nils-herrmann/clean_scirepeval huggingface dataset:
Autonomous navigation robot for landmine detection applications

Modern robotic technologies have provided efficient solutions to protect workers from hazards in the work environments; such as radioactive, toxic, or explosive. This paper presents a design of a holonomic mobile robot for rough terrain that can replace the human role in the demining applications. A suspension system based on the bogie mechanism was adopted in order to overcome rough terrains. The flexible design of the robot enables it to move in any direction at any instant without changing the orientation of the body. The motion of the robot was simulated using the Pro-Engineer software and the robot stability margins for different road conditions were found in order to predict the possibility of tipping-over before any maneuver.
Evolutionary Foundations of Cultural Variation : Evoked Culture and Mate Preferences

We articulate an evolutionary perspective on cultural variation, centering on the concept of evoked culture. We then demonstrate how the framework of evoked culture has been used to predict and explain cultural variation and report new tests of hypotheses about cultural variation in mate preferences. These tests demonstrate the predictive power of ecological variables such as parasite prevalence that are implicated by evolutionary psychological theories. New empirical tests provided little support for the predictions advanced by competing social role theories (e.g., Eagly & Wood, 1999), with some findings running opposite to those predicted by such theories. We propose that a well-articulated evolutionary perspective on cultural variation may be particularly useful because it can specify how variation in cultural practice itself may emerge. We conclude that discussions of cultural variation should move beyond false dichotomies of social versus biological and suggest that evolutionary psychology provides frameworks that transcend these dichotomies.
Permanent magnet-assisted omnidirectional ball drive

We present an omnidirectional ball wheel drive design that utilizes a permanent magnet as the drive roller to generate the contact force. Particularly interesting for novel human-mobile robot interaction scenarios where the users are expected to physically interact with many palm-sized robots, our design combines simplicity, low cost and compactness. We first detail our design and explain its key parameters. Then, we present our implementation and compare it with an omniwheel drive built with identical conditions and similar cost. Finally, we elaborate on the main advantages and drawbacks of our design.
Community-Affiliation Graph Model for Overlapping Network Community Detection

One of the main organizing principles in real-world networks is that of network communities, where sets of nodes organize into densely linked clusters. Communities in networks often overlap as nodes can belong to multiple communities at once. Identifying such overlapping communities is crucial for the understanding the structure as well as the function of real-world networks. Even though community structure in networks has been widely studied in the past, practically all research makes an implicit assumption that overlaps between communities are less densely connected than the non-overlapping parts themselves. Here we validate this assumption on 6 large scale social, collaboration and information networks where nodes explicitly state their community memberships. By examining such ground-truth communities we find that the community overlaps are more densely connected than the non-overlapping parts, which is in sharp contrast to the conventional wisdom that community overlaps are more sparsely connected than the communities themselves. Practically all existing community detection methods fail to detect communities with dense overlaps. We propose Community-Affiliation Graph Model, a model-based community detection method that builds on bipartite node-community affiliation networks. Our method successfully captures overlapping, non-overlapping as well as hierarchically nested communities, and identifies relevant communities more accurately than the state-of-the-art methods in networks ranging from biological to social and information networks.
Delta-Sigma FDC Based Fractional-N PLLs

Fractional-N phase-locked loop frequency synthesizers based on time-to-digital converters (TDC-PLLs) have been proposed to reduce the area and linearity requirements of conventional PLLs based on delta-sigma modulation and charge pumps (ΔΣ-PLLs). Although TDC-PLLs with good performance have been demonstrated, TDC quantization noise has so far kept their phase noise and spurious tone performance below that of the best comparable ΔΣ-PLLs. An alternative approach is to use a delta-sigma frequency-to-digital converter (ΔΣ FDC) in place of a TDC to retain the benefits of TDC-PLLs and ΔΣ-PLLs. This paper proposes a practical ΔΣ FDC based PLL in which the quantization noise is equivalent to that of a ΔΣ-PLL. It presents a linearized model of the PLL, design criteria to avoid spurious tones in the ΔΣFDC quantization noise, and a design methodology for choosing the loop parameters in terms of standard PLL target specifications.
DAISY: An Efficient Dense Descriptor Applied to Wide-Baseline Stereo

In this paper, we introduce a local image descriptor, DAISY, which is very efficient to compute densely. We also present an EM-based algorithm to compute dense depth and occlusion maps from wide-baseline image pairs using this descriptor. This yields much better results in wide-baseline situations than the pixel and correlation-based algorithms that are commonly used in narrow-baseline stereo. Also, using a descriptor makes our algorithm robust against many photometric and geometric transformations. Our descriptor is inspired from earlier ones such as SIFT and GLOH but can be computed much faster for our purposes. Unlike SURF, which can also be computed efficiently at every pixel, it does not introduce artifacts that degrade the matching performance when used densely. It is important to note that our approach is the first algorithm that attempts to estimate dense depth maps from wide-baseline image pairs, and we show that it is a good one at that with many experiments for depth estimation accuracy, occlusion detection, and comparing it against other descriptors on laser-scanned ground truth scenes. We also tested our approach on a variety of indoor and outdoor scenes with different photometric and geometric transformations and our experiments support our claim to being robust against these.
Mobile App Tagging

Mobile app tagging aims to assign a list of keywords indicating core functionalities, main contents, key features or concepts of a mobile app. Mobile app tags can be potentially useful for app ecosystem stakeholders or other parties to improve app search, browsing, categorization, and advertising, etc. However, most mainstream app markets, e.g., Google Play, Apple App Store, etc., currently do not explicitly support such tags for apps. To address this problem, we propose a novel auto mobile app tagging framework for annotating a given mobile app automatically, which is based on a search-based annotation paradigm powered by machine learning techniques. Specifically, given a novel query app without tags, our proposed framework (i) first explores online kernel learning techniques to retrieve a set of top-N similar apps that are semantically most similar to the query app from a large app repository; and (ii) then mines the text data of both the query app and the top-N similar apps to discover the most relevant tags for annotating the query app. To evaluate the efficacy of our proposed framework, we conduct an extensive set of experiments on a large real-world dataset crawled from Google Play. The encouraging results demonstrate that our technique is effective and promising.
Comparative Study of Chronic Kidney Disease Prediction using KNN and SVM

Chronic kidney disease (CKD), also known as chronic renal disease. Chronic kidney disease involves conditions that damage your kidneys and decrease their ability to keep you healthy. You may develop complications like high blood pressure, anemia (low blood count), weak bones, poor nutritional health and nerve damage. . Early detection and treatment can often keep chronic kidney disease from getting worse.  Mining is the term used for knowledge discovery from large databases. The task of data mining is to make use of historical data, to discover regular patterns and improve future decisions, follows from the convergence of several recent trends: the lessening cost of large data storage devices and the everincreasing ease of collecting data over networks; the expansion of robust and efficient machine learning algorithms to process this data; and the lessening cost of computational power, enabling use of computationally intensive methods for data analysis. Machine learning, has already created practical applications in such areas as analyzing medical science outcomes, detecting fraud, detecting fake users etc. Various data mining classification approaches and machine learning algorithms are applied for prediction of chronic diseases. The objective of this research work is to introduce a new decision support system to predict chronic kidney disease. The aim of this work is to compare the performance of Support vector machine (SVM) and K-Nearest Neighbour (KNN) classifier on the basis of its accuracy, precision and execution time for CKD prediction. From the experimental results it is observed that the performance of KNN classifier is better than SVM. Data Mining, Machine learning, Chronic kidney disease, Classification, K-Nearest Neighbour, Support vector machine.
Low-Rank Representation For Enhanced Deep Neural Network Acoustic Models M aster Project Report

Automatic speech recognition (ASR) is a fascinating area of research towards realizing humanmachine interactions. After more than 30 years of exploitation of Gaussian Mixture Models (GMMs), state-of-the-art systems currently rely on Deep Neural Network (DNN) to estimate class-conditional posterior probabilities. The posterior probabilities are used for acoustic modeling in hidden Markov models (HMM), and form a hybrid DNN-HMM which is now the leading edge approach to solve ASR problems. The present work builds upon the hypothesis that the optimal acoustic models are sparse and lie on multiple low-rank probability subspaces. Hence, the main goal of this Master project aimed at investigating different ways to restructure the DNN outputs using low-rank representation. Exploiting a large number of training posterior vectors, the underlying low-dimensional subspace can be identified, and low-rank decomposition enables separation of the “optimal” posteriors from the spurious (unstructured) uncertainties at the DNN output. Experiments demonstrate that low-rank representation can enhance posterior probability estimation, and lead to higher ASR accuracy. The posteriors are grouped according to their subspace similarities, and structured through low-rank decomposition. Furthermore, a novel hashing technique is proposed exploiting the low-rank property of posterior subspaces that enables fast search in the space of posterior exemplars.
Image-based Airborne LiDAR Point Cloud Encoding for 3 D Building Model Retrieval

With the development of Web 2.0 and cyber city modeling, an increasing number of 3D models have been available on web-based model-sharing platforms with many applications such as navigation, urban planning, and virtual reality. Based on the concept of data reuse, a 3D model retrieval system is proposed to retrieve building models similar to a user-specified query. The basic idea behind this system is to reuse these existing 3D building models instead of reconstruction from point clouds. To efficiently retrieve models, the models in databases are compactly encoded by using a shape descriptor generally. However, most of the geometric descriptors in related works are applied to polygonal models. In this study, the input query of the model retrieval system is a point cloud acquired by Light Detection and Ranging (LiDAR) systems because of the efficient scene scanning and spatial information collection. Using Point clouds with sparse, noisy, and incomplete sampling as input queries is more difficult than that by using 3D models. Because that the building roof is more informative than other parts in the airborne LiDAR point cloud, an image-based approach is proposed to encode both point clouds from input queries and 3D models in databases. The main goal of data encoding is that the models in the database and input point clouds can be consistently encoded. Firstly, top-view depth images of buildings are generated to represent the geometry surface of a building roof. Secondly, geometric features are extracted from depth images based on height, edge and plane of building. Finally, descriptors can be extracted by spatial histograms and used in 3D model retrieval system. For data retrieval, the models are retrieved by matching the encoding coefficients of point clouds and building models. In experiments, a database including about 900,000 3D models collected from the Internet is used for evaluation of data retrieval. The results of the proposed method show a clear superiority over related methods.
Cascade Mask Generation Framework for Fast Small Object Detection

Detecting small objects is a challenging task. Existing CNN-based objection detection pipeline faces such a dilemma: using a high-resolution image as input incurs high computational cost, but using a low-resolution image as input loses the feature representation of small objects and therefore leads to low accuracy. In this work, we propose a cascade mask generation framework to tackle this issue. The proposed framework takes in multi-scale images as input and processes them in ascending order of the scale. Each processing stage outputs object proposals as well as a region-of-interest (RoI) mask for the next stage. With RoI convolution, the masked regions can be excluded from the computation in the next stage. The procedure continues until the largest scale image is processed. Finally, the object proposals generated from multiple scales are classified by a post classifier. Extensive experiments on Tsinghua-Tencent 100K traffic sign benchmark demonstrate that our approach achieves state-of-the-art small object detection performance at a significantly improved speed-accuracy tradeoff compared with previous methods.
Comorbidity of anxiety and unipolar mood disorders.

Research on relationships between anxiety and depression has proceeded at a rapid pace since the 1980s. The similarities and differences between these two conditions, as well as many of the important features of the comorbidity of these disorders, are well understood. The genotypic structure of anxiety and depression is also fairly well documented. Generalized anxiety and major depression share a common genetic diathesis, but the anxiety disorders themselves are genetically hetergeneous. Sophisticated phenotypic models have also emerged, with data converging on an integrative hierarchical model of mood and anxiety disorders in which each individual syndrome contains both a common and a unique component. Finally, considerable progress has been made in understanding cognitive aspects of these disorders. This work has focused on both the cognitive content of anxiety and depression and on the effects that anxiety and depression have on information processing for mood-congruent material.
African-American rhinoplasty.

Increased width, loss of definition, and lack of projection characterize the stereotypical African-American nose. Early rhinoplasty surgeons attempted strict adherence to neoclassical aesthetic ideals. However, in reality, the anatomy and aesthetic desires of these patients are much more complex. Building dorsal height, achieving nasal tip definition amidst thick skin, and producing a more aesthetically pleasing alar base are the major challenges. Surgical planning should be sensitive to both individual and cultural differences in aesthetic perception and expectations. Here we describe the techniques used by the senior author (R.W.H.K.).
Video based technology for ambient assisted living: A review of the literature

Ambient assisted living (AAL) has the ambitious goal of improving the quality of life and maintaining independence of older and vulnerable people through the use of technology. Most of the western world will see a very large increase in the number of older people within the next 50 years with limited resources to care for them. AAL is seen as a promising alternative to the current care models and consequently has attracted lots of attention. Recently, a number of researchers have developed solutions based on video cameras and computer vision systems with promising results. However, for the domain to reach maturity, several challenges need to be faced, including the development of systems that are robust in the real-world and are accepted by users, carers and society. In this literature review paper we present a comprehensive survey of the scope of the domain, the existing technical solutions and the challenges to be faced.
2.4 GHz DOA finder using modified butler matrix for 2×2 array antennas

Technique to find direction of arrival (DOA) has been promisingly constructed by smart antenna technology. It consists of array antennas and signal processing unit. Because of the limitation of space in commercial products, the size of antenna array should be designed with the smallest dimension. Also the processing unit is expected for low complexity and expense. Most of DOA finders were recently proposed employing linear array in which the size is linearly increased by a number of antenna elements. Also the processing units were fully comprised with high level of computation. In this paper, the low profile of 2.4 GHz DOA finder is proposed. The DOA finder is designed in the compact size of 2times2 array antennas. Using modified Butler matrix, the processing unit is very simple in which it can be handled by any economic microprocessors. The experimental results confirm the success of proposed DOA finder.
Computing the Entropy of User Navigation in the Web

Navigation through the web, colloquially known as “surfing”, is one of the main activities of users during web interaction. When users follow a navigation trail they often tend to get disoriented in terms of the goals of their original query and thus the discovery of typical user trails could be useful in providing navigation assistance. Herein we give a theoretical underpinning of user navigation in terms of the entropy of an underlying Markov chain modelling the web topology. We present a novel method for online incremental computation of the entropy and a large deviation result regarding the length of a trail to realise the said entropy. We provide an error analysis for our estimation of the entropy in terms of the divergence between the empirical and actual probabilities. We then indicate applications of our algorithm in the area of web data mining. Finally, we present an extension of our technique to higher-order Markov chains by a suitable reduction of a higher-order Markov chain model to a first-order one.
Why do people need self-esteem? A theoretical and empirical review.

Terror management theory (TMT; J. Greenberg, T. Pyszczynski, & S. Solomon, 1986) posits that people are motivated to pursue positive self-evaluations because self-esteem provides a buffer against the omnipresent potential for anxiety engendered by the uniquely human awareness of mortality. Empirical evidence relevant to the theory is reviewed showing that high levels of self-esteem reduce anxiety and anxiety-related defensive behavior, reminders of one's mortality increase self-esteem striving and defense of self-esteem against threats in a variety of domains, high levels of self-esteem eliminate the effect of reminders of mortality on both self-esteem striving and the accessibility of death-related thoughts, and convincing people of the existence of an afterlife eliminates the effect of mortality salience on self-esteem striving. TMT is compared with other explanations for why people need self-esteem, and a critique of the most prominent of these, sociometer theory, is provided.
A Model for Reasoning with Uncertain Rules in Event Composition Systems

In recent years, there has been an increased need for the use of active systems systems required to act automatically based on events, or changes in the environment. Such systems span many areas, from active databases to applications that drive the core business processes of todays enterprises. However, in many cases, the events to which the system must respond are not generated by monitoring tools, but must be inferred from other events based on complex temporal predicates. In addition, in many applications, such inference is inherently uncertain. In this paper, we introduce a formal framework for knowledge representation and reasoning enabling such event inference. Based on probability theory, we dene the representation of the associated uncertainty. In addition, we formally dene the probability space, and show how the relevant probabilities can be calculated by dynamically constructing a Bayesian network. To the best of our knowledge, this is the rst work that enables taking such uncertainty into account in the context of active systems. Therefore, our contribution is twofold: We formally dene the representation and semantics of event composition for probabilistic settings, and show how to apply these extensions to the quantication of the occurrence probability of events. These results enable any active system to handle
Case study of the Miner Botnet

Malware and botnets are one of the most serious threats to today's Internet security. In this paper, we characterise the so-called &Miner Botnet”. It received major media attention after massive distributed denial of service attacks against a wide range of German and Russian websites, mainly during August and September 2011. We use our insights on this botnet to outline current botnet-related money-making concepts and to show that multiple activities of this botnet are actually centred on the virtual anonymised currency Bitcoin, thus justifying the name. Furthermore, we provide a binary-level analysis of the malware's design and components to illustrate the modularity of the previously mentioned concepts. We give an overview of the structure of the command-and-control protocol as well as of the botnet's architecture. Both centralised as well as distributed infrastructure aspects realised through peer-to-peer are present to run the botnet, the latter for increasing its resiliency. Finally, we provide the results of our ongoing tracking efforts that started in September 2011, focusing on the development of the botnet's size and geographic distribution. In addition we point out the challenge that is generally connected with size measurements of botnets due to the reachability of individual nodes and the persistence of IP addresses over time.
Identifying Similar Code with Program Dependence Graphs

We present an approach to identify similar code in programs based on jinding similar subgraphs in attributed directed graphs. This approach is used on program dependence graphs and therefore considers not only the syntactic structure of programs but also the data Jlow within (as an abstraction of the semantics). As a result, there is no tradeoff between precision and recall-our approach is very good in both. An evaluation of our prototype implementation shows that our approach is feasible and gives very good results despite the non polynomial complexity of the problem.