Search results for "Artificial intelligence."
showing 10 items of 6109 documents
Support vector machine integrated with game-theoretic approach and genetic algorithm for the detection and classification of malware
2013
Abstract. —In the modern world, a rapid growth of mali- cious software production has become one of the most signifi- cant threats to the network security. Unfortunately, wides pread signature-based anti-malware strategies can not help to de tect malware unseen previously nor deal with code obfuscation te ch- niques employed by malware designers. In our study, the prob lem of malware detection and classification is solved by applyin g a data-mining-based approach that relies on supervised mach ine- learning. Executable files are presented in the form of byte a nd opcode sequences and n-gram models are employed to extract essential features from these sequences. Feature vectors o btained are…
Depth perception in tablet-based augmented reality at medium- and far-field distances
2013
Current augmented reality (AR) systems often fail to indicate the distance between the user and points of interest in the environment. Empirical evaluations of human depth perception in AR settings compared to real world settings are needed. Our goal in this study was to understand tablet-based AR depth perception by comparing it with real-world depth perception.
Evaluating the performance of artificial neural networks for the classification of freshwater benthic macroinvertebrates
2014
Abstract Macroinvertebrates form an important functional component of aquatic ecosystems. Their ability to indicate various types of anthropogenic stressors is widely recognized which has made them an integral component of freshwater biomonitoring. The use of macroinvertebrates in biomonitoring is dependent on manual taxa identification which is currently a time-consuming and cost-intensive process conducted by highly trained taxonomical experts. Automated taxa identification of macroinvertebrates is a relatively recent research development. Previous studies have displayed great potential for solutions to this demanding data mining application. In this research we have a collection of 1350 …
Using Video Conferencing and Video Recordings for Upper Secondary Distance Teaching: Teachers' View Points
2016
In Finland the “Isoverstas” (formely ISOverkosto) network of schools coordinates the development of upper secondary distance learning services. The community actually is quite extensive with 65 member schools. In this paper we introduce the results related using synchronous and asynchronous online video resources for distance teaching. The topic is approached broadly at the level of schools and different support services as well as the pedagogical practices of individual teachers. The research data consists of wiki stories written by teachers, the interviews of selected teachers, and an online survey. Data-based content analysis was chosen as the main analysis method with the aim of highlig…
A Serendipity-Oriented Greedy Algorithm for Recommendations
2017
Most recommender systems suggest items to a user that are popular among all users and similar to items the user usually consumes. As a result, a user receives recommendations that she/he is already familiar with or would find anyway, leading to low satisfaction. To overcome this problem, a recommender system should suggest novel, relevant and unexpected, i.e. serendipitous items. In this paper, we propose a serendipity-oriented algorithm, which improves serendipity through feature diversification and helps overcome the overspecialization problem. To evaluate our algorithm and compare it with others, we employ a serendipity metric that captures each component of serendipity, unlike the most …
A two-step, user-centered approach to personalized tourist recommendations
2017
Geo-localized, mobile applications can simplify a tourist visit, making the relevant Point of Interests more easily and promptly discernible to users. At the same time, such solutions must avoid creating unfitting or rigid user profiles that impoverish the users' options instead of refining them. Currently, user profiles in recommender systems rely on dimensions whose relevance to the user is more often presumed than empirically defined. To avoid this drawback, we build our recommendation system in a two-step process, where profile parameters are evaluated preliminarily and separately from the recommendations themselves. We describe this two-step evaluation process including an initial surv…
Real-time recognition of personal routes using instance-based learning
2011
Predicting routes is a critical enabler for many new location-based applications and services, such as warning drivers about congestion- or accident-risky areas. Hybrid vehicles can also utilize the route prediction for optimizing their charging and discharging phases. In this paper, a new lightweight route recognition approach using instance-based learning is introduced. In this approach, the current route is compared in real-time against the route instances observed in past, and the most similar route is selected. In order to assess the similarity between the routes, a similarity measure based on the longest common subsequence (LCSS) is employed, and an algorithm for incrementally evaluat…
Semi-automatic literature mapping of participatory design studies 2006--2016
2018
The paper presents a process of semi-automatic literature mapping of a comprehensive set of participatory design studies between 2006--2016. The data of 2939 abstracts were collected from 14 academic search engines and databases. With the presented method, we were able to identify six education-related clusters of PD articles. Furthermore, we point out that the identified clusters cover the majority of education-related words in the whole data. This is the first attempt to systematically map the participatory design literature. We argue that by continuing our work, we can help to perceive a coherent structure in the body of PD research.
Social Collaborative Viewpoint Regression with Explainable Recommendations
2017
A recommendation is called explainable if it not only predicts a numerical rating for an item, but also generates explanations for users' preferences. Most existing methods for explainable recommendation apply topic models to analyze user reviews to provide descriptions along with the recommendations they produce. So far, such methods have neglected user opinions and influences from social relations as a source of information for recommendations, even though these are known to improve the rating prediction. In this paper, we propose a latent variable model, called social collaborative viewpoint regression (sCVR), for predicting item ratings based on user opinions and social relations. To th…
Convolutional neural networks in skin cancer detection using spatial and spectral domain
2019
Skin cancers are world wide deathly health problem, where significant life and cost savings could be achieved if detection of cancer can be done in early phase. Hypespectral imaging is prominent tool for non-invasive screening. In this study we compare how use of both spectral and spatial domain increase classification performance of convolutional neural networks. We compare five different neural network architectures for real patient data. Our models gain same or slightly better positive predictive value as clinicians. Towards more general and reliable model more data is needed and collection of training data should be systematic. peerReviewed