scholarly journals Client-driven animated GIF generation framework using an acoustic feature

2021 ◽  
Author(s):  
Ghulam Mujtaba ◽  
Sangsoon Lee ◽  
Jaehyoun Kim ◽  
Eun-Seok Ryu
Keyword(s):  
Qualitative Evaluation ◽  
Video Segment ◽  
Generation Process ◽  
Computationally Efficient ◽  
Acoustic Feature ◽  
Baseline Method ◽  
Audio File ◽  
Interchange Format ◽  
Computational Resources ◽  
And Storage

AbstractThis paper proposes a novel, lightweight method to generate animated graphical interchange format images (GIFs) using the computational resources of a client device. The method analyzes an acoustic feature from the climax section of an audio file to estimate the timestamp corresponding to the maximum pitch. Further, it processes a small video segment to generate the GIF instead of processing the entire video. This makes the proposed method computationally efficient, unlike baseline approaches that use entire videos to create GIFs. The proposed method retrieves and uses the audio file and video segment so that communication and storage efficiencies are improved in the GIF generation process. Experiments on a set of 16 videos show that the proposed approach is 3.76 times more computationally efficient than a baseline method on an Nvidia Jetson TX2. Additionally, in a qualitative evaluation, the GIFs generated using the proposed method received higher overall ratings compared to those generated by the baseline method. To the best of our knowledge, this is the first technique that uses an acoustic feature in the GIF generation process.

scholarly journals A novel approach for IoT tasks offloading in edge-cloud environments

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Jaber Almutairi ◽  
Mohammad Aldossary
Keyword(s):  
Service Time ◽  
Edge Computing ◽  
Cloud System ◽  
Novel Approach ◽  
Delay Sensitivity ◽  
Cloud Environments ◽  
Application Characteristics ◽  
Iot Devices ◽  
Computational Resources ◽  
And Storage

AbstractRecently, the number of Internet of Things (IoT) devices connected to the Internet has increased dramatically as well as the data produced by these devices. This would require offloading IoT tasks to release heavy computation and storage to the resource-rich nodes such as Edge Computing and Cloud Computing. Although Edge Computing is a promising enabler for latency-sensitive related issues, its deployment produces new challenges. Besides, different service architectures and offloading strategies have a different impact on the service time performance of IoT applications. Therefore, this paper presents a novel approach for task offloading in an Edge-Cloud system in order to minimize the overall service time for latency-sensitive applications. This approach adopts fuzzy logic algorithms, considering application characteristics (e.g., CPU demand, network demand and delay sensitivity) as well as resource utilization and resource heterogeneity. A number of simulation experiments are conducted to evaluate the proposed approach with other related approaches, where it was found to improve the overall service time for latency-sensitive applications and utilize the edge-cloud resources effectively. Also, the results show that different offloading decisions within the Edge-Cloud system can lead to various service time due to the computational resources and communications types.

scholarly journals Towards Generative Design of Computationally Efficient Mathematical Models with Evolutionary Learning

Entropy ◽  
2020 ◽  
Vol 23 (1) ◽  
pp. 28
Author(s):  
Anna V. Kalyuzhnaya ◽  
Nikolay O. Nikitin ◽  
Alexander Hvatov ◽  
Mikhail Maslyaev ◽  
Mikhail Yachmenkov ◽  
...  
Keyword(s):  
Mathematical Models ◽  
Learning Approach ◽  
Model Structure ◽  
Evolutionary Learning ◽  
Learning Models ◽  
Computationally Efficient ◽  
Performance Models ◽  
Generative Design ◽  
Computational Resources ◽  
Machine Learning Models

In this paper, we describe the concept of generative design approach applied to the automated evolutionary learning of mathematical models in a computationally efficient way. To formalize the problems of models’ design and co-design, the generalized formulation of the modeling workflow is proposed. A parallelized evolutionary learning approach for the identification of model structure is described for the equation-based model and composite machine learning models. Moreover, the involvement of the performance models in the design process is analyzed. A set of experiments with various models and computational resources is conducted to verify different aspects of the proposed approach.

ENERGY EFFICIENCY EVALUATION FOR A “GREEN” POWER GENERATION PROCESS WITH MINIMUM EFFORT ON CARBON DIOXIDE CAPTURE AND STORAGE

2012 ◽  
Vol 199 (12) ◽  
pp. 1642-1651 ◽  
Author(s):  
Suttichai Assabumrungrat ◽  
Janewit Phromprasit ◽  
Siriporn Boonkrue ◽  
Worapon Kiatkittipong ◽  
Wisitsree Wiyaratn ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Power Generation ◽  
Energy Efficiency ◽  
Carbon Dioxide Capture ◽  
Efficiency Evaluation ◽  
Generation Process ◽  
Green Power ◽  
Energy Efficiency Evaluation ◽  
And Storage

Efficient computation of Faith's phylogenetic diversity with applications in characterizing microbiomes

2021 ◽  
pp. gr.275777.121
Author(s):  
George W Armstrong ◽  
Kalen Cantrell ◽  
Shi Huang ◽  
Daniel McDonald ◽  
Niina Haiminen ◽  
...  
Keyword(s):  
Carbon Footprint ◽  
Phylogenetic Diversity ◽  
Alpha Diversity ◽  
Previous Method ◽  
Metagenomic Data ◽  
Efficient Computation ◽  
Computationally Efficient ◽  
Dataset Size ◽  
Computational Resources ◽  
Older Populations

The number of publicly available microbiome samples is continually growing. As dataset size increases, bottlenecks arise in standard analytical pipelines. Faith’s phylogenetic diversity is a highly utilized phylogenetic alpha diversity metric that has thus far failed to effectively scale to trees with millions of vertices. Stacked Faith's Phylogenetic Diversity (SFPhD) enables calculation of this widely adopted diversity metric at a much larger scale by implementing a computationally efficient algorithm. The algorithm reduces the amount of computational resources required, resulting in more accessible software with a reduced carbon footprint, as compared to previous approaches. The new algorithm produces identical results to the previous method. We further demonstrate that the phylogenetic aspect of Faith's PD provides increased power in detecting diversity differences between younger and older populations in the FINRISK study's metagenomic data.

Parallel Deep Learning Ensembles for Human Pose Estimation

2018 ◽  
Author(s):  
Hailin Ren ◽  
Anil Kumar ◽  
Xinran Wang ◽  
Pinhas Ben-Tzvi
Keyword(s):  
Pose Estimation ◽  
Parallel Architecture ◽  
Body Parts ◽  
Post Processing ◽  
Computationally Efficient ◽  
Human Pose ◽  
Learning Ensembles ◽  
The Individual ◽  
Computational Resources ◽  
Mutually Independent

This paper presents an efficient method to detect human pose with monocular color imagery using a parallel architecture based on deep neural network. The network presented in this approach consists of two sequentially connected stages of 13 parallel CNN ensembles, where each ensemble is trained to detect one specific kind of linkage of the human skeleton structure. After detecting all skeleton linkages, a voting score-based post-processing algorithm assembles the individual linkages to form a complete human structure. This algorithm exploits human structural heuristics while assembling skeleton links and searches only for adjacent link pairs around the expected common joint area. The use of structural heuristics in the presented approach heavily simplifies the post-processing computations. Furthermore, the parallel architecture of the presented network enables mutually independent computing nodes to be efficiently deployed on parallel computing devices such as GPUs for computationally efficient training. The proposed network has been trained and tested on the COCO 2017 person-keypoints dataset and delivers pose estimation performance matching state-of-art networks. The parallel ensembles architecture improves its adaptability in applications aimed at identifying only specific body parts while saving computational resources.

scholarly journals Just-in-time Point Prediction Using a Computationally-efficient Lebesgue-sampling-based Prognostic Method

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Camilo Reyes ◽  
Francisco Jaramillo ◽  
Bin Zhang ◽  
Chetan Kulkarni ◽  
Marcos Orchard
Keyword(s):  
Electric Vehicles ◽  
Accurate Estimation ◽  
Just In Time ◽  
Computationally Efficient ◽  
Leibler Divergence ◽  
Key Factor ◽  
Discharge Model ◽  
Battery Energy ◽  
Computational Resources ◽  
Time Point

Battery energy systems are becoming increasingly popular in a variety of systems, such as electric vehicles. Accurate estimation of the total discharge of a battery is a key element for energy management. Problems such as path planning for drones or road choices in electric vehicles would benefit greatly knowing beforehand the end of discharge time. These tasks are generally performed online and require continuously quick estimations. We propose a novel prognostic method based on a combination of classic Riemann sampling (RS) and Lebesgue sampling (LS) applied to a discharge model of a battery. The method utilizes an early and inaccurate prediction using a RS-based method combined with a particle-filter based prognostic. Once a fault condition has been detected, subsequent Just-in-Time Point (JITP) estimations are updated using a novel LS-based method. The JITP prediction are triggered when the Kullback-Leibler divergence between the probability density functions (PDF) of the long-term-based prediction and the last filtered state reaches a threshold. The CPU time needed to execute a procedure is used as a measure of the computational resources. Results show that this combined approach is several orders of magnitude faster than the classical prognosis scheme. The combination of these two methods provides a robust JITP prognosis with less computational resources, a key factor to consider in real-time applications in embedded systems.

scholarly journals Human value learning and representation reflects rational adaptation to task demands

2021 ◽  
Author(s):  
Keno Juechems ◽  
Tugba Altun ◽  
Rita Hira ◽  
Andreas Jarvstad
Keyword(s):  
Phase 1 ◽  
Task Demands ◽  
Computationally Efficient ◽  
Human Value ◽  
Wide Range ◽  
Local Contexts ◽  
Irrational Behaviour ◽  
Computational Resources ◽  
Value Learning ◽  
Value Representation

When making decisions about goods and actions, humans and animals often rely on internally represented values. However, to be useful across a wide range of contexts, these values need to be represented on an absolute scale – a coding scheme that is computationally costly. By contrast, representing values in a way that depends entirely on context is highly computationally efficient, but can lead to irrational behaviour when values need to be compared across contexts. Thus, an efficient learner would allocate limited computational resources only when needed according to their expectations about the future. Here, we test the hypothesis that value representation is not fixed, but rationally adapted to expectations in two human learning experiments. Unlike most lab-based tasks, participants could use their initial experience (Phase 1) to optimise behaviour (Phase 2). Phase 1 was designed to cause one group to expect only decisions within local contexts (relative code sufficient), and another group to expect choices across local contexts (relative code insufficient). Despite identical learning experiences, the group whose expectations included choices across local contexts, went on to learn absolute value representations, and learned more absolute-like representations than the other group. Human value representation is neither relative nor absolute, but efficiently and rationally tuned to task demands.

scholarly journals Benchmarking of long-read assemblers for prokaryote whole genome sequencing

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 2138 ◽  
Author(s):  
Ryan R. Wick ◽  
Kathryn E. Holt
Keyword(s):  
Data Sets ◽  
Computationally Efficient ◽  
Short Read Sequencing ◽  
Oxford Nanopore ◽  
Long Read ◽  
Sequencing Platforms ◽  
Computational Resources ◽  
Assembly Algorithms ◽  
Oxford Nanopore Technologies ◽  
Multiple Assembly

Background: Data sets from long-read sequencing platforms (Oxford Nanopore Technologies and Pacific Biosciences) allow for most prokaryote genomes to be completely assembled – one contig per chromosome or plasmid. However, the high per-read error rate of long-read sequencing necessitates different approaches to assembly than those used for short-read sequencing. Multiple assembly tools (assemblers) exist, which use a variety of algorithms for long-read assembly. Methods: We used 500 simulated read sets and 120 real read sets to assess the performance of six long-read assemblers (Canu, Flye, Miniasm/Minipolish, Raven, Redbean and Shasta) across a wide variety of genomes and read parameters. Assemblies were assessed on their structural accuracy/completeness, sequence identity, contig circularisation and computational resources used. Results: Canu v1.9 produced moderately reliable assemblies but had the longest runtimes of all assemblers tested. Flye v2.6 was more reliable and did particularly well with plasmid assembly. Miniasm/Minipolish v0.3 was the only assembler which consistently produced clean contig circularisation. Raven v0.0.5 was the most reliable for chromosome assembly, though it did not perform well on small plasmids and had circularisation issues. Redbean v2.5 and Shasta v0.3.0 were computationally efficient but more likely to produce incomplete assemblies. Conclusions: Of the assemblers tested, Flye, Miniasm/Minipolish and Raven performed best overall. However, no single tool performed well on all metrics, highlighting the need for continued development on long-read assembly algorithms.

scholarly journals A Review of Evolutionary Trends in Cloud Computing and Applications to the Healthcare Ecosystem

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Mbasa Joaquim Molo ◽  
Joke A. Badejo ◽  
Emmanuel Adetiba ◽  
Vingi Patrick Nzanzu ◽  
Etinosa Noma-Osaghae ◽  
...  
Keyword(s):  
Cloud Computing ◽  
New Technologies ◽  
Service Providers ◽  
Fog Computing ◽  
Cloud Service ◽  
The Internet ◽  
On Demand ◽  
Cloud Service Providers ◽  
Computational Resources ◽  
And Storage

Cloud computing is a technology that allows dynamic and flexible computing capability and storage through on-demand delivery and pay-as-you-go services over the Internet. This technology has brought significant advances in the Information Technology (IT) domain. In the last few years, the evolution of cloud computing has led to the development of new technologies such as cloud federation, edge computing, and fog computing. However, with the development of Internet of Things (IoT), several challenges have emerged with these new technologies. Therefore, this paper discusses each of the emerging cloud-based technologies, as well as their architectures, opportunities, and challenges. We present how cloud computing evolved from one paradigm to another through the interplay of benefits such as improvement in computational resources through the combination of the strengths of various Cloud Service Providers (CSPs), decrease in latency, improvement in bandwidth, and so on. Furthermore, the paper highlights the application of different cloud paradigms in the healthcare ecosystem.

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