Reviewerly/exHarmony huggingface dataset:
Do Scientists Need Social Networks for Scientists?

The article analyzes the functioning of social networks for scientists on the Internet. The Internet has emerged as a social network for scientists. Then its development led to the formation of various network segments not related to scientific knowledge. It was based on the normative ideal of science. In the process of development, the Internet began to unite not only scientists. The normative ideal began to penetrate into network segments that were not directly associated to the activities of scientists. The development of the network has led to the formation of special social networks for scientists. However, as shown in the paper, such networks are not able to serve the basis either for the solidarity of scientists, or the formation of a new sociality of scientists, since the development of science has led to the formation of studies that cannot be represented in such networks. Scientists are better use general, not specialized, Internet social networks. In such communicative spaces, they can better deal with the tasks related to the communications with other social actors.
A Task-Oriented Architecture with Priority Queue for BDI Agents Applied to the Multi Agent Programming Contest Scenario

On 2018 we participated of a Multi-Agent Programming Contest, as Akuanduba_UDESC team. We matched upon Agents in the City scenario, with our agents aiming to earn money, fulfilling jobs and missions and also get points through the wells. To accomplish these goals we used a task-oriented architecture with a priorities queue to manage tasks, focusing on developing agents that can choose correctly which tasks they need to perform and change the tasks state to coordinate themselves. We did not reach a good position on the contest, mainly because of a poor strategy for the wells building, but we have saw that all those things we focused on our development returned good results (i.e. the priorities queue and the tasks manager worked successfully).
Design of a Multifunctional Electronic Module Adaptable to a Motorized Wheelchair for People with Motor Disabilities

Attention to the disabled has been an issue of interest to the different areas of science and technology. The development of new technologies focused on disability is of vital importance as it allows improving the quality of life and incorporation into society. This paper presents the development of an electronic module adapted to a motorized wheelchair, which allows a person with some motor disability, in particular with spinal cord injury, to move. The module has a multifunctional capacity to direct the chair, by means of the inclinations of the head detected in an accelerometer implanted in a headset, through voice commands, through a mobile application developed for devices that have the Android operating system. For people who have movement only in their hands, a Flex sensor is included, as well as a myoelectric sensor that reacts with the contractions of a muscle.
Digital Competences Desirable in University Students

Currently, Information and Communication Technologies (ICT) are present in all areas of human development and activities. Banks and financial activities, products and labor activity, the government, the media, the health area, among many others, use digital tools and media to carry out their functions. Education does not escape that reality of being part of a digital society in continuous expansion. Knowledge is produced, disseminated, shared and discussed through digital media. It constitutes the most valuable asset of any organization in the in-formation society. This article is derived from a mixed research based on phenomenology and hermeneutics, whose intent was to generate a theoretical approach to desirable digital competencies in university students. The results indicate that the use of ICT focuses mainly on the use of email and the tools of Microsoft Office PowerPoint and Excel. To a lesser extent, the in-corporation of the use of some social networks is observed. As for the digital ones evaluated, the study demonstrates its mastery only by a third of the interviewees, which implies that two thirds of the students require training and education courses to acquire them, being encouraging that half of the sample understands the importance of this type of skills and the need to achieve them.
Research on Realization of Medical Image Restoration Based on Blind Equalization Algorithm

In order to study the realization of medical image restoration, this study mainly adopts blind equalization algorithm to analyze medical images, and observes the improvement effect of blind equalization technology on medical images. In the process of medical image formation, it is unavoidable to be affected by point spread function, which leads to image degradation and brings great difficulties to diagnosis, and the results of degradation are often unpredictable. The results show that the blind restoration algorithm can restore the image when the degradation process of the medical image is uncertain, which makes the medical image clearer and more accurate, brings great convenience to the diagnosis, and also reduces the diagnostic errors caused by the unclear image.
MT-RRT: a general purpose multithreading library for path planning

Rapidly Random exploring Trees are popular algorithms in the field of motion planning. A feasible path connecting two different poses is found by incrementally building a tree data structure. They are powerful and flexible, but also computationally intense, requiring thousands of iterations before their termination. The aim of this article is to show the capabilities of MT-RRT, a general purpose library which exploits four different multithreading strategies to speed up the planning process of Rapidly Random exploring Trees. MT-RRT will be proved to significantly reduce the computation time on various benchmarks.
Latency Estimation of Big Data Processing Under the MapReduce Framework with Coupling Effects

MapReduce is a model of processing large-scaled data with parallel and distributed algorithms at a cluster, and it is composed of two stages: a map stage for filtering and sorting data and a reduce stage for the operation of summary. We develop a model with two connected queues: one upstream queue for the data flow to access the mappers and one downstream queue for the data flow to access the reducers. Also, we analyze the latency of processing a large scale of data using queueing models, in consideration of the coupling effects between these two queues for map and reduce, respectively. Our analysis results on various datasets and with various algorithms show that the MapReduce framework can almost linearly speed up with increasingly more processors, and adding mappers is usually more efficient than adding reducers to reduce the latency when processing a large-scaled dataset.
SISTEM BOOKING PARKIR MALL DENGAN IDENTIFIKASI PLAT NOMOR KENDARAAN BERBASIS ANDROID

Many urban people spend time relieving fatigue due to the pressure of saturating work by walking in the mall or for mothers hunting for brand items that are being discounted at the mall, unfortunately when going to the mall it's hard to find a large enough parking lot. Look for a parking lot from the basement floor to the top floor and change from building one to another building to just get a parking space. Most malls do not provide information on the availability of parking spaces and the absence of officers directing to parking locations is still available. Some malls have provided information on the availability of parking spaces but have not yet provided which locations are still empty. From the description above we provide a solution to get a parking space when you want to visit the mall so that it can provide comfort when going to parking and time is not wasted to find parking locations. The parking booking system created will make it easy to get a parking space when going to the mall. With a vehicle number plate identification system and combined with an Android system, mall customers will easily get a parking space.
Meyotl: A Web-App Prototype for the Geolocation of People with Cognitive Impairment

Cognitive impairment is a condition that emerges in people who are 60 years old or more. Is probable that a person with this condition tends to blank out and get lost because they have spatial and temporal displacement. To aid in the care of this type of people, the web app Meyotl (which means shadow in Nahuatl) plans to monitor the location of cognitive impaired people with a GPS and GPRS device that they wear discreetly. The following work presents the main function of Meyotl's prototype, its arquitecture, and the technologies used in its development, as well as some graphical interfaces with early results from geolocation tests made by the authors themselves, to validate Meyotl's proper functionality.
Path Planning of Mobile Robot Based on an Improved Ant Colony Algorithm

The ant colony algorithm (ACO) is an intelligent optimization algorithm inspired by the behavior of ants searching for food in the nature. As a general stochastic optimization algorithm, the ant colony algorithm has been successfully applied to TSP, mobile robot path planning and other combinatorial optimization problems, and achieved good results. But because the probability of the algorithm is a typical algorithm, the parameters set in the algorithm is usually determined by experimental method, leading to the optimization of the performance closely related to people’s experience, it is difficult to optimize the algorithm performance. Moreover, the traditional ant colony algorithm has many shortcomings, such as long convergence time and easiness to fall into the local optimal solution. In order to overcome these shortcomings, in this paper, a large number of experimental data are analyzed to obtain the main appropriate parameters of the ant colony algorithm, such as the number $$ M $$ of ants, the number $$ K $$ of iterations, the influence factor $$ \alpha $$ and $$ \beta $$, and a new pheromone updating method that is related to the sine function is proposed in this paper, the simulation results show that the improved algorithm can accelerate the speed by 60%, and the global optimal solution can be found more easily than the original ant colony algorithm.
CDFM-based Secure & Efficient Architecture for Data Management in Cloud Computing

Cloud has become a prominent technology of data storage and computation. This allows user and institutions to depend on Cloud providers for storage and computing to offer system as a service. The growing significance of extensive acquisition and adoption of cloud computing have imposed security assurance on cloud. Security assurance provides integrity, confidentiality and reliability of data by performing computation and retrieving in compliance with the cloud providers. The storage, processing and managing of data for complex application becomes cumbersome and burdensome on cloud provider platforms. A management mechanism is required to manage the resources and reduce the complexity for maximizing performance with minimum human intervention. Even handling security threats occurring with varying workloads and failures in cloud environment is entailed. Motivated from this self management mechanism, we proposed a novel Cuckoo-based Data Fragmentation and Metadata (CDFM) secure and efficient approach. It manages the complex data of Translation and retrieves the translated data securely. In this work, cuckoo pools divide the data into different number of fragments and send it to different data pools. Data pools first encrypt the data fragment and assign color code to this encrypted fragment and then prepare indexing according to this color coding. The performance analysis, exhibit higher performance than existing approach for security and data fragmentation in cloud scenario.
Comparative study of feature detector and descriptor methods for registration

Image registration requires a step of detection and matching of primitives. This phase is important to obtain reliable registration. In this paper, we mainly focus on geometric registration methods which are based on the extraction and matching of distinctive feature points in images. Several methods such as SIFT, SURF, BRIEF, BRISK, ORB, FREAK and FRIF, are already proposed. In this paper, we present a comparative study of feature detector and descripts methods for registration which can be classified according to the type of descriptor and can be local classical or binary. We have presented, through this study, the difference between geometric methods of descriptor leveling as well as points of interest detector used and which have an influence on the resetting registration result. We can see that each method has weak points as well as strong points. The major difference is the level of invariance to the type of processing and the temporal complexity.
Getting "SMART" on Shock Treatment: An Evidence-Based Mnemonic Acronym for the Initial Management of Hemorrhage in Trauma

Treating hemorrhagic shock is challenging, the pathology is complex, and time is critical. Treatment requires resources in mental bandwidth (i.e., focused attention), drugs and blood products, equipment, and personnel. Providers must focus on treatment options in order of priority while also maintaining a dynamic assessment of the patient's response to treatment and considering potential differential diagnoses. In this process, the cognitive load is substantial. To avoid errors of clinical reasoning and practical errors of commission, omission, or becoming fixated, it is necessary to use evidence-based treatment recommendations that are concise, in priority order, and easily recalled. This is particularly the case in the austere, remote, or tactical environment. A simple mnemonic acronym, SMART, is presented in this article. It is a clinical heuristic that can be used as an aide-mémoire during the initial phases of resuscitation of the trauma patient with hemorrhagic shock: Start the clock and Stop the bleeding; Maintain perfusion; Administer antifibrinolytics; Retain heat; Titrate blood products and calcium; Think of alternative causes of shock.
Research on System Architecture Based on Deep Learning Convolutional Neural Network

In recent years, because deep learning technology can effectively learn features from data, it has become a powerful technical means in the field of image recognition. Research on image recognition can better promote the development of artificial intelligence and computer vision. This paper has conducted research and review of this field, introduced its important development and application, and made an attempt to promote further development in this field. Firstly, this paper introduces the structure of the network, and then introduces the common structural model of deep learning with CNN. The technical methods of reducing overfitting method, neural network visualization technology, inception structure, and transforming input images are discussed. Finally, the problems that still need to be solved in this field and the future of deep learning are introduced. It is pointed out that distributed computing, bit number reduction, migration learning, image style transformation, image generation, etc., are further research directions in the field of image recognition.
Person Re-identification with Soft Biometrics Through Deep Learning

Re-identification of persons is usually based on primary biometric features such as their faces, fingerprints, iris or gait. However, in most existing video surveillance systems, it is difficult to obtain these features due to the low resolution of surveillance footages and unconstrained real-world environments. As a result, most of the existing person re-identification techniques only focus on overall visual appearance. Recently, the use of soft biometrics has been proposed to improve the performance of person re-identification. Soft biometrics such as height, gender, age are physical or behavioural features, which can be described by humans. These features can be obtained from low-resolution videos at a distance ideal for person re-identification application. In addition, soft biometrics are traits for describing an individual with human-understandable labels. It allows human verbal descriptions to be used in the person re-identification or person retrieval systems. In some deep learning based person re-identification methods, soft biometrics attributes are integrated into the network to boot the robustness of the feature representation. Biometrics can also be utilised as a domain adaptation bridge for addressing the cross-dataset person re-identification problem. This chapter will review the state-of-the-art deep learning methods involving soft biometrics from three perspectives: supervised, semi-supervised and unsupervised approaches. In the end, we discuss the existing issues that are not addressed by current works.
IntelligentBox for Web-Based VR Applications (WebIBVR) and Its Collaborative Virtual Environments

This paper treats an interactive 3D graphics software development system called IntelligentBox and its extended version for Web-based VR applications called WebIBVR. WebIBVR consists of two main components, a client side part and a server side part. The server side part is almost the same as the original IntelligentBox. Its difference from the original IntelligentBox is functionality of off-screen rendering of 3D scenes and communication with a client side part. The client side part is a JavaScript program runs on a Web-browser and displays the images of 3D scenes sent from the server side. The client side part provides several functionalities for VR applications, i.e., stereo view support and multi-angle view support for a Head Mounted Display (HMD) of a VR goggle with a smartphone. Multiple HMDs can share the same view image of a 3D scene generated by server side IntelligentBox. However, the server side IntelligentBox cannot generate multiple different view images of a 3D scene correspond to the orientation of each of multiple HMDs because it needs much time and the interactivity becomes worse. To overcome this, in this paper the authors proposes the use of multiple server side IntelligentBoxes to generate multiple different view images of a common 3D scene at once by communicating with each other using RoomBox, one of the special purpose components of original IntelligentBox used for building collaborative virtual environments. This paper also explains the performance improvement of image transmission from a server side to a client side.
The Effect of Experiential Learning Models Toward Writing Skills of Narration Primary School Student

Learning to write in elementary school is currently not getting serious attention. This results in the poor quality of students' narrative writing. This study aims to determine the effect of experiential learning models on the narrative writing skills of elementary school students. The method used is a quasi-experimental research design with a nonequivalent control group design. The population in this study was the fourth-grade students at the UPI Lab school Elementary School, amounting to 47 students and divided into two classes, namely the experimental class and the control class. The results showed that there were significant differences between the experimental class posttest and the control class. From these results, it can be concluded that the experiential learning model significantly influences the narrative writing skills of elementary school students. Thus students get meaningful learning and teachers get alternative learning to write for students to create meaningful learning with analytical assessment in the form of content, content, information, grammar, and mechanical organization.
On Data Analysis of Software Repositories

This article discusses the analysis of software repositories using data analysis methods. A review is made of methods for analyzing programs based on information retrieved from the program code stored in code repositories. A review is made of methods for analyzing programs based on information retrieved from the program code stored in repositories. The article reviews the works that apply methods of classification, clustering and depth learning in software development. For example, for classifying and predicting errors, changing the properties of code in the process of its evolution, detecting design flaws and debts, assist for code refactoring. The main ultimate goal for all models is, of course, an automation of programming. In practice, we are talking about more simple tasks. This includes, for example, information retrieval (program code), error prediction, clone detection, link analysis, evolution analysis, etc. Firstly, we discuss recurrent neural networks and their deployment for the analysis of software repositories. In the simplest case, recurrent networks model a programming language as a sequence of characters. Also, the paper covers clustering and topic modeling.
Detecting Target of Sarcasm using Ensemble Methods

We describe our methods in trying to detect the target of sarcasm as part of ALTA 2019 shared task. We use combination of ensemble of clas- sifiers and a rule-based system. Our team ob- tained a Dice-Sorensen Coefficient score of 0.37150, which placed 2nd in the public leader- board. Despite no team beating the baseline score for the private dataset, we present our findings and also some of the challenges and future improvements which can be used in or- der to tackle the problem.
Performance Comparison of De-speckling filters on the Basis of Incremental Iteration in Ultrasound Imaging

Speckle noise is a multiplicative noise that degrades image quality. It is inherently found in ultrasound imaging. Removing spots always trades between noise suppression and loss of information, and experts are very concerned about this. Therefore, we hope to retain as much important information as possible. Here in this paper we tried to find the effect of multiple iteration of any filter to remove the speckle noise from the image. The results are very informative which showed that some filters behave abruptly and gives higher values for lower iteration than showed some stability and again its values increased. The paper tried to show the impact of different filters by increment of iterations during removal of speckle noise from the ultrasound imaging.
An Effective Machine Learning Approach for Sentiment Analysis on Popular Restaurant Reviews in Bangladesh

Sentiment analysis or text mining is making a huge field of research in this cutting-edge period of social media. Different web journals and Social Media (Facebook, Twitter, Instagram) are the most prevalent stage for the consumers and users where most of the time they express their judgement about trending topics, different brands, restaurant, films, books and so on. Analyzing sentiment is an exceptionally brilliant and viable way to discover people views about news, place, restaurant, film, book, brand. It is helpful for both the owners and sellers. In this study, we built a model using natural language processing techniques and machine learning algorithms to automate the approach of classifying a review on around 200 popular restaurants of Bangladesh as Satisfactory or Poor. This would greatly help the owners to gather a view about the consumers on their restaurant. In this paper, we developed an effective machine learning approach to build a model that can predict the sentiment by analyzing the customer’s review of a restaurant. Our model achieved an accuracy of 95% using Support Vector Machine Classifier besides other classification models.
Research on Coding Of Conventional Procedures Based Upon ARINC Specification 424

Most of the conventional procedures could be coded effectively into the navigation database based upon the ARINC specification 424. But in some special cases, it’s hard to code as the procedure’s designer does not comply with the ARINC specification 424 when they design the conventional procedures. This paper will analyze some of these special cases from the practical experience, and will also provide the solutions to these cases after the analysis.