Miracle-dz/UniFAR-FTUs huggingface dataset:
Assistive System for Visually Impaired using Object Recognition

Object recognition based electronic aid is most promising for visually impaired people to get description of nearby objects. With the advancement in computer vision and computing technologies we can afford to develop a system for visually impaired people, which can give audio feedback of surrounding objects and context. This thesis explore object recognition method to assist visually impaired people. We proposed an object recognition algorithm, and an assistive system which is very useful for their safety, quality life and freedom from other person all the time. Consideration of Gabor-recursive neural network along with convolutional recursive neural network(CRNN) with less number of maps have been found to be very promising to achieve better accuracy with less time complexity as compare to CRNN, the extracted feature vector is used to train Softmax classifier which is then used to classify query image into one of the trained categories. Our color recognition algorithm is simple and fast, which is the desiderata of color recognition module, we are using HSI color space with observed threshold and random sampling. The second contribution of this thesis is to use above stated methods to assist visually impaired people, the proposed assistive system is ensemble of two modules (i) object and (ii) color recognition, it is implemented using multimedia processor equipped embedded board and OpenCV. Object recognition module recognize objects such as door, chair, stairs, mobile phone etc. and generate an audio feedback to the user. Color recognition module generates audio description about object color in front of the camera, it is useful in recognizing clothing colors, fruits color etc. The system modules and operation can be selected using an on demand push button panel which contains two push buttons. The object recognition algorithm is evaluated on on-line available dataset as well as on our dataset and compared with state of art methods.
RESEARCH ON LEARNING INTRODUCTORY BIOMECHANICSIMECHANICS

Most students find mechanics and biomechanics concepts munterintuitive and difficult, and therefore have a negative perception of the subject. This paper reviews the research on learning biomechanical concepts, noting recent advances in standardized tests, identifying factors associated with learning, and strategies to increase learning. Active learning pedagogies typically double learning of mechanics and biomechanics concepts compared to traditional lecturellab instruction. The Scholarship of Teachlng and Learnlng (SoTL) literature in many sclences clearly supports use of active learning strategies, focusing on student engagement through personal and career interests, and emphasis on qualitative understanding of concepts. Biomechanics faculty can advance student interest in the field by continued faculty engagement and support of SoTL research.
Method for Adjusting Single Matching Network for High‐Power Transfer Efficiency of Wireless Power Transfer System

A wireless power transfer (WPT) system is generally designed with the optimum source and load impedance in order to achieve the maximum power transfer efficiency (PTE) at a specific coupling coefficient. Empirically or intuitively, however, it is well known that a high PTE can be attained by adjusting either the source or load impedance. In this paper, we estimate the maximum achievable PTE of WPT systems with the given load impedance, and propose the condition of source impedance for the maximum PTE. This condition can be reciprocally applied to the load impedance of a WPT system with the given source impedance. First, we review the transducer power gain of a two‐port network as the PTE of the WPT system. Next, we derive two candidate conditions, the critical coupling and the optimum conditions, from the transducer power gain. Finally, we compare the two conditions carefully, and the results therefore indicate that the optimum condition is more suitable for a highly efficient WPT system with a given load impedance.
Fast Algorithms for Large-Scale Network Analytics

Today’s networks are massive and dynamic; Facebook with a billion of users and a trillion of connections and Twitter with ∼600 millions of users tweeting ∼9,000 times in a second are just a few examples. Making sense of these graphs in static and dynamic scenarios is essential. Most of the existing algorithms assume that the graph is static and it does not change. Today, these assumptions are no more valid. Fast algorithms for streaming and parallel scenarios are necessary to process graphs of massive sizes. Compression techniques are also quite necessary to deal with the size. In our work, we provide compression, streaming, and parallel algorithms for three important graph analytics problems: centrality computation, dense subgraph discovery and community detection. In addition, we introduce new dense subgraph discovery algorithms to better model the cohesion in real-world networks. Centrality metrics, such as betweenness and closeness, quantify how central a node is in a network. They have been used successfully to carry various analyses such as structural analysis of knowledge networks, power grid contingency analysis, quantifying importance in social networks, analysis of covert networks and decision/action networks, and even for finding the best store locations in cities. However, they are
Analyzing Security Decisions with Discrete Event Simulation

As organizations become increasingly more dependent on information security in order to succeed, the security decisions made by the Chief Information Security Officer (CISO) also becomes important and needs to be considered in the context of the organization. However, since the complexity of the organization’s internal processes and the threats the organization is facing, the CISO needs a decision making tool or method in order to determine the effects of a specific security decision. Because of this, we have in this thesis determined the suitability of utilizing Key Performance Indicators (KPIs) and Discrete Event Simulation (DES) as a method to help the CISO make the "best" security decision for his organization. The thesis is based on a health care specific scenario which has been constructed in collaboration with Akershus University Hospital (Ahus), Rheumatism Hospital at Lillehammer and Buypass. The scenario includes a patient treatment process and the processes related to the usage of smart cards and passwords as authentication mechanisms. Furthermore, KPIs which focuses on time usage and number of deviations has been identified, where deviations within this health care specific scenario relates to more traditional security incidents. A case study was then conducted based on the scenario. The results of this case study indicate no statistical significant difference between the two authentication mechanisms with regards to the average time a doctor uses on a business activity. However, based on the number of deviations identified, smart cards were determined the preferred security measure of the two. In order to determine the suitability of the simulation approach, a second case study was also conducted. This second case study was based on the same scenario, but this time with a non-simulation approach. By comparing the process surrounding the two case studies, the non-simulation approach were determined the most cost-effective approach and the approach which provided the most direct link between the input data and the results. Based on this, the non-simulation approach was also determined the most suitable approach. However, we did determine that for "what if" analysis, the simulation approach becomes the best choice of the two. Should a "what if" analysis be desirable, we have in this thesis proposed a new methodology which modelers can utilize in order to reduces the complexity of the model building process. The methodology, called Minimalistic Model Design (MIMD), excludes the temporal relationship between the identified business activities within the business process. This exclusion helps to reduce total time used on the model building process, and enables better scalability.
Improved Face Recognition Method Based on NMF

Non-negative matrix factorization(NMF) is a method of parts-based feature extraction,according to this characteristic.An improved algorithm dealing with NMF basis was introduced to enhance partial occlusion face recognition rate.Firstly,discrete wavelet transformation was used to produce a representation in the low frequency domain,and basic matrix was got according to the NMF method.Secondly,parts of face features which possess outstanding perfor-mance were extracted by threshold value judgments,and they were used to form optimized facial subspace feature.The training and testing images were projected to the optimized subspace feature.Finally support vector machine was used for classification.Experiments show that the improved algorithm's computing time is short,and this method achieves remarkable effects under the partially occluded circumstance.
Augmented Reality Storytelling

Modern storytelling technology enables producing and consuming stories anywhere and anytime. Multimedia content is authored collaboratively and shared with a large audience in the cloud. Although such tools are very accessible and easy to use — possibly the simplest form of expressing a feeling is to click the like button — the produced content is not only short form, but also short-lived. The majority of stories are of a superficial nature and deeper, more complex emotions are lost. In this thesis, we aim to leverage novel digital technology to allow people to craft and experience rich stories together. We demonstrate that Augmented Reality (AR) is a promising partner to storytelling because, first, it can vastly enhance the immersiveness and interactivity of a narrative application and, second, it can create a social setting for multiple people to share the experience. The core challenges we address are (1) computer-assisted collaborative and interactive story authoring, (2) realism and interactivity in AR storytelling and, finally, (3) we present compelling, interactive AR applications that boost creativity in real-world activities. (1) In the story authoring domain, we present novel and innovative tools that improve collaborative as well as interactive storytelling. Our distributed, collaborative story authoring system, Story Version Control (SVC), focuses on version control for stories as well as on graphical visualization techniques to enhance collaboration among authors. We propose a media-agnostic, graph-based story representation that acts as the foundation for version control operations. A user study validated the efficacy of SVC and provided directions for
Energy Efficient Clustering Algorithm with Global and Local Re-clustering for Wireless Sensor Networks

power sensor nodes deployed to monitor the environment and collect data. Gathering information in an energy efﬁcient manner is a critical aspect to prolong the network lifetime. Clustering algorithms have an advantage of enhancing the network lifetime. Current clustering algorithms usually focus on global re-clustering and local re-clustering separately. This paper, proposed a combination of those two re-clustering methods to reduce the energy consumption of the network. Furthermore, the proposed algorithm can apply to homogeneous as well as heterogeneous wireless sensor networks. In addition, the cluster head rotation happens, only when its energy drops below a dynamic threshold value computed by the algorithm. The simulation result shows that the proposed algorithm prolong the network lifetime compared to existing algorithms.
Computational methods for integrated analysis of omics and pathway data

ix 1. GENERAL INTRODUCTION . 1 2. A COMPUTATIONAL PLATFORM TO MAINTAIN AND MIGRATE MANUAL FUNCTIONAL ANNOTATIONS FOR BIOCYC DATABASES 9 Abstract . 9 Background . 10 Implementation . 12 Results and Discussion . 17 Use Case: MaizeCyc and CornCyc GO Term Annotation Migration . 17 Use Case: Creating Strain-specific EcoCyc Databases . 19 Conclusions . 21 3. THE QUALITY OF METABOLIC PATHWAY RESOURCES DEPENDS ON INITIAL ENZYMATIC FUNCTIONAL ASSIGNMENTS AND LEVEL OF MANUAL CURATION: A CASE FOR MAIZE . 33 Abstract . 33 Introduction . 34
Search by Image: A Novel Approach to Content Based Image Retrieval System

Image Retrieval could be a technique of looking out, browsing, and retrieving the pictures from a picture database. The Image search engines largely admit close text options. It’s troublesome for search engines to interpret users’ search intention solely by keywords and this ends up in ambiguous and reedy search results that are removed from satisfactory. It’s necessary to use content primarily based search so as to unravel the paradox in text-based image retrieval. The present paper discusses the idea and functioning of most popular nonprofit Reverse Image Search Engines. We have chosen Dr. S. R. Ranganthan image to ascertain the performance and analysis of the results from most well-liked reverse search engines.
NEUCOGAR: A Neuromodulating Cognitive Architecture for Biomimetic Emotional AI

This paper introduces a new model of artificial cognitive architecture for intelligent systems, the Neuromodulating Cognitive Architecture (NEUCOGAR). The model is biomimetically inspired and adapts the neuromodulators role of human brains into computational environments. This way we aim at achieving more efficient Artificial Intelligence solutions based on the biological inspiration of the deep functioning of human brain, which is highly emotional. The analysis of new data obtained from neurology, psychology philosophy and anthropology allows us to generate a mapping of monoamine neuromodulators and to apply it to computational system parameters. Artificial cognitive systems can then better perform complex tasks (regarding information selection and discrimination, attention, innovation, creativity,…) as well as engaging in affordable emotional relationships with human users.
A Chip for Selforganizing Feature Maps

The use of self-organizing feature maps in real-time applications requires a high computational performance. Especially for embedded systems neural network chips are needed. In this paper a fabricated integrated circuit for self-organizing feature maps is presented. The architecture of this digital chip is based on the idea, that restrictions to the algorithm can simplify the implementation. Using the Manhattan Distance and a special treatment of the adaptation factor α decreases the necessary chip area, so that a high number of processor elements can be integrated on one chip. The effects of these restrictions on the function of the self-organizing feature map are discussed. The paper concludes with performance figures for a system architecture based on these chips
Fostering Better Learning of Science Concepts through Creative Visualization

The study explored the use of creative visualization to foster Grade 6 students' better learning of science concepts. Creative visualization activities were given on a regular basis for one quarter, and students' outputs were compiled in their science folio. The findings demonstrate that, though the young learners were initially doubtful of their ability in doing the activities as it was their first time to do them in their science class, the creative visualization activities made them learn the science concepts better, redounding to a significant increase in the their post-test scores as compared to their pretest scores.
Stochastic Gradient MCMC: Algorithms and Applications

Despite the powerful advantages of Bayesian inference such as quantifying uncertainty, ac- curate averaged prediction, and preventing overfitting, the traditional Markov chain Monte Carlo (MCMC) method has been regarded unsuitable for large-scale problems because it required processing the entire dataset per iteration rather than using a small random mini- batch as performed in the stochastic gradient optimization. The first attempt toward the scalable MCMC method based on stochastic gradients is the stochastic gradient Langevin dynamics (SGLD) proposed by Welling and Teh [2011]. Originated from the Langevin Monte Carlo method, SGLD achieved O(n) computation per iteration (here, n is the size of a minibatch) by using stochastic gradients estimated using minibatches and skipping the Metropolis-Hastings accept-reject test.In this thesis, we introduce recent advances in the stochastic gradient MCMC method since the advent of SGLD. Our contributions are two-fold: algorithms and applications. In the algorithm part, we first propose the stochastic gradient Fisher scoring algorithm (SGFS) which resolves two drawbacks of SGLD: the poor mixing rate and the arbitrarily large bias occurred when using large step sizes. Then, we also propose the distributed SGLD (D-SGLD) algorithm which makes it possible to extend the power of stochastic gradient MCMC to the distributed computing systems. In the second part, we apply the developed SG-MCMC algorithms to the most popular large-scale problems: the topic modeling using the latent Dirichlet allocation model, recommender systems using matrix factorization, and community modeling in social networks using mixed membership stochastic blockmodels. By resolving the unique challenges raised by each of the applications, which make it difficult to directly use the existing SG-MCMC methods, we obtain the-state-of-the-art results outperforming existing approaches using collapsed Gibbs sampling, stochastic variational inference, or dis- tributed stochastic gradient descent.
Do You See What I See? The Neural Bases of Joint Attention

In the simplest social interactions, two people look at each other. Gazing face‐ to‐face is infants' first social experience with their parents, for example. Most social interactions between humans, however, don't stop with mutual gaze. Instead, people quickly switch to sharing attention on an object or topic of communication. I point out the flowers, you mention your sister's graduation, we both smile at the thought. In each of these cases, our social interaction has at least three elements: two people, coordinating attention (and perception, and emotional reactions) on a third element. These core elements of social interaction are called " joint attention " (or sometimes " triadic attention, " to make the distinction from simple mutual gaze). These triadic interactions provide an infant with a platform by which she can learn about her world. Similarly, they provide the caretaker with a platform by which she can guide the infants learning. Joint attention continues to be an important tool throughout life; however, it is particularly critical in infancy and early childhood when social interaction is the primary means of learning. Before an infant has acquired abstract symbols (words) for objects, her medium of communication (and learning) about her world with others is constrained to points,
Planning Useful Evaluations Evaluability Assessment

Thank you for downloading planning useful evaluations evaluability assessment. Maybe you have knowledge that, people have search hundreds times for their chosen readings like this planning useful evaluations evaluability assessment, but end up in malicious downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they are facing with some malicious virus inside their computer.
Individualized Head-Related Transfer Functions: Efficient Modeling and Estimation from Small Sets of Spatial Samples

Acknowledgments This work has been shaped by many great individuals who have unselfishly shared their passions and knowledge with me as I have struggled to absorb and appreciate all that I have been introduced to here at Carnegie Mellon, back at the Air Force Research Labs, and at home. I would like to to thank several of these individuals in particular. First, I'd like to thank my committee members whose guidance and encouragement have helped advance this work since its infancy as a fuzzy idea I brought with me to CMU. Dr. Marios Savvides brought life and interest to my pattern recognition class, and helped me recognize and appreciate the good, the bad, and the ugly. Dr. Bhiksha Raj inspired the first iteration of this work in his machine learning for signal processing course, and opened my eyes to the power of signal processing as a tool for discovery. Dr. Douglas Brungart gave me my first real job several years ago as a freshman at Wright State; He perpetually pushed me with projects well outside my limited knowledge base, and is undeniably responsible for a great amount of my success. And finally Prof. Richard Stern, my advisor, and perhaps the one person at CMU willing to stick with a kid foolish enough to change his major, state, and marital status all his first year of graduate school. He has patiently provided me with invaluable amounts of practical guidance and instruction while giving me the space and confidence to pursue my own ideas, something for which I am continually grateful. Second, I would like to thank my colleagues and mentors at the Battlespace Acoustics Branch; to Dr. Brian Simpson and Dr. Nandini Iyer who along with Doug have raised me up as part of the 6.1 team I will soon join officially, and to Dr. Todd Nelson and Robert McKinley, who help guide me through the chaos that comes along with my unique funding and employment situation. Also, I would like to acknowledge the Science, Mathematics and Research for Transformation (SMART) scholarship program for funding me through my degree program as well as the financial and facilities support provided by the 711th Human Performance Wing of the Air Force Research Labs. Lastly, I would like to thank my family. My parents, Libbie and Terry Romigh, who instilled in me an appreciation for knowledge, but much more than that, they taught me …
Var-CNN: A Data-Efficient Website Fingerprinting Attack Based on Deep Learning

Abstract In recent years, there have been several works that use website fingerprinting techniques to enable a local adversary to determine which website a Tor user visits. While the current state-of-the-art attack, which uses deep learning, outperforms prior art with medium to large amounts of data, it attains marginal to no accuracy improvements when both use small amounts of training data. In this work, we propose Var-CNN, a website fingerprinting attack that leverages deep learning techniques along with novel insights specific to packet sequence classification. In open-world settings with large amounts of data, Var-CNN attains over 1% higher true positive rate (TPR) than state-of-the-art attacks while achieving 4× lower false positive rate (FPR). Var-CNN’s improvements are especially notable in low-data scenarios, where it reduces the FPR of prior art by 3.12% while increasing the TPR by 13%. Overall, insights used to develop Var-CNN can be applied to future deep learning based attacks, and substantially reduce the amount of training data needed to perform a successful website fingerprinting attack. This shortens the time needed for data collection and lowers the likelihood of having data staleness issues.
A boundary formula for reproducing kernel Hilbert spaces of real harmonic functions in Lipschitz domains

This paper develops a new Hilbert space method to characterize a family of reproducing kernel Hilbert spaces of real harmonic functions in a bounded Lipschitz domain . Such method involves some families of positive self-adjoint operators and makes use of characterizations of their trace data and of a special inner product on . We also establish boundary representation results for this family in terms of the -Bergman kernel. In particular, a boundary integral representation for the very weak solution of the Dirichlet problem for Laplace's equation with -boundary data is provided. Reproducing kernels and orthonormal bases for the harmonic spaces are also found.