nils-herrmann/clean_scirepeval huggingface dataset:
The effects of combining Artemisia annua and Curcuma longa ethanolic extracts in broilers challenged with infective oocysts of Eimeria acervulina and E. maxima.

Due to an increasing demand for natural products to control coccidiosis in broilers, we investigated the effects of supplementing a combination of ethanolic extracts of Artemisia annua and Curcuma longa in drinking water. Three different dosages of this herbal mixture were compared with a negative control (uninfected), a positive control (infected and untreated), chemical coccidiostats (nicarbazin+narazin and, later, salinomycin), vaccination, and a product based on oregano. Differences in performance (weight gain, feed intake, and feed conversion rate), mortality, gross intestinal lesions and oocyst excretion were investigated. Broilers given chemical coccidiostats performed better than all other groups. Broilers given the two highest dosages of the herbal mixture had intermediate lesion scores caused by Eimeria acervulina, which was higher than in broilers given coccidiostats, but less than in broilers given vaccination, oregano and in negative controls. There was a trend for lower mortality (P = 0·08) in the later stage of the growing period (23-43 days) in broilers given the highest dosage of herbal mixture compared with broilers given chemical coccidiostats. In conclusion, the delivery strategy of the herbal extracts is easy to implement at farm level, but further studies on dose levels and modes of action are needed.
A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms

In this paper the various driving style analysis solutions are investigated. An in-depth investigation is performed to identify the relevant machine learning and artificial intelligence algorithms utilised in current driver behaviour and driving style analysis systems. This review therefore serves as a trove of information, and will inform the specialist and the student regarding the current state of the art in driver style analysis systems, the application of these systems and the underlying artificial intelligence algorithms applied to these applications. The aim of the investigation is to evaluate the possibilities for unique driver identification utilizing the approaches identified in other driver behaviour studies. It was found that Fuzzy Logic inference systems, Hidden Markov Models and Support Vector Machines consist of promising capabilities to address unique driver identification algorithms if model complexity can be reduced.
Identifying lexical relationships and entailments with distributional semantics

As the field of Natural Language Processing has developed, research has progressed on ambitious semantic tasks like Recognizing Textual Entailment (RTE). Systems that approach these tasks may perform sophisticated inference between sentences, but often depend heavily on lexical resources like WordNet to provide critical information about relationships and entailments between lexical items. However, lexical resources are expensive to create and maintain, and are never fully comprehensive. Distributional Semantics has long provided a method to automatically induce meaning representations for lexical items from large corpora with little or no annotation efforts. The resulting representations are excellent as proxies of semantic similarity: words will have similar representations if their semantic meanings are similar. Yet, knowing two words are similar does not tell us their relationship or whether one entails the other. We present several models for identifying specific relationships and entailments from distributional representations of lexical semantics. Broadly, this work falls into two distinct but related areas: the first predicts specific ontology relations and entailment decisions between lexical items devoid of context; and the second predicts specific lexical paraphrases in complete sentences. We provide insight and analysis of how and why our models are able to generalize to novel lexical items and improve upon prior work. We propose several shortand long-term extensions to our work. In the short term, we propose applying one of our hypernymy-detection models to other relationships and evaluating our more recent work in an end-to-end RTE system. In the long-term, we propose adding consistency constraints to our lexical relationship prediction, better integration of context into our lexical paraphrase model, and new distributional models for improving word representations.
Transformational Leadership and Job Satisfaction

A vast literature describes transformational leadership in terms of leader having charismatic and inspiring personality, stimulating followers, and providing them with individualized consideration. A considerable empirical support exists for transformation leadership in terms of its positive effect on followers with respect to criteria like effectiveness, extra role behaviour and organizational learning. This study aims to explore the effect of transformational leadership characteristics on followers’ job satisfaction. Survey method was utilized to collect the data from the respondents. The study reveals that individualized consideration and intellectual stimulation affect followers’ job satisfaction. However, intellectual stimulation is positively related with job satisfaction and individualized consideration is negatively related with job satisfaction. Leader’s charisma or inspiration was found to be having no affect on the job satisfaction. The three aspects of transformational leadership were tested against job satisfaction through structural equation modeling using Amos.
Learning Concept Taxonomies from Multi-modal Data

We study the problem of automatically building hypernym taxonomies from textual and visual data. Previous works in taxonomy induction generally ignore the increasingly prominent visual data, which encode important perceptual semantics. Instead, we propose a probabilistic model for taxonomy induction by jointly leveraging text and images. To avoid hand-crafted feature engineering, we design end-to-end features based on distributed representations of images and words. The model is discriminatively trained given a small set of existing ontologies and is capable of building full taxonomies from scratch for a collection of unseen conceptual label items with associated images. We evaluate our model and features on the WordNet hierarchies, where our system outperforms previous approaches by a large gap.
von Mises-Fisher Mixture Model-based Deep learning: Application to Face Verification

A number of pattern recognition tasks, e.g., face verification, can be boiled down to classification or clustering of unit length directional feature vectors whose distance can be simply computed by their angle. In this paper, we propose the von Mises-Fisher (vMF) mixture model as the theoretical foundation for an effective deep-learning of such directional features and derive a novel vMF Mixture Loss and its corresponding vMF deep features. The proposed vMF feature learning achieves the characteristics of discriminative learning, i.e., compacting the instances of the same class while increasing the distance of instances from different classes. Moreover, it subsumes a number of popular loss functions as well as an effective method in deep learning, namely normalization. We conduct extensive experiments on face verification using 4 different challenging face datasets, i.e., LFW, YouTube faces, CACD and IJB-A. Results show the effectiveness and excellent generalization ability of the proposed approach as it achieves stateof-the-art results on the LFW, YouTube faces and CACD datasets and competitive results on the IJB-A dataset.
Gender Oppression in Cinematic Content ? A Look at Females On-Screen & Behind-the-Camera in Top-Grossing 2007 Films

Females represent just over half of the United States population. Yet their role in cinematic content does not reflect this reality. Looking at characters in films from 1946 to 1990, one study shows that females only occupy 25-28% of all parts. Another study found that 32% of all primary and secondary roles are filled with females across 100 films released between 1940 and 1980. More recent data reveals a similarly lop-sided scenario, yielding roughly equivalent point statistics for females in film (27.3-32%). 3 Assessing over 15,000 speaking characters across 400 top-grossing theatrically released G, PG, PG-13, and R-rated films, Smith and her colleagues found 2.71 males appear for every one female. Put another way, only 27% of all speaking characters in movies are girls or women. Significant but trivial deviation emerged in the percentage of females by Motion Picture Association of America (MPAA) rating. No change in the percentage of females materialized by release date across three distinct periods of time (i.e., 1990-95, 1996-00, 2001-06).
Effectiveness of early physiotherapy to prevent lymphoedema after surgery for breast cancer: randomised, single blinded, clinical trial

To determine the effectiveness of early physiotherapy in reducing the risk of secondary lymphoedema after surgery for breast cancer.



Randomised, single blinded, clinical trial.



University hospital in Alcalá de Henares, Madrid, Spain.



120 women who had breast surgery involving dissection of axillary lymph nodes between May 2005 and June 2007.



The early physiotherapy group was treated by a physiotherapist with a physiotherapy programme including manual lymph drainage, massage of scar tissue, and progressive active and action assisted shoulder exercises. This group also received an educational strategy. The control group received the educational strategy only.



Incidence of clinically significant secondary lymphoedema (>2 cm increase in arm circumference measured at two adjacent points compared with the non-affected arm).



116 women completed the one year follow-up. Of these, 18 developed secondary lymphoedema (16%): 14 in the control group (25%) and four in the intervention group (7%). The difference was significant (P=0.01); risk ratio 0.28 (95% confidence interval 0.10 to 0.79). A survival analysis showed a significant difference, with secondary lymphoedema being diagnosed four times earlier in the control group than in the intervention group (intervention/control, hazard ratio 0.26, 95% confidence interval 0.09 to 0.79).



Early physiotherapy could be an effective intervention in the prevention of secondary lymphoedema in women for at least one year after surgery for breast cancer involving dissection of axillary lymph nodes.
Coarse and fine grain programmable overlay architectures for FPGAs

Overlay architectures are programmable logic systems that are compiled on top of a traditional FPGA. These architectures give designers flexibility, and have a number of benefits, such as being designed or optimized for specific application domains, making it easier or more efficient to implement solutions, being independent of platform, allowing the ability to do partial reconfiguration regardless of the underlying architecture, and allowing compilation without using vendor tools, in some cases with fully open source tool chains. This thesis describes the implementation of two FPGA overlay architectures, ZUMA and CARBON. These overlay implementations include optimizations to reduce area and increase speed which may be applicable to many other FPGAs and also ASIC systems. ZUMA is a fine-grain overlay which resembles a modern commercial FPGA, and is compatible with the VTR open source compilation tools. The implementation includes a number of novel features tailored to efficient FPGA implementation, including the utilization of reprogrammable LUTRAMs, a novel two-stage local routing crossbar, and an area efficient configuration controller.
Object Oriented Database Management Systems-Concepts, Advantages, Limitations and Comparative Study with Relational Database Management Systems

Object Oriented Databases stores data in the form of objects. An Object is something uniquely identifiable which models a real world entity and has got state and behaviour. In Object Oriented based Databases capabilities of Object based paradigm for Programming and databases are combined due remove the limitations of Relational databases and on the demand of some advanced applications. In this paper, need of Object database, approaches for Object database implementation, requirements for database to an Object database, Perspectives of Object database, architecture approaches for Object databases, the achievements and weakness of Object Databases and comparison with relational database are discussed.
EMNIST: an extension of MNIST to handwritten letters

The MNIST dataset has become a standard benchmark for learning, classification and computer vision systems. Contributing to its widespread adoption are the understandable and intuitive nature of the task, its relatively small size and storage requirements and the accessibility and ease-of-use of the database itself. The MNIST database was derived from a larger dataset known as the NIST Special Database 19 which contains digits, uppercase and lowercase handwritten letters. This paper introduces a variant of the full NIST dataset, which we have called Extended MNIST (EMNIST), which follows the same conversion paradigm used to create the MNIST dataset. The result is a set of datasets that constitute a more challenging classification tasks involving letters and digits, and that shares the same image structure and parameters as the original MNIST task, allowing for direct compatibility with all existing classifiers and systems. Benchmark results are presented along with a validation of the conversion process through the comparison of the classification results on converted NIST digits and the MNIST digits.
Into the Colorful World of Webtoons: Through the Lens of Neural Networks

The task of colorizing black and white images has previously been explored for natural images. In this paper we look at the task of colorization on a different domain: webtoons. To our knowledge this type of dataset hasn't been used before. Webtoons are usually produced in color thus they make a good dataset for analyzing different colorization models. Comics like webtoons also present some additional challenges over natural images, such as occlusion by speech bubbles and text. First we look at some of the previously introduced models' performance on this task and suggest modifications to address their problems. We propose a new model composed of two networks; one network generates sparse color information and a second network uses this generated color information as input to apply color to the whole image. These two networks are trained end-to-end. Our proposed model solves some of the problems observed with other architectures, resulting in better colorizations.
An artificial bee colony approach for clustering

Clustering is a popular data analysis and data mining technique. In this paper, an artificial bee colony clustering algorithm is presented to optimally partition N objects into K clusters. The Deb’s rules are used to direct the search direction of each candidate. This algorithm has been tested on several well-known real datasets and compared with other popular heuristics algorithm in clustering, such as GA, SA, TS, ACO and the recently proposed K–NM–PSO algorithm. The computational simulations reveal very encouraging results in terms of the quality of solution and the processing time required.
Automated Detecting and Repair of Cross-Site Scripting Vulnerabilities

The best practice to prevent Cross Site Scripting (XSS) attacks is to apply encoders to sanitize untrusted data. To balance security and functionality, encoders should be applied to match the web page context, such as HTML body, JavaScript, and style sheets. A common programming error is the use of a wrong type of encoder to sanitize untrusted data, leaving the application vulnerable. We present a security unit testing approach to detect XSS vulnerabilities caused by improper encoding of untrusted data. Unit tests for the XSS vulnerability are constructed out of each web page and then evaluated by a unit test execution framework. A grammar-based attack generator is devised to automatically generate test inputs. We also propose a vulnerability repair technique that can automatically fix detected vulnerabilities in many situations. Evaluation of this approach has been conducted on an open source medical record application with over 200 web pages written in JSP.
DAGER: Deep Age, Gender and Emotion Recognition Using Convolutional Neural Network

This paper describes the details of Sighthound’s fully automated age, gender and emotion recognition system. The backbone of our system consists of several deep convolutional neural networks that are not only computationally inexpensive, but also provide state-of-theart results on several competitive benchmarks. To power our novel deep networks, we collected large labeled datasets through a semi-supervised pipeline to reduce the annotation effort/time. We tested our system on several public benchmarks and report outstanding results. Our age, gender and emotion recognition models are available to developers through the Sighthound Cloud API at https://www.sighthound.com/products/cloud
Detecting tunneled video streams using traffic analysis

Detecting access to video streaming websites is the first step for an organization to regulate unwanted accesses to such sites by its employees. Adversaries often adopt circumvention techniques using proxy servers and Virtual Private Networks (VPNs) in order to avoid such detection. This paper presents a traffic analysis based technique that can detect such tunneled traffic at an organization's firewall using signatures found in traffic amount and timing in targeted video traffic. We present the detection results on the traffic data for several popular video streaming sites. Additional results are presented to validate the detection framework when detecting access to video streaming sites from a wide range of clients with a classifier trained with traffic data collected from a limited number of clients. The results show that the classifier works in both cases. It detects same-client traffic with high true positive rate, while it detects traffic from an unknown client with lower true positive rate but very low false positive rate. The results validate the effectiveness of traffic analysis based detection of video streaming sites.
A Study of the Influence of Gaming Behavior on Academic Performance of IT College Students

Video-game playing is popular among college students. Cognitive and negative consequences have been studied frequently. However, little is known about the influence of gaming behavior on IT college students’ academic performance. An increasing number of college students take online courses, use social network websites for social interactions, and play video games online. To analyze the relationship between college students’ gaming behavior and their academic performance, a research model is proposed and a survey study is conducted. The study result of a multiple regression analysis shows that self-control capability, social interaction using face-to-face or phone communications, and playing video games using a personal computer make statistically significant contributions to the IT college students’ academic performance measured by GPA.
Chatbot Using a Knowledge in Database: Human-to-Machine Conversation Modeling

A chatterbot or chatbot aims to make a conversation between both human and machine. The machine has been embedded knowledge to identify the sentences and making a decision itself as response to answer a question. The response principle is matching the input sentence from user. From input sentence, it will be scored to get the similarity of sentences, the higher score obtained the more similar of reference sentences. The sentence similarity calculation in this paper using bigram which divides input sentence as two letters of input sentence. The knowledge of chatbot are stored in the database. The chatbot consists of core and interface that is accessing that core in relational database management systems (RDBMS). The database has been employed as knowledge storage and interpreter has been employed as stored programs of function and procedure sets for pattern-matching requirement. The interface is standalone which has been built using programing language of Pascal and Java.
ReVision: automated classification, analysis and redesign of chart images

Poorly designed charts are prevalent in reports, magazines, books and on the Web. Most of these charts are only available as bitmap images; without access to the underlying data it is prohibitively difficult for viewers to create more effective visual representations. In response we present ReVision, a system that automatically redesigns visualizations to improve graphical perception. Given a bitmap image of a chart as input, ReVision applies computer vision and machine learning techniques to identify the chart type (e.g., pie chart, bar chart, scatterplot, etc.). It then extracts the graphical marks and infers the underlying data. Using a corpus of images drawn from the web, ReVision achieves image classification accuracy of 96% across ten chart categories. It also accurately extracts marks from 79% of bar charts and 62% of pie charts, and from these charts it successfully extracts data from 71% of bar charts and 64% of pie charts. ReVision then applies perceptually-based design principles to populate an interactive gallery of redesigned charts. With this interface, users can view alternative chart designs and retarget content to different visual styles.
Framing in Social Media: How the U.S. Congress uses Twitter hashtags to frame political issues Introduction

Social media offers politicians an opportunity to bypass traditional media and directly influence their audience's opinions and behavior through framing. Using data from Twitter about how members of the U.S. Congress use hashtags, we examine to what extent politicians participate in framing, which issues received the most framing efforts, and which politicians exhibited the highest rates of framing. We find that politicians actively use social media to frame issues by choosing both topics to discuss and specific hashtags within topics, and that recognizably divisive issues receive the most framing efforts. Finally, we find that voting patterns generally align with tweeting patterns; however, several notable exceptions suggest our methodology can provide a more nuanced picture of Congress than voting records alone.
Natural Language Processing: A Human-Computer Interaction Perspective

Natural language processing has been in existence for more than fifty years. During this time, it has significantly contributed to the field of human-computer interaction in terms of theoretical results and practical applications. As computers continue to become more affordable and accessible, the importance of user interfaces that are effective, robust, unobtrusive, and user-friendly – regardless of user expertise or impediments – becomes more pronounced. Since natural language usually provides for effortless and effective communication in human-human interaction, its significance and potential in human-computer interaction should not be overlooked – either spoken or typewritten, it may effectively complement other available modalities, such as windows, icons, and menus, and pointing; in some cases, such as in users with disabilities, natural language may even be the only applicable modality. This chapter examines the field of natural language processing as it relates to humancomputer interaction by focusing on its history, interactive application areas, theoretical approaches to linguistic modeling, and relevant computational and philosophical issues. It also presents a taxonomy for interactive natural language systems based on their linguistic knowledge and processing requirements, and reviews related applications. Finally, it discusses linguistic coverage issues, and explores the development of natural language widgets and their integration into multimodal user interfaces.