nils-herrmann/clean_scirepeval huggingface dataset:
OINT C LOUDS

Three-dimensional geometric data offer an excellent domain for studying representation learning and generative modeling. In this paper, we look at geometric data represented as point clouds. We introduce a deep autoencoder (AE) network with state-of-the-art reconstruction quality and generalization ability. The learned representations outperform existing methods on 3D recognition tasks and enable basic shape editing via simple algebraic manipulations, such as semantic part editing, shape analogies and shape interpolation. We perform a thorough study of different generative models including: GANs operating on the raw point clouds, significantly improved GANs trained in the fixed latent space of our AEs and, Gaussian mixture models (GMM). For our quantitative evaluation we propose measures of sample fidelity and diversity based on matchings between sets of point clouds. Interestingly, our careful evaluation of generalization, fidelity and diversity reveals that GMMs trained in the latent space of our AEs produce the best results.
Head detection and orientation estimation for pedestrian safety

Head detection and orientation estimation are a vital component in the intention recognition of pedestrians. In this paper we propose a novel framework to detect highly occluded pedestrians and estimate their head orientation. Detection is performed for pedestrian's heads only. For this we employ a part-based classifier with HOG/SVM combinations. Head orientations are estimated using discrete orientation classifiers and LBP features. Results are improved by leveraging orientation estimation for head and torso as well as motion information. The orientation estimation is integrated over time using a Hidden Markov Model. From the discrete model we obtain a contiunous head orientation. We evaluate our approach on image sequences with ground truth orientation measurements. To our best knowledge we outperform state of the art results.
Dynamic ransomware protection using deterministic random bit generator

Ransomware has become a very significant cyber threat. The basic idea of ransomware was presented in the form of a cryptovirus in 1995. However, it was considered as merely a conceptual topic since then for over a decade. In 2017, ransomware has become a reality, with several famous cases of ransomware having compromised important computer systems worldwide. For example, the damage caused by CryptoLocker and WannaCry is huge, as well as global. They encrypt victims' files and require user's payment to decrypt them. Because they utilize public key cryptography, the key for recovery cannot be found in the footprint of the ransomware on the victim's system. Therefore, once infected, the system cannot be recovered without paying for restoration. Various methods to deal this threat have been developed by antivirus researchers and experts in network security. However, it is believed that cryptographic defense is infeasible because recovering a victim's files is computationally as difficult as breaking a public key cryptosystem. Quite recently, various approaches to protect the crypto-API of an OS from malicious codes have been proposed. Most ransomware generate encryption keys using the random number generation service provided by the victim's OS. Thus, if a user can control all random numbers generated by the system, then he/she can recover the random numbers used by the ransomware for the encryption key. In this paper, we propose a dynamic ransomware protection method that replaces the random number generator of the OS with a user-defined generator. As the proposed method causes the virus program to generate keys based on the output from the user-defined generator, it is possible to recover an infected file system by reproducing the keys the attacker used to perform the encryption.
Synthesizing Non Photo-Realistic Rendering Effects of Volumetric Strokes

NPR (Non Photo-Realistic Rendering) is an important issue in Computer Graphics. It presents a totally different style from those using realistic methods. In this paper we take advantage of a 3D NPR system in color presentation, and apply stroke merging and sketch-based modeling to synthesize NPR images with volumetric effects, such as oil painting, bas-relief sculpture, and clay relief. To represent these volumetric visual effects on canvas we adopt a mesh generation method which generates the thickness of strokes. By controlling the ratio of height and width to the base area, the surface curvature is adjusted to reflect the shape differences between impasto oil painting and clay relief. Then a mesh smoothing method is applied to each group of strokes. In addition, users can personalize their own color palette by using keywords such as the name of the artist, or by choosing color sets through our GUI which categorizes color space into an emotional map. Users choose by using keywords, then related images are gathered from online resources using web-mining technology, and the appropriate colors are extracted to construct a color chart by analyzing the images. Then, the color chart is used to control the distribution of colors on the canvas. As shown in the experiment results, our 3D NPR system creates interesting and vivid images of oil painting and clay relief works. Finally, we integrate our volumetric NPR image with a relief mapping technique to provide realtime interaction.
An Item-based Collaborative Filtering Recommendation Algorithm Using Slope One Scheme Smoothing

Collaborative filtering is one of the most important technologies in electronic commerce. With the development of recommender systems, the magnitudes of users and items grow rapidly, resulted in the extreme sparsity of user rating data set. Traditional similarity measure methods work poor in this situation, make the quality of recommendation system decreased dramatically. Poor quality is one major challenge in collaborative filtering recommender systems. Sparsity of users’ ratings is the major reason causing the poor quality. To address this issue, an item-based collaborative filtering recommendation algorithm using slope one scheme smoothing is presented. This approach predicts item ratings that users have not rated by the employ of slope one scheme, and then uses Pearson correlation similarity measurement to find the target items’ neighbors, lastly produces the recommendations. The experiments are made on a common data set using different recommender algorithms. The results show that the proposed approach can improve the accuracy of the collaborative filtering recommender system.
Analyzing Dynamic Binary Instrumentation Overhead Gang -

Robust and powerful software instrumentation tools are essential for dynamic program analysis tasks such as profiling, performance evaluation, and bug detection. Dynamic binary instrumentation (DBI) is a general purpose technique that eases the development of program analysis tools by facilitating automatic low-level instrumentation. DBI-based program analysis can introduce high overhead and it is crucial for tool writers to minimize the cost. Analyzing the performance of instrumentation tools is challenging because most systems use a just-in-time compiler (JIT) to dynamically generate code. In this paper, we describe our method for analyzing the performance of instrumentation tools. The instrumented code is itself instrumented with basic block counters. We implement the profiler in Pin and use it to analyze the behavior of simple and complex instrumentation tools. The analysis yields several unexpected results about the dynamic behavior of instrumented programs. By examining these results, we often find effective solutions to improve performance.
Routing Around Congestion: Defeating DDoS Attacks and Adverse Network Conditions via Reactive BGP Routing

In this paper, we present Nyx, the first system to both effectively mitigate modern Distributed Denial of Service (DDoS) attacks regardless of the amount of traffic under adversarial control and function without outside cooperation or an Internet redesign. Nyx approaches the problem of DDoS mitigation as a routing problem rather than a filtering problem. This conceptual shift allows Nyx to avoid many of the common shortcomings of existing academic and commercial DDoS mitigation systems. By leveraging how Autonomous Systems (ASes) handle route advertisement in the existing Border Gateway Protocol (BGP), Nyx allows the deploying AS to achieve isolation of traffic from a critical upstream AS off of attacked links and onto alternative, uncongested, paths. This isolation removes the need for filtering or de-prioritizing attack traffic. Nyx controls outbound paths through normal BGP path selection, while return paths from critical ASes are controlled through the use of specific techniques we developed using existing traffic engineering principles and require no outside coordination. Using our own realistic Internet-scale simulator, we find that in more than 98% of cases our system can successfully route critical traf?c around network segments under transit-link DDoS attacks; a new form of DDoS attack where the attack traf?c never reaches the victim AS, thus invaliding defensive filtering, throttling, or prioritization strategies. More significantly, in over 95% of those cases, the alternate path provides complete congestion relief from transit-link DDoS. Nyx additionally provides complete congestion relief in over 75% of cases when the deployer is being directly attacked.
How meta-analysis increases statistical power.

One of the most frequently cited reasons for conducting a meta-analysis is the increase in statistical power that it affords a reviewer. This article demonstrates that fixed-effects meta-analysis increases statistical power by reducing the standard error of the weighted average effect size (T.) and, in so doing, shrinks the confidence interval around T.. Small confidence intervals make it more likely for reviewers to detect nonzero population effects, thereby increasing statistical power. Smaller confidence intervals also represent increased precision of the estimated population effect size. Computational examples are provided for 3 effect-size indices: d (standardized mean difference), Pearson's r, and odds ratios. Random-effects meta-analyses also may show increased statistical power and a smaller standard error of the weighted average effect size. However, the authors demonstrate that increasing the number of studies in a random-effects meta-analysis does not always increase statistical power.
Techniques for Machine Learning based Spatial Data Analysis: Research Directions

Today, machine learning techniques play a significant role in data analysis, predictive modeling and visualization. The main aim of the machine learning algorithms is that they first learn from the empirical data and can be utilized in cases for which the modeled phenomenon is hidden or not yet described. Several significant techniques like Artificial Neural Network, Support Vector Regression, k-Nearest Neighbour, Bayesian Classifiers and Decision Trees are developed in past years to acheive this task. The first and most complicated problem concerns is the amount of available data. This paper reviews state-of-the-art in the domain of spatial data analysis by employing machine learning approaches. First various methods have been summarized, which exist in the literature. Further, the current research scenarios which are going on in this area are described. Based on the research done in past years, identification of the problem in the existing system is also presented in this paper and have given future research directions.
Piecewise-Planar StereoScan: Structure and Motion from Plane Primitives

This article describes a pipeline that receives as input a sequence of images acquired by a calibrated stereo rig and outputs the camera motion and a Piecewise-Planar Reconstruction (PPR) of the scene. It firstly detects the 3D planes viewed by each stereo pair from semi-dense depth estimation. This is followed by estimating the pose between consecutive views using a new closed-form minimal algorithm that relies in point correspondences only when plane correspondences are insufficient to fully constrain the motion. Finally, the camera motion and the PPR are jointly refined, alternating between discrete optimization for generating plane hypotheses and continuous bundle adjustment. The approach differs from previous works in PPR by determining the poses from plane-primitives, by jointly estimating motion and piecewise-planar structure, and by operating sequentially, being suitable for applications of SLAM and visual odometry. Experiments are carried in challenging wide-baseline datasets where conventional point-based SfM usually fails.
Automated classification of security requirements

Requirement engineers are not able to elicit and analyze the security requirements clearly, that are essential for the development of secure and reliable software. Proper identification of security requirements present in the Software Requirement Specification (SRS) document has been a problem being faced by the developers. As a result, they are not able to deliver the software free from threats and vulnerabilities. Thus, in this paper, we intend to mine the descriptions of security requirements present in the SRS document and thereafter develop the classification models. The security-based descriptions are analyzed using text mining techniques and are then classified into four types of security requirements viz. authentication-authorization, access control, cryptography-encryption and data integrity using J48 decision tree method. Corresponding to each type of security requirement, a prediction model has been developed. The effectiveness of the prediction models is evaluated against requirement specifications collected from 15 projects which have been developed by MS students at DePaul University. The result analysis indicated that all the four models have performed very well in predicting their respective type of security requirements.
Community Structure Detection for Overlapping Modules through Mathematical Programming in Protein Interaction Networks

Community structure detection has proven to be important in revealing the underlying properties of complex networks. The standard problem, where a partition of disjoint communities is sought, has been continually adapted to offer more realistic models of interactions in these systems. Here, a two-step procedure is outlined for exploring the concept of overlapping communities. First, a hard partition is detected by employing existing methodologies. We then propose a novel mixed integer non linear programming (MINLP) model, known as OverMod, which transforms disjoint communities to overlapping. The procedure is evaluated through its application to protein-protein interaction (PPI) networks of the rat, E. coli, yeast and human organisms. Connector nodes of hard partitions exhibit topological and functional properties indicative of their suitability as candidates for multiple module membership.  identifies two types of connector nodes, inter and intra-connector, each with their own particular characteristics pertaining to their topological and functional role in the organisation of the network. Inter-connector proteins are shown to be highly conserved proteins participating in pathways that control essential cellular processes, such as proliferation, differentiation and apoptosis and their differences with intra-connectors is highlighted. Many of these proteins are shown to possess multiple roles of distinct nature through their participation in different network modules, setting them apart from proteins that are simply 'hubs', i.e. proteins with many interaction partners but with a more specific biochemical role.
Synaptic basis of cortical persistent activity: the importance of NMDA receptors to working memory.

Delay-period activity of prefrontal cortical cells, the neural hallmark of working memory, is generally assumed to be sustained by reverberating synaptic excitation in the prefrontal cortical circuit. Previous model studies of working memory emphasized the high efficacy of recurrent synapses, but did not investigate the role of temporal synaptic dynamics. In this theoretical work, I show that biophysical properties of cortical synaptic transmission are important to the generation and stabilization of a network persistent state. This is especially the case when negative feedback mechanisms (such as spike-frequency adaptation, feedback shunting inhibition, and short-term depression of recurrent excitatory synapses) are included so that the neural firing rates are controlled within a physiological range (10-50 Hz), in spite of the exuberant recurrent excitation. Moreover, it is found that, to achieve a stable persistent state, recurrent excitatory synapses must be dominated by a slow component. If neuronal firings are asynchronous, the synaptic decay time constant needs to be comparable to that of the negative feedback; whereas in the case of partially synchronous dynamics, it needs to be comparable to a typical interspike interval (or oscillation period). Slow synaptic current kinetics also leads to the saturation of synaptic drive at high firing frequencies that contributes to rate control in a persistent state. For these reasons the slow NMDA receptor-mediated synaptic transmission is likely required for sustaining persistent network activity at low firing rates. This result suggests a critical role of the NMDA receptor channels in normal working memory function of the prefrontal cortex.
Summarizing Search Results with Community-Based Question Answering

Previous work on snippet generation focused mainly on how to produce one snippet for an individual search result. This paper aims to generate snippets as a comprehensive overview for an entity query (e.g., flu) in a search-result page. Our approach first extracts the attributes (e.g., Symptom and diagnose) of the categories (e.g., Disease) from a community-based question-answering (CQA) website, and then generates the snippets based on how central a sentence is to the meaning of the query, its category, and how well it diversifies the attributes. Integer Linear Programming (ILP) is adopted to find the optimal sentence set. The experiments are conducted on Wikipedia and Yahoo! Answers. Experimental results demonstrate the effectiveness of our approach, compared to an existing commercial search engine and several summarization baselines.
Spiral Mecanum Wheel achieving omnidirectional locomotion in step-climbing

The vehicle using omnidirectional wheels has ability to move in all directions without changing the body direction unlike a normal four wheel drive vehicle. However most of omnidirectional vehicle are designed for using only on flat ground. In this paper, we propose a new type of omnidirectional wheel, “Spiral Mecanum Wheel”, which enables vehicle climb the step. This new wheel consists of spiral beams and many small rollers, and these small rollers are arranged along the spiral beams. When the vehicle with this spiral Mecanum Wheels moves in normal direction, the edge of the spiral moves to cover the step from above. We have performed the experiments using Spiral Mecanum Wheel and showing the wheel works very well. As a result, in the experiment using single Spiral Mecanum Wheel, this Spiral Mecanum Wheel climbed the step about 83% of the wheel diameter in normal direction motion. The vehicle with Spiral Mecnaum Wheel climbed the step about 37% of the wheel diameter in tangential direction motion and 59% in normal direction.
The Representation and Processing of Coreference in Discourse

A model is presented that addresses both the distribution and comprehension of different forms of referring expressions in language. This model is expressed in a formalism (Kamp & Reyle, 1993) that uses interpretive rules to map syntactic representations onto representations of discourse. Basic interpretive rules are developed for names, pronouns, definite descriptions, and quantified descriptions. These rules are triggered by syntactic input and interact dynamically with representations of discourse to establish reference and coreference. This interaction determines the ease with which coreference can be established for different linguistic forms given the existing discourse context. The performance of the model approximates that observed in studies of intuitive judgments of grammaticality and studies using online measures of language comprehension. The model uses the same basic interpretive mechanisms for coreference within and between sentences, thereby linking the domain traditionally studied by generative linguists to domains that have been of concern primarily to psychologists and computational linguists.
Acquisition and analysis of volatile memory from android devices

The Android operating system for mobile phones, which is still relatively new, is rapidly gaining market share, with dozens of smartphones and tablets either released or set to be released. In this paper, we present the first methodology and toolset for acquisition and deep analysis of volatile physical memory from Android devices. The paper discusses some of the challenges in performing Android memory acquisition, discusses our new kernel module for dumping memory, named dmd, and specifically addresses the difficulties in developing device-independent acquisition tools. Our acquisition tool supports dumping memory to either the SD on the phone or via the network. We also present analysis of kernel structures using newly developed Volatility functionality. The results of this work illustrate the potential that deep memory analysis offers to digital forensics investigators. a 2011
Procedural level generation using multi-layer level representations with MdMCs

The use of statistical and machine learning ap­proaches, such as Markov chains, for procedural content generation (PCG) has been growing in recent years in the field of Game AI. However, many of these level generation approaches account for only the structural properties of the levels. We developed multi-layered representations of levels, where each layer is designed to capture distinct gameplay information. Specifically, we experiment with representing levels using three layers: a structural layer that captures the object placements in the level, a player path layer that captures the path of an agent through the level, and a height layer that captures sections of the level as various height groups. We test our approach by generating levels for Super Mario Bros. with a multi-dimensional Markov chain approach. We compare the levels sampled with our multi-layered representation with those sampled using a single- layer approach that only considers the structural layer.