nils-herrmann/clean_scirepeval huggingface dataset:
Object-Centric Anomaly Detection by Attribute-Based Reasoning

When describing images, humans tend not to talk about the obvious, but rather mention what they find interesting. We argue that abnormalities and deviations from typicalities are among the most important components that form what is worth mentioning. In this paper we introduce the abnormality detection as a recognition problem and show how to model typicalities and, consequently, meaningful deviations from prototypical properties of categories. Our model can recognize abnormalities and report the main reasons of any recognized abnormality. We also show that abnormality predictions can help image categorization. We introduce the abnormality detection dataset and show interesting results on how to reason about abnormalities.
Data Quality in Ontology-based Data Access: The Case of Consistency

Ontology-based data access (OBDA) is a new paradigm aiming at accessing and managing data by means of an ontology, i.e., a conceptual representation of the domain of interest in the underlying information system. In the last years, this new paradigm has been used for providing users with abstract (independent from technological and system-oriented aspects), effective, and reasoning-intensive mechanisms for querying the data residing at the information system sources. In this paper we argue that OBDA, besides querying data, provides the right principles for devising a formal approach to data quality. In particular, we concentrate on one of the most important dimensions considered both in the literature and in the practice of data quality, namely consistency. We define a general framework for data consistency in OBDA, and present algorithms and complexity analysis for several relevant tasks related to the problem of checking data quality under this dimension, both at the extensional level (content of the data sources), and at the intensional level (schema of the
As-awareness in Tor path selection

Tor is an anonymous communications network with thousands of router nodes worldwide. An intuition reflected in much of the literature on anonymous communications is that, as an anonymity network grows, it becomes more secure against a given observer because the observer will see less of the network. In particular, as the Tor network grows from volunteers operating relays all over the world, it becomes less and less likely for a single autonomous system (AS) to be able to observe both ends of an anonymous connection. Yet, as the network continues to grow significantly, no analysis has been done to determine if this intuition is correct. Further, modifications to Tor's path selection algorithm to help clients avoid an AS-level observer have not been proposed and analyzed.
 Five years ago a previous study examined the AS-level threat against client and destination addresses chosen a priori to be likely or interesting to examine. Using an AS-level path inference algorithm with improved accuracy, more extensive Internet routing data, and, most importantly, a model of typical Tor client AS-level sources and destinations based on data gathered from the live network, we demonstrate that the threat of a single AS observing both ends of an anonymous Tor connection is greater than previously thought. We look at the growth of the Tor network over the past five years and show that its explosive growth has had only a small impact on the network's robustness against an AS-level attacker. Finally, we propose and evaluate the effectiveness of some simple, AS-aware path selection algorithms that avoid the computational overhead imposed by full AS-level path inference algorithms. Our results indicate that a novel heuristic we propose is more effective against an AS-level observer than other commonly proposed heuristics for improving location diversity in path selection.
Device-free gesture tracking using acoustic signals

Device-free gesture tracking is an enabling HCI mechanism for small wearable devices because fingers are too big to control the GUI elements on such small screens, and it is also an important HCI mechanism for medium-to-large size mobile devices because it allows users to provide input without blocking screen view. In this paper, we propose LLAP, a device-free gesture tracking scheme that can be deployed on existing mobile devices as software, without any hardware modification. We use speakers and microphones that already exist on most mobile devices to perform device-free tracking of a hand/finger. The key idea is to use acoustic phase to get fine-grained movement direction and movement distance measurements. LLAP first extracts the sound signal reflected by the moving hand/finger after removing the background sound signals that are relatively consistent over time. LLAP then measures the phase changes of the sound signals caused by hand/finger movements and then converts the phase changes into the distance of the movement. We implemented and evaluated LLAP using commercial-off-the-shelf mobile phones. For 1-D hand movement and 2-D drawing in the air, LLAP has a tracking accuracy of 3.5 mm and 4.6 mm, respectively. Using gesture traces tracked by LLAP, we can recognize the characters and short words drawn in the air with an accuracy of 92.3% and 91.2%, respectively.
Relational autoencoder for feature extraction

Feature extraction becomes increasingly important as data grows high dimensional. Autoencoder as a neural network based feature extraction method achieves great success in generating abstract features of high dimensional data. However, it fails to consider the relationships of data samples which may affect experimental results of using original and new features. In this paper, we propose a Relation Autoencoder model considering both data features and their relationships. We also extend it to work with other major autoencoder models including Sparse Autoencoder, Denoising Autoencoder and Variational Autoencoder. The proposed relational autoencoder models are evaluated on a set of benchmark datasets and the experimental results show that considering data relationships can generate more robust features which achieve lower construction loss and then lower error rate in further classification compared to the other variants of autoencoders.
Sensorless control of induction motor drives

rotor field orientation stator field orientation stator model rotor model MRAS, observers, Kalman filter parasitic properties field angle estimation Abstract — Controlled induction motor drives without mechanical speed sensors at the motor shaft have the attractions of low cost and high reliability. To replace the sensor, the information on the rotor speed is extracted from measured stator voltages and currents at the motor terminals. Vector controlled drives require estimating the magnitude and spatial orientation of the fundamental magnetic flux waves in the stator or in the rotor. Open loop estimators or closed loop observers are used for this purpose. They differ with respect to accuracy, robustness, and sensitivity against model parameter variations. Dynamic performance and steady-state speed accuracy in the low speed range can be achieved by exploiting parasitic effects of the machine. The overview in this paper uses signal flow graphs of complex space vector quantities to provide an insightful description of the systems used in sensorless control of induction motors.
Global Diffusion via Cascading Invitations: Structure, Growth, and Homophily

Many of the world's most popular websites catalyze their growth through invitations from existing members. New members can then in turn issue invitations, and so on, creating cascades of member signups that can spread on a global scale. Although these diffusive invitation processes are critical to the popularity and growth of many websites, they have rarely been studied, and their properties remain elusive. For instance, it is not known how viral these cascades structures are, how cascades grow over time, or how diffusive growth affects the resulting distribution of member characteristics present on the site. In this paper, we study the diffusion of LinkedIn, an online professional network comprising over 332 million members, a large fraction of whom joined the site as part of a signup cascade. First we analyze the structural patterns of these signup cascades, and find them to be qualitatively different from previously studied information diffusion cascades. We also examine how signup cascades grow over time, and observe that diffusion via invitations on LinkedIn occurs over much longer timescales than are typically associated with other types of online diffusion. Finally, we connect the cascade structures with rich individual-level attribute data to investigate the interplay between the two. Using novel techniques to study the role of homophily in diffusion, we find striking differences between the local, edge-wise homophily and the global, cascade-level homophily we observe in our data, suggesting that signup cascades form surprisingly coherent groups of members.
Optimizing real-time RDF data streams

The Resource Description Framework (RDF) provides a common data model for the integration of “real-time” social and sensor data streams with the Web and with each other. While there exist numerous protocols and data formats for exchanging dynamic RDF data, or RDF updates, these options should be examined carefully in order to enable a Semantic Web equivalent of the high-throughput, low-latency streams of typical Web 2.0, multimedia, and gaming applications. This paper contains a brief survey of RDF update formats and a high-level discussion of both TCP and UDPbased transport protocols for updates. Its main contribution is the experimental evaluation of a UDP-based architecture which serves as a real-world example of a high-performance RDF streaming application in an Internet-scale distributed environment.
A Cooperative approach to particle swarm optimization

The particle swarm optimizer (PSO) is a stochastic, population-based optimization technique that can be applied to a wide range of problems, including neural network training. This paper presents a variation on the traditional PSO algorithm, called the cooperative particle swarm optimizer, or CPSO, employing cooperative behavior to significantly improve the performance of the original algorithm. This is achieved by using multiple swarms to optimize different components of the solution vector cooperatively. Application of the new PSO algorithm on several benchmark optimization problems shows a marked improvement in performance over the traditional PSO.
Cloud-Scale Entity Resolution: Current State and Open Challenges

Entity resolution (ER) is a process to identify records in information systems, which refer to the same real-world entity. Because in the two recent decades the data volume has grown so large, parallel techniques are called upon to satisfy the ER requirements of high performance and scalability. The development of parallel ER has reached a relatively prosperous stage, and has found its way into several applications. In this work, we first comprehensively survey the state of the art of parallel ER approaches. From the comprehensive overview, we then extract the classification criteria of parallel ER, classify and compare these approaches based on these criteria. Finally, we identify open research questions and challenges and discuss potential solutions and further research potentials in this field. TYPE OF PAPER AND
Speaker identification and verification using Gaussian mixture speaker models

This paper presents high performance speaker identification and verification systems based on Gaussian mixture speaker models: robust, statistically based representations of speaker identity. The identification system is a maximum likelihood classifier and the verification system is a likelihood ratio hypothesis tester using background speaker normalization. The systems are evaluated on four publically available speech databases: TIMIT, NTIMIT, Switchboard and YOHO. The different levels of degradations and variabilities found in these databases allow the examination of system performance for different task domains. Constraints on the speech range from vocabulary-dependent to extemporaneous and speech quality varies from near-ideal, clean speech to noisy, telephone speech. Closed set identification accuracies on the 630 speaker TIMIT and NTIMIT databases were 99.5% and 60.7%, respectively. On a 113 speaker population from the Switchboard database the identification accuracy was 82.8%. Global threshold equal error rates of 0.24%, 7.19%, 5.15% and 0.51% were obtained in verification experiments on the TIMIT, NTIMIT, Switchboard and YOHO databases, respectively.
Safe Exploration Techniques for Reinforcement Learning - An Overview

We overview different approaches to safety in (semi)autonomous robotics. Particularly, we focus on how to achieve safe behavior of a robot if it is requested to perform exploration of unknown states. Presented methods are studied from the viewpoint of reinforcement learning, a partially-supervised machine learning method. To collect training data for this algorithm, the robot is required to freely explore the state space – which can lead to possibly dangerous situations. The role of safe exploration is to provide a framework allowing exploration while preserving safety. The examined methods range from simple algorithms to sophisticated methods based on previous experience or state prediction. Our overview also addresses the issues of how to define safety in the real-world applications (apparently absolute safety is unachievable in the continuous and random real world). In the conclusion we also suggest several ways that are worth researching more thoroughly.
On the Suitability of BPMN for Business Process Modelling

In this paper we examine the suitability of BPMN for business process modelling, using the Workflow Patterns as an evaluation framework. The Workflow Patterns are a collection of patterns developed for assessing control-flow, data and resource capabilities in the area of Process Aware Information Systems (PAIS). In doing so, we provide a comprehensive evaluation of the capabilities of BPMN, and its strengths and weaknesses when utilised for business process modelling. The analysis provided for BPMN is part of a larger effort aiming at an unbiased and vendor-independent survey of the suitability and the expressive power of some mainstream process modelling languages. It is a sequel to an analysis series where languages like BPEL and UML 2.0 A.D are evaluated.
Nonlinear causal discovery with additive noise models

The discovery of causal relationships between a set of observed variables is a fundamental problem in science. For continuous-valued data linear acyclic causal models with additive noise are often used because these models are well understood and there are well-known methods to fit them to data. In reality, of course, many causal relationships are more or less nonlinear, raising some doubts as to the applicability and usefulness of purely linear methods. In this contribution we show that the basic linear framework can be generalized to nonlinear models. In this extended framework, nonlinearities in the data-generating process are in fact a blessing rather than a curse, as they typically provide information on the underlying causal system and allow more aspects of the true data-generating mechanisms to be identified. In addition to theoretical results we show simulations and some simple real data experiments illustrating the identification power provided by nonlinearities.
Multiagent-Based Reinforcement Learning for Optimal Reactive Power Dispatch

This paper proposes a fully distributed multiagent-based reinforcement learning method for optimal reactive power dispatch. According to the method, two agents communicate with each other only if their corresponding buses are electrically coupled. The global rewards that are required for learning are obtained with a consensus-based global information discovery algorithm, which has been demonstrated to be efficient and reliable. Based on the discovered global rewards, a distributed Q-learning algorithm is implemented to minimize the active power loss while satisfying operational constraints. The proposed method does not require accurate system model and can learn from scratch. Simulation studies with power systems of different sizes show that the method is very computationally efficient and able to provide near-optimal solutions. It can be observed that prior knowledge can significantly speed up the learning process and decrease the occurrences of undesirable disturbances. The proposed method has good potential for online implementation.
ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes

A key requirement for leveraging supervised deep learning methods is the availability of large, labeled datasets. Unfortunately, in the context of RGB-D scene understanding, very little data is available &#x2013; current datasets cover a small range of scene views and have limited semantic annotations. To address this issue, we introduce ScanNet, an RGB-D video dataset containing 2.5M views in 1513 scenes annotated with 3D camera poses, surface reconstructions, and semantic segmentations. To collect this data, we designed an easy-to-use and scalable RGB-D capture system that includes automated surface reconstruction and crowdsourced semantic annotation. We show that using this data helps achieve state-of-the-art performance on several 3D scene understanding tasks, including 3D object classification, semantic voxel labeling, and CAD model retrieval.
Design of parallel coupled microstrip bandpass filter for FM Wireless applications

Design, analysis and optimization of a parallel-coupled microstrip bandpass filter for FM Wireless applications is presented in this paper. The filter is designed and optimized at a center frequency of 6 GHz. Half wavelength long resonators and admittance inverters are used to design the filter. A brief description of coupled microstrip lines and immittance inverters is also included. Design equations to compute physical dimensions of the filter are given in the paper. The filter is simulated using ADS (Advanced Design System) design software and implemented on Roger 4003C substrate.
Towards intelligent lower limb wearable robots: Challenges and perspectives - State of the art

Recent technological advances made necessary the use of the robots in various types of applications. Currently, the traditional robot-like scenarios dedicated to industrial applications with repetitive tasks, were replaced by applications which require human interaction. The main field of such applications concerns the rehabilitation and aid of elderly persons. In this study, we present a state-of-the-art of the main research advances in lower limbs actuated orthosis/wearable robots in the literature. This will include a review on researches covering full limb exoskeletons, lower limb exoskeletons and particularly the knee joint orthosis. Rehabilitation using treadmill based device and use of Functional Electrical Stimulation (FES) are also investigated. We discuss finally the challenges not yet solved such as issues related to portability, energy consumption, social constraints and high costs of theses devices.
Criteria of Successful IT Projects from Management's Perspective

The aim of this paper is to compile a model of IT project success from management's perspective. Therefore, a qualitative research approach is proposed by interviewing IT managers on how their companies evaluate the success of IT projects. The evaluation of the survey provides fourteen success criteria and four success dimensions. This paper also thoroughly analyzes which of these criteria the management considers especially important and which ones are being missed in daily practice. Additionally, it attempts to identify the relevance of the discovered criteria and dimensions with regard to the determination of IT project success. It becomes evident here that the old-fashioned Iron Triangle still plays a leading role, but some long-term strategical criteria, such as value of the project, customer perspective or impact on the organization, have meanwhile caught up or pulled even.
An Analysis of Incorporating an External Language Model into a Sequence-to-Sequence Model

Attention-based sequence-to-sequence models for automatic speech recognition jointly train an acoustic model, language model, and alignment mechanism. Thus, the language model component is only trained on transcribed audio-text pairs. This leads to the use of shallow fusion with an external language model at inference time. Shallow fusion refers to log-linear interpolation with a separately trained language model at each step of the beam search. In this work, we investigate the behavior of shallow fusion across a range of conditions: different types of language models, different decoding units, and different tasks. On Google Voice Search, we demonstrate that the use of shallow fusion with an neural LM with wordpieces yields a 9.1% relative word error rate reduction (WERR) over our competitive attention-based sequence-to-sequence model, obviating the need for second-pass rescoring.
Active One-shot Learning

Recent advances in one-shot learning have produced models that can learn from a handful of labeled examples, for passive classification and regression tasks. This paper combines reinforcement learning with one-shot learning, allowing the model to decide, during classification, which examples are worth labeling. We introduce a classification task in which a stream of images are presented and, on each time step, a decision must be made to either predict a label or pay to receive the correct label. We present a recurrent neural network based action-value function, and demonstrate its ability to learn how and when to request labels. Through the choice of reward function, the model can achieve a higher prediction accuracy than a similar model on a purely supervised task, or trade prediction accuracy for fewer label requests.
An ultra-low-power human body motion sensor using static electric field sensing

Wearable sensor systems have been used in the ubiquitous computing community and elsewhere for applications such as activity and gesture recognition, health and wellness monitoring, and elder care. Although the power consumption of accelerometers has already been highly optimized, this work introduces a novel sensing approach which lowers the power requirement for motion sensing by orders of magnitude. We present an ultra-low-power method for passively sensing body motion using static electric fields by measuring the voltage at any single location on the body. We present the feasibility of using this sensing approach to infer the amount and type of body motion anywhere on the body and demonstrate an ultra-low-power motion detector used to wake up more power-hungry sensors. The sensing hardware consumes only 3.3 μW, and wake-up detection is done using an additional 3.3 μW (6.6 μW total).
Building credit scoring models using genetic programming

Credit scoring models have been widely studied in the areas of statistics, machine learning, and artificial intelligence (AI). Many novel approaches such as artificial neural networks (ANNs), rough sets, or decision trees have been proposed to increase the accuracy of credit scoring models. Since an improvement in accuracy of a fraction of a percent might translate into significant savings, a more sophisticated model should be proposed to significantly improving the accuracy of the credit scoring mode. In this paper, genetic programming (GP) is used to build credit scoring models. Two numerical examples will be employed here to compare the error rate to other credit scoring models including the ANN, decision trees, rough sets, and logistic regression. On the basis of the results, we can conclude that GP can provide better performance than other models. q 2005