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Modern machine-learning techniques greatly reduce the efforts required to conduct high-quality program compilation, which, without the aid of machine learning, would otherwise heavily rely on human manipulation as well as expert intervention. The success of the application of machine-learning techniques to compilation tasks can be largely attributed to the recent development and advancement of program...
Pervasive and ubiquitous computing increasingly relies on data-driven models learnt from large datasets. This learning process requires annotations in conjunction with datasets to prepare training data. Ambient Assistive Living (AAL) is one application of pervasive and ubiquitous computing that focuses on providing support for individuals. A subset of AAL solutions exist which model and recognize...
This paper presents a novel tandem human-machine cognition approach for human-in-the-loop control of complex business-critical and mission-critical systems and processes that are monitored by Internet-of-Things (IoT) sensor networks and where it is of utmost importance to mitigate and avoid cognitive overload situations for the human operators. The approach is based on a decision making supervisory...
Social interactions can be inferred on the web using the mailing list and home page links. It also represents the social lives of the individuals, collaborations, communities and relationship. Social networking groups are becoming increasingly important due to the volume and activities. Thus, the structure of the network, connectivity, movement of members from one group to another and change of interest...
Stack Overflow is one of the most popular question-and-answer sites for programmers. However, there are a great number of duplicate questions that are expected to be detected automatically in a short time. In this paper, we introduce two approaches to improve the detection accuracy: splitting body into different types of data and using word-embedding to treat word ambiguities that are not contained...
Human beings possess six highly efficient sensory system such as vision, hearing, touch, taste and smell. Many of these sensory systems are artificially replicated to provide intelligence to the machines. This paper reviews the various techniques and instruments that can be used to design a human olfactory system, which can be used in different applications. The odour detection systems can be broadly...
As Convolutional Neural Networks continue to produce state of the art results, more types of data are being used to see the results that would be produced. Using the heart rate data that was collected using sensors from various subjects who consumed alcohol, we converted it from the 1D waveform into a set of spectrograms. The spectrograms were fed into two pretrained CNNs, CaffeNet and AlexNet, to...
Integer overflow vulnerability is very difficult to locate and patch. From experience speaking the more complicate the integer operation the more error-prone the program. So in this paper, we come up with a new method to leverage static integer operation attributes to predict integer overflows based on machine learning technique. The static integer operation attributes consist of sink, integer operation...
This paper presents the use of a generalized learning technique to automatically generate Bangla poetry. We have trained a Long Sort Term Memory (LSTM) based recurrent neural network model on 350 poems written by Rabindranath Tagore to inspect if the recurrent neural network learns to generate poems. Using the technique described in this paper, we are able to generate poems using seed texts provided...
Dengue fever is one of the major health related issues as reported in World Health Organization (WHO). Therefore, a study is needed on the factors that influencing dengue incidences. This paper presents the influence of dengue incidence with dual climate variable in the 3D form scatter plot. Machine learning techniques such as clustering and regression is done to compare the sum square of residual...
The adequate representation of states in the construction of intelligent agents is fundamental for allowing them to achieve a satisfactory performance, principally for those that actuate in a competitive environment that possesses a high state space. One particular type of representation that is very appropriate for these situations is the NetFeatureMap, which describes by means of features the relevant...
Real-time functional magnetic resonance imaging (rtfMRI) is an emerging approach for studying the functioning of the human brain. Computational challenges combined with high data velocity have to this point restricted rtfMRI analyses to studying regions of the brain independently. However, given that neural processing is accomplished via functional interactions among brain regions, neuroscience could...
This work presents a new approach to prediction of human control error in unstable systems. We consider virtual inverted pendulum (stick) as a characteristic example of such system. The proposed approach is based on applying classification via machine learning to distinguish between the samples of human control corresponding to successful balancing and critical control errors (resulting in stick fall)...
The ubiquitous advance of technology used on the Internet, computers, smart phones and tablets has been conducive to the creation and proliferation of cyber threats resulting in attacks that have grown exponentially. Consequently, anti-virus companies and researchers have developed new approaches for dealing with discovering and classifying malware. Among these, machine learning and big data technologies...
Sentiment classification has gained much attention in big data era. Most existing methods rely on bag-of-words model, which disregard contextual information. In many cases however, the sentiment strength of a word is implicitly associated with its part of speech and context. In this paper, we present a WWE (weighted word embeddings) method that combines word embeddings and part-of-speech (POS) tagging...
With the development of the IoT market, collectable data is increasing exponentially. Recently, various methods for big data analysis are being suggested. Existing general research on data analysis has some problem that if the size of data is getting bigger, the processing speed is rapidly slow. In this paper, we find out the optimal algorithm that efficiently manage the energy data based on Big data...
While state-of-the-art kernels for graphs with discrete labels scale well to graphs with thousands of nodes, the few existing kernels for graphs with continuous attributes, unfortunately, do not scale well. To overcome this limitation, we present hash graph kernels, a general framework to derive kernels for graphs with continuous attributes from discrete ones. The idea is to iteratively turn continuous...
Deep learning models have showed great potential in classification and recognition over the last decade. Deep Belief Networks (DBNs) have been applied in visual, voice fields due to their great feature presentation capability. However, there are a vast number of time consuming calculations in the training of DBNs. Many researches have accelerated the training of DBNs with good speedups on CPU, GPU,...
Smart Grids and Home Energy Management System (HEMS) have been propagated by energy liberalization, and there is a demand for services, which are based on analysis of energy consumption data. For instance, a recommendation on effective utilization of home appliances in order to reduce power consumption. However, it is computationally expensive to analyze data in order to provide an energy-saving handbook,...
Software Defined Networks (SDNs) provides a separation between the control plane and the forwarding plane of networks. The software implementation of the control plane and the built in data collection mechanisms of the OpenFlow protocol promise to be excellent tools to implement Machine Learning (ML) network control applications. A first step in that direction is to understand the type of data that...
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