nils-herrmann/clean_scirepeval huggingface dataset:
MAPO: Mining and Recommending API Usage Patterns

To improve software productivity, when constructing new software systems, programmers often reuse existing libraries or frameworks by invoking methods provided in their APIs. Those API methods, however, are often complex and not well documented. To get familiar with how those API methods are used, programmers often exploit a source code search tool to search for code snippets that use the API methods of interest. However, the returned code snippets are often large in number, and the huge number of snippets places a barrier for programmers to locate useful ones. In order to help programmers overcome this barrier, we have developed an API usage mining framework and its supporting tool called MAPO (Mining API usage Pattern from Open source repositories) for mining API usage patterns automatically. A mined pattern describes that in a certain usage scenario, some API methods are frequently called together and their usages follow some sequential rules. MAPO further recommends the mined API usage patterns and their associated code snippets upon programmers’ requests. Our experimental results show that with these patterns MAPO helps programmers locate useful code snippets more effectively than two state-of-the-art code search tools. To investigate whether MAPO can assist programmers in programming tasks, we further conducted an empirical study. The results show that using MAPO, programmers produce code with fewer bugs when facing relatively complex API usages, comparing with using the two state-of-the-art code search tools.
Analysis of LoRaWAN technology in an Outdoor and an Indoor Scenario in Duisburg-Germany

Low Power Wide Area Network (LPWAN) technology is a new technology that is nowadays drawing a lot of attention in the Internet of Things (IoT) segment. In this study, we analyze the behavior of LoRaWAN technology in urban settings in both an indoor and an outdoor scenario, with respect to parameters such as Received Signal Strength Indicator, Signal to Noise Ratio, Packet Delivery Rate, Coverage, and Deep Indoor Penetration. The results are presented with different Spreading Factors (SF7 to SF12) in an indoor analysis. We built a private LoRaWAN gateway with the help of an IC880a concentrator board. The experiments took place in urban settings with frequency noises and obstacles, such as tall buildings, trees, cars, and people. We compared our study results in Duisburg-Germany downtown with related studies.
Cooperative Path Prediction in Vehicular Environments

The prediction of the future path of the ego vehicle and of other vehicles in the road environment is very important for safety applications, especially for collision avoidance systems. Today's available advanced driver assistance systems are mainly based on sensors that are installed in the vehicle. Due to the evolution of wireless networks the current trend is to exploit the cooperation among vehicles to enhance road safety. In this paper a cooperative path prediction algorithm is presented. This algorithm gathers position, velocity and yaw rate measurements from all vehicles in order to calculate the future paths. A specific care is taken for the manipulation of the latency of the wireless vehicular network. Also map data concerning the road geometry are used to enhance the estimation of path prediction. This work shows both the advances of using communications among road users and the corresponding challenges.
The Role of Adverbs in Sentiment Analysis

Sentiment Analysis, an important area of Natural Language Understanding, often relies on the assumption that lexemes carry inherent sentiment values, as reflected in specialized resources. We examine and measure the contribution that eight intensifying adverbs make to the sentiment value of sentences, as judged by human annotators. Our results show, first, that the intensifying adverbs are not themselves sentiment-laden but strengthen the sentiment conveyed by words in their contexts to different degrees. We consider the consequences for appropriate modifications of the representation of the adverbs in sentiment lexicons.
A Survey of Various Fault Tolerance Checkpointing Algorithms in Distributed System

Sudha Department of Computer Science, Amity University Haryana, India Email: sudhayadav.91@gmail.com Nisha Department of Computer Science, Amity University Haryana, India Email: yadavnisha021@gmail.com ----------------------------------------------------------------ABSTRACT---------------------------------------------------------------A distributed system is a collection of independent entities that cooperate to solve a problem that cannot be individually solved. Checkpoint is defined as a fault tolerant technique. It is a save state of a process during the failure-free execution, enabling it to restart from this checkpointed state upon a failure to reduce the amount of lost work instead of repeating the computation from beginning. The process of restoring form previous checkpointed state is known as rollback recovery. A checkpoint can be saved on either the stable storage or the volatile storage depending on the failure scenarios to be tolerated. Checkpointing is major challenge in mobile ad hoc network. The mobile ad hoc network architecture is one consisting of a set of self configure mobile hosts(MH) capable of communicating with each other without the assistance of base stations, some of processes running on mobile host. The main issues of this environment are insufficient power and limited storage capacity. This paper surveys the algorithms which have been reported in the literature for checkpointing in distributed systems as well as Mobile Distributed systems.
Model-based contextual policy search for data-efficient generalization of robot skills

In robotics, lower-level controllers are typically used to make the robot solve a specific task in a fixed context. For example, the lower-level controller can encode a hitting movement while the context defines the target coordinates to hit. However, in many learning problems the context may change between task executions. To adapt the policy to a new context, we utilize a hierarchical approach by learning an upper-level policy that generalizes the lower-level controllers to new contexts. A common approach to learn such upper-level policies is to use policy search. However, the majority of current contextual policy search approaches are model-free and require a high number of interactions with the robot and its environment. Model-based approaches are known to significantly reduce the amount of robot experiments, however, current model-based techniques cannot be applied straightforwardly to the problem of learning contextual upper-level policies. They rely on specific parametrizations of the policy and the reward function, which are often unrealistic in the contextual policy search formulation. In this paper, we propose a novel model-based contextual policy search algorithm that is able to generalize lower-level controllers, and is data-efficient. Our approach is based on learned probabilistic forward models and information theoretic policy search. Unlike current algorithms, our method does not require any assumption on the parametrization of the policy or the reward function. We show on complex simulated robotic tasks and in a real robot experiment that the proposed learning framework speeds up the learning process by up to two orders of magnitude in comparison to existing methods, while learning high quality policies.
Distinguishing Load Torque Oscillations and Eccentricity Faults in Induction Motors Using Stator Current Wigner Distributions

This paper proposes a novel diagnosis method for detection and discrimination of two typical mechanical failures in induction motors by stator current analysis: load torque oscillations and dynamic rotor eccentricity. A theoretical analysis shows that each fault modulates the stator current in a different way: torque oscillations lead to stator current phase modulation, whereas rotor eccentricities produce stator current amplitude modulation. The use of traditional current spectrum analysis involves identical frequency signatures with the two fault types. A time-frequency analysis of the stator current with the Wigner distribution leads to different fault signatures that can be used for a more accurate diagnosis. The theoretical considerations and the proposed diagnosis techniques are validated on experimental signals.
Unilateral winged scapula: Clinical and electrodiagnostic experience with 128 cases, with special attention to long thoracic nerve palsy.

In this study we report a large series of patients with unilateral winged scapula (WS), with special attention to long thoracic nerve (LTN) palsy.



Clinical and electrodiagnostic data were collected from 128 patients over a 25-year period.



Causes of unilateral WS were LTN palsy (n = 70), spinal accessory nerve (SAN) palsy (n = 39), both LTN and SAN palsy (n = 5), facioscapulohumeral dystrophy (FSH) (n = 5), orthopedic causes (n = 11), voluntary WS (n = 6), and no definite cause (n = 2). LTN palsy was related to neuralgic amyotrophy (NA) in 61 patients and involved the right side in 62 patients.



Clinical data allow for identifying 2 main clinical patterns for LTN and SAN palsy. Electrodiagnostic examination should consider bilateral nerve conduction studies of the LTN and SAN, and needle electromyography of their target muscles. LTN palsy is the most frequent cause of unilateral WS and is usually related to NA. Voluntary WS and FSH must be considered in young patients. Muscle Nerve 57: 913-920, 2018.
Efficient differentiation of human embryonic and induced pluripotent stem cells into functional astrocytes.

Human high-grade gliomas (hHGG) remain a therapeutic challenge in neuro-oncology despite current multimodality treatments. We recently demonstrated that murine embryonic stem cell (mESC)-derived astrocytes conditionally expressing proapoptotic genes can successfully be used to induce apoptosis and tumor shrinkage of hHGG tumor in vitro and in an in vivo mouse model. The first step in the translation of these results to the clinical settings, however, requires availability of human embryonic stem cells (hESC)- and/or induced pluripotent cell (hiPSC)-derived astrocytes engineered to express proapoptotic genes. The potential for directed differentiation of hESCs and hiPSCs to functional postmitotic astrocytes is not fully characterized. In this study, we show that once specified to neuro-epithelial lineage, hiPSC could be differentiated to astrocytes with a similar efficiency as hESC. However, our analyses of 2 hESC and 2 hiPSC cell lines showed some variability in differentiation potential into astrocytic lineages. Both the hESC- and hiPSC-derived astrocytes appeared to follow the functional properties of mESC-derived astrocytes, namely, migration and tropism for hHGG. This work provides evidence that hESC- and hiPSC-derived cells are able to generate functionally active astrocytes. These results demonstrate the feasibility of using iPSC-derived astrocytes, a new potential source for therapeutic use for brain tumors and other neurological diseases.
Polynomial approximation to clothoids via s-power series

Given an arbitrary segment of a clothoid over a finite interval, we propose a novel method for generating a polynomial approximation, based on employing s-power series, the two-point analogue of Taylor expansions. Truncating at the kth term the s-power series furnishes the order-k Hermite interpolant, i.e. the degree-ð2k þ 1Þ polynomial curve that reproduces up to the kth derivative of the original curve at the endpoints of a given interval. By piecing these approximations we obtain a Hermitian spline that exhibits C continuity at the joints and enjoys almost arc-length parameterization. This is a more suitable alternative than the truncated Taylor series or the blending of Taylor expansions advocated by Wang et al.  in a recent article. q 2003
Determinants of Rural Poverty Reduction and Pro-poor Economic Growth in China

China has made remarkable progress in its war against poverty since the launching of economic reform in the late 1970s. This paper examines some of the major driving forces of poverty reduction in China. Based on time series and cross-sectional provincial data, the determinants of rural poverty incidence are estimated. The results show that economic growth is an essential and necessary condition for nationwide poverty reduction. It is not, however, a sufficient condition. While economic growth played a dominant role in reducing poverty through the mid-1990s, its impacts has diminished since that time. Beyond general economic growth, growth in specific sectors of the economy is also found to reduce poverty. For example, the growth the agricultural sector and other pro-rural (vs urban-biased) development efforts can also have significant impacts on rural poverty. Notwithstanding the record of the past, our paper is consistent with the idea that poverty reduction in the future will need to rely on more than broad-based growth and instead be dependent on pro-poor policy interventions (such as national poverty alleviation programs) that can be targeted at the poor, trying to directly help the poor to increase their human capital and incomes. Determinants of Rural Poverty Reduction and Pro-poor Economic Growth in China
Blood-brain barrier shuttle peptides enhance AAV transduction in the brain after systemic administration.

The adeno-associated virus (AAV) vector has been used in preclinical and clinical trials of gene therapy for central nervous system (CNS) diseases. One of the biggest challenges of effectively delivering AAV to the brain is to surmount the blood-brain barrier (BBB). Herein, we identified several potential BBB shuttle peptides that significantly enhanced AAV8 transduction in the brain after a systemic administration, the best of which was the THR peptide. The enhancement of AAV8 brain transduction by THR is dose-dependent, and neurons are the primary THR targets. Mechanism studies revealed that THR directly bound to the AAV8 virion, increasing its ability to cross the endothelial cell barrier. Further experiments showed that binding of THR to the AAV virion did not interfere with AAV8 infection biology, and that THR competitively blocked transferrin from binding to AAV8. Taken together, our results demonstrate, for the first time, that BBB shuttle peptides are able to directly interact with AAV and increase the ability of the AAV vectors to cross the BBB for transduction enhancement in the brain. These results will shed important light on the potential applications of BBB shuttle peptides for enhancing brain transduction with systemic administration of AAV vectors.
Cluster-based sampling of multiclass imbalanced data

The aim of this paper is to improve the classification performance based on the multiclass imbalanced datasets. In this paper, we introduce a new resampling approach based on Clustering with sampling for Multiclass Imbalanced classification using Ensemble (C-MIEN). C-MIEN uses the clustering approach to create a new training set for each cluster. The new training sets consist of the new label of instances with similar characteristics. This step is applied to reduce the number of classes then the complexity problem can be easily solved by C-MIEN. After that, we apply two resampling techniques (oversampling and undersampling) to rebalance the class distribution. Finally, the class distribution of each training set is balanced and ensemble approaches are used to combine the models obtained with the proposed method through majority vote. Moreover, we carefully design the experiments and analyze the behavior of C-MIEN with different parameters (imbalance ratio and number of classifiers). The experimental results show that C-MIEN achieved higher performance than state-of-the-art methods.
Opioid-Sparing Effect of Ketamine in Children: A Meta-Analysis and Trial Sequential Analysis of Published Studies.

Reducing postoperative opioid consumption is a priority given its impact upon recovery, and the efficacy of ketamine as an opioid-sparing agent in children is debated. The goal of this study was to update a previous meta-analysis on the postoperative opioid-sparing effect of ketamine, adding trial sequential analysis (TSA) and four new studies.



A comprehensive literature search was conducted to identify clinical trials that examined ketamine as a perioperative opioid-sparing agent in children and infants. Outcomes measured were postoperative opioid consumption to 48 h (primary outcome: postoperative opioid consumption to 24 h), postoperative pain intensity, postoperative nausea and vomiting and psychotomimetic symptoms. The data were combined to calculate the pooled mean difference, odds ratios or standard mean differences. In addition to this classical meta-analysis approach, a TSA was performed.



Eleven articles were identified, with four added to seven from the previous meta-analysis. Ketamine did not exhibit a global postoperative opioid-sparing effect to 48 postoperative hours, nor did it decrease postoperative pain intensity. This result was confirmed using TSA, which found a lack of power to draw any conclusion regarding the primary outcome of this meta-analysis (postoperative opioid consumption to 24 h). Ketamine did not increase the prevalence of either postoperative nausea and vomiting or psychotomimetic complications.



This meta-analysis did not find a postoperative opioid-sparing effect of ketamine. According to the TSA, this negative result might involve a lack of power of this meta-analysis. Further studies are needed in order to assess the postoperative opioid-sparing effects of ketamine in children.
Multi-model Data Management: What's New and What's Next?

As more businesses realized that data, in all forms and sizes, is critical to making the best possible decisions, we see the continued growth of systems that support massive volume of non-relational or unstructured forms of data. Nothing shows the picture more starkly than the Gartner Magic quadrant for operational database management systems, which assumes that, by 2017, all leading operational DBMSs will offer multiple data models, relational and NoSQL, in a single DBMS platform. Having a single data platform for managing both well-structured data and NoSQL data is beneficial to users; this approach reduces significantly integration, migration, development, maintenance, and operational issues. Therefore, a challenging research work is how to develop efficient consolidated single data management platform covering both relational data and NoSQL to reduce integration issues, simplify operations, and eliminate migration issues. In this tutorial, we review the previous work on multi-model data management and provide the insights on the research challenges and directions for future work. The slides and more materials of this tutorial can be found at http://udbms.cs.helsinki.fi/?tutorials/edbt2017.
Author ' s personal copy Flame synthesis of zinc oxide nanowires

Single-crystalline ZnO nanowires are grown directly on zinc-plated-steel substrates at high rates (microns/minute) using a flame-synthesis method, with no catalysts. The growth of the nanostructures is very sensitive to local gas-phase chemical species concentrations (O2, H2, CO2, H2O) and temperature (800–1500 K) as measured by laser-based spontaneous Raman spectroscopy. Larger-diameter (>100 nm) hexagonal-cross-section nanowires, with [0001] growth direction, are produced at higher temperatures; while smallerdiameter (25–40 nm) nanowires, with 1⁄211 20 growth direction, are produced at lower temperatures, and only on the fuel side of the reaction zone (with no oxygen present). Nanoribbons and other nanowire-based morphologies are also found and discussed.
Medicaid Becomes the First Third-Party Payer to Cover Passive Remote Monitoring for Home Care: Policy Analysis

Recent years have seen an influx of location-tracking, activity-monitoring sensors, and Web-cameras to remotely monitor the safety of older adults in their homes and to reduce reliance on in-person assistance. The state of research on these monitoring technologies leaves open crucial financial, social, and ethical cost-benefit questions, which have prevented widespread use. Medicaid is now the first large third-party payer in the United States to pay for these technologies, and their use is likely to increase as states transition to managed long-term services and supports (MLTSS).



This is the first study to examine how state Medicaid programs are treating passive remote monitoring technologies. This study identifies (1) which states allow location tracking, sensor systems, and cameras; (2) what policies are in place to track their use; (3) what implementation processes and program monitoring mechanisms are in place; and (4) what related insights Medicaid program stakeholders would like to learn from researchers.



Interviews were conducted with 43 state, federal, and managed care organization (MCO) Medicaid program stakeholders about how these technologies are used in state waivers serving community-dwelling older adults in 15 states, and what policies are in place to regulate them. The interviews were analyzed by the research team using the framework analysis method for applied policy research.



Two-thirds of the states cover location tracking and activity-monitoring sensors and one-third cover cameras, but only 3 states have specific service categories that allow them to track when they are paying for any of these technologies, impeding regulation and understanding of their use at the state and federal level. Consideration of ethical and social risks is limited, and states struggle to understand which circumstances warrant use. They are further challenged by extreme resource restrictions and transitions to MLTSS by MCOs inexperienced in serving this growing "high-need, high-cost" population.



Decisions about Medicaid reimbursement of technologies that have the potential to dramatically alter the way older adults receive supportive services are being made without research on their use, social and ethical implications, or outcomes. At a minimum, new service categories are needed to enable oversight. Participants prioritized 3 research aims to inform practice: (1) determine cost-effectiveness; (2) identify what type of information beneficiaries want to be generated and whom they want it to be shared with; and (3) understand how to support ethical decision making for beneficiaries with cognitive impairment. These findings provide direction for future research and reveal that greater interaction between policy makers and researchers in this field is needed.
Using Emotions as Intrinsic Motivation to Accelerate Classic Reinforcement Learning

Aiming at the need for autonomous learning in reinforcement learning (RL), a quantitative emotion-based motivation model is proposed by introducing psychological emotional factors as the intrinsic motivation. The curiosity is used to promote or hold back agents' exploration of unknown states, the happiness index is used to determine the current state-action's happiness level, the control power is used to indicate agents' control ability over its surrounding environment, and together to adjust agents' learning preferences and behavioral patterns. To combine intrinsic emotional motivations with classic RL, two methods are proposed. The first method is to use the intrinsic emotional motivations to explore unknown environment and learn the environment transitioning model ahead of time, while the second method is to combine intrinsic emotional motivations with external rewards as the ultimate joint reward function, directly to drive agents' learning. As the result shows, in the simulation experiments in the rat foraging in maze scenario, both methods have achieved relatively good performance, compared with classic RL purely driven by external rewards.