Training Computational Complexity Research Articles

TECHNOLOGIES AND COMPUTER SIMULATORS FOR TASK TRAINING DURING CHEMICAL EMERGENCIES

In modern educational practice simulators are becoming more common and more accessible means for professional training of recuers. The use of computer training complexes creates the preconditions for better learning of educational material, which is necessary during real rescue activites on chemically hazardous objects. This allows to form emergency preparedness of future rescuers. Conducted analysis show that appropriate methodologies considering introduction of computer training complexes to the educational process must be developed and approved by the State Emergency Service of Ukraine.

Detecting falls with wearable sensors using machine learning techniques.

Falls are a serious public health problem and possibly life threatening for people in fall risk groups. We develop an automated fall detection system with wearable motion sensor units fitted to the subjects' body at six different positions. Each unit comprises three tri-axial devices (accelerometer, gyroscope, and magnetometer/compass). Fourteen volunteers perform a standardized set of movements including 20 voluntary falls and 16 activities of daily living (ADLs), resulting in a large dataset with 2520 trials. To reduce the computational complexity of training and testing the classifiers, we focus on the raw data for each sensor in a 4 s time window around the point of peak total acceleration of the waist sensor, and then perform feature extraction and reduction. Most earlier studies on fall detection employ rule-based approaches that rely on simple thresholding of the sensor outputs. We successfully distinguish falls from ADLs using six machine learning techniques (classifiers): the k-nearest neighbor (k-NN) classifier, least squares method (LSM), support vector machines (SVM), Bayesian decision making (BDM), dynamic time warping (DTW), and artificial neural networks (ANNs). We compare the performance and the computational complexity of the classifiers and achieve the best results with the k-NN classifier and LSM, with sensitivity, specificity, and accuracy all above 99%. These classifiers also have acceptable computational requirements for training and testing. Our approach would be applicable in real-world scenarios where data records of indeterminate length, containing multiple activities in sequence, are recorded.

Factors that affect the accuracy of text-based language identification

The classification accuracy of text-based language identification depends on several factors, including the size of the text fragment to be identified, the amount of training data available, the classification features and algorithm employed, and the similarity of the languages to be identified. To date, no systematic study of these factors and their interactions has been published. We therefore investigate the effects of each of these factors and their relations on the performance of text-based language identification.Our study uses n-gram statistics as features for classification. In particular, we compare support vector machines, Naïve Bayesian and difference-in-frequency classifiers on different amounts of input text and various values of n for different amounts of training data. For a fixed value of n the support vector machines generally outperform the other classifiers, but the simpler classifiers are able to handle larger values of n. The additional computational complexity of training the support vector machine classifier may not be justified in light of importance of using a large value of n, except possibly for small sizes of the input window when limited training data is available.Our training and testing corpora consisted of text from the 11 official languages of South Africa. Within these languages distinct language families can be found. We find that it is much more difficult to discriminate languages within languages families than languages in different families. The overall accuracy on short input strings is low for this reason, but for input strings of 100 characters or more there is only a slight confusion within families and accuracies as high as 99.4% are achieved. For the smallest input strings studied here, which consist of 15 characters, the best accuracy achieved is only 83%, but when languages in different families are grouped together, this corresponds to a usable 95.1% accuracy.The relationship between the amount of training data and the accuracy achieved is found to depend on the window size: for the largest window (300 characters) about 400000 characters are sufficient to achieve close-to-optimal accuracy, whereas improvements in accuracy are found even beyond 1.6 million characters of training data for smaller windows.Our study concludes that the correlation between the factors studied significantly affect classification accuracy; therefore, to assure credible and comparable results, these factors need to be controlled in any text-based language identification task.

Text-based language identification for south african languages

We investigate the performance of text-based language identification systems on the 11 official languages of South Africa, when n-gram statistics are used as features for classification. In particular, we compare support vector machines (SVMs) and likelihood-based classifiers on different amounts of input text, both from a closed domain and an open domain. With as few as 15 words of input text, reliable language identification is possible. Although the SVM is generally more accurate a classifier, the additional computational complexity of training this classifier may not be justified in light of the importance of using a large value for n.

On the Nonlearnability of a Single Spiking Neuron

We study the computational complexity of training a single spiking neuron N with binary coded inputs and output that, in addition to adaptive weights and a threshold, has adjustable synaptic delays. A synchronization technique is introduced so that the results concerning the nonlearnability of spiking neurons with binary delays are generalized to arbitrary real-valued delays. In particular, the consistency problem for N with programmable weights, a threshold, and delays, and its approximation version are proven to be NP-complete. It follows that the spiking neurons with arbitrary synaptic delays are not properly PAC learnable and do not allow robust learning unless RP = NP. In addition, the representation problem for N, a question whether an n-variable Boolean function given in DNF (or as a disjunction of O(n) threshold gates) can be computed by a spiking neuron, is shown to be coNP-hard.