Reviewerly/exHarmony huggingface dataset:
On Multi-modal Fusion for Freehand Gesture Recognition

We present a study of multi-modal freehand gesture recognition relying on three sensory modalities. The modalities are RGB images, depth data, and acceleration data from an IMD attached to the hand. Based on a new self-recorded dataset, we initially establish the ability of a deep Long Short-Term Memory (LSTM) network to correctly classify individual data streams from each modality. Notably, classifying the IMD stream alone generates very good results already. In addition, we investigate two different strategies of multi-modal fusion, since there is no agreement in the literature as to which strategy is preferable. Combining the modalities leads to better recognition performance. Most importantly, fusion considerably improves ahead-of-time classification, i.e., gesture class estimates before sequences are completed, for classes that are difficult to classify on their own.
Deliberating over legislative ends

Abstract This paper outlines a non-exhaustive inventory of presumptive argument schemes that can be used by legislators to rationally argue for and against the legitimacy of legislative ends. The inventory has both a descriptive and normative dimension. The inventory is descriptive because it is partly based on the empirical observation of arguments actually used by legislators in a sample of lawmaking debates. However, the inventory is also normative because – as I shall argue in this paper – the schemes identified in the sample are presumptive arguments schemes. They are therefore schemes with a claim to rationality, provided that certain conditions are met. The schemes included in the inventory are: the scheme of instrumental argumentation, the scheme from unintended consequences, the scheme from values, the schemes from model and antimodel, and the schemes from social demand.
Learning region sparse constraint correlation filter for tracking

Correlation filters (CF) have sparked a lot of interest in visual tracking owing to their impressive performance. Most of previous correlation filter methods learn a filter from all features of the sample region. However, some features may be distractive, like those from occlusions and deformations, leading to tracking drift in the next frame. To mitigate this problem, we propose a novel region sparse constraint correlation filter (RSCF) to adaptively ignore those distractive features. The proposed method is formulated based on the elastic net model, and a binary mask is used to directly limit the sparsity of the filter values corresponding to the target region instead of the whole sample region. Besides, a context-aware term is integrated to enhance the discriminant ability of the filter. Last, an ADMM optimization algorithm is proposed to solve the model. Qualitative evaluations have been conducted on well-known benchmark, such as OTB-2013, OTB-2015, Temple-Color 128 and VOT2016. Experiment results demonstrate that the proposed tracker performs favorably against several state-of-the-art methods.
Automatically learning formal models

The correctness of autonomous driving software is of utmost importance as incorrect behaviour may have catastrophic consequences. Though formal model-based engineering techniques can help guarantee correctness, challenges exist in widespread industrial adoption. One among them is the model construction problem. Manual construction of formal models is expensive, error-prone, and intractable for large systems. Automating model construction would be a great enabler for the use of formal methods to guarantee software correctness and thereby for safe deployment of autonomous vehicles. Such automated techniques can be beneficial in software design, re-engineering, and reverse engineering. In this industrial case study, we apply active learning techniques to obtain formal models from an existing autonomous driving software (in development) implemented in MATLAB. We demonstrate the feasibility of active automata learning algorithms for automotive industrial use. Furthermore, we discuss the practical challenges in applying automata learning and possible directions for integrating automata learning into automotive software development workflow.
Research on collision detection algorithm of multi-robot bonnet polishing system

In a multi-robot bonnet polishing system, the mobile space between robots overlaps with each other, so collision detection is the premise of the safe and stable operation of the system. The sphere and capsule bounding boxes are used to simplify the model of the multi-robot bonnet polishing system, and the collision detection problem is transformed into the judgment of intersection among bounding boxes in the simplified model. The intersection of bounding boxes is preliminarily judged by the second projection method. The bounding box is projected into line segments. It avoids a lot of multiplication and square operation. If the projection line segments are not overlapped, the bounding box will not intersect. If the projection line segments are overlapped, the problem is transformed into the shortest distance solution from point to point, point to line and line to line. Using space geometry method to calculate the shortest distance between point and line segment. Using the method of transforming reference coordinate system to calculate the shortest distance between two line segment. Through the analysis of simulation software, compared with other algorithms, the average time consumed of the algorithm is reduced by 100%, and the detection efficiency is significantly improved. The algorithm can be better applied to multi-robot bonnet polishing system.
Evaluation of Generative Adversarial Network for Human Face Image Synthesis

Meaningful and objective evaluation metric for fair model comparison is crucial for further scientific progress in the field of deep generative modeling. Despite the significant progress and impressive results obtained by Generative Adversarial Networks in recent years, the problem of their objective evaluation remains open. In this paper, we give an overview of qualitative and quantitative evaluation measures most frequently used to assess the quality of generated images and learned representations of an adversarial network together with the empirical comparison of their performance on the problem of human face image synthesis. It is shown that evaluation scores of the two most widely accepted quantitative metrics, Inception Score (IS) and Fréchet Inception Distance (FID), do not correlate. The IS is not an appropriate evaluation metric for a given problem, but FID shows good performance that correlates well with a visual inspection of generated samples. The qualitative evaluation can be used to complement results obtained with quantitative evaluation - to gain further insight into the learned data representation and detect possible overfitting.
Machine Assisted Video Tagging of Elderly Activities in K-Log Centre

In a rapidly aging society, like in South Korea, the number of Alzheimer's Disease (AD) patients is a significant public health problem, and the need for specialized healthcare centers is in high demand. Healthcare providers generally rely on caregivers (CG) for elderly persons with AD to monitor and help them in their daily activities. K-Log Centre is a healthcare provider located in Korea to help AD patients meet their daily needs with assistance from CG in the center. The CG'S in the K-Log Centre need to attend the patients' unique demands and everyday essentials for long-term care. Moreover, the CG also describes and logs the day-to-day activities in Activities of Daily Living (ADL) log, which comprises various events in detail. The CG's logging activities can overburden their work, leading to appalling results like suffering quality of elderly care and hiring additional CG's to maintain the quality of care and a negative feedback cycle. In this paper, we have analyzed this impending issue in K-Log Centre and propose a method to facilitate machine-assisted human tagging of videos for logging of the elderly activities using Human Activity Recognition (HAR). To enable the scenario, we use a You Only Look Once (YOLO-v3)-based deep learning method for object detection and use it for HAR creating a multi-modal machine-assisted human tagging of videos. The proposed algorithm detects the HAR with a precision of 98.4%. After designing the HAR model, we have tested it in a live video feed from the K-Log Centre to test the proposed method. The model showed an accuracy of 81.4% in live data, reducing the logging activities of the CG's.
A practical generative adversarial network architecture for restoring damaged character photographs

Recently, deep learning has been applied to many image restoration tasks. In this work, we focus on studying an efficient deep learning architecture to restore damaged character photographs (DCPs) which are spoiled by natural or human factors including creases, spots, cracks, light, etc. A large amount of work has focused on image restoration such as super-resolution, image inpainting, image deblurring, and image denoising. However, few studies focus on restoring DCPs based on deep learning since DCPs are varied and complex, along with the difficulty of getting paired training dataset. In this work, we propose a new generative adversarial network (GAN) architecture to restore DCPs. Specifically, a residual U-Net (ResU-Net) GAN (RUGAN) is firstly constructed to generate fake DCPs by employing real DCPs, clear character photographs (CCPs), and dirty masks. Then, a ResU-Net conditional GAN (RUCGAN) is built to restore DCPs by exploiting paired CCPs and fake DCPs. To further improve the quality of restored character photographs, a weighted multi-features loss function is adopted in RUCGAN. Finally, numerical results show that our approach can restore spots, creases, cracks, and other spoiled manners in DCPs.
Research on Key Technologies of Data Warehouse Construction in Unified Data Center in Electric Power Enterprises

Abstract On the basis of studying the existing problems of power enterprise informatization at this stage, the paper introduces the application of data warehouse technology in power system, proposes to use enterprise-level data warehouse as the data centre, and describes the information of power enterprise based on data warehouse strategy The overall design of the system model; Finally, specific solutions and application examples are given for the design of the dimensional model of the power enterprise data warehouse.
DynGCN: A Dynamic Graph Convolutional Network Based on Spatial-Temporal Modeling

Representation learning on graphs has recently attracted a lot of interest with graph convolutional networks (GCN) achieving state-of-the-art performance in many graph mining tasks. However, most of existing methods mainly focus on static graphs while ignoring the fact that real-world graphs may be dynamic in nature. Although a few recent studies have gone a step further to incorporate sequence modeling (e.g., RNN) with the GCN framework, they fail to capture the dynamism of graph structural (i.e., spatial) information over time. In this paper, we propose a Dynamic Graph Convolutional Network (DynGCN) that performs spatial and temporal convolutions in an interleaving manner along with a model adapting mechanism that updates model parameters to adapt to new graph snapshots. The model is able to extract both structural dynamism and temporal dynamism on dynamic graphs. We conduct extensive experiments on several real-world datasets for link prediction and edge classification tasks. Results show that DynGCN outperforms state-of-the-art methods.
An Easy-to-Use Multi-Source Recording and Synchronization Software for Experimental Trials

A typical routine in many scientific studies consists of recording data from devices and identifying which segment of data corresponds with an experimental interval. However, many current applications have been designed to obtain and save data from one single device, and synchronizing data with the markers that delimit the test phases can be difficult. To address this issue, we have developed LSLRec, which is based on Lab-Streaming Layer, a C++ library that allows data synchronization. LSLRec is an easy-to-use, open-source, multi-platform, recording system developed on Java that can save data from several devices at the same time, while maintaining synchronization with the experimental phase markers. It supports three explicit sync methods: the first uses one-integer-channel input Lab-Streaming Layer streams. The others use TCP/UDP socket messages and allow any existing software that generates sync markers through TCP/UDP messages to be used under the same conditions. In LSLRec, the markers are saved together with input data, facilitating the process of linking data with test stages. A prerequisite for recording software is to guarantee suitable timing performance, with no data loss and an easy user interface. These features were assessed for LSLRec, with no data loss detected for 20 hours (25 sessions). We evaluated performance by measuring timestamp deviations for sync marker and input data. The sync methods exhibited an average of deviations of around -34μs (which is more than acceptable, e.g., in studies involving living beings), whereas the absolute value of deviation for one-channel data was lower than 40μs. Finally, we assessed the system's usability with the technology acceptance model 3 survey (6 volunteers). Subjects saw the software as useful, and intended to use it in the future. In conclusion, LSLRec is a useful tool for those who need to record data from multiple sources, and maintain synchronization with experimental phases with low delay.
“The people want it”

Abstract This article reflects on the reasonableness of populist arguments supporting a prescriptive standpoint in the context of deliberation (which I call ‘deliberative’ populist arguments). A literature survey shows a divide between authors who claim that populist arguments are always fallacious and those who think that in some situations they can be reasonable, including the context of political deliberation. It is then argued that deliberative populist arguments are based on a linking premise that appeals to majority opinion as a principle of democracy. This linking premise differs from the one underlying the traditional interpretation of a fallacious populist argument ( argumentum ad populum ) and appears at first sight to make the argument reasonable. However, I conclude that a deliberative populist argument is also unreasonable, because it acts merely as a trump card, creating a false impression about democracy and avoiding engagement in real debate and substantive reasons.
Federated Ensemble Regression Using Classification

Abstract Ensemble learning has been shown to significantly improve predictive accuracy in a variety of machine learning problems. For a given predictive task, the goal of ensemble learning is to improve predictive accuracy by combining the predictive power of multiple models. In this paper, we present an ensemble learning algorithm for regression problems which leverages the distribution of the samples in a learning set to achieve improved performance. We apply the proposed algorithm to a problem in precision medicine where the goal is to predict drug perturbation effects on genes in cancer cell lines. The proposed approach significantly outperforms the base case.
Embracing Disease Progression with a Learning System for Real World Evidence Discovery

Electronic Health Records (EHRs) have been widely used in healthcare studies recently, such as the analyses for patient diagnostic outcome and understanding of disease progression. EHR is a treasure for researchers who conduct the Real-World study to discovering Real-World Evidence (RWE). In this paper, we design an end-to-end learning system for disease states discovery based on a data-driven strategy. A large-scale proprietary EHR data mart containing about 55 million patients with over 20 billion data records is used for data extraction and analysis. Given a disease of interest, researchers could easily obtain the hidden disease states. Once our system were operational, biomedical researchers could get the results for downstream analyses such as disease prediction, drug design and outcome analyses.
Analytical derivatives for differentiable renderer: 3D pose estimation by silhouette consistency

Differentiable renderer is widely used in optimization-based 3D reconstruction which requires gradients for optimization. The existing differentiable renderers obtain gradients via numerical techniques. However, these methods are inaccurate and inefficient. Motivated by this fact, we propose a differentiable renderer with analytical gradients. The main obstacle of traditional renderer being differentiable is the discrete sampling operation of rasterization. To obtain a differentiable rasterization renderer, we define pixel intensity as a double integral over the pixel grid, and then derive the analytical gradients with respect to vertices. 3D pose estimation by multi-viewpoint silhouettes is conducted to reveal the effectiveness and efficiency of the proposed method. Experimental results show that 3D pose estimation without 3D and 2D joint supervision can produce competitive results. The findings also indicate that the proposed method has higher accuracy and efficiency than previous differentiable renderers.
Study Analysis of Human Face Recognition using Principal Component Analysis

Principal Component Analysis (PCA) is a very popular facial recognition method. This research aims to analyze the PCA method, where various scenarios are tested to look for things that affect the results of recognition using this method. There are three datasets used in the testing phase, namely the private dataset, JAFFE, and Yale. The accuracy produced in the private dataset is 79%, 82%, 86%, and 85.33% with a variety of different scenarios, while in the JAFFE dataset the maximum recognition accuracy is 100% and in the last experiment on the Yale dataset, the accuracy is 85.33%. From various experiments that have been done, it is found that the things that affect accuracy are the number of people, training data, attributes used, lighting, and background. While facial expressions and gender do not prove to have a major influence on the recognition process, with a variety of facial expressions, the PCA method can still recognize faces well.
RhyRNN: Rhythmic RNN for Recognizing Events in Long and Complex Videos

Though many successful approaches have been proposed for recognizing events in short and homogeneous videos, doing so with long and complex videos remains a challenge. One particular reason is that events in long and complex videos can consist of multiple heterogeneous sub-activities (in terms of rhythms, activity variants, composition order, etc.) within quite a long period. This fact brings about two main difficulties: excessive/varying length and complex video dynamic/rhythm. To address this, we propose Rhythmic RNN (RhyRNN) which is capable of handling long video sequences (up to 3,000 frames) as well as capturing rhythms at different scales. We also propose two novel modules: diversity-driven pooling (DivPool) and bilinear reweighting (BR), which consistently and hierarchically abstract higher-level information. We study the behavior of RhyRNN and empirically show that our method works well even when only event-level labels are available in the training stage (compared to algorithms requiring sub-activity labels for recognition), and thus is more practical when the sub-activity labels are missing or difficult to obtain. Extensive experiments on several public datasets demonstrate that, even without fine-tuning the feature backbones, our method can achieve promising performance for long and complex videos that contain multiple sub-activities.
The use of augmented reality technology in the reconstruction of a lost cultural heritage site

Abstract The paper is devoted to the study of the possibility of using augmented reality technology in the process of hypothetical (virtual) restoration of the authentic appearance of lost cultural heritage structures on the example of an object of regional significance - the cinema “Sever”. Based on the collected archival data and field studies, a historical and retrospective analysis of the cultural heritage object and its territory is presented, the construction history of the monument site is developed, dimensional drawings are restored and three-dimensional models of the object in various historical periods of the monument life are created. A detailed description of actions for creating an augmented reality object and exporting its animated model to an accessible digital platform (Instagram) was developed. The expediency of using the augmented reality approach in the reconstruction of lost cultural heritage objects is shown. This technology allows recreating the appearance of the lost monument in conjunction with the actual architectural component of the urban space. Successful implementation of this approach will allow for more detailed cultural, historical, archaeological, ethnographic, anthropological and other research.
WHO IS DIGITALLY COMPETENT PERSON? EMPIRICAL STUDY PRODUCT STUDY (predominantly) FACULTY OF MASARYK UNIVERSITY

The study aims to describe, based on 92 statements of Masaryk University students, how they imagine a digitally competent person and what their grasp of the concept of competence is.The students included in the research were graduates of courses focusing on the development of digital competences according to the DigComp 2.1 European framework.They are therefore not randomly selected students, but those who have already encountered a digital competence structure and have experience, both practical and theoretical.The research analyzes the conclusions of the questionnaire in both quantitative and qualitative form.It uses coding.The result is a definition that emphasizes the relationship of competence to usability in all areas of human life.To what we can find in the initial framework, then add the need for critical thinking and the ability to learn continuously.Competence has a temporal character; it is nothing permanent.
MVP Architecture Model With Single Endpoint Access For Displaying COVID 19 Patients Information Dynamically

The use of Model View Presenter (MVP) architecture for tracking the data of COVID 19 patients has evolved novel innovation in the already existing architectural diagram used by the software industry.It introduced the use of GraphQL a very efficient backend framework providing dynamic and single-point access to queries made by the user.In contrary to the traditional REST API which uses GET and POST methods for fetching and posting data requiring a different URL for every API call, with GraphQL we can have single-point access.This paper aims to propose a simple backend driven dynamic cards model that can get data from realworld findings of patients' database by trusted sources and freely available research enabling the user of this application to get valuable information of its region, country and the globe and how exposed and vulnerable they are, all of this in a single application of its first type.