nils-herrmann/clean_scirepeval huggingface dataset:
Conducting Meta-Analyses in R with the metafor Package

The metafor package provides functions for conducting meta-analyses in R. The package includes functions for fitting the meta-analytic fixedand random-effects models and allows for the inclusion of moderators variables (study-level covariates) in these models. Meta-regression analyses with continuous and categorical moderators can be conducted in this way. Functions for the Mantel-Haenszel and Peto’s one-step method for metaanalyses of 2× 2 table data are also available. Finally, the package provides various plot functions (for example, for forest, funnel, and radial plots) and functions for assessing the model fit, for obtaining case diagnostics, and for tests of publication bias.
vScale: automatic and efficient processor scaling for SMP virtual machines

SMP virtual machines (VMs) have been deployed extensively in clouds to host multithreaded applications. A widely known problem is that when CPUs are oversubscribed, the scheduling delays due to VM preemption give rise to many performance problems because of the impact of these delays on thread synchronization and I/O efficiency. Dynamically changing the number of virtual CPUs (vCPUs) by considering the available physical CPU (pCPU) cycles has been shown to be a promising approach. Unfortunately, there are currently no efficient mechanisms to support such vCPU-level elasticity.
 We present vScale, a cross-layer design to enable SMP-VMs to adaptively scale their vCPUs, at the cost of only microseconds. vScale consists of two extremely light-weight mechanisms: i) a generic algorithm in the hypervisor scheduler to compute VMs' CPU extendability, based on their proportional shares and CPU consumptions, and ii) an efficient method in the guest OS to quickly reconfigure the vCPUs. vScale can be tightly integrated with existing OS/hypervisor primitives and has very little management complexity. With our prototype in Xen/Linux, we evaluate vScale's performance with several representative multithreaded applications, including NPB suite, PARSEC suite and Apache web server. The results show that vScale can significantly reduce the VM's waiting time, and thus can accelerate many applications, especially synchronization-intensive ones and I/O-intensive ones.
Increased human intestinal barrier permeability plasma biomarkers zonulin and FABP2 correlated with plasma LPS and altered gut microbiome in anxiety or depression.

We read with interest the recent work by Uhde et al, which demonstrated that physically asymptomatic non-coeliac gluten/ wheat sensitivity involves compromised intestinal epithelium barrier dysfunction in conjunction with systemic immune activation events. We also read with interest the recent work by Marchesi et al, which comprehensively reviewed the role of microbiota in physical disorders of the gut and extra-gut organs. But common to these Gut papers was the lack of accounting for anxiety and depression, comorbidities often experienced in gastroenterology clinics. Patients who are otherwise physically asymptomatic often do not explicitly divulge these mental disorders, or the disorders are unintentionally overlooked, yet they report ‘diminished quality of life’. In response to this gap, we explored roles of dysbiosis and gut barrier integrity in individuals physically asymptomatic for gastrointestinal distress, yet nonetheless experiencing mental distress. We hypothesised that anxiety and depressive disorders are linked to human gut dysbiosis with microbiota that secrete lipopolysaccharide (LPS) endotoxin into plasma, which in conjunction with compromised gut barrier integrity has systemic manifestations including the brain. We further hypothesised that this correlates with altered intestinal epithelium paracellular integrity molecules discharged into blood as biomarkers, namely zonulin and intestinal fatty acidbinding protein-2 (FABP2). Zonulin is the master regulator of endothelial and epithelium tight junctions, modulating both gut and blood–brain barriers. We analysed stool faecal microbiota and plasma in 50 subjects who self-reported to be asymptomatic for gastrointestinal physical disorders; 22 volunteers met Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria for a depressive disorder or an anxiety disorder (DEP/ ANX), and 28 more were control reference subjects (NODEPANX). Subjects refrained from probiotics and antibiotics during 30 days prior to providing samples (see online supplementary methods). LEfSe analyses (online supplementary figures S1 and S2) revealed gut dysbiosis in the DEP/ANX subjects, in contrast to microbiome of control subjects. PICRUSt analyses  predicted KEGG orthologue gene differences consistent with a pathophysiological gut metagenomic signature in DEP/ANX versus NODEPANX microbiome. Notable in figure 1 were over-represented LPS biosynthesis genes in the gut microbiome of PostScript Gut Online First, published on August 16, 2017 as 10.1136/gutjnl-2017-314759
ICA with Reconstruction Cost for Efficient Overcomplete Feature Learning

F . Here,Cx denotes the covariance matrix of the data. E is the matrix whose columns are eigenvectors of Cx; andD is a diagonal matrix of eigenvalues of Cx. This result also means that when the data is not whitened, the reconstruction cost becomes a weighted linear regression in the space rotated by eigenvec tors and scaled by eigenvalues. The cost is therefore weighted heavily in the principal vector d irections and less so in the other directions. Also, note that D 1 2E is the well-known PCA whitening matrix. The optimization th erefore builds in an inverse whitening matrix such that W will have to learn the whitening matrix to cancel outED 1 2 .
Multilabel SVM active learning for image classification

Image classification is an important task in computer vision. However, how to assign suitable labels to images is a subjective matter, especially when some images can be categorized into multiple classes simultaneously. Multilabel image classification focuses on the problem that each image can have one or multiple labels. It is known that manually labelling images is time-consuming and expensive. In order to reduce the human effort of labelling images, especially multilabel images, we proposed a multilabel SVM active learning method. We also proposed two selection strategies: Max Loss strategy and Mean Max Loss strategy. Experimental results on both artificial data and real-world images demonstrated the advantage of proposed method.
SAFE : Fast , Verifiable Sanitization for SSDs Or : Why encryption alone is not a solution for sanitizing SSDs

As users, corporations, and government agencies store more data in digital media, managing that data and access to it becomes increasingly important. Reliably removing data from persistent storage (i.e., sanitizing the storage) is an essential aspect of this management process, and several techniques that reliably delete data from hard disks are available as built-in ATA or SCSI commands, software tools, and government standards. Recently, there have been two disruptive developments in storage sanitization. The first is the emergence of flash-based solid-state drives (SSDs) that use silicon chips rather than spinning disks to store data. The second, is the rise of encryption as a means to protect data on the drive and as a means to quickly render it irrecoverable. Reliably erasing data from SSDs is challenging both because of the complex data management schemes they employ and because the built-in facilities for sanitization are sometimes buggy. We have evaluated built-in sanitization facilities by applying a sanitization technique, dismantling the drives, extracting the raw bits from the discrete flash devices inside, and searching for remnant data. The technique takes a few hours, is inexpensive, requires only moderate technical skill, and works independently of the controller. We have used this technique to show that some drives claim to successfully erased the drive when the data remains intact, leaving us with a strong conviction that firmware-based sanitization techniques must be verifiable to be trustworthy. An alternative to overwriting or erasing data is to store the data in encrypted form. When the user wishes to destroy the data, the drive destroys the cryptographic key. In theory this should render the data irrecoverable. An advantage of this technique is that it is fast. It takes a fraction of a second to destroy a cryptographic key while conventional sanitization operations on an SSD may take many seconds. In emergency situations, speed is of the essence and erasure-based techniques may be too slow.
A hybrid bug triage algorithm for developer recommendation

With a great number of software applications that have been developed, software maintenance has become an important and challenging task, particularly due to the increasing scale of software projects. Even if developers can create and update bug reports in bug repositories to support software maintenance, a large software project receives a large number of bug reports each day. For reducing the workload of developers, many researchers and software engineers have begun recommending appropriate developers to fix bugs. This process is called bug triage and is a hot research topic for software maintenance. In this paper, we propose a hybrid bug triage algorithm, combining a probability model and an experience model to rank all candidate developers for fixing a new bug. For this study, we adopted the smoothed Unigram Model (UM) instead of the traditional Vector Space Model (VSM) to search similar bug reports. In the probability model, we used a social network to analyze the probability of fixing a new bug for a candidate developer. We first proposed to add a new feature (the number of re-opened bugs) in order to get the fixing probability. In the experience model, we considered the number of fixed bugs and fixing cost for each candidate developer as the estimate factor. In addition, we introduced a new concept, activity factor, to better model developers' experience. We performed the experiments on two large-scale, open source projects. The results show that our method can effectively recommend the best developer for fixing bugs.
Solidity Parsing Using SmaCC: Challenges and Irregularities

Solidity is a language used to implement smart contracts on a blockchain platform. Since its initial conception in 2014, Solidity has evolved into one of the major languages for the Ethereum platform as well as other blockchain technologies. Due to its popularity, there are many tools specifically designed to handle smart contracts written in Solidity. However, there is a lack of tools for Pharo to handle Solidity contracts. Therefore, we implemented a parser using SmaCC to serve as a base for further developing Solidity support in Pharo. In this paper, we describe the parser creation, the irregularities we found in the Solidity grammar specification, and common practices on how to adapt the grammar to an LR type parser. Our experiences with parsing the Solidity language using SmaCC may help other developers trying to convert similar grammars.
From Factors to Actors : Computational Sociology and Agent-Based Modeling

■ Abstract Sociologists often model social processes as interactions among variables. We review an alternative approach that models social life as interactions among adaptive agents who influence one another in response to the influence they receive. These agent-based models (ABMs) show how simple and predictable local interactions can generate familiar but enigmatic global patterns, such as the diffusion of information, emergence of norms, coordination of conventions, or participation in collective action. Emergent social patterns can also appear unexpectedly and then just as dramatically transform or disappear, as happens in revolutions, market crashes, fads, and feeding frenzies. ABMs provide theoretical leverage where the global patterns of interest are more than the aggregation of individual attributes, but at the same time, the emergent pattern cannot be understood without a bottom up dynamical model of the microfoundations at the relational level. We begin with a brief historical sketch of the shift from “factors” to “actors” in computational sociology that shows how agent-based modeling differs fundamentally from earlier sociological uses of computer simulation. We then review recent contributions focused on the emergence of social structure and social order out of local interaction. Although sociology has lagged behind other social sciences in appreciating this new methodology, a distinctive sociological contribution is evident in the papers we review. First, theoretical interest focuses on dynamic social networks that shape and are shaped by agent interaction. Second, ABMs are used to perform virtual experiments that test macrosociological theories by manipulating structural factors like network topology, social stratification, or spatial mobility. We conclude our review with a series of recommendations for realizing the rich sociological potential of this approach.
A Survey on Device-to-Device Communication in Cellular Networks

Device-to-device (D2D) communications was initially proposed in cellular networks as a new paradigm for enhancing network performance. The emergence of new applications such as content distribution and location-aware advertisement introduced new user cases for D2D communications in cellular networks. The initial studies showed that D2D communications has advantages such as increased spectral efficiency and reduced communication delay. However, this communication mode introduces complications in terms of interference control overhead and protocols that are still open research problems. The feasibility of D2D communications in Long-Term Evolution Advanced is being studied by academia, industry, and standardization bodies. To date, there are more than 100 papers available on D2D communications in cellular networks, but there is no survey on this field. In this paper, we provide a taxonomy based on the D2D communicating spectrum and review the available literature extensively under the proposed taxonomy. Moreover, we provide new insights into the over-explored and under-explored areas that lead us to identify open research problems of D2D communications in cellular networks.
Generating Neural Networks with Neural Networks

Hypernetworks are neural networks that generate weights for another neural network. We formulate the hypernetwork training objective as a compromise between accuracy and diversity, where the diversity takes into account trivial symmetry transformations of the target network. We explain how this simple formulation generalizes variational inference. We use multi-layered perceptrons to form the mapping from the low dimensional input random vector to the high dimensional weight space, and demonstrate how to reduce the number of parameters in this mapping by parameter sharing. We perform experiments and show that the generated weights are diverse and lie on a non-trivial manifold.
A lightweight framework for efficient M2M device management in oneM2M architecture

Recent years have witnessed an explosion in the number and types of physical devices connected to the Internet. This exponential growth in the volume of objects poses challenges in terms of managing the connected M2M devices. A unified approach for efficient management of the M2M devices while preserving scalability is necessary. This paper proposes an M2M device management framework that can be deployed in a cloud system, M2M gateway or even inside a mobile application. CoRE Link Format is used for lightweight description of smart M2M devices. The capabilities of CoRE Link are extended to describe legacy M2M devices as a part of Internet of Things ecosystem. Open Mobile Alliance Lightweight M2M (OMA LwM2M) Technical Specifications are used in the framework to provide M2M service enablement for end users. Self-management of the M2M device configurations is outlined. The capabilities of the framework are exposed using RESTful web services. oneM2M architecture for device management is described which integrates the proposed framework. Its software implementation is examined to be ultra-lightweight. Utilization of CoRE Link settles the heterogeneity of the managed devices and promotes interoperability. Finally the paper summarizes the contributions and concludes with future directions.
Effects of attention and confidence on the hypothesized ERP correlates of recollection and familiarity

Dual-process theories suggest that recognition memory is determined by two separate processes: familiarity and recollection. Experiment 1 behaviorally replicated past studies using the remember/know procedure to indicate that the amount of attention devoted to study influences both recollection and familiarity, but recollection more strongly. Experiments 1 and 2 assessed the effects of attention on two ERP components that have been hypothesized to be related to familiarity (FN400 old/new effect, 300-500 ms, anterior) and recollection (parietal old/new effect, 400-800 ms, posterior). Parietal old/new effects were reduced by divided attention, but FN400 old/new effects were not. Parietal ERPs (400-800 ms) in experiment 2 increased with confidence in recognizing old items, but not new items. These results support the hypothesis that the parietal old/new effect is related to recollection.
Enhancing Reliability of Workflow Execution Using Task Replication and Spot Instances

Cloud environments offer low-cost computing resources as a subscription-based service. These resources are elastically scalable and dynamically provisioned. Furthermore, cloud providers have also pioneered new pricing models like spot instances that are cost-effective. As a result, scientific workflows are increasingly adopting cloud computing. However, spot instances are terminated when the market price exceeds the users bid price. Likewise, cloud is not a utopian environment. Failures are inevitable in such large complex distributed systems. It is also well studied that cloud resources experience fluctuations in the delivered performance. These challenges make fault tolerance an important criterion in workflow scheduling. This article presents an adaptive, just-in-time scheduling algorithm for scientific workflows. This algorithm judiciously uses both spot and on-demand instances to reduce cost and provide fault tolerance. The proposed scheduling algorithm also consolidates resources to further minimize execution time and cost. Extensive simulations show that the proposed heuristics are fault tolerant and are effective, especially under short deadlines, providing robust schedules with minimal makespan and cost.
Survey on Collaborative Filtering, Content-based Filtering and Hybrid Recommendation System

Recommender systems or recommendation systems are a subset of information filtering system that used to anticipate the &apos;evaluation&apos; or &apos;preference&apos; that user would feed to an item. In recent years E-commerce applications are widely using Recommender system. Generally the most popular E-commerce sites are probably music, news, books, research articles, and products. Recommender systems are also available for business experts, jokes, restaurants, financial services, life insurance and twitter followers. Recommender systems have formulated in parallel with the web. Initially Recommender systems were based on demographic, content-based filtering and collaborative filtering. Currently, these systems are incorporating social information for enhancing a quality of recommendation process. For betterment of recommendation process in the future, Recommender systems will use personal, implicit and local information from the Internet. This paper provides an overview of recommender systems that include collaborative filtering, content-based filtering and hybrid approach of recommender system.
Polyphenolic content and antioxidant properties of Moringa oleifera leaf extracts and enzymatic activity of liver from goats supplemented with Moringa oleifera leaves/sunflower seed cake.

The study investigated antioxidant potency of Moringa oleifera leaves in different in vitro systems using standard phytochemical methods. The antioxidative effect on the activities of superoxide dismutase (SOD), catalase (CAT), lipid peroxidation (LPO) and reduced glutathione (GSH) were investigated in goats supplemented with M. oleifera (MOL) or sunflower seed cake (SC). The acetone extract had higher concentrations of total flavonoids (295.01 ± 1.89 QE/g) followed by flavonols (132.74 ± 0.83 QE/g), phenolics (120.33 ± 0.76 TE/g) and then proanthocyanidins (32.59 ± 0.50 CE/g) than the aqueous extract. The reducing power of both solvent extracts showed strong antioxidant activity in a concentration dependent manner. The acetone extract depicted higher percentage inhibition against DPPH, ABTS and nitric oxide radicals which were comparable with reference standard antioxidants (vitamin C and BHT). MOL increased the antioxidant activity of GSH (186%), SOD (97.8%) and catalase (0.177%). Lipid peroxidation was significantly reduced by MOL. The present study suggests that M. oleifera could be a potential source of compounds with strong antioxidant potential.
Lyric-Based Music Recommendation

Traditional music recommendation systems rely on collaborative filtering to recommend songs or artists. This is computationally efficient and performs well method but is not effective when there is limited or no user input. For these cases, it may be useful to consider content-based recommendation. This paper considers a content-based recommendation system based on lyrical data. We compare a complex network of lyrical recommendations to an equivalent collaborative filtering network. We used user generated tag data from Last.fm to produce 23 subgraphs of each network based on tag categories representing musical genre, mood, and gender of vocalist. We analyzed these subgraphs to determine how recommendations within each network tend to stay within tag categories. Finally, we compared the lyrical recommendations to the collaborative filtering recommendations to determine how well lyrical recommendations perform. We see that the lyrical network is significantly more clustered within tag categories than the collaborative filtering network, particularly within small musical niches, and recommendations based on lyrics alone perform 12.6 times better than random recommendations.