nils-herrmann/clean_scirepeval huggingface dataset:
Watermarking of Digital Images

A Note to All Readers This is not an original electronic copy of the master's thesis, but a reproduced version of the authentic hardcopy of the thesis. I lost the original electronic copy during transit from India to USA in December 1999. I could get hold of some of the older version of the files and figures. Some of the missing figures have been scanned from the photocopy version of the hardcopy of the thesis. The scanned figures have been earmarked with an asterisk. Acknowledgement I would like to profusely thank my guide Prof. K. R. Ramakrishnan for is timely advice and encouragement throughout my project work. I would also like to acknowledge Prof. M. Kankanhalli for reviewing my work from time to time. A special note of gratitude goes to Dr. S. H. Srinivas for the support he extended to this work. I would also like to thank all who helped me during my project work.
Recommendations in Signed Social Networks

Recommender systems play a crucial role in mitigating the information overload problem in social media by suggesting relevant information to users. The popularity of pervasively available social activities for social media users has encouraged a large body of literature on exploiting social networks for recommendation. The vast majority of these systems focus on unsigned social networks (or social networks with only positive links), while little work exists for signed social networks (or social networks with positive and negative links). The availability of negative links in signed social networks presents both challenges and opportunities in the recommendation process. We provide a principled and mathematical approach to exploit signed social networks for recommendation, and propose a model, RecSSN, to leverage positive and negative links in signed social networks. Empirical results on real-world datasets demonstrate the effectiveness of the proposed framework. We also perform further experiments to explicitly understand the effect of signed networks in RecSSN.
A new optimal adaptive under frequency load shedding Using Artificial Neural Networks

Different short circuits, load growth, generation shortage, and other faults which disturb the voltage and frequency stability are serious threats to the system security. The frequency and voltage instability causes dispersal of a power system into sub-systems, and leads to blackout as well as heavy damages of the system equipment. This paper presents a fast and optimal adaptive load shedding method, for isolated power system using Artificial Neural Networks (ANN). The proposed method is able to determine the necessary load shedding in all steps simultaneously and is much faster than conventional methods. This method has been tested on the New-England power system. The simulation results show that the proposed algorithm is fast, robust and optimal values of load shedding in different loading scenarios are obtained in comparison with conventional method.
Motion Detection Based on Frame Difference Method

Recent research in computer vision has increasingly focused on building systems for observing humans and understanding their look, activities, and behavior providing advanced interfaces for interacting with humans, and creating sensible models of humans for various purposes. This paper presents a new algorithm for detecting moving objects from a static background scene based on frame difference. Firstly, the first frame is captured through the static camera and after that sequence of frames is captured at regular intervals. Secondly, the absolute difference is calculated between the consecutive frames and the difference image is stored in the system. Thirdly, the difference image is converted into gray image and then translated into binary image. Finally, morphological filtering is done to remove noise.
Implicit learning and consciousness : A graded , dynamic perspective

na a a am m m mi i i ic c c c p p p pe e e er r r rs s s sp p p pe e e ec c c ct t t ti i i iv v v ve e e e While the study of implicit learning is nothing new, the field as a whole has come to embody — over the last decade or so — ongoing questioning about three of the most fundamental debates in the cognitive sciences: The nature of consciousness, the nature of mental representation (in particular the difficult issue of abstraction), and the role of experience in shaping the cognitive system. Our main goal in this chapter is to offer a framework that attempts to integrate current thinking about these three issues in a way that specifically links consciousness with adaptation and learning. Our assumptions about this relationship are rooted in further assumptions about the nature of processing and of representation in cognitive systems. When considered together, we believe that these assumptions offer a new perspective on the relationships between conscious and unconscious processing and on the function of consciousness in cognitive systems. To begin in a way that reflects the goals of this volume, we can ask the question: " What is implicit learning for? " In asking this question, one presupposes that implicit learning is a special process that can be distinguished from, say, explicit learning or, even more pointedly, from learning tout court. The most salient feature attributed to implicit learning is of course that it is implicit, by which most researchers in the area actually mean unconscious. Hence the question "What is implicit learning for?" is in fact a way of asking about the function of consciousness in learning that specifically assumes that conscious and unconscious learning have different functions. The central idea that we will develop in this chapter is that conscious and unconscious learning are actually two different expressions of a single set of constantly operating graded, dynamic processes of adaptation. While this position emphasizes that conscious and unconscious processing differ only in degree rather than in kind, it is nevertheless not incompatible with the notion that consciousness has specific functions in the cognitive economy. Indeed, our main conclusion will be that the function of consciousness is to offer flexible adaptive control over behavior. By adaptive here, we do not mean simply the …
A nanowatt cascadable delay element for compact power-on-reset (POR) circuits

A cascadable power-on-reset (POR) delay element consuming nanowatt of peak power was developed to be used in very compact power-on-reset pulse generator (POR-PG) circuits. Operation principles and features of the POR delay element were presented in this paper. The delay element was designed, and fabricated in a 0.5µm 2P3M CMOS process. It was determined from simulation as well as measurement results that the delay element works wide supply voltage ranges between 1.8 volt and 5 volt and supply voltage rise times between 100nsec and 1msec allowing wide dynamic range POR-PG circuits. It also has very small silicon footprint. Layout size of a single POR delay element was 35µm x 55µm in 0.5µm CMOS process.
Viewstamped Replication: A New Primary Copy Method to Support Highly-Available Distributed Systems

One of the potential benefits of distributed systems is their use in providing highly-available services that are likely to be usable when needed. Availabilay is achieved through replication. By having inore than one copy of information, a service continues to be usable even when some copies are inaccessible, for example, because of a crash of the computer where a copy was stored. This paper presents a new replication algorithm that has desirable performance properties. Our approach is based on the primary copy technique. Computations run at a primary. which notifies its backups of what it has done. If the primary crashes, the backups are reorganized, and one of the backups becomes the new primary. Our method works in a general network with both node crashes and partitions. Replication causes little delay in user computations and little information is lost in a reorganization; we use a special kind of timestamp called a viewstamp to detect lost information.
Ranking Paragraphs for Improving Answer Recall in Open-Domain Question Answering

Recently, open-domain question answering (QA) has been combined with machine comprehension models to find answers in a large knowledge source. As open-domain QA requires retrieving relevant documents from text corpora to answer questions, its performance largely depends on the performance of document retrievers. However, since traditional information retrieval systems are not effective in obtaining documents with a high probability of containing answers, they lower the performance of QA systems. Simply extracting more documents increases the number of irrelevant documents, which also degrades the performance of QA systems. In this paper, we introduce Paragraph Ranker which ranks paragraphs of retrieved documents for a higher answer recall with less noise. We show that ranking paragraphs and aggregating answers using Paragraph Ranker improves performance of open-domain QA pipeline on the four opendomain QA datasets by 7.8% on average.
Neural Attention for Learning to Rank Questions in Community Question Answering

In real-world data, e.g., from Web forums, text is often contaminated with redundant or irrelevant content, which leads to introducing noise in machine learning algorithms. In this paper, we apply Long Short-Term Memory networks with an attention mechanism, which can select important parts of text for the task of similar question retrieval from community Question Answering (cQA) forums. In particular, we use the attention weights for both selecting entire sentences and their subparts, i.e., word/chunk, from shallow syntactic trees. More interestingly, we apply tree kernels to the filtered text representations, thus exploiting the implicit features of the subtree space for learning question reranking. Our results show that the attention-based pruning allows for achieving the top position in the cQA challenge of SemEval 2016, with a relatively large gap from the other participants while greatly decreasing running time.
3D Mesh Labeling via Deep Convolutional Neural Networks

This article presents a novel approach for 3D mesh labeling by using deep Convolutional Neural Networks (CNNs). Many previous methods on 3D mesh labeling achieve impressive performances by using predefined geometric features. However, the generalization abilities of such low-level features, which are heuristically designed to process specific meshes, are often insufficient to handle all types of meshes. To address this problem, we propose to learn a robust mesh representation that can adapt to various 3D meshes by using CNNs. In our approach, CNNs are first trained in a supervised manner by using a large pool of classical geometric features. In the training process, these low-level features are nonlinearly combined and hierarchically compressed to generate a compact and effective representation for each triangle on the mesh. Based on the trained CNNs and the mesh representations, a label vector is initialized for each triangle to indicate its probabilities of belonging to various object parts. Eventually, a graph-based mesh-labeling algorithm is adopted to optimize the labels of triangles by considering the label consistencies. Experimental results on several public benchmarks show that the proposed approach is robust for various 3D meshes, and outperforms state-of-the-art approaches as well as classic learning algorithms in recognizing mesh labels.
3D Photography on Your Desk

A simple and inexpensive approach for extracting the threedimensional shape of objects is presented. It is based on `weak structured lighting'; it di ers from other conventional structured lighting approaches in that it requires very little hardware besides the camera: a desk-lamp, a pencil and a checkerboard. The camera faces the object, which is illuminated by the desk-lamp. The user moves a pencil in front of the light source casting a moving shadow on the object. The 3D shape of the object is extracted from the spatial and temporal location of the observed shadow. Experimental results are presented on three di erent scenes demonstrating that the error in reconstructing the surface is less than 1%.
Intelligent data mining and personalisation for customer relationship management

Customer relationship management (CRM) initiatives have gained much attention in recent years. With the aid of data mining technology, businesses can formulate specific strategies for different customer bases more precisely. Additionally, personalisation is another important issue in CRM - especially when a company has a huge product range. This paper presents a case model and investigates the use of computational intelligent techniques for CRM. These techniques allow the complex functions of relating customer behaviour to internal business processes to be learned more easily and the industry expertise and experience from business managers to be integrated into the modelling framework directly. Hence, they can be used in the CRM framework to enhance the creation of targeted strategies for specific customer bases.
Efficient logo retrieval through hashing shape context descriptors

In this paper we present a method for organizing and indexing logo digital libraries like the ones of the patent and trademark offices. We propose an efficient queried-by-example retrieval system which is able to retrieve logos by similarity from large databases of logo images. Logos are compactly described by a variant of the shape context descriptor. These descriptors are then indexed by a locality-sensitive hashing data structure aiming to perform approximate k-NN search in high dimensional spaces in sub-linear time. The experiments demonstrate the effectiveness and efficiency of this system on realistic datasets as the Tobacco-800 logo database.
Automatic Number Plate Recognition for Australian Conditions

We consider the task of recognition of Australian vehicle number plates (also called license plates or registration plates in other countries). A system for Australian number plate recognition must cope with wide variations in the appearance of the plates. Each state uses its own range of designs with font variations between the designs. There are special designs issued for significant events such as the Sydney 2000 Olympic Games. Also, vehicle owners may place the plates inside glass covered frames or use plates made of non-standard materials. These issues compound the complexity of automatic number plate recognition, making existing approaches inadequate. We have developed a system that incorporates a novel combination of image processing and artificial neural network technologies to successfully locate and read Australian vehicle number plates in digital images. Commercial application of the system is envisaged.
Small Drones for Community-Based Forest Monitoring: An Assessment of Their Feasibility and Potential in Tropical Areas

Data gathered through community-based forest monitoring (CBFM) programs may be as accurate as those gathered by professional scientists, but acquired at a much lower cost and capable of providing more detailed data about the occurrence, extent and drivers of forest loss, degradation and regrowth at the community scale. In addition, CBFM enables greater survey repeatability. Therefore, CBFM should be a fundamental component of national forest monitoring systems and programs to measure, report and verify (MRV) REDD+ activities. To contribute to the development of more effective approaches to CBFM, in this paper we assess: (1) the feasibility of using small, low-cost drones (i.e., remotely piloted aerial vehicles) in CBFM programs; (2) their potential advantages and disadvantages for communities, partner organizations and forest data end-users; and (3) to what extent their utilization, coupled with ground surveys and local ecological knowledge, OPEN ACCESS Forests 2014, 5 1482 would improve tropical forest monitoring. To do so, we reviewed the existing literature regarding environmental applications of drones, including forest monitoring, and drew on our own firsthand experience flying small drones to map and monitor tropical forests and training people to operate them. We believe that the utilization of small drones can enhance CBFM and that this approach is feasible in many locations throughout the tropics if some degree of external assistance and funding is provided to communities. We suggest that the use of small drones can help tropical communities to better manage and conserve their forests whilst benefiting partner organizations, governments and forest data end-users, particularly those engaged in forestry, biodiversity conservation and climate change mitigation projects such as REDD+.
Computational techniques for spatial logistic regression with large data sets

In epidemiological research, outcomes are frequently non-normal, sample sizes may be large, and effect sizes are often small. To relate health outcomes to geographic risk factors, fast and powerful methods for fitting spatial models, particularly for non-normal data, are required. I focus on binary outcomes, with the risk surface a smooth function of space, but the development herein is relevant for non-normal data in general. I compare penalized likelihood models, including the penalized quasi-likelihood (PQL) approach, and Bayesian models based on fit, speed, and ease of implementation.A Bayesian model using a spectral basis representation of the spatial surface via the Fourier basis provides the best tradeoff of sensitivity and specificity in simulations, detecting real spatial features while limiting overfitting and being reasonably computationally efficient. One of the contributions of this work is further development of this underused representation. The spectral basis model outperforms the penalized likelihood methods, which are prone to overfitting, but is slower to fit and not as easily implemented. A Bayesian Markov random field model performs less well statistically than the spectral basis model, but is very computationally efficient. We illustrate the methods on a real dataset of cancer cases in Taiwan.The success of the spectral basis with binary data and similar results with count data suggest that it may be generally useful in spatial models and more complicated hierarchical models.
A safe generic adaptation mechanism for smart cars

Today's vehicles are evolving towards smart cars, which will be able to drive autonomously and adapt to changing contexts. Incorporating self-adaptation in these cyber-physical systems (CPS) promises great benefits, like cheaper software-based redundancy or optimised resource utilisation. As promising as these advantages are, a respective proportion of a vehicle's functionality poses as safety hazards when confronted with fault and failure situations. Consequently, a system's safety has to be ensured with respect to the availability of multiple software applications, thus often resulting in redundant hardware resources, such as dedicated backup control units. To benefit from self-adaptation by means of creating efficient and safe systems, this work introduces a safety concept in form of a generic adaptation mechanism (GAM). In detail, this generic adaptation mechanism is introduced and analysed with respect to generally known and newly created safety hazards, in order to determine a minimal set of system properties and architectural limitations required to safely perform adaptation. Moreover, the approach is applied to the ICT architecture of a smart e-car, thereby highlighting the soundness, general applicability, and advantages of this safety concept and forming the foundation for the currently ongoing implementation of the GAM within a real prototype vehicle.
Predicting tie strength with social media

Social media treats all users the same: trusted friend or total stranger, with little or nothing in between. In reality, relationships fall everywhere along this spectrum, a topic social science has investigated for decades under the theme of tie strength. Our work bridges this gap between theory and practice. In this paper, we present a predictive model that maps social media data to tie strength. The model builds on a dataset of over 2,000 social media ties and performs quite well, distinguishing between strong and weak ties with over 85% accuracy. We complement these quantitative findings with interviews that unpack the relationships we could not predict. The paper concludes by illustrating how modeling tie strength can improve social media design elements, including privacy controls, message routing, friend introductions and information prioritization.
An Ultra-Low-Voltage CMOS Process-Insensitive Self-Biased OTA With Rail-to-Rail Input Range

An operational-transconductance-amplifier (OTA) design for ultra-low voltage ultra-low power applications is proposed. The input stage of the proposed OTA utilizes a bulk-driven pseudo-differential pair to allow minimum supply voltage while achieving a rail-to-rail input range. All the transistors in the proposed OTA operate in the subthreshold region. Using a novel self-biasing technique to bias the OTA obviates the need for extra biasing circuitry and enhances the performance of the OTA. The proposed technique ensures the OTA robustness to process variations and increases design feasibility under ultra-low-voltage conditions. Moreover, the proposed biasing technique significantly improves the common-mode and power-supply rejection of the OTA. To further enhance the bandwidth and allow the use of smaller compensation capacitors, a compensation network based on a damping-factor control circuit is exploited. The OTA is fabricated in a 65 nm CMOS technology. Measurement results show that the OTA provides a low-frequency gain of 46 dB and rail-to-rail input common-mode range with a supply voltage as low as 0.5 V. The dc gain of the OTA is greater than 42 dB for supply voltage as low as 0.35 V. The power dissipation is 182 μW at VDD=0.5 V and 17 μW at VDD=0.35 V.
Improvement of the Quality Factor of RF Integrated Inductors by Layout Optimization

A systematic method to improve the quality ( ) factor of RF integrated inductors is presented in this paper. The proposed method is based on the layout optimization to minimize the series resistance of the inductor coil, taking into account both ohmic losses, due to conduction currents, and magnetically induced losses, due to Eddy currents. The technique is particularly useful when applied to inductors in which the fabrication process includes integration substrate removal. However, it is also applicable to inductors on low-loss substrates. The method optimizes the width of the metal strip for each turn of the inductor coil, leading to a variable strip-width layout. The optimization procedure has been successfully applied to the design of square spiral inductors in a silicon-based multichip-module technology, complemented with silicon micromachining postprocessing. The obtained experimental results corroborate the validity of the proposed method. A factor of about 17 have been obtained for a 35-nH inductor at 1.5 GHz, with values higher than 40 predicted for a 20-nH inductor working at 3.5 GHz. The latter is up to a 60% better than the best results for a single strip-width inductor working at the same frequency.