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Artificial intelligence 1

Brain–machine interface 1

Chlorobenzene 1

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Indoor environmental quality (IEQ) 1

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Simultaneous removal of NO and chlorobenzene on VO/TiO granular catalyst: Kinetic study and performance prediction

Frontiers of Environmental Science & Engineering 2021, Volume 15, Issue 4, doi: 10.1007/s11783-020-1363-5

Abstract:

• A V2O5/TiO2 granular catalyst for simultaneous removal of NO and chlorobenzene.

Keywords： Simultaneous removal     Kinetic study     Performance prediction     V2O5/TiO2     Graphicalabstract    

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Learning deep IA bidirectional intelligence Personal View

Lei XU

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 4,   Pages 558-562 doi: 10.1631/FITEE.1900541

Abstract: There has been a framework sketched for learning deep bidirectional intelligence. The framework has an inbound that features two actions: one is the acquiring action, which gets inputs in appropriate patterns, and the other is A-S cognition, derived from the abbreviated form of words abstraction and self-organization, which abstracts input patterns into concepts that are labeled and understood by self-organizing parts involved in the concept into structural hierarchies. The top inner domain accommodates relations and a priori knowledge with the help of the A-I thinking action that is responsible for the accumulation-amalgamation and induction-inspiration. The framework also has an outbound that comes with two actions. One is called I-S reasoning, which makes inference and synthesis (I-S) and is responsible for performing various tasks including image thinking and problem solving, and the other is called the interacting action, which controls, communicates with, and inspects the environment. Based on this framework, we further discuss the possibilities of design intelligence through synthesis reasoning.

Keywords： Abstraction     Least mean square error reconstruction (Lmser)     Cognition     Image thinking     Abstract thinking    

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Development of an artificial intelligence diagnostic model based on dynamic uncertain causality graph for the differential diagnosis of dyspnea

Yang Jiao, Zhan Zhang, Ting Zhang, Wen Shi, Yan Zhu, Jie Hu, Qin Zhang

Frontiers of Medicine 2020, Volume 14, Issue 4,   Pages 488-497 doi: 10.1007/s11684-020-0762-0

Abstract: Dyspnea is one of the most common manifestations of patients with pulmonary disease, myocardial dysfunction, and neuromuscular disorder, among other conditions. Identifying the causes of dyspnea in clinical practice, especially for the general practitioner, remains a challenge. This pilot study aimed to develop a computer-aided tool for improving the efficiency of differential diagnosis. The disease set with dyspnea as the chief complaint was established on the basis of clinical experience and epidemiological data. Differential diagnosis approaches were established and optimized by clinical experts. The artificial intelligence (AI) diagnosis model was constructed according to the dynamic uncertain causality graph knowledge-based editor. Twenty-eight diseases and syndromes were included in the disease set. The model contained 132 variables of symptoms, signs, and serological and imaging parameters. Medical records from the electronic hospital records of Suining Central Hospital were randomly selected. A total of 202 discharged patients with dyspnea as the chief complaint were included for verification, in which the diagnoses of 195 cases were coincident with the record certified as correct. The overall diagnostic accuracy rate of the model was 96.5%. In conclusion, the diagnostic accuracy of the AI model is promising and may compensate for the limitation of medical experience.

Keywords： knowledge representation     uncertain     causality     graphical model     artificial intelligence     diagnosis     dyspnea    

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Generic, efficient, and effective deobfuscation and semantic-aware attack detection for PowerShell scripts Research Articles

Chunlin XIONG, Zhenyuan LI, Yan CHEN, Tiantian ZHU, Jian WANG, Hai YANG, Wei RUAN,chunlinxiong94@zju.edu.cn,ruanwei@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 3,   Pages 361-381 doi: 10.1631/FITEE.2000436

Abstract: In recent years, has increasingly been reported as appearing in a variety of cyber attacks. However, because the language is dynamic by design and can construct script fragments at different levels, state-of-the-art static analysis based attack detection approaches are inherently vulnerable to obfuscations. In this paper, we design the first generic, effective, and lightweight deobfuscation approach for scripts. To precisely identify the obfuscated script fragments, we define obfuscation based on the differences in the impacts on the s of scripts and propose a novel emulation-based recovery technology. Furthermore, we design the first semantic-aware attack detection system that leverages the classic objective-oriented association mining algorithm and newly identifies 31 semantic signatures. The experimental results on 2342 benign samples and 4141 malicious samples show that our deobfuscation method takes less than 0.5 s on average and increases the similarity between the obfuscated and original scripts from 0.5% to 93.2%. By deploying our deobfuscation method, the attack detection rates for Windows Defender and VirusTotal increase substantially from 0.33% and 2.65% to 78.9% and 94.0%, respectively. Moreover, our detection system outperforms both existing tools with a 96.7% true positive rate and a 0% false positive rate on average.

Keywords： PowerShell     Abstract syntax tree     Obfuscation and deobfuscation     Malicious script detection    

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An intelligent IEQ monitoring and feedback system: Development and applications Article

Yang Geng,Zhongchen Zhang,Juan Yu,Hongzhong Chen,Hao Zhou,Borong Lin,Weimin Zhuang

Engineering 2022, Volume 18, Issue 11,   Pages 218-231 doi: 10.1016/j.eng.2021.09.017

Abstract: Secondly, a wireless data transmission module, a cloud storage module, and graphical user interfaces

Keywords： Indoor environmental quality (IEQ)     Sensors     Continuous monitoring     Graphical user interface     Interactive    

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Toward the Next Generation of Retinal Neuroprosthesis: Visual Computation with Spikes Review

Zhaofei Yu, Jian K. Liu, Shanshan Jia, Yichen Zhang, Yajing Zheng, Yonghong Tian, Tiejun Huang

Engineering 2020, Volume 6, Issue 4,   Pages 449-461 doi: 10.1016/j.eng.2020.02.004

Abstract:

A neuroprosthesis is a type of precision medical device that is intended to manipulate the neuronal signals of the brain in a closed-loop fashion, while simultaneously receiving stimuli from the environment and controlling some part of a human brain or body. Incoming visual information can be processed by the brain in millisecond intervals. The retina computes visual scenes and sends its output to the cortex in the form of neuronal spikes for further computation. Thus, the neuronal signal of interest for a retinal neuroprosthesis is the neuronal spike. Closed-loop computation in a neuroprosthesis includes two stages: encoding a stimulus as a neuronal signal, and decoding it back into a stimulus. In this paper, we review some of the recent progress that has been achieved in visual computation models that use spikes to analyze natural scenes that include static images and dynamic videos. We hypothesize that in order to obtain a better understanding of the computational principles in the retina, a hypercircuit view of the retina is necessary, in which the different functional network motifs that have been revealed in the cortex neuronal network are taken into consideration when interacting with the retina. The different building blocks of the retina, which include a diversity of cell types and synaptic connections—both chemical synapses and electrical synapses (gap junctions)—make the retina an ideal neuronal network for adapting the computational techniques that have been developed in artificial intelligence to model the encoding and decoding of visual scenes. An overall systems approach to visual computation with neuronal spikes is necessary in order to advance the next generation of retinal neuroprosthesis as an artificial visual system.

Keywords： Brain–machine interface     Artificial intelligence     Deep learning     Spiking neural network     Probabilistic graphical    

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Title Author Date Type Operation

Simultaneous removal of NO and chlorobenzene on VO/TiO granular catalyst: Kinetic study and performance prediction

Journal Article

Learning deep IA bidirectional intelligence

Lei XU

Journal Article

Development of an artificial intelligence diagnostic model based on dynamic uncertain causality graph for the differential diagnosis of dyspnea

Yang Jiao, Zhan Zhang, Ting Zhang, Wen Shi, Yan Zhu, Jie Hu, Qin Zhang

Journal Article

Generic, efficient, and effective deobfuscation and semantic-aware attack detection for PowerShell scripts

Chunlin XIONG, Zhenyuan LI, Yan CHEN, Tiantian ZHU, Jian WANG, Hai YANG, Wei RUAN,chunlinxiong94@zju.edu.cn,ruanwei@zju.edu.cn

Journal Article

An intelligent IEQ monitoring and feedback system: Development and applications

Yang Geng,Zhongchen Zhang,Juan Yu,Hongzhong Chen,Hao Zhou,Borong Lin,Weimin Zhuang

Journal Article

Toward the Next Generation of Retinal Neuroprosthesis: Visual Computation with Spikes

Zhaofei Yu, Jian K. Liu, Shanshan Jia, Yichen Zhang, Yajing Zheng, Yonghong Tian, Tiejun Huang

Journal Article