nils-herrmann/clean_scirepeval huggingface dataset:
NetBricks: Taking the V out of NFV

The move from hardware middleboxes to software network functions, as advocated by NFV, has proven more challenging than expected. Developing new NFs remains a tedious process, requiring that developers repeatedly rediscover and reapply the same set of optimizations, while current techniques for providing isolation between NFs (using VMs or containers) incur high performance overheads. In this paper we describe NetBricks, a new NFV framework that tackles both these problems. For building NFs we take inspiration from modern data analytics frameworks (e.g., Spark and Dryad) and build a small set of customizable network processing elements. We also embrace type checking and safe runtimes to provide isolation in software, rather than rely on hardware isolation. NetBricks provides the same memory isolation as containers and VMs, without incurring the same performance penalties. To improve I/O efficiency, we introduce a novel technique called zero-copy software isolation.
A speech enhancement algorithm by iterating single- and multi-microphone processing and its application to robust ASR

We propose a speech enhancement algorithm based on single- and multi-microphone processing techniques. The core of the algorithm estimates a time-frequency mask which represents the target speech and use masking-based beamforming to enhance corrupted speech. Specifically, in single-microphone processing, the received signals of a microphone array are treated as individual signals and we estimate a mask for the signal of each microphone using a deep neural network (DNN). With these masks, in multi-microphone processing, we calculate a spatial covariance matrix of noise and steering vector for beamforming. In addition, we propose a masking-based post-filter to further suppress the noise in the output of beamforming. Then, the enhanced speech is sent back to DNN for mask re-estimation. When these steps are iterated for a few times, we obtain the final enhanced speech. The proposed algorithm is evaluated as a frontend for automatic speech recognition (ASR) and achieves a 5.05% average word error rate (WER) on the real environment test set of CHiME-3, outperforming the current best algorithm by 13.34%.
Cyber-Physical Systems Security: Experimental Analysis of a Vinyl Acetate Monomer Plant

We describe an approach for analysing and attacking the physical part (a process) of a cyber-physical system. The stages of this approach are demonstrated in a case study, a simulation of a vinyl acetate monomer plant. We want to demonstrate in particular where security has to rely on expert knowledge in the domain of the physical components and processes of a system and that there are major challenges for converting cyber attacks into successful cyber-physical attacks.
Intel SGX Explained

Intel’s Software Guard Extensions (SGX) is a set of extensions to the Intel architecture that aims to provide integrity and confidentiality guarantees to securitysensitive computation performed on a computer where all the privileged software (kernel, hypervisor, etc) is potentially malicious. This paper analyzes Intel SGX, based on the 3 papers [14, 79, 139] that introduced it, on the Intel Software Developer’s Manual [101] (which supersedes the SGX manuals [95, 99]), on an ISCA 2015 tutorial [103], and on two patents [110, 138]. We use the papers, reference manuals, and tutorial as primary data sources, and only draw on the patents to fill in missing information. This paper does not reflect the information available in two papers [74, 109] that were published after the first version of this paper. This paper’s contributions are a summary of the Intel-specific architectural and micro-architectural details needed to understand SGX, a detailed and structured presentation of the publicly available information on SGX, a series of intelligent guesses about some important but undocumented aspects of SGX, and an analysis of SGX’s security properties.
Collision-Free Accumulators and Fail-Stop Signature Schemes Without Trees

One-way accumulators, introduced by Benaloh and de Mare, can be used to accumulate a large number of values into a single one, which can then be used to authenticate every input value without the need to transmit the others. However, the one-way property does is not suucient for all applications. In this paper, we generalize the deenition of accumulators and deene and construct a collision-free subtype. As an application, we construct a fail-stop signature scheme in which many one-time public keys are accumulated into one short public key. In contrast to previous constructions with tree authentication, the length of both this public key and the signatures can be independent of the number of messages that can be signed.
Image and Video Abstraction by Anisotropic Kuwahara Filtering

We present a non-photorealistic rendering technique to transform color images and videos into painterly abstractions. It is based on a generalization of the Kuwahara filter that is adapted to the local shape of features, derived from the smoothed structure tensor. Contrary to conventional edge-preserving filters, our filter generates a painting-like flattening effect along the local feature directions while preserving shape boundaries. As opposed to conventional painting algorithms, it produces temporally coherent video abstraction without extra processing. The GPU implementation of our method processes video in real-time. The results have the clearness of cartoon illustrations but also exhibit directional information as found in oil paintings.
Exploiting HMM Sparsity to Perform Online Real-Time Nonintrusive Load Monitoring

Understanding how appliances in a house consume power is important when making intelligent and informed decisions about conserving energy. Appliances can turn ON and OFF either by the actions of occupants or by automatic sensing and actuation (e.g., thermostat). It is also difficult to understand how much a load consumes at any given operational state. Occupants could buy sensors that would help, but this comes at a high financial cost. Power utility companies around the world are now replacing old electro-mechanical meters with digital meters (smart meters) that have enhanced communication capabilities. These smart meters are essentially free sensors that offer an opportunity to use computation to infer what loads are running and how much each load is consuming (i.e., load disaggregation). We present a new load disaggregation algorithm that uses a super-state hidden Markov model and a new Viterbi algorithm variant which preserves dependencies between loads and can disaggregate multi-state loads, all while performing computationally efficient exact inference. Our sparse Viterbi algorithm can efficiently compute sparse matrices with a large number of super-states. Additionally, our disaggregator can run in real-time on an inexpensive embedded processor using low sampling rates.
Activity ranking in LinkedIn feed

Users on an online social network site generate a large number of heterogeneous activities, ranging from connecting with other users, to sharing content, to updating their profiles. The set of activities within a user's network neighborhood forms a stream of updates for the user's consumption. In this paper, we report our experience with the problem of ranking activities in the LinkedIn homepage feed. In particular, we provide a taxonomy of social network activities, describe a system architecture (with a number of key components open-sourced) that supports fast iteration in model development, demonstrate a number of key factors for effective ranking, and report experimental results from extensive online bucket tests.
Simple MOSFET-based high-voltage nanosecond pulse circuit

Using simple but powerful electronics concepts, such as a mass produced Schmitt trigger and integrated MOSFET driver in novel circuit applications, a simple 400-V 75-ns pulse generator (pulser) has been designed, developed, and tested. For a 50-/spl Omega/ matched load, the pulser produces extremely well-defined, repetitive high-voltage pulses that are free from overshoot and ringing. The width of the pulses is adjustable from 75 ns to 10 ms with the fall times of a few tens of nanoseconds for a negative wave and a repetition period of 1.5 /spl mu/s with the existing setup. By upgrading to a more complex driver circuit, much lower pulsewidths are possible. Using a 1-4 mm standard commercial cuvette, it is possible to generate electric fields of 4-1 kV/cm with this pulser. The purpose is to try to do electroporation mediated gene therapy on mammalian cells at higher electrical field strengths and submicrosecond or microsecond pulsewidths compared to conventional 200 V/cm and tens of millisecond-duration pulsewidths.
A Cross-Cultural Study of a Circumplex Model of Affect

In the quest to understand how people conceptualize emotional feelings, one approach has been to seek the dimensions by which they perceive the similarities and differences among feelings. A circumplex model of affect represents this set of mutual relations by placing feeling-related concepts in a circular order in a space formed by two bipolar dimensions: pleasure-displeasure and arousalsleepiness. This article offers evidence that the circumplex structure, rather than being somehow dependent on the English-speaking student population in which it was originally obtained, occurs in different languages and cultures. In Study 1, Estonian, Greek, and Polish Ss judged the similarity between feelings described by 28 words in their respective native languages. In Study 2, Greek and Chinese Ss judged the similarity between feelings conveyed by 10 facial expressions. In all cases, multidimensional scaling of pairwise similarity scores yielded the circular order and underlying dimensions predicted by the circumplex.
Studying Software Engineers: Data Collection Techniques for Software Field Studies

Software engineering is an intensively people-oriented activity, yet too little is known about how designers, maintainers, requirements analysts and all other types of software engineers perform their work. In order to improve software engineering tools and practice, it is therefore essential to conduct field studies, i.e. to study real practitioners as they solve real problems. To do so effectively, however, requires an understanding of the techniques most suited to each type of field study task. In this paper, we provide a taxonomy of techniques, focusing on those for data collection. The taxonomy is organized according to the degree of human intervention each requires. For each technique, we provide examples from the literature, an analysis of some of its advantages and disadvantages, and a discussion of how to use it effectively. We also briefly talk about field study design in general, and data analysis.
On the semantics of noun compounds

The noun compound – a sequence of nouns which function as a single noun – is very common in English texts. No language processing system should ignore expressions like steel soup pot cover if it wants to be serious about such high-end applications of computational linguistics as question answering, information extraction, text summarization, machine translation – the list goes on. Processing noun compounds, however, is far from trouble-free. For one thing, they can be bracketed in various ways: is it steel soup, steel pot or steel cover? Then there are relations inside a compound, annoyingly not signalled by any words: does pot contain soup or is it for cooking soup? These and many other research challenges are the subject of this special issue. The volume opens with Preslav Nakov’s survey paper on the interpretation of noun compounds. It serves as en excellent, thorough introduction to the whole business of studying noun compounds computationally. Both theoretical and computational linguistics consider various formal definitions of the compound, its creation, its types and properties, its applications, its approximation by paraphrases. The discussion is also illustrated by a range of languages other than English. Next, the problem of bracketing is given a few typical solutions. There follows a detailed look at noun compound semantics, including coarse-grained and very fine-grained inventories of relations among nouns in a compound. Finally, a “capstone” project is presented: textual entailment, a tool which can be immensely helpful in many high-end applications. Diarmuid Ó Séaghdha and Ann Copestake tell us how to interpret compound nouns by classifying their relations with kernel methods. The kernels implement intuitive notions of lexical and relational similarity which are computed using distributional information extracted from large text corpora. The classification is tested at three different levels of specificity. Impressively, in all cases a combination of both lexical and relational information improves upon either source taken alone.
Bandit-Based Planning and Learning in Continuous-Action Markov Decision Processes

Recent research leverages results from the continuous-armed bandit literature to create a reinforcement-learning algorithm for continuous state and action spaces. Initially proposed in a theoretical setting, we provide the first examination of the empirical properties of the algorithm. Through experimentation, we demonstrate the effectiveness of this planning method when coupled with exploration and model learning and show that, in addition to its formal guarantees, the approach is very competitive with other continuous-action reinforcement
Reinforcement Learning and Markov Decision Processes

Reinforcement learning (RL) has developed into a large research field. The current state-ofthe-art is comprised of several subfields dealing with, for example, hierarchical abstraction and relational representations. This overview is targeted at researchers interested in RL who want to know where to start when studying RL in general, and where to start within the field of RL when faced with specific problem domains. This overview is by no means complete, nor does it describe all relevant texts. In fact, there are many more. The main function of this overview is to provide a reasonable amount of good entry points into the rich field of RL. All texts are widely available and most of them are online. General and Introductory Texts There are many texts that introduce the exciting field of RL and Markov decision processes (see for example the mentioned PhD theses at the end of this overview). Furthermore, many recent AI and machine learning textbooks cover basic RL. Some of the core texts in the field are the following. I M. L. Puterman. Markov Decision Processes—Discrete Stochastic Dynamic Programming. John Wiley & Sons, Inc., New York, NY, 1994 I D. P. Bertsekas and J. Tsitsiklis. Neuro-Dynamic Programming. Athena Scientific, Belmont, MA, 1996 I L. P. Kaelbling, M. L. Littman, and A. W. Moore. Reinforcement learning: A survey.  Keerthi and B. Ravindran. Reinforcement learning. In E. Fiesler and R. Beale, editors, Handbook of Neural Computation, chapter C3. Institute of Physics and Oxford University Press, New York, New York, 1997 I R. S. Sutton and A. G. Barto. Reinforcement Learning: an Introduction. The MIT Press, Cambridge, 1998 I C. Boutilier, T. Dean, and S. Hanks. Decision theoretic planning: Structural assumptions and computational leverage.  The Logic of Adaptive Behavior: Knowledge Representation and Algorithms for Adaptive Sequential Decision Making under Uncertainty in First-Order and Relational Domains. IOS Press, Amsterdam, The Netherlands, 2009 The book by Sutton and Barto is available online, for free. You can find it at http://www.cs.ualberta.ca/∼ sutton/book/the-book.html Function Approximation, Generalization and Abstraction Because most problems are too large to represent explicitly, the majority of techniques in current RL research employs some form of generalization, abstraction or function approximation. Ergo, there are innumerable texts that deal with these matters. Some interesting starting points are the following.
A Hierarchical Model of Approach and Avoidance Achievement Motivation

A hierarchical model of approach and avoidance achievement motivation was proposed and tested in a college classroom. Mastery, performance-approach, and performance-avoidance goals were assessed and their antecedents and consequences examined. Results indicated that mastery goals were grounded in achievement motivation and high competence expectancies; performance-avoidance goals, in fear of failure and low competence expectancies; and performance-approach goals, in ach.ievement motivation, fear of failure, and high competence expectancies. Mastery goals facilitated intrinsic motivation, performance-approach goals enhanced graded performance, and performanceavoidance goals proved inimical to both intrinsic motivation and graded performance. The proposed model represents an integration of classic and contemporary approaches to the study of achievement motivation.
Color image segmentation: advances and prospects

Image segmentation is very essential and critical to image processing and pattern recognition. This survey provides a summary of color image segmentation techniques available now. Basically, color segmentation approaches are based on monochrome segmentation approaches operating in di!erent color spaces. Therefore, we "rst discuss the major segmentation approaches for segmenting monochrome images: histogram thresholding, characteristic feature clustering, edge detection, region-based methods, fuzzy techniques, neural networks, etc.; then review some major color representation methods and their advantages/disadvantages; "nally summarize the color image segmentation techniques using di!erent color representations. The usage of color models for image segmentation is also discussed. Some novel approaches such as fuzzy method and physics-based method are investigated as well. 2001 Pattern Recognition Society.
Why change programs don't produce change.

Faced with changing markets and tougher competition, more and more companies realize that to compete effectively they must transform how they function. But while senior managers understand the necessity of change, they often misunderstand what it takes to bring it about. They assume that corporate renewal is the product of company-wide change programs and that in order to transform employee behavior, they must alter a company's formal structure and systems. Both these assumptions are wrong, say these authors. Using examples drawn from their four-year study of organizational change at six large corporations, they argue that change programs are, in fact, the greatest obstacle to successful revitalization and that formal structures and systems are the last thing a company should change, not the first. The most successful change efforts begin at the periphery of a corporation, in a single plant or division. Such efforts are led by general managers, not the CEO or corporate staff people. And these general managers concentrate not on changing formal structures and systems but on creating ad hoc organizational arrangements to solve concrete business problems. This focuses energy for change on the work itself, not on abstractions such as "participation" or "culture." Once general managers understand the importance of this grass-roots approach to change, they don't have to wait for senior management to start a process of corporate renewal. The authors describe a six-step change process they call the "critical path."
CGV - An interactive graph visualization system

Previous work on graph visualization has yielded a wealth of efficient graph analysis algorithms and expressive visual mappings. To support the visual exploration of graph structures, a high degree of interactivity is required as well. We present a fully implemented graph visualization system, called CGV (Coordinated Graph Visualization), whose particular emphasis is on interaction. The system incorporates several interactive views that address different aspects of graph visualization. To support different visualization tasks, view ensembles can be created dynamically with the help of a flexible docking framework. Several novel techniques, including enhanced dynamic filtering, graph lenses, and edge-based navigation are presented. The main graph canvas interactions are augmented with several visual cues, among which the infinite grid and the radar view are novel. CGV provides a history mechanism that allows for undo/redo of interaction. CGV is a general system with potential application in many scenarios. It has been designed as a dual-use system that can run as a stand-alone application or as an applet in a web browser. CGV has been used to evaluate graph clustering results, to navigate topological structures of neuronal systems, and to perform analysis of some time-varying graphs.
Identifying Where to Focus in Reading Comprehension for Neural Question Generation

A first step in the task of automatically generating questions for testing reading comprehension is to identify questionworthy sentences, i.e. sentences in a text passage that humans find it worthwhile to ask questions about. We propose a hierarchical neural sentence-level sequence tagging model for this task, which existing approaches to question generation have ignored. The approach is fully data-driven — with no sophisticated NLP pipelines or any hand-crafted rules/features — and compares favorably to a number of baselines when evaluated on the SQuAD data set. When incorporated into an existing neural question generation system, the resulting end-to-end system achieves stateof-the-art performance for paragraph-level question generation for reading comprehension.
Visualizing and Understanding Neural Machine Translation

While neural machine translation (NMT) has made remarkable progress in recent years, it is hard to interpret its internal workings due to the continuous representations and non-linearity of neural networks. In this work, we propose to use layer-wise relevance propagation (LRP) to compute the contribution of each contextual word to arbitrary hidden states in the attention-based encoderdecoder framework. We show that visualization with LRP helps to interpret the internal workings of NMT and analyze translation errors.
Fog Computing: Towards Minimizing Delay in the Internet of Things

With the Internet of Things (IoT) becoming a major component of our daily life, understanding how to improve quality of service (QoS) in IoT networks is becoming a challenging problem. Currently most interaction between the IoT devices and the supporting back-end servers is done through large scale cloud data centers. However, with the exponential growth of IoT devices and the amount of data they produce, communication between "things" and cloud will be costly, inefficient, and in some cases infeasible. Fog computing serves as solution for this as it provides computation, storage, and networking resource for IoT, closer to things and users. One of the promising advantages of fog is reducing service delay for end user applications, whereas cloud provides extensive computation and storage capacity with a higher latency. Thus it is necessary to understand the interplay between fog computing and cloud, and to evaluate the effect of fog computing on the IoT service delay and QoS. In this paper we will introduce a general framework for IoT-fog-cloud applications, and propose a delay-minimizing policy for fog-capable devices that aims to reduce the service delay for IoT applications. We then develop an analytical model to evaluate our policy and show how the proposed framework helps to reduce IoT service delay.