Learning hierarchical spatio-temporal pattern for human activity prediction

Human activity prediction has become increasingly valuable in many applications. This paper, initially from the perspective of cognition science, presents a novel approach to learning a hierarchical spatio-temporal pattern of human activities to predict ongoing activities from videos that contain only the onsets of the activities. Spatio-temporal pattern can be learned by a Hierarchical Self-Organizing Map (HSOM), which consists of two self-organizing maps (i.e., action map and actionlet map) connected via associative links trained by Hebbian learning. Ongoing activities can be predicted by Variable order Markov Model (VMM), which provides the means for capturing both large and small order Markov dependencies based on the training actionlet sequences. Experiments of the proposed method on four challenging 3D action datasets captured by commodity depth cameras show promising results.     

A combined M5P tree and hazard-based duration model for predicting urban freeway traffic accident durations

The duration of freeway traffic accidents duration is an important factor, which affects traffic congestion, environmental pollution, and secondary accidents. Among previous studies, the M5P algorithm has been shown to be an effective tool for predicting incident duration. M5P builds a tree-based model, like the traditional classification and regression tree (CART) method, but with multiple linear regression models as its leaves. The problem with M5P for accident duration prediction, however, is that whereas linear regression assumes that the conditional distribution of accident durations is normally distributed, the distribution for a “time-to-an-event” is almost certainly nonsymmetrical. A hazard-based duration model (HBDM) is a better choice for this kind of a “time-to-event” modeling scenario, and given this, HBDMs have been previously applied to analyze and predict traffic accidents duration. Previous research, however, has not yet applied HBDMs for accident duration prediction, in association with clustering or classification of the dataset to minimize data heterogeneity. The current paper proposes a novel approach for accident duration prediction, which improves on the original M5P tree algorithm through the construction of a M5P-HBDM model, in which the leaves of the M5P tree model are HBDMs instead of linear regression models. Such a model offers the advantage of minimizing data heterogeneity through dataset classification, and avoids the need for the incorrect assumption of normality for traffic accident durations. The proposed model was then tested on two freeway accident datasets. For each dataset, the first 500 records were used to train the following three models: (1) an M5P tree; (2) a HBDM; and (3) the proposed M5P-HBDM, and the remainder of data were used for testing. The results show that the proposed M5P-HBDM managed to identify more significant and meaningful variables than either M5P or HBDMs. Moreover, the M5P-HBDM had the lowest overall mean absolute percentage error (MAPE).     

How drivers decide whether to get a fuel cell vehicle: An ethnographic decision model

This article develops and tests an ethnographic decision model (EDM) of hydrogen fuel cell vehicle (FCV) adoption using interviews with California residents that either actually adopted an FCV or “seriously considered” doing so before deciding against it. We developed an initial model from 25 semi-structured interviews in which respondents self-described their decision-making processes. We iteratively tested and refined the model in a second round of 53 structured interviews. The final model consists of a first stage that assesses FCV adoption feasibility and a second stage that compares FCVs to other vehicle types. The model ultimately correctly predicts 86.8% of cases in the sample. In the first stage, respondents preferred to satisfy their need for a primary refueling station near home but a substantial number were willing to rely on a station near or on the way to work or other destination. Most drivers required a convenient backup station and a means of managing long-distance trips. Vehicle size options eliminated a few respondents. None rejected FCV adoption due to insufficient driving range. In the second stage, nearly all drivers engaged in some kind of cost comparison, though the factors considered varied greatly. Most opted for what they viewed as the less costly option, although a few FCV adopters and non-adopters were willing to pay more for their more preferred option. EDM is a promising qualitative research method for generating insights into how people navigate the decision whether or not to get an alternative-fuel vehicle.     

Development of Phospholipids Vesicular Nanocarrier for Topical Delivery of Tea Tree Oil in Management of Atopic Dermatitis Using BALB/c Mice Model

This work aims to develop and evaluate the efficacy of tea tree oil (TTO) ethosomal cream with improved deposition in skin layers for treatment of atopic dermatitis (AD). Ethosomes of TTO are developed using phosphatidylcholine (2% and 3% w/v) and ethanol (20%, 30%, and 40% w/v). Ethosomes are evaluated for percent entrapment efficiency (%EE), vesicle size, zeta potential, and in vitro drug diffusion. Ethosomal creams with optimized ethosomal dispersion are developed and evaluated for physicochemical parameters, thermal stability, ex vivo permeation, skin retention, and in vitro cytotoxicity using HaCat skin cell lines in comparison to conventional creams of TTO. In vivo investigations of optimized creams are performed using BALB/c mice model. The %EE, vesicle size, and zeta potential for optimized ethosomes are found to be 76.19 ± 3.26%, 333.6 nm, and –35.3 mV, respectively. Ethosomal creams showed higher deposition in the epidermis and dermis. The optimized creams are non-cytotoxic to HaCat cell lines. The creams significantly reduce the inflammatory response by decreasing the clinical score and infiltration of white blood cells, eosinophils, and IgE antibodies. Overall efficacy of ethosomal cream is higher than conventional cream. In conclusion, optimized ethosomal cream of TTO shows good efficacy for treatment of AD. Practical applications : The method used for the formulation of ethosomes is simple and can be easily scaled up on the industrial level. The loading of TTO within ethosomes can increase the efficiency by enhanced drug deposition in the epidermis and might also improve its stability against oxidative degradation. Topical ethosomal cream of TTO can improve patient compliance by avoidance of adverse effects linked with corticosteroids and could be a possible complementary or alternative therapy in management of AD.     

Phishing attacks discovery based on HTML layout similarity

Based on the similarity of the layout structure between the phishing sites and real sites,an approach to discover phishing sites was presented.First,the tag with link attribute as a feature was extracted,and then based on the feature,the page tag sequence branch to identify website was extracted,followed by the page layout similarity-HTMLTagAntiPhish,the alignment of page tag sequence tree into the alignment of page tag sequence branches was converted,this converted two-dimention tree structure into one-dimention string structure,and finally through the substitution matrix of bioinfor-matics BLOSUM62 coding,alignment score quickly to improve the phishing sites detection efficiency was computed.A series of simulation experiments show that this approach is feasible and has higher precision and recall rates.     

系统工程在优化矿山生产管理中的应用

根据某海下石棉矿采矿生产现状,分析目前矿山在地质、回采、运输及管理方面存在的问题,应用系统工程原理建立模型,制定方案并进行综合评价,利用统一规划法建立数量目标树和管理目标树,实现了生产管理的系统化,合理统筹人力、物力,极大的提高了管理水平,各项技术经济指标取得显著的效果.     

Syntactic compression codes at the zero entropy point

Syntactic compression codes compress the tree, which is the syntax of a binary source message. The ones considered here originate from image processing. The syntactic trees usedhave a constant valency and their binary labels distinguish whether the source substring derived from a node is completely zero or not. We compress them simply by deleting some redundant subtrees.

These codes fall into a theoretically new class of codes which is wider than the classical ones. They are here studied in the neighborhood of a zero of the binary entropy function. There, their behavior is close to that of an infinite run length encoding and the optimum valency is three. Finally, we open a problem, related with automata theory, which perhaps could provide a further link between Information Theory and Algorithmic Information Theory.     

虚拟士兵的智能记忆模型研究

为了提高虚拟士兵的真实度与可信度,在分析现有研究成果的基础上,依据生物记忆原理和遗忘特性,提出了一种智能记忆模型.构造了记忆信息的编码形式,建立了基于槽式和广义树结构的记忆分层存储模型；提出了带有参数的遗忘算法,并运用宽度优先算法,实现了记忆信息的搜索和调用.借助Matlab工具对算法进行了验证,验证结果表明该算法与实际情况基本相符.     

Fluctuation control for many-to-one routing in wireless sensor networks

This paper presents a distributed and adaptive fluctuation control scheme for many-to-one routing(FCM) in wireless sensor networks.Unlike well-known topology control schemes,the primary design objective is to reduce the fluctuation which happens due to overload of sensors in a data collection tree.More specifically,an estimation model of a sensor available capacity based on the number of its neighbors is proposed.In addition,this paper proposes a parent selection mechanism by three-way handshake.With such model and the selection mechanism,it is ensured that the load of a sensor does not exceed its available capacity.Finally,an adaptive maintenance mechanism is proposed to adjust the estimation of a sensor available capacity as the network environment changes.Simulation results demonstrate the effectiveness of the scheme.