scholarly journals Inter-Frame Based Interpolation for Top–Bottom Packed Frame of 3D Video

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 702
Author(s):  
Phan Van Duc ◽  
Phu Tran Tin ◽  
Anh Vu Le ◽  
Nguyen Huu Khanh Nhan ◽  
Mohan Rajesh Elara
Keyword(s):  
Missing Values ◽  
State Of The Art ◽  
Original Data ◽  
Interpolation Scheme ◽  
Interpolation Technique ◽  
Broadcasting System ◽  
Left And Right ◽  
Inter Frame ◽  
Bottom Frame

The frame-compatible packing for 3D contents is the feasible approach to archive the compatibility with the existing monocular broadcasting system. To perceive better 3D quality, the packed 3D frames are expanded to the full size at the decoder. In this paper, an interpolation technique enhancing and comparing the quality of enlarged halt vertical left and right stereo video in the top–bottom frame-compatible packing is presented. To this end, the appropriate interpolation modes from fourteen available modes for each row segment, which exploit the correlation between left and right stereoscopic as well as current and adjacent frames of individual view, are estimated at the encoder. Based on the information received from the encoder, at the decoder, the interpolation scheme can select the most appropriate available original data to find the missing values of to-be-discarded row segments. The proposed method outperformed than the state-of-the-art interpolation methods in terms of subjective visualization and numerical PSNRs and SSMI about 11%, with an execution time of about 12% comparisons.

Data science in economics: comprehensive review of advanced machine learning and deep learning methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Ferdinand Filip ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
State Of The Art ◽  
Science Methods ◽  
Learning Models ◽  
Diverse Range ◽  
Hybrid Machine ◽  
Economics Research

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.

Sustainability Failure in the Antimicrobial Stewardship Program in a Secondary Care Hospital from Cuba: A Historical Review of 30 Years and 3 Shots

2020 ◽  
Vol 18 ◽  
Author(s):  
Humberto Guanche Garcell ◽  
Juan José Pisonero Socias ◽  
Gilberto Pardo Gómez
Keyword(s):  
Antimicrobial Stewardship ◽  
Historical Review ◽  
Original Data ◽  
Care Hospital ◽  
Antimicrobial Stewardship Program ◽  
Continuous Quality ◽  
Stewardship Program ◽  
History Of ◽  
Health Organization

Background: During the last 30 years an antimicrobial stewardship program (ASP) was implemented in a facility with periods of weakness. We aim to describe the history of the sustainability failure in the local ASP. Methods: A historical review was conducted using original data from the facility library and papers published. An analysis of factors related to the failure was conducted based on the Doyle approach. Results: The first ASP was implemented from 1989 to 1996 based on the international experiences and contributes to the improvement in the quality of prescription, reduction of 52% in cost and in the incidence of nosocomial infection. The second program restarts in 2008 and decline in 2015, while the third program was guided by the Pan-American Health Organization from 2019. This program, in progress, is more comprehensive than previous ones and introduced as a novel measure the monitoring of antibiotic prophylaxis in surgery. The factors related to the sustainability were considered including the availability of antimicrobials, the leader´s support, safety culture, and infrastructure. Conclusions: The history behind thirty years of experiences in antimicrobial stewardship programs has allowed us to identify the gaps that require proactive strategies and actions to achieve sustainability and continuous quality improvement.

State-of-the-Art: A Systematic Literature Review on Image Segmentation in Latent Fingerprint Forensics

2019 ◽  
Vol 12 ◽  
Author(s):  
Megha Chhabra ◽  
Manoj Kumar Shukla ◽  
Kiran Kumar Ravulakollu
Keyword(s):  
Image Segmentation ◽  
Background Noise ◽  
State Of The Art ◽  
Recognition Rate ◽  
Poor Quality ◽  
Latent Fingerprint ◽  
In The Beginning ◽  
The Comparative Study ◽  
Finger Skin

: Latent fingerprints are unintentional finger skin impressions left as ridge patterns at crime scenes. A major challenge in latent fingerprint forensics is the poor quality of the lifted image from the crime scene. Forensics investigators are in permanent search of novel outbreaks of the effective technologies to capture and process low quality image. The accuracy of the results depends upon the quality of the image captured in the beginning, metrics used to assess the quality and thereafter level of enhancement required. The low quality of the image collected by low quality scanners, unstructured background noise, poor ridge quality, overlapping structured noise result in detection of false minutiae and hence reduce the recognition rate. Traditionally, Image segmentation and enhancement is partially done manually using help of highly skilled experts. Using automated systems for this work, differently challenging quality of images can be investigated faster. This survey amplifies the comparative study of various segmentation techniques available for latent fingerprint forensics.

scholarly journals Comprehensive survey among statistical members of medical ethics committees in Germany on their personal impression of completeness and correctness of biostatistical aspects of submitted study protocols

BMJ Open ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. e032864
Author(s):  
Geraldine Rauch ◽  
Lorena Hafermann ◽  
Ulrich Mansmann ◽  
Iris Pigeot
Keyword(s):  
Medical Ethics ◽  
Missing Values ◽  
Sample Size Calculation ◽  
Ethics Committees ◽  
Secondary Outcome ◽  
Study Protocols ◽  
Individual Assessment ◽  
Statistical Problems ◽  
Multiple Comparison Procedures

ObjectivesTo assess biostatistical quality of study protocols submitted to German medical ethics committees according to personal appraisal of their statistical members.DesignWe conducted a web-based survey among biostatisticians who have been active as members in German medical ethics committees during the past 3 years.SettingThe study population was identified by a comprehensive web search on websites of German medical ethics committees.ParticipantsThe final list comprised 86 eligible persons. In total, 57 (66%) completed the survey.QuestionnaireThe first item checked whether the inclusion criterion was met. The last item assessed satisfaction with the survey. Four items aimed to characterise the medical ethics committee in terms of type and location, one item asked for the urgency of biostatistical training addressed to the medical investigators. The main 2×12 items reported an individual assessment of the quality of biostatistical aspects in the submitted study protocols, while distinguishing studies according to the German Medicines Act (AMG)/German Act on Medical Devices (MPG) and studies non-regulated by these laws.Primary and secondary outcome measuresThe individual assessment of the quality of biostatistical aspects corresponds to the primary objective. Thus, participants were asked to complete the sentence ‘In x% of the submitted study protocols, the following problem occurs’, where 12 different statistical problems were formulated. All other items assess secondary endpoints.ResultsFor all biostatistical aspects, 45 of 49 (91.8%) participants judged the quality of AMG/MPG study protocols much better than that of ‘non-regulated’ studies. The latter are in median affected 20%–60% more often by statistical problems. The highest need for training was reported for sample size calculation, missing values and multiple comparison procedures.ConclusionsBiostatisticians being active in German medical ethics committees classify the biostatistical quality of study protocols as low for ‘non-regulated’ studies, whereas quality is much better for AMG/MPG studies.

UBAR

2021 ◽  
Vol 20 (3) ◽  
pp. 1-25
Author(s):  
Elham Shamsa ◽  
Alma Pröbstl ◽  
Nima TaheriNejad ◽  
Anil Kanduri ◽  
Samarjit Chakraborty ◽  
...  
Keyword(s):  
Resource Management ◽  
Quality Of Experience ◽  
State Of The Art ◽  
State Of Charge ◽  
User Preference ◽  
Management Approach ◽  
High Quality ◽  
Trade Off ◽  
Run Time

Smartphone users require high Battery Cycle Life (BCL) and high Quality of Experience (QoE) during their usage. These two objectives can be conflicting based on the user preference at run-time. Finding the best trade-off between QoE and BCL requires an intelligent resource management approach that considers and learns user preference at run-time. Current approaches focus on one of these two objectives and neglect the other, limiting their efficiency in meeting users’ needs. In this article, we present UBAR, User- and Battery-aware Resource management, which considers dynamic workload, user preference, and user plug-in/out pattern at run-time to provide a suitable trade-off between BCL and QoE. UBAR personalizes this trade-off by learning the user’s habits and using that to satisfy QoE, while considering battery temperature and State of Charge (SOC) pattern to maximize BCL. The evaluation results show that UBAR achieves 10% to 40% improvement compared to the existing state-of-the-art approaches.

scholarly journals Smoothing additive manufactured parts using ns-pulsed laser radiation

2021 ◽  
Author(s):  
Florian Kuisat ◽  
Fernando Lasagni ◽  
Andrés Fabián Lasagni
Keyword(s):  
Surface Roughness ◽  
Mechanical Performance ◽  
State Of The Art ◽  
Pulsed Laser ◽  
Industrial Applications ◽  
Laser Source ◽  
Pulsed Laser Radiation ◽  
Current State ◽  
The Impact

AbstractIt is well known that the surface topography of a part can affect its mechanical performance, which is typical in additive manufacturing. In this context, we report about the surface modification of additive manufactured components made of Titanium 64 (Ti64) and Scalmalloy®, using a pulsed laser, with the aim of reducing their surface roughness. In our experiments, a nanosecond-pulsed infrared laser source with variable pulse durations between 8 and 200 ns was applied. The impact of varying a large number of parameters on the surface quality of the smoothed areas was investigated. The results demonstrated a reduction of surface roughness Sa by more than 80% for Titanium 64 and by 65% for Scalmalloy® samples. This allows to extend the applicability of additive manufactured components beyond the current state of the art and break new ground for the application in various industrial applications such as in aerospace.

scholarly journals Ensemble-Based Out-of-Distribution Detection

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 567
Author(s):  
Donghun Yang ◽  
Kien Mai Mai Ngoc ◽  
Iksoo Shin ◽  
Kyong-Ha Lee ◽  
Myunggwon Hwang
Keyword(s):  
Detection Method ◽  
State Of The Art ◽  
Metric Learning ◽  
Feature Space ◽  
Confidence Score ◽  
Distance Metric Learning ◽  
Current State ◽  
Overall Performance ◽  
Deep Learning Model

To design an efficient deep learning model that can be used in the real-world, it is important to detect out-of-distribution (OOD) data well. Various studies have been conducted to solve the OOD problem. The current state-of-the-art approach uses a confidence score based on the Mahalanobis distance in a feature space. Although it outperformed the previous approaches, the results were sensitive to the quality of the trained model and the dataset complexity. Herein, we propose a novel OOD detection method that can train more efficient feature space for OOD detection. The proposed method uses an ensemble of the features trained using the softmax-based classifier and the network based on distance metric learning (DML). Through the complementary interaction of these two networks, the trained feature space has a more clumped distribution and can fit well on the Gaussian distribution by class. Therefore, OOD data can be efficiently detected by setting a threshold in the trained feature space. To evaluate the proposed method, we applied our method to various combinations of image datasets. The results show that the overall performance of the proposed approach is superior to those of other methods, including the state-of-the-art approach, on any combination of datasets.

scholarly journals Fissure Ridges: A Reappraisal of Faulting and Travertine Deposition (Travitonics)

Geosciences ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 278
Author(s):  
Andrea Brogi ◽  
Enrico Capezzuoli ◽  
Volkan Karabacak ◽  
Mehmet Cihat Alcicek ◽  
Lianchao Luo
Keyword(s):  
State Of The Art ◽  
Tectonic Setting ◽  
Original Data ◽  
Apical Part ◽  
Thermal Waters ◽  
Growth Mechanisms ◽  
Depositional Facies ◽  
Geothermal Fluids ◽  
Tectonic Features ◽  
Travertine Deposition

The mechanical discontinuities in the upper crust (i.e., faults and related fractures) lead to the uprising of geothermal fluids to the Earth’s surface. If fluids are enriched in Ca2+ and HCO3-, masses of CaCO3 (i.e., travertine deposits) can form mainly due to the CO2 leakage from the thermal waters. Among other things, fissure-ridge-type deposits are peculiar travertine bodies made of bedded carbonate that gently to steeply dip away from the apical part where a central fissure is located, corresponding to the fracture trace intersecting the substratum; these morpho-tectonic features are the most useful deposits for tectonic and paleoseismological investigation, as their development is contemporaneous with the activity of faults leading to the enhancement of permeability that serves to guarantee the circulation of fluids and their emergence. Therefore, the fissure ridge architecture sheds light on the interplay among fault activity, travertine deposition, and ridge evolution, providing key geo-chronologic constraints due to the fact that travertine can be dated by different radiometric methods. In recent years, studies dealing with travertine fissure ridges have been considerably improved to provide a large amount of information. In this paper, we report the state of the art of knowledge on this topic refining the literature data as well as adding original data, mainly focusing on the fissure ridge morphology, internal architecture, depositional facies, growth mechanisms, tectonic setting in which the fissure ridges develop, and advantages of using the fissure ridges for neotectonic and seismotectonic studies.

A Simulation Software for the Evaluation of Vulnerabilities in Reputation Management Systems

2021 ◽  
Vol 37 (1-4) ◽  
pp. 1-30
Author(s):  
Vincenzo Agate ◽  
Alessandra De Paola ◽  
Giuseppe Lo Re ◽  
Marco Morana
Keyword(s):  
Quality Of Service ◽  
Distributed Systems ◽  
State Of The Art ◽  
Simulation Software ◽  
Reputation Management ◽  
Management Systems ◽  
Security Attacks ◽  
Vulnerability Evaluation ◽  
Multi Agent

Multi-agent distributed systems are characterized by autonomous entities that interact with each other to provide, and/or request, different kinds of services. In several contexts, especially when a reward is offered according to the quality of service, individual agents (or coordinated groups) may act in a selfish way. To prevent such behaviours, distributed Reputation Management Systems (RMSs) provide every agent with the capability of computing the reputation of the others according to direct past interactions, as well as indirect opinions reported by their neighbourhood. This last point introduces a weakness on gossiped information that makes RMSs vulnerable to malicious agents’ intent on disseminating false reputation values. Given the variety of application scenarios in which RMSs can be adopted, as well as the multitude of behaviours that agents can implement, designers need RMS evaluation tools that allow them to predict the robustness of the system to security attacks, before its actual deployment. To this aim, we present a simulation software for the vulnerability evaluation of RMSs and illustrate three case studies in which this tool was effectively used to model and assess state-of-the-art RMSs.

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