nils-herrmann/clean_scirepeval huggingface dataset:
Direct and realistic handover of a virtual object

We propose a method to induce a realistic two-hand manipulation of virtual objects by representing their reactions to multiple contacts of hands and fingers in a unified way. One of the key problems in achieving physics-based virtual interaction is that there is no accurate haptic feedback to the user's hands and fingers, which must introduce a mismatch between the real hands and their virtual counterparts. This problem becomes more serious when multiple hands or fingers are involved to a single virtual object, for example, a hand-over situation from one hand to the other. The key contribution of this paper is to represent and describe the physical status of a virtual object in the local object space and to change the status in the object point of view in order to avoid its drastic changes by the physical-virtual mismatch. The experiments compare the performance of the proposed method with objects in different shapes when using 2D and 3D visualization devices.
Evaluating intrusion detection systems: the 1998 DARPA off-line intrusion detection evaluation

A intrusion detection evaluation test bed was developed which generated normal traffic similar to that on a government site containing 100’s of users on 1000’s of hosts. More than 300 instances of 38 different automated attacks were launched against victim UNIX hosts in seven weeks of training data and two weeks of test data. Six research groups participated in a blind evaluation and results were analyzed for probe, denialof-service (DoS), remote-to-local (R2L), and user to root (U2R) attacks. The best systems detected old attacks included in the training data, at moderate detection rates ranging from 63% to 93% at a false alarm rate of 10 false alarms per day. Detection rates were much worse for new and novel R2L and DoS attacks included only in the test data. The best systems failed to detect roughly half these new attacks which included damaging access to root-level privileges by remote users. These results suggest that further research should focus on developing techniques to find new attacks instead of extending existing rule-based
Birds of a ‘ ‘ bad ’ ’ feather flock together : The Dark Triad and mate choice

Previous research on the Dark Triad traits (i.e., Machiavellianism, psychopathy, and narcissism) has focused solely on the role the Dark Triad traits played in mate choice of actors. The current study (N = 336) extends this by manipulating the apparent levels of Dark Triad traits in targets and correlating mate choice in these targets with individual differences in the Dark Triad traits in actors. As expected, both sexes preferred partners low in the Dark Triad traits for long-term mating, while those high in these traits were preferred for one-night stands. However, women high in psychopathy considered the Dark Triad traits in potential male partners more physically attractive and desirable for an one-night stand, as well as a potential husband. Men who were high on psychopathy were likewise attracted to psychopathy in potential mothers. Our findings are discussed from an evolutionary personality paradigm.
Car-Park Management using Wireless Sensor Networks

A complete wireless sensor network solution for carpark management is presented in this paper. The system architecture and design are first detailed, followed by a description of the current working implementation, which is based on our DSYS25z sensing nodes. Results of a series of real experimental tests regarding connectivity, sensing and network performance are then discussed. The analysis of link characteristics in the car-park scenario shows unexpected reliability patterns which have a strong influence on MAC and routing protocol design. Two unexpected link reliability patterns are identified and documented. First, the presence of the objects (cars) being sensed can cause significant interference and degradation in communication performance. Second, link quality has a high temporal correlation but a low spatial correlation. From these observations we conclude that a) the construction and maintenance of a fixed topology is not useful and b) spatial rather than temporal message replicates can improve transport reliability
The Skyline Operator

We propose to extend database systems by a Skyline operation. This operation $filters out a set of interesting points from a potentially large set of data points. A point is interesting if it is not dominated by any other point. For example, a hotel might be interesting for somebody traveling to Nassau if no other hotel is both cheaper and closer to the beach. We show how SQL can be extended to pose Skyline queries, present and evaluate alternative algorithms to implement the Skyline operation, and show how this operation can be combined with other database operations, e.g., join.
An efficient DHT-based elastic SDN controller

Elasticity in distributed SDN Controllers , ,  improves fault tolerance, power efficiency, cost efficiency and scalability. We find that the Elasticon  algorithm has high time complexity since it balances the load equally on all the controllers for every event. In this paper, we propose an efficient algorithm for elastic controllers using Chord , a Distributed Hash Table (DHT). In our scheme, we do not balance the load on the controllers until a controller is overloaded. The switch ID space forms the ring in the DHT. The controller is assigned an ID based on the range of switch IDs it is responsible for which changes as the switches migrate from an overloaded controller to its adjacent controllers in the ring. If the average load on the controllers falls below a given threshold, we consolidate the switch allocation to controllers so that some of the controllers can be powered off. We compared the performance of Elasticon and the proposed algorithm in terms of the number of migrations needed, the average number of controllers that are powered on, the time taken to run the algorithm and the standard deviation of the load on the controllers. We find that the number of controllers is similar for our algorithm and Elasticon, the migrations are fewer in our algorithm while the time taken by our algorithm is orders of magnitude less than Elasticon.
Weakly-supervised text-to-speech alignment confidence measure

This work proposes a new confidence measure for evaluating text-to-speech alignment systems outputs, which is a key component for many applications, such as semi-automatic corpus anonymization, lips syncing, film dubbing, corpus preparation for speech synthesis and speech recognition acoustic models training. This confidence measure exploits deep neural networks that are trained on large corpora without direct supervision. It is evaluated on an open-source spontaneous speech corpus and outperforms a confidence score derived from a state-of-the-art text-to-speech aligner. We further show that this confidence measure can be used to fine-tune the output of this aligner and improve the quality of the resulting alignment.
Fixed budget quantized kernel least-mean-square algorithm

We present a quantization-based kernel least mean square (QKLMS) algorithm with a fixed memory budget. In order to deal with the growing support inherent in online kernel methods, the proposed method utilizes a growing and pruning combined technique and defines a criterion, significance, based on weighted statistical contribution of a data center. This method doesn’t need any apriori information and its computational complexity is acceptable, linear with the center number. As we show theoretically and experimentally, the introduced algorithm successfully quantifies the least ‘significant’ datum and preserves the most important ones resulting in less system error.
Policy Gradient Methods for Reinforcement Learning with Function Approximation and Action-Dependent Baselines

1 Notation and Background We assume that the reader is familiar with the seminal paper by Sutton et al. (2000), which shows how the policy gradient theorem (Baxter and Bartlett, 1999) can be extended to include function approximation and action-independent baselines. Our paper is intended to be read immediately after reviewing Section 3 of the paper by Sutton et al. (2000). Although here we adopt the episodic setting, the extension of this work to the average reward setting is straightforward. We use the notational standard MDPNv1 (Thomas and Okal, 2015) and the following additional definitions, where expectations conditioned on θ denote that actions, At, are sampled from π(St, ·, θ) unless otherwise specified:
Introduction to the special section on educational data mining

Educational Data Mining (EDM) is an emerging multidisciplinary research area, in which methods and techniques for exploring data originating from various educational information systems have been developed. EDM is both a learning science, as well as a rich application area for data mining, due to the growing availability of educational data. EDM contributes to the study of how students learn, and the settings in which they learn. It enables data-driven decision making for improving the current educational practice and learning material. We present a brief overview of EDM and introduce four selected EDM papers representing a crosscut of different application areas for data mining in education.
Adaptive Replacement Strategies for MOEA/D

Multiobjective evolutionary algorithms based on decomposition (MOEA/D) decompose a multiobjective optimization problem into a set of simple optimization subproblems and solve them in a collaborative manner. A replacement scheme, which assigns a new solution to a subproblem, plays a key role in balancing diversity and convergence in MOEA/D. This paper proposes a global replacement scheme which assigns a new solution to its most suitable subproblems. We demonstrate that the replacement neighborhood size is critical for population diversity and convergence, and develop an approach for adjusting this size dynamically. A steady-state algorithm and a generational one with this approach have been designed and experimentally studied. The experimental results on a number of test problems have shown that the proposed algorithms have some advantages.
The Virtual Computing Lab (VCL): An Open Source Cloud Computing Solution Designed Specifically for Education and Research

In this paper, we describe the Virtual Computing Lab (VCL) with its main features and services. Also, we introduce the recent advances of the VCL system and its usage in research and education. The VCL is a cloud computing system that has been optimized for the educational services and research needs of the academic community. VCL is an open source cloud orchestration stack with a self-service portal that currently supports a large number of customers and commercial cloud, or cloud-related services and solutions. It was developed by NCSU with support from IBM Corporation. VCLs promise to support researchers and students in all academic levels to fulfill all their computing needs. In addition to supporting students and faculty members at NC State University and other UNC System universities, the NC VCL now also supports students at several NC community colleges. Also, we introduced cloud computing and service science related activities and achievements at Jordan University of Science and Technology.
Graphical models for text mining: knowledge extraction and performance estimation

This thesis would not have been possible unless the constant support of my advisor Professor Fabio Stella. He gave me suggestions, advices and a constant incentive to learn and deeply understand how things works. Special thanks go to Professor Enrico Fagiuoli for its suggestions and its huge knowledge of probabilistic models. internship has been a precious occasion to learn and discuss and to find new friends. Special thanks go my colleagues at DISCo that shared the highs and lows of my Ph.D. In particular, i thank my laboratory colleague Elena Gatti for our discussion and friendship and all the other Ph.D students for the discussion and fun moments. I thanks all the administrative personnel at the department, without them a Ph.D student life is hell. Thanks to all the student who worked with me during this years, part of the work in this thesis has been developed thanks to their contribution. It is a pleasure to thank those who made my Ph.D and thus my thesis possible: Docflow S.p.A. They gave me the possibility to get a sense of business world, their needs and to propose solutions. A special thanks go to all who have tolerated my obsessions and complaints during this years: my family, my friends and Manoela.
Adaptive Mobile Robot Navigation and Mapping

The task of building a map of an unknown environment and concurrently using that map to navigate is a central problem in mobile robotics research. This paper addresses the problem of how to perform concurrent mapping and localization (CML) adaptively using sonar. Stochastic mapping is a feature-based approach to CML that generalizes the extended Kalman lter to incorporate vehicle localization and environmental mapping. We describe an implementation of stochastic mapping that uses a delayed nearest neighbor data association strategy to initialize new features into the map, match measurements to map features, and delete out-of-date features. We introduce a metric for adaptive sensing which is de ned in terms of Fisher information and represents the sum of the areas of the error ellipses of the vehicle and feature estimates in the map. Predicted sensor readings and expected dead-reckoning errors are used to estimate the metric for each potential action of the robot, and the action which yields the lowest cost (i.e., the maximum information) is selected. This technique is demonstrated via simulations, in-air sonar experiments, and underwater sonar experiments. Results are shown for 1) adaptive control of motion and 2) adaptive control of motion and scanning. The vehicle tends to explore selectively di erent objects in the environment. The performance of this adaptive algorithm is shown to be superior to straight-line motion and random motion.
Design of an FMCW Radar Altimeter for Wide-Range and Low Measurement Error

This paper presents the design of a frequency-modulated continuous-wave radar altimeter with wide altitude range and low measurement error. A wide altitude range is attained by employing an optical delay in the path of transmission to reduce the dynamic range of the altitude being measured. The transmitter power and receiver gain are also controlled to facilitate a reduction in the dynamic range of the received power. In addition, low measurement error was obtained by improving the sweep linearity using a direct digital synthesizer and minimizing phase noise by employing a phase-locked loop with the offset frequency driven by a reference clock (Ref_CLK). The performance of the radar altimeter was experimentally verified using an optical-delay simulator, which provided a delay time corresponding to a 200-m radar distance. To assess the performance of the system in a realistic setting, the radar altimeter was tested using a crane setup. The crane test demonstrated that the design described in this paper resulted in a reduced measurement-error rate.
Real-time air quality monitoring through mobile sensing in metropolitan areas

Traditionally, pollution measurements are performed using expensive equipment at fixed locations or dedicated mobile equipment laboratories. This is a coarse-grained and expensive approach where the pollution measurements are few and far in-between. In this paper, we present a vehicular-based mobile approach for measuring fine-grained air quality in real-time. We propose two cost effective data farming models -- one that can be deployed on public transportation and the second a personal sensing device. We present preliminary prototypes and discuss implementation challenges and early experiments.
PEDESTRIAN AND NON-MOTOR VEHICLE DETECTION SYSTEMS WITH LIDAR FOR AUTONOMOUS VEHICLES

The purpose of this paper is to examine the use of Light Detection And Ranging (LiDAR) in obstacle detection systems in autonomous vehicles (AVs). Particular attention will be given to methods being developed to detect nonmotorized bodies such as pedestrians and cyclists due to unique variables they present compared to other vehicles. Due to the increasing societal and professional relevance of AV development, we examine LiDAR’s current role in the development of pedestrian detection systems in autonomous vehicles as well as its implications for sustainable development. We begin our paper with an overview of AV development and the role of LiDAR within it. We then survey currently-proposed LiDAR-based detection methods for nonmotorized bodies and critically examine the potential technical problems associated within. This is followed by an examination of machine-learning techniques being implemented to decrease errors in detection. Waymo, formerly Google’s self-driving car, is used as a case study. We conclude with a discussion of current technical, social, and ethical problems associated with AVs and the possible solutions to reach a qualified position on LiDAR’s role in future AV development.
INSTITUT FÜR INFORMATIK Continuous Monitoring of Software Services : Design and Application of the Kieker Framework

In addition to studying the construction and evolution of software services, the software engineering discipline needs to address the operation of continuously running software services. A requirement for its robust operation are means for effective monitoring of software runtime behavior. In contrast to profiling for construction activities, monitoring of operational services should only impose a small performance overhead. Furthermore, instrumentation should be non-intrusive to the business logic, as far as possible. We present the Kieker framework for monitoring software runtime behavior, e.g., internal performance or (distributed) trace data. The flexible architecture allows to replace or add framework components, including monitoring probes, analysis components, and monitoring record types shared by logging and analysis. As a non-intrusive instrumentation technique, Kieker currently employs, but is not restricted to, aspect-oriented programming. An extensive lab study evaluates and quantifies the low overhead caused by the framework components. Qualitative evaluations provided by industrial case studies demonstrate the practicality of the approach with a telecommunication customer self service and a digital photo submission service. Kieker is available as open-source software, where both the academic and industrial partners contribute to the code. Our experiment data is publicly available, allowing interested researchers to repeat and extend our lab experiments.
Improved Multimodal Deep Learning with Variation of Information

Deep learning has been successfully applied to multimodal representation learning problems, with a common strategy of learning joint representations that are shared across multiple modalities on top of layers of modality-specific networks. Nonetheless, there still remains a question about how to effectively learn associations between heterogeneous data modalities; in particular, a good generative model of multimodal data should be able to reason about missing data modality given the rest of data modalities. In this paper, we propose a novel multimodal representation learning framework that explicitly aims at this goal by training the model to minimize the variation of information rather than maximizing likelihood. We provide a theoretical insight into why the proposed learning objective is sufficient to estimate the data-generating joint distribution of multimodal data. We apply our method to restricted Boltzmann machines and introduce learning algorithms based on contrastive divergence and multi-prediction training. Further, we extend our method to deep networks with recurrent encoding for finetuning. In experiments, we demonstrate the state-of-the-art visual recognition performance on MIR-Flickr and PASCAL VOC2007 database with and without text observations.
Combining Machine Learning Techniques and Natural Language Processing to Infer Emotions Using Spanish Twitter Corpus

In the recent years, microblogging services, as Twitter, have become a popular tool for expressing feelings, opinions, broadcasting news, and communicating with friends. Twitter users produced more than 340 million tweets per day which may be consider a rich source of user information. We take a supervised approach to the problem, but leverage existing hashtags in Twitter for building our training data. Finally, we tested the Spanish emotional corpus applying two different machine learning algorithms for emotion identification reaching about 65% accuracy.
Improving Human Action Recognition through Hierarchical Neural Network Classifiers

Automatic understanding of videos is one of the complex problems in machine learning and computer vision. An important area in the field of video analysis is human action recognition (HAR). Though a large number of HAR systems have already been developed, there is plenty of daily life actions that are difficult to recognize, due to several reasons, such as recording on different devices, poor video quality and similarities among actions. Development in the field of deep learning, especially in convolutional neural networks (CNN), has provided us with methods that are well-suited for the tasks of image and video recognition. This work implements a CNN-based hierarchical recognition approach to recognize 20 most difficult-to-recognize actions from the Kinetics dataset. Experimental results have shown that the application of our method significantly improves the quality of recognition for these actions.
An Operating System for the Home

Network devices for the home such as remotely controllable locks, lights, thermostats, cameras, and motion sensors are now readily available and inexpensive. In theory, this enables scenarios like remotely monitoring cameras from a smartphone or customizing climate control based on occupancy patterns. However, in practice today, such smarthome scenarios are limited to expert hobbyists and the rich because of the high overhead of managing and extending current technology. We present HomeOS, a platform that bridges this gap by presenting users and developers with a PC-like abstraction for technology in the home. It presents network devices as peripherals with abstract interfaces, enables cross-device tasks via applications written against these interfaces, and gives users a management interface designed for the home environment. HomeOS already has tens of applications and supports a wide range of devices. It has been running in 12 real homes for 4–8 months, and 42 students have built new applications and added support for additional devices independent of our efforts.
The challenges of building mobile underwater wireless networks for aquatic applications

The large-scale mobile underwater wireless sensor network (UWSN) is a novel networking paradigm to explore aqueous environments. However, the characteristics of mobile UWSNs, such as low communication bandwidth, large propagation delay, floating node mobility, and high error probability, are significantly different from ground-based wireless sensor networks. The novel networking paradigm poses interdisciplinary challenges that will require new technological solutions. In particular, in this article we adopt a top-down approach to explore the research challenges in mobile UWSN design. Along the layered protocol stack, we proceed roughly from the top application layer to the bottom physical layer. At each layer, a set of new design intricacies is studied. The conclusion is that building scalable mobile UWSNs is a challenge that must be answered by interdisciplinary efforts of acoustic communications, signal processing, and mobile acoustic network protocol design.