nils-herrmann/clean_scirepeval huggingface dataset:
Software-defined optical networks technology and infrastructure: Enabling software-defined optical network operations [invited]

Software-defined networking (SDN) enables programmable SDN control and management functions at a number of layers, allowing applications to control network resources or information across different technology domains, e.g., Ethernet, wireless, and optical. Current cloud-based services are pushing networks to new boundaries by deploying cutting edge optical technologies to provide scalable and flexible services. SDN combined with the latest optical transport technologies, such as elastic optical networks, enables network operators and cloud service providers to customize their infrastructure dynamically to user/application requirements and therefore minimize the extra capital and operational costs required for hosting new services. In this paper a unified control plane architecture based on OpenFlow for optical SDN tailored to cloud services is introduced. Requirements for its implementation are discussed considering emerging optical transport technologies. Implementations of the architecture are proposed and demonstrated across heterogeneous state-of-the-art optical, packet, and IT resource integrated cloud infrastructure. Finally, its performance is evaluated using cloud use cases and its results are discussed.
Similarity and Locality Based Indexing for High Performance Data Deduplication

Data deduplication has gained increasing attention and popularity as a space-efficient approach in backup storage systems. One of the main challenges for centralized data deduplication is the scalability of fingerprint-index search. In this paper, we propose SiLo, a near-exact and scalable deduplication system that effectively and complementarily exploits similarity and locality of data streams to achieve high duplicate elimination, throughput, and well balanced load at extremely low RAM overhead. The main idea behind SiLo is to expose and exploit more similarity by grouping strongly correlated small files into a segment and segmenting large files, and to leverage the locality in the data stream by grouping contiguous segments into blocks to capture similar and duplicate data missed by the probabilistic similarity detection. SiLo also employs a locality based stateless routing algorithm to parallelize and distribute data blocks to multiple backup nodes. By judiciously enhancing similarity through the exploitation of locality and vice versa, SiLo is able to significantly reduce RAM usage for index-lookup, achieve the near-exact efficiency of duplicate elimination, maintain a high deduplication throughput, and obtain load balance among backup nodes.
Gated End-to-End Memory Networks

Machine reading using differentiable reasoning models has recently shown remarkable progress. In this context, End-to-End trainable Memory Networks (MemN2N) have demonstrated promising performance on simple natural language based reasoning tasks such as factual reasoning and basic deduction. However, other tasks, namely multi-fact questionanswering, positional reasoning or dialog related tasks, remain challenging particularly due to the necessity of more complex interactions between the memory and controller modules composing this family of models. In this paper, we introduce a novel end-to-end memory access regulation mechanism inspired by the current progress on the connection short-cutting principle in the field of computer vision. Concretely, we develop a Gated End-toEnd trainable Memory Network architecture (GMemN2N). From the machine learning perspective, this new capability is learned in an end-to-end fashion without the use of any additional supervision signal which is, as far as our knowledge goes, the first of its kind. Our experiments show significant improvements on the most challenging tasks in the 20 bAbI dataset, without the use of any domain knowledge. Then, we show improvements on the Dialog bAbI tasks including the real human-bot conversion-based Dialog State Tracking Challenge (DSTC-2) dataset. On these two datasets, our model sets the new state of the art. ∗work done as an Intern at XRCE
Using Phase Instead of Optical Flow for Action Recognition

Currently, the most common motion representation for action recognition is optical flow. Optical flow is based on particle tracking which adheres to a Lagrangian perspective on dynamics. In contrast to the Lagrangian perspective, the Eulerian model of dynamics does not track, but describes local changes. For video, an Eulerian phase-based motion representation, using complex steerable filters, has been successfully employed recently for motion magnification and video frame interpolation. Inspired by these previous works, here, we proposes learning Eulerian motion representations in a deep architecture for action recognition. We learn filters in the complex domain in an end-to-end manner. We design these complex filters to resemble complex Gabor filters, typically employed for phase-information extraction. We propose a phaseinformation extraction module, based on these complex filters, that can be used in any network architecture for extracting Eulerian representations. We experimentally analyze the added value of Eulerian motion representations, as extracted by our proposed phase extraction module, and compare with existing motion representations based on optical flow, on the UCF101 dataset.
Automatically Evaluating Text Coherence Using Discourse Relations

We present a novel model to represent and assess the discourse coherence of text. Our model assumes that coherent text implicitly favors certain types of discourse relation transitions. We implement this model and apply it towards the text ordering ranking task, which aims to discern an original text from a permuted ordering of its sentences. The experimental results demonstrate that our model is able to significantly outperform the state-ofthe-art coherence model by Barzilay and Lapata (2005), reducing the error rate of the previous approach by an average of 29% over three data sets against human upper bounds. We further show that our model is synergistic with the previous approach, demonstrating an error reduction of 73% when the features from both models are combined for the task.
Efficient evolution of neural network topologies

Neuroevolution, i.e. evolving artificial neural networks with genetic algorithms, has been highly effective in reinforcement learning tasks, particularly those with hidden state information. An important question in neuroevolution is how to gain an advantage from evolving neural network topologies along with weights. We present a method, NeuroEvolution of Augmenting Topologies (NEAT) that outperforms the best fixed-topology methods on a challenging benchmark reinforcement learning task. We claim that the increased efficiency is due to (1) employing a principled method of crossover of different topologies, (2) protectin g structural innovation using speciation, and (3) incrementally growing from minimal structure. We test this claim through a series of ablation studies that demonstrate that each component is necessary to the system as a whole and to each other. What results is significantly faster learning. NEAT is also an important contribution to GAs because it shows how it is possible for evolution to both optimize and complexify solutions simultaneously, making it possible to evolve increasingly complex solutions over time, thereb y strengthening the analogy with biological evolution.
Study of Visual Clutter in Geographic Node-Link Diagrams

An increasing amount of geographic data is now freely available on the Internet, and this number is expected to increase as monitoring systems and sensors are becoming a ubiquitous part of our environment. A substantial subset is structured as networks (or graphs). Notable examples are import/export of goods, networks of climate stations, flight connections, migration flows and Internet traffic. To fully comprehend such networks, it is needed to know both their geographic and relational information. For this purpose, the most common visual representation is the node-link diagram, where vertices are depicted as points and the edges connecting them are drawn as lines. This Thesis focuses on an instance of node-link diagrams, where nodes are over-imposed on a map and fixed according to their geographical information. The major issue of this visualization is the visual clutter of nodes or edges, defined in literature as “the state in which excess items, or their representation or organization, lead to a degradation of performance at some task”. In particular, nodes and links may cause occlusion and ambiguity in the graph representation. Such problems characterize a cluttered diagram because they reduce the potential usefulness of the visualization. The goal of this thesis is to advance the state of the art in Graph Visualization with respect to visual clutter. On the theoretical perspective, our goal is to acquire a deep understanding of existing approaches for visualizing geographic networks and for reducing visual clutter. Initially, we provide a classification of geographic node-link diagrams and a survey on techniques to reduce the visual clutter on such visualizations. Afterwards, we present a schematization of techniques that helps the reader to decide, given a task and a geographical node-link diagram, which are the candidate solutions that help to reduce the visual clutter. On the practical perspective, our goal is the development of visualization and interaction techniques to overcome various issues of the state-of-the-art approaches. On the one hand, we present an interactive lens that faces the issues of links organization over a map. On the other hand, we describe a deformationbased technique that reveals nodes and links otherwise hidden behind the globe surface. Finally, we introduce a method that automatically generates flow map layouts starting from multivariate geographical datasets.
Real-Time Object Detection for "Smart" Vehicles

This paper presents an e cient shape-based object detection method based on Distance Transforms and describes its use for real-time vision on-board vehicles. The method uses a template hierarchy to capture the variety of object shapes; e cient hierarchies can be generated o ine for given shape distributions using stochastic optimization techniques (i.e. simulated annealing). Online, matching involves a simultaneous coarse-tone approach over the shape hierarchy and over the transformation parameters. Very large speedup factors are typically obtained when comparing this approach with the equivalent brute-force formulation; we have measured gains of several orders of magnitudes. We present experimental results on the real-time detection of tra c signs and pedestrians from a moving vehicle. Because of the highly time sensitive nature of these vision tasks, we also discuss some hardwarespeci c implementations of the proposed method as far as SIMD parallelism is concerned.
A distributed attenuator for K-band using standard SMD thin-film chip resistors

This paper presents a distributed attenuator with standard SMD chip components. For low frequencies simple Pi and T-networks with chip resistors offer a good flexibility to design low cost attenuators in a microwave circuit. Nevertheless, with increasing frequency the parasitics of the chip resistors as well as the short additional line sections in the Pi and T network cause the design to fail. Here, a model of an 0201 chip resistor has been setup to show the limited operating range of the Pi and T-networks. Using a distributed attenuator design, two resistors separated by a quarter wavelength transmission line, can overcome these problems and extent the frequency range to K-band.
Maps of Bounded Rationality : Psychology for Behavioral Economicst

The work cited by the Nobel committee was done jointly with Amos Tversky (1937-1996) during a long and unusually close collaboration. Together, we explored the psychology of intuitive beliefs and choices and examined their bounded rationality. Herbert A. Simon (1955, 1979) had proposed much earlier that decision makers should be viewed as boundedly rational, and had offered a model in which utility maximization was replaced by satisficing. Our research attempted to obtain a map of bounded rationality, by exploring the systematic biases that separate the beliefs that people have and the choices they make from the optimal beliefs and choices assumed in rational-agent models.
YouTube2Text: Recognizing and Describing Arbitrary Activities Using Semantic Hierarchies and Zero-Shot Recognition

Despite a recent push towards large-scale object recognition, activity recognition remains limited to narrow domains and small vocabularies of actions. In this paper, we tackle the challenge of recognizing and describing activities ``in-the-wild''. We present a solution that takes a short video clip and outputs a brief sentence that sums up the main activity in the video, such as the actor, the action and its object. Unlike previous work, our approach works on out-of-domain actions: it does not require training videos of the exact activity. If it cannot find an accurate prediction for a pre-trained model, it finds a less specific answer that is also plausible from a pragmatic standpoint. We use semantic hierarchies learned from the data to help to choose an appropriate level of generalization, and priors learned from Web-scale natural language corpora to penalize unlikely combinations of actors/actions/objects, we also use a Web-scale language model to ``fill in'' novel verbs, i.e. when the verb does not appear in the training set. We evaluate our method on a large YouTube corpus and demonstrate it is able to generate short sentence descriptions of video clips better than baseline approaches.
The AMARA Corpus: Building Parallel Language Resources for the Educational Domain

This paper presents the AMARA corpus of on-line educational content: a new parallel corpus of educational video subtitles, multilingually aligned for 20 languages, i.e. 20 monolingual corpora and 190 parallel corpora. This corpus includes both resource-rich languages such as English and Arabic, and resource-poor languages such as Hindi and Thai. In this paper, we describe the gathering, validation, and preprocessing of a large collection of parallel, community-generated subtitles. Furthermore, we describe the methodology used to prepare the data for Machine Translation tasks. Additionally, we provide a document-level, jointly aligned development and test sets for 14 language pairs, designed for tuning and testing Machine Translation systems. We provide baseline results for these tasks, and highlight some of the challenges we face when building machine translation systems for educational content.
SpotON : An Indoor 3 D Location Sensing Technology Based on RF Signal Strength

Providing a reliable technology and architecture for determining the location of real world objects and people will undoubtedly enable applications, customization, and inference not currently possible. This paper documents the creation of SpotON, a new tagging technology for three dimensional location sensing based on radio signal strength analysis. Although there are many aspects to the topic of location sensing and several of them will be briefly discussed, this paper is primarily concerned with the hardware and embedded system development of such a system.
Application of AHP and Taguchi loss functions in supply chain

The purpose of this paper is to develop a decision model to help decision makers with selection of the appropriate supplier.  Supplier selection is a multi-criteria decision-making process encompassing various tangible and intangible factors. Both risks and benefits of using a vendor in supply chain are identified for inclusion in the evaluation process. Since these factors can be objective and subjective, a hybrid approach that applies to both quantitative and qualitative factors is used in the development of the model. Taguchi loss functions are used to measure performance of each supplier candidate with respect to the risks and benefits. Analytical hierarchy process (AHP) is used to determine the relative importance of these factors to the decision maker. The weighted loss scores are then calculated for each supplier by using the relative importance as the weights. The composite weighted loss scores are used for ranking of the suppliers. The supplier with the smallest loss score is recommended for selection.  Inclusion of both risk and benefit categories in the evaluation process provides a comprehensive decision tool.  The proposed model provides guidelines for supply chain managers to make an informed decision regarding supplier selection.  Combining Taguchi loss function and AHP provides a novel approach for ranking of potential suppliers for outsourcing purposes.
INFORMATION TECHNOLOGY AND ORGANIZATIONAL CHANGE : CAUSAL STRUCTURE IN THEORY AND

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org. This article concerns theories about why and how information technology affects organizational life. Good theory guides research, which, when applied, increases the likelihood that information technology will be employed with desirable consequences for users, organizations, and other interested parties. But what is a good theory? Theories are often evaluated in terms of their content-the specific concepts used and the human values served. This article examines theories in terms of their structures-theorists' assumptions about the nature and direction of causal influence. Three dimensions of causal structure are considered-causal agency, logical structure, and level of analysis. Causal agency refers to beliefs about the nature of causality: whether external forces cause change, whether people act purposefully to accomplish intended objectives, or whether changes emerge unpredictably from the interaction of people and events. Logical structure refers to the temporal aspect of theory-static versus dynamic-and to the logical relationships between the "causes" and the outcomes. Level of analysis refers to the entities about which the theory poses concepts and relationships-individuals, groups, organizations, and society. While there are many possible structures for good theory about the role of information technology in organizational change, only a few of these structures can be seen in current theorizing. Increased awareness of the options, open discussion of their advantages and disadvantages , and explicit characterization of future theoretical statements in terms of the dimensions and categories discussed here should, we believe, promote the development of better theory.
netmap: A Novel Framework for Fast Packet I/O

Many applications (routers, traffic monitors, firewalls, etc.) need to send and receive packets at line rate even on very fast links. In this paper we present netmap, a novel framework that enables commodity operating systems to handle the millions of packets per seconds traversing 1..10 Gbit/s links, without requiring custom hardware or changes to applications. In building netmap, we identified and successfully reduced or removed three main packet processing costs: per-packet dynamic memory allocations, removed by preallocating resources; system call overheads, amortized over large batches; and memory copies, eliminated by sharing buffers and metadata between kernel and userspace, while still protecting access to device registers and other kernel memory areas. Separately, some of these techniques have been used in the past. The novelty in our proposal is not only that we exceed the performance of most of previous work, but also that we provide an architecture that is tightly integrated with existing operating system primitives, not tied to specific hardware, and easy to use and maintain. netmap has been implemented in FreeBSD and Linux for several 1 and 10 Gbit/s network adapters. In our prototype, a single core running at 900 MHz can send or receive 14.88 Mpps (the peak packet rate on 10 Gbit/s links). This is more than 20 times faster than conventional APIs. Large speedups (5x and more) are also achieved on user-space Click and other packet forwarding applications using a libpcap emulation library running on top of netmap. This work was funded by the EU FP7 project CHANGE (257422). USENIX plans to publish this paper on the Proceedings of the 2012 USENIX Annual Technical Conference, which will be available at this URL after June 13, 2012. You may also find this paper, with related material, on the author’s site, http://info.iet.unipi.it/ luigi/netmap/
Shake-your-head: revisiting walking-in-place for desktop virtual reality

The Walking-In-Place interaction technique was introduced to navigate infinitely in 3D virtual worlds by walking in place in the real world. The technique has been initially developed for users standing in immersive setups and was built upon sophisticated visual displays and tracking equipments.
 In this paper, we propose to revisit the whole pipeline of the Walking-In-Place technique to match a larger set of configurations and apply it notably to the context of desktop Virtual Reality. With our novel "Shake-Your-Head" technique, the user is left with the possibility to sit down, and to use small screens and standard input devices such as a basic webcam for tracking. The locomotion simulation can compute various motions such as turning, jumping and crawling, using as sole input the head movements of the user. We also introduce the use of additional visual feedback based on camera motions to enhance the walking sensations.
 An experiment was conducted to compare our technique with classical input devices used for navigating in desktop VR. Interestingly, the results showed that our technique could even allow faster navigations when sitting, after a short learning. Our technique was also perceived as more fun and increasing presence, and was generally more appreciated for VR navigation.
Spot noise texture synthesis for data visualization

The use of stochastic texture for the visualization of scalar and vector fields over surfaces is discussed. Current techniques for texture synthesis are not suitable, because they do not provide local control, and are not suited for the design of textures. A new technique, spot noise, is presented that does provide these features. Spot noise is synthesized by addition of randomly weighted and positioned spots. Local control of the texture is realized by variation of the spot. The spot is a useful primitive for texture design, because, in general, the relations between features of the spot and features of the texture are straightforward. Various examples and applications are shown. Spot noise lends itself well for the synthesis of texture over curved surfaces, and is therefore an alternative to solid texturing. The relations of spot noise with a variety of other techniques, such as radom faults, filtering, sparse convolution, and particle systems, are discussed. It appears that spot noise provides a new perspective on those techniques.