental constraints. This calls for extending the life cycle of software systems with a reconfiguration step to go beyond analysis, design, development and deployment. Existing approaches compute a new valid configuration at design time, at run time, or both, inducing computational or validation overheads for each reconfiguration step. We propose an approach that relies on variability models to acquire a representation of the set of valid configurations of a system. We use feature models to automatically generate a JavaBIP run-time variability model. The generated model monitors and controls the application behaviour by intercepting reconfiguration requests and executing them in such a manner as to ensure that all reachable configurations are valid without the need of pre-computing the possible configurations neither at design time nor at run-time while only inducing a minimal run-time computational overhead.