nils-herrmann/clean_scirepeval huggingface dataset:
Nonlinear observers for predicting state-of-charge and state-of-health of lead-acid batteries for hybrid-electric vehicles

This paper describes the application of state-estimation techniques for the real-time prediction of the state-of-charge (SoC) and state-of-health (SoH) of lead-acid cells. Specifically, approaches based on the well-known Kalman Filter (KF) and Extended Kalman Filter (EKF), are presented, using a generic cell model, to provide correction for offset, drift, and long-term state divergence-an unfortunate feature of more traditional coulomb-counting techniques. The underlying dynamic behavior of each cell is modeled using two capacitors (bulk and surface) and three resistors (terminal, surface, and end), from which the SoC is determined from the voltage present on the bulk capacitor. Although the structure of the model has been previously reported for describing the characteristics of lithium-ion cells, here it is shown to also provide an alternative to commonly employed models of lead-acid cells when used in conjunction with a KF to estimate SoC and an EKF to predict state-of-health (SoH). Measurements using real-time road data are used to compare the performance of conventional integration-based methods for estimating SoC with those predicted from the presented state estimation schemes. Results show that the proposed methodologies are superior to more traditional techniques, with accuracy in determining the SoC within 2% being demonstrated. Moreover, by accounting for the nonlinearities present within the dynamic cell model, the application of an EKF is shown to provide verifiable indications of SoH of the cell pack.
A Hybrid Model for Identity Obfuscation by Face Replacement

As more and more personal photos are shared and tagged in social media, avoiding privacy risks such as unintended recognition, becomes increasingly challenging. We propose a new hybrid approach to obfuscate identities in photos by head replacement. Our approach combines state of the art parametric face synthesis with latest advances in Generative Adversarial Networks (GAN) for data-driven image synthesis. On the one hand, the parametric part of our method gives us control over the facial parameters and allows for explicit manipulation of the identity. On the other hand, the data-driven aspects allow for adding fine details and overall realism as well as seamless blending into the scene context. In our experiments we show highly realistic output of our system that improves over the previous state of the art in obfuscation rate while preserving a higher similarity to the original image content.
A passive-mixer-first receiver with baseband-controlled RF impedance matching, ≪ 6dB NF, and ≫ 27dBm wideband IIP3

A software-defined radio (SDR) ideally allows all of the parameters of the radio to be programmed dynamically. SDRs so far have shown flexibility in bandwidth (BW) , local oscillator (LO) frequency [2-4], gain, and modulation type. However, the impedance an RF front-end presents to its antenna has proven difficult to tune . Since antenna impedance can vary significantly across frequency and different environments, a software-controlled input impedance is desirable. Meanwhile, recent work suggests that connecting the antenna directly to a passive mixer without an RF LNA can provide significant benefits, such as extremely low power [6] or greatly increased tuning range and linearity . This paper demonstrates a passive mixer-first SDR with an RF impedance that is controlled by baseband components whose impedance is reflected through the passive mixer to the RF port.
Detecting malicious tweets in trending topics using a statistical analysis of language

0957-4174/$ see front matter 2012  A http://dx.doi.org/10.1016/j.eswa.2012.12.015 ⇑ Corresponding author. E-mail addresses: juaner@lsi.uned.es (J. MartinezAraujo). Twitter spam detection is a recent area of research in which most previous works had focused on the identification of malicious user accounts and honeypot-based approaches. However, in this paper we present a methodology based on two new aspects: the detection of spam tweets in isolation and without previous information of the user; and the application of a statistical analysis of language to detect spam in trending topics. Trending topics capture the emerging Internet trends and topics of discussion that are in everybody’s lips. This growing microblogging phenomenon therefore allows spammers to disseminate malicious tweets quickly and massively. In this paper we present the first work that tries to detect spam tweets in real time using language as the primary tool. We first collected and labeled a large dataset with 34 K trending topics and 20 million tweets. Then, we have proposed a reduced set of features hardly manipulated by spammers. In addition, we have developed a machine learning system with some orthogonal features that can be combined with other sets of features with the aim of analyzing emergent characteristics of spam in social networks. We have also conducted an extensive evaluation process that has allowed us to show how our system is able to obtain an F-measure at the same level as the best state-ofthe-art systems based on the detection of spam accounts. Thus, our system can be applied to Twitter spam detection in trending topics in real time due mainly to the analysis of tweets instead of user accounts.
A visual analytics loop for supporting model development

Threats in cybersecurity come in a variety of forms, and combating such threats involves handling a huge amount of data from different sources. It is absolutely necessary to use algorithmic models to defend against these threats. However, all models are sensitive to deviation from the original contexts in which the models were developed. Hence, it is not really an overstatement to say that `all models are wrong'. In this paper, we propose a visual analytics loop for supporting the continuous development of models during their deployment. We describe the roles of three types of operators (monitors, analysts and modelers), present the visualization techniques used at different stages of model development, and demonstrate the utility of this approach in conjunction with a prototype software system for corporate insider threat detection. In many ways, our environment facilitates an agile approach to the development and deployment of models in cybersecurity.
A Case Study on Implementing False Data Injection Attacks Against Nonlinear State Estimation

Smart grid aims to improve control and monitoring routines to ensure reliable and efficient supply of electricity. The rapid advancements in information and communication technologies of Supervisory Control And Data Acquisition (SCADA) networks, however, have resulted in complex cyber physical systems. This added complexity has broadened the attack surface of power-related applications, amplifying their susceptibility to cyber threats. A particular class of system integrity attacks against the smart grid is False Data Injection (FDI). In a successful FDI attack, an adversary compromises the readings of grid sensors in such a way that errors introduced into estimates of state variables remain undetected. This paper presents an end-to-end case study of how to instantiate real FDI attacks to the Alternating Current (AC) --nonlinear-- State Estimation (SE) process. The attack is realized through firmware modifications of the microprocessor-based remote terminal systems, falsifying the data transmitted to the SE routine, and proceeds regardless of perfect or imperfect knowledge of the current system state. The case study concludes with an investigation of an attack on the IEEE 14 bus system using load data from the New York Independent System Operator (NYISO).
An Ultra-Wideband Horizontally Polarized Omnidirectional Circular Connected Vivaldi Antenna Array

An ultra-wideband horizontally polarized omnidirectional antenna is proposed. The connected Vivaldi antenna array mechanism is exploited to achieve the ultra-wide bandwidths of both the impedance and the radiation patterns simultaneously. Sixteen Vivaldi antenna elements are connected with each other and distributed along a circular substrate to form the proposed antenna. The designed antenna exhibits a relative impedance bandwidth of 159.97% (1.28–11.51 GHz) for <inline-formula> <tex-math notation="LaTeX">$\vert S_{11}\vert <-10$ </tex-math></inline-formula> dB. Meanwhile, uniform omnidirectional radiation patterns with a gain variation of less than 1.5 dB have been obtained within the frequency band of 1.25–7.6 GHz (142.73%).
SMOTE: Synthetic Minority Over-sampling Technique

An approach to the construction of classifiers from imbalanced datasets is described. A dataset is imbalanced if the classification categories are not approximately equally represented. Often real-world data sets are predominately composed of “normal” examples with only a small percentage of “abnormal” or “interesting” examples. It is also the case that the cost of misclassifying an abnormal (interesting) example as a normal example is often much higher than the cost of the reverse error. Under-sampling of the majority (normal) class has been proposed as a good means of increasing the sensitivity of a classifier to the minority class. This paper shows that a combination of our method of over-sampling the minority (abnormal) class and under-sampling the majority (normal) class can achieve better classifier performance (in ROC space) than only under-sampling the majority class. This paper also shows that a combination of our method of over-sampling the minority class and under-sampling the majority class can achieve better classifier performance (in ROC space) than varying the loss ratios in Ripper or class priors in Naive Bayes. Our method of over-sampling the minority class involves creating synthetic minority class examples. Experiments are performed using C4.5, Ripper and a Naive Bayes classifier. The method is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.
The Modeling Process for Stage Models

The purpose of this paper is to present and test a modeling procedure because researchers have struggled for decades to develop stages-of-growth models that are both theoretically founded and empirically validated. This article presents the concept and hypothesis of stages, the history of stage models, and a procedure that may serve as a useful tool in modeling stages of growth. Based on previous research and lessons learned from case study experience of the government sector in Norway, a procedure for the stages of growth modeling process is suggested and demonstrated. The procedure is used for developing a stage model for e-government interoperability. This article provides new insight into issues and challenges faced when engaging in stages of growth research. The paper proposes a new approach to stages of growth modeling. The utility of the suggested procedure is to improve theory building and empirical validation. The contribution to academia is the modeling process that can be applied in future developments of stages of growth. The contribution to practice lies in the stage hypothesis of organizational development over time.
Brain Tumor Detection And Localization In Magnetic Resonance Imaging

A tumor also known as neoplasm is a growth in the abnormal tissue which can be differentiated from the surrounding tissue by its structure. A tumor may lead to cancer, which is a major leading cause of death and responsible for around 13% of all deaths world-wide. Cancer incidence rate is growing at an alarming rate in the world. Great knowledge and experience on radiology are required for accurate tumor detection in medical imaging. Automation of tumor detection is required because there might be a shortage of skilled radiologists at a time of great need. We propose an automatic brain tumor detection and localization framework that can detect and localize brain tumor in magnetic resonance imaging. The proposed brain tumor detection and localization framework comprises five steps: image acquisition, pre-processing, edge detection, modified histogram clustering and morphological operations. After morphological operations, tumors appear as pure white color on pure black backgrounds. We used 50 neuroimages to optimize our system and 100 out-of-sample neuroimages to test our system. The proposed tumor detection and localization system was found to be able to accurately detect and localize brain tumor in magnetic resonance imaging. The preliminary results demonstrate how a simple machine learning classifier with a set of simple image-based features can result in high classification accuracy. The preliminary results also demonstrate the efficacy and efficiency of our five-step brain tumor detection and localization approach and motivate us to extend this framework to detect and localize a variety of other types of tumors in other types of medical imagery.
TwinFusion: High Framerate Non-rigid Fusion through Fast Correspondence Tracking

Real time non-rigid reconstruction pipelines are extremely computationally expensive and easily saturate the highest end GPUs currently available. This requires careful strategic choices to be made about a set of highly interconnected parameters that divide up the limited compute. At the same time, offline systems, prove the value of increasing voxel resolution, more iterations, and higher frame rates. To this end, we demonstrate a set of remarkably simple but effective modifications to these algorithms that significantly reduce the average per-frame computation cost allowing these parameters to be increased. Specifically, we divide the depth stream into sub-frames and fusion-frames, disabling both model accumulation (fusion) and non-rigid alignment (model tracking) on the former. Instead, we efficiently track point correspondences across neighboring sub-frames. We then leverage these correspondences to initialize the standard non-rigid alignment to a fusion-frame where data can then be accumulated into the model. As a result, compute resources in the modified non-rigid reconstruction pipeline can be immediately re-purposed to increase voxel resolution, use more iterations or to increase the frame rate. To demonstrate the latter, we leverage recent high frame rate depth algorithms to build a novel “twin” sensor consisting of a low-res/highfps sub-frame camera and a second low-fps/high-res fusion camera.
SKILL: A System for Skill Identification and Normalization

Named Entity Recognition (NER) and Named Entity Normalization (NEN) refer to the recognition and normalization of raw texts to known entities. From the perspective of recruitment innovation, professional skill characterization and normalization render human capital data more meaningful both commercially and socially. Accurate and detailed normalization of skills is the key for the predictive analysis of labor market dynamics. Such analytics help bridge the skills gap between employers and candidate workers by matching the right talent for the right job and identifying in-demand skills for workforce training programs. This can also work towards the social goal of providing more job opportunities to the community. In this paper we propose an automated approach for skill entity recognition and optimal normalization. The proposed system has two components: 1) Skills taxonomy generation, which employs vocational skill related sections of resumes and Wikipedia categories to define and develop a taxonomy of professional skills; 2) Skills tagging, which leverages properties of semantic word vectors to recognize and normalize relevant skills in input text. By sampling based end-user evaluation, the current system attains 91% accuracy on the taxonomy generation and 82% accuracy on the skills tagging tasks. The beta version of the system is currently applied in various big data and business intelligence applications for workforce analytics and career track projections at CareerBuilder.
The eWrist — A wearable wrist exoskeleton with sEMG-based force control for stroke rehabilitation

Chronic wrist impairment is frequent following stroke and negatively impacts everyday life. Rehabilitation of the dysfunctional limb is possible but requires extensive training and motivation. Wearable training devices might offer new opportunities for rehabilitation. However, few devices are available to train wrist extension even though this movement is highly relevant for many upper limb activities of daily living. As a proof of concept, we developed the eWrist, a wearable one degree-of-freedom powered exoskeleton which supports wrist extension training. Conceptually one might think of an electric bike which provides mechanical support only when the rider moves the pedals, i.e. it enhances motor activity but does not replace it. Stroke patients may not have the ability to produce overt movements, but they might still be able to produce weak muscle activation that can be measured via surface electromyography (sEMG). By combining force and sEMG-based control in an assist-as-needed support strategy, we aim at providing a training device which enhances activity of the wrist extensor muscles in the context of daily life activities, thereby, driving cortical reorganization and recovery. Preliminary results show that the integration of sEMG signals in the control strategy allow for adjustable assistance with respect to a proxy measurement of corticomotor drive.
A Double-Sided LCLC-Compensated Capacitive Power Transfer System for Electric Vehicle Charging

A double-sided LCLC-compensated capacitive power transfer (CPT) system is proposed for the electric vehicle charging application. Two pairs of metal plates are utilized to form two coupling capacitors to transfer power wirelessly. The LCLC-compensated structure can dramatically reduce the voltage stress on the coupling capacitors and maintain unity power factor at both the input and output. A 2.4-kW CPT system is designed with four 610-mm × 610-mm copper plates and an air gap distance of 150 mm. The experimental prototype reaches a dc-dc efficiency of 90.8% at 2.4-kW output power. At 300-mm misalignment case, the output power drops to 2.1 kW with 90.7% efficiency. With a 300-mm air gap distance, the output power drops to 1.6 kW with 89.1% efficiency.
A Novel UWB Monopole Antenna With Tunable Notched Behavior Using Varactor Diode

This letter presents a novel ultrawideband (UWB) antenna with tunable notched band. The antenna is assembled on an FR4 substrate with thickness of 0.8 mm and εr = 4.4. By inserting a π-shaped slot on the radiating patch, band-notch function is achieved. By loading the slot with lumped varactor, tunability of the created notch would be possible. Based on this technique, an electronically controlled notched-band antenna is designed and fabricated including a single varactor diode with a varying capacitance value of 0.63-2.67 pF. Using the cited varactor, notched band tunability of 2.7-7.2 GHz is obtained.
BEYOND STUDENT PERCEPTIONS: ISSUES OF INTERACTION, PRESENCE, AND PERFORMANCE IN AN ONLINE COURSE

The research literature on Web-based learning supports the assumption that interaction is important for a successful course, yet questions exist regarding the nature and extent of the interaction and its effects on student performance. Much of the research is based on student perceptions of the quality and quantity of their interactions and how much they have learned in an online course. The purpose of this study is to examine performance in an online course in relationship to student interaction and sense of presence in the course. Data on multiple independent (measures of interaction and presence) and dependent (measures of performance) variables were collected and subjected to analysis. An attempt was made to go beyond typical institutional performance measures such as grades and withdrawal rates and to examine measures specifically related to course objectives.
Learning to Pinpoint Singing Voice from Weakly Labeled Examples

Building an instrument detector usually requires temporally accurate ground truth that is expensive to create. However, song-wise information on the presence of instruments is often easily available. In this work, we investigate how well we can train a singing voice detection system merely from song-wise annotations of vocal presence. Using convolutional neural networks, multipleinstance learning and saliency maps, we can not only detect singing voice in a test signal with a temporal accuracy close to the state-of-the-art, but also localize the spectral bins with precision and recall close to a recent source separation method. Our recipe may provide a basis for other sequence labeling tasks, for improving source separation or for inspecting neural networks trained on auditory spectrograms.
Copy-move forgery detection: Survey, challenges and future directions

The authenticity and reliability of digital images are increasingly important due to the ease in modifying such images. Thus, the capability to identify image manipulation is a current research focus, and a key domain in digital image authentication is Copy-Move Forgery Detection (CMFD). Copy-move forgery is the process of copying and pasting from one region to another location within the same image. In this paper, we survey the recent developments in CMFD, and describe the entire CMFD process involved. Specifically, we characterize the common CMFD workflow of feature extraction and matching process using block or keypoint-based approaches. Instead of listing the datasets and validations used in the literature, we also categorize the types of copied regions. Finally, we also outline a number of future research directions.
MIKE: Keyphrase Extraction by Integrating Multidimensional Information

Traditional supervised keyphrase extraction models depend on the features of labelled keyphrases while prevailing unsupervised models mainly rely on structure of the word graph, with candidate words as nodes and edges capturing the co-occurrence information between words. However, systematically integrating all these multidimensional heterogeneous information into a unified model is relatively unexplored. In this paper, we focus on how to effectively exploit multidimensional information to improve the keyphrase extraction performance (MIKE). Specifically, we propose a random-walk parametric model, MIKE, that learns the latent representation for a candidate keyphrase that captures the mutual influences among all information, and simultaneously optimizes the parameters and ranking scores of candidates in the word graph. We use the gradient-descent algorithm to optimize our model and show the comprehensive experiments with two publicly-available WWW and KDD datasets in Computer Science. Experimental results demonstrate that our approach significantly outperforms the state-of-the-art graph-based keyphrase extraction approaches.