nils-herrmann/clean_scirepeval huggingface dataset:
A Novel Route Controlling Begomovirus Resistance by the Messenger RNA Surveillance Factor Pelota

Tomato yellow leaf curl virus (TYLCV) is a devastating disease of tomato (Solanum lycopersicum) that can be effectively controlled by the deployment of resistant cultivars. The TYLCV-resistant line TY172 carries a major recessive locus for TYLCV resistance, designated ty-5, on chromosome 4. In this study, the association between 27 polymorphic DNA markers, spanning the ty-5 locus, and the resistance characteristics of individual plants inoculated with TYLCV in 51 segregating recombinant populations were analyzed. These analyses localized ty-5 into a 425 bp region containing two transversions: one in the first exon of a gene encoding the tomato homolog of the messenger RNA surveillance factor Pelota (Pelo), and a second in its proximal promoter. Analyses of susceptible and resistant lines revealed that the relative transcript level of the gene remained unchanged, regardless of whether the plants were infected with TYLCV or not. This suggests that the polymorphism discovered in the coding region of the gene controls the resistance. Silencing of Pelo in a susceptible line rendered the transgenic plants highly resistant, while in the resistant line TY172 had no effect on symptom development. In addition, over-expression of the susceptible allele of the gene in the resistant TY172 line rendered it susceptible, while over-expression of the resistant allele in susceptible plants had no effect. These results confirm that Pelo is the gene controlling resistance at the ty-5 locus. Pelo, implicated in the ribosome recycling-phase of protein synthesis, offers an alternative route to promote resistance to TYLCV and other viruses.
Duplex Generative Adversarial Network for Unsupervised Domain Adaptation

Domain adaptation attempts to transfer the knowledge obtained from the source domain to the target domain, i.e., the domain where the testing data are. The main challenge lies in the distribution discrepancy between source and target domain. Most existing works endeavor to learn domain invariant representation usually by minimizing a distribution distance, e.g., MMD and the discriminator in the recently proposed generative adversarial network (GAN). Following the similar idea of GAN, this work proposes a novel GAN architecture with duplex adversarial discriminators (referred to as DupGAN), which can achieve domain-invariant representation and domain transformation. Specifically, our proposed network consists of three parts, an encoder, a generator and two discriminators. The encoder embeds samples from both domains into the latent representation, and the generator decodes the latent representation to both source and target domains respectively conditioned on a domain code, i.e., achieves domain transformation. The generator is pitted against duplex discriminators, one for source domain and the other for target, to ensure the reality of domain transformation, the latent representation domain invariant and the category information of it preserved as well. Our proposed work achieves the state-of-the-art performance on unsupervised domain adaptation of digit classification and object recognition.
Global Continuous Optimization with Error Bound and Fast Convergence

This paper considers global optimization with a black-box unknown objective function that can be non-convex and non-differentiable. Such a difficult optimization problem arises in many real-world applications, such as parameter tuning in machine learning, engineering design problem, and planning with a complex physics simulator. This paper proposes a new global optimization algorithm, called Locally Oriented Global Optimization (LOGO), to aim for both fast convergence in practice and finite-time error bound in theory. The advantage and usage of the new algorithm are illustrated via theoretical analysis and an experiment conducted with 11 benchmark test functions. Further, we modify the LOGO algorithm to specifically solve a planning problem via policy search with continuous state/action space and long time horizon while maintaining its finite-time error bound. We apply the proposed planning method to accident management of a nuclear power plant. The result of the application study demonstrates the practical utility of our method.
A review of techniques for sentiment analysis Of Twitter data

There has been a rapid increase in the use of social networking websites in the last few years. People most conveniently express their views and opinions on a wide array of topics via such websites. Sentiment analysis of such data which comprises of people's views is very important in order to gauge public opinion on a particular topic of interest. This paper reviews a number of techniques, both lexicon-based approaches as well as learning based methods that can be used for sentiment analysis of text. In order to adapt these techniques for sentiment analysis of data procured from one of the social networking websites, Twitter, a number of issues and challenges need to be addressed, which are put forward in this paper.
A Novel Approach for Rogue Access Point Detection on the Client-Side

There is a big risk for public Wi-Fi users being tricked into connecting to rogue access points. Rogue access point is one of the most serious threats in WLAN, since it exposes a large number of users to MITM and evil twin attack. In this paper we propose a practical method that warns users to avoid connecting to the rogue access points. Proposed method compares the gateways and the routes that a packet travels to determine whether an access point is legitimate or not. This method can easily detect Man-In-The-Middle and evil twin attack without any assistance from the WLAN operator.
A probabilistic approach to modeling and estimating the QoS of web-services-based workflows

Web services promise to become a key enabling technology for B2B e-commerce. One of the most-touted features of Web services is their capability to recursively construct a Web service as a workflow of other existing Web services. The quality of service (QoS) of Web-services-based workflows may be an essential determinant when selecting constituent Web services and determining the service-level agreement with users. To make such a selection possible, it is essential to estimate the QoS of a WS workflow based on the QoSs of its constituent WSs. In the context of WS workflow, this estimation can be made by a method called QoS aggregation. While most of the existing work on QoS aggregation treats the QoS as a deterministic value, we argue that due to some uncertainty related to a WS, it is more realistic to model its QoS as a random variable, and estimate the QoS of a WS workflow probabilistically. In this paper, we identify a set of QoS metrics in the context of WS workflows, and propose a unified probabilistic model for describing QoS values of a broader spectrum of atomic and composite Web services. Emulation data are used to demonstrate the efficiency and accuracy of the proposed approach.
An open-source simulator for cognitive robotics research: the prototype of the iCub humanoid robot simulator

This paper presents the prototype of a new computer simulator for the humanoid robot iCub. The iCub is a new open-source humanoid robot developed as a result of the "RobotCub" project, a collaborative European project aiming at developing a new open-source cognitive robotics platform. The iCub simulator has been developed as part of a joint effort with the European project "ITALK" on the integration and transfer of action and language knowledge in cognitive robots.
Arousal increases social transmission of information.

Social transmission is everywhere. Friends talk about restaurants , policy wonks rant about legislation, analysts trade stock tips, neighbors gossip, and teens chitchat. Further, such interpersonal communication affects everything from decision making and well-But although it is clear that social transmission is both frequent and important, what drives people to share, and why are some stories and information shared more than others? Traditionally, researchers have argued that rumors spread in the " 3 Cs " —times of conflict, crisis, and catastrophe (e.g., wars or natural disasters; Koenig, 1985)―and the major explanation for this phenomenon has been generalized anxiety (i.e., apprehension about negative outcomes). Such theories can explain why rumors flourish in times of panic, but they are less useful in explaining the prevalence of rumors in positive situations, such as the Cannes Film Festival or the dot-com boom. Further, although recent work on the social sharing of emotion suggests that positive emotion may also increase transmission, why emotions drive sharing and why some emotions boost sharing more than others remains unclear. I suggest that transmission is driven in part by arousal. Physiological arousal is characterized by activation of the autonomic nervous system (Heilman, 1997), and the mobilization provided by this excitatory state may boost sharing. This hypothesis not only suggests why content that evokes more of certain emotions (e.g., disgust) may be shared more than other a review), but also suggests a more precise prediction , namely, that emotions characterized by high arousal, such as anxiety or amusement (Gross & Levenson, 1995), will boost sharing more than emotions characterized by low arousal, such as sadness or contentment. This idea was tested in two experiments. They examined how manipulations that increase general arousal (i.e., watching emotional videos or jogging in place) affect the social transmission of unrelated content (e.g., a neutral news article). If arousal increases transmission, even incidental arousal (i.e., outside the focal content being shared) should spill over and boost sharing. In the first experiment, 93 students completed what they were told were two unrelated studies. The first evoked specific emotions by using film clips validated in prior research (Christie & Friedman, 2004; Gross & Levenson, 1995). Participants in the control condition watched a neutral clip; those in the experimental conditions watched an emotional clip. Emotional arousal and valence were manipulated independently so that high-and low-arousal emotions of both a positive (amusement vs. contentment) and a negative (anxiety vs. …
Malware detection based on opcode frequency

Malware is a computer program or a piece of software that is designed to penetrate and detriment computers without owner's permission. There are different malware types such as viruses, rootkits, keyloggers, worms, trojans, spywares, ransomware, backdoors, bots, logic bomb, etc. Volume, Variant and speed of propagation of malwares are increasing every year. Antivirus companies are receiving thousands of malwares on the daily basis, so detection of malwares is complex and time consuming task. There are many malwares detection techniques like signature based detection, behavior based detection and machine learning based techniques, etc. The signatures based detection system fails for new unknown malware. In case of behavior based detection, if the antivirus program identify attempt to change or alter a file or communication over internet then it will generate alarm signal, but still there is a chance of false positive rate. Also the obfuscation and polymorphism techniques are hinderers the malware detection process. In this paper we propose new method to detect malwares based on the frequency of opcodes in the portable executable file. This research applied machine learning algorithm to find false positives, false negatives, true positives and true negatives for malwares and got 96.67 per cent success rate.
Distributed Road Surface Condition Monitoring Using Mobile Phones

The objective of this research is to improve traffic safety through collecting and distributing up-to-date road surface condition information using mobile phones. Road surface condition information is seen useful for both travellers and for the road network maintenance. The problem we consider is to detect road surface anomalies that, when left unreported, can cause wear of vehicles, lesser driving comfort and vehicle controllability, or an accident. In this work we developed a pattern recognition system for detecting road condition from accelerometer and GPS readings. We present experimental results from real urban driving data that demonstrate the usefulness of the system. Our contributions are: 1) Performing a throughout spectral analysis of tri-axis acceleration signals in order to get reliable road surface anomaly labels. 2) Comprehensive preprocessing of GPS and acceleration signals. 3) Proposing a speed dependence removal approach for feature extraction and demonstrating its positive effect in multiple feature sets for the road surface anomaly detection task. 4) A framework for visually analyzing the classifier predictions over the validation data and labels.
Channel Gain Map Tracking via Distributed Kriging

A collaborative algorithm is developed to estimate the channel gains of wireless links in a geographical area. The spatiotemporal evolution of shadow fading is characterized by judiciously extending an experimentally verified spatial-loss field model. Kriged Kalman filtering (KKF), which is a tool with widely appreciated merits in spatial statistics and geosciences, is adopted and implemented in a distributed fashion to track the time-varying shadowing field using a network of radiometers. The novel distributed KKF requires only local message passing yet achieves a global view of the radio frequency environment through consensus iterations. Numerical tests demonstrate superior tracking accuracy of the collaborative algorithm compared with its noncollaborative counterpart. Furthermore, the efficacy of the global channel gain knowledge obtained is showcased in the context of cognitive radio resource allocation.
HOT SPOTS OF CRIME AND CRIMINAL CAREERS OF PLACES

The explanation of crime has been preoccupied with individuals and communities as units of analysis. Recent work on offender decision making (Cornish and Clarke, 1986), situations (Clarke, 1983, 1992), environments (Brantingham and Brantingham 1981, 1993), routine activities (Cohen and Felson, 1979; Felson, 1994), and the spatial organization of drug dealing in the U.S. suggest a new unit of analysis: places. Crime is concentrated heavily in a Jew "hot spots" of crime (Sherman et aL 1989). The concentration of crime among repeat places is more intensive than it is among repeat offenders (Spelman and Eck, 1989). The components of this concentration are analogous to the components of the criminal careers of persons: onset, desistance, continuance, specialization, and desistance. The theoretical explanationfor variance in these components is also stronger at the level of places than it is for individuals. These facts suggest a need for rethinking theories of crime, as well as a new approach to theorizing about crime for
Security attacks in IoT: A survey

Internet of Things (IoT) is one of the most buzzing and discussed topic in research field today. Some of the researchers are also looking future of the world in this technology. Since then significant research and development have taken place on IoT, however various vulnerabilities are observed which shall keep IoT as a technology in danger. As a result, there are so many attacks on IoT have been invented before actual commercial implementation of it. The present study discusses about various IoT attacks happening, classify them, its countermeasures and finding the most prominent attacks in IoT. A state of the art survey about the various attacks have been presented and compared including their efficiency and damage level in IoT.
The Nature of Rapport and Its Nonverbal Correlates

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Understanding other minds: linking developmental psychology and functional neuroimaging.

Evidence from developmental psychology suggests that understanding other minds constitutes a special domain of cognition with at least two components: an early-developing system for reasoning about goals, perceptions, and emotions, and a later-developing system for representing the contents of beliefs. Neuroimaging reinforces and elaborates upon this view by providing evidence that (a) domain-specific brain regions exist for representing belief contents, (b) these regions are apparently distinct from other regions engaged in reasoning about goals and actions (suggesting that the two developmental stages reflect the emergence of two distinct systems, rather than the elaboration of a single system), and (c) these regions are distinct from brain regions engaged in inhibitory control and in syntactic processing. The clear neural distinction between these processes is evidence that belief attribution is not dependent on either inhibitory control or syntax, but is subserved by a specialized neural system for theory of mind.
On the Accuracy of Hyper-local Geotagging of Social Media Content

Social media users share billions of items per year, only a small fraction of which is geotagged. We present a data-driven approach for identifying non-geotagged content items that can be associated with a hyper-local geographic area by modeling the location distributions of n-grams that appear in the text. We explore the trade-off between accuracy and coverage of this method. Further, we explore differences across content received from multiple platforms and devices, and show, for example, that content shared via different sources and applications produces significantly different geographic distributions, and that it is preferred to model and predict location for items according to their source. Our findings show the potential and the bounds of a data-driven approach to assigning location data to short social media texts, and offer implications for all applications that use data-driven approaches to locate content.
Developer identification methods for integrated data from various sources

Studying a software project by mining data from a single repository has been a very active research field in software engineering during the last years. However, few efforts have been devoted to perform studies by integrating data from various repositories, with different kinds of information, which would, for instance, track the different activities of developers. One of the main problems of these multi-repository studies is the different identities that developers use when they interact with different tools in different contexts. This makes them appear as different entities when data is mined from different repositories (and in some cases, even from a single one). In this paper we propose an approach, based on the application of heuristics, to identify the many identities of developers in such cases, and a data structure for allowing both the anonymized distribution of information, and the tracking of identities for verification purposes. The methodology will be presented in general, and applied to the GNOME project as a case example. Privacy issues and partial merging with new data sources will also be considered and discussed.
Boosting bottom-up and top-down visual features for saliency estimation

Despite significant recent progress, the best available visual saliency models still lag behind human performance in predicting eye fixations in free-viewing of natural scenes. Majority of models are based on low-level visual features and the importance of top-down factors has not yet been fully explored or modeled. Here, we combine low-level features such as orientation, color, intensity, saliency maps of previous best bottom-up models with top-down cognitive visual features (e.g., faces, humans, cars, etc.) and learn a direct mapping from those features to eye fixations using Regression, SVM, and AdaBoost classifiers. By extensive experimenting over three benchmark eye-tracking datasets using three popular evaluation scores, we show that our boosting model outperforms 27 state-of-the-art models and is so far the closest model to the accuracy of human model for fixation prediction. Furthermore, our model successfully detects the most salient object in a scene without sophisticated image processings such as region segmentation.
CloudCmp: comparing public cloud providers

While many public cloud providers offer pay-as-you-go computing, their varying approaches to infrastructure, virtualization, and software services lead to a problem of plenty. To help customers pick a cloud that fits their needs, we develop CloudCmp, a systematic comparator of the performance and cost of cloud providers. CloudCmp measures the elastic computing, persistent storage, and networking services offered by a cloud along metrics that directly reflect their impact on the performance of customer applications. CloudCmp strives to ensure fairness, representativeness, and compliance of these measurements while limiting measurement cost. Applying CloudCmp to four cloud providers that together account for most of the cloud customers today, we find that their offered services vary widely in performance and costs, underscoring the need for thoughtful provider selection. From case studies on three representative cloud applications, we show that CloudCmp can guide customers in selecting the best-performing provider for their applications.
Incorporating Side Information into Recurrent Neural Network Language Models

Recurrent neural network language models (RNNLM) have recently demonstrated vast potential in modelling long-term dependencies for NLP problems, ranging from speech recognition to machine translation. In this work, we propose methods for conditioning RNNLMs on external side information, e.g., metadata such as keywords or document title. Our experiments show consistent improvements of RNNLMs using side information over the baselines for two different datasets and genres in two languages. Interestingly, we found that side information in a foreign language can be highly beneficial in modelling texts in another language, serving as a form of cross-lingual language modelling.