A personal route prediction system based on trajectory data mining

This paper presents a system where the personal route of a user is predicted using a probabilistic model built from the historical trajectory data. Route patterns are extracted from personal trajectory data using a novel mining algorithm, Continuous Route Pattern Mining (CRPM), which can tolerate different kinds of disturbance in trajectory data. Furthermore, a client–server architecture is employed which has the dual purpose of guaranteeing the privacy of personal data and greatly reducing the computational load on mobile devices. An evaluation using a corpus of trajectory data from 17 people demonstrates that CRPM can extract longer route patterns than current methods. Moreover, the average correct rate of one step prediction of our system is greater than 71%, and the average Levenshtein distance of continuous route prediction of our system is about 30% shorter than that of the Markov model based method

Detecting Traffic Congestions Using Cell Phone Accelerometers

Abstract In this paper, we propose a system that detects traffic congestions by using cell phone accelerometers, which have many advantages (e.g. energy-efficient, unobtrusive, impervious to environmental noise, etc.). However, it is challenging to extract well-targeted and accurate features (e.g. speed) for detecting traffic congestions in a complex daily-living environment using a single cell phone accelerometer. The proposed system comprises a vehicular movement detection module, and a module for likelihood estimation of traffic congestions. Experimental results based on real datasets have demonstrated the effectiveness of the proposed system

TinyDroid: A Lightweight and Efficient Model for Android Malware Detection and Classification

With the popularity of Android applications, Android malware has an exponential growth trend. In order to detect Android malware effectively, this paper proposes a novel lightweight static detection model, TinyDroid, using instruction simplification and machine learning technique. First, a symbol-based simplification method is proposed to abstract the opcode sequence decompiled from Android Dalvik Executable files. Then, N-gram is employed to extract features from the simplified opcode sequence, and a classifier is trained for the malware detection and classification tasks. To improve the efficiency and scalability of the proposed detection model, a compression procedure is also used to reduce features and select exemplars for the malware sample dataset. TinyDroid is compared against the state-of-the-art antivirus tools in real world using Drebin dataset. The experimental results show that TinyDroid can get a higher accuracy rate and lower false alarm rate with satisfied efficiency

Language Inclusion Checking of Timed Automata Based on Property Patterns

The language inclusion checking of timed automata is described as the following: given two timed automata M and N, where M is a system model and N is a specification model (which represents the properties that the system needs to satisfy), check whether the language of M is included in the language of N. The language inclusion checking of timed automata can detect whether a system model satisfies a given property under the time constraints. There exist excellent studies on verifying real-time systems using timed automata. However, there is no thorough method of timed automata language inclusion checking for real-life systems. Therefore, this paper proposes a language inclusion checking method of timed automata based on the property patterns. On the one hand, we summarize commonly used property patterns described by timed automata, which can guide people to model the properties with time constraints. On the other hand, the system model M often contains a large number of events, but in general, the property N only needs to pay attention to the sequences and time limits of a few events. Therefore, the timed automata language inclusion checking algorithm is improved so that only the concerned events are required. Our method is applied to a water disposal system and it is also evaluated using benchmark systems. The determinization problem of timed automata is undecidable, which may lead to an infinite state space. However, our method is still practical because the properties established according to property patterns are often deterministic

Language Inclusion Checking of Timed Automata Based on Property Patterns

The language inclusion checking of timed automata is described as the following: given two timed automata M and N, where M is a system model and N is a specification model (which represents the properties that the system needs to satisfy), check whether the language of M is included in the language of N. The language inclusion checking of timed automata can detect whether a system model satisfies a given property under the time constraints. There exist excellent studies on verifying real-time systems using timed automata. However, there is no thorough method of timed automata language inclusion checking for real-life systems. Therefore, this paper proposes a language inclusion checking method of timed automata based on the property patterns. On the one hand, we summarize commonly used property patterns described by timed automata, which can guide people to model the properties with time constraints. On the other hand, the system model M often contains a large number of events, but in general, the property N only needs to pay attention to the sequences and time limits of a few events. Therefore, the timed automata language inclusion checking algorithm is improved so that only the concerned events are required. Our method is applied to a water disposal system and it is also evaluated using benchmark systems. The determinization problem of timed automata is undecidable, which may lead to an infinite state space. However, our method is still practical because the properties established according to property patterns are often deterministic

Social defeat stress before pregnancy induces depressive-like behaviours and cognitive deficits in adult male offspring: correlation with neurobiological changes

Abstract Background Epidemiological surveys and studies with animal models have established a relationship between maternal stress and affective disorders in their offspring. However, whether maternal depression before pregnancy influences behaviour and related neurobiological mechanisms in the offspring has not been studied. Results A social defeat stress (SDS) maternal rat model was established using the resident-intruder paradigm with female specific pathogen-free Wistar rats and evaluated with behavioural tests. SDS maternal rats showed a significant reduction in sucrose preference and locomotor and exploratory activities after 4 weeks of stress. In the third week of the experiment, a reduction in body weight gain was observed in SDS animals. Sucrose preference, open field, the elevated-plus maze, light–dark box, object recognition, the Morris water maze, and forced swimming tests were performed using the 2-month-old male offspring of the female SDS rats. Offspring subjected to pre-gestational SDS displayed enhanced anxiety-like behaviours, reduced exploratory behaviours, reduced sucrose preference, and atypical despair behaviours. With regard to cognition, the offspring showed significant impairments in the retention phase of the object recognition test, but no effect was observed in the acquisition phase. These animals also showed impairments in recognition memory, as the discrimination index in the Morris water maze test in this group was significantly lower for both 1 h and 24 h memory retention compared to controls. Corticosterone, adrenocorticotropic hormone, and monoamine neurotransmitters levels were determined using enzyme immunoassays or radioimmunoassays in plasma, hypothalamus, left hippocampus, and left prefrontal cortex samples from the offspring of the SDS rats. These markers of hypothalamic–pituitary–adrenal axis responsiveness and the monoaminergic system were significantly altered in pre-gestationally stressed offspring. Brain-derived neurotrophic factor (BDNF), cyclic adenosine monophosphate response element binding protein (CREB), phosphorylated CREB (pCREB), and serotonin transporter (SERT) protein levels were evaluated using western blotting with right hippocampus and right prefrontal cortex samples. Expression levels of BDNF, pCREB, and SERT in the offspring were also altered in the hippocampus and in the prefrontal cortex; however, there was no effect on CREB. Conclusion We conclude that SDS before pregnancy might induce depressive-like behaviours, cognitive deficits, and neurobiological alterations in the offspring