nils-herrmann/clean_scirepeval huggingface dataset:
Monitoring of Multivariate Processes Through the Regression Adjustment Procedure Based on Artificial Neural Networks
Venue: IEEE Latin America Transactions
Year: 2019

In current production systems, organizations must control the total of the variables that have a correlated influence on the final quality of the products. For this task, multivariate statistical control is used. One of the most used multivariate control methods is the so called regression adjustment whose scheme proposes that to monitor a final quality characteristic in a product, a multiple regression should be applied on the previous variables that define it in the process and the resulting residue will be controlled from a univariate control chart to detect changes in the variations, however, the utility of the procedure is directly related to the efficiency of the method used to perform the adjustment task between the dependent and independent variables. The present work proposed to use an artificial neural network to carry out the adjustment operation, the results showed a remarkable superiority of the proposed scheme in comparison with the current scheme (Least Squares) and adjustment methods proposed in the literature (LAD and M) The proposed multivariate control approach allows the evolution of the regression adjustment method with the incorporation of artificial intelligence.
Computational Fluid Dynamics Suggests Ecological Diversification among Stem Gnathostomes
Venue: Current Biology
Year: 2020

The evolutionary assembly of the vertebrate bodyplan has been characterized as a long term ecological trend toward increasingly active and predatory lifestyles, culminating in jawed vertebrates that dominate modern vertebrate biodiversity [1 8] This contrast is no more stark than between the earliest jawed vertebrates and their immediate relatives, the extinct jawless, dermal armor encased osteostracans, which have conventionally been interpreted as benthic mud grubbers with poor swimming capabilities and low maneuverability [9 12] Using computational fluid dynamics, we show that osteostracan headshield morphology is compatible with a diversity of hydrodynamic efficiencies including passive control of water flow around the body; these could have increased versatility for adopting diverse locomotor strategies. Hydrodynamic performance varies with morphology, proximity to the substrate, and angle of attack (inclination) Morphotypes with dorsoventrally oblate headshields are hydrodynamically more efficient when swimming close to the substrate, whereas those with dorsoventrally more prolate headshields exhibit maximum hydrodynamic efficiency when swimming free from substrate effects. These results suggest different hydrofoil functions among osteostracan headshield morphologies, compatible with ecological diversification and undermining the traditional view that jawless stem gnathostomes were ecologically constrained [9 12] with the origin of jaws as the key innovation that precipitated the ecological diversification of the group [13, 14]
Linking Merger and Acquisition Strategies to Postmerger Integration: A Configurational Perspective of Human Resource Management
Year: 2018

The extant literature tends to frame mergers and acquisitions (M&As) and postmerger integration (PMI) as strategies and outcomes, but this framing often leaves their underlying processes underexplored. We address this gap by redirecting attention to the view that M&As are largely embedded in social and human practices. Our conceptual study identifies three generic M&A strategies annex assimilate, harvest protect, and link promote and matches them with three well known PMI outcomes (i.e. absorption, preservation, and symbiosis, respectively) Using a configurational perspective and drawing upon the ability motivation opportunity (AMO) model, we develop a conceptual framework that reveals why and how AMO enhancing human resource management (HRM) practices can link M&A strategies and PMI outcomes. Finally, we elaborate on the theoretical and practical contributions and chart a course for future inquiry and research applications for the M&A HRM PMI triad and its processes.
Phonological awareness in non fluent preschool children
Year: 2020

The relation between disfluency and aspects of language, including phonology, has been investigated for many years. Whereas past literature reported that the incidence of phonological difficulties is higher for children who stutter when compared to normally fluent children, the suggestion of association between the two disorders also drew several critical evaluations. This study investigated the phonological awareness abilities of four and five year old non fluent preschool children.  25 non fluent preschool children (21 boy and 4 girl) were paired according to age, sex and socioeconomic status to their typically fluent peers. Participants completed multiple measures of phonological awareness abilities, as well as measures of expressive and receptive vocabulary and articulation.  Regarding the scores of phonological awareness test there were significant differences between non fluent children and their typically fluent peers. Four year old who non fluent performed significantly less well than their typically fluent peers on tasks of syllable awareness, intra syllable awareness and phoneme syllable awareness. five year old non fluent preschool children performed significantly less well than their typically fluent peers on tasks of intra syllable awareness and phoneme syllable awareness.  Results suggest that non fluent preschool children have phonological differences in aspects of phonological awareness that may contribute to an unstable language planning system in preschool children who stutter.
RA UNet: A Hybrid Deep Attention Aware Network to Extract Liver and Tumor in CT Scans
Venue: Frontiers in Bioengineering and Biotechnology
Year: 2020

Automatic extraction of liver and tumor from CT volumes is a challenging task due to their heterogeneous and diffusive shapes. Recently, 2D deep convolutional neural networks have become popular in medical image segmentation tasks because of the utilization of large labeled datasets to learn hierarchical features. However, few studies investigate 3D networks for liver tumor segmentation. In this paper, we propose a 3D hybrid residual attention aware segmentation method, i.e. RA UNet, to precisely extract the liver region and segment tumors from the liver. The proposed network has a basic architecture as U Net which extracts contextual information combining low level feature maps with high level ones. Attention residual modules are integrated so that the attention aware features change adaptively. This is the first work that an attention residual mechanism is used to segment tumors from 3D medical volumetric images. We evaluated our framework on the public MICCAI 2017 Liver Tumor Segmentation dataset and tested the generalization on the 3DIRCADb dataset. The experiments show that our architecture obtains competitive results.
Travel Plans in Public Transit Networks Using Artificial Intelligence Planning Models
Venue: Appl. Artif. Intell.
Year: 2019

Users of public transit networks require tools that generate travel plans to traverse them. The main issue is that public transit networks are time and space dependent. Travel plans depend on the current location of users and transit units, along with a set of user preferences and time restrictions. In this work, we propose the design and development of artificial intelligence (AI) planning models for engineering travel plans for such networks. The proposed models consider temporal actions, bus locations, and user preferences as constraints, to restrict the set of travel plans generated. Our approach decouples model design from algorithm construction, providing a greater level of flexibility and richness of solutions. We also introduce an integer linear programming formulation, and a fast preprocessing procedure, to evaluate the quality of the solutions returned by the proposed planning models. Experimental results show that AI planning models can efficiently generate close to optimal solutions. Furthermore, our analysis identifies user preferences as the most critical factor that increases solution complexity for planning models.
2D LiDAR SLAM Back End Optimization with Control Network Constraint for Mobile Mapping
Venue: Sensors
Year: 2018

Simultaneous localization and mapping (SLAM) has been investigated in the field of robotics for two decades, as it is considered to be an effective method for solving the positioning and mapping problem in a single framework. In the SLAM community, the Extended Kalman Filter (EKF) based SLAM and particle filter SLAM are the most mature technologies. After years of development, graph based SLAM is becoming the most promising technology and a lot of progress has been made recently with respect to accuracy and efficiency. No matter which SLAM method is used, loop closure is a vital part for overcoming the accumulated errors. However, in 2D Light Detection and Ranging (LiDAR) SLAM, on one hand, it is relatively difficult to extract distinctive features in LiDAR scans for loop closure detection, as 2D LiDAR scans encode much less information than images; on the other hand, there is also some special mapping scenery, where no loop closure exists. Thereby, in this paper, instead of loop closure detection, we first propose the method to introduce extra control network constraint (CNC) to the back end optimization of graph based SLAM, by aligning the LiDAR scan center with the control vertex of the presurveyed control network to optimize all the poses of scans and submaps. Field tests were carried out in a typical urban Global Navigation Satellite System (GNSS) weak outdoor area. The results prove that the position Root Mean Square (RMS) error of the selected key points is 0.3614 m, evaluated with a reference map produced by Terrestrial Laser Scanner (TLS) Mapping accuracy is significantly improved, compared to the mapping RMS of 1.6462 m without control network constraint. Adding distance constraints of the control network to the back end optimization is an effective and practical method to solve the drift accumulation of LiDAR front end scan matching.
Design of Secure Authentication Protocol for Cloud Assisted Telecare Medical Information System Using Blockchain
Venue: IEEE Access
Year: 2020

Telecare medical information system (TMIS) implemented in wireless body area network (WBAN) is convenient and time saving for patients and doctors. TMIS is realized using wearable devices worn by a patient, and wearable devices generate patient health data and transmit them to a server through a public channel. Unfortunately, a malicious attacker can attempt performing various attacks through such a channel. Therefore, establishing a secure authentication process between a patient and a server is essential. Moreover, wearable devices have limited storage power. Cloud computing can be considered to resolve this problem by providing a storage service in the TMIS environment. In this environment, access control of the patient health data is essential for the quality of healthcare. Furthermore, the database of the cloud server is a major target for an attacker. The attacker can try to modify, forge, or delete the stored data. To resolve these problems, we propose a secure authentication protocol for a cloud assisted TMIS with access control using blockchain. We employ ciphertext policy attribute based encryption (CP ABE) to establish access control for health data stored in the cloud server, and apply blockchain to guarantee data integrity. To prove robustness of the proposed protocol, we conduct informal analysis and Burrows Adabi Needham (BAN) logic analysis, and we formally validate the proposed protocol using automated validation of internet security protocols and applications (AVISPA) Consequently, we show that the proposed protocol provides more security and has better efficiency compared to related protocols. Therefore, the proposed protocol is proper for a practical TMIS environment.
P SIF: Document Embeddings Using Partition Averaging
Venue: AAAI
Year: 2020

Simple weighted averaging of word vectors often yields effective representations for sentences which outperform sophisticated seq2seq neural models in many tasks. While it is desirable to use the same method to represent documents as well, unfortunately, the effectiveness is lost when representing long documents involving multiple sentences. One of the key reasons is that a longer document is likely to contain words from many different topics; hence, creating a single vector while ignoring all the topical structure is unlikely to yield an effective document representation. This problem is less acute in single sentences and other short text fragments where the presence of a single topic is most likely. To alleviate this problem, we present P SIF, a partitioned word averaging model to represent long documents. P SIF retains the simplicity of simple weighted word averaging while taking a document's topical structure into account. In particular, P SIF learns topic specific vectors from a document and finally concatenates them all to represent the overall document. We provide theoretical justifications on the correctness of P SIF. Through a comprehensive set of experiments, we demonstrate P SIF's effectiveness compared to simple weighted averaging and many other baselines.
[Development of a low cost single chamber microbial fuel cell type BOD sensor]
Venue: Huan jing ke xue= Huanjing kexue
Year: 2010

The principle of the detector is based on the effect of microbial toxicity of water sample on the electricity generation in microbial fuel cell (MFC) The performance of the MFC type biotoxicity detector was evaluated with the synthetic water containing heavy metals of Cd2+ and Cu2+ The experimental results demonstrated that: (1) relative to the conventional methods, the MFC type detector is easy to operate, and suitable for on line measurements with high sensitivity; (2) it only requires 4 h to complete measurements, and can get ready for next measurement within 4 h; (3) there is a significant linear correlation between the concentration of toxic metal(s) and inhibition ratios in Coulombic yields of MFC. As the IC20 (concentration causing 20% inhibition) of Cd2+ Cu2+ and mixed metals (Cd2+ and Cu2+ were 0.6, 0.8 and 0.25 mg/L, the regression coefficients were shown to be 0.9960, 0.9744 and 0.9907.
Children's causal inferences about past vs. future events
Venue: CogSci
Year: 2019

Causal and temporal reasoning are fundamentally linked, but few studies have directly examined how the ability to make causal inferences about the past vs. the future develops. We used a counterfactual reasoning task to explore 4to 6 year old children's understanding of the causal relationships among past, present, and future events. Like adults, even 4 year olds judged that future, but not past, events could be altered by interventions in the present. This early sensitivity to the causal asymmetry between the past and future became more pronounced with age. We also found that children and adults selectively and appropriately use evidence about the present to make inferences about past events. Implications for theoretical accounts of the development of causal reasoning and abstract concepts of time are discussed.
HIPE MAGIC: a technology aware synthesis and mapping flow for highly parallel execution of memristor aided LoGIC
Venue: ISLPED
Year: 2020

Recent efforts for finding novel computing paradigms that meet today's design requirements have given rise to a new trend of processing in memory relying on non volatile memories. In this paper, we present HIPE MAGIC, a technology aware synthesis and mapping flow for highly parallel execution of the memristor based logic. Our framework is built upon two fundamental contributions: balancing techniques during the logic synthesis, mainly targeting benefits of the parallelism offered by memristive crossbar arrays (MCAs) and an efficient technology mapping framework to maximize the performance and area efficiency of the memristor based logic. Our experimental evaluations across several benchmark suites demonstrate the superior performance of HIPE MAGIC in terms of throughput and energy efficiency compared to recently developed synthesis and mapping flows targeting MCAs, as well as the conventional CPU computing.
Global Ten Moment Multifluid Simulations of the Solar Wind Interaction with Mercury: From the Planetary Conducting Core to the Dynamic Magnetosphere
Venue: Geophysical Research Letters
Year: 2019

For the first time, we explore the tightly coupled interior magnetosphere system of Mercury by employing a three dimensional ten moment multifluid model. This novel fluid model incorporates the non ideal effects including the Hall effect, inertia, and tensorial pressures that are critical for collisionless magnetic reconnection; therefore, it is particularly well suited for investigating $collisionless$ magnetic reconnection in Mercury's magnetotail and at the planet's magnetopause. The model is able to reproduce the observed magnetic field vectors, field aligned currents, and cross tail current sheet asymmetry (beyond the MHD approach) and the simulation results are in good agreement with spacecraft observations. We also study the magnetospheric response of Mercury to a hypothetical extreme event with an enhanced solar wind dynamic pressure, which demonstrates the significance of induction effects resulting from the electromagnetically coupled interior. More interestingly, plasmoids (or flux ropes) are formed in Mercury's magnetotail during the event, indicating the highly dynamic nature of Mercury's magnetosphere.
Effective and Robust Detection of Adversarial Examples via Benford Fourier Coefficients
Venue: ArXiv
Year: 2020

Adversarial examples have been well known as a serious threat to deep neural networks (DNNs) In this work, we study the detection of adversarial examples, based on the assumption that the output and internal responses of one DNN model for both adversarial and benign examples follow the generalized Gaussian distribution (GGD) but with different parameters (i.e. shape factor, mean, and variance) GGD is a general distribution family to cover many popular distributions (e.g. Laplacian, Gaussian, or uniform) It is more likely to approximate the intrinsic distributions of internal responses than any specific distribution. Besides, since the shape factor is more robust to different databases rather than the other two parameters, we propose to construct discriminative features via the shape factor for adversarial detection, employing the magnitude of Benford Fourier coefficients (MBF) which can be easily estimated using responses. Finally, a support vector machine is trained as the adversarial detector through leveraging the MBF features. Extensive experiments in terms of image classification demonstrate that the proposed detector is much more effective and robust on detecting adversarial examples of different crafting methods and different sources, compared to state of the art adversarial detection methods.
How to measure diversity when you must.
Venue: Psychological methods
Year: 2012

Racial/ethnic diversity has become an increasingly important variable in the social sciences. Research from multiple disciplines consistently demonstrates the tremendous impact of ethnic diversity on individuals and organizations. Investigators use a variety of measures, and their choices can affect the conclusions that can be drawn and limit the ability to compare and generalize results across studies effectively. The current article reviews 3 popular approaches to the measurement of diversity: the simplistic majority minority approach and 2 multiple categories variants, the generalized variance and the lesser used entropy statistic. We discuss the properties of each approach and reject the majority minority approach. We provide 5 examples using the generalized variance and entropy statistics and illustrate their versatility and flexibility. We urge investigators to adopt these multicategory measures and to use our discussion to determine which measure of diversity is most appropriate given the nature of one's data set and research question.
"You're Not Welcome Here" A Grounded Theory of Family Distancing
Year: 2019

A constructivist grounded theory of family distancing emerged from the narrative accounts of 52 adult children in the process of estrangement from their parents. The grounded theory conceptualizes estrangement as a continuum comprised of eight components: (1) communication quality, (2) communication quantity, (3) physical distance, (4) presence/absence of emotion, (5) positive/negative affect, (6) reconciliation/desire to be a family, (7) role reciprocity, and (8) legal action. In addition, 10 communicative behaviors are identified that can help family members move along the estrangement continuum toward greater distance or maintain their position. Together they interplay to constitute a holistic picture of the estrangement process. Scholarly uses, theoretical implications, and practical applications will be discussed.
Neurosurgical Interventions for Occipital Encephalocele
Venue: Asian journal of neurosurgery
Year: 2018

The aim of this study is to find the outcome of repair and resection of the occipital encephalocele.  The clinical data of fifty consecutive occipital encephalocele patients were retrieved from medical records including operative notes, postoperative follow up visits, and postsurgical complications were noted for analysis from November 2009 to November 2013 at the Department of Neurosurgery, Jinnah Postgraduate Medical Centre, Karachi, Pakistan. All patients were assessed by computed tomography scan, magnetic resonance imaging brain, and ultrasound when needed. Physician's assessment, physical examination, and his/her questions to the family at follow up were used as a tool to determine if there was a developmental delay rather than quantitative analysis like hydrocephalus questionnaires. Patients who developed complications and delayed milestone were regarded as no improvement and those who did not develop complications and achieved appropriate milestone were regarded as improved at 18 months follow up.  Of 50 patients, 17 were males and 33 were females. The average age at presentation was 2.4 months. 16 (32% patients had increased head circumference and hydrocephalus, 2 (4% had associated Dandy Walker cyst, 3 (6% developed developmental delays, and 8 (15% had a seizure disorder. None of our patients had neurological deficits. The size of the sac ranged from 2 cm x 3 cm to 27 cm x 15 cm. 9 (18% patients were admitted with the complication of sac rupture and 2 (4% patients sac ruptured after admission. Only one patient (2% had a cerebrospinal fluid leak postoperatively that was repaired primarily without patch graft or dura seal while 4 (8% developed hydrocephalus after repair of the sac which was treated with placement of ventriculoperitoneal shunt. One (2% patient did not recover from anesthesia and expired.  Encephalocele is commonly seen in the practice of neurosurgery in the world as well as in Pakistan. Modern neuroimaging, neurosurgical techniques, and neonatal neurological intensive care have greatly improved morbidity and mortality in the care of encephalocele.