nils-herrmann/clean_scirepeval huggingface dataset:
Downstream processing of stevioside and its potential applications.

Stevioside is a natural sweetener extracted from leaves of Stevia rebaudiana Bertoni, which is commercially produced by conventional (chemical/physical) processes. This article gives an overview of the stevioside structure, various analysis technique, new technologies required and the advances achieved in recent years. An enzymatic process is established, by which the maximum efficacy and benefit of the process can be achieved. The efficiency of the enzymatic process is quite comparable to that of other physical and chemical methods. Finally, we believe that in the future, the enzyme-based extraction will ensure more cost-effective availability of stevioside, thus assisting in the development of more food-based applications.
Liberal Entity Extraction: Rapid Construction of Fine-Grained Entity Typing Systems.

The ability of automatically recognizing and typing entities in natural language without prior knowledge (e.g., predefined entity types) is a major challenge in processing such data. Most existing entity typing systems are limited to certain domains, genres, and languages. In this article, we propose a novel unsupervised entity-typing framework by combining symbolic and distributional semantics. We start from learning three types of representations for each entity mention: general semantic representation, specific context representation, and knowledge representation based on knowledge bases. Then we develop a novel joint hierarchical clustering and linking algorithm to type all mentions using these representations. This framework does not rely on any annotated data, predefined typing schema, or handcrafted features; therefore, it can be quickly adapted to a new domain, genre, and/or language. Experiments on genres (news and discussion forum) show comparable performance with state-of-the-art supervised typing systems trained from a large amount of labeled data. Results on various languages (English, Chinese, Japanese, Hausa, and Yoruba) and domains (general and biomedical) demonstrate the portability of our framework.
Use of a water flosser for interdental cleaning.

Interdental cleaning is an important part of a patient's personal oral care regimen. Water flossers, also known as oral irrigators or dental water jets, can play a vital, effective role in interdental hygiene. Evidence has shown a significant reduction in plaque biofilm from tooth surfaces and the reduction of subgingival pathogenic bacteria from pockets as deep as 6 mm with the use of water flossing. In addition, water flossers have been shown to reduce gingivitis, bleeding, probing pocket depth, host inflammatory mediators, and calculus. Educating patients on the use of a water flosser as part of their oral hygiene routine can be a valuable tool in maintaining oral health.
Been There, Done That: Meta-Learning with Episodic Recall

Meta-learning agents excel at rapidly learning new tasks from open-ended task distributions; yet, they forget what they learn about each task as soon as the next begins. When tasks reoccur – as they do in natural environments – metalearning agents must explore again instead of immediately exploiting previously discovered solutions. We propose a formalism for generating open-ended yet repetitious environments, then develop a meta-learning architecture for solving these environments. This architecture melds the standard LSTM working memory with a differentiable neural episodic memory. We explore the capabilities of agents with this episodic LSTM in five meta-learning environments with reoccurring tasks, ranging from bandits to navigation and stochastic sequential decision problems.
Metacognitive awareness of learning strategies in undergraduates.

Two studies examined undergraduates' metacognitive awareness of six empirically-supported learning strategies. Study 1 results overall suggested an inability to predict the learning outcomes of educational scenarios describing the strategies of dual-coding, static-media presentations, low-interest extraneous details, testing, and spacing; there was, however, weak endorsement of the strategy of generating one's own study materials. In addition, an independent measure of metacognitive self-regulation was correlated with scenario performance. Study 2 demonstrated higher prediction accuracy for students who had received targeted instruction on applied memory topics in their psychology courses, and the best performance for those students directly exposed to the original empirical studies from which the scenarios were derived. In sum, this research suggests that undergraduates are largely unaware of several specific strategies that could benefit memory for course information; further, training in applied learning and memory topics has the potential to improve metacognitive judgments in these domains.
Strategies in the design of nanoparticles for therapeutic applications

Engineered nanoparticles have the potential to revolutionize the diagnosis and treatment of many diseases; for example, by allowing the targeted delivery of a drug to particular subsets of cells. However, so far, such nanoparticles have not proved capable of surmounting all of the biological barriers required to achieve this goal. Nevertheless, advances in nanoparticle engineering, as well as advances in understanding the importance of nanoparticle characteristics such as size, shape and surface properties for biological interactions, are creating new opportunities for the development of nanoparticles for therapeutic applications. This Review focuses on recent progress important for the rational design of such nanoparticles and discusses the challenges to realizing the potential of nanoparticles.
Sentiment analysis of top colleges in India using Twitter data

In today's world, opinions and reviews accessible to us are one of the most critical factors in formulating our views and influencing the success of a brand, product or service. With the advent and growth of social media in the world, stakeholders often take to expressing their opinions on popular social media, namely Twitter. While Twitter data is extremely informative, it presents a challenge for analysis because of its humongous and disorganized nature. This paper is a thorough effort to dive into the novel domain of performing sentiment analysis of people's opinions regarding top colleges in India. Besides taking additional preprocessing measures like the expansion of net lingo and removal of duplicate tweets, a probabilistic model based on Bayes' theorem was used for spelling correction, which is overlooked in other research studies. This paper also highlights a comparison between the results obtained by exploiting the following machine learning algorithms: Naïve Bayes and Support Vector Machine and an Artificial Neural Network model: Multilayer Perceptron. Furthermore, a contrast has been presented between four different kernels of SVM: RBF, linear, polynomial and sigmoid.
Urdu Word Segmentation using Machine Learning Approaches

Word Segmentation is considered a basic NLP task and in diverse NLP areas, it plays a significant role. The main areas which can be benefited from Word segmentation are IR, POS, NER, sentiment analysis, etc. Urdu Word Segmentation is a challenging task. There can be a number of reasons but Space Insertion Problem and Space Omission Problems are the major ones. Compared to Urdu, the tools and resources developed for word segmentation of English and English like other western languages have record-setting performance. Some languages provide a clear indication for words just like English which having space or capitalization of the first character in a word. But there are many languages which do not have proper delimitation in between words e.g. Thai, Lao, Urdu, etc. The objective of this research work is to present a machine learning based approach for Urdu word segmentation. We adopted the use of conditional random fields (CRF) to achieve the subject task. Some other challenges faced in Urdu text are compound words and reduplicated words. In this paper, we tried to overcome such challenges in Urdu text by machine learning methodology. Part-of-speech (POS); NER; word segmentation; information retrieval; Natural Language Processing (NLP); conditional random fields (CRF)
Research on passing values between ASP.NET Web Forms

In ASP.NET, Programmers maybe use POST or GET to pass parameter's value. Two methods are easy to come true. But In ASP.NET, It is not easy to pass parameter's value. In ASP.NET, Programmers maybe use many methods to pass parameter's value, such as using Application, Session, Querying, Cookies, and Forms variables. In this paper, by way of pass value from WebForm1.aspx to WebForm2.aspx and show out the value on WebForm2. We can give and explain actually examples in ASP.NET language to introduce these methods.
A Fresh Look at Followership:

Followership has been an understudied topic in the academic literature and an underappreciated topic among practitioners. Although it has always been important, the study of followership has become even more crucial with the advent of the information age and dramatic changes in the workplace. This paper provides a fresh look at followership by providing a synthesis of the literature and presents a new model for matching followership styles to leadership styles. The model’s practical value lies in its usefulness for describing how leaders can best work with followers, and how followers can best work with leaders.
Exploring Learners' Sequential Behavioral Patterns, Flow Experience, and Learning Performance in an Anti-Phishing Educational Game

The purposes of this study were to provide a game-based anti-phishing lesson to 110 elementary school students in Taiwan, explore their learning behavioral patterns, and investigate the effects of the flow states on their learning behavioral patterns and learning achievement. The study recorded behaviour logs, and applied a preand post-test on phishing knowledge and a flow state measurement to analyze the learning process. The study used lag sequential analysis to infer the students’ behavioural patterns. The results showed that the learning materials used in this study can enable learners’ flow experience, whereby they can acquire anti-phishing knowledge through trial and error via a repeated “learning with gaming” behavioral pattern. We recommend that future educators and researchers on this topic appropriately increase the level of difficulty of the games used, and design learning materials with flexible difficulty based on learners’ flow states.
Automatic Irrigation System with Data Log Creation

Water being a crucial resource should be judiciously used, especially when it comes to agriculture. It has been seen that right amount of water helps the farmer to have a good yield and the quality is also better. The paper proposes a technique to automate the irrigation system using Arduino Mega 2560 along with GSM 800L which is controlled through a mobile application module and create a log of the sensor data collected from the field on a webpage.
A Two-step Information Accumulation Strategy for Learning from Highly Imbalanced Data

Highly imbalanced data is common in the real world and it is important but difficult to train an effective classifier. In this paper, Our major point is that the imbalance is the observed phenomenon but not the cause of the problem. The challenge is that useful information is been overshadowed in the large scale of data in both majority and minority classes. We propose a novel two-step strategy, Information Accumulation, which first selects the most discriminative data by the Zooming-in phase, and then leverages unlabeled data by pseudo active learning and self-training in the phase of Learning from Learned  Comparative experiments are conducted on large-scale highly imbalanced real customer service data on complaint detection task (where less than 2% of data is positive). The results on eight state-of-the-art classification algorithms show that significant improvements are observed on the performances of all algorithms with Information Accumulation(for example, the F-Measure score of Xgboost is increased by 197% from 0.115 to 0.347), which demonstrates the effectiveness and general applicability of the proposed strategy. This work explores a new idea on dealing with highly imbalanced data that we do not aim to balance the training examples as usual, but focus on finding the most discriminative information from labeled data and the learning results of unlabeled data.
Analysis of the WinZip encryption method

Analysis of the WinZip en ryption method Tadayoshi Kohno May 8, 2004 Abstra t WinZip is a popular ompression utility for Mi rosoft Windows omputers, the latest version of whi h is advertised as having \easy-to-use AES en ryption to prote t your sensitive data." We exhibit several atta ks against WinZip's new en ryption method, dubbed \AE-2" or \Advan ed En ryption, version two." We then dis uss se ure alternatives. Sin e at a high level the underlying WinZip en ryption method appears se ure (the ore is exa tly En ryptthen-Authenti ate using AES-CTR and HMAC-SHA1), and sin e one of our atta ks was made possible be ause of the way that WinZip Computing, In . de ided to x a di erent se urity problem with its previous en ryption method AE-1, our atta ks further unders ore the subtlety of designing ryptographi ally se ure software.
A Taxonomy and Survey of Cloud Resource Orchestration Techniques

Cloud services and applications prove indispensable amid today’s modern utility-based computing. The cloud has displayed a disruptive and growing impact on everyday computing tasks. However, facilitating the orchestration of cloud resources to build such cloud services and applications is yet to unleash its entire magnitude of power. Accordingly, it is paramount to devise a unified and comprehensive analysis framework to accelerate fundamental understanding of cloud resource orchestration in terms of concepts, paradigms, languages, models, and tools. This framework is essential to empower effective research, comprehension, comparison, and selection of cloud resource orchestration models, languages, platforms, and tools. This article provides such a comprehensive framework while analyzing the relevant state of the art in cloud resource orchestration from a novel and holistic viewpoint.
Effects of Talent Management on Employees Retention : The Mediate Effect of Organizational Trust

Talent management is found to be important for modern organizations because of the advent of the Modern economy, new generations entering the human resource and the need for businesses to become more strategic and competitive, which implies new ways of managing resource and human capital. In this research, the relationship between Talent management, employee Retention and organizational trust is investigated. The aim of the article is to examine the effect of Talent management on employee Retention through organizational trust among staffs of Isfahan University in Iran. The research method is a descriptive survey. The statistical population consists of staffs of Isfahan University in Iran. The sample included 280 employees, which were selected randomly. Data have been collected by a researcher-developed questionnaire and sampling has been done through census and analyzed using SPSS and AMOS software. The validity of the instrument was achieved through content validity and the reliability through Cronbach Alpha. The results of hypothesis testing indicate that there is a significant relationship between Talent management, employee Retention and organizational trust. The study is significant in that it draws attention to the effects of talent management on organizational trust and employees Retention in organization.
Learning Subjective Language

Subjectivity in natural language refers to aspects of language used to express opinions, evaluations, and speculations. There are numerous natural language processing applications for which subjectivity analysis is relevant, including information extraction and text categorization. The goal of this work is learning subjective language from corpora. Clues of subjectivity are generated and tested, including low-frequency words, collocations, and adjectives and verbs identified using distributional similarity. The features are also examined working together in concert. The features, generated from different data sets using different procedures, exhibit consistency in performance in that they all do better and worse on the same data sets. In addition, this article shows that the density of subjectivity clues in the surrounding context strongly affects how likely it is that a word is subjective, and it provides the results of an annotation study assessing the subjectivity of sentences with high-density features. Finally, the clues are used to perform opinion piece recognition (a type of text categorization and genre detection) to demonstrate the utility of the knowledge acquired in this article.
Human-aligned artificial intelligence is a multiobjective problem

As the capabilities of artificial intelligence (AI) systems improve, it becomes important to constrain their actions to ensure their behaviour remains beneficial to humanity. A variety of ethical, legal and safety-based frameworks have been proposed as a basis for designing these constraints. Despite their variations, these frameworks share the common characteristic that decision-making must consider multiple potentially conflicting factors. We demonstrate that these alignment frameworks can be represented as utility functions, but that the widely used Maximum Expected Utility (MEU) paradigm provides insufficient support for such multiobjective decision-making. We show that a Multiobjective Maximum Expected Utility paradigm based on the combination of vector utilities and non-linear action–selection can overcome many of the issues which limit MEU’s effectiveness in implementing aligned AI. We examine existing approaches to multiobjective AI, and identify how these can contribute to the development of human-aligned intelligent agents.
A Substrate Integrated Waveguide Circular Polarized Slot Radiator and Its Linear Array

A top-wall substrate integrated waveguide (SIW) slot radiator for generating circular polarized (CP) field is proposed and characterized in this letter. The reflection of the slot radiator is extremely weak, which simplifies the linear traveling wave array design. Based on such a structure, a 16-element CP SIW traveling wave antenna array is designed, fabricated, and measured at 16 GHz. A -23 dB side lobe level (SLL) with an axial ratio (AR) of 1.95 dB is experimentally achieved. The size of the proposed SIW CP linear array antenna is 285 mm times 22 mm. The measured gain is 18.9 dB, and the usable bandwidth is 2.5%.
Time-efficient architecture for face detection from images using support vector machine

One of the most prolific topics of research in the field of computer vision is pattern detection in images. A large number of practical applications for face detection exist. Contemporary work even suggests that any of the results from specialized detectors can be approximated by using fast detection classifiers. In this project, we developed an algorithm which detected faces from the input image with a lower false detection rate and lower computation cost using the ensemble effects of computer vision concepts. This algorithm utilized the concepts of recognizing skin color, filtering the binary image, detecting blobs and extracting different features from the face. The result is supported by the statistics obtained from calculating the parameters defining the parts of the face. The project also implements the highly powerful concept of Support Vector Machine that is used for the classification of images into face and non-face class. This classification is based on the training data set and indicators of luminance value, chrominance value, saturation value, elliptical value and eye and mouth map values.
Predicting customer churn in banking industry using neural networks

The aim of this article is to present a case study of usage of one of the data mining methods, neural network, in knowledge discovery from databases in the banking industry. Data mining is automated process of analysing, organization or grouping a large set of data from different perspectives and summarizing it into useful information using special algorithms. Data mining can help to resolve banking problems by finding some regularity, causality and correlation to business information which are not visible at first sight because they are hidden in large amounts of data. In this paper, we used one of the data mining methods, neural network, within the software package Alyuda NeuroInteligence to predict customer churn in bank. The focus on customer churn is to determinate the customers who are at risk of leaving and analysing whether those customers are worth retaining. Neural network is statistical learning model inspired by biological neural and it is used to estimate or approximate functions that can depend on a large number of inputs which are generally unknown. Although the method itself is complicated, there are tools that enable the use of neural networks without much prior knowledge of how they operate. The results show that clients who use more bank services (products) are more loyal, so bank should focus on those clients who use less than three products, and offer them products according to their needs. Similar results are obtained for different network topologies.
A Deep Neural Network Model for Target-based Sentiment Analysis

In recent years, with the development of social networks, sentiment analysis has become one of the most important research topics in the field of natural language processing. The deep neural network model combining attention mechanism has achieved remarkable success in the task of target-based sentiment analysis. In current research, however, the attention mechanism is more combined with LSTM networks, such neural network- based architectures generally rely on complex computation and only focus on the single target, thus it is difficult to effectively distinguish the different polarities of variant targets in the same sentence. To address this problem, we propose a deep neural network model combining convolutional neural network and regional long short-term memory (CNN-RLSTM) for the task of target-based sentiment analysis. The approach can reduce the training time of neural network model through a regional LSTM. At the same time, the CNN-RLSTM uses a sentence-level CNN to extract sentiment features of the whole sentence, and controls the transmission of information through different weight matrices, which can effectively infer the sentiment polarities of different targets in the same sentence. Finally, experimental results on multi-domain datasets of two languages from SemEval2016 and auto data show that, our approach yields better performance than SVM and several other neural network models.
Occlusion Resistant Object Rotation Regression from Point Cloud Segments

Rotation estimation of known rigid objects is important for robotic applications such as dexterous manipulation. Most existing methods for rotation estimation use intermediate representations such as templates, global or local feature descriptors, or object coordinates, which require multiple steps in order to infer the object pose. We propose to directly regress a pose vector from point cloud segments using a convolutional neural network. Experimental results show that our method can potentially achieve competitive performance compared to a state-of-theart method, while also showing more robustness against occlusion. Our method does not require any post processing such as refinement with the iterative closest point algorithm.