Semantic lossless encoded image representation for malware classification.

Combining artificial intelligence with static analysis is an effective method for classifying malicious code. Due to the development of anti-analysis techniques, malicious code commonly employs obfuscation methods like packing, which result in garbled assembly code and the loss of original semantics. Consequently, existing pre-trained code language models are rendered ineffective in such scenarios.

Investigations on improved Box-Cox sparsity measures for machine condition monitoring.

Sparsity measures are commonly utilized as health indicators for machine condition monitoring. Recently, with the assistance of Box-Cox transformation, kurtosis and negative entropy have been smoothly extended to form Box-Cox sparsity measures (BCSMs). However, traditional BCSMs do not generate sparsity measures that outperform negative entropy, which means it is meaningless to some extent.

ExCS: accelerating code search with code expansion.

Efficiently searching and reusing code from expansive codebases is pivotal for enhancing developers' productivity. In recent times, the emergence of deep learning-driven neural ranking models, characterized by their vast dimensions and intricate interaction mechanisms, has been noteworthy. Yet, these models, in real-world scenarios, pose computational challenges due to their high dimensionality.

Feasibility study on using house-tree-person drawings for automatic analysis of depression.

Major depression is a severe psychological disorder typically diagnosed using scale tests and through the subjective assessment of medical professionals. Along with the continuous development of machine learning techniques, computer technology has been increasingly employed to identify depression in recent years. Traditional methods of automatic depression recognition rely on using the patient's physiological data, such as facial expressions, voice, electroencephalography (EEG), and magnetic resonance imaging (MRI) as input.

A new method to select frequency band for vibration signal demodulation and condition estimation of rolling bearings.

The narrowband amplitude demodulation of vibration signals is widely used to extract components carrying information about rolling bearing faults for condition estimation. However, precisely selecting the frequency band determines the quality of demodulation. Although transient detection based methods have achieved satisfactory performance in some scenarios, they may be severely disturbed by strong random impulses or harmonics.

Breakdancing Movement Based on Image Recognition Promotes Preschool Children's Executive Function and Intervention Plan.

With the continuous development of science and technology, people can apply more and more technology to the cultivation of children's abilities. In the process of cultivating children's ability, the most fancy is the study of executive function, and this is the research topic of this article. In the past, training methods such as music, mindfulness, and exercise have been used in the study of children's executive abilities to promote the development of preschool children's executive functions.