nils-herrmann/clean_scirepeval huggingface dataset:
Estimation of vehicle's vertical and lateral tire forces considering road angle and road irregularity

Vehicle dynamics is an essential topic in development of safety driving systems. These complex and integrated control units require precise information about vehicle dynamics, especially, tire/road contact forces. Nevertheless, it is lacking an effective and low-cost sensor to measure them directly. Therefore, this study presents a new method to estimate these parameters by using observer technologies and low-cost sensors which are available on the passenger cars in real environment. In our previous work, observers have been designed to estimate the vehicle tire/road contact forces and sideslip angles. However, the previous study just considered the situation of the vehicles running on a level road. In our recent study, vehicle mathematical models are reconstructed to suit banked road and inclined road. Then, Kalman Filter is used to improve the estimation of vehicle dynamics. Finally, the estimator is tested both on simulation CALLAS and on the experimental vehicle DYNA.
A comparative analysis of Switched-Capacitor and inductor-based DC-DC conversion technologies

This paper compares the performance of Switched-Capacitor (SC) and inductor-based DC-DC conversion technologies. A metric to compare between the two topologies is discussed, and is used to compare switch utilization. Fundamental limits on utilization of reactive elements developed in the literature for all DC-DC converters are also reviewed and discussed, and this analysis shows that popular SC and inductor-based converters achieve the limits of utilization for reactive components. These limits are stated in terms of the ratio of output power to required stored energy in reactive elements. A detailed analysis of available surface mount discrete components and on-die devices reveals that capacitors have substantially higher energy and power density than their magnetic counterparts. The challenging regulation task for SC converters is also discussed, with a promising strategy outlined. The SC converter is evidently a promising candidate for future high power density integrated DC-DC converters.
Multi-fingered robotic hand employing strings transmission named “Twist Drive”

A goal of our research is to produce a light-weight, low-cost five fingered robotic hand that has similar degrees of freedom as a human hand. The joints in the fingers of the developed robotic hand are powered by a newly proposed strings transmission named “Twist Drive”. The transmission converts torque into a pulling force by using a pair of strings that twist on each other. The basic characteristics of the transmission are given in the paper. A robotic hand prototype with 18 joints of which 14 are independently powered by Twist Drives was produced. The size of the hand is equal to the size of an adult human's hand and its weight including the power circuits is approximately 800 grams. The mechanical and the control systems of the hand are presented in the paper.
FOR THE TASK OF PRODUCT RECOMMENDATION

We propose a unified product embedded representation that is optimized for the task of retrieval-based product recommendation. We generate this representation using Content2Vec, a new deep architecture that merges product content information such as text and image, and we analyze its performance on hard recommendation setups such as cold-start and cross-category recommendations. In the case of a normal recommendation regime where collaborative information signal is available, we merge the product co-occurrence information and propose a second architecture Content2vec+ and show its lift in performance versus non-hybrid approaches in both cold start and normal recommendation regimes.
The Role of Culture in Health Literacy and Chronic Disease Screening and Management

Cultural and language differences and socioeconomic status interact with and contribute to low health literacy, defined as the inability to understand or act on medical/therapeutic instructions. Health literacy is increasingly recognized as an important factor in patient compliance, cancer screening utilization, and chronic disease outcomes. Commendable efforts have been initiated by the American Medical Association and other organizations to address low health literacy among patients. Less work has been done, however, to place health literacy in the broader context of socioeconomic and cultural differences among patients and providers that hinder communication and compliance. This review examines cultural influences on health literacy, cancer screening and chronic disease outcomes. We argue that cultural beliefs around health and illness contribute to an individual’s ability to understand and act on a health care provider’s instructions. This paper proposes key aspects of the intersection between health literacy and culturally varying beliefs about health which merit further exploration.
Career motivation and burnout among medical students in Hungary - could altruism be a protection factor?

Burnout is a major issue among medical students. Its general characteristics are loss of interest in study and lack of motivation. A study of the phenomenon must extend beyond the university environment and personality factors to consider whether career choice has a role in the occurrence of burnout.



Quantitative, national survey (n = 733) among medical students, using a 12-item career motivation list compiled from published research results and a pilot study. We measured burnout by the validated Hungarian version of MBI-SS.



The most significant career choice factor was altruistic motivation, followed by extrinsic motivations: gaining a degree, finding a job, accessing career opportunities. Lack of altruism was found to be a major risk factor, in addition to the traditional risk factors, for cynicism and reduced academic efficacy. Our study confirmed the influence of gender differences on both career choice motivations and burnout.



The structure of career motivation is a major issue in the transformation of the medical profession. Since altruism is a prominent motivation for many women studying medicine, their entry into the profession in increasing numbers may reinforce its traditional character and act against the present trend of deprofessionalization.
On the TDD subframe structure for beyond 4G radio access network

The purpose of a Beyond 4G (B4G) radio access technology, is to cope with the expected exponential increase of mobile data traffic in local area (LA). The requirements related to physical layer control signaling latencies and to hybrid ARQ (HARQ) round trip time (RTT) are in the order of ~1ms. In this paper, we propose a flexible orthogonal frequency division multiplexing (OFDM) based time division duplex (TDD) physical subframe structure optimized for B4G LA environment. We show that the proposed optimizations allow very frequent link direction switching, thus reaching the tight B4G HARQ RTT requirement and significant control signaling latency reductions compared to existing LTE-Advanced and WiMAX technologies.
Learning Mid-level Filters for Person Re-identification

In this paper, we propose a novel approach of learning mid-level filters from automatically discovered patch clusters for person re-identification. It is well motivated by our study on what are good filters for person re-identification. Our mid-level filters are discriminatively learned for identifying specific visual patterns and distinguishing persons, and have good cross-view invariance. First, local patches are qualitatively measured and classified with their discriminative power. Discriminative and representative patches are collected for filter learning. Second, patch clusters with coherent appearance are obtained by pruning hierarchical clustering trees, and a simple but effective cross-view training strategy is proposed to learn filters that are view-invariant and discriminative. Third, filter responses are integrated with patch matching scores in RankSVM training. The effectiveness of our approach is validated on the VIPeR dataset and the CUHK01 dataset. The learned mid-level features are complementary to existing handcrafted low-level features, and improve the best Rank-1 matching rate on the VIPeR dataset by 14%.
A Syllable Based Word Recognition Model for Korean Noun Extraction

Noun extraction is very important for many NLP applications such as information retrieval, automatic text classification, and information extraction. Most of the previous Korean noun extraction systems use a morphological analyzer or a Partof-Speech (POS) tagger. Therefore, they require much of the linguistic knowledge such as morpheme dictionaries and rules (e.g. morphosyntactic rules and morphological rules). This paper proposes a new noun extraction method that uses the syllable based word recognition model. It finds the most probable syllable-tag sequence of the input sentence by using automatically acquired statistical information from the POS tagged corpus and extracts nouns by detecting word boundaries. Furthermore, it does not require any labor for constructing and maintaining linguistic knowledge. We have performed various experiments with a wide range of variables influencing the performance. The experimental results show that without morphological analysis or POS tagging, the proposed method achieves comparable performance with the previous methods.
IFCIoT: Integrated Fog Cloud IoT: A novel architectural paradigm for the future Internet of Things.

The Internet of Things (IoT) is a network of physical things, objects, or devices, such as radio-frequency identification tags, sensors, actuators, mobile phones, and laptops. The IoT enables objects to be sensed and controlled remotely across existing network infrastructure, including the Internet, thereby creating opportunities for more direct integration of the physical world into the cyber world. The IoT becomes an instance of cyberphysical systems (CPSs) with the incorporation of sensors and actuators in IoT devices. Objects in the IoT have the potential to be grouped into geographical or logical clusters. Various IoT clusters generate huge amounts of data from diverse locations, which creates the need to process these data more efficiently. Efficient processing of these data can involve a combination of different computation models, such as in situ processing and offloading to surrogate devices and cloud-data centers.
Quadruplet Network with One-Shot Learning for Visual Tracking

As a discriminative method of one-shot learning, Siamese deep network allows recognizing an object from a single exemplar with the same class label. However, it does not take the advantage of the underlying structure and relationship among a multitude of instances since it only relies on pairs of instances for training. In this paper, we propose a quadruplet deep network to examine the potential connections among the training instances, aiming to achieve a more powerful representation. We design four shared networks that receive multi-tuple of instances as inputs and are connected by a novel loss function consisting of pair-loss and tripletloss. According to the similarity metric, we select the most similar and the most dissimilar instances as the positive and negative inputs of triplet loss from each multi-tuple. We show that this scheme improves the training performance and convergence speed. Furthermore, we introduce a new weighted pair loss for an additional acceleration of the convergence. We demonstrate promising results for model-free tracking-by-detection of objects from a single initial exemplar in the Visual Object Tracking benchmark.
MalwareVis: entity-based visualization of malware network traces

This paper presents MalwareVis, a utility that provides security researchers a method to browse, filter, view and compare malware network traces as entities.
 Specifically, we propose a cell-like visualization model to view the network traces of a malware sample's execution. This model is a intuitive representation of the heterogeneous attributes (protocol, host ip, transmission size, packet number, duration) of a list of network streams associated with a malware instance. We encode these features into colors and basic geometric properties of common shapes. The list of streams is organized circularly in a clock-wise fashion to form an entity. Our design takes into account of the sparse and skew nature of these attributes' distributions and proposes mapping and layout strategies to allow a clear global view of a malware sample's behaviors.
 We demonstrate MalwareVis on a real-world corpus of malware samples and display their individual activity patterns. We show that it is a simple to use utility that provides intriguing visual representations that facilitate user interaction to perform security analysis.
Data-aggregation techniques in sensor networks: a survey

Wireless sensor networks consist of sensor nodes with sensing and communication capabilities. We focus on data aggregation problems in energy constrained sensor networks. The main goal of data aggregation algorithms is to gather and aggregate data in an energy efficient manner so that network lifetime is enhanced. In this paper, we present a survey of data aggregation algorithms in wireless sensor networks. We compare and contrast different algorithms on the basis of performance measures such as lifetime, latency and data accuracy. We conclude with possible future research directions.
Digital Health Data: A Comprehensive Review of Privacy and Security Risks and Some Recommendations

In todays world, health data are being produced in everincreasing amounts due to extensive use of medical devices generating data in digital form. These data are stored in diverse formats at different health information systems. Medical practitioners and researchers can be benefited significantly if these massive heterogeneous data could be integrated and made accessible through a common platform. On the other hand, digital health data containing protected health information (PHI) are the main target of the cybercriminals. In this paper, we have provided a state of the art review of the security threats in the integrated healthcare information systems. According to our analysis, healthcare data servers are leading target of the hackers because of monetary value. At present, attacks on healthcare organizations’ data are 1.25 times higher compared to five years ago. We have provided some important recommendations to minimize the risk of attacks and to reduce the chance of compromising patients’ privacy after any successful attack.
Secure and Private Data Aggregation for Energy Consumption Scheduling in Smart Grids

The recent proposed solutions for demand side energy management leverage the two-way communication infrastructure provided by modern smart-meters and sharing the usage information with the other users. In this paper, we first highlight the privacy and security issues involved in the distributed demand management protocols. We propose a novel protocol to share required information among users providing privacy, confidentiality, and integrity. We also propose a new clustering-based, distributed multi-party computation (MPC) protocol. Through simulation experiments we demonstrate the efficiency of our proposed solution. The existing solutions typically usually thwart selfish and malicious behavior of consumers by deploying billing mechanisms based on total consumption during a few time slots. However, the billing is typically based on the total usage in each time slot in smart grids. In the second part of this paper, we formally prove that under the per-slot based charging policy, users have incentive to deviate from the proposed protocols. We also propose a protocol to identify untruthful users in these networks. Finally, considering a repeated interaction among honest and dishonest users, we derive the conditions under which the smart grid can enforce cooperation among users and prevents dishonest declaration of consumption.
Prolonged behavioral stress enhances synaptic connectivity in the basolateral amygdala

Recently identified cellular and molecular correlates of stress-induced plasticity suggest a putative link between neuronal remodeling in the amygdala and the development of anxiety-like behavior. Rodent models of immobilization stress, applied for 10 consecutive days, have been reported to enhance anxiety, and also cause dendritic elongation and spine formation in the basolateral amygdala (BLA). Paradoxically, longer exposure to stress, which is also anxiogenic, fails to affect key molecular markers of neuronal remodeling in the BLA. This has raised the possibility of homeostatic mechanisms being triggered by more prolonged stress that could potentially dampen the morphological effects of stress in the BLA. Therefore, we examined the cellular and behavioral impact of increasing the duration of stress in rats. We find that prolonged immobilization stress (PIS), spanning 21 days, caused significant enhancement in dendritic arborization of spiny BLA neurons. Spine density was also enhanced along these elongated dendrites in response to PIS. Finally, this striking increase in synaptic connectivity was accompanied by enhanced anxiety-like behavior in the elevated plus-maze. Thus, we did not detect any obvious morphological correlate of adaptive changes within the BLA that may have been activated by prolonged and repeated application of the same stressor for 21 days. These findings add to accumulating evidence that structural encoding of aversive experiences, through enhanced availability of postsynaptic dendritic surface and synaptic inputs on principal neurons of the BLA, may contribute to the affective symptoms of stress disorders.
Implementation of the phase correlation algorithm : motion estimation in the frequency domain

In a practical training for the Faculty of Electrical Engineering at Eindhoven University of Technology, a literature search to the past developments concerning the Phase Correlation algorithm was carried out and the algorithm was implemented. The Phase Correlation algorithm is a method for measuring the motion in a video sequence, based on the Fourier shift theorem. It was found that an enhanced implementation of the Phase Correlation algorithm gives very good results, comparable to the results of 3-D Recursive Search (a popular method for motion estimation in consumer electronics equipment). The estimated motion vectors have a close relation to the true motion in a scene, which makes this algorithm very suitable for video format conversion. It is concluded that Phase Correlation and 3-D Recursive Search, together with object based motion estimation, are the best methods for motion estimation currently available.
A Cognition Based Attention Model for Sentiment Analysis

Attention models are proposed in sentiment analysis because some words are more important than others. However, most existing methods either use local context based text information or user preference information. In this work, we propose a novel attention model trained by cognition grounded eye-tracking data. A reading prediction model is first built using eye-tracking data as dependent data and other features in the context as independent data. The predicted reading time is then used to build a cognition based attention (CBA) layer for neural sentiment analysis. As a comprehensive model, We can capture attentions of words in sentences as well as sentences in documents. Different attention mechanisms can also be incorporated to capture other aspects of attentions. Evaluations show the CBA based method outperforms the state-of-the-art local context based attention methods significantly. This brings insight to how cognition grounded data can be brought into NLP tasks.
Synchronization of Parallel Single-Phase Inverters With Virtual Oscillator Control

A method to synchronize and control a system of parallel single-phase inverters without communication is presented. Inspired by the phenomenon of synchronization in networks of coupled oscillators, we propose that each inverter be controlled to emulate the dynamics of a nonlinear dead-zone oscillator. As a consequence of the electrical coupling between inverters, they synchronize and share the load in proportion to their ratings. We outline a sufficient condition for global asymptotic synchronization and formulate a methodology for controller design such that the inverter terminal voltages oscillate at the desired frequency, and the load voltage is maintained within prescribed bounds. We also introduce a technique to facilitate the seamless addition of inverters controlled with the proposed approach into an energized system. Experimental results for a system of three inverters demonstrate power sharing in proportion to power ratings for both linear and nonlinear loads.
Regular and irregular progressive edge-growth tanner graphs

We propose a general method for constructing Tanner graphs having a large girth by establishing edges or connections between symbol and check nodes in an edge-by-edge manner, called progressive edge-growth (PEG) algorithm. Lower bounds on the girth of PEG Tanner graphs and on the minimum distance of the resulting low-density parity-check (LDPC) codes are derived in terms of parameters of the graphs. Simple variations of the PEG algorithm can also be applied to generate linear-time encodeable LDPC codes. Regular and irregular LDPC codes using PEG Tanner graphs and allowing symbol nodes to take values over GF(q) (q>2) are investigated. Simulation results show that the PEG algorithm is a powerful algorithm to generate good short-block-length LDPC codes.
Sediment-water distribution of organic contaminants in aquatic ecosystems: the role of organic carbon mineralization.

The distribution between sediments and water plays a key role in the food-chain transfer of hydrophobic organic chemicals. Current models and assessment methods of sediment-water distribution predominantly rely on chemical equilibrium partitioning despite several observations reporting an "enrichment" of chemical concentrations in suspended sediments. In this study we propose and derive a fugacity based model of chemical magnification due to organic carbon decomposition throughout the process of sediment diagenesis. We compare the behavior of the model to observations of bottom sediment-water, suspended sediments-water, and plankton-water distribution coefficients of a range of hydrophobic organic chemicals in five Great Lakes. We observe that (i) sediment-water distribution coefficients of organic chemicals between bottom sediments and water and between suspended sediments and water are considerably greaterthan expected from chemical partitioning and that the degree sediment-water disequilibrium appears to follow a relationship with the depth of the lake; (ii) concentrations increase from plankton to suspended sediments to bottom sediments and follow an inverse ratherthan a proportional relationship with the organic carbon content and (iii) the degree of disequilibrium between bottom sediment and water, suspended sediments and water, and plankton and water increases when the octanol-water partition coefficient K(ow) drops. We demonstrate that these observations can be explained by a proposed organic carbon mineralization model. Our findings imply that sediment-water distribution is not solely a chemical partitioning process but is to a large degree controlled by lake specific organic carbon mineralization processes.