nils-herrmann/clean_scirepeval huggingface dataset:
A delay-tolerant network architecture for challenged internets

The highly successful architecture and protocols of today's Internet may operate poorly in environments characterized by very long delay paths and frequent network partitions. These problems are exacerbated by end nodes with limited power or memory resources. Often deployed in mobile and extreme environments lacking continuous connectivity, many such networks have their own specialized protocols, and do not utilize IP. To achieve interoperability between them, we propose a network architecture and application interface structured around optionally-reliable asynchronous message forwarding, with limited expectations of end-to-end connectivity and node resources. The architecture operates as an overlay above the transport layers of the networks it interconnects, and provides key services such as in-network data storage and retransmission, interoperable naming, authenticated forwarding and a coarse-grained class of service.
USING PRE-TRAINED NEURAL NETWORKS

Although content is fundamental to our music listening preferences, the leading performance in music recommendation is achieved by collaborative-filtering-based methods which exploit the similarity patterns in user’s listening history rather than the audio content of songs. Meanwhile, collaborative filtering has the well-known “cold-start” problem, i.e., it is unable to work with new songs that no one has listened to. Efforts on incorporating content information into collaborative filtering methods have shown success in many non-musical applications, such as scientific article recommendation. Inspired by the related work, we train a neural network on semantic tagging information as a content model and use it as a prior in a collaborative filtering model. Such a system still allows the user listening data to “speak for itself”. The proposed system is evaluated on the Million Song Dataset and shows comparably better result than the collaborative filtering approaches, in addition to the favorable performance in the cold-start case.
Learning to Grade Short Answer Questions using Semantic Similarity Measures and Dependency Graph Alignments

In this work we address the task of computerassisted assessment of short student answers. We combine several graph alignment features with lexical semantic similarity measures using machine learning techniques and show that the student answers can be more accurately graded than if the semantic measures were used in isolation. We also present a first attempt to align the dependency graphs of the student and the instructor answers in order to make use of a structural component in the automatic grading of student answers.
A group mobility model for ad hoc wireless networks

In this paper, we present a survey of various mobility models in both cellular networks and multi-hop networks. We show that group motion occurs frequently in ad hoc networks, and introduce a novel group mobility model Reference Point Group Mobility (RPGM) to represent the relationship among mobile hosts. RPGM can be readily applied to many existing applications. Moreover, by proper choice of parameters, RPGM can be used to model several mobility models which were previously proposed. One of the main themes of this paper is to investigate the impact of the mobility model on the performance of a specific network protocol or application. To this end, we have applied our RPGM model to two different network protocol scenarios, clustering and routing, and have evaluated network performance under different mobility patterns and for different protocol implementations. As expected, the results indicate that different mobility patterns affect the various protocols in different ways. In particular, the ranking of routing algorithms is influenced by the choice of mobility pattern.
The Spectrum Kernel: A String Kernel for SVM Protein Classification

We introduce a new sequence-similarity kernel, the spectrum kernel, for use with support vector machines (SVMs) in a discriminative approach to the protein classification problem. Our kernel is conceptually simple and efficient to compute and, in experiments on the SCOP database, performs well in comparison with state-of-the-art methods for homology detection. Moreover, our method produces an SVM classifier that allows linear time classification of test sequences. Our experiments provide evidence that string-based kernels, in conjunction with SVMs, could offer a viable and computationally efficient alternative to other methods of protein classification and homology detection.
An Integrated Experimental Environment for Distributed Systems and Networks

Three experimental environments traditionally support network and distributed systems research: network emulators, network simulators, and live networks. The continued use of multiple approaches highlights both the value and inadequacy of each. Netbed, a descendant of Emulab, provides an experimentation facility that integrates these approaches, allowing researchers to configure and access networks composed of emulated, simulated, and wide-area nodes and links. Netbed's primary goals are ease of use, control, and realism, achieved through consistent use of virtualization and abstraction.By providing operating system-like services, such as resource allocation and scheduling, and by virtualizing heterogeneous resources, Netbed acts as a virtual machine for network experimentation. This paper presents Netbed's overall design and implementation and demonstrates its ability to improve experimental automation and efficiency. These, in turn, lead to new methods of experimentation, including automated parameter-space studies within emulation and straightforward comparisons of simulated, emulated, and wide-area scenarios.
Panda: Public Auditing for Shared Data with Efficient User Revocation in the Cloud

With data storage and sharing services in the cloud, users can easily modify and share data as a group. To ensure shared data integrity can be verified publicly, users in the group need to compute signatures on all the blocks in shared data. Different blocks in shared data are generally signed by different users due to data modifications performed by different users. For security reasons, once a user is revoked from the group, the blocks which were previously signed by this revoked user must be re-signed by an existing user. The straightforward method, which allows an existing user to download the corresponding part of shared data and re-sign it during user revocation, is inefficient due to the large size of shared data in the cloud. In this paper, we propose a novel public auditing mechanism for the integrity of shared data with efficient user revocation in mind. By utilizing the idea of proxy re-signatures, we allow the cloud to re-sign blocks on behalf of existing users during user revocation, so that existing users do not need to download and re-sign blocks by themselves. In addition, a public verifier is always able to audit the integrity of shared data without retrieving the entire data from the cloud, even if some part of shared data has been re-signed by the cloud. Moreover, our mechanism is able to support batch auditing by verifying multiple auditing tasks simultaneously. Experimental results show that our mechanism can significantly improve the efficiency of user revocation.
Action Recognition with Improved Trajectories

Recently dense trajectories were shown to be an efficient video representation for action recognition and achieved state-of-the-art results on a variety of datasets. This paper improves their performance by taking into account camera motion to correct them. To estimate camera motion, we match feature points between frames using SURF descriptors and dense optical flow, which are shown to be complementary. These matches are, then, used to robustly estimate a homography with RANSAC. Human motion is in general different from camera motion and generates inconsistent matches. To improve the estimation, a human detector is employed to remove these matches. Given the estimated camera motion, we remove trajectories consistent with it. We also use this estimation to cancel out camera motion from the optical flow. This significantly improves motion-based descriptors, such as HOF and MBH. Experimental results on four challenging action datasets (i.e., Hollywood2, HMDB51, Olympic Sports and UCF50) significantly outperform the current state of the art.
Visual Relationship Detection With Deep Structural Ranking

Visual relationship detection aims to describe the interactions between pairs of objects. Different from individual object learning tasks, the number of possible relationships are much larger, which makes it hard to explore only based on the visual appearance of objects. In addition, due to the limited human effort, the annotations for visual relationships are usually incomplete which increases the difficulty of model training and evaluation. In this paper, we propose a novel framework, called Deep Structural Ranking, for visual relationship detection. To complement the representation ability of visual appearance, we integrate multiple cues for predicting the relationships contained in an input image. Moreover, we design a new ranking objective function by enforcing the annotated relationships to have higher relevance scores. Unlike previous works, our proposed method can both facilitate the co-occurrence of relationships and mitigate the incompleteness problem. Experimental results show that our proposed method outperforms the state-of-the-art on the two widely used datasets. We also demonstrate its superiority in detecting zero-shot relationships.
TensorLayer: A Versatile Library for Efficient Deep Learning Development

Recently we have observed emerging uses of deep learning techniques in multimedia systems. Developing a practical deep learning system is arduous and complex. It involves labor-intensive tasks for constructing sophisticated neural networks, coordinating multiple network models, and managing a large amount of training-related data. To facilitate such a development process, we propose TensorLayer which is a Python-based versatile deep learning library. TensorLayer provides high-level modules that abstract sophisticated operations towards neuron layers, network models, training data and dependent training jobs. In spite of offering simplicity, it has transparent module interfaces that allows developers to flexibly embed low-level controls within a backend engine, with the aim of supporting fine-grain tuning towards training. Real-world cluster experiment results show that TensorLayeris able to achieve competitive performance and scalability in critical deep learning tasks. TensorLayer was released in September 2016 on GitHub. Since after, it soon become one of the most popular open-sourced deep learning library used by researchers and practitioners.
Influence and passivity in social media

The ever-increasing amount of information flowing through Social Media forces the members of these networks to compete for attention and influence by relying on other people to spread their message. A large study of information propagation within Twitter reveals that the majority of users act as passive information consumers and do not forward the content to the network. Therefore, in order for individuals to become influential they must not only obtain attention and thus be popular, but also overcome user passivity. We propose an algorithm that determines the influence and passivity of users based on their information forwarding activity. An evaluation performed with a 2.5 million user dataset shows that our influence measure is a good predictor of URL clicks, outperforming several other measures that do not explicitly take user passivity into account. We demonstrate that high popularity does not necessarily imply high influence and vice-versa.
Analysing the visual dynamics of spatial morphology

Recently there has been a revival of interest in visibility analysis of architectural configurations. The new analyses rely heavily on computing power and statistical analysis, two factors which, according to the postpositivist school of geography, should immediately cause us to be wary. The danger, they would suggest, is in the application of a reductionist formal mathematical description in order to `explain' multilayered sociospatial phenomena. The author presents an attempt to rationalise how we can use visibility analysis to explore architecture in this multilayered context by considering the dynamics that lead to the visual experience. In particular, it is recommended that we assess the visual process of inhabitation, rather than assess the visibility in vacuo. In order to investigate the possibilities and limitations of the methodology, an urban environment is analysed by means of an agent-based model of visual actors within the configuration. The results obtained from the model are compared with actual pedestrian movement and other analytic measurements of the area: the agents correlate well both with human movement patterns and with configurational relationship as analysed by space-syntax methods. The application of both methods in combination improves on the correlation with observed movement of either, which in turn implies that an understanding of both the process of inhabitation and the principles of configuration may play a crucial role in determining the social usage of space.
Asymmetric Transitivity Preserving Graph Embedding

Graph embedding algorithms embed a graph into a vector space where the structure and the inherent properties of the graph are preserved. The existing graph embedding methods cannot preserve the asymmetric transitivity well, which is a critical property of directed graphs. Asymmetric transitivity depicts the correlation among directed edges, that is, if there is a directed path from u to v, then there is likely a directed edge from u to v. Asymmetric transitivity can help in capturing structures of graphs and recovering from partially observed graphs. To tackle this challenge, we propose the idea of preserving asymmetric transitivity by approximating high-order proximity which are based on asymmetric transitivity. In particular, we develop a novel graph embedding algorithm, High-Order Proximity preserved Embedding (HOPE for short), which is scalable to preserve high-order proximities of large scale graphs and capable of capturing the asymmetric transitivity. More specifically, we first derive a general formulation that cover multiple popular high-order proximity measurements, then propose a scalable embedding algorithm to approximate the high-order proximity measurements based on their general formulation. Moreover, we provide a theoretical upper bound on the RMSE (Root Mean Squared Error) of the approximation. Our empirical experiments on a synthetic dataset and three real-world datasets demonstrate that HOPE can approximate the high-order proximities significantly better than the state-of-art algorithms and outperform the state-of-art algorithms in tasks of reconstruction, link prediction and vertex recommendation.
Learning to predict slip for ground robots

In this paper we predict the amount of slip an exploration rover would experience using stereo imagery by learning from previous examples of traversing similar terrain. To do that, the information of terrain appearance and geometry regarding some location is correlated to the slip measured by the rover while this location is being traversed. This relationship is learned from previous experience, so slip can be predicted later at a distance from visual information only. The advantages of the approach are: 1) learning from examples allows the system to adapt to unknown terrains rather than using fixed heuristics or predefined rules; 2) the feedback about the observed slip is received from the vehicle's own sensors which can fully automate the process; 3) learning slip from previous experience can replace complex mechanical modeling of vehicle or terrain, which is time consuming and not necessarily feasible. Predicting slip is motivated by the need to assess the risk of getting trapped before entering a particular terrain. For example, a planning algorithm can utilize slip information by taking into consideration that a slippery terrain is costly or hazardous to traverse. A generic nonlinear regression framework is proposed in which the terrain type is determined from appearance and then a nonlinear model of slip is learned for a particular terrain type. In this paper we focus only on the latter problem and provide slip learning and prediction results for terrain types, such as soil, sand, gravel, and asphalt. The slip prediction error achieved is about 15% which is comparable to the measurement errors for slip itself
RASL: Robust Alignment by Sparse and Low-Rank Decomposition for Linearly Correlated Images

This paper studies the problem of simultaneously aligning a batch of linearly correlated images despite gross corruption (such as occlusion). Our method seeks an optimal set of image domain transformations such that the matrix of transformed images can be decomposed as the sum of a sparse matrix of errors and a low-rank matrix of recovered aligned images. We reduce this extremely challenging optimization problem to a sequence of convex programs that minimize the sum of ℓ1-norm and nuclear norm of the two component matrices, which can be efficiently solved by scalable convex optimization techniques. We verify the efficacy of the proposed robust alignment algorithm with extensive experiments on both controlled and uncontrolled real data, demonstrating higher accuracy and efficiency than existing methods over a wide range of realistic misalignments and corruptions.
Frangipani: A Scalable Distributed File System

The ideal distributed file system would provide all its users with coherent, shared access to the same set of files,yet would be arb itrarily scalable to provide more storage space and higher performan ce to a growing user community. It would be highly available in spi te of component failures. It would require minimal human adminis tration, and administration would not become more complex as mo re components were added. Frangipani is a new file system that approximates this ideal, y t was relatively easy to build because of its two-layer struct ure. The lower layer is Petal (described in an earlier paper), a distr ibuted storage service that provides incrementally scalable, hig hly available, automatically managed virtual disks. In the upper lay er, multiple machines run the same Frangipani file system code on top of a shared Petal virtual disk, using a distributed lock serv ic to ensure coherence. Frangipani is meant to run in a cluster of machines that are un d r a common administration and can communicate securely. Thus the machines trust one another and the shared virtual disk appro ch is practical. Of course, a Frangipani file system can be exporte d to untrusted machines using ordinary network file access proto cols. We have implemented Frangipani on a collection of Alphas running DIGITAL Unix 4.0. Initial measurements indicate th a Frangipani has excellent single-server performance and sc ale well as servers are added.
Optical Motion Capture: Theory and Implementation

Motion capture is the process of recording real life movement of a subject as sequences of Cartesian coordinates in 3D space. Optical motion capture (OMC) uses cameras to reconstruct the body posture of the performer. One approach employs a set of multiple synchronized cameras to capture markers placed in strategic locations on the body. A motion capture system has applications in computer graphics for character animation, in virtual reality for human control-interface, and in video games for realistic simulation of human motion. In this tutorial, we discuss the theoretical and empirical aspects of an optical motion capture system. Basically, for a motion capture system implementation; the resources required consist of a number of synchronized cameras, an image acquisition system, a capturing area, and a special suit with markers. The locations of the markers on the suit are designed such that the required body parts (e.g. joints) are covered. We present our motion capture system using a framework that identifies different sub-problems to be solved in a modular way. The sub-problems involved in OMC are initialization, marker detection, spatial correspondence, temporal correspondence, and postprocessing. In this tutorial, we discuss the theory involved in each sub-problem and the corresponding novel techniques used in the current implementation. The initialization includes setting up a human model and the computation of intrinsic and extrinsic camera calibration. Marker detection involves finding 1 Computer Vision Laboratory, Center for Automation Research. University of Maryland, College Park, MD 20742-3275.  Optical Motion Capture: Theory and Implementation 2 RITA • Volume XII • Número 2 • 2005 the 2D pixel coordinates of markers in the images. The spatial correspondence problem consists in finding pairs of detected markers in different images captured at the same time with different viewpoints such that each pair corresponds to the projections of the same scene point. Given camera calibration and the spatial matching, the 3D reconstruction of markers (translational data) is achieved by triangulating the various camera views. The temporal correspondence problem (tracking) involves matching two clouds of 3D points representing detected markers at two consecutive frames, respectively. The temporal correspondence module builds a track for each marker where the marker’s 3D coordinates are concatenated according to time. Post-processing consists in labeling each track with a marker code, finding missing markers lost by occlusions, correcting possible gross errors, and filtering noise. Once the translational data is processed, a hierarchical human model may be used to compute rotational data (joint angles). We consider standard data formats available for motion capture data (e.g. bvh, acclaim). Other important techniques used to improve consistency in the motion data are volumetric reconstruction, inverse kinematics, and inverse dynamics. We also cover topics related to editing and manipulation of motion data. Optical Motion Capture: Theory and Implementation RITA • Volume XII • Número 2 • 2005 3
Entropy-Based Subword Mining with an Application to Word Embeddings

Recent literature has shown a wide variety of benefits to mapping traditional onehot representations of words and phrases to lower-dimensional real-valued vectors known as word embeddings. Traditionally, most word embedding algorithms treat each word as the finest meaningful semantic granularity and perform embedding by learning distinct embedding vectors for each word. Contrary to this line of thought, technical domains such as scientific and medical literature compose words from subword structures such as prefixes, suffixes, and root-words as well as compound words. Treating individual words as the finest-granularity unit discards meaningful shared semantic structure between words sharing substructures. This not only leads to poor embeddings for text corpora that have longtail distributions, but also heuristic methods for handling out-of-vocabulary words. In this paper we propose SubwordMine, an entropybased subword mining algorithm that is fast, unsupervised, and fully data-driven. We show that this allows for great cross-domain performance in identifying semantically meaningful subwords. We then investigate utilizing the mined subwords within the FastText embedding model and compare performance of the learned representations in a downstream language modeling task.