nils-herrmann/clean_scirepeval huggingface dataset:
Sampling-based motion planning with temporal goals

This paper presents a geometry-based, multi-layered synergistic approach to solve motion planning problems for mobile robots involving temporal goals. The temporal goals are described over subsets of the workspace (called propositions) using temporal logic. A multi-layered synergistic framework has been proposed recently for solving planning problems involving significant discrete structure. In this framework, a high-level planner uses a discrete abstraction of the system and the exploration information to suggest feasible high-level plans. A low-level sampling-based planner uses the physical model of the system, and the suggested high-level plans, to explore the state-space for feasible solutions. In this paper, we advocate the use of geometry within the above framework to solve motion planning problems involving temporal goals. We present a technique to construct the discrete abstraction using the geometry of the obstacles and the propositions defined over the workspace. Furthermore, we show through experiments that the use of geometry results in significant computational speedups compared to previous work. Traces corresponding to trajectories of the system are defined employing the sampling interval used by the low-level algorithm. The applicability of the approach is shown for second-order nonlinear robot models in challenging workspace environments with obstacles, and for a variety of temporal logic specifications.
On the design of deformable input- / state-lattice graphs

In this paper we describe a novel and simple to implement yet effective lattice design algorithm, which simultaneously produces input and state-space sampled lattice graphs. The presented method is an extension to the ideas suggested by Bicchi et al. on input lattices and is applicable to systems which can be brought into (2,n) chained form, such as kinematic models of unicycles, bicycles, differential-drive robots and car-like vehicles (pulling several trailers). We further show that a transformation from chained form to path coordinates allows the resulting lattice to be bent along any C1 continuous path. We exploit this fact by shaping it along the skeleton of arbitrary structured environments, such as the center of road lanes and corridors. In our experiments in both structured (i.e. on-road) and unstructured (i.e. parking lot) scenarios, we successfully demonstrate for the first time the applicability of lattice-based planning approaches to search queries in arbitrary environments.
Automatic failure recovery for software-defined networks

Tolerating and recovering from link and switch failures are fundamental requirements of most networks, including Software-Defined Networks (SDNs). However, instead of traditional behaviors such as network-wide routing re-convergence, failure recovery in an SDN is determined by the specific software logic running at the controller. While this admits more freedom to respond to a failure event, it ultimately means that each controller application must include its own recovery logic, which makes the code more difficult to write and potentially more error-prone.
 In this paper, we propose a runtime system that automates failure recovery and enables network developers to write simpler, failure-agnostic code. To this end, upon detecting a failure, our approach first spawns a new controller instance that runs in an emulated environment consisting of the network topology excluding the failed elements. Then, it quickly replays inputs observed by the controller before the failure occurred, leading the emulated network into the forwarding state that accounts for the failed elements. Finally, it recovers the network by installing the difference ruleset between emulated and current forwarding states.
A Pattern Decomposition (PD) Algorithm for Finding All Frequent Patterns in Large Datasets

Efficient algorithms to mine frequent patterns are crucial to many tasks in data mining. Since the Apriori algorithm was proposed in 1994, there have been several methods proposed to improve its performance. However, most still adopt its candidate set generation-and-test approach. We propose a pattern decomposition (PD) algorithm that can significantly reduce the size of the dataset on each pass making it more efficient to mine frequent patterns in a large dataset. The proposed algorithm avoids the costly process of candidate set generation and saves time by reducing dataset. Our empirical evaluation shows that the algorithm outperforms Apriori by one order of magnitude and is faster than FP-tree. Further, PD is more scalable than both Apriori and FP-tree.
Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks

This paper presents a deep learning approach for automatic detection and visual analysis of invasive ductal carcinoma (IDC) tissue regions in whole slide images (WSI) of breast cancer (BCa). Deep learning approaches are learn-from-data methods involving computational modeling of the learning process. This approach is similar to how human brain works using different interpretation levels or layers of most representative and useful features resulting into a hierarchical learned representation. These methods have been shown to outpace traditional approaches of most challenging problems in several areas such as speech recognition and object detection. Invasive breast cancer detection is a time consuming and challenging task primarily because it involves a pathologist scanning large swathes of benign regions to ultimately identify the areas of malignancy. Precise delineation of IDC in WSI is crucial to the subsequent estimation of grading tumor aggressiveness and predicting patient outcome. DL approaches are particularly adept at handling these types of problems, especially if a large number of samples are available for training, which would also ensure the generalizability of the learned features and classifier. The DL framework in this paper extends a number of convolutional neural networks (CNN) for visual semantic analysis of tumor regions for diagnosis support. The CNN is trained over a large amount of image patches (tissue regions) from WSI to learn a hierarchical part-based representation. The method was evaluated over a WSI dataset from 162 patients diagnosed with IDC. 113 slides were selected for training and 49 slides were held out for independent testing. Ground truth for quantitative evaluation was provided via expert delineation of the region of cancer by an expert pathologist on the digitized slides. The experimental evaluation was designed to measure classifier accuracy in detecting IDC tissue regions in WSI. Our method yielded the best quantitative results for automatic detection of IDC regions in WSI in terms of F-measure and balanced accuracy (71.80%, 84.23%), in comparison with an approach using handcrafted image features (color, texture and edges, nuclear textural and architecture), and a machine learning classifier for invasive tumor classification using a Random Forest. The best performing handcrafted features were fuzzy color histogram (67.53%, 78.74%) and RGB histogram (66.64%, 77.24%). Our results also suggest that at least some of the tissue classification mistakes (false positives and false negatives) were less due to any fundamental problems associated with the approach, than the inherent limitations in obtaining a very highly granular annotation of the diseased area of interest by an expert pathologist.
Neural Emoji Recommendation in Dialogue Systems

Emoji is an essential component in dialogues which has been broadly utilized on almost all social platforms. It could express more delicate feelings beyond plain texts and thus smooth the communications between users, making dialogue systems more anthropomorphic and vivid. In this paper, we focus on automatically recommending appropriate emojis given the contextual information in multiturn dialogue systems, where the challenges locate in understanding the whole conversations. More specifically, we propose the hierarchical long shortterm memory model (H-LSTM) to construct dialogue representations, followed by a softmax classifier for emoji classification. We evaluate our models on the task of emoji classification in a real-world dataset, with some further explorations on parameter sensitivity and case study. Experimental results demonstrate that our method achieves the best performances on all evaluation metrics. It indicates that our method could well capture the contextual information and emotion flow in dialogues, which is significant for emoji recommendation.
ACRec: a co-authorship based random walk model for academic collaboration recommendation

Recent academic procedures have depicted that work involving scientific research tends to be more prolific through collaboration and cooperation among researchers and research groups. On the other hand, discovering new collaborators who are smart enough to conduct joint-research work is accompanied with both difficulties and opportunities. One notable difficulty as well as opportunity is the big scholarly data. In this paper, we satisfy the demand of collaboration recommendation through co-authorship in an academic network. We propose a random walk model using three academic metrics as basics for recommending new collaborations. Each metric is studied through mutual paper co-authoring information and serves to compute the link importance such that a random walker is more likely to visit the valuable nodes. Our experiments on DBLP dataset show that our approach can improve the precision, recall rate and coverage rate of recommendation, compared with other state-of-the-art approaches.
Ellipsoidal decision regions for motif-based patterned fabric defect detection

This paper presents a study of using ellipsoidal decision regions for motif-based patterned fabric defect detection, the result of which is found to improve the original detection success using max–min decision region of the energy-variance values. In our previous research, max–min decision region was found to be effective in distinct cases but ill detect the ambiguous false-positive and false-negative cases. To alleviate this problem, we first assume that the energy-variance values can be described by a Gaussian mixture model. Second, we apply k-means clustering to roughly identify the various clusters that make up the entire data population. Third, convex hull of each cluster is employed as a basis for fitting an ellipsoidal decision region over it. Defect detection is then based on these ellipsoidal regions. To validate the method, three wallpaper groups are evaluated using the new ellipsoidal regions, and compared with those results obtained using the max–min decision region. For the p2 group, success rate improves from 93.43% to 100%. For the pmm group, success rate improves from 95.9% to 96.72%, while the p4 m group records the same success rate at 90.77%. This demonstrates the superiority of using ellipsoidal decision regions in motif-based defect detection. & 2009 Elsevier Ltd. All rights reserved.
Using Agile Methods in Software Product Development: A Case Study

The mythos surrounding the use of agile methods emphasizes improved customer satisfaction, developer morale, and end-product quality. While the difficulty of adopting these methods is mentioned, it is often glossed over in the discussion. This paper presents an in-depth case study of agile methods adoption in a software product development firm. The choice of the firm as the unit of analysis enables the identification of organizational, social and technological challenges with respect to using agile methods. Using a mix of interviews, observation and archival data, the evolution of agile adoption within the firm is reconstructed. The data analysis highlights the importance of the four areas of requirements management, scrum implementation, organizational learning, and verification & validation activities.
The renaissance of field experimentation in evaluating interventions.

Most experiments are done in laboratories. However, there is also a theory and practice of field experimentation. It has had its successes and failures over the past four decades but is now increasingly used for answering causal questions. This is true for both randomized and-perhaps more surprisingly-nonrandomized experiments. In this article, we review the history of the use of field experiments, discuss some of the reasons for their current renaissance, and focus the bulk of the article on the particular technical developments that have made this renaissance possible across four kinds of widely used experimental and quasi-experimental designs-randomized experiments, regression discontinuity designs in which those units above a cutoff get one treatment and those below get another, short interrupted time series, and nonrandomized experiments using a nonequivalent comparison group. We focus this review on some of the key technical developments addressing problems that previously stymied accurate effect estimation, the solution of which opens the way for accurate estimation of effects under the often difficult conditions of field implementation-the estimation of treatment effects under partial treatment implementation, the prevention and analysis of attrition, analysis of nested designs, new analytic developments for both regression discontinuity designs and short interrupted time series, and propensity score analysis. We also cover the key empirical evidence showing the conditions under which some nonrandomized experiments may be able to approximate results from randomized experiments.
Deeply-Fused Nets

In this paper, we present a novel deep learning approach, deeply-fused nets. The central idea of our approach is deep fusion, i.e., combine the intermediate representations of base networks, where the fused output serves as the input of the remaining part of each base network, and perform such combinations deeply over several intermediate representations. The resulting deeply fused net enjoys several benefits. First, it is able to learn multi-scale representations as it enjoys the benefits of more base networks, which could form the same fused network, other than the initial group of base networks. Second, in our suggested fused net formed by one deep and one shallow base networks, the flows of the information from the earlier intermediate layer of the deep base network to the output and from the input to the later intermediate layer of the deep base network are both improved. Last, the deep and shallow base networks are jointly learnt and can benefit from each other. More interestingly, the essential depth of a fused net composed from a deep base network and a shallow base network is reduced because the fused net could be composed from a less deep base network, and thus training the fused net is less difficult than training the initial deep base network. Empirical results demonstrate that our approach achieves superior performance over two closely-related methods, ResNet and Highway, and competitive performance compared to the state-of-the-arts.
Scholarly publishing and linked data: describing roles, statuses, temporal and contextual extents

Recently, several ontologies have been introduced for semantic publishing. However, scholarly publishing, like other real-world domains, needs to be described also in terms of precise temporal durations and the particular contexts in which the relevant processes take place. For instance, a document changes status during its publication process, e.g., from "draft" to "submitted" to "under review" to "accepted for publication", and so on. Similarly, one's roles may change with time: one's affiliation with an academic institution or one's role as a journal editor are likely to change over time. Existing well-known ontologies used to describe individuals and bibliographic entities in the Linked Data are currently not able to model situations of temporary or context-dependent possession (e.g., the holding of a status or of a role). In this paper, we address this issue by introducing two ontologies for semantic publishing, the Publishing Roles Ontology and the Publishing Status Ontology, that define the roles of people and the statuses of documents in the scholarly publishing domain.
GeoSRS: A hybrid social recommender system for geolocated data

We present GeoSRS, a hybrid recommender system for a popular locationbased social network (LBSN), in which users are able to write short reviews on the places of interest they visit. Using state-of-the-art text mining techniques, our system recommends locations to users using as source the whole set of text reviews in addition to their geographical location. To evaluate our system, we have collected our own datasets by crawling the social network Foursquare. To do this efficiently, we propose the use of a parallel version of the Quadtree technique, which may be applicable to crawling/exploring other spatially distributed sources. Finally, we study the performance of GeoSRS on our collected dataset and conclude that by combining sentiment analysis and text modelling, GeoSRS generates more accurate recommendations. The performance of the system improves as more reviews are available, which further motivates the use of large-scale crawling techniques such as the Quadtree.
A System for Interactive Visual Analysis of Large Graphs Using Motifs in Graph Editing and Aggregation

Network analysis is an important task in a wide variety of application domains including analysis of social, financial, or transportation networks, to name a few. The appropriate visualization of graphs may reveal useful insight into relationships between network entities and subnetworks. However, often further algorithmic analysis of network structures is needed. In this paper, we propose a system for effective visual analysis of graphs which supports multiple analytic tasks. Our system enhances any graph layout algorithm by an analysis stage which detects predefined or arbitrarily specified subgraph structures (motifs). These motifs in turn are used to filter or aggregate the given network, which is particularly useful for search and analysis of interesting structures in large graphs. Our approach is fully interactive and can be iteratively refined, supporting analysis of graph structures at multiple levels of abstraction. Furthermore, our system supports the analysis of dataor user-driven graph dynamics by showing the implications of graph changes on the identified subgraph structures. The interactive facilities may be flexibly combined for gaining deep insight into the network structures for a wide range of analysis tasks. While we focus on directed, weighted graphs, the proposed tools can be easily extended to undirected and unweighted graphs. The usefulness of our approach is demonstrated by application on a phone call data set .
Analysis of SMS Spam in Mobility Networks

 The Short Messaging Service (SMS), one of the most successful cellular services, generating millions of dollars in revenue for mobile operators yearly. Current estimations indicate that billions of SMSs are sent every day. Nevertheless, text messaging is becoming a source of customer dissatisfaction due to the rapid surge of messaging abuse activities. Although spam is a well tackled problem in the email world, SMS spam experiences a yearly growth larger than 500%. In this paper we expand our previous analysis on SMS spam traffic from a tier-1 cellular operator presented in , aiming to highlight the main characteristics of such messaging fraud activity. Communication patterns of spammers are compared to those of legitimate cell-phone users and Machine to Machine (M2M) connected appliances. The results indicate that M2M systems exhibit communication profiles similar to spammers, which could mislead spam filters. We find the main geographical sources of messaging abuse in the US. We also find evidence of spammer mobility, voice and data traffic resembling the behavior of legitimate customers. Finally, we include new findings on the invariance of the main characteristics of spam messages and spammers over time. Also, we present results that indicate a clear device reuse strategy in SMS spam activities.
Mobile-Assisted Language Learning: A Selected Annotated Bibliography of Implementation Studies 1994-2012.

To enhance the information contained in these references, over 90% of the entries are complemented by a brief (~80 word) summary. To the extent that the publication provides such information, each annotation identifies the country of origin of the study, native language (L1) and/or the second or foreign language (L2) involved, the mobile technology used, the learning area(s) targeted, the type of learners, their numbers, the duration of the study, and a summary of the results (i.e., learning outcomes and survey opinions). Since nearly 60% of MALL implementation studies appear outside of professional journals, in conference proceedings, project reports, academic dissertations, and so forth, locating copies of these publications poses a major challenge in itself. For this reason, where possible, links are included to copies of the works cited.
Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks

One of the challenges in modeling cognitive events from electroencephalogram (EEG) data is finding representations that are invariant to interand intra-subject differences, as well as to inherent noise associated with such data. Herein, we propose a novel approach for learning such representations from multi-channel EEG time-series, and demonstrate its advantages in the context of mental load classification task. First, we transform EEG activities into a sequence of topologypreserving multi-spectral images, as opposed to standard EEG analysis techniques that ignore such spatial information. Next, we train a deep recurrent-convolutional network inspired by state-of-the-art video classification to learn robust representations from the sequence of images. The proposed approach is designed to preserve the spatial, spectral, and temporal structure of EEG which leads to finding features that are less sensitive to variations and distortions within each dimension. Empirical evaluation on the cognitive load classification task demonstrated significant improvements in classification accuracy over current state-of-the-art approaches in this field.
Personalized disease prediction using a CNN-based similarity learning method

Predicting patients' risk of developing certain diseases is an important research topic in healthcare. Personalized predictive modeling, which focuses on building specific models for individual patients, has shown its advantages on utilizing heterogeneous health data compared to global models trained on the entire population. Personalized predictive models use information from similar patient cohorts, in order to capture the specific characteristics. Accurately identifying and ranking the similarity among patients based on their historical records is a key step in personalized modeling. The electric health records (EHRs), which are irregular sampled and have varied patient visit lengths, cannot be directly used to measure patient similarity due to lack of an appropriate vector representation. In this paper, we build a novel time fusion CNN framework to simultaneously learn patient representations and measure pairwise similarity. Compared to a traditional CNN, our time fusion CNN can learn not only the local temporal relationships but also the contributions from each time interval. Along with the similarity learning process, the output information which is the probability distribution is used to rank similar patients. Utilizing the similarity scores, we perform personalized disease predictions, and compare the effect of different vector representations and similarity learning metrics.