nils-herrmann/clean_scirepeval huggingface dataset:
USED: a large-scale social event detection dataset

Event discovery from single pictures is a challenging problem that has raised significant interest in the last decade. During this time, a number of interesting solutions have been proposed to tackle event discovery in still images. However, a large scale benchmarking image dataset for the evaluation and comparison of event discovery algorithms from single images is still lagging behind. To this aim, in this paper we provide a large-scale properly annotated and balanced dataset of 490,000 images, covering every aspect of 14 different types of social events, selected among the most shared ones in the social network. Such a large scale collection of event-related images is intended to become a powerful support tool for the research community in multimedia analysis by providing a common benchmark for training, testing, validation and comparison of existing and novel algorithms. In this paper, we provide a detailed description of how the dataset is collected, organized and how it can be beneficial for the researchers in the multimedia analysis domain. Moreover, a deep learning based approach is introduced into event discovery from single images as one of the possible applications of this dataset with a belief that deep learning can prove to be a breakthrough also in this research area. By providing this dataset, we hope to gather research community in the multimedia and signal processing domains to advance this application.
Automatic Fingerprint Recognition Systems

This article summarizes the major developments in the history of efforts to use fingerprint patterns to identify individuals, from the earliest fingerprint classification systems of Vucetich and Henry in the 1890s through the advent of automated fingerprint identification. By chronicling the history of “manual” systems for recording storing, matching, and retrieving fingerprints, the article puts advances in automatic fingerprint recognition in historical context and highlights their historical and social significance.
Basic principles of ROC analysis.

The limitations of diagnostic "accuracy" as a measure of decision performance require introduction of the concepts of the "sensitivity" and "specificity" of a diagnostic test. These measures and the related indices, "true positive fraction" and "false positive fraction," are more meaningful than "accuracy," yet do not provide a unique description of diagnostic performance because they depend on the arbitrary selection of a decision threshold. The receiver operating characteristic (ROC) curve is shown to be a simple yet complete empirical description of this decision threshold effect, indicating all possible combinations of the relative frequencies of the various kinds of correct and incorrect decisions. Practical experimental techniques for measuring ROC curves are described, and the issues of case selection and curve-fitting are discussed briefly. Possible generalizations of conventional ROC analysis to account for decision performance in complex diagnostic tasks are indicated. ROC analysis is shown to be related in a direct and natural way to cost/benefit analysis of diagnostic decision making. The concepts of "average diagnostic cost" and "average net benefit" are developed and used to identify the optimal compromise among various kinds of diagnostic error. Finally, the way in which ROC analysis can be employed to optimize diagnostic strategies is suggested.
Feature Extraction or Feature Selection for Text Classification: A Case Study on Phishing Email Detection

Dimensionality reduction is generally performed when high dimensional data like text are classified. This can be done either by using feature extraction techniques or by using feature selection techniques. This paper analyses which dimension reduction technique is better for classifying text data like emails. Email classification is difficult due to its high dimensional sparse features that affect the generalization performance of classifiers. In phishing email detection, dimensionality reduction techniques are used to keep the most instructive and discriminative features from a collection of emails, consists of both phishing and legitimate, for better detection. Two feature selection techniques Chi-Square and Information Gain Ratio and two feature extraction techniques – Principal Component Analysis and Latent Semantic Analysis are used for the analysis. It is found that feature extraction techniques offer better performance for the classification, give stable classification results with the different number of features chosen, and robustly keep the performance over time.
How to measure similarity for multiple categorical data sets?

How to measure similarity or distance for multiple categorical data? It is an important step for Data Mining and Knowledge Management process to measure similarity or distance between objects appropriately. Measurements for continuous data have been well-defined and relatively easy to be calculated. However, the notion of similarity for categorical data is not simple, since categorical data usually is not simply translated into the numerical format, and they also have their own priority with structures and data distribution. In this paper, we propose a new measure for multiple categorical data sets using data distribution. Our new measure, MCSM (Multiple Categorical Similarity Measure), can solve conventional drawbacks of multiple categorical data sets successfully in which we prove the verification of our measure with mathematical proofs and experimentation. The experimental result shows that our measure is powerful for multiple categorical data sets with proper data distributions.
A simple interface circuit to measure very small capacitance changes in capacitive sensors

This paper presents an easy-to-design interface circuit to measure very small-percentage capacitance variations in capacitive sensors, especially suitable for industrial measurements. A computer-controlled 24-bit A/D converter is employed to obtain a higher resolution. This interface circuit can be used with various types of capacitive sensors. The most interesting thing is, that the measurement results through this interface circuit are independent of the initial capacitance of the sensor. In addition, the double differential operating principle used here minimizes the error caused by coupling and stray capacitance of sensor probes. The operating principle of the designed interface circuit, the major assumptions made, test data, and possible future developments are discussed.
Applying a Multi-Level Modeling Theory to Assess Taxonomic Hierarchies in Wikidata

Wikidata captures structured data on a number of subject domains, managing, among others, the information underlying Wikipedia and other Wikimedia projects. Wikidata serves as a repository of structured data, whose purpose is to support the consistent sharing and linking of data on the Web. To support these purposes, it is key that Wikidata is built on consistent data models and representation schemas, which are constructed and managed in a collaborative platform. In this paper, we address the quality of taxonomic hierarchies in Wikidata. We focus on taxonomic hierarchies with entities at different classification levels (particular individuals, types of individuals, types of types of individuals, etc.). We use an axiomatic theory for multi-level modeling to analyze current Wikidata content, and identify a significant number of problematic classification and taxonomic statements. The problems seem to arise from an inadequate use of instantiation and subclassing in certain Wikidata hierarchies.
Research on Cooperative Learning and Achievement : What We Know , What We Need to Know

Research on cooperative learning is one of the greatest success stories in the history of educational research. While there was some research on this topic from the early days of this century, the amount and quality of that research greatly accelerated in the early 1970's, and continues unabated today, a quarter-century later. Hundreds of studies have compared cooperative learning to various control methods on a broad range of measures, but by far the most frequent objective of this research is to determine the effects of cooperative learning on student achievement. Studies of the achievement effects of cooperative learning have taken place in every major subject, at all grade levels, in all types of schools in many countries. Both field studies and laboratory studies have produced a great deal of knowledge about the effects of many types of cooperative interventions and about the mechanisms responsible for these effects. Further, cooperative learning is not only a subject of research and theory; it is used at some level by millions of teachers. A recent national survey (Puma, Jones, Rock, & Fernandez, 1993) found that 79% of elementary teachers and 62% of middle school teachers reported making some sustained use of cooperative learning.
A Framework for Dynamic Image Sampling Based on Supervised Learning

Sparse sampling schemes can broadly be classified into two main categories: static sampling where the sampling pattern is predetermined, and dynamic sampling where each new measurement location is selected based on information obtained from previous measurements. Dynamic sampling methods are particularly appropriate for pointwise imaging methods, in which pixels are measured sequentially in arbitrary order. Examples of pointwise imaging schemes include certain implementations of atomic force microscopy, electron back scatter diffraction, and synchrotron X-ray imaging. In these pointwise imaging applications, dynamic sparse sampling methods have the potential to dramatically reduce the number of measurements required to achieve a desired level of fidelity. However, the existing dynamic sampling methods tend to be computationally expensive and are, therefore, too slow for many practical applications. In this paper, we present a framework for dynamic sampling based on machine learning techniques, which we call a supervised learning approach for dynamic sampling (SLADS). In each step of SLADS, the objective is to find the pixel that maximizes the expected reduction in distortion (ERD) given previous measurements. SLADS is fast because we use a simple regression function to compute the ERD, and it is accurate because the regression function is trained using datasets that are representative of the specific application. In addition, we introduce an approximate method to terminate dynamic sampling at a desired level of distortion. We then extend our algorithm to incorporate multiple measurements at each step, which we call groupwise SLADS. Finally, we present results on computationally generated synthetic data and experimentally collected data to demonstrate a dramatic improvement over state-of-the-art static sampling
Delphi method: techniques and applications

Cross-impact analysis is a method for revising estimated probabilities of future events in terms of estimated interactions among those events. This Report presents an elementary cross-impact model where the cross-impacts are formulated as relative probabilities. Conditions are derived for the consistency of the matrix of relative probabilities of n events. An extension also provides a necessary condition for the vector of absolute probabilities to be consistent with the relative probability matrix. An averaging technique is formulated for resolving inconsistencies in the matrix, and a nearest-point computation derived for resolving inconsistencies between the set of absolute probabilities and the matrix. Although elementary, the present model clarifies some of the conceptual problems associated with cross-impact analysis, and supplies a relatively sound basis for revising probability estimates in the limited case where interactions can be approximated by relative probabilities.
Strike (with) a Pose: Neural Networks Are Easily Fooled by Strange Poses of Familiar Objects

Despite excellent performance on stationary test sets, deep neural networks (DNNs) can fail to generalize to out-of-distribution (OoD) inputs, including natural, nonadversarial ones, which are common in real-world settings. In this paper, we present a framework for discovering DNN failures that harnesses 3D renderers and 3D models. That is, we estimate the parameters of a 3D renderer that cause a target DNN to misbehave in response to the rendered image. Using our framework and a self-assembled dataset of 3D objects, we investigate the vulnerability of DNNs to OoD poses of well-known objects in ImageNet. For objects that are readily recognized by DNNs in their canonical poses, DNNs incorrectly classify 97% of their pose space. In addition, DNNs are highly sensitive to slight pose perturbations. Importantly, adversarial poses transfer across models and datasets. We find that 99.9% and 99.4% of the poses misclassified by Inception-v3 also transfer to the AlexNet and ResNet-50 image classifiers trained on the same ImageNet dataset, respectively, and 75.5% transfer to the YOLOv3 object detector trained on MS COCO.
Cooperative mobile edge computing system for VANET-based software-defined content delivery

Next-generation smart cities and Internet of Things (IoT) are getting more mature in terms of services and infrastructure requirements. Multiple smart vehicle applications are being conceived these days, including road traffic, road safety and infotainment, all of which are suffering from the WAN-latency problem. In this paper, we propose a Vehicular Adhoc Network (VANET)based Software-Defined Edge Computing infrastructure supporting content delivery services among connected vehicles. The proposed approach leverages network base stations to embed mobile edge computing (MEC) services closer to the vehicles. Our approach can enable the delivery of more competitive services with reduced-latency by utilizing cooperative MEC search strategy for vehicle to infrastructure (V2I) communications as well as utilizing vehicle-level caching for vehicle to vehicle (V2V) communications between peers. The framework prototype has been implemented as a clean extension of the Mininet-WiFi emulator. Preliminary results serve as validation of the proposed framework and point out the potential benefits of the approach in mitigating WAN-latency in VANET.
Mediation analysis allowing for exposure-mediator interactions and causal interpretation: theoretical assumptions and implementation with SAS and SPSS macros.

Mediation analysis is a useful and widely employed approach to studies in the field of psychology and in the social and biomedical sciences. The contributions of this article are several-fold. First we seek to bring the developments in mediation analysis for nonlinear models within the counterfactual framework to the psychology audience in an accessible format and compare the sorts of inferences about mediation that are possible in the presence of exposure-mediator interaction when using a counterfactual versus the standard statistical approach. Second, the work by VanderWeele and Vansteelandt (2009, 2010) is extended here to allow for dichotomous mediators and count outcomes. Third, we provide SAS and SPSS macros to implement all of these mediation analysis techniques automatically, and we compare the types of inferences about mediation that are allowed by a variety of software macros.
Product Feature Mining: Semantic Clues versus Syntactic Constituents

Product feature mining is a key subtask in fine-grained opinion mining. Previous works often use syntax constituents in this task. However, syntax-based methods can only use discrete contextual information, which may suffer from data sparsity. This paper proposes a novel product feature mining method which leverages lexical and contextual semantic clues. Lexical semantic clue verifies whether a candidate term is related to the target product, and contextual semantic clue serves as a soft pattern miner to find candidates, which exploits semantics of each word in context so as to alleviate the data sparsity problem. We build a semantic similarity graph to encode lexical semantic clue, and employ a convolutional neural model to capture contextual semantic clue. Then Label Propagation is applied to combine both semantic clues. Experimental results show that our semantics-based method significantly outperforms conventional syntaxbased approaches, which not only mines product features more accurately, but also extracts more infrequent product features.
Automatic text summarization based on sentences clustering and extraction

Technology of automatic text summarization plays an important role in information retrieval and text classification, and may provide a solution to the information overload problem. Text summarization is a process of reducing the size of a text while preserving its information content. This paper proposes a sentences clustering based summarization approach. The proposed approach consists of three steps: first clusters the sentences based on the semantic distance among sentences in the document, and then on each cluster calculates the accumulative sentence similarity based on the multi-features combination method, at last chooses the topic sentences by some extraction rules. The purpose of present paper is to show that summarization result is not only depends the sentence features, but also depends on the sentence similarity measure. The experimental result on the DUC 2003 dataset show that our proposed approach can improve the performance compared to other summarization methods.
Device to device interaction analysis in IoT based Smart Traffic Management System: An experimental approach

Device to Device Communication pattern is analyzed for IoT based Smart Traffic Management System in the paper. In this type of communication pattern there is no any human intervention in a system because of direct communication between the devices, hence it is completely automated. D2D Communication Patterns are in great demand in IoT sector where a Thing (Device) is controlled by Internet from any remote place by using another Device. Here in an experiment of Smart Traffic Management System the method to determine Traffic Density on a particular lane is discussed. Ultrasonic Sensors are employed as the data acquisition unit to collect the information regarding Traffic Density and Raspberry Pi is used as Control Unit for processing the data. The information regarding traffic density sensed is processed and transmitted to the users provided with Android Application, where the user get a notification about the traffic density in that particular lane and hence assist to take an alternate path.
A Survey on Outlier Detection Techniques for Credit Card Fraud Detection

Credit card fraud detection is an important application of outlier detection. Due to drastic increase in digital frauds, there is a loss of billions dollars and therefore various techniques are evolved for fraud detection and applied to diverse business fields. The traditional fraud detection schemes use data analysis methods that require knowledge about different domains such as financial, economics, law and business practices. The current fraud detection techniques may be offline or online, and may use neural networks, clustering, genetic algorithm, decision tree etc. There are various outlier detection techniques are available such as statistical based, density based, clustering based and so on. This paper projected to find credit card fraud by using appropriate outlier detection technique, which is suitable for online applications where large scale data is involved. The method should also work efficiently for applications where memory and computation limitations are present. Here we have discussed one such unsupervised method Principal Component Analysis(PCA) to detect an outlier.
Non-Orthogonal Multiple Access With Cooperative Full-Duplex Relaying

We propose a full-duplex (FD) cooperative non-orthogonal multiple access (NOMA) system with dual users, where a dedicated FD relay assists the information transmission to the user with weak channel condition. Under the realistic assumption of imperfect self-interference cancellation, the achievable outage probability of both users and ergodic sum capacity are investigated, and exact analytical expressions are derived. Simulation results demonstrate that the proposed FD cooperative NOMA system attains better performance than the half-duplex cooperative NOMA system in the moderate signal-to-noise ratio regime.
Correction of the retracted alar base.

Alar base retraction is a common yet difficult problem faced by the rhinoplasty surgeon. It may be caused by weakened, overresected lateral crura, vestibular lining deficiencies, or congenital alar malpositioning. Methods of correction include soft tissue manipulation, auricular composite grafting, and cartilage grafting. We present the senior author's graded approach to alar retraction using auricular composite grafting, alar rim grafting, and lateral crural strut graft placement with caudal lateral crural repositioning.
On the Semantic Mapping of Schema-agnostic Queries : A Preliminary Study

The growing size, heterogeneity and complexity of databases demand the creation of strategies to facilitate users and systems to consume data. Ideally, query mechanisms should be schema-agnostic or vocabulary-independent, i.e. they should be able to match user queries in their own vocabulary and syntax to the data, abstracting data consumers from the representation of the data. Despite being a central requirement across natural language interfaces and entity search, there is a lack on the conceptual analysis of schema-agnosticism and on the associated semantic differences between queries and databases. This work aims at providing an initial conceptualization for schema-agnostic queries aiming at providing a fine-grained classification which can support the scoping, evaluation and development of semantic matching approaches for schema-agnostic queries.
Adaptation impact and environment models for architecture-based self-adaptive systems

Self-adaptive systems have the ability to adapt their behavior to dynamic operating conditions. In reaction to changes in the environment, these systems determine the appropriate corrective actions based in part on information about which action will have the best impact on the system. Existing models used to describe the impact of adaptations are either unable to capture the underlying uncertainty and variability of such dynamic environments, or are not compositional and described at a level of abstraction too low to scale in terms of specification effort required for non-trivial systems. In this paper, we address these shortcomings by describing an approach to the specification of impact models based on architectural system descriptions, which at the same time allows us to represent both variability and uncertainty in the outcome of adaptations, hence improving the selection of the best corrective action. The core of our approach is a language equipped with a formal semantics defined in terms of Discrete Time Markov Chains that enables us to describe both the impact of adaptation tactics, as well as the assumptions about the environment. To validate our approach, we show how employing our language can improve the accuracy of predictions used for decision-making in the Rainbow framework for architecture-based self-adaptation.
Analysis of Convolutional Neural Networks for Document Image Classification

Convolutional Neural Networks (CNNs) are state-of-the-art models for document image classification tasks. However, many of these approaches rely on parameters and architectures designed for classifying natural images, which differ from document images. We question whether this is appropriate and conduct a large empirical study to find what aspects of CNNs most affect performance on document images. Among other results, we exceed the state-of-the-art on the RVL-CDIP dataset by using shear transform data augmentation and an architecture designed for a larger input image. Additionally, we analyze the learned features and find evidence that CNNs trained on RVL-CDIP learn region-specific layout features.