nils-herrmann/clean_scirepeval huggingface dataset:
Cleansing Data for Mining and Warehousing

Given the rapid growth of data it is important to extract mine and discover useful information from databases and data ware houses The process of data cleansing is crucial because of the garbage in garbage out principle Dirty data les are prevalent because of incorrect or missing data values inconsistent value naming conventions and incomplete information Hence we may have multiple records refer ing to the same real world entity In this paper we examine the problem of detecting and removing duplicating records We present several e cient techniques to pre process the records before sorting them so that potentially matching records will be brought to a close neighbourhood Based on these techniques we implement a data cleansing system which can detect and remove more duplicate records than existing methods
How Does Disagreement Benefit Co-teaching?

Learning with noisy labels is one of the most important question in weakly-supervised learning domain. Classical approaches focus on adding the regularization or estimating the noise transition matrix. However, either a regularization bias is permanently introduced, or the noise transition matrix is hard to be estimated accurately. In this paper, following a novel path to train on small-loss samples, we propose a robust learning paradigm called Co-teaching+. This paradigm naturally bridges “Update by Disagreement” strategy with Co-teaching that trains two deep neural networks, thus consists of disagreement-update step and cross-update step. In disagreement-update step, two networks predicts all data first, and feeds forward prediction disagreement data only. Then, in cross-update step, each network selects its small-loss data from such disagreement data, but back propagates the small-loss data by its peer network and updates itself parameters. Empirical results on noisy versions of MNIST, CIFAR10 and NEWS demonstrate that Co-teaching+ is much superior to the state-of-the-art methods in the robustness of trained deep models.
Adapting Visual Category Models to New Domains

Domain adaptation is an important emerging topic in computer vision. In this paper, we present one of the first studies of domain shift in the context of object recognition. We introduce a method that adapts object models acquired in a particular visual domain to new imaging conditions by learning a transformation that minimizes the effect of domain-induced changes in the feature distribution. The transformation is learned in a supervised manner and can be applied to categories for which there are no labeled examples in the new domain. While we focus our evaluation on object recognition tasks, the transform-based adaptation technique we develop is general and could be applied to non-image data. Another contribution is a new multi-domain object database, freely available for download. We experimentally demonstrate the ability of our method to improve recognition on categories with few or no target domain labels and moderate to large changes in the imaging conditions.
A classification of transaction processing systems

The problems in building a transaction processing system are discussed, and it is shown that the difficulties are a function of specific attributes of the underlying database system. A model of a transaction processing system is presented, and five system dimensions important in classifying transaction processing systems-the process, machine, heterogeneity, data, and site components-are introduced. The specific problems posed by various combinations of system characteristics are analyzed. The evolution of transaction processing systems are described in terms of the framework.<<ETX>>
Human wayfinding in information networks

Navigating information spaces is an essential part of our everyday lives, and in order to design efficient and user-friendly information systems, it is important to understand how humans navigate and find the information they are looking for. We perform a large-scale study of human wayfinding, in which, given a network of links between the concepts of Wikipedia, people play a game of finding a short path from a given start to a given target concept by following hyperlinks. What distinguishes our setup from other studies of human Web-browsing behavior is that in our case people navigate a graph of connections between concepts, and that the exact goal of the navigation is known ahead of time. We study more than 30,000 goal-directed human search paths and identify strategies people use when navigating information spaces. We find that human wayfinding, while mostly very efficient, differs from shortest paths in characteristic ways. Most subjects navigate through high-degree hubs in the early phase, while their search is guided by content features thereafter. We also observe a trade-off between simplicity and efficiency: conceptually simple solutions are more common but tend to be less efficient than more complex ones. Finally, we consider the task of predicting the target a user is trying to reach. We design a model and an efficient learning algorithm. Such predictive models of human wayfinding can be applied in intelligent browsing interfaces.
Personalized Web Search in Web Mining

Web is a collection of inter-related files on one or more Web servers. Web mining is the application of data mining techniques to extract knowledge from Web data. Web mining involves the process to apply data mining techniques to extract and uncover knowledge from web documents and services. Web mining has been explored to a vast level and different techniques have been defined for variety of applications like Web Search, Web Classification and Personalization etc. Personalized search is a way to improve the accuracy of web search. However personalized search requires collection and aggregation of user information, which often raise serious problems of breach of confidential data for many users. So, personalized web search in web mining is one of the techniques to improve privacy concerns of the user’s data.
Common Attack Pattern Enumeration and Classification — CAPEC TM A Community Knowledge Resource for Building Secure Software

To build secure software, builders must ensure that they have protected every relevant potential vulnerability. Yet, to attack software, attackers often have to find and exploit only a single exposed vulnerability. To identify and mitigate relevant vulnerabilities in software, the development community needs more than just good software engineering and analytical practices, a solid grasp of software security features, and a powerful set of tools. All of these things are necessary but not sufficient. To respond effectively, the community needs to think outside of the box and have a firm grasp of the attacker’s perspective and the approaches used to exploit software systems. CAPEC provides this information to the community in order to help enhance security throughout the software development lifecycle and to support the needs of developers, testers, and educators.
Empirical Analysis of Detection Cascades of Boosted Classifiers for Rapid Object Detection

Recently Viola et al. have introduced a rapid object detection scheme based on a boosted cascade of simple feature classifiers. In this paper we introduce and empirically analysis two extensions to their approach: Firstly, a novel set of rotated haar-like features is introduced. These novel features significantly enrich the simple features of [6] and can also be calculated efficiently. With these new rotated features our sample face detector shows off on average a 10% lower false alarm rate at a given hit rate. Secondly, we present a through analysis of different boosting algorithms (namely Discrete, Real and Gentle Adaboost) and weak classifiers on the detection performance and computational complexity. We will see that Gentle Adaboost with small CART trees as base classifiers outperform Discrete Adaboost and stumps. The complete object detection training and detection system as well as a trained face detector are available in the Open Computer Vision Library at sourceforge.net .
On unifying key-frame and voxel-based dense visual SLAM at large scales

This paper proposes an approach to real-time dense localisation and mapping that aims at unifying two different representations commonly used to define dense models. On one hand, much research has looked at 3D dense model representations using voxel grids in 3D. On the other hand, image-based key-frame representations for dense environment mapping have been developed. Both techniques have their relative advantages and disadvantages which will be analysed in this paper. In particular each representation's space-size requirements, their effective resolution, the computation efficiency, their accuracy and robustness will be compared. This paper then proposes a new model which unifies various concepts and exhibits the main advantages of each approach within a common framework. One of the main results of the proposed approach is its ability to perform large scale reconstruction accurately at the scale of mapping a building.
Interactive Test Tool for Interoperable C-ITS Development

This paper presents the architecture of an Interactive Test Tool (ITT) for interoperability testing of Cooperative Intelligent Transport Systems (C-ITS). Cooperative systems are developed by different manufacturers at different locations, which makes interoperability testing a tedious task. Up until now, interoperability testing is performed during physical meetings where the C-ITS devices are placed within range of wireless communication, and messages are exchanged. The ITT allows distributed (e.g. over Internet) interoperability testing starting from the network Transport Layer and all the way up to the Application Layer, e.g. to platooning. ITT clients can be implemented as Hardware-in-the-Loop, thus allowing to combine physical and virtual vehicles. Since the ITT considers each client as a black box, manufacturers can test together without revealing internal implementations to each other. The architecture of the ITT allows users to easily switch between physical wireless networking and virtual ITT networking. Therefore, only one implementation of the ITS communication stack is required for both development and testing, which reduces the work overhead and ensures that the stack that is used during the testing is the one deployed in the real world.
An Unequal Wilkinson Power Divider for a Frequency and Its First Harmonic

This letter presents a Wilkinson power divider operating at a frequency and its first harmonic with unequal power divider ratio. To obtain the unequal property, four groups of 1/6 wavelength transmission lines with different characteristic impedances are needed to match all ports. Theoretically, closed-form equations for the design are derived based on transmission line theory. Experimental results have indicated that all the features of this novel power divider can be fulfilled at f 0 and 2f 0 simultaneously.
Lifelong Machine Learning November , 2016

Lifelong machine learning (or lifelong learning) is an advanced machine learning paradigm that learns continuously, accumulates the knowledge learned in previous tasks, and uses it to help future learning. In the process, the learner becomes more and more knowledgeable and effective at learning. This learning ability is one of the hallmarks of human intelligence. However, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model. It makes no attempt to retain the learned knowledge and use it in future learning. Although this isolated learning paradigm has been very successful, it requires a large number of training examples, and is only suitable for well-defined and narrow tasks. In comparison, we humans can learn effectively with a few examples because we have accumulated so much knowledge in the past which enables us to learn with little data or effort. Lifelong learning aims to achieve this capability. As statistical machine learning matures, it is time to make a major effort to break the isolated learning tradition and to study lifelong learning to bring machine learning to a new height. Applications such as intelligent assistants, chatbots, and physical robots that interact with humans and systems in real-life environments are also calling for such lifelong learning capabilities. Without the ability to accumulate the learned knowledge and use it to learn more knowledge incrementally, a system will probably never be truly intelligent. This book serves as an introductory text and survey to lifelong learning.
Unifying Low-Level and High-Level Music Similarity Measures

Measuring music similarity is essential for multimedia retrieval. For music items, this task can be regarded as obtaining a suitable distance measurement between songs defined on a certain feature space. In this paper, we propose three of such distance measures based on the audio content: first, a low-level measure based on tempo-related description; second, a high-level semantic measure based on the inference of different musical dimensions by support vector machines. These dimensions include genre, culture, moods, instruments, rhythm, and tempo annotations. Third, a hybrid measure which combines the above-mentioned distance measures with two existing low-level measures: a Euclidean distance based on principal component analysis of timbral, temporal, and tonal descriptors, and a timbral distance based on single Gaussian Mel-frequency cepstral coefficient (MFCC) modeling. We evaluate our proposed measures against a number of baseline measures. We do this objectively based on a comprehensive set of music collections, and subjectively based on listeners' ratings. Results show that the proposed methods achieve accuracies comparable to the baseline approaches in the case of the tempo and classifier-based measures. The highest accuracies are obtained by the hybrid distance. Furthermore, the proposed classifier-based approach opens up the possibility to explore distance measures that are based on semantic notions.
Solving sybil attacks using evolutionary game theory

Recommender systems have become quite popular recently. However, such systems are vulnerable to several types of attacks that target user ratings. One such attack is the Sybil attack where an entity masquerades as several identities with the intention of diverting user ratings. In this work, we propose evolutionary game theory as a possible solution to the Sybil attack in recommender systems. After modeling the attack, we use replicator dynamics to solve for evolutionary stable strategies. Our results show that under certain conditions that are easily achievable by a system administrator, the probability of an attack strategy drops to zero implying degraded fitness for Sybil nodes that eventually die out.
Automating the translation of assertions using natural language processing techniques

In order to verify natural language assertions from a specification automatically, they need to be translated into formal representations. This process is error-prone and can lead to a product that does not meet the initial intentions.We automate this process by first partitioning all assertions into subsets based on sentence similarity and then providing a translation template for each subset which must be completed by the designer. Since many assertions are described by similar sentences, the number of manual translation steps can be decreased significantly. We evaluated our approach by translating English constraint sentences from an industrial specification into SystemVerilog assertions.
3 D Stereo Reconstruction Using Multiple Spherical Views

360° stereoscopic image capture makes it easier to capture full scenes in a limited number of images. Because of this quality, it is useful to utilize the these spherical images in computer vision and virtual reality applications such as depth estimation and 3D scene reconstruction. Using a single pair of spherical images allows us to perform depth estimation from one perspective, but utilizing multiple viewpoints during scene reconstruction can allow for more robustness when creating translated views. We aim to use disparity maps generated from multiple viewpoints to improve 3D reconstruction of spherical scenes.
Concept-Based Knowledge Discovery in Texts Extracted from the Web

This paper presents an approach for knowledge discovery in texts extracted from the Web. Instead of analyzing words or attribute values, the approach is based on concepts, which are extracted from texts to be used as characteristics in the mining process. Statistical techniques are applied on concepts in order to find interesting patterns in concept distributions or associations. In this way, users can perform discovery in a high level, since concepts describe real world events, objects, thoughts, etc. For identifying concepts in texts, a categorization algorithm is used associated to a previous classification task for concept definitions. Two experiments are presented: one for political analysis and other for competitive intelligence. At the end, the approach is discussed, examining its problems and advantages in the Web context.
Semantic segmentation of microscopic images of H&E stained prostatic tissue using CNN

There is a need for an automatic Gleason scoring system that can be used for prostate cancer diagnosis. Today the diagnoses are determined by pathologists manually, which is both a complex and a time-consuming task. To reduce the pathologists' workload, but also to reduce variations between different pathologists, an automatic classification system would be of great use. Some previous works have aimed for this, but still more work needs to be done. It is probable that such a tool would benefit from having access to individually segmented, pathologically relevant objects from the images. Therefore, we have developed an algorithm for semantic segmentation of the microscopic images of H&E stained prostate tissue into Background, Stroma, Epithelial Cytoplasm and Nuclei. This algorithm is based on deep learning, or more specifically a convolutional neural network. The network design is inspired by architectures that previously have been proved successful in different applications. It consists of a contracting and an expanding part, which are symmetrical. We have reached an accuracy of 80 %, as measured by the mean of the intersection over union, for segmentation into four classes. Previous works have only investigated nuclei segmentation, and our network performed similar but for the more challenging task of four class segmentation.
Dimensionality reduction of objectives and constraints in multi-objective optimization problems: A system design perspective

The notion of optimal system design holds that in order to dasiatrulypsila maximize/minimize an objective function, the feasible set needs to be optimized. Inspired by it, the attempt in our recent work was to incorporate constraint-reduction in our earlier proposed procedures on dimensionality reduction of objectives. In that, while targetting constrained single-objective optimization problems (SOPs), we could arrive at a critical set of constraints and also their importance based rank-ordering. This information was used to study the shift from the constrained to the unconstrained optima. The methodology above was based on treating the a priori stated constraints as objectives besides the original-objective, and on applying (K. Deb et al., 2006), (D.K. Saxena et al., 2007) to this combined objective set-but-without constraints. In this work, the endeavor is to extend the above notion to the realm of multi-objective optimization problems (MOPs). Towards it, while we hire much from the above methodology, we make a fundamental shift, in that, we retain the a priori stated constraints, while evaluating the combined objective set. The motivation for this shift lies, in that, it allows more effective realization of the notion of system design than the approach in (D.K. Saxen et al., 2007). Reasonable effort has been spent on establishing this argument. Incorporating this change, a procedure for simultaneous reduction in objectives and constraints (for both SOPs, MOPs) is proposed, which also defines a realizable path towards optimal system design. Finally, the procedure is demonstrated on two test problems and one real world problem.
Towards Operational Measures of Computer Security

Ideally, a measure of the security of a system should capture quantitatively the intuitive notion of ‘the ability of the system to resist attack’. That is, it should be operational, reflecting the degree to which the system can be expected to remain free of security breaches under particular conditions of operation (including attack). Instead, current security levels at best merely reflect the extensiveness of safeguards introduced during the design and development of a system. Whilst we might expect a system developed to a higher level than another to exhibit ‘more secure behaviour’ in operation, this cannot be guaranteed; more particularly, we cannot infer what the actual security behaviour will be from knowledge of such a level. In the paper we discuss similarities between reliability and security with the intention of working towards measures of ‘operational security’ similar to those that we have for reliability of systems. Very informally, these measures could involve expressions such as the rate of occurrence of security breaches (cf rate of occurrence of failures in reliability), or the probability that a specified ‘mission’ can be accomplished without a security breach (cf reliability function). This new approach is based on the analogy between system failure and security breach. A number of other analogies to support this view are introduced. We examine this duality critically, and have identified a number of important open questions that need to be answered before this quantitative approach can be taken further. The work described here is therefore somewhat tentative, and one of our major intentions is to invite discussion about the plausibility and feasibility of this new approach. Towards Operational Measures of Computer Security 2 _____________________________________________________________________
Requirements engineering and the creative process in the video game industry

The software engineering process in video game development is not clearly understood, hindering the development of reliable practices and processes for this field. An investigation of factors leading to success or failure in video game development suggests that many failures can be traced to problems with the transition from preproduction to production. Three examples, drawn from real video games, illustrate specific problems: 1) how to transform documentation from its preproduction form to a form that can be used as a basis for production;, 2) how to identify implied information in preproduction documents; and 3) how to apply domain knowledge without hindering the creative process. We identify 3 levels of implication and show that there is a strong correlation between experience and the ability to identify issues at each level. The accumulated evidence clearly identifies the need to extend traditional requirements engineering techniques to support the creative process in video game development.