scholarly journals A State-of-the-Art Survey of Tasks for Tree Design and Evaluation with a Curated Task Dataset

2021 ◽  
Author(s):  
Aditeya Pandey ◽  
Uzma Haque Syeda ◽  
Chaitya Shah ◽  
John Alexis Guerra Gomez ◽  
Michelle Borkin
Keyword(s):  
Information Visualization ◽  
State Of The Art ◽  
Evaluation Studies ◽  
The Novel ◽  
Knowledge Repositories ◽  
Visual Encoding ◽  
Different Types ◽  
Multi Level ◽  
The Creation ◽  
Tree Visualization

In the field of information visualization, the concept of ``tasks'' is an essential component of theories and methodologies for how a visualization researcher or a practitioner understands what tasks a user needs to perform and how to approach the creation of a new design. In this paper, we focus on the collection of tasks for tree visualizations, a common visual encoding in many domains ranging from biology to computer science to geography. In spite of their commonality, no prior efforts exist to collect and abstractly define tree visualization tasks. We present a literature review of tree visualization papers and generate a curated dataset of over 200 tasks. To enable effective task abstraction for trees, we also contribute a novel extension of the Multi-Level Task Typology to include more specificity to support tree-specific tasks as well as a systematic procedure to conduct task abstractions for tree visualizations. All tasks in the dataset were abstracted with the novel typology extension and analyzed to gain a better understanding of the state of tree visualizations. These abstracted tasks can benefit visualization researchers and practitioners as they design evaluation studies or compare their analytical tasks with ones previously studied in the literature to make informed decisions about their design. We also reflect on our novel methodology and advocate more broadly for the creation of task-based knowledge repositories for different types of visualizations. The Supplemental Material will be maintained on OSF:~\url{https://osf.io/u5ehs/

scholarly journals Finding the Perfect Match: Different Heavy-Duty Mobile Applications Call for Different Actuators

Proceedings ◽  
2020 ◽  
Vol 64 (1) ◽  
pp. 22
Author(s):  
David Fassbender ◽  
Tatina Minav
Keyword(s):  
Mobile Applications ◽  
State Of The Art ◽  
Perfect Match ◽  
The Novel ◽  
Hydraulic Systems ◽  
Heavy Duty ◽  
Different Types ◽  
Evaluation Algorithm ◽  
Novel Concept ◽  
Valve Control

For the longest time, valve-controlled, centralized hydraulic systems have been the state-of-the-art technology to actuate heavy-duty mobile machine (HDMM) implements. Due to the typically low energy efficiency of those systems, a high number of promising, more-efficient actuator concepts has been proposed by academia as well as industry over the last decades as potential replacements for valve control—e.g., independent metering, displacement control, different types of electro-hydraulic actuators (EHAs), electro-mechanic actuators, or hydraulic transformers. This paper takes a closer look on specific HDMM applications for these actuator concepts to figure out where which novel concept can be a better alternative to conventional actuator concepts, and where novel concepts might fail to improve. For this purpose, a novel evaluation algorithm for actuator–HDMM matches is developed based on problem aspects that can indicate an unsuitable actuator–HDMM match. To demonstrate the functionality of the match evaluation algorithm, four actuator concepts and four HDMM types are analyzed and rated in order to form 16 potential actuator–HDMM matches that can be evaluated by the novel algorithm. The four actuator concepts comprise a conventional valve-controlled concept and three different types of EHAs. The HDMM types are excavator, wheel loader, backhoe, and telehandler. Finally, the evaluation of the 16 matches results in 16 mismatch values, of which the lowest indicates the “perfect match”. Low mismatch values could be found in general for EHAs in combination with most HDMMs but also for a valve-controlled actuator concept in combination with a backhoe. Furthermore, an analysis of the concept limitations with suggestions for improvement is included.

scholarly journals Use of Artificial Intelligence to Improve Resilience and Preparedness Against Adverse Flood Events

Water ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 973 ◽  
Author(s):  
Sara Saravi ◽  
Roy Kalawsky ◽  
Demetrios Joannou ◽  
Monica Rivas Casado ◽  
Guangtao Fu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
State Of The Art ◽  
Flash Flood ◽  
Weather Forecast ◽  
Specific Pattern ◽  
The Novel ◽  
Flood Events ◽  
Ice Melt ◽  
The Past ◽  
Different Types

The main focus of this paper is the novel use of Artificial Intelligence (AI) in natural disaster, more specifically flooding, to improve flood resilience and preparedness. Different types of flood have varying consequences and are followed by a specific pattern. For example, a flash flood can be a result of snow or ice melt and can occur in specific geographic places and certain season. The motivation behind this research has been raised from the Building Resilience into Risk Management (BRIM) project, looking at resilience in water systems. This research uses the application of the state-of-the-art techniques i.e., AI, more specifically Machin Learning (ML) approaches on big data, collected from previous flood events to learn from the past to extract patterns and information and understand flood behaviours in order to improve resilience, prevent damage, and save lives. In this paper, various ML models have been developed and evaluated for classifying floods, i.e., flash flood, lakeshore flood, etc. using current information i.e., weather forecast in different locations. The analytical results show that the Random Forest technique provides the highest accuracy of classification, followed by J48 decision tree and Lazy methods. The classification results can lead to better decision-making on what measures can be taken for prevention and preparedness and thus improve flood resilience.

The Role of Evaluative Tools in the Creation of the Memorial Media Portrait of Jacques Chirac (1932—2019)

2020 ◽  
pp. 97-110
Author(s):  
E. N. Mikhailova ◽  
V. A. Telegina
Keyword(s):  
Print Media ◽  
Political Elite ◽  
Systematic Analysis ◽  
Research Material ◽  
Political Orientations ◽  
Different Types ◽  
The Media ◽  
The Creation ◽  
Entire Text

The article is devoted to the study of evaluative tools used in modern French media in order to form the media image of a representative of the political elite. The techniques used in the creation of a memorial media portrait of Jacques Chirac (1932—2019), President of France from 1995 to 2007 are considered. The research material was the most prestigious French print media of various political orientations, published in late September — early October 2019 in connection with the death of the ex-President of the French Republic. The relevance of the research topic is dictated by the close attention of modern linguistics to axiological phenomena, differently presented in different types of discursive practices. The novelty of the study is due to the appeal to the analysis of the complex of evaluation tools used in the French print media when characterizing the former leader of the state during the nation’s farewell period. The estimated potential of the title of the article and its influence on the formation of the estimated vector of the entire text of the publication are shown. A systematic analysis of the assessment expression means, reflected in the memorial media portrait of the politician, is given. The factors that influenced the peculiarities of their use in this type of media portrait are revealed.

scholarly journals Multiple objects tracking in the UAV system based on hierarchical deep high-resolution network

2021 ◽  
Author(s):  
Wei Huang ◽  
Xiaoshu Zhou ◽  
Mingchao Dong ◽  
Huaiyu Xu
Keyword(s):  
High Resolution ◽  
Object Tracking ◽  
High Performance ◽  
State Of The Art ◽  
Class Imbalance ◽  
Unified Framework ◽  
Multiple Objects ◽  
Tracking Process ◽  
Objects Tracking ◽  
Different Types

AbstractRobust and high-performance visual multi-object tracking is a big challenge in computer vision, especially in a drone scenario. In this paper, an online Multi-Object Tracking (MOT) approach in the UAV system is proposed to handle small target detections and class imbalance challenges, which integrates the merits of deep high-resolution representation network and data association method in a unified framework. Specifically, while applying tracking-by-detection architecture to our tracking framework, a Hierarchical Deep High-resolution network (HDHNet) is proposed, which encourages the model to handle different types and scales of targets, and extract more effective and comprehensive features during online learning. After that, the extracted features are fed into different prediction networks for interesting targets recognition. Besides, an adjustable fusion loss function is proposed by combining focal loss and GIoU loss to solve the problems of class imbalance and hard samples. During the tracking process, these detection results are applied to an improved DeepSORT MOT algorithm in each frame, which is available to make full use of the target appearance features to match one by one on a practical basis. The experimental results on the VisDrone2019 MOT benchmark show that the proposed UAV MOT system achieves the highest accuracy and the best robustness compared with state-of-the-art methods.

scholarly journals CRISPR-Cas System: The Powerful Modulator of Accessory Genomes in Prokaryotes

2021 ◽  
pp. 1-16
Author(s):  
Anca Butiuc-Keul ◽  
Anca Farkas ◽  
Rahela Carpa ◽  
Dumitrana Iordache
Keyword(s):  
Nucleic Acids ◽  
Pathogenic Bacteria ◽  
The Novel ◽  
Defense System ◽  
Guide Rnas ◽  
Crispr Array ◽  
Different Types ◽  
Evolutionary Trajectories ◽  
Novel Coronavirus ◽  
Cas Proteins

Being frequently exposed to foreign nucleic acids, bacteria and archaea have developed an ingenious adaptive defense system, called CRISPR-Cas. The system is composed of the Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) array, together with CRISPR (<i>cas</i>)-associated genes. This system consists of a complex machinery that integrates fragments of foreign nucleic acids from viruses and mobile genetic elements (MGEs), into CRISPR arrays. The inserted segments (spacers) are transcribed and then used by cas proteins as guide RNAs for recognition and inactivation of the targets. Different types and families of CRISPR-Cas systems consist of distinct adaptation and effector modules with evolutionary trajectories, partially independent. The origin of the effector modules and the mechanism of spacer integration/deletion is far less clear. A review of the most recent data regarding the structure, ecology, and evolution of CRISPR-Cas systems and their role in the modulation of accessory genomes in prokaryotes is proposed in this article. The CRISPR-Cas system&apos;s impact on the physiology and ecology of prokaryotes, modulation of horizontal gene transfer events, is also discussed here. This system gained popularity after it was proposed as a tool for plant and animal embryo editing, in cancer therapy, as antimicrobial against pathogenic bacteria, and even for combating the novel coronavirus – SARS-CoV-2; thus, the newest and promising applications are reviewed as well.

scholarly journals Plant-Based Alternatives to Yogurt: State-of-the-Art and Perspectives of New Biotechnological Challenges

Foods ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 316
Author(s):  
Marco Montemurro ◽  
Erica Pontonio ◽  
Rossana Coda ◽  
Carlo Giuseppe Rizzello
Keyword(s):  
Lactic Acid ◽  
Lactic Acid Bacteria ◽  
State Of The Art ◽  
Protein Digestibility ◽  
The Novel ◽  
Sensory Features ◽  
Long Time ◽  
Biotechnological Processes ◽  
Nutritional Compounds ◽  
Increasing Demand

Due to the increasing demand for milk alternatives, related to both health and ethical needs, plant-based yogurt-like products have been widely explored in recent years. With the main goal to obtain snacks similar to the conventional yogurt in terms of textural and sensory properties and ability to host viable lactic acid bacteria for a long-time storage, several plant-derived ingredients (e.g., cereals, pseudocereals, legumes, and fruits) as well as technological solutions (e.g., enzymatic and thermal treatments) have been investigated. The central role of fermentation in yogurt-like production led to specific selections of lactic acid bacteria strains to be used as starters to guarantee optimal textural (e.g., through the synthesis of exo-polysaccharydes), nutritional (high protein digestibility and low content of anti-nutritional compounds), and functional (synthesis of bioactive compounds) features of the products. This review provides an overview of the novel insights on fermented yogurt-like products. The state-of-the-art on the use of unconventional ingredients, traditional and innovative biotechnological processes, and the effects of fermentation on the textural, nutritional, functional, and sensory features, and the shelf life are described. The supplementation of prebiotics and probiotics and the related health effects are also reviewed.

scholarly journals Fighting Together against the Pandemic: Learning Multiple Models on Tomography Images for COVID-19 Diagnosis

AI ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 261-273
Author(s):  
Mario Manzo ◽  
Simone Pellino
Keyword(s):  
Network Architecture ◽  
State Of The Art ◽  
Ensemble Classification ◽  
Effective Vaccine ◽  
Rt Pcr ◽  
Neural Network Architecture ◽  
Experimental Phase ◽  
Different Types ◽  
Polymerase Chain ◽  
Better Than

COVID-19 has been a great challenge for humanity since the year 2020. The whole world has made a huge effort to find an effective vaccine in order to save those not yet infected. The alternative solution is early diagnosis, carried out through real-time polymerase chain reaction (RT-PCR) tests or thorax Computer Tomography (CT) scan images. Deep learning algorithms, specifically convolutional neural networks, represent a methodology for image analysis. They optimize the classification design task, which is essential for an automatic approach with different types of images, including medical. In this paper, we adopt a pretrained deep convolutional neural network architecture in order to diagnose COVID-19 disease from CT images. Our idea is inspired by what the whole of humanity is achieving, as the set of multiple contributions is better than any single one for the fight against the pandemic. First, we adapt, and subsequently retrain for our assumption, some neural architectures that have been adopted in other application domains. Secondly, we combine the knowledge extracted from images by the neural architectures in an ensemble classification context. Our experimental phase is performed on a CT image dataset, and the results obtained show the effectiveness of the proposed approach with respect to the state-of-the-art competitors.

scholarly journals Contextualized Filtering for Shared Cyber Threat Information

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4890
Author(s):  
Athanasios Dimitriadis ◽  
Christos Prassas ◽  
Jose Luis Flores ◽  
Boonserm Kulvatunyou ◽  
Nenad Ivezic ◽  
...  
Keyword(s):  
Information Sharing ◽  
Business Processes ◽  
State Of The Art ◽  
Contextual Information ◽  
Coarse Grained ◽  
Business Information ◽  
Cyber Threat ◽  
Domain Expertise ◽  
Multi Level ◽  
Filtering Approach

Cyber threat information sharing is an imperative process towards achieving collaborative security, but it poses several challenges. One crucial challenge is the plethora of shared threat information. Therefore, there is a need to advance filtering of such information. While the state-of-the-art in filtering relies primarily on keyword- and domain-based searching, these approaches require sizable human involvement and rarely available domain expertise. Recent research revealed the need for harvesting of business information to fill the gap in filtering, albeit it resulted in providing coarse-grained filtering based on the utilization of such information. This paper presents a novel contextualized filtering approach that exploits standardized and multi-level contextual information of business processes. The contextual information describes the conditions under which a given threat information is actionable from an organization perspective. Therefore, it can automate filtering by measuring the equivalence between the context of the shared threat information and the context of the consuming organization. The paper directly contributes to filtering challenge and indirectly to automated customized threat information sharing. Moreover, the paper proposes the architecture of a cyber threat information sharing ecosystem that operates according to the proposed filtering approach and defines the characteristics that are advantageous to filtering approaches. Implementation of the proposed approach can support compliance with the Special Publication 800-150 of the National Institute of Standards and Technology.

scholarly journals Full-length transcriptome analysis reveals the mechanism of acupuncture at PC6 improves cardiac function in myocardial ischemia model

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Jing Yuan ◽  
Jun-Meng Wang ◽  
Zhi-Wei Li ◽  
Cheng-Shun Zhang ◽  
Bin Cheng ◽  
...  
Keyword(s):  
Myocardial Ischemia ◽  
Cardiac Function ◽  
Acupuncture Treatment ◽  
Pathological Process ◽  
Full Length ◽  
The Novel ◽  
New Genes ◽  
Myocardial Infraction ◽  
Differential Expression Genes ◽  
Multi Level

Abstract Background The pathological process of myocardial ischemia (MI) is very complicated. Acupuncture at PC6 has been proved to be effective against MI injury, but the mechanism remains unclear. This study investigated the mechanism that underlies the effect of acupuncture on MI through full-length transcriptome. Methods Adult male C57/BL6 mice were randomly divided into control, MI, and PC6 groups. Mice in MI and PC6 group generated MI model by ligating the left anterior descending (LAD) coronary artery. The samples were collected 5 days after acupuncture treatment. Results The results showed that treatment by acupuncture improved cardiac function, decreased myocardial infraction area, and reduced the levels of cTnT and cTnI. Based on full-length transcriptome sequencing, 5083 differential expression genes (DEGs) and 324 DEGs were identified in the MI group and PC6 group, respectively. These genes regulated by acupuncture were mainly enriched in the inflammatory response pathway. Alternative splicing (AS) is a post-transcriptional action that contributes to the diversity of protein. In all samples, 8237 AS events associated with 1994 genes were found. Some differential AS-involved genes were enriched in the pathway related to heart disease. We also identified 602 new genes, 4 of which may the novel targets of acupuncture in MI. Conclusions Our findings suggest that the effect of acupuncture on MI may be based on the multi-level regulation of the transcriptome.

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