nils-herrmann/clean_scirepeval huggingface dataset:
Eigenvector-centrality - a node-centrality?

Networks of social relations can be represented by graphs and socioor adjacency-matrices and their structure can be analyzed using different concepts, one of them called centrality. We will provide a new formalization of a “node-centrality” which leads to some properties a measure of centrality has to satisfy. These properties allow to test given measures, for example measures based on degree, closeness, betweenness or Bonacich’s eigenvector-centrality. It turns out that it depends on normalization whether eigenvector-centrality does satisfy the expected properties or not.
Tank-Like Module-Based Climbing Robot Using Passive Compliant Joints

This paper proposes an underactuated modular climbing robot with flat dry elastomer adhesives. This robot is designed to achieve high speed, high payload, and dexterous motions that are typical drawbacks of previous climbing robots. Each module is designed as a tread-wheeled mechanism to simultaneously realize high speed and high adhesive force. Two modules are connected by compliant joints, which induce a positive preload on the front wheels resulting in stable climbing and high payload capacity. Compliant joints also help the robot to perform various transitions. An active tail is adopted to regulate the preload of the second module. Force transfer equations are derived and stable operating conditions are verified. The stiffness coefficients of the compliant joints and the active tail force are determined optimally to satisfy the constraints of stable operation. The prototype two-module robot achieves 6-cm/s speed and 500-g payload capacity on vertical surfaces. The abilities of flat surface locomotion, internal, external, and thin-wall transitions, and overcoming various sized obstacles are validated through experiment. The principle of joint compliance can be adopted in other climbing robots to enhance their stability and transition capability.
The impact of machine translation error types on post-editing effort indicators

In this paper, we report on a post-editing study fo r general text types from English into Dutch conducted with master's students of translati on. We used a fine-grained machine translation (MT) quality assessment method with err or weights that correspond to severity levels and are related to cognitive load. Linear mi xed effects models are applied to analyze the impact of MT quality on potential post-editing effort indicators. The impact of MT quality is evaluated on three different levels, each wi th an increasing granularity. We find that MT quality is a significant predictor of all differ nt types of post-editing effort indicators and that different types of MT errors predict diffe rent post-editing effort indicators.
A Hygroscopic Sensor Electrode for Fast Stabilized Non-Contact ECG Signal Acquisition

A capacitive electrocardiography (cECG) technique using a non-invasive ECG measuring technology that does not require direct contact between the sensor and the skin has attracted much interest. The system encounters several challenges when the sensor electrode and subject's skin are weakly coupled. Because there is no direct physical contact between the subject and any grounding point, there is no discharge path for the built-up electrostatic charge. Subsequently, the electrostatic charge build-up can temporarily contaminate the ECG signal from being clearly visible; a stabilization period (3-15 min) is required for the measurement of a clean, stable ECG signal at low humidity levels (below 55% relative humidity). Therefore, to obtain a clear ECG signal without noise and to reduce the ECG signal stabilization time to within 2 min in a dry ambient environment, we have developed a fabric electrode with embedded polymer (FEEP). The designed hygroscopic FEEP has an embedded superabsorbent polymer layer. The principle of FEEP as a conductive electrode is to provide humidity to the capacitive coupling to ensure strong coupling and to allow for the measurement of a stable, clear biomedical signal. The evaluation results show that hygroscopic FEEP is capable of rapidly measuring high-accuracy ECG signals with a higher SNR ratio.
Supervised distance metric learning through maximization of the Jeffrey divergence

Over the past decades, distance metric learning has attracted a lot of interest in machine learning and related fields. In this work, we propose an optimization framework for distance metric learning via linear transformations by maximizing the Jeffrey divergence between two multivariate Gaussian distributions derived from local pairwise constraints. In our method, the distance metric is trained on positive and negative difference spaces, which are built from the neighborhood of each training instance, so that the local discriminative information is preserved. We show how to solve this problem with a closed-form solution rather than using tedious optimization procedures. The solution is easy to implement, and tractable for large-scale problems. Experimental results are presented for both a linear and a kernelized version of the proposed method for k-nearest neighbors classification. We obtain classification accuracies superior to the state-of-theart distance metric learning methods in several cases while being competitive in others.
Controller design and fault tolerance analysis of 4-phase floating interleaved boost converter for fuel cell electric vehicles

Owing to the energy shortage and the increasingly serious environmental pollution, fuel cell electric vehicles (FCEV) with zero-emission and high-efficiency have been expected to be the most potential candidate to substitute the conventional vehicles. The DC/DC converter is the interface between the fuel cell (FC) and the driveline of FCEV. It not only needs high voltage gain to convert the wide FC voltage into an appropriate voltage level, but also needs the capacity of fault tolerance to enhance the reliability of the system. For this reason, floating interleaved boost converters (FIBC) seem to be the optimal selection. Despite this topology can continue operating without interruption under the action of the proper control scheme in the case of power switch open circuit fault (OCF), operating in degraded mode has adverse impacts on the component stress and the input current ripple. Hence, this paper aims to design an effective controller to maintain the dc bus voltage constant and to demonstrate thorough theoretical analysis and simulation verification of these undesirable effects.
Domain Adaptation for Relation Extraction with Domain Adversarial Neural Network

Relations are expressed in many domains such as newswire, weblogs and phone conversations. Trained on a source domain, a relation extractor’s performance degrades when applied to target domains other than the source. A common yet labor-intensive method for domain adaptation is to construct a target-domainspecific labeled dataset for adapting the extractor. In response, we present an unsupervised domain adaptation method which only requires labels from the source domain. Our method is a joint model consisting of a CNN-based relation classifier and a domain-adversarial classifier. The two components are optimized jointly to learn a domain-independent representation for prediction on the target domain. Our model outperforms the state-of-the-art on all three test domains of ACE 2005.
Real-time 3 D Camera Tracking for Industrial Augmented Reality Applications

In this paper we present a new solution for real-time 3D camera pose estimation for Augmented Reality (AR) applications. The tracking system does not require special engineering of the environment, such as placing markers or beacons. The required input data are a CAD model of the target object to be tracked, and a calibrated reference image of it. We consider the whole process of camera tracking, and developed both an autonomous initialization and a real-time tracking procedure. The system is robust to abrupt camera motions, strong changes of the lighting conditions and partial occlusions. To avoid typical jitter and drift problems the tracker performs feature matching not only in an iterative manner, but also against stable reference features, which are dynamically cached in case of high confidence. We present experimental results generated with help of synthetic ground truth, real off-line and on-line image sequences using different types of target objects.
On-Line Analytical Processing

On-line analytical processing (OLAP) describes an approach to decision support, which aims to extract knowledge from a data warehouse, or more specifically, from data marts. Its main idea is providing navigation through data to non-expert users, so that they are able to interactively generate ad hoc queries without the intervention of IT professionals. This name was introduced in contrast to on-line transactional processing (OLTP), so that it reflected the different requirements and characteristics between these classes of uses. The concept falls in the area of business intelligence.
Deringing cartoons by image analogies

In this article, we propose a novel method to reduce ringing artifacts in BDCT-encoded cartoon images using image analogies. The quantization procedure of BDCT compression (such as JPEG and MPEG) introduces annoying visual artifacts. Our main focus is on the removal of ringing artifacts that is seldom addressed by existing methods. In the proposed method, the contaminated image is modeled as a Markov random field (MRF). We “learn” the behavior of contamination by extracting massive numbers of artifact patterns from a training set, and organizing them using tree-structured vector quantization (TSVQ). Instead of postfiltering the input contaminated image, we synthesize an artifact-reduced image. Our method is noniterative and hence, can remove artifacts within a very short period of time. We show that substantial improvement is achieved using the proposed method in terms of visual quality and statistics.
Trainable frontend for robust and far-field keyword spotting

Robust and far-field speech recognition is critical to enable true hands-free communication. In far-field conditions, signals are attenuated due to distance. To improve robustness to loudness variation, we introduce a novel frontend called per-channel energy normalization (PCEN). The key ingredient of PCEN is the use of an automatic gain control based dynamic compression to replace the widely used static (such as log or root) compression. We evaluate PCEN on the keyword spotting task. On our large rerecorded noisy and far-field eval sets, we show that PCEN significantly improves recognition performance. Furthermore, we model PCEN as neural network layers and optimize high-dimensional PCEN parameters jointly with the keyword spotting acoustic model. The trained PCEN frontend demonstrates significant further improvements without increasing model complexity or inference-time cost.
Deep Learning-Based Multimedia Analytics: A Review

The multimedia community has witnessed the rise of deep learning–based techniques in analyzing multimedia content more effectively. In the past decade, the convergence of deep-learning and multimedia analytics has boosted the performance of several traditional tasks, such as classification, detection, and regression, and has also fundamentally changed the landscape of several relatively new areas, such as semantic segmentation, captioning, and content generation. This article aims to review the development path of major tasks in multimedia analytics and take a look into future directions. We start by summarizing the fundamental deep techniques related to multimedia analytics, especially in the visual domain, and then review representative high-level tasks powered by recent advances. Moreover, the performance review of popular benchmarks gives a pathway to technology advancement and helps identify both milestone works and future directions.
Modeling and Control of a Small Autonomous Aircraft Having Two Tilting Rotors

This paper presents recent work concerning a small tiltrotor aircraft with a reduced number of rotors. The design consists of two propellers which can tilt laterally and longitudinally. A model of the full birotor dynamics is provided, and a controller based on the backstepping procedure is synthesized for the purposes of stabilization and trajectory tracking. The proposed control strategy has been tested in simulation
Drag and drop the apple: the semantic weight of words and images in touch-based interaction

In this paper we report a user study to investigate the effect of semantic weight in a touch-based drag and drop task. The study was motivated by our own interest in exploring potential factors that influence touch behavior and supported by results in related neuroscience research. The question we intended to answer is: "Do people drag the representation of a smaller and lighter real world object (e.g. an apple) different than the representation of a heavier and larger real world object (e.g. a car)?". Participants were asked to perform a drag and drop task repeatedly on a tablet device. Dragged objects were the same physical size on screen, but represented real world objects that were either heavy and large or light and small. We studied two representation modalities (i.e. image and text). In both representation modalities, semantically heavier objects were dragged significantly faster than semantically lighter objects.
Classical grasp quality evaluation: New algorithms and theory

This paper investigates theoretical properties of a well-known L<sup>1</sup> grasp quality measure Q whose approximation Q<sup>-</sup><sub>l</sub> is commonly used for the evaluation of grasps and where the precision of Q<sup>-</sup><sub>l</sub> depends on an approximation of a cone by a convex polyhedral cone with l edges. We prove the Lipschitz continuity of Q and provide an explicit Lipschitz bound that can be used to infer the stability of grasps lying in a neighbourhood of a known grasp. We think of Q<sup>-</sup><sub>l</sub> as a lower bound estimate to Q and describe an algorithm for computing an upper bound Q<sup>+</sup>. We provide worst-case error bounds relating Q and Q<sup>-</sup><sub>l</sub>. Furthermore, we develop a novel grasp hypothesis rejection algorithm which can exclude unstable grasps much faster than current implementations. Our algorithm is based on a formulation of the grasp quality evaluation problem as an optimization problem, and we show how our algorithm can be used to improve the efficiency of sampling based grasp hypotheses generation methods.
On Inferring Browsing Activity on Smartphones via USB Power Analysis Side-Channel

In this paper, we show that public USB charging stations pose a significant privacy risk to smartphone users even when no data communication is possible between the station and the user’s mobile device. We present a side-channel attack that allows a charging station to identify which Webpages are loaded while the smartphone is charging. To evaluate this side-channel, we collected power traces of Alexa top 50 Websites on multiple smartphones under several conditions, including battery charging level, browser cache enabled/disabled, taps on the screen, Wi-Fi/LTE, TLS encryption enabled/disabled, time elapsed between collection of training and testing data, and location of the Website. The results of our evaluation show that the attack is highly successful: in many settings, we were able to achieve over 90% Webpage identification accuracy. On the other hand, our experiments also show that this side-channel is sensitive to some of the aforementioned conditions. For instance, when training and testing traces were collected 70 days apart, accuracies were as low as 2.2%. Although there are studies that show that power-based side-channels can predict browsing activity on laptops, this paper is unique, because it is the first to study this side-channel on smartphones, under smartphone-specific constraints. Further, we demonstrate that Websites can be correctly identified within a short time span of  $2\times6$  seconds, which is in contrast with prior work, which uses 15-s traces. This is important, because users typically spend less than 15 s on a Webpage.
Robust Passivity-Based Control of a Buck–Boost-Converter/DC-Motor System: An Active Disturbance Rejection Approach

This paper presents an active disturbance rejection (ADR) approach for the control of a buck-boost-converter feeding a dc motor. The presence of arbitrary, time-varying, load torque inputs on the dc motor and the lack of direct measurability of the motor's angular velocity variable prompts a generalized proportional integral (GPI) observer-based ADR controller which is synthesized on the basis of passivity considerations. The GPI observer simultaneously estimates the angular velocity and the exogenous disturbance torque input in an on-line cancellation scheme, known as the ADR control. The proposed control scheme is thus a sensorless one with robustness features added to the traditional energy shaping plus damping injection methodology. The discrete switching control realization of the designed continuous feedback control law is accomplished by means of a traditional PWM-modulation scheme. Additionally, an input to state stability property of the closed-loop system is established. Experimental and simulation results are provided.
Trends and Lessons from Three Years Fighting Malicious Extensions

In this work we expose wide-spread efforts by criminals to abuse the Chrome Web Store as a platform for distributing malicious extensions. A central component of our study is the design and implementation of WebEval, the first system that broadly identifies malicious extensions with a concrete, measurable detection rate of 96.5%. Over the last three years we detected 9,523 malicious extensions: nearly 10% of every extension submitted to the store. Despite a short window of operation—we removed 50% of malware within 25 minutes of creation— a handful of under 100 extensions escaped immediate detection and infected over 50 million Chrome users. Our results highlight that the extension abuse ecosystem is drastically different from malicious binaries: miscreants profit from web traffic and user tracking rather than email spam or banking theft.
Comparative Study of CNN and LSTM based Attention Neural Networks for Aspect-Level Opinion Mining

Aspect-level opinion mining aims to find and aggregate opinions on opinion targets. Previous work has demonstrated that precise modeling of opinion targets within the surrounding context can improve performances. However, how to effectively and efficiently learn hidden word semantics and better represent targets and the context still needs to be further studied. In this paper, we propose and compare two interactive attention neural networks for aspect-level opinion mining, one employs two bi-directional Long-Short-Term-Memory (BLSTM) and the other employs two Convolutional Neural Networks (CNN). Both frameworks learn opinion targets and the context respectively, followed by an attention mechanism that integrates hidden states learned from both the targets and context. We compare our model with state-of-the-art baselines on two SemEval 2014 datasets1. Experiment results show that our models obtain competitive performances against the baselines on both datasets. Our work contributes to the improvement of state-of-the-art aspect-level opinion mining methods and offers a new approach to support human decision-making process based on opinion mining results. The quantitative and qualitative comparisons in our work aim to give basic guidance for neural network selection in similar tasks.
Habitat suitability analysis for hippopotamus ( H . amphibious ) using GIS and remote sensing in Lake Tana and its environs , Ethiopia

This research was carried out from October 2013 to May 2014. Hippopotamus amphibious is a mammalian species distributed in different lakes and rivers where ecological requirements are fulfilled for its survival. Lake Tana and its environs are home to Hippopotamus amphibious. The species is identified as vulnerable worldwide due to habitat loss and poaching. However, despite its vulnerability, there is no research conducted regarding the species, and its environmental requirements in Ethiopia. Therefore, the main objective of this study was to carryout habitat suitability analysis and find out suitable habitat sites of hippopotamus within the Lake Tana and its environs using the integration of GIS and remote sensing techniques. The softwares such as, Arc GIS10.2, ERDAS IMAGINE2010, and Virtual satellite image downloader were used in this research. The data used were SPOT image of 2012 of the study area, bathymetric data of Lake Tana, DEM, Google Earth data and GCP. Running a suitability model requires estimation of weights by expertise for each individual criterion on GIS software. Thus, the habitats in Lake Tana and its environs ranging from most suitable to not suitable for hippopotamus were identified. It was shown that 50.88% of the areas under study was highly disturbed and became unsuitable to hippopotamus, 42.29% of the areas were moderately disturbed, and only 1.81% of the areas were revealed to be undisturbed. As the study result showed that in and around Lake Tana, a human factor was considered to be outweighing the physical factors to minimize the habitat for the aforementioned animal. The results revealed that only 22.54% of the study areas were identified as most suitable for the animal under study of which the large portions of the areas are located at the backside of settlements which are not easily accessible by the species, while 40.5% of the areas were found to be moderately suitable, and 36.96% were unsuitable habitats for hippopotamus. Based on the findings of the present study it was concluded that there was high interference of human being in the habitats of hippopotamus especially at the shores of the lake since the land were looked-for agricultural activities. Therefore, too much proximity of human activities in identified hippopotamus habitats have to be protected and conservation buffer surrounding the Lake has to be developed.
An Ultrasonic Transducer Interface IC With Integrated Push-Pull 40 Vpp, 400 mA Current Output, 8-bit DAC and Integrated HV Multiplexer

We present an ultrasonic transducer interface IC that includes an 8-bit, 40 Vpp, 400 mA current output DAC for arbitrary waveform transducer excitation and a ±25 V analog multiplexer. The IC was fabricated using a 0.35 μm, 50 V CMOS process. The design eliminates the need for an external power amplifier as the piezoelectric transducer is driven directly from a segmented push-pull current output DAC, which simplifies the overall system design. This approach has the advantage of simple and rapid glitch-free power-up/down, which is especially important in integrated high-output-power drivers. The DAC has been evaluated when operating at a 150 MHz sampling rate with a ±400 mA output current and a 50 Ω load. Measured performance includes 37 dB SNDR and 46 dB SFDR at 10 MHz output frequency. By implementing an additional reference DAC and extending the receiver isolation switch into an analog multiplexer, we enable on-line calibration for the purpose of reducing the driver and receiver signal path uncertainty. Measurements show a greater than ten-fold improvement in delay uncertainty to approximately 20 ps for temperature variations of 0 to 70 degrees Celsius.