nils-herrmann/clean_scirepeval huggingface dataset:
Characterizing and Detecting Hateful Users on Twitter

Most current approaches to characterize and detect hate speech focus on content posted in Online Social Networks. They face shortcomings to collect and annotate hateful speech due to the incompleteness and noisiness of OSN text and the subjectivity of hate speech. These limitations are often aided with constraints that oversimplify the problem, such as considering only tweets containing hate-related words. In this work we partially address these issues by shifting the focus towards users. We develop and employ a robust methodology to collect and annotate hateful users which does not depend directly on lexicon and where the users are annotated given their entire profile. This results in a sample of Twitter’s retweet graph containing 100, 386 users, out of which 4, 972 were annotated. We also collect the users who were banned in the three months that followed the data collection. We show that hateful users differ from normal ones in terms of their activity patterns, word usage and as well as network structure. We obtain similar results comparing the neighbors of hateful vs. neighbors of normal users and also suspended users vs. active users, increasing the robustness of our analysis. We observe that hateful users are densely connected, and thus formulate the hate speech detection problem as a task of semi-supervised learning over a graph, exploiting the network of connections on Twitter. We find that a node embedding algorithm, which exploits the graph structure, outperforms content-based approaches for the detection of both hateful (95% AUC vs 88% AUC) and suspended users (93% AUC vs 88% AUC). Altogether, we present a user-centric view of hate speech, paving the way for better detection and understanding of this relevant and challenging issue.
Decontaminating eukaryotic genome assemblies with machine learning

High-throughput sequencing has made it theoretically possible to obtain high-quality de novo assembled genome sequences but in practice DNA extracts are often contaminated with sequences from other organisms. Currently, there are few existing methods for rigorously decontaminating eukaryotic assemblies. Those that do exist filter sequences based on nucleotide similarity to contaminants and risk eliminating sequences from the target organism. We introduce a novel application of an established machine learning method, a decision tree, that can rigorously classify sequences. The major strength of the decision tree is that it can take any measured feature as input and does not require a priori identification of significant descriptors. We use the decision tree to classify de novo assembled sequences and compare the method to published protocols. A decision tree performs better than existing methods when classifying sequences in eukaryotic de novo assemblies. It is efficient, readily implemented, and accurately identifies target and contaminant sequences. Importantly, a decision tree can be used to classify sequences according to measured descriptors and has potentially many uses in distilling biological datasets.
Digital Games in Education: The Design of Games-Based Learning Environments.

In recentyears, electronicgames have assumed an importantplace in the lives of children and adolescents. Children acquire digital literacy informally, through plaj and neither schools nor other educational institutions take suficient account of this important aspect. We consider that multimedia design for training and education should combine the mostpowerfulfedtures of interactive multimedia design with the most effective principles of technologically-mediated learning. An examination of the evolution of the design of videogames is a good way to analyze the main contributions and characteristics ofgames-ba~edlearnin~ environments. At the same time, we will discuss the main obstacles and challenges to the use ofgames for learning.
Autonomous driving for vehicular networks with nonlinear dynamics

In this paper, we introduce cooperative autonomous driving algorithms for vehicular networks with nonlinear mobile robot dynamics in urban environments that take human safety into account and are capable of performing vehicle-to-vehicle (V2V) and vehicle-to-pedestrian (V2P) collision avoidance. We argue that “flocks” are multi-agent models of vehicular traffic on roads and propose novel autonomous driving architectures and algorithms for cyber-physical vehicles capable of performing autonomous driving tasks such as lane-driving, lane-changing, braking, passing, and making turns. Our proposed autonomous driving algorithms are inspired by Olfati-Saber's flocking theory. Though, there are notable differences between autonomous driving on urban roads and flocking behavior - flocks have a single desired destination whereas most drivers on road do not share the same destination. We refer to this collective behavior (driving) as “multi-objective flocking.” The self-driving vehicles in our framework turn out to be hybrid systems with a finite number of discrete states that are related to the driving modes of vehicles. Complex driving maneuvers can be performed using a sequence of mode switchings. We use near-identity nonlinear transformations to extend the application of particle-based autonomous driving algorithms to multi-robot networks with nonlinear dynamics. The derivation of the mode switching conditions that preserve safety is non-trivial and an important part of the design of autonomous driving algorithms. We present several examples of driving tasks that can be effectively performed using our proposed driving algorithms.
A Novel Depth-Based Virtual View Synthesis Method for Free Viewpoint Video

Free-viewpoint rendering (FVR) has become a popular topic in 3-D research. A promising technology in FVR is to generate virtual views using a single texture image and the corresponding depth image. A critical problem that occurs when generating virtual views is that the regions covered by the foreground objects in the original view may be disoccluded in the synthesized views. In this paper, a depth based disocclusion filling algorithm using patch-based texture synthesis is proposed. In contrast to the existing patch-based virtual view synthesis methods, the filling priority is driven by the robust structure tensor that efficiently reflects the overall structure of an image part and a new confidence term that produces fine synthesis results even near the foreground boundaries. Moreover, the best-matched patch is searched in the background regions and finally it is chosen through a new patch distance measure. Significant superiority of the proposed method over the state-of-the-art methods is presented by comparing the experimental results.
Importance of data mining in healthcare: A survey

In this survey, we collect the related information that demonstrate the importance of data mining in healthcare. As the amount of collected health data is increasing significantly every day, it is believed that a strong analysis tool that is capable of handling and analyzing large health data is essential. Analyzing the health datasets gathered by electronic health record (EHR) systems, insurance claims, health surveys, and other sources, using data mining techniques is very complex and is faced with very specific challenges, including data quality and privacy issues. However, the applications of data mining in healthcare, advantages of data mining techniques over traditional methods, special characteristics of health data, and new health condition mysteries have made data mining very necessary for health data analysis.
Self-Driving Cars: Ethical Responsibilities of Design Engineers

In the wake of the exposure of Volkswagen's diesel engine test-rigging, a Bloomberg Business journalist described the company as "driven by engineering-crazed executives"  and The New York Times ran a story noting how with today's complex computer systems in automobiles, there are numerous opportunities for misdeeds both by automakers and hackers . With the advent of so-called autonomous or self-driving cars, such issues may become even more pervasive and problematic. From a legal perspective, a key focal point is who would be at fault if and when an accident occurs . Much also has been written about the ethical complexities posed by self-driving cars [5]-[6]. In accordance with Moore's Law, "[a]s technological revolutions increase their social impact, ethical problems increase" . Yet relatively little has been said about the ethical responsibilities of the designers of self-driving cars.
Real-Time Biologically Inspired Action Recognition from Key Poses Using a Neuromorphic Architecture

Intelligent agents, such as robots, have to serve a multitude of autonomous functions. Examples are, e.g., collision avoidance, navigation and route planning, active sensing of its environment, or the interaction and non-verbal communication with people in the extended reach space. Here, we focus on the recognition of the action of a human agent based on a biologically inspired visual architecture of analyzing articulated movements. The proposed processing architecture builds upon coarsely segregated streams of sensory processing along different pathways which separately process form and motion information (Layher et al., 2014). Action recognition is performed in an event-based scheme by identifying representations of characteristic pose configurations (key poses) in an image sequence. In line with perceptual studies, key poses are selected unsupervised utilizing a feature-driven criterion which combines extrema in the motion energy with the horizontal and the vertical extendedness of a body shape. Per class representations of key pose frames are learned using a deep convolutional neural network consisting of 15 convolutional layers. The network is trained using the energy-efficient deep neuromorphic networks (Eedn) framework (Esser et al., 2016), which realizes the mapping of the trained synaptic weights onto the IBM Neurosynaptic System platform (Merolla et al., 2014). After the mapping, the trained network achieves real-time capabilities for processing input streams and classify input images at about 1,000 frames per second while the computational stages only consume about 70 mW of energy (without spike transduction). Particularly regarding mobile robotic systems, a low energy profile might be crucial in a variety of application scenarios. Cross-validation results are reported for two different datasets and compared to state-of-the-art action recognition approaches. The results demonstrate, that (I) the presented approach is on par with other key pose based methods described in the literature, which select key pose frames by optimizing classification accuracy, (II) compared to the training on the full set of frames, representations trained on key pose frames result in a higher confidence in class assignments, and (III) key pose representations show promising generalization capabilities in a cross-dataset evaluation.
Extracting Business Process Models Using Natural Language Processing (NLP) Techniques

This Doctoral Consortium paper discusses how NLP can be applied in the domain of BPM in order to automatically generate business process models from existing documentation within the organization. The main idea is that from the syntactic and grammatical structure of a sentence, the components of a business process model can be derived (i.e. activities, resources, tasks, patterns). The result would be a business process model depicted using BPMN - a dedicated business process modeling technique
What are mobile developers asking about? A large scale study using stack overflow

The popularity of mobile devices has been steadily growing in recent years. These devices heavily depend on software from the underlying operating systems to the applications they run. Prior research showed that mobile software is different than traditional, large software systems. However, to date most of our research has been conducted on traditional software systems. Very little work has focused on the issues that mobile developers face. Therefore, in this paper, we use data from the popular online Q&A site, Stack Overflow, and analyze 13,232,821 posts to examine what mobile developers ask about. We employ Latent Dirichlet allocation-based topic models to help us summarize the mobile-related questions. Our findings show that developers are asking about app distribution, mobile APIs, data management, sensors and context, mobile tools, and user interface development. We also determine what popular mobile-related issues are the most difficult, explore platform specific issues, and investigate the types (e.g., what, how, or why) of questions mobile developers ask. Our findings help highlight the challenges facing mobile developers that require more attention from the software engineering research and development communities in the future and establish a novel approach for analyzing questions asked on Q&A forums.
On-line fingerprint verification

Fingerprint verification is one of the most reliable personal identification methods. However, manual fingerprint verification is so tedious, time-consuming, and expensive that it is incapable of meeting today’s increasing performance requirements. An automatic fingerprint identification system (AFIS) is widely needed. It plays a very important role in forensic and civilian applications such as criminal identification, access control, and ATM card verification. This paper describes the design and implementation of an on-line fingerprint verification system which operates in two stages: minutia extraction and minutia matching. An improved version of the minutia extraction algorithm proposed by Ratha et al., which is much faster and more reliable, is implemented for extracting features from an input fingerprint image captured with an on-line inkless scanner. For minutia matching, an alignment-based elastic matching algorithm has been developed. This algorithm is capable of finding the correspondences between minutiae in the input image and the stored template without resorting to exhaustive search and has the ability of adaptively compensating for the nonlinear deformations and inexact pose transformations between fingerprints. The system has been tested on two sets of fingerprint images captured with inkless scanners. The verification accuracy is found to be acceptable. Typically, a complete fingerprint verification procedure takes, on an average, about eight seconds on a SPARC 20 workstation. These experimental results show that our system meets the response time requirements of on-line verification with high accuracy. Index Terms —Biometrics, fingerprints, matching, verification, minutia, orientation field, ridge extraction. —————————— ✦ ——————————
A Database of Narrative Schemas

This paper describes a new language resource of events and semantic roles that characterize real-world situations. Narrative schemas contain sets of related events (edit and publish), a temporal ordering of the events (edit before publish), and the semantic roles of the participants (authors publish books). This type of world knowledge was central to early research in natural language understanding. Scripts were one of the main formalisms, representing common sequences of events that occur in the world. Unfortunately, most of this knowledge was hand-coded and time consuming to create. Current machine learning techniques, as well as a new approach to learning through coreference chains, has allowed us to automatically extract rich event structure from open domain text in the form of narrative schemas. The narrative schema resource described in this paper contains approximately 5000 unique events combined into schemas of varying sizes. We describe the resource, how it is learned, and a new evaluation of the coverage of these schemas over unseen documents.
Combining Naive Bayes and Decision Tree for Adaptive Intrusion Detection

In this paper, a new learning algorithm for adaptive network intrusion detection using naive Bayesian classifier and decision tree is presented, which performs balance detections and keeps false positives at acceptable level for different types of network attacks, and eliminates redundant attributes as well as contradictory examples from training data that make the detection model complex. The proposed algorithm also addresses some difficulties of data mining such as handling continuous attribute, dealing with missing attribute values, and reducing noise in training data. Due to the large volumes of security audit data as well as the complex and dynamic properties of intrusion behaviours, several data miningbased intrusion detection techniques have been applied to network-based traffic data and host-based data in the last decades. However, there remain various issues needed to be examined towards current intrusion detection systems (IDS). We tested the performance of our proposed algorithm with existing learning algorithms by employing on the KDD99 benchmark intrusion detection dataset. The experimental results prove that the proposed algorithm achieved high detection rates (DR) and significant reduce false positives (FP) for different types of network intrusions using limited computational resources.
Handling Obstacles in Goal-Oriented Requirements Engineering

is nd ABSTRACT Requirements engineering is concerned with the elicitatio of high-level goals to be achieved by the envisioned syste the refinement of such goals and their operationalizatio into specifications of services and constraints, and th assignment of responsibilities for the resulting requiremen to agents such as humans, devices, and software. Requirements engineering processes often result in goa requirements and assumptions about agent behavior that too ideal; some of them are likely to be not satisfied from time to time in the running system due to unexpected age behavior. The lack of anticipation of exceptional behavior results in unrealistic, unachievable and/or incomple requirements. As a consequence, the software develop from those requirements will not be robust enough and w inevitably result in poor performance or failures, sometime with critical consequences on the environment. The paper presents formal techniques for reasoning abo obstacles to the satisfaction of goals, requirements, a assumptions elaborated in the requirements engineer process. A first set of techniques allow obstacles to be ge erated systematically from goal formulations and doma properties. A second set of techniques allow resolutions be generated once the obstacles have been identifi thereby. Our techniques are based on a temporal logic formalizatio of goals and domain properties; they are integrated into existing method for goal-oriented requirements elaboratio with the aim of deriving more realistic, complete and robus requirements specifications. A key principle in this paper is to handle exceptions a requirements engineering time and at the goal level, so th more freedom is left for resolving them in a satisfactor way. The various techniques proposed are illustrated a assessed in the context of a real safety-critical system.
An empirical analysis of dropout in piecewise linear networks

The recently introduced dropout training criterion for neural networks has been the subject of much attention due to its simplicity and remarkable effectiveness as a regularizer, as well as its interpretation as a training procedure for an exponentially large ensemble of networks that share parameters. In this work we empirically investigate several questions related to the efficacy of dropout, specifically as it concerns networks employing the popular rectified linear activation function. We investigate the quality of the test time weight-scaling inference procedure by evaluating the geometric average exactly in small models, as well as compare the performance of the geometric mean to the arithmetic mean more commonly employed by ensemble techniques. We explore the effect of tied weights on the ensemble interpretation by training ensembles of masked networks without tied weights. Finally, we investigate an alternative criterion based on a biased estimator of the maximum likelihood ensemble gradient.
Bundle Adjustment in the Large

We present the design and implementation of a new inexact Newton type algorithm for solving large-scale bundle adjustment problems with tens of thousands of images. We explore the use of Conjugate Gradients for calculating the Newton step and its performance as a function of some simple and computationally efficient preconditioners. We show that the common Schur complement trick is not limited to factorization-based methods and that it can be interpreted as a form of preconditioning. Using photos from a street-side dataset and several community photo collections, we generate a variety of bundle adjustment problems and use them to evaluate the performance of six different bundle adjustment algorithms. Our experiments show that truncated Newton methods, when paired with relatively simple preconditioners, offer state of the art performance for large-scale bundle adjustment. The code, test problems and detailed performance data are available at http://grail.cs.washington.edu/projects/bal.
RASR - The RWTH Aachen University Open Source Speech Recognition Toolkit

RASR is the open source version of the well-proven speech recognition toolkit developed and used at RWTH Aachen University. The current version of the package includes state of the art speech recognition technology for acoustic model training and decoding. Speaker adaptation, speaker adaptive training, unsupervised training, discriminative training, lattice processing tools, flexible signal analysis, a finite state automata library, and an efficient dynamic network decoder are notable components. Comprehensive documentation, example setups for training and recognition, and tutorials are provided to support newcomers.