nils-herrmann/clean_scirepeval huggingface dataset:
Commiphora myrrha and commiphora Africana essential oils

From generation to generation in all countries all around the world medicinal plants play an important role in our live from ancient time till these days of wide drugs and pharmacological high technique industries , the studding of biological and pharmacological activities of plant essential oils attracted the attention to the potential use of these natural products from chemical and pharmacological investigation to their therapeutic aspects. In this paper two resins commiphora Africana and commiphora myrrha were selected to discuss their essential oils for chemical analysis and biological aspect the results of GCMS shows that the two resins are rich in sesqiuterpenes and sesqiuterpene lactones compounds that possess anti-inflammatory and antitumor activity Antibacterial and antifungal bioassay shows antibacterial and antifungal activity higher in the myrrha oil than the Africana oil while antiviral bioassay shows higher antiviral activity in the Africana oil than myrrha oil
DNN's Sharpest Directions Along the SGD Trajectory

•At the beginning of training, a high learning rate or small batch size influences SGD to visit flatter loss regions. •The evolution of the largest eigenvalues always follow a similar pattern, with a fast increase in the first epochs and a steady decrease thereafter, where the peak value is determined by the learning rate and batch size. •By altering the learning rate in the sharpest direction, SGD can be steered towards regions which are an order of magnitude sharper with similar generalization.
Adversarial Feature Adaptation for Cross-lingual Relation Classification

Classification aims to classify the semantic relationship between two marked entities in a given sentence. It plays a vital role in a variety of natural language processing applications. Most existing methods focus on exploiting mono-lingual data, e.g., in English, due to the lack of annotated data in other languages. In this paper, we come up with a feature adaptation approach for cross-lingual relation classification, which employs a generative adversarial network (GAN) to transfer feature representations from one language with rich annotated data to another language with scarce annotated data. Such a feature adaptation approach enables feature imitation via the competition between a relation classification network and a rival discriminator. Experimental results on the ACE 2005 multilingual training corpus, treating English as the source language and Chinese the target, demonstrate the effectiveness of our proposed approach, yielding an improvement of 5.7% over the state-of-the-art.
SALSA-TEXT : self attentive latent space based adversarial text generation

Inspired by the success of self attention mechanism and Transformer architecture in sequence transduction and image generation applications, we propose novel self attention-based architectures to improve the performance of adversarial latent codebased schemes in text generation. Adversarial latent code-based text generation has recently gained a lot of attention due to its promising results. In this paper, we take a step to fortify the architectures used in these setups, specifically AAE and ARAE. We benchmark two latent code-based methods (AAE and ARAE) designed based on adversarial setups. In our experiments, the Google sentence compression dataset is utilized to compare our method with these methods using various objective and subjective measures. The experiments demonstrate the proposed (self) attention-based models outperform the state-of-the-art in adversarial code-based text generation.
An End-to-End Model for Question Answering over Knowledge Base with Cross-Attention Combining Global Knowledge

With the rapid growth of knowledge bases (KBs) on the web, how to take full advantage of them becomes increasingly important. Question answering over knowledge base (KB-QA) is one of the promising approaches to access the substantial knowledge. Meanwhile, as the neural networkbased (NN-based) methods develop, NNbased KB-QA has already achieved impressive results. However, previous work did not put more emphasis on question representation, and the question is converted into a fixed vector regardless of its candidate answers. This simple representation strategy is not easy to express the proper information in the question. Hence, we present an end-to-end neural network model to represent the questions and their corresponding scores dynamically according to the various candidate answer aspects via cross-attention mechanism. In addition, we leverage the global knowledge inside the underlying KB, aiming at integrating the rich KB information into the representation of the answers. As a result, it could alleviates the out-of-vocabulary (OOV) problem, which helps the crossattention model to represent the question more precisely. The experimental results on WebQuestions demonstrate the effectiveness of the proposed approach.
Generating fuzzy rules by learning from examples

A general method is developed to generate fuzzy rules from numerical data. This new method consists of five steps: Step 1 divides the input and output spaces of the given numerical data into fuzzy regions; Step 2 generates fuzzy rules from the given data; Step 3 assigns a degree of each of the generated rules for the purpose of resolving conflicts among the generated rules; Step 4 creates a combined fuzzy rule base based on both the generated rules and linguistic rules of human experts; and, Step 5 determines a mapping from input space to output space based on the combined fuzzy rule base using a defuzzifying procedure. The mapping is proved to be capable of approximating any real continuous function on a compact set to arbitrary accuracy. Applications to truck backer-upper control and time series prediction problems are presented. For the truck control problem, the performance of this new method is compared with a neural network controller and a pure limited-rule fuzzy controller; the new method shows the best performance. For the time series prediction problem, results are compared by using the new method and a neural network predictor for the Mackey-Glass chaotic time series.
Metaphor comprehension: a computational theory.

Metaphor comprehension involves an interaction between the meaning of the topic and the vehicle terms of the metaphor. Meaning is represented by vectors in a high-dimensional semantic space. Predication modifies the topic vector by merging it with selected features of the vehicle vector. The resulting metaphor vector can be evaluated by comparing it with known landmarks in the semantic space. Thus, metaphorical prediction is treated in the present model in exactly the same way as literal predication. Some experimental results concerning metaphor comprehension are simulated within this framework, such as the nonreversibility of metaphors, priming of metaphors with literal statements, and priming of literal statements with metaphors.
Network-Level Structure-Function Relationships in Human Neocortex

The dynamics of spontaneous fluctuations in neural activity are shaped by underlying patterns of anatomical connectivity. While numerous studies have demonstrated edge-wise correspondence between structural and functional connections, much less is known about how large-scale coherent functional network patterns emerge from the topology of structural networks. In the present study, we deploy a multivariate statistical technique, partial least squares, to investigate the association between spatially extended structural networks and functional networks. We find multiple statistically robust patterns, reflecting reliable combinations of structural and functional subnetworks that are optimally associated with one another. Importantly, these patterns generally do not show a one-to-one correspondence between structural and functional edges, but are instead distributed and heterogeneous, with many functional relationships arising from nonoverlapping sets of anatomical connections. We also find that structural connections between high-degree hubs are disproportionately represented, suggesting that these connections are particularly important in establishing coherent functional networks. Altogether, these results demonstrate that the network organization of the cerebral cortex supports the emergence of diverse functional network configurations that often diverge from the underlying anatomical substrate.
Principles and Standards for School Mathematics : A Guide for Mathematicians

868 NOTICES OF THE AMS VOLUME 47, NUMBER 8 In April 2000 the National Council of Teachers of Mathematics (NCTM) released Principles and Standards for School Mathematics—the culmination of a multifaceted, three-year effort to update NCTM’s earlier standards documents and to set forth goals and recommendations for mathematics education in the prekindergarten-through-grade-twelve years. As the chair of the Writing Group, I had the privilege to interact with all aspects of the development and review of this document and with the committed groups of people, including the members of the Writing Group, who contributed immeasurably to this process. This article provides some background about NCTM and the standards, the process of development, efforts to gather input and feedback, and ways in which feedback from the mathematics community influenced the document. The article concludes with a section that provides some suggestions for mathematicians who are interested in using Principles and Standards.
CrowdStory: Fine-Grained Event Storyline Generation by Fusion of Multi-Modal Crowdsourced Data

Event summarization based on crowdsourced microblog data is a promising research area, and several researchers have recently focused on this field. However, these previous works fail to characterize the fine-grained evolution of an event and the rich correlations among posts. The semantic associations among the multi-modal data in posts are also not investigated as a means to enhance the summarization performance. To address these issues, this study presents CrowdStory, which aims to characterize an event as a fine-grained, evolutionary, and correlation-rich storyline. A crowd-powered event model and a generic event storyline generation framework are first proposed, based on which a multi-clue--based approach to fine-grained event summarization is presented. The implicit human intelligence (HI) extracted from visual contents and community interactions is then used to identify inter-clue associations. Finally, a cross-media mining approach to selective visual story presentation is proposed. The experiment results indicate that, compared with the state-of-the-art methods, CrowdStory enables fine-grained event summarization (e.g., dynamic evolution) and correctly identifies up to 60% strong correlations (e.g., causality) of clues. The cross-media approach shows diversity and relevancy in visual data selection.
Carbon emission and mitigation cost comparisons between fossil fuel , nuclear and renewable energy resources for electricity generation

A study was conducted to compare the electricity generation costs of a number of current commercial technologies with technologies expected to become commercially available within the coming decade or so. The amount of greenhouse gas emissions resulting per kWh of electricity generated were evaluated. A range of fossil fuel alternatives (with and without physical carbon sequestration), were compared with the baseline case of a pulverised coal, steam cycle power plant. Nuclear, hydro, wind, bioenergy and solar generating plants were also evaluated. The objectives were to assess the comparative costs of mitigation per tonne of carbon emissions avoided, and to estimate the total amount of carbon mitigation that could result from the global electricity sector by 2010 and 2020 as a result of fuel switching, carbon dioxide sequestration and the greater uptake of renewable energy. Most technologies showed potential to reduce both generating costs and carbon emission avoidance by 2020 with the exception of solar power and carbon dioxide sequestration. The global electricity industry has potential to reduce its carbon emissions by over 15% by 2020 together with cost saving benefits compared with existing generation. r 2002 Elsevier
Reconfigurable Single-Armed Square Spiral Microstrip Antenna Design

In the past decade, wireless technologies have advanced dramatically [1-2]. Software Defined Radio (SDR) and Multiple-Input and Multiple-Output (MIMO) are two emerging technologies to deliver the desired functionality over a wide range of frequencies and provide dramatic increases in throughput. To support and enable the cutting edge wireless technologies, antenna designs need to meet the dynamic requirements for different frequency operating bands, radiation patterns and polarizations. Reconfigurable antennas are good candidates to meet those requirements. Several researchers have developed multiple reconfigurable antenna designs in various structures to deliver antenna frequency tunability, pattern reformability, and polarization selection with PIN diodes, MESFETs and RF MEMs [3-8]. In this paper, a frequency and pattern reconfigurable single-armed square spiral microstrip antenna is presented. Simulations indicate an average 3-7dBi gain can be achieved with this small single-armed square spiral antenna.
EAI Endorsed Transactions on Context-aware Systems and Applications Research

Context-aware Systems (CASs) are becoming increasingly popular and can be found in the areas of wearable computing, mobile computing, robotics, adaptive and intelligent user interfaces. Sensors are the corner stone of context capturing however, sensed context data are commonly prone to imperfection due to the technical limitations of sensors, their availability, dysfunction, and highly dynamic nature of environment. Consequently, sensed context data might be imprecise, erroneous, conflicting, or simply missing. To limit the impact of context imperfection on the behavior of a context-aware system, a notion of Quality of Context (QoC) is used to measure quality of any information that is used as context information. Adaptation is performed only if the context data used in the decision-making has an appropriate quality level. This paper reports an analytical review the state of the art on quality of context in context-aware systems and points to future research directions.
Hierarchical differential image filters for skin analysis

In this paper we present a framework for analyzing skin parameters from portrait images and videos. Using a series of Hierarchical Differential Image Filters (HDIF), it becomes possible to detect different skin features such as wrinkles, spots, and roughness. These detected features are used to compute skin ratings that are compared to actual ratings by dermatologists. Analyzing a database of 49 images with ratings by a panel of dermatologists, the proposed HDIF method is able to detect skin roughness, dark spots, and deep wrinkles with an average rating error of 11.3%, 17.6%, and 15.6%, respectively, as compared to individual dermatologist rating errors of 8.2%, 7.4%, and 6.5%. Although dermatologist ratings are more accurate than the proposed HDIF method, the ratings are close enough that the HDIF ratings can be a viable solution where dermatologist ratings are not readily available.
An Incremental Construction of Deep Neuro Fuzzy System for Continual Learning of Non-stationary Data Streams

Existing fuzzy neural networks (FNNs) are mostly developed under a shallow network configuration having lower generalization power than those of deep structures. This paper proposes a novel self-organizing deep fuzzy neural network, namely deep evolving fuzzy neural networks (DEVFNN). Fuzzy rules can be automatically extracted from data streams or removed if they play little role during their lifespan. The structure of the network can be deepened on demand by stacking additional layers using a drift detection method which not only detects the covariate drift, variations of input space, but also accurately identifies the real drift, dynamic changes of both feature space and target space. DEVFNN is developed under the stacked generalization principle via the feature augmentation concept where a recently developed algorithm, namely Generic Classifier (gClass), drives the hidden layer. It is equipped by an automatic feature selection method which controls activation and deactivation of input attributes to induce varying subsets of input features. A deep network simplification procedure is put forward using the concept of hidden layer merging to prevent uncontrollable growth of input space dimension due to the nature of feature augmentation approach in building a deep network structure. DEVFNN works in the sample-wise fashion and is compatible for data stream applications. The efficacy of DEVFNN has been thoroughly evaluated using six datasets with non-stationary properties under the prequential test-then-train protocol. It has been compared with four state-ofthe art data stream methods and its shallow counterpart where DEVFNN demonstrates improvement of classification accuracy. Moreover, it is also shown that the concept drift detection method is an effective tool to control the depth of network structure while the hidden layer merging scenario is capable of simplifying the network complexity of a deep network with negligible compromise of generalization performance.
Education Vs Employability-The Need to Bridge the Skills Gap among the Engineering and Management Graduates in Andhra Pradesh

Education and training create assets in the form of knowledge and skills which increase the productive capacity of manpower and this is referred to as human capital. Education is considered to be a process of skill formation and in this aspect it is treated at par with the process of capital formation. Economists argue that as demand for educational training increases, the systems need to meet the country’s requirement for people with high levels of skill and knowledge. But the major stumbling block in this growth path is the inadequate skill set of the workforce. While on the one side we have the world’s large stock of scientists, engineers and management graduates, we have been unable to derive full economic benefit from this talent base because of the mismatch between industry needs and university output. Skillful management of the intellectual capital could be a driver for growth and is imperative for Indian economy. The purpose of this study is to identify the employability skills required by young graduates and assess how there can be a value creation through effective knowledge management in terms of pedagogy, evaluation process and feedback mechanisms.
PROPOSING CARDINAL VIRTUES

RECENT WORK in virtue ethics, particularly sustained reflection on specific virtues, makes it possible to argue that the classical list of cardinal virtues (prudence, justice, temperance, and fortitude) is inadequate, and that we need to articulate the cardinal virtues more correctly. With that end in view, the first section of this article describes the challenges of espousing cardinal virtues today, the second considers the inadequacy of the classical listing of cardinal virtues, and the third makes a proposal. Since virtues, no matter how general, should always relate to concrete living, the article is framed by a case.
Deep Communicating Agents for Abstractive Summarization

We present deep communicating agents in an encoder-decoder architecture to address the challenges of representing a long document for abstractive summarization. With deep communicating agents, the task of encoding a long text is divided across multiple collaborating agents, each in charge of a subsection of the input text. These encoders are connected to a single decoder, trained end-to-end using reinforcement learning to generate a focused and coherent summary. Empirical results demonstrate that multiple communicating encoders lead to a higher quality summary compared to several strong baselines, including those based on a single encoder or multiple non-communicating encoders.
Follow the Moving Leader in Deep Learning

Deep networks are highly nonlinear and difficult to optimize. During training, the parameter iterate may move from one local basin to another, or the data distribution may even change. Inspired by the close connection between stochastic optimization and online learning, we propose a variant of the follow the regularized leader (FTRL) algorithm called follow the moving leader (FTML). Unlike the FTRL family of algorithms, the recent samples are weighted more heavily in each iteration and so FTML can adapt more quickly to changes. We show that FTML enjoys the nice properties of RMSprop and Adam, while avoiding their pitfalls. Experimental results on a number of deep learning models and tasks demonstrate that FTML converges quickly, and outperforms other state-ofthe-art optimizers.
PUSHOVER ANALYSIS OF A 19 STORY CONCRETE SHEAR WALL BUILDING

Pushover analysis was performed on a nineteen story, slender concrete tower building located in San Francisco with a gross area of 430,000 square feet. Lateral system of the building consists of concrete shear walls. The building is newly designed conforming to 1997 Uniform Building Code, and pushover analysis was performed to verify code's underlying intent of Life Safety performance under design earthquake. Procedure followed for carrying out the analysis and results are presented in this paper.
LIFE-CYCLE ANALYSIS OF WOOD PRODUCTS : CRADLE-TO-GATE LCI OF RESIDENTIAL WOOD BUILDING MATERIALS

This study compares the cradle-to-gate total energy and major emissions for the extraction of raw materials, production, and transportation of the common wood building materials from the CORRIM 2004 reports. A life-cycle inventory produced the raw materials, including fuel resources and emission to air, water, and land for glued-laminated timbers, kiln-dried and green softwood lumber, laminated veneer lumber, softwood plywood, and oriented strandboard. Major findings from these comparisons were that the production of wood products, by the nature of the industry, uses a third of their energy consumption from renewable resources and the remainder from fossil-based, non-renewable resources when the system boundaries consider forest regeneration and harvesting, wood products and resin production, and transportation life-cycle stages. When the system boundaries are reduced to a gate-to-gate (manufacturing life-cycle stage) model for the wood products, the biomass component of the manufacturing energy increases to nearly 50% for most products and as high as 78% for lumber production from the Southeast. The manufacturing life-cycle stage consumed the most energy over all the products when resin is considered part of the production process. Extraction of log resources and transportation of raw materials for production had the least environmental impact.