nils-herrmann/clean_scirepeval huggingface dataset:
Nonlinear Dynamics of Information Diffusion in Social Networks

The recent explosion in the adoption of search engines and new media such as blogs and Twitter have facilitated the faster propagation of news and rumors. How quickly does a piece of news spread over these media? How does its popularity diminish over time? Does the rising and falling pattern follow a simple universal law? In this article, we propose SpikeM, a concise yet flexible analytical model of the rise and fall patterns of information diffusion. Our model has the following advantages. First, unification power: it explains earlier empirical observations and generalizes theoretical models including the SI and SIR models. We provide the threshold of the take-off versus die-out conditions for SpikeM and discuss the generality of our model by applying it to an arbitrary graph topology. Second, practicality: it matches the observed behavior of diverse sets of real data. Third, parsimony: it requires only a handful of parameters. Fourth, usefulness: it makes it possible to perform analytic tasks such as forecasting, spotting anomalies, and interpretation by reverse engineering the system parameters of interest (quality of news, number of interested bloggers, etc.). We also introduce an efficient and effective algorithm for the real-time monitoring of information diffusion, namely SpikeStream, which identifies multiple diffusion patterns in a large collection of online event streams. Extensive experiments on real datasets demonstrate that SpikeM accurately and succinctly describes all patterns of the rise and fall spikes in social networks.
SECURE: Self-Protection Approach in Cloud Resource Management

In the current scenario of cloud computing, heterogeneous resources are located in various geographical locations requiring security-aware resource management to handle security threats. However, existing techniques are unable to protect systems from security attacks. To provide a secure cloud service, a security-based resource management technique is required that manages cloud resources automatically and delivers secure cloud services. In this paper, we propose a self-protection approach in cloud resource management called SECURE, which offers self-protection against security attacks and ensures continued availability of services to authorized users. The performance of SECURE has been evaluated using SNORT. The experimental results demonstrate that SECURE performs effectively in terms of both the intrusion detection rate and false positive rate. Further, the impact of security on quality of service (QoS) has been analyzed.
Dysregulation of the right brain: a fundamental mechanism of traumatic attachment and the psychopathogenesis of posttraumatic stress disorder.

This review integrates recent advances in attachment theory, affective neuroscience, developmental stress research, and infant psychiatry in order to delineate the developmental precursors of posttraumatic stress disorder.



Existing attachment, stress physiology, trauma, and neuroscience literatures were collected using Index Medicus/Medline and  This converging interdisciplinary data was used as a theoretical base for modelling the effects of early relational trauma on the developing central and autonomic nervous system activities that drive attachment functions.



Current trends that integrate neuropsychiatry, infant psychiatry, and clinical psychiatry are generating more powerful models of the early genesis of a predisposition to psychiatric disorders, including PTSD. Data are presented which suggest that traumatic attachments, expressed in episodes of hyperarousal and dissociation, are imprinted into the developing limbic and autonomic nervous systems of the early maturing right brain. These enduring structural changes lead to the inefficient stress coping mechanisms that lie at the core of infant, child, and adult posttraumatic stress disorders.



Disorganised-disoriented insecure attachment, a pattern common in infants abused in the first 2 years of life, is psychologically manifest as an inability to generate a coherent strategy for coping with relational stress. Early abuse negatively impacts the developmental trajectory of the right brain, dominant for attachment, affect regulation, and stress modulation, thereby setting a template for the coping deficits of both mind and body that characterise PTSD symptomatology. These data suggest that early intervention programs can significantly alter the intergenerational transmission of posttraumatic stress disorders.
Detecting code clones in binary executables

Large software projects contain significant code duplication, mainly due to copying and pasting code. Many techniques have been developed to identify duplicated code to enable applications such as refactoring, detecting bugs, and protecting intellectual property. Because source code is often unavailable, especially for third-party software, finding duplicated code in binaries becomes particularly important. However, existing techniques operate primarily on source code, and no effective tool exists for binaries.
 In this paper, we describe the first practical clone detection algorithm for binary executables. Our algorithm extends an existing tree similarity framework based on clustering of characteristic vectors of labeled trees with novel techniques to normalize assembly instructions and to accurately and compactly model their structural information. We have implemented our technique and evaluated it on Windows XP system binaries totaling over 50 million assembly instructions. Results show that it is both scalable and precise: it analyzed Windows XP system binaries in a few hours and produced few false positives. We believe our technique is a practical, enabling technology for many applications dealing with binary code.
Blockchain - From the Anarchy of Cryptocurrencies to the Enterprise (Keynote Abstract)

A blockchain is a public ledger for recording transactions, maintained by many nodes without central authority through a distributed cryptographic protocol. All nodes validate the information to be appended to the blockchain, and a consensus protocol ensures that the nodes agree on a unique order in which entries are appended. Distributed protocols tolerating faults and adversarial attacks, coupled with cryptographic tools are needed for this. The recent interest in blockchains has revived research on consensus protocols, ranging from the proof-of-work method in Bitcoin’s “mining” protocol to classical Byzantine agreement. Going far beyond its use in cryptocurrencies, blockchain is today viewed as a promising technology to simplify trusted exchanges of data and goods among companies. In this context, the Hyperledger Project has been established in early 2016 as an industry-wide collaborative effort to develop an open-source blockchain. This talk will present an overview of blockchain concepts, cryptographic building blocks and consensus mechanisms. It will also introduce Hyperledger Fabric, an implementation of blockchain technology intended for enterprise applications. Being one of the key partners in the Hyperledger Project, IBM is actively involved in the development of this blockchain platform.
Identification of extremism on Twitter

Identifying extremist-associated conversations on Twitter is an open problem. Extremist groups have been leveraging Twitter (1) to spread their message and (2) to gain recruits. In this paper, we investigate the problem of determining whether a particular Twitter user engages in extremist conversation. We explore different Twitter metrics as proxies for misbehavior, including the sentiment of the user's published tweets, the polarity of the user's ego-network, and user mentions. We compare different known classifiers using these different features on manually annotated tweets involving the ISIS extremist group and find that combining all these features leads to the highest accuracy for detecting extremism on Twitter.
Sensor-Based Activity Recognition

Research on sensor-based activity recognition has, recently, made significant progress and is attracting growing attention in a number of disciplines and application domains. However, there is a lack of high-level overview on this topic that can inform related communities of the research state of the art. In this paper, we present a comprehensive survey to examine the development and current status of various aspects of sensor-based activity recognition. We first discuss the general rationale and distinctions of vision-based and sensor-based activity recognition. Then, we review the major approaches and methods associated with sensor-based activity monitoring, modeling, and recognition from which strengths and weaknesses of those approaches are highlighted. We make a primary distinction in this paper between data-driven and knowledge-driven approaches, and use this distinction to structure our survey. We also discuss some promising directions for future research.
K-means Document Clustering Based on Latent Dirichlet Allocation

K-means is a popular algorithm in document clustering, which is fast and efficient. The disadvantages of K-means are that it requires one to set the number of clusters first and select the initial clustering centers randomly. Latent Dirichlet Allocation (LDA) is a mature probabilistic topic model, which aids in document dimensionality reduction, semantic mining and information retrieval. We present a document clustering method based on LDA and K-means (LDA_K-means). In order to improve document clustering effect with K-means, we discover the initial clustering centers by finding the typical latent topics extracted by LDA. The effectiveness of LDA_K-means is evaluated on the 20 Newsgroups data sets. We show that LDA_K-means can significantly improve the clustering effect in contrast to clustering based on random initialization of K-means and LDA (LDA_KMR).
Molecular mechanisms and clinical implications of bacterial persistence.

Any bacterial population harbors a small number of phenotypic variants that survive exposure to high concentrations of antibiotic. Importantly, these so-called 'persister cells' compromise successful antibiotic therapy of bacterial infections and are thought to contribute to the development of antibiotic resistance. Intriguingly, drug-tolerant persisters have also been identified as a factor underlying failure of chemotherapy in tumor cell populations. Recent studies have begun to unravel the complex molecular mechanisms underlying persister formation and revolve around stress responses and toxin-antitoxin modules. Additionally, in vitro evolution experiments are revealing insights into the evolutionary and adaptive aspects of this phenotype. Furthermore, ever-improving experimental techniques are stimulating efforts to investigate persisters in their natural, infection-associated, in vivo environment. This review summarizes recent insights into the molecular mechanisms of persister formation, explains how persisters complicate antibiotic treatment of infections, and outlines emerging strategies to combat these tolerant cells.
Fractional Fourier Transform Based Co-Radar Waveform: Experimental Validation

A Fractional Fourier Transform (FrFT) based waveform design for joint radar-communication systems (Co-Radar) that embeds data into chirp sub-carriers with different time-frequency rates has been recently presented. Simulations demonstrated the possibility to reach data rates as high as 3.660 Mb/s while maintaining good radar performance compared to a Linear Frequency Modulated (LFM) pulse that occupies the same bandwidth. In this paper the experimental validation of the concept is presented. The system is considered in its basic configuration, with a mono-static radar that generates the waveforms and performs basic radar tasks, and a communication receiver in charge of the pulse demodulation. The entire network is implemented on a Software Defined Radio (SDR) device. The system is then used to acquire data and assess radar and communication capabilities.
Financial time series forecasting using independent component analysis and support vector regression

As financial time series are inherently noisy and non-stationary, it is regarded as one of the most challenging applications of time series forecasting. Due to the advantages of generalization capability in obtaining a unique solution, support vector regression (SVR) has also been successfully applied in financial time series forecasting. In the modeling of financial time series using SVR, one of the key problems is the inherent high noise. Thus, detecting and removing the noise are important but difficult tasks when building an SVR forecasting model. To alleviate the influence of noise, a two-stage modeling approach using independent component analysis (ICA) and support vector regression is proposed in financial time series forecasting. ICA is a novel statistical signal processing technique that was originally proposed to find the latent source signals from observed mixture signals without having any prior knowledge of the mixing mechanism. The proposed approach first uses ICA to the forecasting variables for generating the independent components (ICs). After identifying and removing the ICs containing the noise, the rest of the ICs are then used to reconstruct the forecasting variables which contain less noise and served as the input variables of the SVR forecasting model. In order to evaluate the performance of the proposed approach, the Nikkei 225 opening index and TAIEX closing index are used as illustrative examples. Experimental results show that the proposed model outperforms the SVR model with non-filtered forecasting variables and a random walk model.
The Role of Business Intelligence and Communication Technologies in Organizational Agility: A Configurational Approach

This study examines the role that business intelligence (BI) and communication technologies play in how firms may achieve organizational sensing agility, decision making agility, and acting agility in different organizational and environmental contexts. Based on the information-processing view of organizations and dynamic capability theory, we suggest a configurational analytic framework that departs from the standard linear paradigm to examine how IT’s effect on agility is embedded in a configuration of organizational and environmental elements. In line with this approach, we use fuzzy-set qualitative comparative analysis (fsQCA) to analyze field survey data from diverse industries. Our findings suggest equifinal pathways to organizational agility and the specific boundary conditions of our middle-range theory that determine what role BI and communication technologies play in organizations’ achieving organizational agility. We discuss implications for theory and practice and discuss future research avenues.
Managing the Pre- and Post-analytical Phases of the Total Testing Process

For many years, the clinical laboratory's focus on analytical quality has resulted in an error rate of 4-5 sigma, which surpasses most other areas in healthcare. However, greater appreciation of the prevalence of errors in the pre- and post-analytical phases and their potential for patient harm has led to increasing requirements for laboratories to take greater responsibility for activities outside their immediate control. Accreditation bodies such as the Joint Commission International (JCI) and the College of American Pathologists (CAP) now require clear and effective procedures for patient/sample identification and communication of critical results. There are a variety of free on-line resources available to aid in managing the extra-analytical phase and the recent publication of quality indicators and proposed performance levels by the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) working group on laboratory errors and patient safety provides particularly useful benchmarking data. Managing the extra-laboratory phase of the total testing cycle is the next challenge for laboratory medicine. By building on its existing quality management expertise, quantitative scientific background and familiarity with information technology, the clinical laboratory is well suited to play a greater role in reducing errors and improving patient safety outside the confines of the laboratory.
Parent-mediated early intervention for young children with autism spectrum disorder.

Recent estimates concerning the prevalence of autistic spectrum disorder are much higher than those reported 30 years ago, with at least 1 in 400 children affected. This group of children and families have important service needs. The involvement of parents in implementing intervention strategies designed to help their autistic children has long been accepted as helpful. The potential benefits are increased skills and reduced stress for parents as well as children.



The objective of this review was to determine the extent to which parent-mediated early intervention has been shown to be effective in the treatment of children aged 1 year to 6 years 11 months with autistic spectrum disorder. In particular, it aimed to assess the effectiveness of such interventions in terms of the benefits for both children and their parents.


SEARCH STRATEGY
A range of psychological, educational and biomedical databases were searched. Bibliographies and reference lists of key articles were searched, field experts were contacted and key journals were hand searched.



Only randomised or quasi-randomised studies were included. Study interventions had a significant focus on parent-implemented early intervention, compared to a group of children who received no treatment, a waiting list group or a different form of intervention. There was at least one objective, child related outcome measure.



Appraisal of the methodological quality of included studies was carried out independently by two reviewers. Differences between the included studies in terms of the type of intervention, the comparison groups used and the outcome measures were too great to allow for direct comparison.



The results of this review are based on data from two studies. Two significant results were found to favour parent training in one study: child language and maternal knowledge of autism. In the other, intensive intervention (involving parents, but primarily delivered by professionals) was associated with better child outcomes on direct measurement than were found for parent-mediated early intervention, but no differences were found in relation to measures of parent and teacher perceptions of skills and behaviours.



This review has little to offer in the way of implications for practice: there were only two studies, the numbers of participants included were small, and the two studies could not be compared directly to one another. In terms of research, randomised controlled trials involving large samples need to be carried out, involving both short and long-term outcome information and full economic evaluations. Research in this area is hampered by barriers to randomisation, such as availability of equivalent services.
The ideal restoration of endodontically treated teeth - structural and esthetic considerations: a review of the literature and clinical guidelines for the restorative clinician.

In restorative dentistry, the non-vital tooth and its restoration have been extensively studied from both its structural and esthetic aspects. The restoration of endodontically treated teeth has much in common with modern implantology: both must include multifaceted biological, biomechanical and esthetic considerations with a profound understanding of materials and techniques; both are technique sensitive and both require a multidisciplinary approach. And for both, two fundamental principles from team sports apply well: firstly, the weakest link determines the limits, and secondly, it is a very long way to the top, but a very short way to failure. Nevertheless, there is one major difference: if the tooth fails, there is the option of the implant, but if the implant fails, there is only another implant or nothing. The aim of this essay is to try to answer some clinically relevant conceptual questions and to give some clinical guidelines regarding the reconstructive aspects, based on scientific evidence and clinical expertise.
EULERIAN SERIES AS MODULAR FORMS

In 1988, Hickerson proved the celebrated “mock theta conjectures”, a collection of ten identities from Ramanujan’s “lost notebook” which express certain modular forms as linear combinations of mock theta functions. In the context of Maass forms, these identities arise from the peculiar phenomenon that two different harmonic Maass forms may have the same non-holomorphic parts. Using this perspective, we construct several infinite families of modular forms which are differences of mock theta functions.
Semantics of Linear/Modal Lambda Calculus

In previous work the author has introduced a lambda calculus SLR with modal and linear types which serves as an extension of Bellantoni-Cook's function algebra BC to higher types. It is a step towards a functional programming language in which all programs run in polynomial time. While this previous work was concerned with the syntactic metatheory of SLR in this paper we develop a semantics of SLR in terms of Chu spaces over a certain category of sheaves from which it follows that all expressible functions are indeed in PTIME. We notice a similarity between the Chu space interpretation and CPS translation which as we hope will have further applications in functional programming.
Socio-Economic Distance and Spatial Patterns in Unemployment

This paper examines the spatial patterns of unemployment in Chicago between 1980 and 1990. We study unemployment clustering with respect to different social and economic distance metrics that reßect the structure of agents social networks. SpeciÞcally, we use physical distance, travel time, and differences in ethnic and occupational distribution between locations. Our goal is to determine whether our estimates of spatial dependence are consistent with models in which agents employment status is affected by information exchanged locally within their social networks. We present non-parametric estimates of correlation across Census tracts as a function of each distance metric as well as pairs of metrics, both for unemployment rate itself and after conditioning on a set of tract characteristics. Our results indicate that there is a strong positive and statistically signiÞcant degree of spatial dependence in the distribution of raw unemployment rates, for all our metrics. However, once we condition on a set of covariates, most of the spatial autocorrelation is eliminated, with the exception of physical and occupational distance. Racial and ethnic composition variables are the single most important factor in explaining the observed correlation patterns.
Learning Cross-Spectral Similarity Measures with Deep Convolutional Neural Networks

The simultaneous use of images from different spectra can be helpful to improve the performance of many computer vision tasks. The core idea behind the usage of crossspectral approaches is to take advantage of the strengths of each spectral band providing a richer representation of a scene, which cannot be obtained with just images from one spectral band. In this work we tackle the cross-spectral image similarity problem by using Convolutional Neural Networks (CNNs). We explore three different CNN architectures to compare the similarity of cross-spectral image patches. Specifically, we train each network with images from the visible and the near-infrared spectrum, and then test the result with two public cross-spectral datasets. Experimental results show that CNN approaches outperform the current state-of-art on both cross-spectral datasets. Additionally, our experiments show that some CNN architectures are capable of generalizing between different crossspectral domains.