nils-herrmann/clean_scirepeval huggingface dataset:
Exploiting Cyclic Symmetry in Convolutional Neural Networks

Many classes of images exhibit rotational symmetry. Convolutional neural networks are sometimes trained using data augmentation to exploit this, but they are still required to learn the rotation equivariance properties from the data. Encoding these properties into the network architecture, as we are already used to doing for translation equivariance by using convolutional layers, could result in a more efficient use of the parameter budget by relieving the model from learning them. We introduce four operations which can be inserted into neural network models as layers, and which can be combined to make these models partially equivariant to rotations. They also enable parameter sharing across different orientations. We evaluate the effect of these architectural modifications on three datasets which exhibit rotational symmetry and demonstrate improved performance with smaller models.
Antimicrobial effect of cinnamon (Cinnamomum verum J. Presl) bark essential oil in cream-filled cakes and pastries

Food poisoning has been always a major concern in health system of every community and cream-filled products are one of the most widespread food poisoning causes in humans. In present study, we examined the preservative effect of the cinnamon oil in cream-filled cakes.  Antimicrobial activity of Cinnamomum verum J. Presl (Cinnamon) bark essential oil was examined against five food-borne pathogens (Staphylococcus aureus, Escherichia coli, Candida albicans, Bacillus cereus and Salmonella typhimurium) to investigate its potential for use as a natural preservative in cream-filled baked goods. Chemical constituents of the oil were determined by gas chromatography/mass spectrometry. For evaluation of preservative sufficiency of the oil, pathogens were added to cream-filled cakes manually and 1 μL/mL of the essential oil was added to all samples except the blank.  Chemical constituents of the oil were determined by gas chromatography/mass spectrometry and twenty five components were identified where cinnamaldehyde (79.73%), linalool (4.08%), cinnamaldehyde para-methoxy (2.66%), eugenol (2.37%) and trans-caryophyllene (2.05%) were the major constituents. Cinnamon essential oil showed strong antimicrobial activity against selected pathogens in vitro and the minimum inhibitory concentration values against all tested microorganisms were determined as 0.5 μL/disc except for S. aureus for which, the oil was not effective in tested concentrations. After baking, no observable microorganism was observed in all susceptible microorganisms count in 72h stored samples.  It was concluded that by analysing the sensory quality of the preserved food, cinnamon oil may be considered as a natural preservative in food industry, especially for cream-filled cakes and
Visual Grounding via Accumulated Attention

Visual Grounding (VG) aims to locate the most relevant object or region in an image, based on a natural language query. The query can be a phrase, a sentence or even a multi-round dialogue. There are three main challenges in VG: 1) what is the main focus in a query; 2) how to understand an image; 3) how to locate an object. Most existing methods combine all the information curtly, which may suffer from the problem of information redundancy (i.e. ambiguous query, complicated image and a large number of objects). In this paper, we formulate these challenges as three attention problems and propose an accumulated attention (A-ATT) mechanism to reason among them jointly. Our A-ATT mechanism can circularly accumulate the attention for useful information in image, query, and objects, while the noises are ignored gradually. We evaluate the performance of A-ATT on four popular datasets (namely Refer-COCO, ReferCOCO+, ReferCOCOg, and Guesswhat?!), and the experimental results show the superiority of the proposed method in term of accuracy.
IEC 61850-9-2 Process Bus and Its Impact on Power System Protection and Control Reliability

This paper analyzes the Sampled Value (SV) Process Bus concept that was recently introduced by the IEC 61850-9-2 standard. This standard proposes that the Current and Voltage Transformer (CT, PT) outputs that are presently hard wired to various devices (relays, meters, IED, and SCADA) be digitized at the source and then communicated to those devices using an Ethernet-Based Local Area Network (LAN). The approach is especially interesting for modern optical CT/PT devices that possess high quality information about the primary voltage/current waveforms, but are often forced to degrade output signal accuracy in order to meet traditional analog interface requirements (5 A/120 V). While very promising, the SV-based process bus brings along a distinct set of issues regarding the overall reliability of the new Ethernet communications-based protection and control system. This paper looks at the Merging Unit Concept, analyzes the protection system reliability in the process bus environment, and proposes an alternate approach that can be used to successfully deploy this technology. Multiple scenarios used with the associated equipment configurations are compared. Additional issues that need to be addressed by various standards bodies and interoperability challenges posed by the SV process bus LAN on real-time monitoring and control applications (substation HMI, SCADA, engineering access) are also identified.
A High Step-Down Isolated Three-Phase AC-DC Converter

In this paper, a single-stage three-phase isolated ac-dc converter topology utilizing SiC MOSFETs is proposed for power rectification with a stepped-down output voltage. Unlike the conventional two-stage [front-end power factor correction (PFC) stage and isolated dc-dc stage] ac-dc converters, the full/half bridge structure in dc-dc stage is eliminated in this structure. The high-frequency pulsating voltage is obtained directly from the PFC stage and is applied across the high-frequency transformer, leading to a more compact design. In addition, there is an advantage of zero voltage switching (ZVS) in four PFC MOSFETs connected to the high-frequency tank, which is not achievable in the case of a conventional two-staged ac-dc converter. A sine-pulse width modulation (PWM)-based control scheme is applied with the common-mode duty ratio injection method to minimize the current harmonics without affecting the power factor. An LC filter is used after the PFC semistage to suppress the line-frequency voltage ripple. Furthermore, the intermediate dc-link capacitor value can be greatly reduced through no additional ripple constraints. Experimental and simulation results are included for a laboratory prototype, which converts 115-V, 400-Hz three-phase input voltage to 28-V dc output voltage. The experimental results demonstrate a power factor of 0.993 with a conversion efficiency of 95.4%, and total harmonic distortion (THD) as low as 3.5% at 2.1-kW load condition.
Ideology and discourse analysis

Contrary to most traditional approaches, ideologies are defined here within a multidisciplinary framework that combines a social, cognitive and discursive component. As 'systems of ideas', ideologies are sociocognitively defined as shared representations of social groups, and more specifically as the `axiomatic ' principies of such representations. As the basis of a social group's selfimage, ideologies organize its identity, actions, aims, norms and values, and resources as well as its relations to other social groups. Ideologies are distinct from the sociocognitive basis of broader cultural communities, within which different ideological groups share fundamental beliefs such as their cultural knowledge. Ideologies are expressed and generally reproduced in the social practices of their members, and more particularly acquired, confirmed, changed and perpetuated through discourse. Although general properties of language and discourse are not, as such, ideologically marked, systematic discourse analysis offers powerful methods to study the structures and functions of underlying' ideologies. The ideological polarization between ingroups and outgroups— a prominent feature of the structure of ideologies—may also be systematically studied at all levels of text and talk, e.g. by analysing how members of ingroups typically emphasize their own good deeds and properties and the bad ones of the outgroup, and mitigate or deny their own bad ones and the good ones of the outgroup.
A case of early sports specialization in an adolescent athlete.

Early sport specialization (ESS) refers to intense year round training in a specific sport with the exclusion of other sports at a young age. This approach to training is heavily debated and there are claims both in support and against ESS. ESS is considered to be more common in the modern day youth athlete and could be a source of overuse injuries and burnout. This case describes a 16 year old elite level baseball pitcher who engaged in high volume, intense training at a young age which lead to several significant throwing related injuries. The case highlights the historical context of ESS, the potential risk and benefits as well as the evidence for its effectiveness. It is important for health care professionals to be informed on the topic of ESS in order to educate athletes, parents, coaches and organizations of the potential risks and benefits.
The Gesture Recognition Toolkit

The Gesture Recognition Toolkit is a cross-platform open-source C++ library designed to make real-time machine learning and gesture recognition more accessible for non-specialists. Emphasis is placed on ease of use, with a consistent, minimalist design that promotes accessibility while supporting flexibility and customization for advanced users. The toolkit features a broad range of classification and regression algorithms and has extensive support for building real-time systems. This includes algorithms for signal processing, feature extraction and automatic gesture spotting.
Combining bagging, boosting, rotation forest and random subspace methods

Bagging, boosting, rotation forest and random subspace methods are well known re-sampling ensemble methods that generate and combine a diversity of learners using the same learning algorithm for the base-classifiers. Boosting and rotation forest algorithms are considered stronger than bagging and random subspace methods on noise-free data. However, there are strong empirical indications that bagging and random subspace methods are much more robust than boosting and rotation forest in noisy settings. For this reason, in this work we built an ensemble of bagging, boosting, rotation forest and random subspace methods ensembles with 6 sub-classifiers in each one and then a voting methodology is used for the final prediction. We performed a comparison with simple bagging, boosting, rotation forest and random subspace methods ensembles with 25 sub-classifiers, as well as other well known combining methods, on standard benchmark datasets and the proposed technique had better accuracy in most cases.
Wisdom or Madness? Comparing Crowds with Expert Evaluation in Funding the Arts

In fields as diverse as technology entrepreneurship and the arts, crowds of interested stakeholders are increasingly responsible for deciding which innovations to fund, a privilege that was previously reserved for a few experts, such as venture capitalists and grant-making bodies. Little is known about the degree to which the crowd differs from experts in judging which ideas to fund, and, indeed, whether the crowd is even rational in making funding decisions. Drawing on a panel of national experts and comprehensive data from the largest crowdfunding site, we examine funding decisions for proposed theater projects, a category where expert and crowd preferences might be expected to differ greatly. We instead find significant agreement between the funding decisions of crowds and experts. Where crowds and experts disagree, it is far more likely to be a case where the crowd is willing to fund projects that experts may not. Examining the outcomes of these projects, we find no quantitative or qualitative differences between projects funded by the crowd alone and those that were selected by both the crowd and experts. Our findings suggest that crowdfunding can play an important role in complementing expert decisions, particularly in sectors where the crowds are end users, by allowing projects the option to receive multiple evaluations and thereby lowering the incidence of “false negatives.”
Security Operation Center Concepts & Implementation

A Security Operation Center (SOC) is made up of five distinct modules: event generators, event collectors, message database, analysis engines and reaction management software. The main problem encountered when building a SOC is the integration of all these modules, usually built as autonomous parts, while matching availability, integrity and security of data and their transmission channels. In this paper we will discuss the functional architecture needed to integrate those modules. Chapter one will introduce the concepts behind each module and briefly describe common problems encountered with each of them. In chapter two we will design the global architecture of the SOC. We will then focus on collection & analysis of data generated by sensors in chapters three and four. A short conclusion will describe further research & analysis to be performed in the field of SOC design.
Interaction Screening: Efficient and Sample-Optimal Learning of Ising Models

We consider the problem of learning the underlying graph of an unknown Ising model on p spins from a collection of i.i.d. samples generated from the model. We suggest a new estimator that is computationally efficient and requires a number of samples that is near-optimal with respect to previously established informationtheoretic lower-bound. Our statistical estimator has a physical interpretation in terms of “interaction screening”. The estimator is consistent and is efficiently implemented using convex optimization. We prove that with appropriate regularization, the estimator recovers the underlying graph using a number of samples that is logarithmic in the system size p and exponential in the maximum coupling-intensity and maximum node-degree.
Category-specific object reconstruction from a single image

Object reconstruction from a single image - in the wild - is a problem where we can make progress and get meaningful results today. This is the main message of this paper, which introduces an automated pipeline with pixels as inputs and 3D surfaces of various rigid categories as outputs in images of realistic scenes. At the core of our approach are deformable 3D models that can be learned from 2D annotations available in existing object detection datasets, that can be driven by noisy automatic object segmentations and which we complement with a bottom-up module for recovering high-frequency shape details. We perform a comprehensive quantitative analysis and ablation study of our approach using the recently introduced PASCAL 3D+ dataset and show very encouraging automatic reconstructions on PASCAL VOC.
word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method

The word2vec software of Tomas Mikolov and colleagues 1 has gained a lot of traction lately, and provides state-of-the-art word embeddings. The learning models behind the software are described in two research papers [1, 2]. We found the description of the models in these papers to be somewhat cryptic and hard to follow. While the motivations and presentation may be obvious to the neural-networks language-modeling crowd, we had to struggle quite a bit to figure out the rationale behind the equations. This note is an attempt to explain equation (4) (negative sampling) in " Distributed Representations of Words and Phrases and their Compositionality " by 1 The skip-gram model The departure point of the paper is the skip-gram model. In this model we are given a corpus of words w and their contexts c. We consider the conditional probabilities p(c|w), and given a corpus T ext, the goal is to set the parameters θ of p(c|w; θ) so as to maximize the corpus probability: arg max θ w∈T ext   c∈C(w) p(c|w; θ)   (1) in this equation, C(w) is the set of contexts of word w. Alternatively: arg max θ (w,c)∈D p(c|w; θ) (2) here D is the set of all word and context pairs we extract from the text.
Types and Effects for Asymmetric Cryptographic Protocols

We present the first type and effect system for proving authenticity properties of security protocols based on asymmetric cryptography. The most significant new features of our type system are: (1) a separation of public types (for data possibly sent to the opponent) from tainted types (for data possibly received from the opponent) via a subtype relation; (2) trust effects, to guarantee that tainted data does not, in fact, originate from the opponent; and (3) challenge/response types to support a variety of idioms used to guarantee message freshness. We illustrate the applicability of our system via protocol examples.
Machine Reading with Background Knowledge

Intelligent systems capable of automatically understanding natural language text are important for many artificial intelligence applications including mobile phone voice assistants, computer vision, and robotics. Understanding language often constitutes fitting new information into a previously acquired view of the world. However, many machine reading systems rely on the text alone to infer its meaning. In this paper, we pursue a different approach; machine reading methods that make use of background knowledge to facilitate language understanding. To this end, we have developed two methods: The first method addresses prepositional phrase attachment ambiguity. It uses background knowledge within a semi-supervised machine learning algorithm that learns from both labeled and unlabeled data. This approach yields state-of-the-art results on two datasets against strong baselines; The second method extracts relationships from compound nouns. Our knowledge-aware method for compound noun analysis accurately extracts relationships and significantly outperforms a baseline that does not make use of background knowledge.
How social Q&A sites are changing knowledge sharing in open source software communities

Historically, mailing lists have been the preferred means for coordinating development and user support activities. With the emergence and popularity growth of social Q&A sites such as the StackExchange network (e.g., StackOverflow), this is beginning to change. Such sites offer different socio-technical incentives to their participants than mailing lists do, e.g., rich web environments to store and manage content collaboratively, or a place to showcase their knowledge and expertise more vividly to peers or potential recruiters. A key difference between StackExchange and mailing lists is gamification, i.e., StackExchange participants compete to obtain reputation points and badges. In this paper, we use a case study of R (a widely-used tool for data analysis) to investigate how mailing list participation has evolved since the launch of StackExchange. Our main contribution is the assembly of a joint data set from the two sources, in which participants in both the texttt{r-help} mailing list and StackExchange are identifiable. This permits their activities to be linked across the two resources and also over time. With this data set we found that user support activities show a strong shift away from texttt{r-help}. In particular, mailing list experts are migrating to StackExchange, where their behaviour is different. First, participants active both on texttt{r-help} and on StackExchange are more active than those who focus exclusively on only one of the two. Second, they provide faster answers on StackExchange than on texttt{r-help}, suggesting they are motivated by the emph{gamified} environment. To our knowledge, our study is the first to directly chart the changes in behaviour of specific contributors as they migrate into gamified environments, and has important implications for knowledge management in software engineering.
MRSA in Africa: Filling the Global Map of Antimicrobial Resistance

We sought to assess the prevalence of methicillin-resistance among Staphylococcus aureus isolates in Africa. We included articles published in 2005 or later reporting for the prevalence of MRSA among S. aureus clinical isolates. Thirty-two studies were included. In Tunisia, the prevalence of MRSA increased from 16% to 41% between 2002-2007, while in Libya it was 31% in 2007. In South Africa, the prevalence decreased from 36% in 2006 to 24% during 2007-2011. In Botswana, the prevalence varied from 23-44% between 2000-2007. In Algeria and Egypt, the prevalence was 45% and 52% between 2003-2005, respectively. In Nigeria, the prevalence was greater in the northern than the southern part. In Ethiopia and the Ivory Coast, the prevalence was 55% and 39%, respectively. The prevalence of MRSA was lower than 50% in most of the African countries, although it appears to have risen since 2000 in many African countries, except for South Africa.
Sparse and Dense Data with CNNs: Depth Completion and Semantic Segmentation

Convolutional neural networks are designed for dense data, but vision data is often sparse (stereo depth, point clouds, pen stroke, etc.). We present a method to handle sparse depth data with optional dense RGB, and accomplish depth completion and semantic segmentation changing only the last layer. Our proposal efficiently learns sparse features without the need of an additional validity mask. We show how to ensure network robustness to varying input sparsities. Our method even works with densities as low as 0.8% (8 layer lidar), and outperforms all published state-of-the-art on the Kitti depth completion benchmark.
Dialogue Act Semantic Representation and Classification Using Recurrent Neural Networks

In this work, we present a model that incorporates Dialogue Act (DA) semantics in the framework of Recurrent Neural Networks (RNNs) for DA classification. Specifically, we propose a novel scheme for automatically encoding DA semantics via the extraction of salient keywords that are representative of the DA tags. The proposed model is applied to the Switchboard corpus and achieves 1.7% (absolute) improvement in classification accuracy with respect to the baseline model. We demonstrate that the addition of discourse-level features enhances the DA classification as well as makes the algorithm more robust: the proposed model does not require the preprocessing of dialogue transcriptions.
Advances in Clickstream Data Analysis in Marketing

Clickstream data are defined as the electronic record of Internet usage collected by Web servers or third-party services. The authors discuss the nature of clickstream data, noting key strengths and limitations of these data for research in marketing. The paper reviews major developments from the analysis of these data, covering advances in understanding (1) browsing and site usage behavior on the Internet, (2) the Internet’s role and efficacy as a new medium for advertising and persuasion, and (3) shopping behavior on the Internet (i.e., electronic commerce). The authors outline opportunities for new research and highlight several emerging areas likely to grow in future importance. Inherent limitations of clickstream data for understanding and predicting the behavior of Internet users or researching marketing phenomena are also discussed.