nils-herrmann/clean_scirepeval huggingface dataset:
Adaptive Load Balancing for Parallel IDS on Multi-Core Systems Using Prioritized Flows

We describe a load balancing system for parallel intrusion detection on multi-core systems using a novel model allowing fine-grained selection of the network traffic to be analyzed. The system receives data from a network and distributes it to multiple IDSs running on individual CPU cores. In contrast to related approaches, we do not assume a static association of flows to IDS processes but adaptively determine the load of each IDS process to allocate network flows for a limited time window. We developed a priority model for the selection of network data and the assignment process. Special emphasis is given to environments with highly dynamic network traffic, where only a fraction of all data can be analyzed due to system constraints. We show that IDSs analyzing packet payload data disproportionately suffer from random packet drops due to overload. Our proposed system ensures loss-free analysis for selected data streams in a specified time interval. Our primary focus lies on the treatment of dynamic network behavior: neither data should be lost unintentionally, nor analysis processes should be needlessly idle. To evaluate the priority model and assignment systems, we implemented a prototype and evaluated it with real network traffic.
Genetic Algorithm Performance with Different Selection Methods in Solving Multi-Objective Network Design Problem

Selection is one of the key operations of genetic algorithm (GA). This paper presents a comparative analysis of GA performance in solving multi-objective network design problem (MONDP) using different parent selection methods. Three problem instances were tested and results show that on the average tournament selection is the most effective and most efficient for 10-node network design problem, while Ranking & Scaling is the least effective and least efficient. For 21-node and 36-node network problems, Roulette Wheel is the least effective but most efficient while Ranking & Scaling equals and outperformed tournament in effectiveness and efficiency respectively.
Sufficient Conditions for Convergence of the Sum–Product Algorithm

Novel conditions are derived that guarantee convergence of the sum-product algorithm (also known as loopy belief propagation or simply belief propagation (BP)) to a unique fixed point, irrespective of the initial messages, for parallel (synchronous) updates. The computational complexity of the conditions is polynomial in the number of variables. In contrast with previously existing conditions, our results are directly applicable to arbitrary factor graphs (with discrete variables) and are shown to be valid also in the case of factors containing zeros, under some additional conditions. The conditions are compared with existing ones, numerically and, if possible, analytically. For binary variables with pairwise interactions, sufficient conditions are derived that take into account local evidence (i.e., single-variable factors) and the type of pair interactions (attractive or repulsive). It is shown empirically that this bound outperforms existing bounds.
Object detection using Haar-cascade Classifier

Object detection is an important feature of computer science. The benefits of object detection is however not limited to someone with a doctorate of informatics. Instead, object detection is growing deeper and deeper into the common parts of the information society, lending a helping hand wherever needed. This paper will address one such possibility, namely the help of a Haar-cascade classifier. The main focus will be on the case study of a vehicle detection and counting system and the possibilities it will provide in a semi-enclosed area both the statistical kind and also for the common man. The goal of the system to be developed is to further ease and augment the everyday part of our lives.
Android Malware Detection & Protection: A Survey

Android has become the most popular smartphone operating system. This rapidly increasing adoption of Android has resulted in significant increase in the number of malwares when compared with previous years. There exist lots of antimalware programs which are designed to effectively protect the users’ sensitive data in mobile systems from such attacks. In this paper, our contribution is twofold. Firstly, we have analyzed the Android malwares and their penetration techniques used for attacking the systems and antivirus programs that act against malwares to protect Android systems. We categorize many of the most recent antimalware techniques on the basis of their detection methods. We aim to provide an easy and concise view of the malware detection and protection mechanisms and deduce their benefits and limitations. Secondly, we have forecast Android market trends for the year up to 2018 and provide a unique hybrid security solution and take into account both the static and dynamic analysis an android application. Android; Permissions; Signature
Secrets of GrabCut and Kernel K-Means

The log-likelihood energy term in popular model-fitting segmentation methods, e.g. [39, 8, 28, 10], is presented as a generalized "probabilistic K-means" energy [16] for color space clustering. This interpretation reveals some limitations, e.g. over-fitting. We propose an alternative approach to color clustering using kernel K-means energy with well-known properties such as non-linear separation and scalability to higher-dimensional feature spaces. Our bound formulation for kernel K-means allows to combine general pair-wise feature clustering methods with image grid regularization using graph cuts, similarly to standard color model fitting techniques for segmentation. Unlike histogram or GMM fitting [39, 28], our approach is closely related to average association and normalized cut. But, in contrast to previous pairwise clustering algorithms, our approach can incorporate any standard geometric regularization in the image domain. We analyze extreme cases for kernel bandwidth (e.g. Gini bias) and demonstrate effectiveness of KNN-based adaptive bandwidth strategies. Our kernel K-means approach to segmentation benefits from higher-dimensional features where standard model fitting fails.
Characterizing the Errors of Data-Driven Dependency Parsing Models

We present a comparative error analysis of the two dominant approaches in datadriven dependency parsing: global, exhaustive, graph-based models, and local, greedy, transition-based models. We show that, in spite of similar performance overall, the two models produce different types of errors, in a way that can be explained by theoretical properties of the two models. This analysis leads to new directions for parser development.
Machine discovery of effective admissible heuristics

Admissible heuristics are an important class of heuristics worth discovering: they guarantee shortest path solutions in search algorithms such asA* and they guarantee less expensively produced, but boundedly longer solutions in search algorithms such as dynamic weighting. Unfortunately, effective (accurate and cheap to compute) admissible heuristics can take years for people to discover. Several researchers have suggested that certain transformations of a problem can be used to generate admissible heuristics. This article defines a more general class of transformations, calledabstractions, that are guaranteed to generate only admissible heuristics. It also describes and evaluates an implemented program (Absolver II) that uses a means-ends analysis search control strategy to discover abstracted problems that result in effective admissible heuristics. Absolver II discovered several well-known and a few novel admissible heuristics, including the first known effective one for Rubik's Cube, thus concretely demonstrating that effective admissible heuristics can be tractably discovered by a machine.
An advanced ACO algorithm for feature subset selection

Feature selection is an important task for data analysis and information retrieval processing, pattern classification systems, and data mining applications. It reduces the number of features by removing noisy, irrelevant and redundant data. In this paper, a novel feature selection algorithm based on Ant Colony Optimization (ACO), called Advanced Binary ACO (ABACO), is presented. Features are treated as graph nodes to construct a graph model and are fully connected to each other. In this graph, each node has two sub-nodes, one for selecting and the other for deselecting the feature. Ant colony algorithm is used to select nodes while ants should visit all features. The use of several statistical measures is examined as the heuristic function for visibility of the edges in the graph. At the end of a tour, each ant has a binary vector with the same length as the number of features, where 1 implies selecting and 0 implies deselecting the corresponding feature. The performance of proposed algorithm is compared to the performance of Binary Genetic Algorithm (BGA), Binary Particle Swarm Optimization (BPSO), CatfishBPSO, Improved Binary Gravitational Search Algorithm (IBGSA), and some prominent ACO-based algorithms on the task of feature selection on 12 well-known UCI datasets. Simulation results verify that the algorithm provides a suitable feature subset with good classification accuracy using a smaller feature set than competing feature selection methods. KeywordsFeature selection; Wrraper; Ant colony optimization (ACO); Binary ACO;
Predicting Abnormal Returns From News Using Text Classification

We show how text from news articles can be used to predict intr aday price movements of financial assets using support vector machines. Multiple kernel learni ng is used to combine equity returns with text as predictive features to increase classification performa nce nd we develop an analytic center cutting plane method to solve the kernel learning problem efficientl y. We observe that while the direction of returns is not predictable using either text or returns, their size is, with text features producing significantly better performance than historical returns alone.
A High Accuracy Fuzzy Logic Based Map Matching Algorithm for Road Transport

Recent research on map matching algorithms for land vehicle navigation has been based on either a conventional topological analysis or a probabilistic approach. The input to these algorithms normally comes from the global positioning system and digital map data. Although the performance of some of these algorithms is good in relatively sparse road networks, they are not always reliable for complex roundabouts, merging or diverging sections of motorways and complex urban road networks. In high road density areas where the average distance between roads is less than 100 metres, there may be many road patterns matching the trajectory of the vehicle reported by the positioning system at any given moment. Consequently, it may be difficult to precisely identify the road on which the vehicle is travelling. Therefore, techniques for dealing with qualitative terms such as likeliness are essential for map matching algorithms to identify a correct link. Fuzzy logic is one technique that is an effective way to deal with qualitative terms, linguistic vagueness, and human intervention. This paper develops a map matching algorithm based on fuzzy logic theory. The inputs to the proposed algorithm are from the global positioning system augmented with data from deduced reckoning sensors to provide continuous navigation. The algorithm is tested on different road networks of varying complexity. The validation of this algorithm is carried out using high precision positioning data obtained from GPS carrier phase observables. The performance of the developed map matching algorithm is evaluated against the performance of several well-accepted existing map matching algorithms. The results show that the fuzzy logic-based map matching algorithm provides a significant improvement over existing map matching algorithms both in terms of identifying correct links and estimating the vehicle position on the links.
An Efficient and Secure Dynamic Auditing Protocol for Data Storage in Cloud Computing

In cloud computing, data owners host their data on cloud servers and users (data consumers) can access the data from cloud servers. Due to the data outsourcing, however, this new paradigm of data hosting service also introduces new security challenges, which requires an independent auditing service to check the data integrity in the cloud. Some existing remote integrity checking methods can only serve for static archive data and, thus, cannot be applied to the auditing service since the data in the cloud can be dynamically updated. Thus, an efficient and secure dynamic auditing protocol is desired to convince data owners that the data are correctly stored in the cloud. In this paper, we first design an auditing framework for cloud storage systems and propose an efficient and privacy-preserving auditing protocol. Then, we extend our auditing protocol to support the data dynamic operations, which is efficient and provably secure in the random oracle model. We further extend our auditing protocol to support batch auditing for both multiple owners and multiple clouds, without using any trusted organizer. The analysis and simulation results show that our proposed auditing protocols are secure and efficient, especially it reduce the computation cost of the auditor.
Geographical and Energy Aware Routing : a recursive data dissemination protocol for wireless sensor networks

Future sensor networks will be composed of a large number of densely deployed sensors/actuators. A key feature of such networks is that their nodes are untethered and unattended. Consequently, energy efficiency is an important design consideration for these networks. Motivated by the fac t that sensor network queries may often be geographical, we design and evaluate an energy efficient routing algorithm that propagates a query to the appropriate geographical region, without flooding. The proposed Geographic and Energy Aware Routing (GEAR) algorithm uses energy aware neighbor selection to route a packet towards the target regi on and Recursive Geographic Forwarding or Restricted Flooding algorithm to disseminate the packet inside the destina-
Action Language BC: Preliminary Report

The action description languages B andC have significant common core. Nevertheless, some expressive possibilities ofB are difficult or impossible to simulate inC, and the other way around. The main advantage ofB is that it allows the user to give Prolog-style recursive definitions, which is important in applications. On the other hand, B solves the frame problem by incorporating the commonsense law of inertia in its semantics, which makes it difficult to talk about fluents whose behavior is described by defaults other than inertia. In C and in its extensionC+, the inertia assumption is expressed by axioms that the user is free to include or not to include, and other defaults can be postulated as well. This paper defines a new action description language, calledBC, that combines the attractive features of B andC+. Examples of formalizing commonsense domains discussed in the paper illustrate the expressive capabilities of BC and the use of answer set solvers for the automation of reasoning about actions described in this language.
Missing Modality Transfer Learning via Latent Low-Rank Constraint

Transfer learning is usually exploited to leverage previously well-learned source domain for evaluating the unknown target domain; however, it may fail if no target data are available in the training stage. This problem arises when the data are multi-modal. For example, the target domain is in one modality, while the source domain is in another. To overcome this, we first borrow an auxiliary database with complete modalities, then consider knowledge transfer across databases and across modalities within databases simultaneously in a unified framework. The contributions are threefold: 1) a latent factor is introduced to uncover the underlying structure of the missing modality from the known data; 2) transfer learning in two directions allows the data alignment between both modalities and databases, giving rise to a very promising recovery; and 3) an efficient solution with theoretical guarantees to the proposed latent low-rank transfer learning algorithm. Comprehensive experiments on multi-modal knowledge transfer with missing target modality verify that our method can successfully inherit knowledge from both auxiliary database and source modality, and therefore significantly improve the recognition performance even when test modality is inaccessible in the training stage.
Benefits of Cloud Computing: Literature Review in a Maturity Model Perspective

Cloud computing is drawing attention from both practitioners and researchers, and its adoption among organizations is on the rise. The focus has mainly been on minimizing fixed IT costs and using the IT resource flexibility offered by the cloud. However, the promise of cloud computing is much greater. As a disruptive technology, it enables innovative new services and business models that decrease time to market, create operational efficiencies and engage customers and citizens in new ways. However, we are still in the early days of cloud computing, and, for organizations to exploit the full potential, we need knowledge of the potential applications and pitfalls of cloud computing. Maturity models provide effective methods for organizations to assess, evaluate, and benchmark their capabilities as bases for developing roadmaps for improving weaknesses. Adopting the business-IT maturity model by Pearlson & Saunders (2007) as analytical framework, we synthesize the existing literature, identify levels of cloud computing benefits, and establish propositions for practice in terms of how to realize these benefits.
A syntactic tree matching approach to finding similar questions in community-based qa services

While traditional question answering (QA) systems tailored to the TREC QA task work relatively well for simple questions, they do not suffice to answer real world questions. The community-based QA systems offer this service well, as they contain large archives of such questions where manually crafted answers are directly available. However, finding similar questions in the QA archive is not trivial. In this paper, we propose a new retrieval framework based on syntactic tree structure to tackle the similar question matching problem. We build a ground-truth set from Yahoo! Answers, and experimental results show that our method outperforms traditional bag-of-word or tree kernel based methods by 8.3% in mean average precision. It further achieves up to 50% improvement by incorporating semantic features as well as matching of potential answers. Our model does not rely on training, and it is demonstrated to be robust against grammatical errors as well.
A Neural Network Approach for Knowledge-Driven Response Generation

We present a novel response generation system. The system assumes the hypothesis that participants in a conversation base their response not only on previous dialog utterances but also on their background knowledge. Our model is based on a Recurrent Neural Network (RNN) that is trained over concatenated sequences of comments, a Convolution Neural Network that is trained over Wikipedia sentences and a formulation that couples the two trained embeddings in a multimodal space. We create a dataset of aligned Wikipedia sentences and sequences of Reddit utterances, which we we use to train our model. Given a sequence of past utterances and a set of sentences that represent the background knowledge, our end-to-end learnable model is able to generate context-sensitive and knowledge-driven responses by leveraging the alignment of two different data sources. Our approach achieves up to 55% improvement in perplexity compared to purely sequential models based on RNNs that are trained only on sequences of utterances.
TAC-GAN - Text Conditioned Auxiliary Classifier Generative Adversarial Network

In this work, we present the Text Conditioned Auxiliary Classifier Generative Adversarial Network, (TAC-GAN) a text to image Generative Adversarial Network (GAN) for synthesizing images from their text descriptions. Former approaches have tried to condition the generative process on the textual data; but allying it to the usage of class information, known to diversify the generated samples and improve their structural coherence, has not been explored. We trained the presented TAC-GAN model on the Oxford102 dataset of flowers, and evaluated the discriminability of the generated images with Inception-Score, as well as their diversity using the Multi-Scale Structural Similarity Index (MS-SSIM). Our approach outperforms the stateof-the-art models, i.e., its inception score is 3.45, corresponding to a relative increase of 7.8% compared to the recently introduced StackGan. A comparison of the mean MS-SSIM scores of the training and generated samples per class shows that our approach is able to generate highly diverse images with an average MS-SSIM of 0.14 over all generated classes.
Fast and Globally Convergent Pose Estimation from Video Images

ÐDetermining the rigid transformation relating 2D images to known 3D geometry is a classical problem in photogrammetry and computer vision. Heretofore, the best methods for solving the problem have relied on iterative optimization methods which cannot be proven to converge and/or which do not effectively account for the orthonormal structure of rotation matrices. We show that the pose estimation problem can be formulated as that of minimizing an error metric based on collinearity in object (as opposed to image) space. Using object space collinearity error, we derive an iterative algorithm which directly computes orthogonal rotation matrices and which is globally convergent. Experimentally, we show that the method is computationally efficient, that it is no less accurate than the best currently employed optimization methods, and that it outperforms all tested methods in robustness to outliers. Index TermsÐPose estimation, absolute orientation, optimization,weak-perspective camera models, numerical optimization.
Drive circuits for ultra-fast and reliable actuation of Thomson coil actuators used in hybrid AC and DC circuit breakers

Thomson coil actuators (also known as repulsion coil actuators) are well suited for vacuum circuit breakers when fast operation is desired such as for hybrid AC and DC circuit breaker applications. This paper presents investigations on how the actuator drive circuit configurations as well as their discharging pulse patterns affect the magnetic force and therefore the acceleration, as well as the mechanical robustness of these actuators. Comprehensive multi-physics finite-element simulations of the Thomson coil actuated fast mechanical switch are carried out to study the operation transients and how to maximize the actuation speed. Different drive circuits are compared: three single switch circuits are evaluated; the pulse pattern of a typical pulse forming network circuit is studied, concerning both actuation speed and maximum stress; a two stage drive circuit is also investigated. A 630 A, 15 kV / 1 ms prototype employing a vacuum interrupter with 6 mm maximum open gap was developed and tested. The total moving mass accelerated by the actuator is about 1.2 kg. The measured results match well with simulated results in the FEA study.