nils-herrmann/clean_scirepeval huggingface dataset:
Learning to detect natural image boundaries using local brightness, color, and texture cues

The goal of this work is to accurately detect and localize boundaries in natural scenes using local image measurements. We formulate features that respond to characteristic changes in brightness, color, and texture associated with natural boundaries. In order to combine the information from these features in an optimal way, we train a classifier using human labeled images as ground truth. The output of this classifier provides the posterior probability of a boundary at each image location and orientation. We present precision-recall curves showing that the resulting detector significantly outperforms existing approaches. Our two main results are 1) that cue combination can be performed adequately with a simple linear model and 2) that a proper, explicit treatment of texture is required to detect boundaries in natural images.
Combining the Best of Convolutional Layers and Recurrent Layers: A Hybrid Network for Semantic Segmentation

State-of-the-art results of semantic segmentation are established by Fully Convolutional neural Networks (FCNs). FCNs rely on cascaded convolutional and pooling layers to gradually enlarge the receptive fields of neurons, resulting in an indirect way of modeling the distant contextual dependence. In this work, we advocate the use of spatially recurrent layers (i.e. ReNet layers) which directly capture global contexts and lead to improved feature representations. We demonstrate the effectiveness of ReNet layers by building a Naive deep ReNet (N-ReNet), which achieves competitive performance on Stanford Background dataset. Furthermore, we integrate ReNet layers with FCNs, and develop a novel Hybrid deep ReNet (H-ReNet). It enjoys a few remarkable properties, including full-image receptive fields, end-to-end training, and efficient network execution. On the PASCAL VOC 2012 benchmark, the H-ReNet improves the results of state-of-the-art approaches Piecewise , CRFasRNN  and DeepParsing  by 3.6%, 2.3% and 0.2%, respectively, and achieves the highest IoUs for 13 out of the 20 object classes.
Characterization of clustered microcalcifications in digitized mammograms using neural networks and support vector machines

Detection and characterization of microcalcification clusters in mammograms is vital in daily clinical practice. The scope of this work is to present a novel computer-based automated method for the characterization of microcalcification clusters in digitized mammograms.



The proposed method has been implemented in three stages: (a) the cluster detection stage to identify clusters of microcalcifications, (b) the feature extraction stage to compute the important features of each cluster and (c) the classification stage, which provides with the final characterization. In the classification stage, a rule-based system, an artificial neural network (ANN) and a support vector machine (SVM) have been implemented and evaluated using receiver operating characteristic (ROC) analysis. The proposed method was evaluated using the Nijmegen and Mammographic Image Analysis Society (MIAS) mammographic databases. The original feature set was enhanced by the addition of four rule-based features.



In the case of Nijmegen dataset, the performance of the SVM was Az=0.79 and 0.77 for the original and enhanced feature set, respectively, while for the MIAS dataset the corresponding characterization scores were Az=0.81 and 0.80. Utilizing neural network classification methodology, the corresponding performance for the Nijmegen dataset was Az=0.70 and 0.76 while for the MIAS dataset it was Az=0.73 and 0.78. Although the obtained high classification performance can be successfully applied to microcalcification clusters characterization, further studies must be carried out for the clinical evaluation of the system using larger datasets. The use of additional features originating either from the image itself (such as cluster location and orientation) or from the patient data may further improve the diagnostic value of the system.
Knowledge harvesting in the big-data era

The proliferation of knowledge-sharing communities such as Wikipedia and the progress in scalable information extraction from Web and text sources have enabled the automatic construction of very large knowledge bases. Endeavors of this kind include projects such as DBpedia, Freebase, KnowItAll, ReadTheWeb, and YAGO. These projects provide automatically constructed knowledge bases of facts about named entities, their semantic classes, and their mutual relationships. They contain millions of entities and hundreds of millions of facts about them. Such world knowledge in turn enables cognitive applications and knowledge-centric services like disambiguating natural-language text, semantic search for entities and relations in Web and enterprise data, and entity-oriented analytics over unstructured contents. Prominent examples of how knowledge bases can be harnessed include the Google Knowledge Graph and the IBM Watson question answering system. This tutorial presents state-of-the-art methods, recent advances, research opportunities, and open challenges along this avenue of knowledge harvesting and its applications. Particular emphasis will be on the twofold role of knowledge bases for big-data analytics: using scalable distributed algorithms for harvesting knowledge from Web and text sources, and leveraging entity-centric knowledge for deeper interpretation of and better intelligence with Big Data.
Toward a Systematic Approach to the Design and Evaluation of Automated Mobility-on-Demand Systems: A Case Study in Singapore

The objective of this work is to provide analytical guidelines and financial justification for the design of shared-vehicle mobility-on-demand systems. Specifically, we consider the fundamental issue of determining the appropriate number of vehicles to field in the fleet, and estimate the financial benefits of several models of car sharing. As a case study, we consider replacing all modes of personal transportation in a city such as Singapore with a fleet of shared automated vehicles, able to drive themselves, e.g., to move to a customer’s location. Using actual transportation data, our analysis suggests a shared-vehicle mobility solution can meet the personal mobility needs of the entire population with a fleet whose size is approximately 1/3 of the total number of passenger vehicles currently in operation.
Old Communication, New Literacies: Social Network Sites as Social Learning Resources

This study examined the role of a social network site (SNS) in the lives of 11 high school teenagers from low-income families in the U.S. We conducted interviews, talk-alouds and content analysis of MySpace profiles. Qualitative analysis of these data revealed three themes. First, SNSs facilitated emotional support, helped maintain relationships, and provided a platform for self-presentation. Second, students used their online social network to fulfill essential social learning functions. Third, within their SNS, students engaged in a complex array of communicative and creative endeavors. In several instances, students’ use of social network sites demonstrated the new literacy practices currently being discussed within education reform efforts. Based on our findings, we suggest additional directions for related research and educational practices.
Native Mobile Applications For Personal Well-Being: A Persuasive Systems Design Evaluation

Smartphone applications have shown promise in supporting people to adopt healthy lifestyles. Hence, it is critical to understand persuasive design strategies incorporated in native mobile applications that facilitate behavior change. The aim of our study was to identify distinct persuasive software features assimilated in twelve selected applications using Persuasive Systems Design (PSD) model and provide a methodical framework for systems developers and IS researchers to extract and evaluate such features. Further, this study aimed to provide deeper comprehension of persuasive design and strategies by learning from practice. Exhaustive evaluations were performed by four researchers specializing in persuasive information systems simulating users walking through the applications stepby-step performing regular tasks. The results disclose the need for improvement in designing and incorporating persuasive techniques in personal well-being applications. While self-monitoring and personalization were moderately exploited, tailoring, a key persuasive feature, was not identified among the evaluated applications. In addition, evaluated applications lacked features that could augment human-computer dialogue as well as social support. The contribution of this paper is twofold: while it exposes weakness in persuasive design of native mobile applications for personal wellbeing, it provides a methodical approach for enhancing general persuasiveness of such applications for instance, through enhanced dialogue support. We propose that designers and IS researchers perform rigorous evaluations of persuasive features incorporated in personal well-being applications.
Learning Compatibility Across Categories for Heterogeneous Item Recommendation

Identifying relationships between items is a key task of an online recommender system, in order to help users discover items that are functionally complementary or visually compatible. In domains like clothing recommendation, this task is particularly challenging since a successful system should be capable of handling a large corpus of items, a huge amount of relationships among them, as well as the high-dimensional and semantically complicated features involved. Furthermore, the human notion of "compatibility" to capture goes beyond mere similarity: For two items to be compatible—whether jeans and a t-shirt, or a laptop and a charger—they should be similar in some ways, but systematically different in others. In this paper we propose a novel method, Monomer, to learn complicated and heterogeneous relationships between items in product recommendation settings. Recently, scalable methods have been developed that address this task by learning similarity metrics on top of the content of the products involved. Here our method relaxes the metricity assumption inherent in previous work and models multiple localized notions of 'relatedness,' so as to uncover ways in which related items should be systematically similar, and systematically different. Quantitatively, we show that our system achieves state-of-the-art performance on large-scale compatibility prediction tasks, especially in cases where there is substantial heterogeneity between related items.
Trading end-to-end latency for composability

The periodic resource model for hierarchical, compositional scheduling abstracts task groups by resource requirements. We study this model in the presence of dataflow constraints between the tasks within a group (intragroup dependencies), and between tasks in different groups (inter-group dependencies). We consider two natural semantics for dataflow constraints, namely, RTW (real-time workshop) semantics and LET (logical execution time) semantics. We show that while RTW semantics offers better end-to-end latency on the task group level, LET semantics allows tighter resource bounds in the abstraction hierarchy and therefore provides better composability properties. This result holds both for intragroup and intergroup dependencies, as well as for shared and for distributed resources
Visual data mining in software archives

Software archives contain historical information about the development process of a software system. Using data mining techniques rules can be extracted from these archives. In this paper we discuss how standard visualization techniques can be applied to interactively explore these rules. To this end we extended the standard visualization techniques for association rules and sequence rules to also show the hierarchical order of items. Clusters and outliers in the resulting visualizations provide interesting insights into the relation between the temporal development of a system and its static structure. As an example we look at the large software archive of the MOZILLA open source project. Finally we discuss what kind of regularities and anomalies we found and how these can then be leveraged to support software engineers.
An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data

This paper proposes a novel approach named AGM to e ciently mine the association rules among the frequently appearing substructures in a given graph data set. A graph transaction is represented by an adjacency matrix, and the frequent patterns appearing in the matrices are mined through the extended algorithm of the basket analysis. Its performance has been evaluated for the arti cial simulation data and the carcinogenesis data of Oxford University and NTP. Its high e ciency has been con rmed for the size of a real-world problem. . . .
Advanced FOUP purge using diffusers for FOUP door-off application

In a number of key IC fabrication steps in-process wafers are sensitive to moisture, oxygen and other airborne molecular contaminants in the air. Nitrogen purge of closed Front Opening Unified Pods (FOUP) have been implemented in many fabs to minimize wafer's exposure to the contaminants (or CDA purge if oxygen is not of concern). As the technology node advances, the need for minimizing the exposure has become even more stringent and in some processes requires FOUP purge while the FOUP door is off on an EFEM loadport. This requirement brings unique challenges to FOUP purge, especially at the front locations near FOUP opening, where EFEM air constantly tries to enter the FOUP. In this paper we present Entegris' latest experimental study on understanding the unique challenges of FOUP door-off purge and the excellent test results of newly designed advanced FOUP with purge flow distribution manifolds (diffusers).
Feature engineering strategies for credit card fraud detection

Every year billions of Euros are lost worldwide due to credit card fraud. Thus, forcing financial institutions to continuously improve their fraud detection systems. In recent years, several studies have proposed the use of machine learning and data mining techniques to address this problem. However, most studies used some sort of misclassification measure to evaluate the different solutions, and do not take into account the actual financial costs associated with the fraud detection process. Moreover, when constructing a credit card fraud detection model, it is very important how to extract the right features from the transactional data. This is usually done by aggregating the transactions in order to observe the spending behavioral patterns of the customers. In this paper we expand the transaction aggregation strategy, and propose to create a new set of features based on analyzing the periodic behavior of the time of a transaction using the von Mises distribution. Then, using a real credit card fraud dataset provided by a large European card processing company, we compare state-of-the-art credit card fraud detection models, and evaluate how the different sets of features have an impact on the results. By including the proposed periodic features into the methods, the results show an average increase in savings of 13%.
Modeling Relational Data with Graph Convolutional Networks

Knowledge graphs enable a wide variety of applications, including question answering and information retrieval. Despite the great effort invested in their creation and maintenance, even the largest (e.g., Yago, DBPedia or Wikidata) remain incomplete. We introduce Relational Graph Convolutional Networks (R-GCNs) and apply them to two standard knowledge base completion tasks: Link prediction (recovery of missing facts, i.e. subject-predicate-object triples) and entity classification (recovery of missing entity attributes). R-GCNs are related to a recent class of neural networks operating on graphs, and are developed specifically to handle the highly multi-relational data characteristic of realistic knowledge bases. We demonstrate the effectiveness of R-GCNs as a stand-alone model for entity classification. We further show that factorization models for link prediction such as DistMult can be significantly improved through the use of an R-GCN encoder model to accumulate evidence over multiple inference steps in the graph, demonstrating a large improvement of 29.8% on FB15k-237 over a decoder-only baseline.
Learning and memory functions of the Basal Ganglia.

Although the mammalian basal ganglia have long been implicated in motor behavior, it is generally recognized that the behavioral functions of this subcortical group of structures are not exclusively motoric in nature. Extensive evidence now indicates a role for the basal ganglia, in particular the dorsal striatum, in learning and memory. One prominent hypothesis is that this brain region mediates a form of learning in which stimulus-response (S-R) associations or habits are incrementally acquired. Support for this hypothesis is provided by numerous neurobehavioral studies in different mammalian species, including rats, monkeys, and humans. In rats and monkeys, localized brain lesion and pharmacological approaches have been used to examine the role of the basal ganglia in S-R learning. In humans, study of patients with neurodegenerative diseases that compromise the basal ganglia, as well as research using brain neuroimaging techniques, also provide evidence of a role for the basal ganglia in habit learning. Several of these studies have dissociated the role of the basal ganglia in S-R learning from those of a cognitive or declarative medial temporal lobe memory system that includes the hippocampus as a primary component. Evidence suggests that during learning, basal ganglia and medial temporal lobe memory systems are activated simultaneously and that in some learning situations competitive interference exists between these two systems.
CMOS image sensors: Electronic camera-on-a-chip

CMOS active pixel sensors (APS) have performance competitive with charge-coupled device (CCD) technology, and offer advantages in on-chip functionality, system power reduction, cost, and miniaturization. This paper discusses the requirements for CMOS image sensors and their historical development. CMOS devices and circuits for pixels, analog signal chain, and on-chip analog-to-digital conversion are reviewed and discussed.
Finding the k Shortest Paths

We give algorithms for finding the k shortest paths (not required to be simple) connecting a pair of vertices in a digraph. Our algorithms output an implicit representation of these paths in a digraph with n vertices and m edges, in time O(m + nlogn + k). We can also find the k shortest paths from a given source s to each vertex in the graph, in total time O(m + n logn + kn). We describe applications to dynamic programming problems including the knapsack problem, sequence alignment, and maximum inscribed
Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting

function parameters = main(); % File main.m, created by Eugene Izhikevich. August 28, 2001 % Uses voltage-clamp data from N voltage-step experiments to % determine (in)activation parameters of a transient current. % Data provided by user: global v times current E p q load v.dat % N by 2 matrix of voltage steps % [from, to; from, to;...] load times.dat % Time mesh of the voltage-clamped data load current.dat % Matrix of the current values. E = 50; % Reverse potential p = 3; % The number of activation gates q = 1; % The number of inactivation gates % Guess of initial values of parameters % activation V_1/2 k V_max sigma C_amp C_base par(1:6) = [ -50 20 -40 30 0.5 0.1]; % inactivation V_1/2 k V_max sigma C_amp C_base par(7:12) =[ -60 -5 -70 20 5 1]; par(13) = 1; % Maximal conductance g_max % If E, p, or q are not known, add par(14)=60, etc. % and modify test.m parameters = fmins(’test’,par);
Process Mining Manifesto

The Poisson gravity model along with pseudo ML methods has become a popular way to model international trade flows. This approach has several econometric advantages that we outline in the paper. We argue that estimating the parameters by ML would only be justified statistically if the trade flows were independent. Such an assumption, however, is generally not valid, and a failure to account for spatial dependence may lead to biased parameter estimates and misleading inferences. To overcome this estimation problem we suggest eigenvector spatial filtering variants of the Poisson gravity model (without and with zero-inflation) along with pseudo ML estimation.
Neocognitron: A hierarchical neural network capable of visual pattern recognition

-A neural network model for visual pattern recognition, called the "neocognitron, "' was previously proposed by the author In this paper, we discuss the mechanism of the model in detail. In order to demonstrate the ability of the neocognitron, we also discuss a pattern-recognition system which works with the mechanism of the neocognitron. The system has been implemented on a minicomputer and has been trained to recognize handwritten numerals. The neocognitron is a hierarchical network consisting of many layers of cells, and has variable connections between the cells in adjoining layers. It can acquire the ability to recognize patterns by learning, and can be trained to recognize any set of patterns. After finishing the process of learning, pattern recognition is performed on the basis of similarity in shape between patterns, and is not affected by deformation, nor by changes in size, nor by shifts in the position of the input patterns. In the hierarchical network of the neocognitron, local features of the input pattern are extracted by the cells of a lower stage, and they are gradually integrated into more global features. Finally, each cell of the highest stage integrates all the information of the input pattern, and responds only to one specific pattern. Thus, the response of the cells of the highest stage shows the final result of the pattern-recognition of the network. During this process of extracting and integrating features, errors in the relative position of local features are gradually tolerated. The operation of tolerating positional error a little at a time at each stage, rather than all in one step, plays an important role in endowing the network with an ability to recognize even distorted patterns.
Real time Google map and Arduino based vehicle tracking system

A vehicle tracking system is very useful for tracking the movement of a vehicle from any location at any time. In this work, real time Google map and Arduino based vehicle tracking system is implemented with Global Positioning System (GPS) and Global system for mobile communication (GSM) technology. GPS module provides geographic coordinates at regular time intervals. Then the GSM module transmits the location of vehicle to cell phone of owner/user in terms of latitude and longitude. At the same time, location is displayed on LCD. Finally, Google map displays the location and name of the place on cell phone. Thus, owner/user will be able to continuously monitor a moving vehicle using the cell phone. In order to show the feasibility and effectiveness of the system, this work presents experimental result of the vehicle tracking system. The proposed system is user friendly and ensures safety and surveillance at low maintenance cost.