nils-herrmann/clean_scirepeval huggingface dataset:
An IoT based smart solution for leaf disease detection

Internet of things is a system consists of actuators or sensors or both that provides connectivity to the internet directly or indirectly. Internet of Things (IoT) advances can be used in smart farming to enhance quality of agriculture. Agriculture, the backbone of Indian economy, contributes to the overall economic growth of the country. But our productivity is very less as compared to world standards due to the use of obsolete farming technology, and nowadays people from rural areas migrate to an urban area for other profitable businesses, and they can't focus on agriculture. Innovation in farming is not new but IoT is set to push smart farming to next level Internet of things is a system consists of actuators or sensors or both provides connectivity to the internet directly or indirectly. This paper includes various features like detection of leaf disease, server based remote monitoring system, Humidity and temperature sensing, Soil the Moisture Sensing etc. It makes use of sensors networks for measurement of moisture, temperature, and humidity instead of manual check. Various Sensors are deployed in various locations of farms, to control all these sensors it has been used one controller called Raspberry PI (RPI). Leaf disease can be detected camera interfacing with RPI. Immediate status of a farm like a leaf disease and other environmental factors affecting crop like humidity, temperature and moisture is send using WIFI Server through RPI to the farmers. The paper presents the study of IOT techniques to engross the use of technology in Agriculture.
Context in NEHRP Recommended Provisions Design basis : Strength limit state Using NEHRP Recommended Provisions : Structural design criteria : Chap . 4 Structural analysis procedures

This topic covers various aspects of seismic design of reinforced concrete structures with an emphasis on Design for regions of high seismicity. Because the requirement for greater ductility in earthquake-resistant Buildings represents the principal departure from the conventional design for gravity and wind loading, the Major part of the discussion in this chapter will be devoted to considerations associated with providing Ductility in members and structures. The discussion in this chapter will be confined to monolithically cast Reinforced-concrete buildings. The concepts of seismic demand and capacity are introduced and elaborated On. Specific provisions for design of seismic resistant reinforced concrete members and systems are Presented in detail. Appropriate seismic detailing considerations are discussed. Finally, a numerical example is presented where these principles are applied.
Kohlberg ’ s Stages of Moral Development

Lawrence Kohlberg (1958) agreed with Piaget's (1932) theory of moral development in principle but wanted to develop his ideas further. He used Piaget’s storytelling technique to tell people stories involving moral dilemmas. In each case, he presented a choice to be considered, for example, between the rights of some authority and the needs of some deserving individual who is being unfairly treated. One of the best known of Kohlberg’s (1958) stories concerns a man called Heinz who lived somewhere in Europe. Heinz’s wife was dying from a particular type of cancer. Doctors said a new drug might save her. The drug had been discovered by a local chemist, and the Heinz tried desperately to buy some, but the chemist was charging ten times the money it cost to make the drug, and this was much more than the Heinz could afford. Heinz could only raise half the money, even after help from family and friends. He explained to the chemist that his wife was dying and asked if he could have the drug cheaper or pay the rest of the money later. The chemist refused, saying that he had discovered the drug and was going to make money from it. The husband was desperate to save his wife, so later that night he broke into the chemist’s and stole the drug.
Uncertainty Driven Multi-loss Fully Convolutional Networks for Histopathology

Different works have shown that the combination of multiple loss functions is beneficial when training deep neural networks for a variety of prediction tasks. Generally, such multi-loss approaches are implemented via a weighted multi-loss objective function in which each term encodes a different desired inference criterion. The importance of each term is often set using empirically tuned hyper-parameters. In this work, we analyze the importance of the relative weighting between the different terms of a multi-loss function and propose to leverage the model’s uncertainty with respect to each loss as an automatically learned weighting parameter. We consider the application of colon gland analysis from histopathology images for which various multi-loss functions have been proposed. We show improvements in classification and segmentation accuracy when using the proposed uncertainty driven multi-loss function.
OCCUPATION AND LOCAL TIMES FOR SKEW BROWNIAN MOTION WITH APPLICATIONS TO DISPERSION ACROSS AN INTERFACE∗

Advective skew dispersion is a natural Markov process defined by a diffusion with drift across an interface of jump discontinuity in a piecewise constant diffusion coefficient. In the absence of drift this process may be represented as a function of α-skew Brownian motion for a uniquely determined value of α = α∗; see Ramirez, Thomann, Waymire, Haggerty and Wood (2006). In the present paper the analysis is extended to the case of non-zero drift. A determination of the (joint) distributions of key functionals of standard skew Brownian motion together with some associated probabilistic semigroup and local time theory is given for these purposes. An application to the dispersion of a solute concentration across an interface is provided that explains certain symmetries and asymmetries in recently reported laboratory experiments conducted at Lawrence-Livermore Berkeley Labs by Berkowitz, Cortis, Dror and Scher (2009).
THE ROLE OF CUSTOMER-BASED BRAND EQUITY IN THE BRAND PERSONALITY EFFECT ON PURCHASE INTENTION

In the Brand literature, few studies especially in Iran investigated the brand functions and business success. Hence, this study aims to provide the desirable model to creation and developing a deeper insight into the role of brand equity in the relationship between brand personality and customers purchase intention. The study statistical population consists of the whole Mellat Bank customers in Qazvin province, which used a questionnaire to collect data from them. In addition to, four hypotheses were announced and tested using structural equation modeling techniques. Research findings show the significant and positive effects of the brand personality on brand equity and purchase intention. Likewise, the results revealed that brand equity has a positive influence on customers' purchase intention and has a positive mediator role for the other two variables. According to the results of study, it is recommended to organizations and those marketing managers to take action to create a positive brand personality until make differentiation in customers minds compared to other brands and enhance brand equity and achieved to the comprehensive understanding of consumer behavior.
A Hierarchical Bayesian Approach to Improve Media Mix Models Using Category Data

One of the major problems in developing media mix models is that the data that is generally available to the modeler lacks sufficient quantity and information content to reliably estimate the parameters in a model of even moderate complexity. Pooling data from different brands within the same product category provides more observations and greater variability in media spend patterns. We either directly use the results from a hierarchical Bayesian model built on the category dataset, or pass the information learned from the category model to a brand-specific media mix model via informative priors within a Bayesian framework, depending on the data sharing restriction across brands. We demonstrate using both simulation and real case studies that our category analysis can improve parameter estimation and reduce uncertainty of model prediction and extrapolation.
Natural Language Generation with Vocabulary Constraints

We investigate data driven natural language generation under the constraints that all words must come from a fixed vocabulary and a specified word must appear in the generated sentence, motivated by the possibility for automatic generation of language education exercises. We present fast and accurate approximations to the ideal rejection samplers for these constraints and compare various sentence level generative language models. Our best systems produce output that is with high frequency both novel and error free, which we validate with human and automatic evaluations.
Middle School Students ' Perceptions on Academic Motivation and Student Engagement

This qualitative study investigates the perceptions of suburban middle school students’ on academic motivation and student engagement. Ten students, grades 6-8, were randomly selected by the researcher from school counselors’ caseloads and the primary data collection techniques included two types of interviews; individual interviews and focus group interviews. Findings indicate students’ motivation and engagement in middle school is strongly influenced by the social relationships in their lives. The interpersonal factors identified by students were peer influence, teacher support and teacher characteristics, and parental behaviors. Each of these factors consisted of academic and social-emotional support which hindered and/or encouraged motivation and engagement. Students identified socializing with their friends as a means to want to be in school and to engage in learning. Also, students are more engaged and motivated if they believe their teachers care about their academic success and value their job. Lastly, parental involvement in academics appeared to be more crucial for younger students than older students in order to encourage motivation and engagement in school. MIDDLE SCHOOL STUDENTS’ PERCEPTIONS 5 Middle School Students’ Perceptions on Student Engagement and Academic Motivation Middle School Students’ Perceptions on Student Engagement and Academic Motivation Early adolescence marks a time for change for students academically and socially. Students are challenged academically in the sense that there is greater emphasis on developing specific intellectual and cognitive capabilities in school, while at the same time they are attempting to develop social skills and meaningful relationships. It is often easy to overlook the social and interpersonal challenges faced by students in the classroom when there is a large focus on grades in education, especially since teachers’ competencies are often assessed on their students’ academic performance. When schools do not consider psychosocial needs of students, there is a decrease in academic motivation and interest, lower levels of student engagement and poorer academic performance (i.e. grades) for middle school students (Wang & Eccles, 2013). In fact, students who report high levels of engagement in school are 75% more likely to have higher grades and higher attendance rates. Disengaged students tend to have lower grades and are more likely to drop out of school (Klem & Connell, 2004). Therefore, this research has focused on understanding the connections between certain interpersonal influences and academic motivation and engagement.
Chatbots Meet eHealth: Automatizing Healthcare

The aim of this work is to investigate the effectiveness of novel human-machine interaction paradigms for eHealth applications. In particular, we propose to replace usual human-machine interaction mechanisms with an approach that leverages a chat-bot program, opportunely designed and trained in order to act and interact with patients as a human being. Moreover, we have validated the proposed interaction paradigm in a real clinical context, where the chat-bot has been employed within a medical decision support system having the goal of providing useful recommendations concerning several disease prevention pathways. More in details, the chat-bot has been realized to help patients in choosing the most proper disease prevention pathway by asking for different information (starting from a general level up to specific pathways questions) and to support the related prevention check-up and the final diagnosis. Preliminary experiments about the effectiveness of the proposed approach are reported.
Unpacking cognitive reappraisal: goals, tactics, and outcomes.

Studies of emotion regulation typically contrast two or more strategies (e.g., reappraisal vs. suppression) and ignore variation within each strategy. To address such variation, we focused on cognitive reappraisal and considered the effects of goals (i.e., what people are trying to achieve) and tactics (i.e., what people actually do) on outcomes (i.e., how affective responses change). To examine goals, we randomly assigned participants to either increase positive emotion or decrease negative emotion to a negative stimulus. To examine tactics, we categorized participants' reports of how they reappraised. To examine reappraisal outcomes, we measured experience and electrodermal responding. Findings indicated that (a) the goal of increasing positive emotion led to greater increases in positive affect and smaller decreases in skin conductance than the goal of decreasing negative emotion, and (b) use of the reality challenge tactic was associated with smaller increases in positive affect during reappraisal. These findings suggest that reappraisal can be implemented in the service of different emotion goals, using different tactics. Such differences are associated with different outcomes, and they should be considered in future research and applied attempts to maximize reappraisal success.
Relationship between Corporate Image and Customer Loyalty in the Mobile Telecommunication Market in Kenya

In the 21 st century, organisations cannot succeed in marketing by focusing only on the marketing mix without a focus on its impact on creating customer loyalty. Customer loyalty is considered to be a key ingredient in enhancing the survival of businesses especially in the situations faced by highly competitive industries. While the antecedents of customer loyalty connected with the marketing mix factors have been well investigated, much still remain regarding some of the intermediate conditions created by the marketing mix factors and customer loyalty. This study sought to investigate the relationship between corporate image and customer loyalty in the mobile telecommunication market in Kenya. The study was guided by several hypotheses that tested the nature of the relationship between four aspects of corporate image and customer loyalty. The study adopted the descriptive survey research design and used a multistage stratified sampling technique to obtain 320 respondents from among students across the campuses of Kenyatta University. Primary data was obtained with questionnaire and analysed using Pearson product-moment correlation coefficient and regression analysis to test the degree of association between the dependent and the independent variables with the aid of the Statistical Product and Service Solutions (SPSS). The new findings of the study showed a positive and statistically significant relationship between the dimensions of corporate image and customer loyalty. The variables significantly predicted customer loyalty. The reported findings in the study raise implications for marketing theory and practice suitable to inform strategic decisions for firms in the telecommunication sector in Kenya.
Multi-Task Deep Neural Networks for Natural Language Understanding

In this paper, we present a Multi-Task Deep Neural Network (MT-DNN) for learning representations across multiple natural language understanding (NLU) tasks. MT-DNN not only leverages large amounts of cross-task data, but also benefits from a regularization effect that leads to more general representations to help adapt to new tasks and domains. MTDNN extends the model proposed in Liu et al. (2015) by incorporating a pre-trained bidirectional transformer language model, known as BERT (Devlin et al., 2018). MT-DNN obtains new state-of-the-art results on ten NLU tasks, including SNLI, SciTail, and eight out of nine GLUE tasks, pushing the GLUE benchmark to 82.2% (1.8% absolute improvement). We also demonstrate using the SNLI and SciTail datasets that the representations learned by MT-DNN allow domain adaptation with substantially fewer in-domain labels than the pre-trained BERT representations. Our code and pre-trained models will be made publicly available.
From Algorithmic Black Boxes to Adaptive White Boxes: Declarative Decision-Theoretic Ethical Programs as Codes of Ethics

Ethics of algorithms is an emerging topic in various disciplines such as social science, law, and philosophy, but also artificial intelligence (AI). The value alignment problem expresses the challenge of (machine) learning values that are, in some way, aligned with human requirements or values. In this paper I argue for looking at how humans have formalized and communicated values, in professional codes of ethics, and for exploring declarative decision-theoretic ethical programs (DDTEP) to formalize codes of ethics. This renders machine ethical reasoning and decision-making, as well as learning, more transparent and hopefully more accountable. The paper includes proof-of-concept examples of known toy dilemmas and gatekeeping domains such as archives and libraries.
Security of ADS-B: State of the Art and Beyond

Automatic dependent surveillance-broadcast (ADSB) is the communications protocol currently being rolled out as part of next generation air transportation systems. As the heart of modern air traffic control, it will play an essential role in the protection of two billion passengers per year, besides being crucial to many other interest groups in aviation. The inherent lack of security measures in the ADS-B protocol has long been a topic in both aviation circles as well as the academic community. Due to recently published proof-of-concept attacks, the topic is becoming ever more pressing, especially with the deadline for mandatory implementation in most airspaces fast approaching. This survey first summarizes the attacks and problems that have been reported in relation to ADS-B security. Thereafter, we survey both the theoretical and practical efforts which have been conducted previously concerning these issues, including possible countermeasures proposed in the literature. In addition, the survey seeks to go beyond the current state of the art and gives a detailed assessment of security measures which have been developed more generally for related wireless networks such as sensor networks and vehicular ad hoc networks, including a taxonomy of all considered approaches.
Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue

A significant weakness of most current deep Convolutional Neural Networks is the need to train them using vast amounts of manually labelled data. In this work we propose a unsupervised framework to learn a deep convolutional neural network for single view depth prediction, without requiring a pre-training stage or annotated ground truth depths. We achieve this by training the network in a manner analogous to an autoencoder. At training time we consider a pair of images, source and target, with small, known camera motion between the two such as a stereo pair. We train the convolutional encoder for the task of predicting the depth map for the source image. To do so, we explicitly generate an inverse warp of the target image using the predicted depth and known inter-view displacement, to reconstruct the source image; the photometric error in the reconstruction is the reconstruction loss for the encoder. The acquisition of this training data is considerably simpler than for equivalent systems, requiring no manual annotation, nor calibration of depth sensor to camera. We show that our network trained on less than half of the KITTI dataset (without any further augmentation) gives comparable performance to that of the state of art supervised methods for single view depth estimation.
Automated Discovery of Process Models from Event Logs: Review and Benchmark

Process mining allows analysts to exploit logs of historical executions of business processes to extract insights regarding the actual performance of these processes. One of the most widely studied process mining operations is automated process discovery. An automated process discovery method takes as input an event log, and produces as output a business process model that captures the control-flow relations between tasks that are observed in or implied by the event log. Various automated process discovery methods have been proposed in the past two decades, striking different tradeoffs between scalability, accuracy, and complexity of the resulting models. However, these methods have been evaluated in an ad-hoc manner, employing different datasets, experimental setups, evaluation measures, and baselines, often leading to incomparable conclusions and sometimes unreproducible results due to the use of closed datasets. This article provides a systematic review and comparative evaluation of automated process discovery methods, using an open-source benchmark and covering 12 publicly-available real-life event logs, 12 proprietary real-life event logs, and nine quality metrics. The results highlight gaps and unexplored tradeoffs in the field, including the lack of scalability of some methods and a strong divergence in their performance with respect to the different quality metrics used.
Memory Networks for Recommendation

Memory networks are a recently introduced model that combines reasoning, attention and memory for solving tasks in the areas of language understanding and dialogue -- where one exciting direction is the use of these models for dialogue-based recommendation. In this talk we describe these models and how they can learn to discuss, answer questions about, and recommend sets of items to a user. The ultimate goal of this research is to produce a full dialogue-based recommendation assistant. We will discuss recent datasets and evaluation tasks that have been built to assess these models abilities to see how far we have come.
Anonymity in Social Media: Effects of Content Controversiality and Social Endorsement on Sharing Behavior

The amount of information shared via social media is rapidly increasing amid growing concerns over online privacy. This study investigates the effect of controversiality and social endorsement of media content on sharing behavior when choosing between sharing publicly or anonymously. Anonymous sharing is found to be a popular choice (59% of shares), especially for controversial content which is 3.2x more likely to be shard anonymously. Social endorsement was not found to affect sharing behavior, except for sports-related content. Implications for social media interface design are dis-