nils-herrmann/clean_scirepeval huggingface dataset:
Modern B-tree techniques

In summary, the core design of B-trees has remained unchanged in 40 years: balanced trees, pages or other units of I/O as nodes, efficient root-to-leaf search, splitting and merging nodes, etc. On the other hand, an enormous amount of research and development has improved every aspect of B-trees including data contents such as multi-dimensional data, access algorithms such as multi-dimensional queries, data organization within each node such as compression and cache optimization, concurrency control such as separation of latching and locking, recovery such as multi-level recovery, etc. Gray and Reuter believed in 1993 that “B-trees are by far the most important access path structure in database and file systems.” It seems that this statement remains true today. B-tree indexes are likely to gain new importance in relational databases due to the advent of flash storage. Fast access latencies permit many more random I/O operations than traditional disk storage, thus shifting the break-even point between a full-bandwidth scan and a B-tree index search, even if the scan has the benefit of columnar database storage. We hope that this tutorial of B-tree techniques will stimulate research and development of modern B-tree indexing techniques for future data management systems.
Open innovation : State of the art and future perspectives

Open innovation has become one of the hottest topics in innovation management. This article intends to explore the limits in our understanding of the open innovation concept. In doing so, I address the questions of what (the content of open innovation), when (the context dependency) and how (the process). Open innovation is a rich concept, that can be implemented in many different ways. The context dependency of open innovation is one of the least understood topics; more research is needed on the internal and external environment characteristics affecting performance. The open innovation process relates to both the transition towards open innovation, and the various open innovation practices. As with any new concept, initial studies focus on successful and early adopters, are based on case studies, and descriptive. However, not all lessons learned from the early adopters may be applicable to following firms. Case study research increases our understanding of how things work and enables us to identify important phenomena. They should be followed by quantitative studies involving large samples to determine the relative importance of factors, to build path models to understand chains of effects, and to formally test for context dependencies. However, the evidence shows that open innovation has been a valuable concept for so many firms and in so many contexts, that it is on its way to find its final place in innovation management. & 2010 Elsevier Ltd. All rights reserved.
A cryptosystem based on Vigenère cipher with varying key

— Privacy is one of the key issues information Security addresses. Through encryption one can prevent a third party from understanding raw data during signal transmission. The encryption methods for enhancing the security of digital contents has gained high significance in the current era of breach of security and misuse of the confidential information intercepted and misused by the unauthorized parties. This paper sets out to contribute to the general body of knowledge in the area of cryptography application and by developing a new way of implementing Vigenère cipher encryption algorithm by automatically changing the cipher key after each encryption step. The new method will use successive keys that will be dependent on the initial key value during the encryption process. The algorithm ultimately makes it possible for encryption and decryption of the text. The ciphertext will have different encryption key pattern and the Vigenère cryptosystem will be more difficult to decipher using frequency attack. The implementation will be done using java programming.
Intelligent UAV-assisted routing protocol for urban VANETs

The process of routing in Vehicular Ad hoc Networks (VANET) is a challenging task in city environments. Finding the shortest end-to-end connected path satisfying delay restriction and minimal overhead is confronted with many constraints and difficulties. Such difficulties are due to the high mobility of vehicles, the frequent path failures, and the various obstructions, which may affect the reliability of the data transmission and routing. Commercial Unmanned Aerial Vehicles (UAVs) or what are commonly referred to as drones can come in handy in dealing with these constraints. In this paper, we study how UAVs operating in ad hoc mode can cooperate with VANET on the ground so as to assist in the routing process and improve the reliability of the data delivery by bridging the communication gap whenever it is possible. In a previous work, we have proposed UVAR – a UAV-Assisted VANETs routing protocol, which improves data routing and connectivity of the vehicles on the ground through the use of UAVs. However, UVAR does not fully exploit UAVs in the sky for data forwarding because it uses UAVs only when the network is poorly dense. In ∗Corresponding author Email addresses: s.oubbati@lagh-univ.dz (Omar Sami Oubbati), alakas@uaeu.ac.ae (Abderrahmane Lakas), fen.zhou@univ-avignon.fr (Fen Zhou), mesut.guenes@ovgu.de (Mesut Güneş), n.lagraa@lagh-univ.dz (Nasreddine Lagraa), m.yagoubi@lagh-univ.dz (Mohamed Bachir Yagoubi) Preprint submitted to Computer Communications April 4, 2017
Spark: Cluster Computing with Working Sets

MapReduce and its variants have been highly successful in implementing large-scale data-intensive applications on commodity clusters. However, most of these systems are built around an acyclic data flow model that is not suitable for other popular applications. This paper focuses on one such class of applications: those that reuse a working set of data across multiple parallel operations. This includes many iterative machine learning algorithms, as well as interactive data analysis tools. We propose a new framework called Spark that supports these applications while retaining the scalability and fault tolerance of MapReduce. To achieve these goals, Spark introduces an abstraction called resilient distributed datasets (RDDs). An RDD is a read-only collection of objects partitioned across a set of machines that can be rebuilt if a partition is lost. Spark can outperform Hadoop by 10x in iterative machine learning jobs, and can be used to interactively query a 39 GB dataset with sub-second response time.
Effect of written emotional expression on immune function in patients with human immunodeficiency virus infection: a randomized trial.

To determine whether writing about emotional topics compared with writing about neutral topics could affect CD4+ lymphocyte count and human immunodeficiency virus (HIV) viral load among HIV-infected patients.



Thirty-seven HIV-infected patients were randomly allocated to 2 writing conditions focusing on emotional or control topics. Participants wrote for 4 days, 30 minutes per day. The CD4+ lymphocyte count and HIV viral load were measured at baseline and at 2 weeks, 3 months, and 6 months after writing.



The emotional writing participants rated their essays as more personal, valuable, and emotional than those in the control condition. Relative to the drop in HIV viral load, CD4+ lymphocyte counts increased after the intervention for participants in the emotional writing condition compared with control writing participants.



The results are consistent with those of previous studies using emotional writing in other patient groups. Based on the self-reports of the value of writing and the preliminary laboratory findings, the results suggest that emotional writing may provide benefit for patients with HIV infection.
Memory resource management in VMware ESX server

VMware ESX Server is a thin software layer designed to multiplex hardware resources efficiently among virtual machines running unmodified commodity operating systems. This paper introduces several novel ESX Server mechanisms and policies for managing memory. A ballooning technique reclaims the pages considered least valuable by the operating system running in a virtual machine. An idle memory tax achieves efficient memory utilization while maintaining performance isolation guarantees. Content-based page sharing and hot I/O page remapping exploit transparent page remapping to eliminate redundancy and reduce copying overheads. These techniques are combined to efficiently support virtual machine workloads that overcommit memory.
Voting-based pose estimation for robotic assembly using a 3D sensor

We propose a voting-based pose estimation algorithm applicable to 3D sensors, which are fast replacing their 2D counterparts in many robotics, computer vision, and gaming applications. It was recently shown that a pair of oriented 3D points, which are points on the object surface with normals, in a voting framework enables fast and robust pose estimation. Although oriented surface points are discriminative for objects with sufficient curvature changes, they are not compact and discriminative enough for many industrial and real-world objects that are mostly planar. As edges play the key role in 2D registration, depth discontinuities are crucial in 3D. In this paper, we investigate and develop a family of pose estimation algorithms that better exploit this boundary information. In addition to oriented surface points, we use two other primitives: boundary points with directions and boundary line segments. Our experiments show that these carefully chosen primitives encode more information compactly and thereby provide higher accuracy for a wide class of industrial parts and enable faster computation. We demonstrate a practical robotic bin-picking system using the proposed algorithm and a 3D sensor.
Discovering Unknown Connections - the DBpedia Relationship Finder

The Relationship Finder is a tool for exploring connections between objects in a Semantic Web knowledge base. It offers a new way to get insights about elements in an ontology, in particular for large amounts of instance data. For this reason, we applied the idea to the DBpedia data set, which contains an enormous amount of knowledge extracted from Wikipedia. We describe the workings of the Relationship Finder algorithm and present some interesting statistical discoveries about DBpedia and Wikipedia.
Gecko Inspired Surface Climbing Robots

Many applications call for robots to perform tasks in workspaces where traditional vehicles cannot reach. Using robots to perform these tasks can afford better human safety as well as lower cost operations. This paper focuses on the development of gecko inspired synthetic dry adhesives for wall climbing robots which can scale vertical walls. Many applications are of great interest for this kind of robot such as inspection, repair, cleaning, and exploration. The fabrication of synthetic dry adhesives inspired by nature is discussed as well as the design of prototype wall climbing robots. Results are presented and discussed to show the feasibility of novel gecko inspired robots
MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases

We present a new algorithm for mining maximal frequent itemsets from a transactional database. Our algorithm is especially efficient when the itemsets in the database are very long. The search strategy of our algorithm integrates a depth-first traversal of the itemset lattice with effective pruning mechanisms. Our implementation of the search strategy combines a vertical bitmap representation of the database with an efficient relative bitmap compression schema. In a thorough experimental analysis of our algorithm on real data, we isolate the effect of the individual components of the algorithm. Our performance numbers show that our algorithm outperforms previous work by a factor of three to five.
THE ROLE OF DIGITAL AND SOCIAL MEDIA MARKETING IN CONSUMER BEHAVIOR

This article reviews recently published research about consumers in digital and social media marketing settings. Five themes are identified: (i) consumer digital culture, (ii) responses to digital advertising, (iii) effects of digital environments on consumer behavior, (iv) mobile environments, and (v) online word of mouth (WOM). Collectively these articles shed light from many different angles on how consumers experience, influence, and are influenced by the digital environments in which they are situated as part of their daily lives. Much is still to be understood, and existing knowledge tends to be disproportionately focused on WOM, which is only part of the digital consumer experience. Several directions for future research are advanced to encourage researchers to consider a broader range of phenomena.
Big data integration

The Big Data era is upon us: data is being generated, collected and analyzed at an unprecedented scale, and data-driven decision making is sweeping through all aspects of society. Since the value of data explodes when it can be linked and fused with other data, addressing the big data integration (BDI) challenge is critical to realizing the promise of Big Data. BDI differs from traditional data integration in many dimensions: (i) the number of data sources, even for a single domain, has grown to be in the tens of thousands, (ii) many of the data sources are very dynamic, as a huge amount of newly collected data are continuously made available, (iii) the data sources are extremely heterogeneous in their structure, with considerable variety even for substantially similar entities, and (iv) the data sources are of widely differing qualities, with significant differences in the coverage, accuracy and timeliness of data provided. This seminar explores the progress that has been made by the data integration community on the topics of schema mapping, record linkage and data fusion in addressing these novel challenges faced by big data integration, and identifies a range of open problems for the community.
Accurate human motion capture in large areas by combining IMU- and laser-based people tracking

A large number of applications use motion capture systems to track the location and the body posture of people. For instance, the movie industry captures actors to animate virtual characters that perform stunts. Today's tracking systems either operate with statically mounted cameras and thus can be used in confined areas only or rely on inertial sensors that allow for free and large-scale motion but suffer from drift in the pose estimate. This paper presents a novel tracking approach that aims to provide globally aligned full body posture estimates by combining a mobile robot and an inertial motion capture system. In our approach, a mobile robot equipped with a laser scanner is used to anchor the pose estimates of a person given a map of the environment. It uses a particle filter to globally localize a person wearing a motion capture suit and to robustly track the person's position. To obtain a smooth and globally aligned trajectory of the person, we solve a least squares optimization problem formulated from the motion capture suite and tracking data. Our approach has been implemented on a real robot and exhaustively tested. As the experimental evaluation shows, our system is able to provide locally precise and globally aligned estimates of the person's full body posture.
Learning Hierarchical Features for Scene Labeling

Scene labeling consists of labeling each pixel in an image with the category of the object it belongs to. We propose a method that uses a multiscale convolutional network trained from raw pixels to extract dense feature vectors that encode regions of multiple sizes centered on each pixel. The method alleviates the need for engineered features, and produces a powerful representation that captures texture, shape, and contextual information. We report results using multiple postprocessing methods to produce the final labeling. Among those, we propose a technique to automatically retrieve, from a pool of segmentation components, an optimal set of components that best explain the scene; these components are arbitrary, for example, they can be taken from a segmentation tree or from any family of oversegmentations. The system yields record accuracies on the SIFT Flow dataset (33 classes) and the Barcelona dataset (170 classes) and near-record accuracy on Stanford background dataset (eight classes), while being an order of magnitude faster than competing approaches, producing a 320×240 image labeling in less than a second, including feature extraction.
Von Mises-Fisher Clustering Models

This paper proposes a suite of models for clustering high-dimensional data on a unit sphere based on von Mises-Fisher (vMF) distribution and for discovering more intuitive clusters than existing approaches. The proposed models include a) A Bayesian formulation of vMF mixture that enables information sharing among clusters, b) a Hierarchical vMF mixture that provides multiscale shrinkage and tree structured view of the data and c) a Temporal vMF mixture that captures evolution of clusters in temporal data. For posterior inference, we develop fast variational methods as well as collapsed Gibbs sampling techniques for all three models. Our experiments on six datasets provide strong empirical support in favour of vMF based clustering models over other popular tools such as K-means, Multinomial Mixtures and Latent Dirichlet Allocation.
Beyond Gauss: Image-Set Matching on the Riemannian Manifold of PDFs

State-of-the-art image-set matching techniques typically implicitly model each image-set with a Gaussian distribution. Here, we propose to go beyond these representations and model image-sets as probability distribution functions (PDFs) using kernel density estimators. To compare and match image-sets, we exploit Csiszar f-divergences, which bear strong connections to the geodesic distance defined on the space of PDFs, i.e., the statistical manifold. Furthermore, we introduce valid positive definite kernels on the statistical manifolds, which let us make use of more powerful classification schemes to match image-sets. Finally, we introduce a supervised dimensionality reduction technique that learns a latent space where f-divergences reflect the class labels of the data. Our experiments on diverse problems, such as video-based face recognition and dynamic texture classification, evidence the benefits of our approach over the state-of-the-art image-set matching methods.
Does the IEEE 802.11 MAC protocol work well in multihop wireless ad hoc networks

The IEEE 802.11 MAC protocol is the standard for wireless LANs; it is widely used in testbeds and simulations for wireless multihop ad hoc networks. However, this protocol was not designed for multihop networks. Although it can support some ad hoc network architecture, it is not intended to support the wireless mobile ad hoc network, in which multihop connectivity is one of the most prominent features. In this article we focus on the following question: Can the IEEE 802.11 MAC protocol function well in multihop networks? By presenting several serious problems encountered in an IEEE 802.11-based multihop network and revealing the in-depth cause of these problems, we conclude that the current version of this wireless LAN protocol does not function well in multihop ad hoc networks. We thus doubt whether the WaveLAN-based system is workable as a mobile ad hoc testbed.
Simulating LTE Cellular Systems: An Open-Source Framework

Long-term evolution (LTE) represents an emerging and promising technology for providing broadband ubiquitous Internet access. For this reason, several research groups are trying to optimize its performance. Unfortunately, at present, to the best of our knowledge, no open-source simulation platforms, which the scientific community can use to evaluate the performance of the entire LTE system, are freely available. The lack of a common reference simulator does not help the work of researchers and poses limitations on the comparison of results claimed by different research groups. To bridge this gap, herein, the open-source framework LTE-Sim is presented to provide a complete performance verification of LTE networks. LTE-Sim has been conceived to simulate uplink and downlink scheduling strategies in multicell/multiuser environments, taking into account user mobility, radio resource optimization, frequency reuse techniques, the adaptive modulation and coding module, and other aspects that are very relevant to the industrial and scientific communities. The effectiveness of the proposed simulator has been tested and verified considering 1) the software scalability test, which analyzes both memory and simulation time requirements; and 2) the performance evaluation of a realistic LTE network providing a comparison among well-known scheduling strategies.
Improved SVRG for Non-Strongly-Convex or Sum-of-Non-Convex Objectives

Many classical algorithms are found until several years later to outlive the confines in which they were conceived, and continue to be relevant in unforeseen settings. In this paper, we show that SVRG is one such method: originally designed for strongly convex objectives, is also very robust under non-strongly convex or sum-of-non-convex settings. If f(x) is a sum of smooth, convex functions but f is not strongly convex (such as Lasso or logistic regression), we propose a variant SVRG that makes a novel choice of growing epoch length on top of SVRG. SVRG is a direct, faster variant of SVRG in this setting. If f(x) is a sum of non-convex functions but f is strongly convex, we show that the convergence of SVRG linearly depends on the non-convexity parameter of the summands. This improves the best known result in this setting, and gives better running time for stochastic PCA.
A Differential Dual-Polarized Cavity-Backed Microstrip Patch Antenna With Independent Frequency Tuning

A dual-polarized cavity-backed microstrip patch antenna has been demonstrated with independent tuning of both polarizations from 0.6 GHz to ~1.0 GHz using varactor diodes (1.2-5.4 pF). The 10-dB impedance bandwidth varies from 0.9% to 1.8% over that range. Differential coaxial feeds and the placement of the tuning varactors in the appropriate symmetry planes are employed to achieve a differential-mode port-to-port isolation of >28 dB over the tuning range. Accordingly, the cross-polarization level is <; -20 dB at broadside. The antenna is 100 × 100 × 6.4 mm3 and is fabricated on a dielectric substrate (εr = 2.2). The feed point and matching circuit for an impedance match to 50 Ω are determined using the transmission line model. This model underestimates the sensitivity of the resonance frequency to the loading capacitance, but leads to a good initial design. To the authors' knowledge, this is the first dual-polarized antenna with independent tuning over a 1.7:1 bandwidth and single-sided radiation.
Born Again Neural Networks

Knowledge Distillation (KD) consists of transferring “knowledge” from one machine learning model (the teacher) to another (the student). Commonly, the teacher is a high-capacity model with formidable performance, while the student is more compact. By transferring knowledge, one hopes to benefit from the student’s compactness, without sacrificing too much performance. We study KD from a new perspective: rather than compressing models, we train students parameterized identically to their teachers. Surprisingly, these Born-Again Networks (BANs), outperform their teachers significantly, both on computer vision and language modeling tasks. Our experiments with BANs based on DenseNets demonstrate state-of-the-art performance on the CIFAR-10 (3.5%) and CIFAR-100 (15.5%) datasets, by validation error. Additional experiments explore two distillation objectives: (i) Confidence-Weighted by Teacher Max (CWTM) and (ii) Dark Knowledge with Permuted Predictions (DKPP). Both methods elucidate the essential components of KD, demonstrating the effect of the teacher outputs on both predicted and nonpredicted classes.