Reviewerly/exHarmony huggingface dataset:
The Effect of Accounting Package Software ACOSYS Operator’s Ability to The Accounting Information Quality

Accounting in company nowdays have been helped by accounting package software which grows rapidly. The usage of the accounting package software provides many conveniences and can improve the generated accounting information quality. It is also possibile to be applied in MSMEs. However, it is feared that the limited number of employees and various level of competence in MSMEs could pose some risks to the generated information. Therefore, the purpose of this study is to determine the effect of accounting software operator’s competence on the accounting information quality at the MSME CV Putra Tunggal. The population of this study are the manager and employees of CV Putra Tunggal. The data method used in this study is a questionnaire and will use simple linear regression analysis. The results of this study indicate that the competence of the ACOSYS accounting software operator has a positive and significant effect on the accounting information quality.
Self-organized intelligent quantum controller: quantum deep learning and quantum genetic algorithm – QSCOptKBTM toolkit

Strategy of intelligent cognitive control systems of ill-defined control objects based on quantum and soft computing presented.Quantum self-organization knowledge base synergetic effect extracted from intelligent fuzzy controller's imperfect knowledge bases described.That technology improved of robustness of intelligent cognitive control systems in hazard control situations described with different types of robot cooperation.Examples demonstrated the introduction of quantum fuzzy inference gate design as prepared programmable algorithmic solution for board embedded control systems.The physical interpretation of the process of controlling self-organization at the quantum level is discussed on the basis of quantum information-thermodynamic models of exchange and extraction of quantum (hidden) valuable information from/between classical particle trajectories in the "swarm of interacting particles" model.The main physical and information-thermodynamic aspects of the model of quantum intelligent control of classical control objects are discussed and described.An approach is considered for constructing reference control models based on new laws of quantum deep machine learning applying neural networks in mega-science project NICA.
Methods and algorithms of the analytical platform for analyzing the labor market and the compliance of the higher education system with market needs

The article discusses the methods and algorithms that underlie the analytical platform for automated monitoring and analysis of the labor market in the Russian Federation, as well as the analysis of the higher education system's compliance with the labor market's current needs.The study involved natural language processing methods and Big Data technologies.The general scheme corresponds to end-to-end processing -from data collection and storage, their transformation, analysis, and modeling, to visualization of results and decision-making.The analytical core of the system is a module for intellectual analysis of the texts of job advertisements in the labor market.The vacancies are collected from the most complete databases in Russia (namely HeadHunter, Work in Russia and SuperJob).Job descriptions of vacancies are matched with the official list of professions of the Ministry of Labor and Social Protection of Russia using semantic analysis based on neural models trained on large arrays of texts.Also, using semantic analysis, automated monitoring and intellectual analysis of the staffing needs of the all-Russian and regional labor markets are carried out according to the range of specialties of the university.Data gathering has been ongoing from 2015 up to now.
Using artificial neural networks to understand the human brain

Decoding AITo the uninitiated, AI -in particular, the subset of AI known as deep learning -is an unfathomable black box.Put simply, however, deeplearning algorithms or deep neural networks are computer-coded maths that function in comparable ways to the human brain.The algorithms can decipher complex and complicated patterns within huge amounts of data.Once trained on information that represents 'ground truth', they can then assume new knowledge from similar data that is thrown at them.In daily life, these algorithms are suggesting which online items you should put in your shopping cart next and beating world champions at the game Go.But deep learning has the potential to improve our lives in profound ways, one example being healthcare.Schematic diagram of a method for estimating cognitive impairment and brain atrophy using deep learning from health check-up data, based on the relationship between brain and systemic metabolic status.Metabolic disorders of dementia risks.Personalised care for prevention of dementia based on deep-learning analysis of health check-up data.researchfeatures.com researchfeatures.com
Desain Penerapan Sistem Informasi Akuntansi Syariah di Rumah Sakit Islam Jawa Timur

The purpose of this study was to determine the design of the application of Islamic accounting information systems in Islamic hospitals in East Java. The design of the application is based on the Iterative method according to the stages carried out. This research is a qualitative descriptive study. The types and sources of data used in this study are primary data and secondary data. The number of informants in this study were 3 people. The results of the study indicate that the design of the application of sharia accounting information systems in Islamic hospitals in East Java, namely: a.) The emergence of the need for financial reports that are in accordance with sharia principles, especially for hospitals that have been certified sharia and for hospitals belonging to Islamic organizations towards sharia. b.) Development of web- based Sharia SIA into SI ASIYAH vs 2.0 application c) The implementation phase requires an implementation model based on the development of Iterative SIA, where an initial introduction to the SI AISYAH application is required to receive responses from hospital participants who are participating in the training.&#x0D; &#x0D; Keywords: Application, Sharia Accounting Information System, Hospital.&#x0D;
Data-independent Low-complexity KLT Approximations for Image and Video Coding

The Karhunen-Lo\`eve transform (KLT) is often used for data decorrelation and dimensionality reduction. The KLT is able to optimally retain the signal energy in only few transform components, being mathematically suitable for image and video compression. However, in practice, because of its high computational cost and dependence on the input signal, its application in real-time scenarios is precluded. This work proposes low-computational cost approximations for the KLT. We focus on the blocklengths $N \in \{4, 8, 16, 32 \}$ because they are widely employed in image and video coding standards such as JPEG and high efficiency video coding (HEVC). Extensive computational experiments demonstrate the suitability of the proposed low-complexity transforms for image and video compression.
Spherical Matrix Factorization

Matrix Factorization plays an important role in machine learning such as Non-negative Matrix Factorization, Principal Component Analysis, Dictionary Learning, etc. However, most of the studies aim to minimize the loss by measuring the Euclidean distance, though in some fields, angle distance is known to be more important and critical for analysis. In this paper, we propose a method by adding constraints on factors to unify the Euclidean and angle distance. However, due to non-convexity of the objective and constraints, the optimized solution is not easy to obtain. In this paper we propose a general framework to systematically solve it with provable convergence guarantee with various constraints.
Scaling ASR Improves Zero and Few Shot Learning

With 4.5 million hours of English speech from 10 different sources across 120 countries and models of up to 10 billion parameters, we explore the frontiers of scale for automatic speech recognition. We propose data selection techniques to efficiently scale training data to find the most valuable samples in massive datasets. To efficiently scale model sizes, we leverage various optimizations such as sparse transducer loss and model sharding. By training 1-10B parameter universal English ASR models, we push the limits of speech recognition performance across many domains. Furthermore, our models learn powerful speech representations with zero and few-shot capabilities on novel domains and styles of speech, exceeding previous results across multiple in-house and public benchmarks. For speakers with disorders due to brain damage, our best zero-shot and few-shot models achieve 22% and 60% relative improvement on the AphasiaBank test set, respectively, while realizing the best performance on public social media videos. Furthermore, the same universal model reaches equivalent performance with 500x less in-domain data on the SPGISpeech financial-domain dataset.
SUPER-Net: Trustworthy Medical Image Segmentation with Uncertainty Propagation in Encoder-Decoder Networks

Deep Learning (DL) holds great promise in reshaping the healthcare industry owing to its precision, efficiency, and objectivity. However, the brittleness of DL models to noisy and out-of-distribution inputs is ailing their deployment in the clinic. Most models produce point estimates without further information about model uncertainty or confidence. This paper introduces a new Bayesian DL framework for uncertainty quantification in segmentation neural networks: SUPER-Net: trustworthy medical image Segmentation with Uncertainty Propagation in Encoder-decodeR Networks. SUPER-Net analytically propagates, using Taylor series approximations, the first two moments (mean and covariance) of the posterior distribution of the model parameters across the nonlinear layers. In particular, SUPER-Net simultaneously learns the mean and covariance without expensive post-hoc Monte Carlo sampling or model ensembling. The output consists of two simultaneous maps: the segmented image and its pixelwise uncertainty map, which corresponds to the covariance matrix of the predictive distribution. We conduct an extensive evaluation of SUPER-Net on medical image segmentation of Magnetic Resonances Imaging and Computed Tomography scans under various noisy and adversarial conditions. Our experiments on multiple benchmark datasets demonstrate that SUPER-Net is more robust to noise and adversarial attacks than state-of-the-art segmentation models. Moreover, the uncertainty map of the proposed SUPER-Net associates low confidence (or equivalently high uncertainty) to patches in the test input images that are corrupted with noise, artifacts, or adversarial attacks. Perhaps more importantly, the model exhibits the ability of self-assessment of its segmentation decisions, notably when making erroneous predictions due to noise or adversarial examples.
Style Transfer with Target Feature Palette and Attention Coloring

Style transfer has attracted a lot of attentions, as it can change a given image into one with splendid artistic styles while preserving the image structure. However, conventional approaches easily lose image details and tend to produce unpleasant artifacts during style transfer. In this paper, to solve these problems, a novel artistic stylization method with target feature palettes is proposed, which can transfer key features accurately. Specifically, our method contains two modules, namely feature palette composition (FPC) and attention coloring (AC) modules. The FPC module captures representative features based on K-means clustering and produces a feature target palette. The following AC module calculates attention maps between content and style images, and transfers colors and patterns based on the attention map and the target palette. These modules enable the proposed stylization to focus on key features and generate plausibly transferred images. Thus, the contributions of the proposed method are to propose a novel deep learning-based style transfer method and present target feature palette and attention coloring modules, and provide in-depth analysis and insight on the proposed method via exhaustive ablation study. Qualitative and quantitative results show that our stylized images exhibit state-of-the-art performance, with strength in preserving core structures and details of the content image.
Continuous Human Action Detection Based on Wearable Inertial Data

Human action detection is a hot topic, which is widely used in video surveillance, human machine interface, healthcare monitoring, gaming, dancing training and musical instrument teaching. As inertial sensors are low cost, portable, and having no operating space, it is suitable to detect human action. In real-world applications, actions that are of interest appear among actions of non interest without pauses in between. Recognizing and detecting actions of interests from continuous action streams is more challenging and useful for real applications. Based on inertial sensor and C-MHAD smart TV gesture recognition dataset, this paper utilized different inertial sensor feature formats, then compared the performance with different deep neural network structures according to these feature formats. Experiment results show the best performance was achieved by image based inertial feature with convolution neural network, which got 51.1% F1 score.
Relevant CommonSense Subgraphs for "What if..." Procedural Reasoning

We study the challenge of learning causal reasoning over procedural text to answer "What if..." questions when external commonsense knowledge is required. We propose a novel multi-hop graph reasoning model to 1) efficiently extract a commonsense subgraph with the most relevant information from a large knowledge graph; 2) predict the causal answer by reasoning over the representations obtained from the commonsense subgraph and the contextual interactions between the questions and context. We evaluate our model on WIQA benchmark and achieve state-of-the-art performance compared to the recent models.
A Review of Deep Learning Methods for Detection of Gatherings and Abnormal Events for Public Security

Public security is a concept that, in western societies, is not given enough importance since the crime rates are at socially acceptable levels. Nowadays, with the help of artificial intelligence methods, it is possible to detect abnormal behaviour patterns in groups of people. This information can then be used to predict what is happening or about to happen in an event which may create a risk to public security. In this paper, a systematic review of artificial intelligence methods, essentially in deep learning and computer vision, able to detect gatherings of people and abnormal events is proposed. Some of the open research areas are identified, including the lack of works addressing multiple cases of anomalies in large concentrations of people, which leaves open an important avenue for future scientific work.
Fibonacci-like sequences for variants of the tower of Hanoi, and corresponding graphs and gray codes

We modify the rules of the classical Tower of Hanoi puzzle in a quite natural way to get the Fibonacci sequence involved in the optimal algorithm of resolution, and show some nice properties of such a variant. In particular, we deduce from this Tower of Hanoi-Fibonacci a Gray-like code on the set of binary words without the factor 11, which has some properties intersting for itself and from which an iterative algorithm for the Tower of Hanoi-Fibonacci is obtained. Such an algorithm involves the Fibonacci substitution. Eventually, we briefly extend the study to some natural generalizations.
Crossword Puzzle Learning Media to Improve Indonesian Vocabulary Mastery for Grade 2 Elementary School Students

Vocabulary is an important aspect for students in conveying messages and opinions. However, several studies show that elementary school students' vocabulary mastery is still low. One of the efforts that can be done is to develop learning media. This study aims to develop crossword puzzle learning media in Indonesian language learning to improve vocabulary mastery of second grade students at SDN Pulogebang 11. The research method used in this study is a qualitative research method. This research was conducted at SDN Pulogebang 11 with 32 students as the subject of class II, consisting of 17 female students and 15 male students. The research data were collected using observation and documentation techniques. Based on the data obtained from the assessment results, the crossword puzzle learning media developed was feasible to be tested. The response of students to Indonesian learning material playing in my environment through the question and answer method with crossword puzzle learning media showed a good response from the second grade students of SDN Pulogebang 11.
ShapeY: Measuring Shape Recognition Capacity Using Nearest Neighbor Matching

Object recognition in humans depends primarily on shape cues. We have developed a new approach to measuring the shape recognition performance of a vision system based on nearest neighbor view matching within the system's embedding space. Our performance benchmark, ShapeY, allows for precise control of task difficulty, by enforcing that view matching span a specified degree of 3D viewpoint change and/or appearance change. As a first test case we measured the performance of ResNet50 pre-trained on ImageNet. Matching error rates were high. For example, a 27 degree change in object pitch led ResNet50 to match the incorrect object 45% of the time. Appearance changes were also highly disruptive. Examination of false matches indicates that ResNet50's embedding space is severely "tangled". These findings suggest ShapeY can be a useful tool for charting the progress of artificial vision systems towards human-level shape recognition capabilities.
Graph Transplant: Node Saliency-Guided Graph Mixup with Local Structure Preservation

Graph-structured datasets usually have irregular graph sizes and connectivities, rendering the use of recent data augmentation techniques, such as Mixup, difficult. To tackle this challenge, we present the first Mixup-like graph augmentation method at the graph-level called Graph Transplant, which mixes irregular graphs in data space. To be well defined on various scales of the graph, our method identifies the sub-structure as a mix unit that can preserve the local information. Since the mixup-based methods without special consideration of the context are prone to generate noisy samples, our method explicitly employs the node saliency information to select meaningful subgraphs and adaptively determine the labels. We extensively validate our method with diverse GNN architectures on multiple graph classification benchmark datasets from a wide range of graph domains of different sizes. Experimental results show the consistent superiority of our method over other basic data augmentation baselines. We also demonstrate that Graph Transplant enhances the performance in terms of robustness and model calibration.
STEM: Unsupervised STructural EMbedding for Stance Detection

Stance detection is an important task, supporting many downstream tasks such as discourse parsing and modeling the propagation of fake news, rumors, and science denial. In this paper, we propose a novel framework for stance detection. Our framework is unsupervised and domain-independent. Given a claim and a multi-participant discussion - we construct the interaction network from which we derive topological embedding for each speaker. These speaker embedding enjoy the following property: speakers with the same stance tend to be represented by similar vectors, while antipodal vectors represent speakers with opposing stances. These embedding are then used to divide the speakers into stance-partitions. We evaluate our method on three different datasets from different platforms. Our method outperforms or is comparable with supervised models while providing confidence levels for its output. Furthermore, we demonstrate how the structural embedding relate to the valence expressed by the speakers. Finally, we discuss some limitations inherent to the framework.
Peningkatan Kemampuan Berbicara Melalui Media Gambar Seri pada Anak Kelompok B TK Kuncup Harapan Soborejo Pringsurat Temanggung Semester I Tahun Pelajaran 2017/2018

The Language is a very important communication tool in human life, because it serves as a tool to express thoughts and feelings to others. The problem faced by children in Group B of TK Kuncup Harapan Soborejo is that the children's speaking ability is still low. The purpose of this study was to determine the process of improvement and the magnitude of the increase in the speaking ability of the children of Group B Kindergarten Kuncup Harapan Soborejo Pringsurat in the first semester of the 2017/2018 academic year. The subjects of this study were 15 students in Group B of TK Kuncup Harapan Soborejo. The research was conducted in September 2017. Data collection methods used are observation, documentation and interviews. Based on the results of the research and discussion, it can be concluded that learning activities through serial picture media can improve the speaking ability of children in Group B Kindergarten Kuncup Harapan Soborejo Pringsurat Temanggung Semester I 2017/2018 Academic Year. This is evidenced by an increase in children's speaking ability at the time of pre-action by 13.3%, increasing to 33.3% in Cycle I, and reaching 86.7% in Cycle II. The success of learning improvement in improving children's speaking skills is indicated by the children being able to speak fluently, the children being able to ask and answering questions, and being able to speak in complete sentences (S-V-O/S-V-A).