nils-herrmann/clean_scirepeval huggingface dataset:
Sequence Data Mining Techniques and Applications

Many interesting real-life mining applications rely on modeling data as sequences of discrete multi-attribute records. Mining models for network intrusion detection view data as sequences of TCP/IP packets. Text information extraction systems model the input text as a sequence of words and delimiters. Customer data mining applications profile buying habits of customers as a sequence of items purchased. In computational biology, DNA, RNA and protein data are all best modeled as sequences. Classifying, clustering and characterizing such sequence data presents interesting issues in feature engineering, discretization and pattern discovery. In this seminar we will review techniques ranging from item set counting, MDL-based discretization and Markov modeling to perform various supervised and unsupervised pattern discovery tasks on sequences. We will present case studies from network intrusion detection and DNA sequence mining to illustrate these techniques. Sunita Sarawagi researches in the fields of databases, data mining, machine learning and data warehousing. She is a member of the faculty at IIT Bombay. Prior to that she was a research staff member at IBM Almaden Research Center. She got her PhD in databases from the University of California at Berkeley. Proceedings of the 19th International Conference on Data Engineering (ICDE’03) 1063-6382/03 $ 17.00
Clinical Abbreviation Disambiguation Using Neural Word Embeddings

This study examined the use of neural word embeddings for clinical abbreviation disambiguation, a special case of word sense disambiguation (WSD). We investigated three different methods for deriving word embeddings from a large unlabeled clinical corpus: one existing method called Surrounding based embedding feature (SBE), and two newly developed methods: Left-Right surrounding based embedding feature (LR_SBE) and MAX surrounding based embedding feature (MAX_SBE). We then added these word embeddings as additional features to a Support Vector Machines (SVM) based WSD system. Evaluation using the clinical abbreviation datasets from both the Vanderbilt University and the University of Minnesota showed that neural word embedding features improved the performance of the SVMbased clinical abbreviation disambiguation system. More specifically, the new MAX_SBE method outperformed the other two methods and achieved the state-of-the-art performance on both clinical abbreviation datasets.
Algorithmic motion planning

Motion planning is a fundamental problem in robotics. It comes in a variety of forms, but the simplest version is as follows. We are given a robot system B, which may consist of several rigid objects attached to each other through various joints, hinges, and links, or moving independently, and a 2D or 3D environment V cluttered with obstacles. We assume that the shape and location of the obstacles and the shape of B are known to the planning system. Given an initial placement Z1 and a final placement Z2 of B, we wish to determine whether there exists a collisionavoiding motion of B from Z1 to Z2, and, if so, to plan such a motion. In this simplified and purely geometric setup, we ignore issues such as incomplete information, nonholonomic constraints, control issues related to inaccuracies in sensing and motion, nonstationary obstacles, optimality of the planned motion, and so on. Since the early 1980s, motion planning has been an intensive area of study in robotics and computational geometry. In this chapter we will focus on algorithmic motion planning, emphasizing theoretical algorithmic analysis of the problem and seeking worst-case asymptotic bounds, and only mention briefly practical heuristic approaches to the problem. The majority of this chapter is devoted to the simplified version of motion planning, as stated above. Section 51.1 presents general techniques and lower bounds. Section 51.2 considers efficient solutions to a variety of specific moving systems with a small number of degrees of freedom. These efficient solutions exploit various sophisticated methods in computational and combinatorial geometry related to arrangements of curves and surfaces (Chapter 30). Section 51.3 then briefly discusses various extensions of the motion planning problem such as computing optimal paths with respect to various quality measures, computing the path of a tethered robot, incorporating uncertainty, moving obstacles, and more.
Complex Fully Convolutional Neural Networks for MR Image Reconstruction

Undersampling the k -space data is widely adopted for acceleration of Magnetic Resonance Imaging (MRI). Current deep learning based approaches for supervised learning of MRI image reconstruction employ real-valued operations and representations by treating complex valued k-space/spatial-space as real values. In this paper, we propose complex dense fully convolutional neural network (CDFNet) for learning to de-alias the reconstruction artifacts within undersampled MRI images. We fashioned a densely-connected fully convolutional block tailored for complex-valued inputs by introducing dedicated layers such as complex convolution, batch normalization, non-linearities etc. CDFNet leverages the inherently complex-valued nature of input k -space and learns richer representations. We demonstrate improved perceptual quality and recovery of anatomical structures through CDFNet in contrast to its realvalued counterparts.
Avoiding plagiarism, self-plagiarism, and other questionable writing practices: A guide to ethical writing

In recognizing the importance of educating aspiring scientists in the responsible conduct of research (RCR), the Office of Research Integrity (ORI) began sponsoring the creation of instructional resources to address this pressing need in 2002. The present guide on avoiding plagiarism and other inappropriate writing practices was created to help students, as well as professionals, identify and prevent such malpractices and to develop an awareness of ethical writing and authorship. This guide is one of the many products stemming from ORI’s effort to promote the RCR.
Twitter under crisis: can we trust what we RT?

In this article we explore the behavior of Twitter users under an emergency situation. In particular, we analyze the activity related to the 2010 earthquake in Chile and characterize Twitter in the hours and days following this disaster. Furthermore, we perform a preliminary study of certain social phenomenons, such as the dissemination of false rumors and confirmed news. We analyze how this information propagated through the Twitter network, with the purpose of assessing the reliability of Twitter as an information source under extreme circumstances. Our analysis shows that the propagation of tweets that correspond to rumors differs from tweets that spread news because rumors tend to be questioned more than news by the Twitter community. This result shows that it is posible to detect rumors by using aggregate analysis on tweets.
Software-Defined Networking (SDN) and Distributed Denial of Service (DDoS) Attacks in Cloud Computing Environments: A Survey, Some Research Issues, and Challenges

Distributed Denial of Service (DDoS) attacks in cloud computing environments are growing due to the essential characteristics of cloud computing. With recent advances in software-defined networking (SDN), SDN-based cloud brings us new chances to defeat DDoS attacks in cloud computing environments. Nevertheless, there is a contradictory relationship between SDN and DDoS attacks. On one hand, the capabilities of SDN, including software-based traffic analysis, centralized control, global view of the network, dynamic updating of forwarding rules, make it easier to detect and react to DDoS attacks. On the other hand, the security of SDN itself remains to be addressed, and potential DDoS vulnerabilities exist across SDN platforms. In this paper, we discuss the new trends and characteristics of DDoS attacks in cloud computing, and provide a comprehensive survey of defense mechanisms against DDoS attacks using SDN. In addition, we review the studies about launching DDoS attacks on SDN, as well as the methods against DDoS attacks in SDN. To the best of our knowledge, the contradictory relationship between SDN and DDoS attacks has not been well addressed in previous works. This work can help to understand how to make full use of SDN's advantages to defeat DDoS attacks in cloud computing environments and how to prevent SDN itself from becoming a victim of DDoS attacks, which are important for the smooth evolution of SDN-based cloud without the distraction of DDoS attacks.
Graph Convolutional Matrix Completion

We consider matrix completion for recommender systems from the point of view of link prediction on graphs. Interaction data such as movie ratings can be represented by a bipartite user-item graph with labeled edges denoting observed ratings. This representation is especially useful in the setting where additional graph-based side information is present. Building on recent progress in deep learning on graph-structured data, we propose a graph auto-encoder framework based on differentiable message passing on the bipartite interaction graph. In settings where complimentary feature information or structured data such as a social network is available, our framework outperforms recent state-of-the-art methods. Furthermore, to validate the proposed message passing scheme, we test our model on standard collaborative filtering tasks and show competitive results.
RFID-Enabled Visibility and Retail Inventory Record Inaccuracy : Experiments in the Field Bill

A ccurate inventory records are key to effective store execution, affecting forecasting, ordering, and replenishment. Prior empirical research, however, shows that retailer inventory records are inherently inaccurate. Radio Frequency Identification (RFID) enables visibility into the movement of inventories in the supply chain. Using two different field experiments, the current research investigates the effectiveness of this visibility in reducing retail store inventory record inaccuracy (IRI). Study 1 used an interrupted time-series design and involved daily physical counts of all products in one category in 13 stores (8 treatments and 5 controls) of a major global retailer over 23 weeks. Results indicate a significant decrease in IRI of approximately 26% due to RFID-enabled visibility. Using an untreated control group design with pre-test and post-test, Study 2 expands the number of categories to five and the number of stores to 62 (31 treatment and 31 control stores). Results show that the effectiveness of RFID in reducing IRI varies by category (ranging from no statistically significant improvement to 81%). Results also suggest that RFID ameliorates the effects of known determinants of IRI and provide the key insight that the technology is most effective for product categories characterized by these determinants.
A Formal Measure of Machine Intelligence

As this is generally considered the most intelligent thing to do, we should test agents in such a way that they are, at least on average, rewarded for correctly applying Occam’s razor. That is, test in such a way that simpler environments really are more likely. In our framework this means that our a priori distribution over environments should be weighted towards simpler environments. However to do this we need a way to measure the complexity of environments.
Towards adaptive Gamification: a synthesis of current Developments

Adaptive gamification is an emerging and fast-growing research stream, that enhances traditional gamification approaches with user-centered, personalized and adaptive incentive mechanisms, tailored to a specific characteristic of different users and contexts. While game-like elements have been successfully applied to increase end-user engagement, satisfaction and task performance in different domains, the effectiveness has often been mixed, highly context specific and varied among individuals. In order to understand how adaptive gamification approaches can be developed that overcome such problems, we have conducted a systematic literature review that identifies main issues and challenges in current literature on adaptive gamification. The analysis corpus is composed of 43 identified studies and includes supporting theoretical contributions from related research areas. The performed analysis provides several contributions. First, a conceptual matrix of adaptive gamification design is proposed that identifies major dimensions of current approaches and classifies these accordingly. Second, we came up with a thematic overview where the identified literature and their related studies are assigned to the designated areas. Finally, we identify five research challenges and propose a research agenda that can serve as a basis for future research directions and for practitioners who want to apply adaptive gamification strategies in real-world applications.
Structured Generative Models of Natural Source Code

We study the problem of building generative models of natural source code (NSC); that is, source code written and understood by humans. Our primary contribution is to describe a family of generative models for NSC that have three key properties: First, they incorporate both sequential and hierarchical structure. Second, we learn a distributed representation of source code elements. Finally, they integrate closely with a compiler, which allows leveraging compiler logic and abstractions when building structure into the model. We also develop an extension that includes more complex structure, refining how the model generates identifier tokens based on what variables are currently in scope. Our models can be learned efficiently, and we show empirically that including appropriate structure greatly improves the models, measured by the probability of generating test programs.
Safe Charging for Wireless Power Transfer

As battery-powered mobile devices become more popular and energy hungry, wireless power transfer technology, which allows the power to be transferred from a charger to ambient devices wirelessly, receives intensive interests. Existing schemes mainly focus on the power transfer efficiency but overlook the health impairments caused by RF exposure. In this paper, we study the safe charging problem SCP of scheduling power chargers so that more energy can be received while no location in the field has electromagnetic radiation EMR exceeding a given threshold $R_{t}$ . We show that SCP is NP-hard and propose a solution, which provably outperforms the optimal solution to SCP with a relaxed EMR threshold $1-\epsilon R_{t}$ . Testbed results based on 8 Powercast TX91501 chargers validate our results. Extensive simulation results show that the gap between our solution and the optimal one is only 6.7% when $\epsilon = 0.1$ , while a naive greedy algorithm is 34.6% below our solution.
Why do dentists struggle with removable partial denture design? An assessment of financial and educational issues

studies in the international dental literature illustrate that the quality of prescription and fabrication of cobalt-chromium removable partial dentures (CCRPDs) by general dental practitioners frequently fail to comply with ethical and legal requirements. The reasons cited for this in the past have broadly related to either financial or educational issues. The aim of this investigation is to determine the effect of financial and educational factors on the quality of CCRPD design and fabrication by general dental practitioners. This investigation was completed in two parts. (1) A pre-piloted pro-forma was distributed to a number of dental laboratories throughout the UK and Ireland. These sought information relating to the quality of written instructions for CCRPDs received by these laboratories, and details of the remunerative scheme under which they were being provided. Three categories of remunerative scheme were considered, private CCRPDs in Ireland, private CCRPDs in the UK, and CCRPDs being provided by salaried NHS practitioners. (2) A pre-piloted questionnaire was distributed to vocational dental practitioners in the UK and Ireland. This sought information relating to their attitudes, opinions, and educational and clinical experiences of CCRPD design and fabrication. Three hundred completed pro-formas were returned from dental laboratories, 100 of which related to each of the three remunerative schemes. Poor or no written instructions were provided in 47% (n = 47) of CCRPD cases funded privately in the UK, 46% (n = 46) of CCRPD cases funded privately in Ireland, and 50% (n = 50) of CCRPDs being provided by salaried NHS practitioners. (2) One hundred and seven completed questionnaires were returned from vocational trainees. Vocational dental practitioners had completed fewer CCRPDs during VT than in dental school (dental school: median = 4, inter-quartile range = 3 to 5; VT: median = 2, inter-quartile range = 1 to 4). One-fifth of respondents (n = 22) had not completed any CCRPDs during VT. Nine per cent of VT practices (n = 10) had a surveyor on their premises. Only 15% (n = 16) of respondents felt the time they had spent in VT had increased their confidence in the design of CCRPDs. Financial factors did not have as significant an effect on the quality of prescription and fabrication of CCRPDs as did educational factors. Serious deficiencies in the teaching of CCRPDs during vocational training were identified.
State-of-the-Art Speech Recognition with Sequence-to-Sequence Models

Attention-based encoder-decoder architectures such as Listen, Attend, and Spell (LAS), subsume the acoustic, pronunciation and language model components of a traditional automatic speech recognition (ASR) system into a single neural network. In previous work, we have shown that such architectures are comparable to state-of-the-art ASR systems on dictation tasks, but it was not clear if such architectures would be practical for more challenging tasks such as voice search. In this work, we explore a variety of structural and optimization improvements to our LAS model which significantly improve performance. On the structural side, we show that word piece models can be used instead of graphemes. We also introduce a multi-head attention architecture, which offers improvements over the commonly-used single-head attention. On the optimization side, we explore synchronous training, scheduled sampling, label smoothing, and minimum word error rate optimization, which are all shown to improve accuracy. We present results with a unidirectional LSTM encoder for streaming recognition. On a 12, 500 hour voice search task, we find that the proposed changes improve the WER from 9.2% to 5.6%, while the best conventional system achieves 6.7%; on a dictation task our model achieves a WER of 4.1% compared to 5% for the conventional system.
GumBolt: Extending Gumbel trick to Boltzmann priors

Boltzmann machines (BMs) are appealing candidates for powerful priors in variational autoencoders (VAEs), as they are capable of capturing nontrivial and multimodal distributions over discrete variables. However, non-differentiability of the discrete units prohibits using the reparameterization trick, essential for low-noise back propagation. The Gumbel trick resolves this problem in a consistent way by relaxing the variables and distributions, but it is incompatible with BM priors. Here, we propose the GumBolt, a model that extends the Gumbel trick to BM priors in VAEs. GumBolt is significantly simpler than the recently proposed methods with BM prior and outperforms them by a considerable margin. It achieves state-of-theart performance on permutation invariant MNIST and OMNIGLOT datasets in the scope of models with only discrete latent variables. Moreover, the performance can be further improved by allowing multi-sampled (importance-weighted) estimation of log-likelihood in training, which was not possible with previous models.
Fast Dynamic Routing Based on Weighted Kernel Density Estimation

Capsules as well as dynamic routing between them are most recently proposed structures for deep neural networks. A capsule groups data into vectors or matrices as poses rather than conventional scalars to represent specific properties of target instance. Besides of pose, a capsule should be attached with a probability (often denoted as activation) for its presence. The dynamic routing helps capsules achieve more generalization capacity with many fewer model parameters. However, the bottleneck that prevents widespread applications of capsule is the expense of computation during routing. To address this problem, we generalize existing routing methods within the framework of weighted kernel density estimation, and propose two fast routing methods with different optimization strategies. Our methods prompt the time efficiency of routing by nearly 40% with negligible performance degradation. By stacking a hybrid of convolutional layers and capsule layers, we construct a network architecture to handle inputs at a resolution of 64× 64 pixels. The proposed models achieve a parallel performance with other leading methods in multiple benchmarks.
Learning to Compose Task-Specific Tree Structures

For years, recursive neural networks (RvNNs) have been shown to be suitable for representing text into fixed-length vectors and achieved good performance on several natural language processing tasks. However, the main drawback of RvNNs is that they require structured input, which makes data preparation and model implementation hard. In this paper, we propose Gumbel Tree-LSTM, a novel tree-structured long short-term memory architecture that learns how to compose task-specific tree structures only from plain text data efficiently. Our model uses Straight-Through Gumbel-Softmax estimator to decide the parent node among candidates dynamically and to calculate gradients of the discrete decision. We evaluate the proposed model on natural language inference and sentiment analysis, and show that our model outperforms or is at least comparable to previous models. We also find that our model converges significantly faster than other models.
Plagiarism Detection with Genetic-Based Parameter Tuning

A crucial step in plagiarism detection is text alignment. This task consists in ̄nding similar text fragments between two given documents. We introduce an optimization methodology based on genetic algorithms to improve the performance of a plagiarism detection model by optimizing its input parameters. The implementation of the genetic algorithm is based on nonbinary representation of individuals, elitism selection, uniform crossover, and high mutation rate. The obtained parameter settings allow the plagiarism detection model to achieve better results than the state-of-the-art approaches.
Mode-Finding for Mixtures of Gaussian Distributions

I consider the problem of finding all the modes of a mixture of multivariate Gaussian distributions, which has applications in clustering and regression. I derive exact formulas for the gradient and Hessian and give a partial proof that the number of modes cannot be more than the number of components, and are contained in the convex hull of the component centroids. Then, I develop two exhaustive mode search algorithms: one based on combined quadratic maximisation and gradient ascent and the other one based on a fixed-point iterative scheme. Appropriate values for the search control parameters are derived by taking into account theoretical results regarding the bounds for the gradient and Hessian of the mixture. The significance of the modes is quantified locally (for each mode) by error bars, or confidence intervals (estimated using the values of the Hessian at each mode); and globally by the sparseness of the mixture, measured by its differential entropy (estimated through bounds). I conclude with some reflections about bump-finding.