nils-herrmann/clean_scirepeval huggingface dataset:
Challenges in Monocular Visual Odometry: Photometric Calibration, Motion Bias, and Rolling Shutter Effect

Monocular visual odometry (VO) and simultaneous localization and mapping (SLAM) have seen tremendous improvements in accuracy, robustness, and efficiency, and have gained increasing popularity over recent years. Nevertheless, not so many discussions have been carried out to reveal the influences of three very influential yet easily overlooked aspects, such as photometric calibration, motion bias, and rolling shutter effect. In this work, we evaluate these three aspects quantitatively on the state of the art of direct, feature-based, and semi-direct methods, providing the community with useful practical knowledge both for better applying existing methods and developing new algorithms of VO and SLAM.  (some of which are counterintuitive) are drawn with both technical and empirical analyses to all of our experiments. Possible improvements on existing methods are directed or proposed, such as a subpixel accuracy refinement of oriented fast and rotated brief (ORB)-SLAM, which boosts its performance.
The combinatorial aspect of motion planning: Maneuver variants in structured environments

Motion planning plays a key role in autonomous driving. In this work, we introduce the combinatorial aspect of motion planning which tackles the fact that there are usually many possible and locally optimal solutions to accomplish a given task. Those options we call maneuver variants. We argue that by partitioning the trajectory space into discrete solution classes, such that local optimization methods yield an optimum within each discrete class, we can improve the chance of finding the global optimum as the optimum trajectory among the manuever variants. This work provides methods to enumerate the maneuver variants as well as constraints to enforce them. The return of the effort put into the problem modification as suggested is gaining assuredness in the convergency behaviour of the optimization algorithm. We show an experiment where we identify three local optima that would not have been found with local optimization methods.
A Novel Lane Detection System With Efficient Ground Truth Generation

A new night-time lane detection system and its accompanying framework are presented in this paper. The accompanying framework consists of an automated ground truth process and systematic storage of captured videos that will be used for training and testing. The proposed Advanced Lane Detector 2.0 (ALD 2.0) is an improvement over the ALD 1.0 or Layered Approach with integration of pixel remapping, outlier removal, and prediction with tracking. Additionally, a novel procedure to generate the ground truth data for lane marker locations is also proposed. The procedure consists of an original process called time slicing, which provides the user with unique visualization of the captured video and enables quick generation of ground truth information. Finally, the setup and implementation of a database hosting lane detection videos and standardized data sets for testing are also described. The ALD 2.0 is evaluated by means of the user-created annotations accompanying the videos. Finally, the planned improvements and remaining work are addressed.
Adaptive, Model-driven Autoscaling for Cloud Applications

Applications with a dynamic workload demand need access to a flexible infrastructure to meet performance guarantees and minimize resource costs. While cloud computing provides the elasticity to scale the infrastructure on demand, cloud service providers lack control and visibility of user space applications, making it difficult to accurately scale the underlying infrastructure. Thus, the burden of scaling falls on the user. In this paper, we propose a new cloud service, Dependable Compute Cloud (DC2), that automatically scales the infrastructure to meet the user-specified performance requirements. DC2 employs Kalman filtering to automatically learn the (possibly changing) system parameters for each application, allowing it to proactively scale the infrastructure to meet performance guarantees. DC2 is designed for the cloud it is application-agnostic and does not require any offline application profiling or benchmarking. Our implementation results on OpenStack using a multi-tier application under a range of workload traces demonstrate the robustness and superiority of DC2 over existing rule-based approaches.
A Case for A Collaborative Query Management System

Over the past 40 years, database management systems (DBMSs) have evolved to provide a sophisticated variety of data management capabilities. At the same time, tools for managing queries over the data have remained relatively primitive. One reason for this is that queries are typically issued through applications. They are thus debugged once and re-used repeatedly. This mode of interaction, however, is changing. As scientists (and others) store and share increasingly large volumes of data in data centers, they need the ability to analyze the data by issuing exploratory queries. In this paper, we argue that, in these new settings, data management systems must provide powerful query management capabilities, from query browsing to automatic query recommendations. We first discuss the requirements for a collaborative query management system. We outline an early system architecture and discuss the many research challenges associated with building such an engine.
TencentRec: Real-time Stream Recommendation in Practice

With the arrival of the big data era, opportunities as well as challenges arise in both industry and academia. As an important service in most web applications, accurate real-time recommendation in the context of big data is of high demand. Traditional recommender systems that analyze data and update models at regular time intervals cannot satisfy the requirements of modern web applications, calling for real-time recommender systems.
 In this paper, we tackle the ``big", ``real-time" and ``accurate" challenges in real-time recommendation, and propose a general real-time stream recommender system built on Storm named TencentRec from three aspects, i.e., ``system", ``algorithm", and ``data". We analyze the large amount of data streams from a wide range of applications leveraging the considerable computation ability of Storm, together with a data access component and a data storage component developed by us. To deal with various application specific demands, we have implemented several classic practical recommendation algorithms in TencentRec, including the item-based collaborative filtering, the content based, and the demographic based algorithms. Specially, we present a practical scalable item-based CF algorithm in detail, with the super characteristics such as robust to the implicit feedback problem, incremental update and real-time pruning. With the enhancement of real-time data collection and processing, we can capture the recommendation changes in real-time. We deploy the TencentRec in a series of production applications, and observe the superiority of TencentRec in providing accurate real-time recommendations for 10 billion user requests everyday.
DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker

Artificial intelligence has seen a number of breakthroughs in recent years, with games often serving as significant milestones. A common feature of games with these successes is that they involve information symmetry among the players, where all players have identical information. This property of perfect information, though, is far more common in games than in real-world problems. Poker is the quintessential game of imperfect information, and it has been a longstanding challenge problem in artificial intelligence. In this paper we introduce DeepStack, a new algorithm for imperfect information settings such as poker. It combines recursive reasoning to handle information asymmetry, decomposition to focus computation on the relevant decision, and a form of intuition about arbitrary poker situations that is automatically learned from selfplay games using deep learning. In a study involving dozens of participants and 44,000 hands of poker, DeepStack becomes the first computer program to beat professional poker players in heads-up no-limit Texas hold’em. Furthermore, we show this approach dramatically reduces worst-case exploitability compared to the abstraction paradigm that has been favored for over a decade.
Optimization of Dilution and Mixing of Biochemical Samples Using Digital Microfluidic Biochips

The recent emergence of lab-on-a-chip (LoC) technology has led to a paradigm shift in many healthcare-related application areas, e.g., point-of-care clinical diagnostics, high-throughput sequencing, and proteomics. A promising category of LoCs is digital microfluidic (DMF)-based biochips, in which nanoliter-volume fluid droplets are manipulated on a 2-D electrode array. A key challenge in designing such chips and mapping lab-bench protocols to a LoC is to carry out the dilution process of biochemical samples efficiently. As an optimization and automation technique, we present a dilution/mixing algorithm that significantly reduces the production of waste droplets. This algorithm takes O(n) time to compute at most n sequential mix/split operations required to achieve any given target concentration with an error in concentration factor less than [1/(2n)]. To implement the algorithm, we design an architectural layout of a DMF-based LoC consisting of two O(n)-size rotary mixers and O(n) storage electrodes. Simulation results show that the proposed technique always yields nonnegative savings in the number of waste droplets and also in the total number of input droplets compared to earlier methods.
Resistive Switching by Voltage-Driven Ion Migration in Bipolar RRAM—Part I: Experimental Study

Resistive-switching random access memory (RRAM) based on the formation and the dissolution of a conductive filament (CF) through insulating materials, e.g., transition metal oxides, may find applications as novel memory and logic devices. Understanding the resistive-switching mechanism is essential for predicting and controlling the scaling and reliability performances of the RRAM. This paper addresses the set/reset characteristics of RRAM devices based on $\hbox{HfO}_{x}$. The set process is analyzed as a function of the initial high-resistance state and of the current compliance. The reset process is studied as a function of the initial low-resistance state. Finally, the intermediate set states, obtained by set at variable compliance current, and reset states, obtained by reset at variable stopping voltage, are characterized with respect to their reset voltage, allowing for a microscopic interpretation of intermediate states in terms of different filament morphologies.
Learning and Classification of Malware Behavior

Malicious software in form of Internet worms, computer viru ses, and Trojan horses poses a major threat to the security of network ed systems. The diversity and amount of its variants severely undermine the effectiveness of classical signature-based detection. Yet variants of malware f milies share typical behavioral patternsreflecting its origin and purpose. We aim to exploit these shared patterns for classification of malware and propose a m thod for learning and discrimination of malware behavior. Our method proceed s in three stages: (a) behavior of collected malware is monitored in a sandbox envi ro ment, (b) based on a corpus of malware labeled by an anti-virus scanner a malware behavior classifieris trained using learning techniques and (c) discriminativ e features of the behavior models are ranked for explanation of classifica tion decisions. Experiments with di fferent heterogeneous test data collected over several month s using honeypots demonstrate the e ffectiveness of our method, especially in detecting novel instances of malware families previously not recognized by commercial anti-virus software.
Automated scenario generation: toward tailored and optimized military training in virtual environments

Scenario-based training exemplifies the learning-by-doing approach to human performance improvement. In this paper, we enumerate the advantages of incorporating automated scenario generation technologies into the traditional scenario development pipeline. An automated scenario generator is a system that creates training scenarios from scratch, augmenting human authoring to rapidly develop new scenarios, providing a richer diversity of tailored training opportunities, and delivering training scenarios on demand. We introduce a combinatorial optimization approach to scenario generation to deliver the requisite diversity and quality of scenarios while tailoring the scenarios to a particular learner's needs and abilities. We propose a set of evaluation metrics appropriate to scenario generation technologies and present preliminary evidence for the suitability of our approach compared to other scenario generation approaches.
Biobehavioral responses to stress in females: tend-and-befriend, not fight-or-flight.

The human stress response has been characterized, both physiologically and behaviorally, as "fight-or-flight." Although fight-or-flight may characterize the primary physiological responses to stress for both males and females, we propose that, behaviorally, females' responses are more marked by a pattern of "tend-and-befriend." Tending involves nurturant activities designed to protect the self and offspring that promote safety and reduce distress; befriending is the creation and maintenance of social networks that may aid in this process. The biobehavioral mechanism that underlies the tend-and-befriend pattern appears to draw on the attachment-caregiving system, and neuroendocrine evidence from animal and human studies suggests that oxytocin, in conjunction with female reproductive hormones and endogenous opioid peptide mechanisms, may be at its core. This previously unexplored stress regulatory system has manifold implications for the study of stress.
iSAM: An iPhone Stealth Airborne Malware

Modern and powerful mobile devices comprise an attractive target for any potential intruder or malicious code. The usual goal of an attack is to acquire users’ sensitive data or compromise the device so as to use it as a stepping stone (or bot) to unleash a number of attacks to other targets. In this paper, we focus on the popular iPhone device. We create a new stealth and airborne malware namely iSAM able to wirelessly infect and self-propagate to iPhone devices. iSAM incorporates six different malware mechanisms, and is able to connect back to the iSAM bot master server to update its programming logic or to obey commands and unleash a synchronized attack. Our analysis unveils the internal mechanics of iSAM and discusses the way all iSAM components contribute towards achieving its goals. Although iSAM has been specifically designed for iPhone it can be easily modified to attack any iOS-based device.
The impact of including husbands in antenatal health education services on maternal health practices in urban Nepal: results from a randomized controlled trial.

Observational studies suggest that including men in reproductive health interventions can enhance positive health outcomes. A randomized controlled trial was designed to test the impact of involving male partners in antenatal health education on maternal health care utilization and birth preparedness in urban Nepal. In total, 442 women seeking antenatal services during second trimester of pregnancy were randomized into three groups: women who received education with their husbands, women who received education alone and women who received no education. The education intervention consisted of two 35-min health education sessions. Women were followed until after delivery. Women who received education with husbands were more likely to attend a post-partum visit than women who received education alone [RR = 1.25, 95% CI = (1.01, 1.54)] or no education [RR = 1.29, 95% CI = (1.04, 1.60)]. Women who received education with their husbands were also nearly twice as likely as control group women to report making >3 birth preparations [RR = 1.99, 95% CI = (1.10, 3.59)]. Study groups were similar with respect to attending the recommended number of antenatal care checkups, delivering in a health institution or having a skilled provider at birth. These data provide evidence that educating pregnant women and their male partners yields a greater net impact on maternal health behaviors compared with educating women alone.
Scalable Semi-Supervised Query Classification Using Matrix Sketching

The enormous scale of unlabeled text available today necessitates scalable schemes for representation learning in natural language processing. For instance, in this paper we are interested in classifying the intent of a user query. While our labeled data is quite limited, we have access to virtually an unlimited amount of unlabeled queries, which could be used to induce useful representations: for instance by principal component analysis (PCA). However, it is prohibitive to even store the data in memory due to its sheer size, let alone apply conventional batch algorithms. In this work, we apply the recently proposed matrix sketching algorithm to entirely obviate the problem with scalability (Liberty, 2013). This algorithm approximates the data within a specified memory bound while preserving the covariance structure necessary for PCA. Using matrix sketching, we significantly improve the user intent classification accuracy by leveraging large amounts of unlabeled queries.
Fusing data with correlations

Many applications rely on Web data and extraction systems to accomplish knowledge-driven tasks. Web information is not curated, so many sources provide inaccurate, or conflicting information. Moreover, extraction systems introduce additional noise to the data. We wish to automatically distinguish correct data and erroneous data for creating a cleaner set of integrated data. Previous work has shown that a naive voting strategy that trusts data provided by the majority or at least a certain number of sources may not work well in the presence of copying between the sources. However, correlation between sources can be much broader than copying: sources may provide data from complementary domains (negative correlation), extractors may focus on different types of information (negative correlation), and extractors may apply common rules in extraction (positive correlation, without copying). In this paper we present novel techniques modeling correlations between sources and applying it in truth finding. We provide a comprehensive evaluation of our approach on three real-world datasets with different characteristics, as well as on synthetic data, showing that our algorithms outperform the existing state-of-the-art techniques.
Phoenix++: modular MapReduce for shared-memory systems

This paper describes our rewrite of Phoenix, a MapReduce framework for shared-memory CMPs and SMPs. Despite successfully demonstrating the applicability of a MapReduce-style pipeline to shared-memory machines, Phoenix has a number of limitations; its uniform intermediate storage of key-value pairs, inefficient combiner implementation, and poor task overhead amortization fail to efficiently support a wide range of MapReduce applications, encouraging users to manually circumvent the framework. We describe an alternative implementation, Phoenix++, that provides a modular, flexible pipeline that can be easily adapted by the user to the characteristics of a particular workload. Compared to Phoenix, this new approach achieves a 4.7-fold performance improvement and increased scalability, while allowing users to write simple, strict MapReduce code.
Provenance management in curated databases

Curated databases in bioinformatics and other disciplines are the result of a great deal of manual annotation, correction and transfer of data from other sources. Provenance information concerning the creation, attribution, or version history of such data is crucial for assessing its integrity and scientific value. General purpose database systems provide little support for tracking provenance, especially when data moves among databases. This paper investigates general-purpose techniques for recording provenance for data that is copied among databases. We describe an approach in which we track the user's actions while browsing source databases and copying data into a curated database, in order to record the user's actions in a convenient, queryable form. We present an implementation of this technique and use it to evaluate the feasibility of database support for provenance management. Our experiments show that although the overhead of a naive approach is fairly high, it can be decreased to an acceptable level using simple optimizations.
Unsupervised and Semi-supervised Clustering : a Brief Survey ∗

Clustering (or cluster analysis) aims to organize a collection of data items into clusters, such that items within a cluster are more “similar” to each other than they are to items in the other clusters. This notion of similarity can be expressed in very different ways, according to the purpose of the study, to domain-specific assumptions and to prior knowledge of the problem. Clustering is usually performed when no information is available concerning the membership of data items to predefined classes. For this reason, clustering is traditionally seen as part of unsupervised learning. We nevertheless speak here of unsupervised clustering to distinguish it from a more recent and less common approach that makes use of a small amount of supervision to “guide” or “adjust” clustering (see section 2). To support the extensive use of clustering in computer vision, pattern recognition, information retrieval, data mining, etc., very many different methods were developed in several communities. Detailed surveys of this domain can be found in ,  or [26]. In the following, we attempt to briefly review a few core concepts of cluster analysis and describe categories of clustering methods that are best represented in the literature. We also take this opportunity to provide some pointers to more recent work on clustering.
Detection and Accurate Localization of Circular Fiducials under Highly Challenging Conditions

Using fiducial markers ensures reliable detection and identification of planar features in images. Fiducials are used in a wide range of applications, especially when a reliable visual reference is needed, e.g., to track the camera in cluttered or textureless environments. A marker designed for such applications must be robust to partial occlusions, varying distances and angles of view, and fast camera motions. In this paper, we present a robust, highly accurate fiducial system, whose markers consist of concentric rings, along with its theoretical foundations. Relying on projective properties, it allows to robustly localize the imaged marker and to accurately detect the position of the image of the (common) circle center. We demonstrate that our system can detect and accurately localize these circular fiducials under very challenging conditions and the experimental results reveal that it outperforms other recent fiducial systems.
Simple and effective defense against evil twin access points

Wireless networking is widespread in public places such as cafes. Unsuspecting users may become victims of attacks based on "evil twin" access points. These rogue access points are operated by criminals in an attempt to launch man-in-the-middle attacks. We present a simple protection mechanism against binding to an evil twin. The mechanism leverages short authentication string protocols for the exchange of cryptographic keys. The short string verification is performed by encoding the short strings as a sequence of colors, rendered sequentially by the user's device and by the designated access point of the cafe. The access point must have a light capable of showing two colors and must be mounted prominently in a position where users can have confidence in its authenticity. We conducted a usability study with patrons in several cafes and participants found our mechanism very usable.