Avatar captcha : telling computers and humans apart via face classification and mouse dynamics.

Bots are malicious, automated computer programs that execute malicious scripts and predefined functions on an affected computer. They pose cybersecurity threats and are one of the most sophisticated and common types of cybercrime tools today. They spread viruses, generate spam, steal personal sensitive information, rig online polls and commit other types of online crime and fraud. They sneak into unprotected systems through the Internet by seeking vulnerable entry points. They access the system’s resources like a human user does. Now the question arises how do we counter this? How do we prevent bots and on the other hand allow human users to access the system resources? One solution is by designing a CAPTCHA (Completely Automated Public Turing Tests to tell Computers and Humans Apart), a program that can generate and grade tests that most humans can pass but computers cannot. It is used as a tool to distinguish humans from malicious bots. They are a class of Human Interactive Proofs (HIPs) meant to be easily solvable by humans and economically infeasible for computers. Text CAPTCHAs are very popular and commonly used. For each challenge, they generate a sequence of alphabets by distorting standard fonts, requesting users to identify them and type them out. However, they are vulnerable to character segmentation attacks by bots, English language dependent and are increasingly becoming too complex for people to solve. A solution to this is to design Image CAPTCHAs that use images instead of text and require users to identify certain images to solve the challenges. They are user-friendly and convenient for human users and a much more challenging problem for bots to solve. In today’s Internet world the role of user profiling or user identification has gained a lot of significance. Identity thefts, etc. can be prevented by providing authorized access to resources. To achieve timely response to a security breach frequent user verification is needed. However, this process must be passive, transparent and non-obtrusive. In order for such a system to be practical it must be accurate, efficient and difficult to forge. Behavioral biometric systems are usually less prominent however, they provide numerous and significant advantages over traditional biometric systems. Collection of behavior data is non-obtrusive and cost-effective as it requires no special hardware. While these systems are not unique enough to provide reliable human identification, they have shown to be highly accurate in identity verification. In accomplishing everyday tasks, human beings use different styles, strategies, apply unique skills and knowledge, etc. These define the behavioral traits of the user. Behavioral biometrics attempts to quantify these traits to profile users and establish their identity. Human computer interaction (HCI)-based biometrics comprise of interaction strategies and styles between a human and a computer. These unique user traits are quantified to build profiles for identification. A specific category of HCI-based biometrics is based on recording human interactions with mouse as the input device and is known as Mouse Dynamics. By monitoring the mouse usage activities produced by a user during interaction with the GUI, a unique profile can be created for that user that can help identify him/her. Mouse-based verification approaches do not record sensitive user credentials like usernames and passwords. Thus, they avoid privacy issues. An image CAPTCHA is proposed that incorporates Mouse Dynamics to help fortify it. It displays random images obtained from Yahoo’s Flickr. To solve the challenge the user must identify and select a certain class of images. Two theme-based challenges have been designed. They are Avatar CAPTCHA and Zoo CAPTCHA. The former displays human and avatar faces whereas the latter displays different animal species. In addition to the dynamically selected images, while attempting to solve the CAPTCHA, the way each user interacts with the mouse i.e. mouse clicks, mouse movements, mouse cursor screen co-ordinates, etc. are recorded nonobtrusively at regular time intervals. These recorded mouse movements constitute the Mouse Dynamics Signature (MDS) of the user. This MDS provides an additional secure technique to segregate humans from bots. The security of the CAPTCHA is tested by an adversary executing a mouse bot attempting to solve the CAPTCHA challenges

Early Detection of Diabetic Retinopathy Based Artificial Intelligent Techniques

The eye is impacted by several disorders, either directly or indirectly. As a result, eye exams are a crucial component of general healthcare. One of the effects of diabetes is diabetic retinopathy (DR), which affects the blood vessels that supply and nourish the retina and causes severe visual loss. One of the prevalent eye conditions and a consequence of diabetes that affects the eyes is diabetic retinopathy. The symptoms of diabetic retinopathy may be absent or minimal. It may eventually result in blindness. Therefore, seeing symptoms early could aid in preventing blindness. This paper aims to research automatic methods for detecting diabetic retinopathy and create a reliable system for doing so. A modified extracted feature for the automatic identification of DR in digital fundus pictures is presented. The properties of exudates, blood vessels, and microaneurysms—three elements of diabetic retinopathy—are reported utilizing a variety of image processing techniques. Back Propagation Neural Networks (BPNN) and Support Vector Machine (SVM) classifiers are used to categorize the phases. SVM, which has accuracy, sensitivity, and specificity of 96.5, 97.2, and 93.3 percent, respectively, is the model that performs the best overall.The eye is impacted by several disorders, either directly or indirectly. As a result, eye exams are a crucial component of general healthcare. One of the effects of diabetes is diabetic retinopathy (DR), which affects the blood vessels that supply and nourish the retina and causes severe visual loss. One of the prevalent eye conditions and a consequence of diabetes that affects the eyes is diabetic retinopathy. The symptoms of diabetic retinopathy may be absent or minimal. It may eventually result in blindness. Therefore, seeing symptoms early could aid in preventing blindness. This paper aims to research automatic methods for detecting diabetic retinopathy and create a reliable system for doing so. A modified extracted feature for the automatic identification of DR in digital fundus pictures is presented. The properties of exudates, blood vessels, and microaneurysms—three elements of diabetic retinopathy—are reported utilizing a variety of image processing techniques. Back Propagation Neural Networks and Support Vector Machine classifiers are used to categorize the phases. SVM, which has accuracy, sensitivity, and specificity of 96.5, 97.2, and 93.3 percent, respectively, is the model that performs the best overall.

Decision-feedback equalization for digital communication over dispersive channels.

ESD-TR-67-466.Bibliography: p.85.Contract AF 19(628)-5167

Dynamic construction of back-propagation artificial neural networks.

by Korris Fu-lai Chung.Thesis (M.Phil.) -- Chinese University of Hong Kong, 1991.Bibliography: leaves R-1 - R-5.LIST OF FIGURES --- p.viLIST OF TABLES --- p.viiiChapter 1 --- INTRODUCTIONChapter 1.1 --- Recent Resurgence of Artificial Neural Networks --- p.1-1Chapter 1.2 --- A Design Problem in Applying Back-Propagation Networks --- p.1-4Chapter 1.3 --- Related Works --- p.1-6Chapter 1.4 --- Objective of the Research --- p.1-8Chapter 1.5 --- Thesis Organization --- p.1-9Chapter 2 --- MULTILAYER FEEDFORWARD NETWORKS (MFNs) AND BACK-PRO- PAGATION (BP) LEARNING ALGORITHMChapter 2.1 --- Introduction --- p.2-1Chapter 2.2 --- From Perceptrons to MFNs --- p.2-2Chapter 2.3 --- From Delta Rule to BP Algorithm --- p.2-6Chapter 2.4 --- A Variant of BP Algorithm --- p.2-12Chapter 3 --- INTERPRETATIONS AND PROPERTIES OF BP NETWORKSChapter 3.1 --- Introduction --- p.3-1Chapter 3.2 --- A Pattern Classification View on BP Networks --- p.3-2Chapter 3.2.1 --- Pattern Space Interpretation of BP Networks --- p.3-2Chapter 3.2.2 --- Weight Space Interpretation of BP Networks --- p.3-3Chapter 3.3 --- Local Minimum --- p.3-5Chapter 3.4 --- Generalization --- p.3-6Chapter 4 --- GROWTH OF BP NETWORKSChapter 4.1 --- Introduction --- p.4-1Chapter 4.2 --- Problem Formulation --- p.4-1Chapter 4.3 --- Learning an Additional Pattern --- p.4-2Chapter 4.4 --- A Progressive Training Algorithm --- p.4-4Chapter 4.5 --- Experimental Results and Performance Analysis --- p.4-7Chapter 4.6 --- Concluding Remarks --- p.4-16Chapter 5 --- PRUNING OF BP NETWORKSChapter 5.1 --- Introduction --- p.5-1Chapter 5.2 --- Characteristics of Hidden Nodes in Oversized Networks --- p.5-2Chapter 5.2.1 --- Observations from an Empirical Study --- p.5-2Chapter 5.2.2 --- Four Categories of Excessive Nodes --- p.5-3Chapter 5.2.3 --- Why are they excessive ？ --- p.5-6Chapter 5.3 --- Pruning of Excessive Nodes --- p.5-9Chapter 5.4 --- Experimental Results and Performance Analysis --- p.5-13Chapter 5.5 --- Concluding Remarks --- p.5-19Chapter 6 --- DYNAMIC CONSTRUCTION OF BP NETWORKSChapter 6.1 --- A Hybrid Approach --- p.6-1Chapter 6.2 --- Experimental Results and Performance Analysis --- p.6-2Chapter 6.3 --- Concluding Remarks --- p.6-7Chapter 7 --- CONCLUSIONS --- p.7-1Chapter 7.1 --- Contributions --- p.7-1Chapter 7.2 --- Limitations and Suggestions for Further Research --- p.7-2REFERENCES --- p.R-lAPPENDIXChapter A.1 --- A Handwriting Numeral Recognition Experiment: Feature Extraction Technique and Sampling Process --- p.A-1Chapter A.2 --- Determining the distance d= δ2/2r in Lemma 1 --- p.A-

Nonlinear receivers for DS-CDMA

The growing demand for capacity in wireless communications is the driving force behind improving established networks and the deployment of a new worldwide mobile standard. Capacity calculations show that the direct sequence code division multiple access (DS-CDMA) technique has more capacity than the time division multiple access technique. Therefore, most 3rd generation mobile systems will incorporate some sort of DS-CDMA. In this thesis DS-CDMA receiver structures are investigated from the view point of pattern recognition which leads to new DS-CDMA receiver structures. It is known that the optimum DS-CDMA receiver has a nonlinear structure with prohibitive complexity for practical implementation. It is also known that the currently implemented receiver in 2nd generation DSCDMA mobile handsets has poor performance, because it suffers from multiuser interference. Consequently, this work focuses on sub-optimum nonlinear receivers for DS-CDMA in the downlink scenario. First, the thesis reviews DS-CDMA, established equalisers, DS-CDMA receivers and pattern recognition techniques. Then the new receivers are proposed. It is shown that DS-CDMA can be considered as a pattern recognition problem and hence, pattern recognition techniques can be exploited in order to develop DS-CDMA receivers. Another approach is to apply known equaliser structures for DS-CDMA. One proposed receiver is based on the Volterra series expansion and processes the received signal at the chip rate. Another receiver is a symbol rate radial basis function network (RBFN) receiver with reduced complexity. Subsequently, a receiver is proposed based on linear programming (LP) which is especially tailored for nonlinearly separable scenarios. The LP based receiver performance is equivalent to the known decorrelating detector in linearly separable scenarios. Finally, a hybrid receiver is proposed which combines LP and RBFN and which exploits knowledge gained from pattern recognition. This structure has lower complexity than the full RBF and good performance, and has a large potential for further improvements. Monte-Carlo simulations compare the proposed DS-CDMA receivers against established linear and nonlinear receivers. It is shown that all proposed receivers outperform the known linear receivers. The Volterra receiver’s complexity is relatively high for the performance gain achieved and might not suit practical implementation. The other receiver’s complexity was greatly reduced but it performs nearly as well as an optimum symbol by symbol detector. This thesis shows that DS-CDMA is a pattern recognition problem and that pattern recognition techniques can simplify DS-CDMA receiver structures. Knowledge is gained from the DSCDMA signal patterns which help to understand the problem of a DS-CDMA receiver. It should be noted that from the large number of known techniques, only a few pattern recognition techniques are considered in this work, and any further work should look at other techniques. Pattern recognition techniques can reduce the complexity of existing DS-CDMA receivers while maintaining performance, leading to novel receiver structures

An Exercise and Sports Equipment Recognition System

Most mobile health management applications today require manual input or use sensors like the accelerometer or GPS to record user data. The onboard camera remains underused. We propose an Exercise and Sports Equipment Recognition System (ESRS) that can recognize physical activity equipment from raw image data. This system can be integrated with mobile phones to allow the camera to become a primary input device for recording physical activity. We employ a deep convolutional neural network to train models capable of recognizing 14 different equipment categories. Furthermore, we propose a preprocessing scheme that uses color normalization and denoising techniques to improve recognition accuracy. Our best model is able to achieve a a top-3 accuracy of 83.3% on the test dataset. We demonstrate that our model improves upon GoogLeNet for this dataset, the state-of-the-art network which won the ILSVRC 2014 challenge. Our work is extendable as improving the quality and size of the training dataset can further boost predictive accuracy

Model-driven and Data-driven Approaches for some Object Recognition Problems

Recognizing objects from images and videos has been a long standing problem in computer vision. The recent surge in the prevalence of visual cameras has given rise to two main challenges where, (i) it is important to understand different sources of object variations in more unconstrained scenarios, and (ii) rather than describing an object in isolation, efficient learning methods for modeling object-scene `contextual' relations are required to resolve visual ambiguities. This dissertation addresses some aspects of these challenges, and consists of two parts. First part of the work focuses on obtaining object descriptors that are largely preserved across certain sources of variations, by utilizing models for image formation and local image features. Given a single instance of an object, we investigate the following three problems. (i) Representing a 2D projection of a 3D non-planar shape invariant to articulations, when there are no self-occlusions. We propose an articulation invariant distance that is preserved across piece-wise affine transformations of a non-rigid object `parts', under a weak perspective imaging model, and then obtain a shape context-like descriptor to perform recognition; (ii) Understanding the space of `arbitrary' blurred images of an object, by representing an unknown blur kernel of a known maximum size using a complete set of orthonormal basis functions spanning that space, and showing that subspaces resulting from convolving a clean object and its blurred versions with these basis functions are equal under some assumptions. We then view the invariant subspaces as points on a Grassmann manifold, and use statistical tools that account for the underlying non-Euclidean nature of the space of these invariants to perform recognition across blur; (iii) Analyzing the robustness of local feature descriptors to different illumination conditions. We perform an empirical study of these descriptors for the problem of face recognition under lighting change, and show that the direction of image gradient largely preserves object properties across varying lighting conditions. The second part of the dissertation utilizes information conveyed by large quantity of data to learn contextual information shared by an object (or an entity) with its surroundings. (i) We first consider a supervised two-class problem of detecting lane markings from road video sequences, where we learn relevant feature-level contextual information through a machine learning algorithm based on boosting. We then focus on unsupervised object classification scenarios where, (ii) we perform clustering using maximum margin principles, by deriving some basic properties on the affinity of `a pair of points' belonging to the same cluster using the information conveyed by `all' points in the system, and (iii) then consider correspondence-free adaptation of statistical classifiers across domain shifting transformations, by generating meaningful `intermediate domains' that incrementally convey potential information about the domain change