Reviewerly/exHarmony huggingface dataset:
Exploiting Intersentence Information for Better Question-Driven Abstractive Summarization: Algorithm Development and Validation

Question-driven summarization has become a practical and accurate approach to summarizing the source document. The generated summary should be concise and consistent with the concerned question, and thus, it could be regarded as the answer to the nonfactoid question. Existing methods do not fully exploit question information over documents and dependencies across sentences. Besides, most existing summarization evaluation tools like recall-oriented understudy for gisting evaluation (ROUGE) calculate N-gram overlaps between the generated summary and the reference summary while neglecting the factual consistency problem.This paper proposes a novel question-driven abstractive summarization model based on transformer, including a two-step attention mechanism and an overall integration mechanism, which can generate concise and consistent summaries for nonfactoid question answering.Specifically, the two-step attention mechanism is proposed to exploit the mutual information both of question to context and sentence over other sentences. We further introduced an overall integration mechanism and a novel pointer network for information integration. We conducted a question-answering task to evaluate the factual consistency between the generated summary and the reference summary.The experimental results of question-driven summarization on the PubMedQA data set showed that our model achieved ROUGE-1, ROUGE-2, and ROUGE-L measures of 36.01, 15.59, and 30.22, respectively, which is superior to the state-of-the-art methods with a gain of 0.79 (absolute) in the ROUGE-2 score. The question-answering task demonstrates that the generated summaries of our model have better factual constancy. Our method achieved 94.2% accuracy and a 77.57% F1 score.Our proposed question-driven summarization model effectively exploits the mutual information among the question, document, and summary to generate concise and consistent summaries.
ANALISA PERANCANGAN SIMPAN PINJAM PADA BANK PERKRIDITAN RAKYAT BERBASIS CLIENT SERVER

Bima Credit and Savings Cooperative Credit Union is a financial institution that provides transaction services to store and borrow money to its members. Bima Savings and Loan Cooperative needs to have an information system to facilitate data processing, carry out tasks appropriately, and minimize errors in recording transactions and financial calculations. To meet the above needs, it is necessary to design a savings and loan information system. The system development method used is SDLC (System Development Life Cycle) which stands for several stages, namely the planning stage, the analysis phase, the design stage, the implementation stage and the maintenance phase, but the maintenance stage is not included in the development of this system. This design uses a programming language that is C # in its creation and mysql as its database. The results of this study are to provide convenience for officers in managing savings and loan data. Desktop-based Savings and Loan Information System, can help to manage, search, make savings and loan reports quickly and easily, and can make savings and loan arrangements and calculate interest directly through the application without being done manually.
RESPONSE TO HOME LEARNING SYSTEM POLICY IN LEARNING INDONESIAN LANGUAGE

The emergence of the Covid 19 outbreak has affected various sectors in the world, including in Indonesia. The world of education is one of the areas affected by this outbreak. All levels of education from PAUD to tertiary institutions must implement the Home Learning System (BDR). Various obstacles arise with the implementation of this policy. The purpose of this study was to analyze the problems faced by teachers and students when implementing BDR policies. The method used in this research is interview and survey to describe social phenomena that occur through data in the form of diagrams taken from several students' questionnaires and teacher interviews. The results showed that the problems faced by students were difficulties in understanding lessons and network constraints that were also experienced by the teacher. It can be concluded that online learning must continue to be implemented, regardless of the existing obstacles, so that there is no lost learner generation. In the world of education, in this case, the teacher must innovate so that the effectiveness of learning can be achieved.
Formations of Finite Groups in Polynomial Time: $\mathfrak{F}$-residuals and $\mathfrak{F}$-subnormality

For a wide family of formations $\mathfrak{F}$ it is proved that the $ \mathfrak{F}$-residual of a permutation finite group can be computed in a polynomial time. Moreover, if in the previous case $\mathfrak{F}$ is hereditary, then an $\mathfrak{F}$-subnormality of a subgroup can be checked in a polynomial time.
Planning of content of professionally pedagogical education of students of art creative specialities

Planning of content of professionally pedagogical education of students of art creative specialities. In the article the features of professionally pedagogical education of students of creative higher educational establishment are examining, the criteria ofplanning of its maintenance are selecting.
Superposition of causal order in quantum walks: non-Markovianity and causal asymmetry

We set the criteria for a quantum walk to exhibit nontrivial dynamics when placed in an indefinite causal order and study two-period quantum walks when the evolution operators are arranged in a causally ordered sequence and in an indefinite causal order using quantum switch. When either forward or backward causal sequence is implemented, one observes a causal asymmetry in the dynamics, in the sense that the reduced dynamics of the coin state is more non-Markovian for one particular temporal order of operations than that of the other. When the dynamics is defined using evolution operators in a superposition of causal orders, the reduced dynamics of the coin space exhibit higher non-Markovian behavior than either of the definite causal orders. This effect can be interpreted as a Parrondo-like effect in non-Markovianity of the reduced state dynamics of the coin. We further generalize the qualitative description of our results pertaining to the dynamics when the walk has a higher number of periods.
Application of per-pixel and sub-pixel unmixing methods on ASTER images to map hydrothermal alterations in a highly metamorphosed terrain: a case of the Musina copper deposit field

This study focuses on mapping hydrothermal alterations in Musina Copper Deposit Field which is characterized by unique concentric zonation of hydrothermal alterations from an outer zone of sericitization (phyllic) followed by inward zones of chloritization and epidotization (propylitic). Per-pixel and mixture tuned matched filtering (MTMF) sub-pixel unmixing methods were applied on ASTER data to map hydrothermal alterations. False colour composite (FCC) generated from combination of propylitic, phyllic and host rock index was effective in delineating alteration zones. The spatial distribution of alteration group minerals: chlorite–epidote and sericite–muscovite from MTMF classification correspond with their associated alteration zones. Correlation coefficient (R2) was utilized to assess accuracy of the results by comparing MF scores of alteration minerals with copper concentrations. R2 was 0.78 for chlorite–epidote and 0.69 for sericite– muscovite. Copper concentration levels increase towards chlorite–epidote region which represents the inward propylitic zone. Following the geometry and alteration zonation, follow-up targets were generated.
Audio Data Augmentation for Acoustic-to-articulatory Speech Inversion using Bidirectional Gated RNNs

Data augmentation has proven to be a promising prospect in improving the performance of deep learning models by adding variability to training data. In previous work with developing a noise robust acoustic-to-articulatory speech inversion system, we have shown the importance of noise augmentation to improve the performance of speech inversion in noisy speech. In this work, we compare and contrast different ways of doing data augmentation and show how this technique improves the performance of articulatory speech inversion not only on noisy speech, but also on clean speech data. We also propose a Bidirectional Gated Recurrent Neural Network as the speech inversion system instead of the previously used feed forward neural network. The inversion system uses mel-frequency cepstral coefficients (MFCCs) as the input acoustic features and six vocal tract-variables (TVs) as the output articulatory features. The Performance of the system was measured by computing the correlation between estimated and actual TVs on the U. Wisc. X-ray Microbeam database. The proposed speech inversion system shows a 5% relative improvement in correlation over the baseline noise robust system for clean speech data. The pre-trained model, when adapted to each unseen speaker in the test set, improves the average correlation by another 6%.
An Information-Theoretic Framework for Supervised Learning

Each year, deep learning demonstrates new and improved empirical results with deeper and wider neural networks. Meanwhile, with existing theoretical frameworks, it is difficult to analyze networks deeper than two layers without resorting to counting parameters or encountering sample complexity bounds that are exponential in depth. Perhaps it may be fruitful to try to analyze modern machine learning under a different lens. In this paper, we propose a novel information-theoretic framework with its own notions of regret and sample complexity for analyzing the data requirements of machine learning. With our framework, we first work through some classical examples such as scalar estimation and linear regression to build intuition and introduce general techniques. Then, we use the framework to study the sample complexity of learning from data generated by deep neural networks with ReLU activation units. For a particular prior distribution on weights, we establish sample complexity bounds that are simultaneously width independent and linear in depth. This prior distribution gives rise to high-dimensional latent representations that, with high probability, admit reasonably accurate low-dimensional approximations. We conclude by corroborating our theoretical results with experimental analysis of random single-hidden-layer neural networks.
Robustness to Label Noise Depends on the Shape of the Noise Distribution in Feature Space

Machine learning classifiers have been demonstrated, both empirically and theoretically, to be robust to label noise under certain conditions -- notably the typical assumption is that label noise is independent of the features given the class label. We provide a theoretical framework that generalizes beyond this typical assumption by modeling label noise as a distribution over feature space. We show that both the scale and the shape of the noise distribution influence the posterior likelihood; and the shape of the noise distribution has a stronger impact on classification performance if the noise is concentrated in feature space where the decision boundary can be moved. For the special case of uniform label noise (independent of features and the class label), we show that the Bayes optimal classifier for $c$ classes is robust to label noise until the ratio of noisy samples goes above $\frac{c-1}{c}$ (e.g. 90% for 10 classes), which we call the tipping point. However, for the special case of class-dependent label noise (independent of features given the class label), the tipping point can be as low as 50%. Most importantly, we show that when the noise distribution targets decision boundaries (label noise is directly dependent on feature space), classification robustness can drop off even at a small scale of noise. Even when evaluating recent label-noise mitigation methods we see reduced accuracy when label noise is dependent on features. These findings explain why machine learning often handles label noise well if the noise distribution is uniform in feature-space; yet it also points to the difficulty of overcoming label noise when it is concentrated in a region of feature space where a decision boundary can move.
An arrangement of the number of K-grams in the performance of Rabin Karp algorithm in text adjustment

Rabin Karp algorithm is frequently used to determine the similarity between texts, using the hash function to compare the string identified and the substring in the text. The choice of the k value in the K-gram is often unrestricted. The number of k values used when cutting some terms will take longer if tried one by one. This research will perform a word cutting test on a script using K-gram 0 to 8. The results will cover the effect of the value of each K used on the similarity percentage produced. This research aims to determine the effect of the number of K-grams on the performance of Rabin Karp in text matching. The test underwent 20 sentences and 10 times using the dice coefficient for text similarity testing. The conclusion of this research should not use the K-gram 0 to 2 due to the K-gram basic principle: character deduction. Subsequently, if the character is 0,1,2, it does not have a meaning yet; thus, it gets a high similarity percentage. Based on trials by taking samples of K-gram 0 to 8 from 10 test data sets; the K-gram 3 is the best among K-grams 0 to 8.
Facial Expression Recognition from Multi-Perspective Visual Inputs and Soft Voting

Automatic identification of human facial expressions has many potential applications in today's connected world, from mental health monitoring to feedback for onscreen content or shop windows and sign-language prosodic identification. In this work we use visual information as input, namely, a dataset of face points delivered by a Kinect device. The most recent work on facial expression recognition uses Machine Learning techniques, to use a modular data-driven path of development instead of using human-invented ad hoc rules. In this paper, we present a Machine-Learning based method for automatic facial expression recognition that leverages information fusion architecture techniques from our previous work and soft voting. Our approach shows an average prediction performance clearly above the best state-of-the-art results for the dataset considered. These results provide further evidence of the usefulness of information fusion architectures rather than adopting the default ML approach of features aggregation.
New Hash-based Sequence Alignment Algorithm

With the gradual deepening of human understanding of the disease and the continuous improvement of diagnosis and treatment, precision medicine, a new medical concept medical model, has been proposed. High-throughput and high-accuracy gene sequence reading and high-accuracy and rapid gene sequence alignment provide the basis for diagnosing and treating precision medicine. This paper presents a new method based on matrix and linked-list to store sequence information. The hash values of the two hash functions are used as the coordinate values of the sequence in the matrix, and they can be used to find the target sequence. The algorithm is compared with five classical comparison algorithms.
Bayesian Linear Regression: Teaching resources and software

Bayesian models are very important in modern data science. These models can be used to derive estimatesfor noisy and sparse data. This manuscript outlines the basics and derivations of a Bayesian linearregression model. Source code for performing Bayesian linear regression is also provided. I hope thisresource will enable broader understanding of the basics of Bayesian models.
The predictors of adopting cloud computing e-learning in Iraq: the role of technology readiness

Cloud computing electronic learning (CCEL) is a new technology that enables educators and students to extend the storage and access learning materials. The use of this technology is limited worldwide. However, in the time of COVID 19, this technology becomes essential for the educational processes. The purpose of this study is to examine the predictors of using CCEL among academic staff and students in Iraq. Based on literature, this study proposed that perceived ease of use (PEOU), perceived usefulness (PU), and subjective norms (SN) will affect the intention to use (IU) which in turn will affect the actual use (AU). Attitude (AT) is proposed to mediate the relationships while technology readiness (TR) is proposed as a moderator. The data is collected from 331 students and academic staff in three Iraqi universities using stratified sampling. Data analysis is conducted using SmartPLS. The results showed that PEOU, PU, and SN affect positively IU which in turn affected AU. AT mediated partially the effect of PEOU, PU, and SN on IU while TR moderated only the effect of PEOU on IU. Decision makers are recommended to simplify the usage of CCEL and to conduct workshops about the usage and benefits of CCEL.
Editorial paper for Pattern Recognition Letters VSI on cross model understanding for visual question answering

Referring expression grounding plays a fundamental role in vision-language understanding, which aims at locating a certain target region in an image described by a natural language expression. It needs to understand high-level semantic correlations between objects in the image according to the referred expression for the task. Thus, it inherently requires reasoning the context information, i.e., appearance context and relationship context. While most existing approaches either ignore to explore the appearance details of the target region or rely on a manually designed reasoning structure and treat the context information of each neighboring object equivalently, inflexible to the scenario where referring expressions are complicated. In this paper, we put forward Multi-context Reasoning Network (MCRN) for referring expression grounding task, which can apply appearance context reasoning and relationship context reasoning simultaneously. Methodologically, for appearance context reasoning, we propose a local node attention to obtain local representation of the target object, which gives a more focus on its appearance details. For relationship context reasoning, we approach it as a language-guided multi-step reasoning problem and design a multi-step graph reasoning module to capture intra-context and inter-context between the target region of its intra-class and inter-class neighboring objects in an iterative way, which makes the reasoning process more reliable and interpretable. Our method demonstrates superiority based on extensive experimental outputs on three popular benchmark datasets.
Levels of formation of diagnostic culture of future primary school teachers in an innovative educational environment and its contents

The article describes the level of formation of the diagnostic culture of future primary school teachers in an innovative educational environment. classification of skills, feedback on the implementation of experimental work on the formation of a diagnostic culture based on a general description of each level.
Towards better Interpretable and Generalizable AD detection using Collective Artificial Intelligence

Alzheimer's Disease is the most common cause of dementia. Accurate diagnosis and prognosis of this disease are essential to design an appropriate treatment plan, increasing the life expectancy of the patient. Intense research has been conducted on the use of machine learning to identify Alzheimer's Disease from neuroimaging data, such as structural magnetic resonance imaging. In recent years, advances of deep learning in computer vision suggest a new research direction for this problem. Current deep learning-based approaches in this field, however, have a number of drawbacks, including the interpretability of model decisions, a lack of generalizability information and a lower performance compared to traditional machine learning techniques. In this paper, we design a two-stage framework to overcome these limitations. In the first stage, an ensemble of 125 U-Nets is used to grade the input image, producing a 3D map that reflects the disease severity at voxel-level. This map can help to localize abnormal brain areas caused by the disease. In the second stage, we model a graph per individual using the generated grading map and other information about the subject. We propose to use a graph convolutional neural network classifier for the final classification. As a result, our framework demonstrates comparative performance to the state-of-the-art methods in different datasets for both diagnosis and prognosis. We also demonstrate that the use of a large ensemble of U-Nets offers a better generalization capacity for our framework.
Researching Graduated Cyber Security Students – Reflecting Employment and Job Responsibilities through NICE framework

Most research and development on Cyber Security education is currently focusing on what should be taught, how much, and where within the degree programmes. Different Cyber Security frameworks are currently evolving to include Cyber Security education parallel to older paradigms of Computing Education, existing alongside with such as “Information Technology” and “Software Engineering”. Different Cyber Security specialisations or even whole degree programmes have started within universities before the frameworks have been defined into standardised degree structures. This is mainly the result of a dire industry need of well-educated cyber security personnel, a phenomenon affecting the industry globally.&#x0D; Our research concentrates on Finnish alumni students who have already graduated from a bachelor’s degree programme in Information Technology with a specialisation in Cyber Security in Finland. Within our gathered research data, we analysed what is the industry sector where their current job resides, and what are the cyber security responsibilities in their current work. The questionnaire also contained an after-reflection section where the graduated students could choose what they would study were they about to start and plan their studies again.&#x0D; The results verify that Cyber Security is still the most favoured specialisation within the former Cyber Security alumni students. Slight variation is evident from the data, which in the authors’ perspective, verifies the multifaceted nature of Cyber Security. When analysing alumni students’ job responsibilities, the main category of work resides in the “Protect and Defend” category of the NICE Framework, which in the terms of the conference, relates to Critical Infrastructure Protection being the main subject of employment for fresh graduates.&#x0D; These results give insight to other education organisations on how to develop their curricula to further emphasise the employment of students or to offer modules which are of interest for newly employed Cyber Security professionals. In addition, it gives an insight of industry demand for freshly graduated students within the target group.