Due to the great advance in machine learning (ML) techniques, numerous ML models are expanding their application domains in recent years. To adapt for resource-constrained platforms such as mobile and Internet of Thin...
Due to the great advance in machine learning (ML) techniques, numerous ML models are expanding their application domains in recent years. To adapt for resource-constrained platforms such as mobile and Internet of Things (IoT) devices, pre-trained models are often processed to enhance their efficiency and compactness, using optimization techniques such as pruning and quantization. Similar to the optimization process in other complex systems, e.g., program compilers and databases, optimizations for ML models can contain bugs, leading to severe consequences such as system crashes and financial loss. While bugs in training, compiling and deployment stages have been extensively studied, there is still a lack of systematic understanding and characterization of model optimization bugs (MOBs). In this work, we conduct the first empirical study to identify and characterize MOBs. We collect a comprehensive dataset containing 371 MOBs from TensorFlow and PyTorch, the most extensively used open-source ML frameworks, covering the entire development time span of their optimizers (May 2019 to August 2022). We then investigate the collected bugs from various perspectives, including their symptoms, root causes, life cycles, detection and fixes. Our work unveils the status quo of MOBs in the wild, and reveals their features on which future detection techniques can be based. Our findings also serve as a warning to the developers and the users of ML frameworks, and an appeal to our research community to enact dedicated countermeasures.
Smart computing has been particularly notable in the development of wireless sensor networks (WSNs), which have many applications. Battery-powered, self-configuring sensor nodes are the basis of these networks. Energy...
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ISBN:
(数字)9798350350067
ISBN:
(纸本)9798350350074
Smart computing has been particularly notable in the development of wireless sensor networks (WSNs), which have many applications. Battery-powered, self-configuring sensor nodes are the basis of these networks. Energy and resources are limited when it comes to sensors. Unbalanced nodes in the network consume more power, which adversely affects the network’s lifespan. An improved energy-efficient cluster-based routing protocol for heterogeneous wireless sensor networks (WSNs) based on the Internet of Things is proposed in this study. Several performance metrics were used to evaluate the proposed model’s effectiveness, including energy efficiency, alive nodes, dead nodes, network lifetime, and residual energy. The results of the comparison were compared with existing methodologies, such as LEACH, PSO, and hybrid PSO. The proposed approach outperforms the existing model by a substantial margin in networks with 50 and 100 nodes, according to the simulation results.
Electric Vehicles (EV s) such as e-bikes, e-scooters, and e-skateboards become the most popular Eco-friendly personal modes of transportation in the United States. These EVs are mostly recharged via the power grid'...
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Electric Vehicles (EV s) such as e-bikes, e-scooters, and e-skateboards become the most popular Eco-friendly personal modes of transportation in the United States. These EVs are mostly recharged via the power grid's stations. Grid power is generated through multiple means including hydroelectric, thermal, solar, and wind. Even with using EVs to reduce the dangerous effects of fuel burning, there is a real need to take major steps towards green charging approaches. Charging EVs through renewable energy resources is maximizing the ecologically friendly potentials. The aim of this work is to provide a green and sustainable charging system for personal electric vehicles. Hence, a solar-powered charging dock has been built and controlled by an Arduino Mega 2560 adding to a Raspberry Pi 4. Subsequently, solar panel tracker linear actuator has been applied to be integrated with the installed dock solar panel tracking system for delivering maximum precision while operating. To get economic and reliable dock, it has been constructed from wood.
Proteins are macromolecules responsible for essential functions in almost all living organisms. Designing reasonable proteins with desired functions is crucial. A protein's sequence and structure are strongly corr...
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The proposed antenna with a size of 90 mm x 90 mm, and the ground portion is 64 mm x 64 mm, which material is FR4 glass epoxy substrate with the thickness of 1.6 mm, relative permittivity of 4.3 and loss tangent of 0....
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ISBN:
(数字)9789463968119
ISBN:
(纸本)9798350359497
The proposed antenna with a size of 90 mm x 90 mm, and the ground portion is 64 mm x 64 mm, which material is FR4 glass epoxy substrate with the thickness of 1.6 mm, relative permittivity of 4.3 and loss tangent of 0.023, consists of four modified dipole elements and four power dividers, as shown in Figure. 1. For the multi-band and broadband operation, three dipole antennas were shunted to be a dipole element. The electrical-length of the dipole element can be determined from the one quarter-wave length at the 2.45 GHz, 5.5 GHz and 6.525 GHz, for covering 2400 MHz-2500 MHz, 5150 MHz-5850 MHz and 5925 MHz-7125 MHz. In order to operates in various modes such as normal mode and axial mode, the modified four port triple-band microstrip series power divider was designed. In normal mode, it radiates horizontally polarized waves where as in axial mode, it radiates circularly polarized waves. The radiated patterns of the proposed antenna as shown in Figure. 2. The features of the proposed antenna are shown in the Table I. Impressive radiated gains and efficiencies are obtained.
The semiconductor industry is experiencing an accelerated transformation to overcome the scaling limits of the transistor and to adapt to new requirements in terms of data storage and computation, especially driven by...
Wireless communication has connected millions of people across the world to internet to enable them to reap benefits of digital economy in present scenario. Almost all the sectors of economy including healthcare, agri...
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Leveraging knowledge from electronic health records (EHRs) to predict a patient’s condition is essential to the effective delivery of appropriate care. Clinical notes of patient EHRs contain valuable information from...
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Objective: Glucose homeostasis is the only way to manage diabetic progression as all medications used do not cure diabetes. This study was aimed at verifying the feasibility of lowering glucose with non-invasive ultra...
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Coherence analysis plays a vital role in the study of functional brain connectivity. However, coherence captures only linear spectral associations, and thus can produce misleading findings when ignoring variations of ...
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