Reviewerly/exHarmony huggingface dataset:
The Implementation of Online-Based New Student Admissions in Surakarta

Abstract The purpose of implementing this research is to determine the implementation of online-based new student admissions in the city of Surakarta, and the effectiveness of the implementation of online-based new student admissions in Surakarta. In the implementation this research using descriptive research with a qualitative approach. Technique for search subject with purposive sampling and snowball sampling, for collection data observation, interview and documentation, and to testing the validity of the data used data triangulation and methodological triangulation The results showed that the implementation of online-based new student admissions in Surakarta was implemented using a zoning system. Implementation of online-based new student admissions, there are several stages of implementation, namely starting with the preparations made by Education Department of Surakarta, then the implementation of online-based new student admissions carried out by Junior High Schools supervised by the Education Department of Surakarta. Online-based admission of new students begins with registration, filing of requirements, inputting data into the system, selecting, announcing the results of selection and re-registration activities; and in implementing online-based new student admissions at the Education Department of Surakarta, it already has an effective system in terms of indicators of goal achievement, availability of resources, good internal processes, and constituency satisfaction in implementing online-based new student admissions.
Continuous Interaction for a Virtual Reality Exposure Therapy System

In exposure therapy and virtual reality exposure therapy, therapists face highly individual patients that have individual conditions. This leads to the challenge of individualized therapy, for which virtual reality systems need to account for. This paper investigates a prototype that allows a flexible therapy of public speaking anxiety using live-interactions from a therapist, which contain transitions from 2D to 3D interactions. Therapists are able to embody an avatar in the patient's virtual audience on purpose. We oriented our design at continuous interactions in the three dimensions: cognitive, perceptual, and functional continuity. We evaluated whether the design is beneficial for the users. For this evaluation, we conducted a study with eight non-therapist participants to assess the general usability and design aspects of continuous interaction. Our results indicate that using continuous interactions is beneficial for the use in this kind of system. For the design, we need to find ways to balance the cognitive load with the opportunities that arise, especially when these systems are scaling bigger.
Design and Implementation of a Location-Based Service With Emphasis on a Geographical Database

One of the most popular apps is location-based services (LBSs) such as navigation, location-based social networks, and location-based advertisements. However, building an LBS is not a simple task because it involves various subjects and techniques like mobile computing, databases, and security and privacy. One of the major LBS components is geographical databases, which are used to store geographic data like locations and functions (such as restaurants and gas stations). A geographical database is usually hosted on a server because of its huge size and should facilitate geographic data storage, indexing, searching, and matching. This article tries to mitigate the high difficulty of LBS construction by showing the construction step by step with a focus on connecting a mobile device to a server-side geographical database. After reading this article, readers will be able to build an LBS prototype for their research or applications.
Money Leave(s) Portugal: an Aesthetic Exploration of Public Investments

The field of Information Visualization has undergone major changes in the last decades due to the growing computational power and easier access to various technologies by a greater number of people. However, Information Visualization and its techniques literacy continue to be a knowledge associated to a reduced audience. In order to surpass this condition of Information Visualization, new practitioners have applied techniques from other areas, such as the arts, to develop visualizations that could transmit information in a more casual and accessible way to a larger number of people, weighing heavily on the artistic component. In this paper, we present a visualization that portrays information about public contracts held in Portugal, that despite being public data, is not analyzed or much less visualized by the majority. To do so we taken a casual representation approach with a strong aesthetics component in mind with the objective of promoting awareness about the dimension and distribution of the money applied daily throughout Portugal. We perform a phenomenological experiment to assess the effectiveness of our work in transmitting the information and evoking the desired insights. The experiment allowed us to collect distinct interpretations that could lead to further approaches and improvements in future iterations.
Technology and Body Art: An Appraisal of Tattoo Renaissance Across Cultures

People across the globe have tattooed human skin en route for communing diverse ontological, psychological, and socio-cultural conceptions including cultural identity, status and position, medicine, beauty, and supernatural protection. As a scheme for communication of knowledge, tattooing persists to be a visual lingo of the skin whereby culture is inscribed, experienced, and preserved in a myriad of ways. This paper evaluates the attitudes various cultures have had toward tattooing and how individuals have affected today’s burgeoning acceptance of tattoos. It also tries to appraise the influence of technology on the tattooing industry—the way technology is redefining the art of tattooing in strange, new ways and how smart tattoos are used for practical applications in daily life.
Monitoring of Docker Containers with Round Robin Database

Provided that the use of docker containers is commonly favoured over traditional virtual machines due to their apparent advantages in optimizing resource utilization, tracking such docker applications often becomes very important. It is essential for an enterprise using multiple numbers of docker containers to evaluate and track these applications. We will look at how we can use the “Round Robin Database” (RRD) to create our own database and store timely docker statistics within it. We can then extract and evaluate data from this archive, using various techniques. We will focus on metrics such as memory usage, memory percentage, network input, network output and CPU percentage in this paper. To obtain docker statistics, we will use the docker python software development kit (SDK) and the python-rrdtool module to store the data in the 'rrd' format. And then for basic interpretations, we would be able to access data and turn them into graphs.
New psychoactive substances as a modern threat agent to public health

Based on the international legal documents, this article presents an extensive description of the concept of new psychoactive substances (NPS), their quantitative and qualitative composition, and relevant classification. The data is provided on the level of illicit drug trafficking, as well as circulation and consequences of their use trough injection in the Russian Federation in relation to other countries. The author discloses the content of measures applied to control illegal trafficking of NPS and criminal liability in accordance with the legislation of foreign countries (peculiarities, differences, classification). Comparative legal method allows reviewing the provisions of the Russian legislation on prevention of illegal trafficking of the new potentially dangerous psychoactive substances. Attention is turned to the problem of the ineffective norms in the Russian criminal law; the approaches towards of its resolution and prevention are proposed. Analysis is conducted on the legislative policy of the use of generally recognized international legal concepts under an alias and with different content, as well as its impact upon the quality and development of criminal legislation.
Semantic Segmentation with Deep Convolutional Neural Networks for Automated Dust Detection in Goes-R Satellite Imagery

Earth and Space Science Open Archive Presented WorkOpen AccessYou are viewing the latest version by default [v1]Semantic Segmentation with Deep Convolutional Neural Networks for Automated Dust Detection in Goes-R Satellite ImageryAuthorsTalhaKhaniDNicholasElmeriDMuthukumaranRamasubramanianEmilyBerndtIkshaGurungAaronKaulfusManilMaskeyiDRahulRamachandranSee all authors Talha KhaniDCorresponding Author• Submitting AuthorNASA Marshall Space Flight CenteriDhttps://orcid.org/0000-0003-3874-4075view email addressThe email was not providedcopy email addressNicholas ElmeriDUniversity of Alabama in HuntsvilleiDhttps://orcid.org/0000-0002-6343-6210view email addressThe email was not providedcopy email addressMuthukumaran RamasubramanianUniversity of Alabama in Huntsvilleview email addressThe email was not providedcopy email addressEmily BerndtNASA Marshall Space Flight Centerview email addressThe email was not providedcopy email addressIksha GurungUniversity of Alabama in Huntsvilleview email addressThe email was not providedcopy email addressAaron KaulfusUniversity of Alabama in Huntsvilleview email addressThe email was not providedcopy email addressManil MaskeyiDUniversity of Alabama in HuntsvilleiDhttps://orcid.org/0000-0002-5087-6903view email addressThe email was not providedcopy email addressRahul RamachandranNASA Marshall Space Flight Centerview email addressThe email was not providedcopy email address
The Problem of Clustering Countries of the World on the Geopolitical Data

Abstract This article deals with the problem of clustering countries by a specific set of geo-political features, including demographic, geographical, economic, and other data. The K-means method was chosen as the clustering algorithm. This clustering allows us to identify the specifics and important trends in the development of the current geopolitical situation.
Look into Multi-Person: A New Benchmark for Pose Estimation and Human Parsing

Human parsing and pose estimation, regarded as two fundamental tasks to analyze human in the wild, are the basis of upper-level tasks, such as human action recognition and person re-identification. The lack of a comprehensive multi-person dataset, which contains the annotations of both human part labels and skeleton keypoint labels, makes that most of the joint learning work for pose estimation and human parsing can only focus on the single-person scene. To fill this gap, we proposed a comprehensive multi-person dataset Look into Multi-Person (LIMP) with 10,082 multi-person images. And we adopt data augmentation strategies to enrich the dataset's diversity which contains more texture, more occlusion, and more in-the-wild images than CIHP. To the best of our knowledge, this is the first comprehensive multi-person dataset for pose estimation and human parsing.
Predicting Solar Diffuse and Direct Components Using Deep Neural Networks

With the increased reliance on solar energy generation, accurate solar irradiance data is important for calculating the value of existing and planned generation. Historical and real-time data on global horizontal irradiance (GHI) is widely available, however; the diffuse horizontal irradiance (DHI) and direct normal irradiance (DNI) components of GHI value are not. Solar photovoltaic and concentrated solar power production models rely on these two components for precise calculations of generator output. In this research, we used a deep neural network model to capture the sparse connections and complex interactions between many different meteorological parameters to accurately calculate DHI and DNI from GHI. Our model proved to outperform other previous parametric and decomposition models in the literature. It is also generalizable to different locations. We have released our model as an open source library here:https://github.com/tpt5cu/solarIrradiancePredictor.
Soft morphological filtering using hypergraphs

Abstract A new framework of soft mathematical morphology on hypergraph spaces is studied. Application in image processing for filtering objects defined in hypergraph spaces are illustrated using several soft morphological operators-openings, closings, granulometries and ASF acting (a) on the subset of vertex and hyperedge set of a hypergraph and (b) on the subhypergraphs of a hypergraph. Experimental results dealing with the extension of soft morphological operators to gray scale images are presented in this paper. The results obtained are promising and is a better substitute for the prevailing methods.
Fast Learning from Imperfect Labels to Segment Brain Based on Active Contour Model and 3D U-Net

Brain extraction is an essential segmentation step in many neuroimage analysis studies. Convolutional neural network approaches have been proposed in the literature and widely used for medical image segmentation, outperforming traditional segmentation approaches. However, these learning-based methods highly rely on ground truth data which needs significant time and labor inputs from experienced experts. In addition, the independent identical distribution assumption is difficult to comply with in practice, leading to the overfitting problem across different datasets. This paper introduces a 3D U-Net based self-fine-tuning network for brain volumetric segmentation that learns from quickly available but imperfect labeled images generated by a model-based approach. We outline two typical application scenarios of our method: (1) In a ground truth absence setup, an improved brain extraction tool (IBET) algorithm is used to generate brain label images fully automatically. The self-fine-tuning network learns from these imperfect labels and yields more accurate predictions. (2) In a transfer learning setup, the IBET algorithm is used to fast extract dataset-specific labels for fine tuning the pretrained network (overfitting prevention). We have evaluated the performance of the proposed method on four datasets and achieved satisfactory results.
Vehicle Reidentification via Multifeature Hypergraph Fusion

Vehicle reidentification refers to the mission of matching vehicles across nonoverlapping cameras, which is one of the critical problems of the intelligent transportation system. Due to the resemblance of the appearance of the vehicles on road, traditional methods could not perform well on vehicles with high similarity. In this paper, we utilize hypergraph representation to integrate image features and tackle the issue of vehicles re-ID via hypergraph learning algorithms. A feature descriptor can only extract features from a single aspect. To merge multiple feature descriptors, an efficient and appropriate representation is particularly necessary, and a hypergraph is naturally suitable for modeling high-order relationships. In addition, the spatiotemporal correlation of traffic status between cameras is the constraint beyond the image, which can greatly improve the re-ID accuracy of different vehicles with similar appearances. The method proposed in this paper uses hypergraph optimization to learn about the similarity between the query image and images in the library. By using the pair and higher-order relationship between query objects and image library, the similarity measurement method is improved compared to direct matching. The experiments conducted on the image library constructed in this paper demonstrates the effectiveness of using multifeature hypergraph fusion and the spatiotemporal correlation model to address issues in vehicle reidentification.
광업에서 재해율과 중대재해의 예측치에 의한 산업재해 방지 방안

Mining industry is a high-risk workplace and a blind spot in safety. The prevention of industrial accidents in this industry is very important because it causes many fatal accidents. Accordingly, predicted results considering the industrial accident rate and fatal accident rate in the mining industry are required to prevent the occupational accidents. Therefore, this paper describes very efficient methods for prevention of industrial accidents with these predicted results in the mining industry. Also, this paper proposes minimization of the sum of square errors (SSE) to find the predicted results between static prediction methods to dynamic method with existing accident data in mining industry. The minimum value of SSE in the mining industry was found in 82.7028 and 147.3107 in the occupational accident rate and fatal accident rate, respectively. Double exponential smoothing method (DESM) and kalman filtering model (KFM) are ideally applied in the accident rate. In addition, the optimal method of prediction by fatal accident was found in auto-regressive integrated moving average (ARIMA) model the mining industry. Finally, this paper provides very efficient and systematic method in order to prevent accidents through the trend of predicted results with accidents and fatal accidents in mining industry.
Saliency Detection Based on Dark Channel Prior and Foreground Saliency Probability

Great achievements have been made in resent saliency detection approaches. However, it is still challenging to detect accurate salient regions using these approaches when an object closely touches the image boundaries. To address the above problem, in this paper, we propose a novel model for saliency detection based on the dark channel and foreground saliency probability. First, we construct a linear combination image called color space volume based on the LAB color space, which can greatly highlight salient regions, while suppressing background regions. After that, a novel fusion algorithm is proposed to obtain a robust and uniform salient image based on the foreground saliency probability and weighted saliency probability map. Finally, experimental results on two large benchmarks demonstrate that the proposed method has achieved better performance than several state-of-the-art methods in terms of precision, F-measure, mean absolute error, and recall.
Artefacts: the big picture in broad terms

My aim in this programmatic paper is to explore the relationship among three important notions: intentionality, disposition and artefact. There wouldn’t be artefacts without what I call “intentional work,” a sustained activity directed to the production of some good. I first present contextualism as a method. Then I use it to delimit the problematic concept ARTEFACT, with the intention to apply it to repertoires of mental dispositions that affect directly our personal identity. The unavoidable but loose criterion of human intervention is used, at least to some degree. Attitudes are intentional states with conceptual content, and concepts are dispositions. We acquire concepts during our lives, sometimes unconsciously, sometimes explicitly through definition of some kind, and each cognitive agent has a unique repertoire of concepts and a unique idiolect as well. The idea that our mental representations (at least some of them) are artefacts might sound strange at first sight, but I shall try to show that it makes full sense. Most of our mental dispositions –those provided with a conceptual content– are themselves artefacts. At the end, we are all different psychologically and culturally because our idiolects and repertoires of concepts are different. For a large part, what makes our species so special is an ongoing process through which homo sapiens makes itself what it is.Keywords: Intentionality, disposition, artefact, contextualism, repertoire.
Deep Learning Approaches in Pandemic and Disaster Management

A quick decision-making process in response and management of epidemics has been the most common approach, as accurate and relevant decisions have been demonstrated to have beneficial impacts on life preservation as well as on global and local economies. However, any disaster or epidemic is rarely represented by a set of single and linear parameters, as they often exhibit highly complex and chaotic behaviours, where interconnected unknowns rapidly evolve. As a consequence, any such decision-making approach must be computationally robust and able to process large amounts of data, whilst evaluating the potential outcomes based on specific decisions in real time.
PCSP: A Protected Cloud Storage Provider employing light weight techniques

One of the major practices of cloud computing is the storage service that it offers. In spite of its many creditable advantages, it also has some disadvantages like data security and data availability. These two are the main issues that a user face. Many models have been proposed to solve these issues. These models use cryptographic methods to secure the data and data redundancy method to ensure data availability. Both these methods solved the issues at the cost of extra storage space and increased time consumption both at the user and at the server side. This paper recommends a model PCSP (Protected Cloud Service Provider) which solves these issues in a novel way. The model uses light weight techniques which does not employ cryptographic methods. PCSP uses a layered approach, with three entities – the user, the PCSP, and the vendor. Due to the use of light weight techniques, the execution time is reduced by 80% and the storage needed is also reduced by 60%. Thus, there is still more reduction in the storage space. The implementation and analysis serve as the proof of concept
Interdependence of Software and Progress of Mathematics in OR: Some Illustrative Cases and Challenges

This paper points out that delayed or no supply of software can kill potential benefits associated with new mathematical ideas that have led to development of new mathematics in OR.It also points out that it is a self-created situation by the scientific community.This situation needs attention and should be resolved urgently.Many illustrative examples have been given to justify the claim.
Q-learning for Statically Scheduling DAGs

Data parallel frameworks (e.g. Hive, Spark or Tez) can be used to execute complex data analyses consisting of many dependent tasks represented by a Directed Acylical Graph (DAG). Minimising the job completion time (i.e. makespan) is still an open problem for large graphs.We propose a novel deep Q-learning (DQN) approach to statically scheduling DAGs and minimising the makespan. Our approach learns to schedule DAGs from scratch instead of learning how to imitate some heuristic. We show that our current approach learns fast and steadily. Furthermore, our approach can schedule DAGs almost 15 times faster than a Forward List Scheduling (FLS) heuristic.