从暴力的“人肉搜索”到友善的“人机搜索”——“人肉搜索”传播应用价值再认识

本文从现有的"人肉搜索"现象中抽象出其传播模式,在对其传播机制进行剖析之后,乐观地指出从暴力的"人肉搜索"到友善的"人机搜索"实现跨越的可能。

A novel speech emotion recognition algorithm based on combination of emotion data field and ant colony search strategy

In order to effectively conduct emotion recognition from spontaneous, non-prototypical and unsegmented speech so as to create a more natural human-machine interaction; a novel speech emotion recognition algorithm based on the combination of the emotional data field （EDF） and the ant colony search （ACS） strategy, called the EDF-ACS algorithm, is proposed. More specifically, the inter- relationship among the turn-based acoustic feature vectors of different labels are established by using the potential function in the EDF. To perform the spontaneous speech emotion recognition, the artificial colony is used to mimic the turn- based acoustic feature vectors. Then, the canonical ACS strategy is used to investigate the movement direction of each artificial ant in the EDF, which is regarded as the emotional label of the corresponding turn-based acoustic feature vector. The proposed EDF-ACS algorithm is evaluated on the continueous audio）＇visual emotion challenge （AVEC） 2012 dataset, which contains the spontaneous, non-prototypical and unsegmented speech emotion data. The experimental results show that the proposed EDF-ACS algorithm outperforms the existing state-of-the-art algorithm in turn-based speech emotion recognition.

Decentralized Searching of Multiple Unknown and Transient Radio Sources with Paired Robots

In this paper, we develop a decentralized algorithm to coord inate a group of mobile robots to search for unknown and transient radio sources. In addition to limited mobility and ranges of communication and sensing, the robot team has to deal with challenges from signal source anonymity, short transmission duration, and variable transmission power. We propose a two-step approach: First, we decentralize belief functions that robots use to track source locations using checkpoint-based synchronization, and second, we propose a decentralized planning strategy to coordinate robots to ensure the existence of checkpoints. We analyze memory usage, data amount in communication, and searching time for the proposed algorithm. We have implemented the proposed algorithm and compared it with two heuristics. The experimental results show that our algorithm successfully trades a modest amount of memory for the fastest searching time among the three methods.

3D structure parameterization design and modeling for irregular structure of stope bottom

Stope mining design is a very important and complicated task in daily production design and technical management of an underground mine.Based on workface technology and human-computer interaction technology,this study introduces a method of 3D parametric design for the irregular structure of stope bottoms,and focuses on solving technical problems in surface modeling of stope bottom structure.Optimization of the minimum span length algorithm(MSLA) and the shortest path search algorithm(SPSA) is conducted to solve the problem of contour-line based instant modeling of stope bottom structures,which makes possible the 3D parametric design for irregular structure of stope bottom.Implementation process and relevant methods of the proposed algorithms are also presented.Feasibility and reliability of the proposed modeling method are testified in a case study.In practice,the proposed 3 D parameterization design method for irregular structure stope bottom proves to be very helpful to precise 3D parametric design.This method is capable of contributing to improved efficiency and precision of stope design,and is worthy of promotion.

Discovering optimal features using static analysis and a genetic search based method for Android malware detection

Mobile device manufacturers are rapidly producing miscellaneous Android versions worldwide. Simultaneously, cyber criminals are executing malicious actions, such as tracking user activities, stealing personal data, and committing bank fraud. These criminals gain numerous benefits as too many people use Android for their daily routines, including important communications. With this in mind, security practitioners have conducted static and dynamic analyses to identify malware. This study used static analysis because of its overall code coverage, low resource consumption, and rapid processing. However, static analysis requires a minimum number of features to efficiently classify malware. Therefore, we used genetic search（GS）, which is a search based on a genetic algorithm（GA）, to select the features among 106 strings. To evaluate the best features determined by GS, we used five machine learning classifiers, namely, Na？ve Bayes（NB）, functional trees（FT）, J48, random forest（RF）, and multilayer perceptron（MLP）. Among these classifiers, FT gave the highest accuracy（95%） and true positive rate（TPR）（96.7%） with the use of only six features.

Finding misplaced items using a mobile robot in a smart home environment

Smart homes can provide complementary information to assist home service robots.We present a robotic misplaced item finding(MIF)system,which uses human historical trajectory data obtained in a smart home environment.First,a multi-sensor fusion method is developed to localize and track a resident.Second,a path-planning method is developed to generate the robot movement plan,which considers the knowledge of the human historical trajectory.Third,a real-time object detector based on a convolutional neural network is applied to detect the misplaced item.We present MIF experiments in a smart home testbed and the experimental results verify the accuracy and efficiency of our solution.