nils-herrmann/clean_scirepeval huggingface dataset:
A Sentence Simplification System for Improving Relation Extraction

In this demo paper, we present a text simplification approach that is directed at improving the performance of state-of-the-art Open Relation Extraction (RE) systems. As syntactically complex sentences often pose a challenge for current Open RE approaches, we have developed a simplification framework that performs a pre-processing step by taking a single sentence as input and using a set of syntactic-based transformation rules to create a textual input that is easier to process for subsequently applied Open RE systems.
Action Recognition and Detection by Combining Motion and Appearance Features

We present an action recognition and detection system from temporally untrimmed videos by combining motion and appearance features. Motion and appearance are two kinds of complementary cues for human action understanding from video. For motion features, we adopt the Fisher vector representation with improved dense trajectories due to its rich descriptive capacity. For appearance feature, we choose the deep convolutional neural network activations due to its recent success in image based tasks. With this fused feature of iDT and CNN, we train a SVM classifier for each action class in the one-vs-all scheme. We report both the recognition and detection results of our system on Thumos 14 Challenge. From the results, we see that our method rank 4 in the action recognition task and 2 in the action detection task.
Spatial synchronization of an insole pressure distribution system with a 3D motion analysis system for center of pressure measurements

Insole pressure systems are often more appropriate than force platforms for analysing center of pressure (CoP) as they are more flexible in use and indicate the position of the CoP that characterizes the contact foot/shoe during gait with shoes. However, these systems are typically not synchronized with 3D motion analysis systems. The present paper proposes a direct method that does not require a force platform for synchronizing an insole pressure system with a 3D motion analysis system. The distance separating 24 different CoPs measured optically and their equivalents measured by the insoles and transformed in the global coordinate system did not exceed 2 mm, confirming the suitability of the method proposed. Additionally, during static single limb stance, distances smaller than 7 mm and correlations higher than 0.94 were found between CoP trajectories measured with insoles and force platforms. Similar measurements were performed during gait to illustrate the characteristics of the CoP measured with each system. The distance separating the two CoPs was below 19 mm and the coefficient of correlation above 0.86. The proposed method offers the possibility to conduct new experiments, such as the investigation of proprioception in climbing stairs or in the presence of obstacles.
Deep Learning for Aspect Based Sentiment Detection

This paper describes our submissions to the GermEval 2017 Shared Task, which focused on the analysis of customer feedback about the Deutsche Bahn AG. We used sentence embeddings and an ensemble of classifiers for two sub-tasks as well as state-of-the-art sequence taggers for two other sub-tasks. Relevant aspects to reproduce our experiments are available from https://github.com/UKPLab/ germeval2017-sentiment-detection.
Feature-selection-based dynamic transfer ensemble model for customer churn prediction

Customer churn prediction is one of the key steps to maximize the value of customers for an enterprise. It is difficult to get satisfactory prediction effect by traditional models constructed on the assumption that the training and test data are subject to the same distribution, because the customers usually come from different districts and may be subject to different distributions in reality. This study proposes a feature-selection-based dynamic transfer ensemble (FSDTE) model that aims to introduce transfer learning theory for utilizing the customer data in both the target and related source domains. The model mainly conducts a two-layer feature selection. In the first layer, an initial feature subset is selected by GMDH-type neural network only in the target domain. In the second layer, several appropriate patterns from the source domain to target training set are selected, and some features with higher mutual information between them and the class variable are combined with the initial subset to construct a new feature subset. The selection in the second layer is repeated several times to generate a series of new feature subsets, and then, we train a base classifier in each one. Finally, a best base classifier is selected dynamically for each test pattern. The experimental results in two customer churn prediction datasets show that FSDTE can achieve better performance compared with the traditional churn prediction strategies, as well as three existing transfer learning strategies.
Question Generation with Minimal Recursion Semantics

Question Generation (QG) is the task of generating reasonable questions from a text. It is a relatively new research topic and has its potential usage in intelligent tutoring systems and closed-domain question answering systems. Current approaches include template or syntax based methods. This thesis proposes a novel approach based entirely on semantics. Minimal Recursion Semantics (MRS) is a meta-level semantic representation with emphasis on scope underspecification. With the English Resource Grammar and various tools from the DELPH-IN community, a natural language sentence can be interpreted as an MRS structure by parsing, and an MRS structure can be realized as a natural language sentence through generation. There are three issues emerging from semantics-based QG: (1) sentence simplification for complex sentences, (2) question transformation for declarative sentences, and (3) generation ranking. Three solutions are also proposed: (1) MRS decomposition through a Connected Dependency MRS Graph, (2) MRS transformation from declarative sentences to interrogative sentences, and (3) question ranking by simple language models atop a MaxEnt-based model. The evaluation is conducted in the context of the Question Generation Shared Task and Generation Challenge 2010. The performance of proposed method is compared against other syntax and rule based systems. The result also reveals the challenges of current research on question generation and indicates direction for future work.
Neural Programming Language

Analog recurrent neural networks have attracted much attention lately as powerful tools of automatic learning. We formally define a high level language, called NEural Langage, which is rich enough to express any computer algorithm or rulebased system. We show how to compile a NEL program to a network which computes exactly as the original program and requires the same computation time. We suggest this language along with its compiler as the ultimate bridge from symbolic to analog computation, and propose its outcome as an initial network for learning.
Discriminative Embedded Clustering: A Framework for Grouping High-Dimensional Data

In many real applications of machine learning and data mining, we are often confronted with high-dimensional data. How to cluster high-dimensional data is still a challenging problem due to the curse of dimensionality. In this paper, we try to address this problem using joint dimensionality reduction and clustering. Different from traditional approaches that conduct dimensionality reduction and clustering in sequence, we propose a novel framework referred to as discriminative embedded clustering which alternates them iteratively. Within this framework, we are able not only to view several traditional approaches and reveal their intrinsic relationships, but also to be stimulated to develop a new method. We also propose an effective approach for solving the formulated nonconvex optimization problem. Comprehensive analyses, including convergence behavior, parameter determination, and computational complexity, together with the relationship to other related approaches, are also presented. Plenty of experimental results on benchmark data sets illustrate that the proposed method outperforms related state-of-the-art clustering approaches and existing joint dimensionality reduction and clustering methods.
Bibliometrics and beyond: some thoughts on web-based citation analysis

The idea of a unified citation index to the literature of science was first outlined by Eugene Garfield  in 1955 in the journal Science. Science Citation Index has since established itself as the gold standard for scientific information retrieval. It has also become the database of choice for citation analysts and evaluative bibliometricians worldwide. As scientific publication moves to the web, and novel approaches to scholarly communication and peer review establish themselves, new methods of citation and link analysis will emerge to capture often liminal expressions of peer esteem, influence and approbation. The web thus affords bibliometricians rich opportunities to apply and adapt their techniques to new contexts and content: the age of ‘bibliometric spectroscopy’  is dawning.
An image-warping VR-architecture: design, implementation and applications

We describe an architecture that provides a programmable display layer in order to allow the execution of custom programs on consecutive display frames. This replaces the default display behavior of repeating application frames until an update is available. The architecture is implemented using a multi-GPU system. We will show three applications of the architecture typical to VR. First, smooth motion is provided by generating intermediate display frames by per-pixel depth-image warping using 3D motion fields. Smooth motion can be beneficial for walk-throughs of large scenes. Second, we implement fine-grained latency reduction at the display frame level using a synchronized prediction of simulation objects and the viewpoint. This improves the average quality and consistency of latency reduction. Third, a crosstalk reduction algorithm for consecutive display frames is implemented, which improves the quality of stereoscopic images.
Gyrophone: Recognizing Speech from Gyroscope Signals

We show that the MEMS gyroscopes found on modern smart phones are sufficiently sensitive to measure acoustic signals in the vicinity of the phone. The resulting signals contain only very low-frequency information (<200Hz). Nevertheless we show, using signal processing and machine learning, that this information is sufficient to identify speaker information and even parse speech. Since iOS and Android require no special permissions to access the gyro, our results show that apps and active web content that cannot access the microphone can nevertheless eavesdrop on speech in the vicinity of the phone.
Adaptive Robust Optimization for the Security Constrained Unit Commitment Problem

Unit commitment, one of the most critical tasks in electric power system operations, faces new challenges as the supply and demand uncertainty increases dramatically due to the integration of variable generation resources such as wind power and price responsive demand. To meet these challenges, we propose a two-stage adaptive robust unit commitment model for the security constrained unit commitment problem in the presence of nodal net injection uncertainty. Compared to the conventional stochastic programming approach, the proposed model is more practical in that it only requires a deterministic uncertainty set, rather than a hard-to-obtain probability distribution on the uncertain data. The unit commitment solutions of the proposed model are robust against all possible realizations of the modeled uncertainty. We develop a practical solution methodology based on a combination of Benders decomposition type algorithm and the outer approximation technique. We present an extensive numerical study on the real-world large scale power system operated by the ISO New England. Computational results demonstrate the economic and operational advantages of our model over the traditional reserve adjustment approach.
Feasibility study of mobile phone WiFi detection in aerial search and rescue operations

In this paper, we show that it is feasible for a mobile phone to be used as an SOS beacon in an aerial search and rescue operation. We show with various experiments that we can reliably detect WiFi-enabled mobile phones from the air at distances up to 200 m. By using a custom mobile application that triggers WiFi scanning with the display off, we can simultaneously extend battery life and increase WiFi scanning frequency, compared to keeping the phone in the default scanning mode. Even if an application is not installed or used, our measurement study suggests that it may be possible to detect mobile devices from their background WiFi emissions alone.
Space and attention in parietal cortex.

The space around us is represented not once but many times in parietal cortex. These multiple representations encode locations and objects of interest in several egocentric reference frames. Stimulus representations are transformed from the coordinates of receptor surfaces, such as the retina or the cochlea, into the coordinates of effectors, such as the eye, head, or hand. The transformation is accomplished by dynamic updating of spatial representations in conjunction with voluntary movements. This direct sensory-to-motor coordinate transformation obviates the need for a single representation of space in environmental coordinates. In addition to representing object locations in motoric coordinates, parietal neurons exhibit strong modulation by attention. Both top-down and bottom-up mechanisms of attention contribute to the enhancement of visual responses. The saliance of a stimulus is the primary factor in determining the neural response to it. Although parietal neurons represent objects in motor coordinates, visual responses are independent of the intention to perform specific motor acts.
Modeling Past and Future for Neural Machine Translation

Existing neural machine translation systems do not explicitly model what has been translated and what has not during the decoding phase. To address this problem, we propose a novel mechanism that separates the source information into two parts: translated Past contents and untranslated Future contents, which are modeled by two additional recurrent layers. The Past and Future contents are fed to both the attention model and the decoder states, which provides Neural Machine Translation (NMT) systems with the knowledge of translated and untranslated contents. Experimental results show that the proposed approach significantly improves the performance in Chinese-English, German-English, and English-German translation tasks. Specifically, the proposed model outperforms the conventional coverage model in terms of both the translation quality and the alignment error rate.
A Novel Methodology for Designing Linear Phase IIR Filters

This paper presents a novel technique for designing an Infinite Impulse Response (IIR) Filter with Linear Phase Response. The design of IIR filter is always a challenging task due to the reason that a Linear Phase Response is not realizable in this kind. The conventional techniques involve large number of samples and higher order filter for better approximation resulting in complex hardware for implementing the same. In addition, an extensive computational resource for obtaining the inverse of huge matrices is required. However, we propose a technique, which uses the frequency domain sampling along with the linear programming concept to achieve a filter design, which gives a best approximation for the linear phase response. The proposed method can give the closest response with less number of samples (only 10) and is computationally simple. We have presented the filter design along with its formulation and solving methodology. Numerical results are used to substantiate the efficiency of the proposed method.
Website fingerprinting in onion routing based anonymization networks

Low-latency anonymization networks such as Tor and JAP claim to hide the recipient and the content of communications from a local observer, i.e., an entity that can eavesdrop the traffic between the user and the first anonymization node. Especially users in totalitarian regimes strongly depend on such networks to freely communicate. For these people, anonymity is particularly important and an analysis of the anonymization methods against various attacks is necessary to ensure adequate protection. In this paper we show that anonymity in Tor and JAP is not as strong as expected so far and cannot resist website fingerprinting attacks under certain circumstances. We first define features for website fingerprinting solely based on volume, time, and direction of the traffic. As a result, the subsequent classification becomes much easier. We apply support vector machines with the introduced features. We are able to improve recognition results of existing works on a given state-of-the-art dataset in Tor from 3% to 55% and in JAP from 20% to 80%. The datasets assume a closed-world with 775 websites only. In a next step, we transfer our findings to a more complex and realistic open-world scenario, i.e., recognition of several websites in a set of thousands of random unknown websites. To the best of our knowledge, this work is the first successful attack in the open-world scenario. We achieve a surprisingly high true positive rate of up to 73% for a false positive rate of 0.05%. Finally, we show preliminary results of a proof-of-concept implementation that applies camouflage as a countermeasure to hamper the fingerprinting attack. For JAP, the detection rate decreases from 80% to 4% and for Tor it drops from 55% to about 3%.
A Case for Efficient Hardware / Software Cooperative Management of Storage and Memory

Most applications manipulate persistent data, yet traditional systems decouple data manipulation from persistence in a two-level storage model. Programming languages and system software manipulate data in one set of formats in volatile main memory (DRAM) using a load/store interface, while storage systems maintain persistence in another set of formats in non-volatile memories, such as Flash and hard disk drives in traditional systems, using a file system interface. Unfortunately, such an approach suffers from the system performance and energy overheads of locating data, moving data, and translating data between the different formats of these two levels of storage that are accessed via two vastly different interfaces. Yet today, new non-volatile memory (NVM) technologies show the promise of storage capacity and endurance similar to or better than Flash at latencies comparable to DRAM, making them prime candidates for providing applications a persistent single-level store with a single load/store interface to access all system data. Our key insight is that in future systems equipped with NVM, the energy consumed executing operating system and file system code to access persistent data in traditional systems becomes an increasingly large contributor to total energy. The goal of this work is to explore the design of a Persistent Memory Manager that coordinates the management of memory and storage under a single hardware unit in a single address space. Our initial simulation-based exploration shows that such a system with a persistent memory can improve energy efficiency and performance by eliminating the instructions and data movement traditionally used to perform I/O operations.
An enhanced e-learning ecosystem based on an integration between cloud computing and Web2.0

What we know is less important than our capacity to continue to learn more until e-learning appeared. While e-learning technology has matured considerably since its inception, there are still many problems that practitioners find when come to implementing e-learning. Today's knowledge society of the 21st century requires a flexible learning environment which is capable to adapt according to teaching and learning objectives, students' profiles and preferences for information and communication technologies and services. Advances in technology offer new opportunities in enhancing teaching and learning. Many advances in learning technologies are taking place throughout the world. The new technologies enable individuals to personalize the environment in which they work or learn, utilizing a range of tools to meet their interests and needs. Research community has believed that an e-learning ecosystem is the next generation e-learning but has faced challenges in optimizing resource allocations, dealing with dynamic demands on getting information and knowledge anywhere and anytime, handling rapid storage growth requirements, cost controlling and greater flexibility. Additionally, e-learning ecosystems need to improve its infrastructure, which can devote the required computation and storage resources for e-learning ecosystems. So, we need flourish, growing, up-to-date and strong infrastructure e-learning ecosystems in a productive and cost-effective way to be able to face rapidly-changing environments. In this paper, an e-learning ecosystem (ELES) which supports modern technologies is introduced and implemented. An integration between cloud computing and Web 2.0 technologies and services will be used to support the development of e-learning ecosystems; cloud computing as an adoptable technology for many of the organizations with its dynamic scalability and usage of virtualized resources as a service through the Internet. Web 2.0 brings new instruments help building dynamic e-learning ecosystem on the web.
Finding your Way in the Fog: Towards a Comprehensive Definition of Fog Computing

The cloud is migrating to the edge of the network, where routers themselves may become the virtualisation infrastructure, in an evolution labelled as "the fog". However, many other complementary technologies are reaching a high level of maturity. Their interplay may dramatically shift the information and communication technology landscape in the following years, bringing separate technologies into a common ground. This paper offers a comprehensive definition of the fog, comprehending technologies as diverse as cloud, sensor networks, peer-to-peer networks, network virtualisation functions or configuration management techniques. We highlight the main challenges faced by this potentially breakthrough technology amalgamation.
Variance Reduction in SGD by Distributed Importance Sampling

Humans are able to accelerate their learning by selecting training materials that are the most informative and at the appropriate level of difficulty. We propose a framework for distributing deep learning in which one set of workers search for the most informative examples in parallel while a single worker updates the model on examples selected by importance sampling. This leads the model to update using an unbiased estimate of the gradient which also has minimum variance when the sampling proposal is proportional to the L2-norm of the gradient. We show experimentally that this method reduces gradient variance even in a context where the cost of synchronization across machines cannot be ignored, and where the factors for importance sampling are not updated instantly across the training set.
Vivaldi Antennas for Microwave Imaging: Theoretical Analysis and Design Considerations

The geometrical and electrical features of the Vivaldi antenna are studied in the light of the frequency-independent antenna theory. A scaling principle is derived for the exponential tapering of the antenna, and a closed-form model for the current distribution is provided. Such theoretical results are in good agreement with several numerical simulations performed by using the NEC2 code. Furthermore, a practical feeding system, based on a double-Y balun, is developed and tested to obtain a more systematic approach to the design of the aforesaid antennas