Efficient Penetration Testing Path Planning Based on Reinforcement Learning with Episodic Memory

Intelligent penetration testing is of great significance for the improvement of the security of information systems,and the critical issue is the planning of penetration test paths.In view of the difficulty for attackers to obtain complete network information in realistic network scenarios,Reinforcement Learning(RL)is a promising solution to discover the optimal penetration path under incomplete information about the target network.Existing RL-based methods are challenged by the sizeable discrete action space,which leads to difficulties in the convergence.Moreover,most methods still rely on experts’knowledge.To address these issues,this paper proposes a penetration path planning method based on reinforcement learning with episodic memory.First,the penetration testing problem is formally described in terms of reinforcement learning.To speed up the training process without specific prior knowledge,the proposed algorithm introduces episodic memory to store experienced advantageous strategies for the first time.Furthermore,the method offers an exploration strategy based on episodic memory to guide the agents in learning.The design makes full use of historical experience to achieve the purpose of reducing blind exploration and improving planning efficiency.Ultimately,comparison experiments are carried out with the existing RL-based methods.The results reveal that the proposed method has better convergence performance.The running time is reduced by more than 20%.

Constructive thinking,rational intelligence and irritable bowel syndrome

AIM： To evaluate rational and experiential intelligence in irritable bowel syndrome （IBS） sufferers. METHODS： We recruited 100 subjects with IBS as per Rome 11 criteria （50 consulters and 50 non-consulters） and 100 healthy controls, matched by age, sex and educational level. Cases and controls completed a clinical questionnaire （including symptom characteristics and medical consultation） and the following tests： rational- intelligence （Wechsler Adult Intelligence Scale, 3rd edition）; experiential-intelligence （Constructive Thinking Inventory）; personality （NEO personality inventory）; psychopathology （MMPI-2）, anxiety （state-trait anxiety inventory） and life events （social readjustment rating scale）. Analysis of variance was used to compare the test results of IBS-sufferers and controls, and a logistic regression model was then constructed and adjusted for age, sex and educational level to evaluate any possible association with IBS. RESULTS： No differences were found between IBS cases and controls in terms of IQ （102.0 ± 10.8 vs 102.8 ± 12.6）, but IBS sufferers scored significantly lower in global constructive thinking （43.7± 9.4 vs 49.6 ± 9.7）. In the logistic regression model, global constructive thinking score was independently linked to suffering from IBS [OR 0.92 （0.87-0.97）], without significant OR for total IQ. CONCLUSION： IBS subjects do not show lower rational intelligence than controls, but lower experiential intelligence is nevertheless associated with IBS.

The Testing Intelligence System Based on Factor Models and Self-Organizing Feature Maps

Presented is a new testing system based on using the factor models and self-organizing feature maps as well as the method of filtering undesirable environment influence. Testing process is described by the factor model with simplex structure, which represents the influences of genetics and environmental factors on the observed parameters - the answers to the questions of the test subjects in one case and for the time, which is spent on responding to each test question to another. The Monte Carlo method is applied to get sufficient samples for training self-organizing feature maps, which are used to estimate model goodness-of-fit measures and, consequently, ability level. A prototype of the system is implemented using the Raven＇s Progressive Matrices （Advanced Progressive Matrices） - an intelligence test of abstract reasoning. Elimination of environment influence results is performed by comparing the observed and predicted answers to the test tasks using the Kalman filter, which is adapted to solve the problem. The testing procedure is optimized by reducing the number of tasks using the distribution of measures to belong to different ability levels after performing each test task provided the required level of conclusion reliability is obtained.

Intelligence Test

1.If you drop a white hat into the Red Sea,what does itbecome?2.What do people do in clock factories?3.Whv do seagulls live near the sea?4.A cowboy rode to an inn on Friday,stayed two nightsand 1eft on Friday.How could that be?5.Where does a bird go when it loses its tail?(Key:1.Wet.2.They make faces all day. 3.Because ifthey live near the bay they will be called bagels.4.Hishorse’s name was Friday.5.The retail store.)

English Intelligence Test

ANow,imagine(设想)there is a large live duckinside a very large bottle.

Clinical Observation on Acupuncture Treatment of Cerebrovascular Dementia——A Report of 32 Cases

Cerebrovascular dementia is a common disease in the middle-aged and old people. Its incidence makes up about 10-20% of all kinds of dementia. It is mainly caused by general degeneration of the brain function resulted from cerebral arteriosclerosis and cerebral infarction. The author has treated 32 cases of cerebrovascular dementia with acupuncture in the recent years with satisfactory therapeutic results. A report follows.

Fast determination of meso-level mechanical parameters of PFC models

To solve the problems of blindness and inefficiency existing in the determination of meso-level mechanical parameters of particle flow code (PFC) models, we firstly designed and numerically carried out orthogonal tests on rock samples to investigate the correlations between macro-and meso-level mechanical parameters of rock-like bonded granular materials. Then based on the artificial intelligent technology, the intelligent prediction systems for nine meso-level mechanical parameters of PFC models were obtained by creating, training and testing the prediction models with the set of data got from the orthogonal tests. Lastly the prediction systems were used to predict the meso-level mechanical parameters of one kind of sandy mudstone, and according to the predicted results the macroscopic properties of the rock were obtained by numerical tests. The maximum relative error between the numerical test results and real rock properties is 3.28% which satisfies the precision requirement in engineering. It shows that this paper provides a fast and accurate method for the determination of meso-level mechanical parameters of PFC models.

Effect of mild head injury on intelligence in Zahedan, Iran

Objective： To investigate the effects of mild head injury （HI） on the victims＇ intelligence by measuring their intelligence quotient （IQ）.Methods： This cohort study was performed in Khatamol-Anbia Hospital, Zahedan, Iran and the IQs of 30 mild HI patients were measured right after the injury （IQ0） and six months later （IQ6）. The IQs of 90 close relatives of the patients were also measured at the same period of time as the non-exposure group. The IQs were measured with Wechsler adult intelligence scale-revised （WAIS-R）. The IQ0, IQ6 and their differences （IQ change） were compared in HI patients and their relatives using the Student＇s t test.Results： The mean IQ0 of the HI patients was similar to their relatives. The IQ6 of HI patients appeared to be less than those of their relatives. Moreover, the IQ6 of the HI patients appeared to be less than their initial scores. HI was associated with more decrease in IQ6 compared with IQ0and the female subjects showed more decrease in IQ6 compared with their IQ0.Conclusion： HI seems to be associated with decrease in IQ six months after the injury and it is more evident in female HI patients.

Seguin Form Board as an intelligence tool for young children in an Indian urban slum

Objective:The present study evaluates the concurrent and predictive validity of the Seguin Form Board Test(SFBT)as an intelligence tool for children in low-and middle-income countries.Methods:In a cohort of normal children,followed up in South India,two cross-sectional analyses were done at 3 and 7 years of age on 95 children.The SFBT and Vineland Social Matu-rity Scale(VSMS)were done at 3 years of age and Malin’s Intelligence Scale for Indian Children(MISIC)and the VSMS were done at 7 years of age,and the results were compared for concurrent and predictive validity for the SFBT.Results:Intelligence quotient and social quotient had positive correlations at 3 years of age,indicating fair concurrent validity.The SFBT done at around 3 years of age had good positive cor-relation with MISIC at 7 years of age,indicating good predictive validity.Conclusion:This study shows the utility of the SFBT as a community-based intelligence tool with acceptable concurrent and predictive validity.

Validation studies of the Para DNA^(■) Intelligence System with artificial evidence items

Short tandem repeat(STR)profiling is one of the mostly used systems for forensic applications.In certain circumstances,STR profiling is time-consuming and costly,which potentially leads to delays in criminal investigations.LGC(Laboratory of the Government Chemist,UK)Forensics has developed a robust STR profiling platform called the ParaDNAVR Intelligence Test System which can provide early tactical intelligence and aid investigators in making informed decisions on sample prioritization for detection.Here,we validated the ParaDNA^(■) intelligence test for its application in forensic cases using a range of mock evidence items following guidelines set by the Scientific Working Group on DNA Analysis Methods(SWGDAM).Specifically,we tested the sensitivity and accuracy of the ParaDNA intelligence test,as well as the success rates for detecting mock samples and for use in case scenarios.Our findings demonstrate that the ParaDNA intelligence test generates useful DNA profiles,especially for samples such as blood,saliva,and semen that contain ample DNA,indicating the benefits of including ParaDNA as a prior step in forensic STR profiling pipelines.

Autonomous Vehicles Testing Considering Utility-Based Operable Tasks

Virtual simulation testing of Autonomous Vehicles(AVs)is gradually being accepted as a mandatory way to test the feasibility of driving strategies for AVs.Mainstream methods focus on improving testing efficiency by extracting critical scenarios from naturalistic driving datasets.However,the criticalities defined in their testing tasks are based on fixed assumptions,the obtained scenarios cannot pose a challenge to AVs with different strategies.To fill this gap,we propose an intelligent testing method based on operable testing tasks.We found that the driving behavior of Surrounding Vehicles(SVs)has a critical impact on AV,which can be used to adjust the testing task difficulty to find more challenging scenarios.To model different driving behaviors,we utilize behavioral utility functions with binary driving strategies.Further,we construct a vehicle interaction model,based on which we theoretically analyze the impact of changing the driving behaviors on the testing task difficulty.Finally,by adjusting SV’s strategies,we can generate more corner cases when testing different AVs in a finite number of simulations.

An intelligent serological SERS test toward early-stage hepatocellular carcinoma diagnosis through ultrasensitive nanobiosensing

Early or very early detection of hepatocellular carcinoma(HCC)is an effective means to resolve the low cure rates,but there currently lacks a method that fulfills clinical requirements.One of the most prospective approaches to detecting early-stage HCC is directly testing a compendium of disease-relevant biomolecules contained within human serum through surface-enhanced Raman scattering(SERS)nanobiosensing and recognizing the biomolecular patterns.We report a novel Si-based bimetallic nanoensembles-functionalized SERS substrate(its analytical enhancement factor reaches 1.47×10^(12))and introduce an ultrasensitive nanobiosensing for capturing the subtle characteristic changes in SERS spectra caused by HCC,hepatitis B,or cirrhosis.Toward early diagnosis,we created an intelligent serological test with this nanobiosensing and the deep learning algorithm to gain key biomolecular phenotypes of early-stage HCC.Using clinical samples from four target populations(normal,HCC,cirrhosis,and hepatitis B),the proof-of-principle result indicates that the test yielded a predictive accuracy of 98.75%on a held-out dataset(randomly drew 4 out of 28 samples per population).On the same held-out dataset,the sensitivity and specificity of the test were both 100%for distinguishing HCC.Such a new-concept liquid biopsy could provide an opportunity for early diagnosis of HCC.