nils-herrmann/clean_scirepeval huggingface dataset:
Identification of Cotton Diseases Based on Cross Information Gain _ Deep Forward Neural Network Classifier with PSO Feature Selection

This work exposes the automatic computation system to analyse the cotton leaf spot diseases. First to initialize the images from the database (Image features) that are highly related to the test image (new image), where test image is given by the user. Three features are used for matching the train image features in database images, namely color feature variance, shape and texture feature variance. These features are extracted by PSO. The feature selection method which helps to identify the injured leaf spot of cotton and at the same time improve the accuracy of the system and reduce the error rate also. These features are calculated by different techniques. The proposed Skew divergence color variance feature is calculated by color histogram and color descriptor. The shape Skew divergence feature is calculated by Sobel and Canny through the find out edge variance, edge location using Edge detection method. The skew divergence texture feature is calculated by Gober filter and texture descriptor. This investigation is based on six types of diseases like Bacterial Blight, Fusarium wilt, Leaf Blight, Root rot, Micro Nutrient, Verticillium wilt. This work utilizes these three features and combined the classifier of proposed Cross Information Gain Deep forward Neural Network (CIGDFNN) which helps to recognize and identify cotton leaf spot diseases. The forceful feature vector set is a combination of three features to obtain the higher accuracy rate and sensitivity, specificity when tested with the cotton leaf dataset. KeywordsParticle Swarm Optimization, Skew divergence Color Variance feature, Skew divergence Edge variance feature, Skew divergence texture variance feature, Cross Information Gain Deep forward Neural Network (CIGDFNN) classifier. The paper is addressed with the following Sections: Section 2: Literature Review, Section 3: Material and Methods, Section 4: Result and Discussion,  Conclusion and Acknowledgement, Reference.
Influences of semi-conductive coatings on the electric field distribution of GIS spacer interface

Ultra high voltage (UHV) AC Gas Insulated Switchgear (GIS) spacer is one of the most important components in GIS. In the process of long-term operation, partial discharge often occurs at the interface between the center conductor and epoxy resin (EP) basin body if this interface has some defects, and the discharge sometimes even leads to the crack of basin. It has become a bottleneck for the safe and stable operation of GIS equipment. In this paper, the interface material with EP, hydroxy-terminated butadiene nitrile liquid rubber (HTBN) and conductive carbon black was created, and its parameters were measured. With three-dimensional finite element method, the interfacial structure with semi-conductive coatings were designed, the calculation models of GIS spacer which had interfacial layer structure were built. The influences of semi-conductive coatings on the electric field distribution of the interface between center conductor and basin body were studied. To different defects on the conductor surface, the improvement of the semi-conductive coatings to the electric field distribution was analyzed. Compared with the interfacial layer without coatings, there is an 80 percent decrease of electric field intensity of surface defects than that without coatings, which proves its significant shielding effect. The rationality of GIS spacer's performance of material and structure of semi-conductive coatings are verified, which is helpful to improve the uneven distribution of electric field at the interface of UHV electrical equipment.
User acceptance of a G2B system: a case of electronic procurement system in Malaysia

The purpose of this paper is to determine the factors that influence the intention to use and actual usage of a G2B system such as electronic procurement system (EPS) by various ministries in the Government of Malaysia.  The research uses an extension of DeLone and McLean’s model of IS success by including trust, facilitating conditions, and web design quality. The model is tested using an empirical approach. A questionnaire was designed and responses from 358 users from various ministries were collected and analyzed using structural equation modeling (SEM).  The findings of the study indicate that: perceived usefulness, perceived ease of use, assurance of service by service providers, responsiveness of service providers, facilitating conditions, web design (service quality) are strongly linked to intention to use EPS; and intention to use is strongly linked to actual usage behavior.  Typically, governments of developing countries spend millions of dollars to implement e-government systems. The investments can be considered useful only if the usage rate is high. The study can help ICT decision makers in government to recognize the critical factors that are responsible for the success of a G2B system like EPS.  The model used in the study is one of the few models designed to determine factors influencing intention to use and actual usage behavior in a G2B system in a fast-developing country like Malaysia.
Deep Inverse Optimization

Given a set of observations generated by an optimization process, the goal of inverse optimization is to determine likely parameters of that process. We cast inverse optimization as a form of deep learning. Our method, called deep inverse optimization, is to unroll an iterative optimization process and then use backpropagation to learn parameters that generate the observations. We demonstrate that by backpropagating through the interior point algorithm we can learn the coefficients determining the cost vector and the constraints, independently or jointly, for both non-parametric and parametric linear programs, starting from one or multiple observations. With this approach, inverse optimization can leverage concepts and algorithms from deep learning.
A latent variable approach to executive control in healthy ageing

It is a well-established finding that the central executive is fractionated in at least three separable component processes: Updating, Shifting, and Inhibition of information (Miyake et al., 2000). However, the fractionation of the central executive among the elderly has been less well explored, and Miyake's et al. latent structure has not yet been integrated with other models that propose additional components, such as access to long-term information. Here we administered a battery of classic and newer neuropsychological tests of executive functions to 122 healthy individuals aged between 48 and 91 years. The test scores were subjected to a latent variable analysis (LISREL), and yielded four factors. The factor structure obtained was broadly consistent with Miyake et al.'s three-factor model. However, an additional factor, which was labeled 'efficiency of access to long-term memory', and a mediator factor ('speed of processing') were apparent in our structural equation analysis. Furthermore, the best model that described executive functioning in our sample of healthy elderly adults included a two-factor solution, thus indicating a possible mechanism of dedifferentiation, which involves larger correlations and interdependence of latent variables as a consequence of cognitive ageing. These results are discussed in the light of current models of prefrontal cortex functioning.
Fast Image Interpolation via Random Forests

This paper proposes a two-stage framework for fast image interpolation via random forests (FIRF). The proposed FIRF method gives high accuracy, as well as requires low computation. The underlying idea of this proposed work is to apply random forests to classify the natural image patch space into numerous subspaces and learn a linear regression model for each subspace to map the low-resolution image patch to high-resolution image patch. The FIRF framework consists of two stages. Stage 1 of the framework removes most of the ringing and aliasing artifacts in the initial bicubic interpolated image, while Stage 2 further refines the Stage 1 interpolated image. By varying the number of decision trees in the random forests and the number of stages applied, the proposed FIRF method can realize computationally scalable image interpolation. Extensive experimental results show that the proposed FIRF(3, 2) method achieves more than 0.3 dB improvement in peak signal-to-noise ratio over the state-of-the-art nonlocal autoregressive modeling (NARM) method. Moreover, the proposed FIRF(1, 1) obtains similar or better results as NARM while only takes its 0.3% computational time.
Scannerless 3D imaging sensors

This contribution describes the research activity on the development of different smart pixel topologies aimed at three-dimensional (3D) vision applications exploiting the multiple-pulse indirect time-of-flight (TOF) and standard direct TOF techniques. The proposed approaches allow for the realization of scannerless laser ranging systems capable of fast collection of 3D data sets, as required in a growing number of applications like, automotive, security, surveillance and robotic guidance. Single channel approach, as well as matrix-organized sensors, will be described, facing the demanding constraints of specific applications, like the high dynamic range capability and the background immunity. Real time range (3D) and intensity (2D) imaging of non-cooperative targets, also in presence of strong background illumination, has been successfully performed in the 2m-9m range with a precision better than 5% and an accuracy of about 1%.
A 154–165 GHz LNA and receiver in CMOS 65 nm technology

This paper presents a fully differential G-band receiver design in CMOS 65 nm technology. First a low noise amplifier (LNA) was designed using neutralized, cross-coupled common source (CC CS) differential pair topology. The measured peak power gain was 36 dB and the minimum noise figure (NF) was 8.3 dB at around 158 GHz. These are the best gain and NF results in CMOS beyond 110 GHz. With exceptionally low DC power consumption of 34.8 mW, this LNA achieves a record gain efficiency of 1.03 dB/mW compared to any integrated circuit technology at this band. Due to the excellent performance of the LNA, a highly sensitive low noise G band receiver was also designed by adding a single transistor passive mixer at the LNA output. The measured receiver conversion gain was up to 20 dB at 158 GHz and the minimum NF was 8 dB. To the best knowledge of the authors, these are the best results in CMOS receivers beyond 110 GHz.
Overview of the NLPCC-ICCPOL 2016 Shared Task: Chinese Word Similarity Measurement

Word similarity computation is a fundamental task for natural language processing. We organize a semantic campaign of Chinese word similarity measurement at NLPCC-ICCPOL 2016. This task provides a benchmark dataset of Chinese word similarity (PKU-500 dataset), including 500 word pairs with their similarity scores. There are 21 teams submitting 24 systems in this campaign. In this paper, we describe clearly the data preparation and word similarity annotation, make an in-depth analysis on the evaluation results and give a brief introduction to participating systems.
Network Flow Query Language—Design, Implementation, Performance, and Applications

Cisco’s NetFlow protocol and Internet engineering task force’s Internet protocol flow information export open standard are widely deployed protocols for collecting network flow statistics. Understanding intricate traffic patterns in these network statistics requires sophisticated flow analysis tools that can efficiently mine network flow records. We present a network flow query language (NFQL), which can be used to write expressive queries to process flow records, aggregate them into groups, apply absolute or relative filters, and invoke Allen interval algebra rules to merge group records. We demonstrate nfql, an implementation of the language that has comparable execution times to SiLK and flow-tools with absolute filters. However, it trades performance when grouping and merging flows in favor of more operational capabilities that help increase the expressiveness of NFQL. We present two applications to demonstrate richer capabilities of the language. We show queries to identify flow signatures of popular applications and behavioural signatures to identify SSH compromise detection attacks.
Stochastic expression dynamics of a transcription factor revealed by single-molecule noise analysis

Gene expression is inherently stochastic; precise gene regulation by transcription factors is important for cell-fate determination. Many transcription factors regulate their own expression, suggesting that autoregulation counters intrinsic stochasticity in gene expression. Using a new strategy, cotranslational activation by cleavage (CoTrAC), we probed the stochastic expression dynamics of cI, which encodes the bacteriophage λ repressor CI, a fate-determining transcription factor. CI concentration fluctuations influence both lysogenic stability and induction of bacteriophage λ. We found that the intrinsic stochasticity in cI expression was largely determined by CI expression level irrespective of autoregulation. Furthermore, extrinsic, cell-to-cell variation was primarily responsible for CI concentration fluctuations, and negative autoregulation minimized CI concentration heterogeneity by counteracting extrinsic noise and introducing memory. This quantitative study of transcription factor expression dynamics sheds light on the mechanisms cells use to control noise in gene regulatory networks.
In-vehicle communication networks-a historical perspective and review ∗

The use of networks for communications between the Electronic Control Units (ECU) of a vehicle in production cars dates from the beginning of the 90s. The speci c requirements of the di erent car domains have led to the development of a large number of automotive networks such as LIN, CAN, CAN FD, FlexRay, MOST, automotive Ethernet AVB, etc.. This report rst introduces the context of in-vehicle embedded systems and, in particular, the requirements imposed on the communication systems. Then, a review of the most widely used, as well as the emerging automotive networks is given. Next, the current e orts of the automotive industry on middleware technologies which may be of great help in mastering the heterogeneity, are reviewed, with a special focus on the proposals of the AUTOSAR consortium. Finally, we highlight future trends in the development of automotive communication systems. ∗This technical report is an updated version of two earlier review papers on automotive networks: N. Navet, Y.-Q. Song, F. Simonot-Lion, C. Wilwert, "Trends in Automotive Communication Systems", . An updated version of this IEEE Proceedings then appeared as chapter 4 in The Automotive Embedded Systems Handbook in 2008 [62].
The physiology/life-history nexus

histories. However, many potential combinations of life-history traits do not actually occur in nature . Indeed, the few major axes of life-history variation stand in stark contrast to the variety of selective pressures on life histories: physical conditions, seasonality and unpredictability of the environment, food availability, predators and disease organisms, and relationships within social and family groups. Most life-history thinking has been concerned with constrained evolutionary responses to the environment. Differences among the life histories of species are viewed commonly as having a genetic basis and reflecting the optimization of phenotypes with respect to their environments. The optimal balance between parental investment and adult self-maintenance is also influenced by the life table of the population, particularly the relative value of present and future reproduction [1,3–5]. Constraints on adaptive responses are established by the allocation of limited time, energy and nutrients among competing functions [6,7]. Relatively less attention has been paid to nongenetic responses to the environment, such as adjustment of parental investment in response to perceived risk, except for the study of phenotypic flexibility (the reaction norm) as a life-history character itself [4,8,9]. Here we argue that physiological function, including endocrine control mechanisms, mediates the relationship of the organism to its environment and therefore is essential to our understanding of the diversification of life histories. Much of the variation in life histories, particularly variation in parental investment and self-maintenance, reflects phenotypic responses of individuals to environmental stresses and perceived risks. As a result, the organization of behavioral and physiological control mechanisms might constrain individual (and evolutionary) responses and limit life-history variation among species.
Individualizing Cybersecurity Lab Exercises with Labtainers

Hurdles to creating and assessing exploratory cybersecurity laboratory exercises include their development, setup, and configuration; resource requirements; student sharing of results; and arduous grading. The Labtainers framework supports multi-component networks and simplifies the creation of labs that are consistent and fair, can be parameterized to discourage students from submitting results either created by others or mined from the Internet, and can include practical, instructor-friendly automated assessment. Implemented using Linux Docker containers and easily accommodated on students’ modest computers, each stand-alone cybersecurity lab exercise has one or more associated containers that ensure a consistent execution environment within the lab. Here we discuss three approaches to lab parameterization that encourage individual work and are compatible with automated assessment: watermarks, per-student artifacts, and per-student solutions.
Individual Differences in Cyber Security Behaviors: An Examination of Who Is Sharing Passwords

In spite of the number of public advice campaigns, researchers have found that individuals still engage in risky password practices. There is a dearth of research available on individual differences in cyber security behaviors. This study focused on the risky practice of sharing passwords. As predicted, we found that individuals who scored high on a lack of perseverance were more likely to share passwords. Contrary to our hypotheses, we found younger [corrected] people and individuals who score high on self-monitoring were more likely to share passwords. We speculate on the reasons behind these findings, and examine how they might be considered in future cyber security educational campaigns.
Automatic Car Number Plate Recognition System for Authorization

In present day scenario the security and authentication is very much needed to make a safety world. Beside all security one vital issue is recognition of number plate from the car for Authorization. In the busy world everything cannot be monitor by a human, so automatic license plate recognition is one of the best application for authorization without involvement of human power. In the proposed method we have make the problem into three fold, firstly extraction of number plate region, secondly segmentation of character and finally Authorization through recognition and classification. For number plate extraction and segmentation we have used morphological based approaches where as for classification we have used Neural Network as classifier. The proposed method is working well in varieties of scenario and the performance level is quiet good..
Emerging pollutants in the EU: 10 years of NORMAN in support of environmental policies and regulations

In 2005, the European Commission funded the NORMAN project to promote a permanent network of reference laboratories and research centers, including academia, industry, standardization bodies, and NGOs. Since then, NORMAN has (i) facilitated a more rapid and wide-scope exchange of data on the occurrence and effects of contaminants of emerging concern (CECs), (ii) improved data quality and comparability via validation and harmonization of common sampling and measurement methods (chemical and biological), (iii) provided more transparent information and monitoring data on CECs, and (iv) established an independent and competent forum for the technical/scientific debate on issues related to emerging substances. NORMAN plays a significant role as an independent organization at the interface between science and policy, with the advantage of speaking to the European Commission and other public institutions with the "bigger voice" of more than 70 members from 20 countries. This article provides a summary of the first 10 years of the NORMAN network. It takes stock of the work done so far and outlines NORMAN's vision for a Europe-wide collaboration on CECs and sustainable links from research to policy-making. It contains an overview of the state of play in prioritizing and monitoring emerging substances with reference to several innovative technologies and monitoring approaches. It provides the point of view of the NORMAN network on a burning issue-the regulation of CECs-and presents the positions of various stakeholders in the field (DG ENV, EEA, ECHA, and national agencies) who participated in the NORMAN workshop in October 2016. The main messages and conclusions from the round table discussions are briefly presented.
Beyond News Contents: The Role of Social Context for Fake News Detection

Social media is becoming popular for news consumption due to its fast dissemination, easy access, and low cost. However, it also enables the wide propagation of fake news, i.e., news with intentionally false information. Detecting fake news is an important task, which not only ensures users receive authentic information but also helps maintain a trustworthy news ecosystem. The majority of existing detection algorithms focus on finding clues from news contents, which are generally not effective because fake news is often intentionally written to mislead users by mimicking true news. Therefore, we need to explore auxiliary information to improve detection. The social context during news dissemination process on social media forms the inherent tri-relationship, the relationship among publishers, news pieces, and users, which has the potential to improve fake news detection. For example, partisan-biased publishers are more likely to publish fake news, and low-credible users are more likely to share fake news. In this paper, we study the novel problem of exploiting social context for fake news detection. We propose a tri-relationship embedding framework TriFN, which models publisher-news relations and user-news interactions simultaneously for fake news classification. We conduct experiments on two real-world datasets, which demonstrate that the proposed approach significantly outperforms other baseline methods for fake news detection.