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Image sharpness is one of the most determining factors for image readability and scene understanding. How to accurately quantify it is a hot topic. This paper systematically validates a previously proposed index for full-reference image sharpness assessment (edge preservation ratio, EPR). Based on Gaussian blurring images in LIVE, CSIQ, TID2008 and TID2013 databases, we firstly evaluated EPR accuracy...
In spite of significant on-going research, the Border gateway protocol (BGP) still encompasses conceptual vulnerability issues regarding impersonating the ownership of IP prefixes for ASes (Autonomous Systems). In this context, a number of research studies focused on securing BGP through historical-based and statistical-based behavioural models. This paper suggests a novel method based on tracking...
Current deep learning methods have achieved human-level performance on Labeled Faces in the Wild (LFW) database, but we think it is because that the limited number of pairs on LFW do not capture the real difficulty of large-scale unconstrained face verification problem. Besides the intra-class variations like pose, illumination, occlusion and expression, highly visually similarity of different persons'...
This paper proposes a method of gait recognition using a convolutional neural network (CNN). Inspired by the great successes of CNNs in image recognition tasks, we feed in the most prevalent image-based gait representation, that is, the gait energy image (GEI), as an input to a CNN designed for gait recognition called GEINet. More specifically, GEINet is composed of two sequential triplets of convolution,...
Although automated classification of soft biometric traits in terms of gender, ethnicity and age is a well-studied problem with a history of more than three decades, it is still far from being considered a solved problem for the case of difficult exposure conditions, such as during night-time, in environments with unconstrained lighting, or at large distances from the camera. In this paper, we investigate...
The performance of a simple yet efficient local receptive field feature extractor is evaluated on state of the art handwritten databases showing that after the proper optimization of its parameters, very good accuracy performances can be obtained using a shallow classifier (e.g. the support vector machine), close to the ones achieved using more sophisticated techniques such as deep-learning classifiers...
In this paper an introduction to an elastic group recommendation system is made where recommendation happens by collaborative and content filtering at three phases for multivariate dynamic attributes. Most regular recommendation systems work on static data contents where as an elastic group recommendation is designed to work for dynamic data inputs and on different use cases. recommendation systems...
Gesticulation, together with the speech, is an important part of natural and affective human-human interaction. Analysis of gesticulation and speech is expected to help designing more natural human-computer interaction (HCI) systems. We build the JestKOD database, which consists of speech and motion capture recordings of dyadic interactions. In this paper we describe our annotation efforts and present...
This paper proposes a new system of categorization and classification using data mining techniques based on certain criteria/topics. We describe the design and implementation of proposed system that automatically categorizes a restaurant as being good or bad, using data mining techniques, based on users' reviews. For this study we took a data set consisting of approximately 9,000 reviews for 2,355...
Affective computing opens a new area of research in computer science with the aim to improve the way how humans and machines interact. Recognition of human emotions by machines is becoming a significant focus in recent research in different disciplines related to information sciences and Human-Computer Interaction (HCI). In particular, emotion recognition in human speech is important, as it is the...
We have entered in data deluge already. Data Deluge means data generated by IoT devices and humans simultaneously. The data deluge is a Big threat for technologist but beneficial for end users. Now the coming problem is the security of this data. Big Data is too big, too fast and too diverse that does not compile with traditional data base system. Traditional data base systems are very good to analyze...
This work presents the design, implementation, and evaluation of a learning platform that addresses two main objectives: first it provides and on-line quiz tool for students which can be used as a complementary learning approach to the classroom courses. Secondly, this tool performs a detailed analysis of learners use, considering not only the number of mistakes students have made but also the student...
Emotion analysis and recognition has become an interesting topic of research among the computer vision research community. In this paper, we first present the emoF-BVP database of multimodal (face, body gesture, voice and physiological signals) recordings of actors enacting various expressions of emotions. The database consists of audio and video sequences of actors displaying three different intensities...
Users of online social networks often use multiple identities. This paper investigates the possibility of identifying a user from his or her chat behavior in such a setting. We have collected a large corpus of multiparty chat records in Turkish, obtained from a multiplayer game database. The most active 978 users are selected according to their participation in game chat sessions. This corpus is used...
Health care system's instantaneous answering method using machine-learning process has hindered the cross-system operability and the inter-user reusability. The scheme consists of two naturally reinforced components of database, namely Local Repository and Web. Primarily the user can access the local-repository database with simple keywords for the health data from the local repository. In other cases,...
Fetal electrocardiogram (FECG) monitoring has become essential due to the current increase in the relative number of cardiac patients worldwide. This paper proposes to use a deep learning approach to compress/recover FECG signals, improving the computation speed in a telemonitoring system. The problem is analogous to the reconstruction of a non-sparse signal in compressive sensing (CS) framework....
Body Sensor Networks aim to capture the state of the user and its environment by utilizing from information heterogeneous sensors, and allow continuous monitoring of numerous physiological signals, where these sensors are attached to the subject's body. This can be immensely useful in activity recognition for identity verification, health and ageing and sport and exercise monitoring applications....
In fault-prone large-scale distributed environments stochastic gradient descent (SGD) is a popular approach to implement machine learning algorithms. Data privacy is a key concern in such environments, which is often addressed within the framework of differential privacy. The output quality of differentially private SGD implementations as a function of design choices has not yet been thoroughly evaluated...
In fault-prone large-scale distributed environments stochastic gradient descent (SGD) is a popular approach to implement machine learning algorithms. Data privacy is a key concern in such environments, which is often addressed within the framework of differential privacy. The output quality of differentially private SGD implementations as a function of design choices has not yet been thoroughly evaluated...
Lectures are one of the medium used in Teaching-Learning strategy. However, amount of knowledge gained by the student is not always equal to the amount of knowledge shared in the lecture. This can be due to several reasons like long lecture hours, lack of concentration of student and many others. If the concepts which are not clearly understood are pre-requisite to understand next/other concept/topic...
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