nils-herrmann/clean_scirepeval huggingface dataset:
Robot Arm Platform for Additive Manufacturing : MultiPlane Printing

A conventional 3D printer utilizes horizontal plane layerings to produce a 3D printed part. However, there are drawbacks associated with horizontal plane layerings motions, e.g., support material needed to printed an overhang structure. To enable multi-plane printing, an industrial robot arm platform is proposed for additive manufacturing. The concept being explored is the integration of existing additive manufacturing process technologies with an industrial robot arm to create a 3D printer with a multi-plane layering capability. The objective is to perform multi-plane toolpath motions that will leverage the increased capability of the robot arm platform compared to conventional gantry-style 3D printers. This approach enables print layering in multiple planes whereas existing conventional 3D printers are restricted to a single toolpath plane (e.g. x-y plane). This integration combines the fused deposition modeling techniques using an extruder head that is typically used in 3D printing and a 6 degree of freedom robot arm. Here, a Motoman SV3X is used as the platform for the robot arm. A higher level controller is used to coordinate the robot and extruder. For the higher level controller to communicate with the robot arm controller, interface software based on the MotoCom SDK libraries was implemented. The integration of the robotic arm and extruder enables multiplane toolpath motions to be utilized in the production of 3D printed parts. Using this integrated system, a test block with an overhang structure has been 3D printed without the use of support material .
Current controlled delay line elements' improvement study

The chain of delay elements creating delay lines are the basic building blocks of delay locked loops (DLLs) applied in clock distribution network in many VLSI circuits and systems. In the paper Current Controlled delay line (CCDL) elements with Duty Cycle Correction (DCC) has been described and investigated. The architecture of these elements is based on Switched-Current Mirror Inverter (SCMI) and CMOS standard or Schmitt type inverters. The primary characteristics of the described CCDL element have been compared with characteristics of two most popular ones: current starved, and shunt capacitor delay elements. The simulation results with real foundry parameters models in 180 nm, 1.8 V CMOS technology from UMC are also included. Simulations have been done using BSIM3V3 device models for Spectre from Cadence Design Systems.
Adversarial Spheres

Machine learning models with very low test error have been shown to be consistently vulnerable to small, adversarially chosen perturbations of the input. We hypothesize that this counterintuitive behavior is a result of the high-dimensional geometry of the data manifold, and explore this hypothesis on a simple highdimensional dataset. For this dataset we show a fundamental bound relating the classification error rate to the average distance to the nearest misclassification, which is independent of the model. We train different neural network architectures on this dataset and show their error sets approach this theoretical bound. As a result of the theory, the vulnerability of machine learning models to small adversarial perturbations is a logical consequence of the amount of test error observed. We hope that our theoretical analysis of this foundational synthetic case will point a way forward to explore how the geometry of complex real-world data sets leads to adversarial examples.
An evaluation of region based object detection strategies within X-ray baggage security imagery

Here we explore the applicability of traditional sliding window based convolutional neural network (CNN) detection pipeline and region based object detection techniques such as Faster Region-based CNN (R-CNN) and Region-based Fully Convolutional Networks (R-FCN) on the problem of object detection in X-ray security imagery. Within this context, with limited dataset availability, we employ a transfer learning paradigm for network training tackling both single and multiple object detection problems over a number of R-CNN/R-FCN variants. The use of first-stage region proposal within the Faster RCNN and R-FCN provide superior results than traditional sliding window driven CNN (SWCNN) approach. With the use of Faster RCNN with VGG16, pretrained on the ImageNet dataset, we achieve 88.3 mAP for a six object class X-ray detection problem. The use of R-FCN with ResNet-101, yields 96.3 mAP for the two class firearm detection problem requiring 0.1 second computation per image. Overall we illustrate the comparative performance of these techniques as object localization strategies within cluttered X-ray security imagery.
Ranking of fuzzy numbers based on centroid point and spread

Centroid and spread are commonly used approaches in ranking fuzzy numbers. Some experts rank fuzzy numbers using centroid or spread alone while others tend to integrate them together. Although a lot of methods for ranking fuzzy numbers that are related to both approaches have been presented, there are still limitations whereby the ranking obtained is inconsistent with human intuition. This paper proposes a novel method for ranking fuzzy numbers that integrates the centroid point and the spread approaches and overcomes the limitations and weaknesses of most existing methods. Proves and justifications with regard to the proposed ranking method are also presented.
A compact dual-polarized magneto-electric dipole antenna for 2G/3G/LTE applications

A compact ±45° dual-polarized magneto-electric (ME) dipole base station antenna is proposed for 2G/3G/LTE applications. The antenna is excited by two Γ-shaped probes placed at a convenient location and two orthogonally octagonal loop electric dipoles are employed to achieve a wide impedance bandwidth. A stable antenna gain and a stable radiation pattern are realized by using a rectangular box-shaped reflector instead of planar one. The antenna is prototype and measured. Measured results show overlapped impedance bandwidth is 58% with standing-wave ratio (SWR) ≤ 1.5 from 1.68 to 3.05 GHz, port-to-port isolation is large than 26 dB within the bandwidth, and stable antenna gains of 8.6 ± 0.8 dBi and 8.3 ± 0.6 dBi for port 1 and port 2, respectively. Nearly symmetrical radiation patterns with low back lobe radiation both in horizontal and vertical planes, and narrow beamwidth can be also obtained. Moreover, the size of the antenna is very compact, which is only 0.79λ0 × 0.79λ0 × 0.26λ0. The proposed antenna can be used for multiband base stations in next generation communication systems.
VR-Drop: Exploring the Use of Walking-in-Place to Create Immersive VR Games

Low-cost smartphone adapters can bring virtual reality to the masses, but input is typically limited to using head tracking, which makes it difficult to perform complex tasks like navigation. Walking-in-place (WIP) offers a natural and immersive form of virtual locomotion that can reduce simulation sickness. We present VR-Drop; an immersive puzzle game that illustrates the use of WIP for virtual locomotion. Our WIP implementation doesn't require any instrumentation as it is implemented using a smartphone's inertial sensors. VR-Drop demonstrates that WIP can significantly increase VR input options and allows for a deep and immersive VR experience.
Location Diversity: Enhanced Privacy Protection in Location Based Services

Location-based Services are emerging as popular applications in pervasive computing. Spatial k-anonymity is used in Locationbased Services to protect privacy, by hiding the association of a specific query with a specific user. Unfortunately, this approach fails in many practical cases such as: (i) personalized services, where the user identity is required, or (ii) applications involving groups of users (e.g., employees of the same company); in this case, associating a query to any member of the group, violates privacy. In this paper, we introduce the concept of Location Diversity, which solves the above-mentioned problems. Location Diversity improves Spatial k-anonymity by ensuring that each query can be associated with at least ` different semantic locations (e.g., school, shop, hospital, etc). We present an attack model that maps each observed query to a linear equation involving semantic locations, and we show that a necessary condition to preserve privacy is the existence of infinite solutions in the resulting system of linear equations. Based on this observation, we develop algorithms that generate groups of semantic locations, which preserve privacy and minimize the expected query processing and communication cost. The experimental evaluation demonstrates that our approach reduces significantly the privacy threats, while incurring minimal overhead.
Exogenous Approach to Grid Cost Allocation in Peer-to-Peer Electricity Markets

The deployment of distributed energy resources, combined with a more proactive demand side, is inducing a new paradigm in power system operation and electricity markets. Within a consumer-centric market framework, peer-to-peer approaches have gained substantial interest. Peer-to-peer markets rely on multi-bilateral direct negotiation among all players to match supply and demand, and with product differentiation. These markets can yield a complete mapping of exchanges onto the grid, hence allowing to rethink our approach to sharing costs related to usage of common infrastructure and services. We propose here to attribute such costs in a number of alternative ways that reflects different views on usage of the grid and on cost allocation, i.e., uniformly and based on the electrical distance between players. Since attribution mechanisms are defined in an exogenous manner and made transparent they eventually affect the trades of the market participants and related grid usage. The interest of our approach is illustrated on a test case using the IEEE 39 bus test system, underlying the impact of attribution mechanisms on trades and grid usage.
Cognitive analysis of a case of pure dysgraphia

An Italian patient with a pure dysgraphia who incorrectly spelled words and nonwords is described. The spelling errors made by the patient were not affected by lexical factors (e.g., frequency, form class) and were qualitatively the same for words and nonwords. The pattern of writing performance is discussed in relation to current models of writing and, specifically, in relation to the role of the Output Grapheme Buffer and Phoneme-Grapheme Conversion in writing.
Heuristics - intelligent search strategies for computer problem solving

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A Hidden Markov Model based driver intention prediction system

Awareness of other vehicle's intention may help human drivers or autonomous vehicles judge the risk and avoid traffic accidents. This paper proposed an approach to predicting driver's intentions using Hidden Markov Model (HMM) which is able to access the control and the state of the vehicle. The driver performs maneuvers including stop/non-stop, change lane left/right and turn left/right in a simulator in both highway and urban environments. Moreover, the structure of the road (curved road) is also taken into account for classification. Experiments were conducted with different input sets (steering wheel data with and without vehicle state data) to compare the system performance.
RFID Smart Shelf With Confined Detection Volume at UHF

This letter presents a smart shelf configuration for radio frequency identification (RFID) application. The proposed shelf has an embedded leaking microstrip transmission line with extended ground plane. This structure, when connected to an RFID reader, allows detecting tagged objects in close proximity with proper field confinement to avoid undesired reading of neighboring shelves. The working frequency band covers simultaneously the three world assigned RFID subbands at ultrahigh frequency (UHF). The concept is explored by full-wave simulations and it is validated with thorough experimental tests.
Protective effects of sweet orange (Citrus sinensis) peel and their bioactive compounds on oxidative stress.

Protective effects of sweet orange (Citrus sinensis) peel and their bioactive compounds on oxidative stress were investigated. According to HPLC-DAD and HPLC-MS/MS analysis, hesperidin (HD), hesperetin (HT), nobiletin (NT), and tangeretin (TT) were present in water extracts of sweet orange peel (WESP). The cytotoxic effect in 0.2mM t-BHP-induced HepG2 cells was inhibited by WESP and their bioactive compounds. The protective effect of WESP and their bioactive compounds in 0.2mM t-BHP-induced HepG2 cells may be associated with positive regulation of GSH levels and antioxidant enzymes, decrease in ROS formation and TBARS generation, increase in the mitochondria membrane potential and Bcl-2/Bax ratio, as well as decrease in caspase-3 activation. Overall, WESP displayed a significant cytoprotective effect against oxidative stress, which may be most likely because of the phenolics-related bioactive compounds in WESP, leading to maintenance of the normal redox status of cells.
A longitudinal study of electronic cigarette users.

To assess behavior change over 12 months in users of e-cigarettes ("vapers").



Longitudinal Internet survey, 2011 to 2013. Participants were enrolled on websites dedicated to e-cigarettes and smoking cessation. We assessed use of e-cigarettes and tobacco among the same cohort at baseline, after one month (n=477) and one year (n=367).



Most participants (72%) were former smokers, and 76% were using e-cigarettes daily. At baseline, current users had been using e-cigarettes for 3 months, took 150 puffs/day on their e-cigarette and used refill liquids containing 16 mg/ml of nicotine, on average. Almost all the daily vapers at baseline were still vaping daily after one month (98%) and one year (89%). Of those who had been vaping daily for less than one month at baseline, 93% were still vaping daily after one month, and 81% after one year. In daily vapers, the number of puffs/day on e-cigarettes remained unchanged between baseline and one year. Among former smokers who were vaping daily at baseline, 6% had relapsed to smoking after one month and also 6% after one year. Among dual users (smokers who were vaping daily at baseline), 22% had stopped smoking after one month and 46% after one year. In dual users who were still smoking at follow-up, cigarette consumption decreased by 5.3 cig/day after one month (from 11.3 to 6.0 cig./day, p=0.006), but remained unchanged between baseline and 1-year follow-up.



E-cigarettes may contribute to relapse prevention in former smokers and smoking cessation in current smokers.
A Statistical Language Modeling Approach to Online Deception Detection

Online deception is disrupting our daily life, organizational process, and even national security. Existing approaches to online deception detection follow a traditional paradigm by using a set of cues as antecedents for deception detection, which may be hindered by ineffective cue identification. Motivated by the strength of statistical language models (SLMs) in capturing the dependency of words in text without explicit feature extraction, we developed SLMs to detect online deception. We also addressed the data sparsity problem in building SLMs in general and in deception detection in specific using smoothing and vocabulary pruning techniques. The developed SLMs were evaluated empirically with diverse datasets. The results showed that the proposed SLM approach to deception detection outperformed a state-of-the-art text categorization method as well as traditional feature-based methods.
Prevalence and predictors of compassion fatigue, burnout and compassion satisfaction among oncology nurses: A cross-sectional survey.

Cancer is a leading cause of death worldwide. Given the complexity of caring work, recent studies have focused on the professional quality of life of oncology nurses. China, the world's largest developing country, faces heavy burdens of care for cancer patients. Chinese oncology nurses may be encountering the negative side of their professional life. However, studies in this field are scarce, and little is known about the prevalence and predictors of oncology nurses' professional quality of life.



To describe and explore the prevalence of predictors of professional quality of life (compassion fatigue, burnout and compassion satisfaction) among Chinese oncology nurses under the guidance of two theoretical models.



A cross-sectional design with a survey.



Ten tertiary hospitals and five secondary hospitals in Shanghai, China.



A convenience and cluster sample of 669 oncology nurses was used. All of the nurses worked in oncology departments and had over 1 year of oncology nursing experience. Of the selected nurses, 650 returned valid questionnaires that were used for statistical analyses.



The participants completed the demographic and work-related questionnaire, the Chinese version of the Professional Quality of Life Scale for Nurses, the Chinese version of the Jefferson Scales of Empathy, the Simplified Coping Style Questionnaire, the Perceived Social Support Scale, and the Chinese Big Five Personality Inventory brief version. Descriptive statistics, t-tests, one-way analysis of variance, simple and multiple linear regressions were used to determine the predictors of the main research variables.



Higher compassion fatigue and burnout were found among oncology nurses who had more years of nursing experience, worked in secondary hospitals and adopted passive coping styles. Cognitive empathy, training and support from organizations were identified as significant protectors, and 'perspective taking' was the strongest predictor of compassion satisfaction, explaining 23.0% of the variance. Personality traits of openness and conscientiousness were positively associated with compassion satisfaction, while neuroticism was a negative predictor, accounting for 24.2% and 19.8% of the variance in compassion fatigue and burnout, respectively.



Oncology care has unique features, and oncology nurses may suffer from more work-related stressors compared with other types of nurses. Various predictors can influence the professional quality of life, and some of these should be considered in the Chinese nursing context. The results may provide clues to help nurse administrators identify oncology nurses' vulnerability to compassion fatigue and burnout and develop comprehensive strategies to improve their professional quality of life.
VINE: an open source interactive data visualization tool for neuroevolution

Recent advances in deep neuroevolution have demonstrated that evolutionary algorithms, such as evolution strategies (ES) and genetic algorithms (GA), can scale to train deep neural networks to solve difficult reinforcement learning (RL) problems. However, it remains a challenge to analyze and interpret the underlying process of neuroevolution in such high dimensions. To begin to address this challenge, this paper presents an interactive data visualization tool called VINE (Visual Inspector for NeuroEvolution) aimed at helping neuroevolution researchers and end-users better understand and explore this family of algorithms. VINE works seamlessly with a breadth of neuroevolution algorithms, including ES and GA, and addresses the difficulty of observing the underlying dynamics of the learning process through an interactive visualization of the evolving agent's behavior characterizations over generations. As neuroevolution scales to neural networks with millions or more connections, visualization tools like VINE that offer fresh insight into the underlying dynamics of evolution become increasingly valuable and important for inspiring new innovations and applications.
A Multiworld Testing Decision Service

Applications and systems are constantly faced with decisions to make, often using a policy to pick from a set of actions based on some contextual information. We create a service that uses machine learning to accomplish this goal. The service uses exploration, logging, and online learning to create a counterfactually sound system supporting a full data lifecycle. The system is general: it works for any discrete choices, with respect to any reward metric, and can work with many learning algorithms and feature representations. The service has a simple API, and was designed to be modular and reproducible to ease deployment and debugging, respectively. We demonstrate how these properties enable learning systems that are robust and safe. Our evaluation shows that the Decision Service makes decisions in real time and incorporates new data quickly into learned policies. A large-scale deployment for a personalized news website has been handling all traffic since Jan. 2016, resulting in a 25% relative lift in clicks. By making the Decision Service externally available, we hope to make optimal decision making available to all.
Nonlinear Dynamics of Chaotic Attractor of Chua Circuit and Its Application for Secure Communication

The Chua circuit is among the simplest non-linear circuits that shows most complex dynamical behavior, including chaos which exhibits a variety of bifurcation phenomena and attractors. In this paper, Chua attractor’s chaotic oscillator, synchronization and masking communication circuits were designed and simulated. The electronic circuit oscilloscope outputs of the realized Chua system is also presented. Simulation and oscilloscope outputs are used to illustrate the accuracy of the designed and realized Chua chaotic oscillator circuits. The Chua system is addressed suitable for chaotic synchronization circuits and chaotic masking communication circuits using Matlab® and MultiSIM® software. Simulation results are used to visualize and illustrate the effectiveness of Chua chaotic system in synchronization and application of secure communication.
BFQA: A Bengali Factoid Question Answering System

Answering (QA) research for factoid questions has recently achieved great success. Presently, QA systems developed for European, Middle Eastern and Asian languages are capable of providing answers with reasonable accuracy. However, Bengali being among themost spoken languages in theworld, no factoid question answering system is available for Bengali till date. This paper describes the first attempt on building a factoid question answering system for Bengali language. The challenges in developing a question answering system for Bengali have been discussed. Extraction and ranking of relevant sentences have also been proposed. Also extraction strategy of the ranked answers from the relevant sentences are suggested for Bengali question answering system.