Low-light images have low contrast and unclear details, resulting in the reduction of available information for human vision. The current mainstream enhancement algorithms have problems such as noise amplification, color distortion, and dependence on ...

Fall detection is an emerging topic for health care and smart surveillance. Non-intrusive radar-based fall detection is a challenging but promising problem. Compared with ideal fall samples in the training set, the fall events in practical situations ...

In computational fluid dynamics, the Boussinesq approximation is a popular model for the numerical simulation of natural convection problems. Although using the Boussinesq approximation leads to significant performance gains over a full-fledged ...

Modern consumer electronic devices have started executing deep learning-based intelligence services on devices, not cloud servers, to keep personal data on devices and to reduce network and cloud costs. We find such a trend as the opportunity to ...

Context: The proliferation of technologies based on Large Language Models (LLM) is reshaping various domains, also impacting on programming code creation and review. Problem: The decision-making process in adopting LLM in software development demands an ...

In the field of software engineering, effectively managing software systems is essential. A key aspect of this management is the issue-commit link, which connects reported problems or enhancement requests (issues) with the actual code changes implemented ...

Adaptive output feedback control based on output feedback exponential passivity (OFEP) has a simple structure and strong robustness in regard to disturbances and system uncertainties. However, it is difficult for most nonlinear systems to ...

Osteoporosis is a medical disorder that causes bone tissue to deteriorate and lose density, increasing the risk of fractures. Applying Neural Networks (NN) to analyze medical imaging data and detect the presence or severity of osteoporosis ...

We introduce On-NAS, a memory-efficient on-device neural architecture search (NAS) solution, that enables memory-constrained embedded devices to find the best deep model architecture and train it on the device. Based on the cell-based differentiable NAS, ...

Current neural network approaches mostly consider the synaptic signal transfer as a basis of interneuronal communication. At the same time, extrasynaptic signaling plays important role in the animal behavior. In the paper processes of information ...

Purpose of I&C (instrumentation and control) system is monitor and control of processing procedure and equipment for reaching main goal of nuclear power plant (NPP) – electric energy generation providing nuclear and radiological safety and ...

Fuzzy testing is one of the most popular vulnerability mining techniques recently, it plays a huge role in exploiting software security vulnerabilities and improving software security. Fuzzy testing mainly performs specific variations on the collected ...

Applying services computing to neural networks, a service provider may provide inference with a pre-trained neural network as a service. Clients use the service to get the neural network's output on their input. To protect sensitive data, secure neural ...

Deploying a lightweight quantized model in compute-in-memory (CIM) might result in significant accuracy degradation due to reduced signal-noise rate (SNR). To address this issue, this paper presents ZEBRA, a zero-bit robust-accumulation CIM approach, ...

The implementation of neural networks (NN) on edge devices enables local processing of wireless data but faces challenges such as high computational complexity and memory requirements when deep neural networks (DNN) are used. Shallow neural networks ...

Recently, most studies in the field have focused on integrating reviews behind ratings to improve recommendation performance. However, two main problems remain (1) Most works use a unified data form and the same processing method to address the user and ...

In this paper, we consider the problem of the queue length estimation if only some small number of a so-called tagged customers is observable. The problem is treated in terms of the queueing of vehicles behind a traffic light. A supervised machine ...

Document-level event extraction is a long-standing challenging information retrieval problem involving a sequence of sub-tasks: entity extraction, event type judgment, and event type-specific multi-event extraction. However, addressing the problem as ...

The 2019 COVID-19 pandemic has affected people worldwide. Social media has become a global platform for individuals to voice their diverse perspectives on the pandemic, which has significantly altered their lives during and beyond lockdown periods. ...

With the growth of social networking website, social media has become a major platform for marketing, such as social business, political manipulation, influence and brand management, etc. However, social media marketing is very different to traditional ...