Reviewerly/exHarmony huggingface dataset:
Evaluating importance of edge types when using graph neural network for predicting return types of Python functions

The static prediction of types for dynamic programming languages is a challenging and important problem. Some success for Python was demonstrated by analyzing docstrings, still, a large portion of code comes without thorough documentation. To target this problem in this work we attempt to predict return type annotations for Python functions by looking at function usage patterns. We analyzed a collection of Python packages and created a graph that captures global relationships between source code elements such as imports, calls, and definitions. Moreover, we train embeddings for functions and evaluate how the performance of predicting return types is affected by removing one of the relationship types from the dataset.
Optimization of Decision Tree with PSO on Sharia Cooperative Customer Funding

Abstract Credit is a service for Sharia Cooperatives to provide funding to customers who are paid in installments. The accuracy of the customer paying the payments is a determining factor for the smooth operation of the Sharia Cooperative. In addition to timeliness, the ability of customers to pay installments is also the most critical factor for credit returns. Bad credit is a significant threat for Sharia Cooperatives where customers cannot pay payments according to agreed agreements. That makes the prediction of the smooth operation of Sharia Cooperative customers needed. One way that can be done to make predictions is to apply data mining techniques. To make a prediction, several attributes of the customer are used, such as gender, age, status, residency status, number of dependents owned, education, employment, remittance, ceiling, type of loan, credit period, method of payment and collat- eral used. The research will use the J48 as one of the Decision Tree algorithms with particle swarm optimization techniques to improve algorithm performance. The accuracy of J48 with PSO is lower than without PSO.
Composition Harmony Assessment Method Based on Rule of Thirds

Composition harmony mainly refers to the various components of the image that are sensed together as a harmonious and unified organism, it plays a key role in the assessment of image aesthetic quality. In this paper, a composition harmony assessment method based on the rule of thirds was proposed. The eye-tracking dataset was used to extract the saliency regions of the image and the composition harmony assessment method was designed by computing how close the centroid of the largest saliency region and the rule of thirds. Moreover, the effectiveness of the assessment method was analyzed by the subjective evaluation experiment results of the composition harmony. The experimental results showed that the composition harmony assessment method proposed in this paper is effective.
No-Reference Video Quality Assessment by Multilayer Selected Deep Features and Neural Networks

In this work, we propose a general-purpose no-reference (NR) video quality assessment (VQA) metric based on the cascade combination of 2D convolutional neural network (CNN), multi-layer perceptron (MLP), and support vector regression (SVR) model. The features are extracted from both spatial and spatiotemporal domains by using a 2D CNN. These features can capture different aspects of video frames. We take these features as inputs of the MLP to obtain estimated quality scores on different perspectives. Finally, these quality scores are combined as a final quality score by an SVR model. The proposed method is evaluated on the well-known LIVE Video quality database with other state-of-the-art VQA metrics. And the experimental result demonstrates that our method is competitive with other full-reference and NR VQA metrics.
CatDetect, a framework for detecting Catalan tweets

This work deals with language detection. It includes new proposals ranging from lexicon and morphological analysis to an increasing use of machine learning solutions. In this case, the language study is focused on Catalan, a minority language. In the context of the Twitter social network, this increases difficulty in detecting tweets (messages written on the Twitter social network). To achieve that, a Catalan-Twitter corpus was generated using lexical and morphological approaches, which then will be used to create supervised models based on machine learning techniques. They were also evaluated in order to see which obtains the best prediction score and thus, is the most suitable to be used. We demonstrate how our proposal is successful with Twitter in the case of minority languages. The best model is to be used on a website, where users can test the algorithm interactively in the front-end webpage and in background by means of a webservice across a RESTful API.
Two realizations of the pattern informativity idea for the method of data logical analysis

Abstract Two realizations of the idea underlying the concept of “pattern informativity” are proposed. The mechanism for implementing the idea consists in the formation of optimization models to obtain patterns. A comparison is made in terms of accuracy for practical problems of classifiers composed of rules obtained on the basis of the proposed optimization models.
Support Vector Machine Based Diagnosis of Breast Cancer

In recent years, the incidence of breast cancer is constantly increasing, for the current medical incurable advanced breast cancer, early accurate diagnosis is the most important to save the lives of patients. In this paper, support vector machine (SVM) is applied to the diagnosis of breast cancer, and the performance of four commonly used kernel functions in different data sets is explored. The experimental results show that the classification accuracy of this method in the breast cancer data set is 98.25%. Compared with the original research using SVM algorithm, this method has a better effect in the auxiliary diagnosis of breast cancer and can help patients and medical institutions to detect the disease more quickly and effectively.
On the amount of nonconstructivity in learning formal languages from text

Nonconstructive computations by various types of machines and automata have been considered by, for example, Karp and Lipton as well as Freivalds. They allow to regard more complicated algorithms from the viewpoint of much more primitive computational devices. The amount of nonconstructivity is a quantitative characterization of the distance between types of computational devices with respect to solving a specific problem. This paper studies the amount of nonconstructivity needed to learn classes of formal languages. Different learning types are compared with respect to the amount of nonconstructivity needed to learn indexable classes and recursively enumerable classes, respectively, of formal languages from positive data. Matching upper and lower bounds for the amount of nonconstructivity needed are shown.
Manipulation of Web Using Gestures

Gesture recognition is a type of perceptual computing user interface that allows computers to capture and interpret human gestures as commands. Gestures are generally the movement of the hands which is a form of non-verbal communication. We use gestures as input to control devices or applications or websites. By using the gestures, we directly open or manipulate the website for the users. When the user uses the gesture, the system captures the gesture and compares it with the stored gesture data. If the gesture matches the data, then the required website will be manipulated. These gestures are widely helpful for the people who are blind and unable to type. The main objective of the project is to deliver user friendly access to the web contents. The vision based technology of hand gesture recognition is an important part of human-computer interaction (HCI), thus stepping into the path of future and our project is one such kind of stepping stone. Our project really concerns the people who are not able to search contents on the web through text search. We provide an improvisable solution where it paves a way for research and development.
Improved Automated Recognition of Leopards from Photographs

Two improvements on the automated image recognition of individual leopards (Panthera pardus) are presented. Firstly, a new method of automated segmentation on images, with a guaranteed side view of the animal, allows for a region of interest to be calculated automatically, reducing the manual effort of the image processing substantially. Secondly, improvements on the automated recognition of citizen science images with extreme viewpoints are detailed.
Narrowing the semantic gap between real and synthetic data

Image-to-image translation is an emerging method of computer vision dataset augmentation, which allows transferring the style of real life images onto synthetic ones, making them more realistic. In our work we propose an incremental improvement over the adversarial learning generator architectures used by image-to-image translation models. First, we managed to use a single network, instead of 2, thus creating a more memory-efficient model, which allowed for an end-to-end training on high resolutions. Second, inspired from recent work on semantic segmentation architectures, we enhanced our model by implying a multi-scale encoding and stylization phase, allowing for a better control over the contextual and spatial features. Given a synthetic image, our framework allows for its multimodal translation into the real domain. Our model shows promising results at narrowing the semantic gap between synthetic and real data.
Improving Occluded Face Recognition with Image Fusion

The application of vision technology has penetrated into many fields such as tourism, education, government affairs, housing, Internet applications, etc. Among them, face recognition technology has been closely related to our lives, such as mobile phone unlocking, attendance check-in, residential access control, etc., resulting in an increasing demand for high success rate face recognition. The recent new crown virus epidemic has caused many people to wear masks to go out, and the masks covering the face will reduce the effective recognition rate. In order to improve the inconvenience caused by masks to the application of face recognition technology, we try to apply image fusion technology to improve the success rate of face recognition. We compared the recognition rates of six common image fusion technologies. These technologies use the Labeled Faces in the Wild database as the data source and the Iterative Weighted Regular Robust Coding (IR3C) algorithm as the evaluation method. The final results show that the use of these image fusion technologies can greatly improve the recognition success rate of images occluded by masks. In particular, the weighted average technique and principal component analysis give a stable performance on recognition accuracy of 99.6% and 99.0% in a relatively small-scale data set, while the LFW simulated masked face dataset provided by Wuhan University that did not perform image fusion Only has a recognition rate of 85.4%. The application of image fusion can solve some limitations in the use of face recognition and bring more convenience to various fields.
Weighted Aggregating Feature Pyramid Network for Object Detection

Feature pyramid is crucial to state-of-the-art object detection methods. In contrast to traditional feature pyramid network, which adopts layer-by-layer architecture, we propose an end-to-end network, weighted aggregating feature pyramid network (WAFPN), which aggregates multi-scale features by rescaling these maps to single size. WAFPN aims at generating more discriminative pyramidal representations efficiently via fusing the features from different levels of backbone network. First, we apply learnable weights to WAFPN for recalibrating the features rather than directly merging them by summation or concatenation operations. Second, we use a lightweight convolutional module to reconstruct the representations of different scale features. To evaluate the proposed method, we mainly implement WAFPN in basic RetinaNet system. Besides, we also apply WAFPN to SSD framework for verifying its extensiveness. Our proposed method achieves more significant performance over the comparable detection approaches on PASCAL VOC 2007 benchmark.
Leveraging aesthetic expression in visualization to communicate positive facts during COVID-19 pandemic

The COVID-19 pandemic is a global public health crisis. Alongside the rapid geographical development and constantly rising number of confirmed cases, mixed spread of information and disinformation also plagued the public with distress, fear, and anxiety, transmitting a negative sentiment across many groups. Facing this, we experiment with the aesthetics of visualizations in educating laymen about positive facts during the pandemic. By blending artistic expressions into the visual encoding, we intend to tell a compelling data story describing the international scientific dedication in fighting the disease collaboratively. We present the visualization to collect feedback and evaluate the expected functions from general audience instead of traditional expert user groups.
3D Reconstruction From Monocular Images Based on Deep Convolutional Networks

3D reconstruction from monocular images is an essential part of machine vision. The effective reconstruction of 3D models of objects or scenes has become a research focus in computer vision. In this article, we concentrate on restoring the 3D model through the depth information of a monocular image. First and foremost, we use deep learning to get the depth of a single image. Next, the point cloud is reconstructed from the depth map according to the camera parameters to achieve the purpose of the 3D reconstruction. Compared with traditional methods, our approach is faster and more relaxed, and the reconstructed three-dimensional information of scenes can be recovered efficiently, our reconstructed 3D models also have better authenticity.
IMACS - an &lt;u&gt;i&lt;/u&gt;nteractive cognitive assistant &lt;u&gt;m&lt;/u&gt;odule for &lt;u&gt;c&lt;/u&gt;ardiac &lt;u&gt;a&lt;/u&gt;rrest cases in emergency medical &lt;u&gt;s&lt;/u&gt;ervice

IMACS is an intelligent, interactive cognitive assistant dedicated to cardiac arrest cases in Emergency Medical Service (EMS). EMS providers deal with many cardiac cases. IMACS interacts with EMS providers in real-time and collects vital information from the providers' conversation, including names of interventions, timestamps of interventions, and dosage amount. Throughout the process, IMACS provides necessary reminders and creates a summary report afterward. Using the dynamic behavioral model of two different cardiac arrest recovery protocols, we have developed a critical risk-index based approach to provide time-sensitive feedback and suggest alternatives to the providers in real-time. Our experiments reveal an F1-score of 83% with 300 test cases. A qualitative study also reflects that seven out of ten of the EMS providers rate the system as very helpful in correctly executing cardiac arrest EMS protocols.
Undetectable steganographic method for palette-based images

Steganography is a technique applied to ensure secure communication. It is challenged by visual observation or statistical analysis to ascertain whether a message is hidden within a cover medium. Several steganographic methods have been proposed for palette-based images. These methods maintain image quality but cannot resist some statistical and visual attacks. To overcome this problem, two parity assignments with performances similar to those of existing parity assignments are proposed. An innovative embedding process that randomly selects one parity assignment while embedding secret bits in each pixel is provided. Finally, a steganographic method using the proposed embedding process and a specified adaptive scheme is presented. Experimental results revealed that the proposed method is undetectable under some statistical and visual attacks even as it maintains image quality.
Blockchain Technology Enables Fixed Assets Investment Statistics

Abstract Blockchain technology is a kind of decentralized transaction and data management technology, which has been extended to many economic and social fields, such as digital finance, Internet of things, intelligent manufacturing, supply chain management, digital asset trading and other economic and social fields, but the application of statistical work is still less. The current investment statistics business of Power Grid Companies involves multi-department collaboration and multi-level interactions. There are uneven data maintenance and low data quality among various business departments. The statistics majors and the basic data providing departments and the statistical data reporting units are both at the upper and lower levels. There are varying degrees of “mutual trust issues”. According to the characteristics of blockchain Smart Contract, consensus mechanism and linked storage structure, multi-party sharing of data on the chain and higher credibility, this paper explores how to integrate the technical characteristics of blockchain with the investment statistics business, and change the business model of investment statistics.
FaceMagic: Real-time Facial Detail Effects on Mobile

We present a novel real-time face detail reconstruction method capable of recovering high quality geometry on consumer mobile devices. Our system firstly uses a morphable model and semantic segmentation of facial parts to achieve robust self-calibration. We then capture fine-scale surface details using a patch-based Shape from Shading (SfS) approach. We pre-compute the patch-wise constant Moore–Penrose inverse matrix of the resulting linear system to achieve real-time performance. Our method achieves high interactive frame-rates and experiments show that our new approach is capable of reconstructing high-fidelity geometry with corresponding results to off-line techniques. We illustrate this through comparisons with off-line and on-line related works, and include demonstrations of novel face detail shader effects processing.
Methods and tools for developing an information system for validation of non-formal learning outcomes

The publication describes methods and tools for development an information system (IS) for acceptance of the non-formal and informal learning results on base of computer ontologies. ESCO ontology of the multilingual skills classifier is used as a prototype of domain ontology that provides knowledge for developed IS. We propose the main stages of development of IS for validation of the results of non-formal learning that include creation of an ontology schema, process of integration of the obtained ontology into the RDF repository, development of application architecture and creation of user interface. The modern approaches to design and development of knowledge-oriented distributed applications intended for functioning in the open Web environment are analyzed, the existing methods and software tools for the presentation and analysis of knowledge and their suitability for solving the task are considered. We propose an innovative method of IS development based on the use of such elements of the Semantic Web project as ontologies, Web services and software agents. The paper describes the tools used in process of development of IS for validation of non-formal learning outcomes. In particular, we analyse Neo4j database management system serving the GraphDB database, the specifics of connectors and SPARQL requests to the data stored in the RDF repository and the tools used for web server creation. Comparison of PHP frameworks for web applications is performed in consideration of task requirements. Functional modeling of IS in order to determine its main functionalities is performed, and the DFD data flow diagrams of system are designed. The benefits of Laravel software are established on base of the analysis of such criteria as security, readiness to installation of plugins and libraries, support for the MVC (Model-View-Controller) concept. User interface is developed to ensure user dialogue with IS. Authers analyse software tools oriented on development of user interface and select React framework that works efficiently with all software tools selected for IS development on the previous stages of analysis.
Estimation of Person-Specific Visual Attention via Selection of Similar Persons

This paper presents a method for estimation of person-specific visual attention based on estimated similar persons' visual attention. For improving the estimation performance of person-specific visual attention, the proposed method uses the dataset including the large number of images and corresponding gaze data of many persons not including the target person and trains an estimation model based on deep learning. By using the estimated visual attention of similar persons for the target image, the proposed method estimates the visual attention of the target person with the small amount of gaze data. Experimental results show that the proposed method is effective for estimation of person-specific visual attention.
Multi-criteria collaborative filtering using rough sets theory

Recommender systems have recently become a significant part of e-commerce applications. Through the different types of recommender systems, collaborative filtering is the most popular and successful recommender system for providing recommendations. Recent studies have shown that using multi-criteria ratings helps the system to know the customers better. However, bringing multi aspects to collaborative filtering causes new challenges such as scalability and sparsity. Additionally, revealing the relation between criteria is yet another optimization problem. Hence, increasing the accuracy in prediction is a challenge. In this paper, an aggregation-function based multi-criteria collaborative filtering system using Rough Sets Theory is proposed as a novel approach. Rough Sets Theory is used to uncover the relationship between the overall criterion and the individual criteria. Experimental results show that the proposed model (RoughMCCF) successfully improves the predictive accuracy without compromising on online performance.
Meta-requirements for Immersive Collaborative Spaces in Industrial Workplace Learning: Towards a Design Theory

As the digital transformation of society accelerates, sophisticated Information Technologies (IT) emerge and are introduced into organizations and institutions of society. An example of such technologies is the Virtual Reality (VR) technology that has re-emerged and become a frequently used solution for supporting collaboration and workplace learning in immersive spaces. The deep sense of immersion in VR enables users to learn and develop working skills in an authentic virtual space, which facilitates an increased feeling of user presence and mapping with real world working scenarios. However, due to the emerging nature of using VR for collaboration and workplace learning, there are wide possibilities for Information Systems (IS) scholars to explore the prescriptive knowledge space of how to design and theorize VR technologies through Design Science Research (DSR). In this research in progress paper, we scrutinize the question of how to design immersive collaborative spaces for industrial workplace learning, and subsequently perform a preliminary round of literature review to address the question. Consequently, the findings of the review are reported as three meta-requirements, which we target as the contribution and input for further development of an information systems design theory.