nils-herrmann/clean_scirepeval huggingface dataset:
A new greedy approach for facility location problems

We present a simple and natural greedy algorithm for the metric uncapacitated facility location problem achieving an approximation guarantee of 1.61. We use this algorithm to find better approximation algorithms for the capacitated facility location problem with soft capacities and for a common generalization of the k-median and facility location problems. We also prove a lower bound of 1+2/e on the approximability of the k-median problem. At the end, we present a discussion about the techniques we have used in the analysis of our algorithm, including a computer-aided method for proving bounds on the approximation factor.
Injecting Logical Background Knowledge into Embeddings for Relation Extraction

Matrix factorization approaches to relation extraction provide several attractive features: they support distant supervision, handle open schemas, and leverage unlabeled data. Unfortunately, these methods share a shortcoming with all other distantly supervised approaches: they cannot learn to extract target relations without existing data in the knowledge base, and likewise, these models are inaccurate for relations with sparse data. Rule-based extractors, on the other hand, can be easily extended to novel relations and improved for existing but inaccurate relations, through first-order formulae that capture auxiliary domain knowledge. However, usually a large set of such formulae is necessary to achieve generalization. In this paper, we introduce a paradigm for learning low-dimensional embeddings of entity-pairs and relations that combine the advantages of matrix factorization with first-order logic domain knowledge. We introduce simple approaches for estimating such embeddings, as well as a novel training algorithm to jointly optimize over factual and first-order logic information. Our results show that this method is able to learn accurate extractors with little or no distant supervision alignments, while at the same time generalizing to textual patterns that do not appear in the formulae.
Clustering Data Streams Based on Shared Density between Micro-Clusters

As more and more applications produce streaming data, clustering data streams has become an important technique for data and knowledge engineering. A typical approach is to summarize the data stream in real-time with an online process into a large number of so called micro-clusters. Micro-clusters represent local density estimates by aggregating the information of many data points in a defined area. On demand, a (modified) conventional clustering algorithm is used in a second offline step to recluster the micro-clusters into larger final clusters. For reclustering, the centers of the micro-clusters are used as pseudo points with the density estimates used as their weights. However, information about density in the area between micro-clusters is not preserved in the online process and reclustering is based on possibly inaccurate assumptions about the distribution of data within and between micro-clusters (e.g., uniform or Gaussian). This paper describes DBSTREAM, the first micro-cluster-based online clustering component that explicitly captures the density between micro-clusters via a shared density graph. The density information in this graph is then exploited for reclustering based on actual density between adjacent micro-clusters. We discuss the space and time complexity of maintaining the shared density graph. Experiments on a wide range of synthetic and real data sets highlight that using shared density improves clustering quality over other popular data stream clustering methods which require the creation of a larger number of smaller micro-clusters to achieve comparable results.
Implementation of Internet of Things for Home Automation

Internet of Things (IoT) is a concept that envisions all objects around us as part of internet. IoT coverage is very wide and includes variety of objects like smart phones, tablets, digital cameras and sensors. Once all these devices are connected to each other, they enable more and more smart processes and services that support our basic needs, environment and health. Such enormous number of devices connected to internet provides many kinds of services. They also produce huge amount of data and information. Cloud computing is one such model for on-demand access to a shared pool of configurable resources (computer, networks, servers, storage, applications, services, and software) that can be provisioned as infrastructures ,software and applications. Cloud based platforms help to connect to the things around us so that we can access anything at any time and any place in a user friendly manner using customized portals and in built applications. Hence, cloud acts as a front end to access IoT. Applications that interact with devices like sensors have special requirements of massive storage to store big data, huge computation power to enable the real time processing of the data, information and high speed network to stream audio or video. Here we have describe how Internet of Things and Cloud computing can work together can address the Big Data problems. We have also illustrated about Sensing as a service on cloud using few applications like Augmented Reality, Agriculture, Environment monitoring,etc. Finally, we propose a prototype model for providing sensing as a service on cloud.
Introducing the Webb Spam Corpus: Using Email Spam to Identify Web Spam Automatically

Just as email spam has negatively impacted the user messaging experience, the rise of Web spam is threatening to severely degrade the quality of information on the World Wide Web. Fundamentally, Web spam is designed to pollute search engines and corrupt the user experience by driving traffic to particular spammed Web pages, regardless of the merits of those pages. In this paper, we identify an interesting link between email spam and Web spam, and we use this link to propose a novel technique for extracting large Web spam samples from the Web. Then, we present the Webb Spam Corpus – a first-of-its-kind, large-scale, and publicly available Web spam data set that was created using our automated Web spam collection method. The corpus consists of nearly 350,000 Web spam pages, making it more than two orders of magnitude larger than any other previously cited Web spam data set. Finally, we identify several application areas where the Webb Spam Corpus may be especially helpful. Interestingly, since the Webb Spam Corpus bridges the worlds of email spam and Web spam, we note that it can be used to aid traditional email spam classification algorithms through an analysis of the characteristics of the Web pages referenced by email messages.
Pragmatic ambiguity detection in natural language requirements

This paper presents an approach for pragmatic ambiguity detection in natural language requirements. Pragmatic ambiguities depend on the context of a requirement, which includes the background knowledge of the reader: different backgrounds can lead to different interpretations. The presented approach employs a graph-based modelling of the background knowledge of different readers, and uses a shortest-path search algorithm to model the pragmatic interpretation of a requirement. The comparison of different pragmatic interpretations is used to decide if a requirement is ambiguous or not. The paper also provides a case study on real-world requirements, where we have assessed the effectiveness of the approach.
Revisiting Frank-Wolfe: Projection-Free Sparse Convex Optimization

We provide stronger and more general primal-dual convergence results for FrankWolfe-type algorithms (a.k.a. conditional gradient) for constrained convex optimization, enabled by a simple framework of duality gap certificates. Our analysis also holds if the linear subproblems are only solved approximately (as well as if the gradients are inexact), and is proven to be worst-case optimal in the sparsity of the obtained solutions. On the application side, this allows us to unify a large variety of existing sparse greedy methods, in particular for optimization over convex hulls of an atomic set, even if those sets can only be approximated, including sparse (or structured sparse) vectors or matrices, low-rank matrices, permutation matrices, or max-norm bounded matrices. We present a new general framework for convex optimization over matrix factorizations, where every Frank-Wolfe iteration will consist of a low-rank update, and discuss the broad application areas of this approach.
Real-time non-rigid reconstruction using an RGB-D camera

We present a combined hardware and software solution for markerless reconstruction of non-rigidly deforming physical objects with arbitrary shape in real-time. Our system uses a single self-contained stereo camera unit built from off-the-shelf components and consumer graphics hardware to generate spatio-temporally coherent 3D models at 30 Hz. A new stereo matching algorithm estimates real-time RGB-D data. We start by scanning a smooth template model of the subject as they move rigidly. This geometric surface prior avoids strong scene assumptions, such as a kinematic human skeleton or a parametric shape model. Next, a novel GPU pipeline performs non-rigid registration of live RGB-D data to the smooth template using an extended non-linear as-rigid-as-possible (ARAP) framework. High-frequency details are fused onto the final mesh using a linear deformation model. The system is an order of magnitude faster than state-of-the-art methods, while matching the quality and robustness of many offline algorithms. We show precise real-time reconstructions of diverse scenes, including: large deformations of users' heads, hands, and upper bodies; fine-scale wrinkles and folds of skin and clothing; and non-rigid interactions performed by users on flexible objects such as toys. We demonstrate how acquired models can be used for many interactive scenarios, including re-texturing, online performance capture and preview, and real-time shape and motion re-targeting.
Optimization of Load Balancing in Hybrid LiFi/RF Networks

Light fidelity (LiFi) uses light emitting diodes (LEDs) for high-speed wireless communications. Since an LED lamp covers a small area, a LiFi system with multiple access points (APs) can offer a significantly high spatial throughput. However, the spatial distribution of data rates achieved by LiFi fluctuates because users experience inter-cell interference from neighboring LiFi APs. In order to guarantee a quality of service (QoS) for all users in the network, an RF network is considered as an additional wireless networking layer. This hybrid LiFi/RF network enables users with low levels of optical signals to achieve the desired QoS by migrating to the RF network. With regard to moving users, the hybrid LiFi/RF system dynamically allocates either a LiFi AP or an RF AP to users based on their channel state information. In this paper, a dynamic load balancing scheme is proposed, which considers the handover overhead in order to improve the overall system throughput. Joint optimization algorithm (JOA) and separate optimization algorithm (SOA), which jointly and separately optimize the AP assignment and resource allocation, respectively, are proposed. Simulation results show that SOA can offer a better performance/complexity tradeoff than JOA for system load balancing.
Six-stator spherical induction motor for balancing mobile robots

This paper describes the design, construction, and operation of a closed-loop spherical induction motor (SIM) ball wheel for a balancing mobile robot (ballbot). Following earlier work, this new design has a smaller rotor and higher torques due to the use of six stators in a skewed layout. Actuation and sensing kinematics as well as control methods are presented. In its current implementation, torques of up to 8 Nm are produced by the motor with rise and decay times of 100 ms. Results are presented supporting its potential as a prime mover for mobile robots.
Depression, loneliness and Internet addiction: How important is low self-control?

This study aimed to explore both the direct and indirect relationships between depression, loneliness, low self-control, and Internet addiction in a sample of Turkish youth, based on a cognitive-behavioral model of generalized problematic Internet use. Data for the present study were collected from 648 undergraduate students with a mean age of 22.46 years (SD = 2.45). Participants completed scales for depression, loneliness, self-control and Internet addiction. Structural equation modeling was used to test the model in which depression and loneliness predicted Internet addiction through low self-control. The findings revealed that of the two factors, only loneliness was related to Internet addiction through low self-control. The results are discussed in terms of the cognitive-behavioral model of generalized problematic Internet use, and implications for practice are considered.
Segmentation and recognition of text written in 3D using Leap motion interface

In this paper, we present a word extraction and recognition methodology from online cursive handwritten text-lines recorded by Leap motion controller The online text, drawn by 3D gesture in air, is distinct from usual online pen-based strokes. The 3D gestures are recorded in air, hence they produce often non-uniform text style and jitter-effect while writing. Also, due to the constraint of writing in air, the pause of stroke-flow between words is missing. Instead all words and lines are connected by a continuous stroke. In this paper, we have used a simple but effective heuristic to segment words written in air. Here, we propose a segmentation methodology of continuous 3D strokes into text-lines and words. Separation of text lines is achieved by heuristically finding the large gap-information between end and start-positions of successive text lines. Word segmentation is characterized in our system as a two class problem. In the next phase, we have used Hidden Markov Model-based approach to recognize these segmented words. Our experimental validation with a large dataset consisting with 320 sentences reveals that the proposed heuristic based word segmentation algorithm performs with accuracy as high as 80.3%c and an accuracy of 77.6% has been recorded by HMM-based word recognition when these segmented words are fed to HMM. The results show that the framework is efficient even with cluttered gestures.
Implicit Regularization in Deep Learning

In an attempt to better understand generalization in deep learning, we study several possible explanations. We show that implicit regularization induced by the optimization method is playing a key role in generalization and success of deep learning models. Motivated by this view, we study how different complexity measures can ensure generalization and explain how optimization algorithms can implicitly regularize complexity measures. We empirically investigate the ability of these measures to explain different observed phenomena in deep learning. We further study the invariances in neural networks, suggest complexity measures and optimization algorithms that have similar invariances to those in neural networks and evaluate them on a number of learning tasks.
A Recursive Recurrent Neural Network for Statistical Machine Translation

In this paper, we propose a novel recursive recurrent neural network (R2NN) to model the end-to-end decoding process for statistical machine translation. R2NN is a combination of recursive neural network and recurrent neural network, and in turn integrates their respective capabilities: (1) new information can be used to generate the next hidden state, like recurrent neural networks, so that language model and translation model can be integrated naturally; (2) a tree structure can be built, as recursive neural networks, so as to generate the translation candidates in a bottom up manner. A semi-supervised training approach is proposed to train the parameters, and the phrase pair embedding is explored to model translation confidence directly. Experiments on a Chinese to English translation task show that our proposed R2NN can outperform the stateof-the-art baseline by about 1.5 points in BLEU.
Supervised Models for Coreference Resolution

Traditional learning-based coreference resolvers operate by training amentionpair classifier for determining whether two mentions are coreferent or not. Two independent lines of recent research have attempted to improve these mention-pair classifiers, one by learning amentionranking model to rank preceding mentions for a given anaphor, and the other by training an entity-mention classifier to determine whether a preceding cluster is coreferent with a given mention. We propose a cluster-ranking approach to coreference resolution that combines the strengths of mention rankers and entitymention models. We additionally show how our cluster-ranking framework naturally allows discourse-new entity detection to be learned jointly with coreference resolution. Experimental results on the ACE data sets demonstrate its superior performance to competing approaches.
Linking People in Videos with "Their" Names Using Coreference Resolution

Natural language descriptions of videos provide a potentially rich and vast source of supervision. However, the highly-varied nature of language presents a major barrier to its effective use. What is needed are models that can reason over uncertainty over both videos and text. In this paper, we tackle the core task of person naming: assigning names of people in the cast to human tracks in TV videos. Screenplay scripts accompanying the video provide some crude supervision about who’s in the video. However, even the basic problem of knowing who is mentioned in the script is often difficult, since language often refers to people using pronouns (e.g., “he”) and nominals (e.g., “man”) rather than actual names (e.g., “Susan”). Resolving the identity of these mentions is the task of coreference resolution, which is an active area of research in natural language processing. We develop a joint model for person naming and coreference resolution, and in the process, infer a latent alignment between tracks and mentions. We evaluate our model on both vision and NLP tasks on a new dataset of 19 TV episodes. On both tasks, we significantly outperform the independent baselines.
The suppressive influence of SMA on M1 in motor imagery revealed by fMRI and dynamic causal modeling

Although motor imagery is widely used for motor learning in rehabilitation and sports training, the underlying mechanisms are still poorly understood. Based on fMRI data sets acquired with very high temporal resolution (300 ms) under motor execution and imagery conditions, we utilized Dynamic Causal Modeling (DCM) to determine effective connectivity measures between supplementary motor area (SMA) and primary motor cortex (M1). A set of 28 models was tested in a Bayesian framework and the by-far best-performing model revealed a strong suppressive influence of the motor imagery condition on the forward connection between SMA and M1. Our results clearly indicate that the lack of activation in M1 during motor imagery is caused by suppression from the SMA. These results highlight the importance of the SMA not only for the preparation and execution of intended movements, but also for suppressing movements that are represented in the motor system but not to be performed.
Voltage and Transient Stability Support by Wind Farms Complying With the E.ON Netz Grid Code

In this paper, the impact of the response of a wind farm (WF) on the operation of a nearby grid is investigated during network disturbances. Only modern variable speed wind turbines are treated in this work. The new E.ON Netz fault response code for WF is taken as the base case for the study. The results found in this paper are that the performance of the used Cigre 32-bus test system during disturbances is improved when the WF is complying with the E.ON code compared to the traditional unity power factor operation. Further improvements are found when the slope of the reactive current support line is increased from the E.ON specified value. In addition, a larger converter of a variable speed wind turbine is exploited that is to be used in order to improve the stability of a nearby grid by extending the reactive current support. By doing so, it is shown in this paper that the voltage profile at the point of common coupling (pcc) as well as the transient stability of the grid are improved compared to the original E.ON code, in addition to the improvements already achieved by using the E.ON code in its original form. Finally, regarding the utilization of a larger converter, it is important to point out that the possible reactive power in-feed into the pcc from an offshore WF decreases with increasing cable length during network faults, making it difficult to support the grid with extra reactive power during disturbances.
Reviewing the flipped classroom research: reflections for computer science education

Recent technical and infrastructural developments posit flipped (or inverted) classroom approaches ripe for exploration. Flipped classroom approaches have students use technology to access the lecture and other instructional resources outside the classroom in order to engage them in active learning during in-class time. Scholars and educators have reported a variety of outcomes of a flipped approach to instruction; however, the lack of a summary from these empirical studies prevents stakeholders from having a clear view of the benefits and challenges of this style of instruction. The purpose of this article is to provide a review of the flipped classroom approach in order to summarize the findings, to guide future studies, and to reflect the major achievements in the area of Computer Science (CS) education. 32 peer-reviewed articles were collected from a systematic literature search and analyzed based on a categorization of their main elements. The results of this survey show the direction of flipped classroom research during recent years and summarize the benefits and challenges of adopting a flipped approach in the classroom. Suggestions for future research include: describing in-detail the flipped approach; performing controlled experiments; and triangulating data from diverse sources. These future research efforts will reveal which aspects of a flipped classroom work better and under which circumstances and student groups. The findings will ultimately allow us to form best practices and a unified framework for guiding/assisting educators who want to adopt this teaching style.
Learning Montezuma's Revenge from a Single Demonstration

We propose a new method for learning from a single demonstration to solve hard exploration tasks like the Atari game Montezuma’s Revenge. Instead of imitating human demonstrations, as proposed in other recent works, our approach is to maximize rewards directly. Our agent is trained using off-the-shelf reinforcement learning, but starts every episode by resetting to a state from a demonstration. By starting from such demonstration states, the agent requires much less exploration to learn a game compared to when it starts from the beginning of the game at every episode. We analyze reinforcement learning for tasks with sparse rewards in a simple toy environment, where we show that the run-time of standard RL methods scales exponentially in the number of states between rewards. Our method reduces this to quadratic scaling, opening up many tasks that were previously infeasible. We then apply our method to Montezuma’s Revenge, for which we present a trained agent achieving a high-score of 74,500, better than any previously published result.