nils-herrmann/clean_scirepeval huggingface dataset:
Herbal Remedies for Coccidiosis Control: A Review of Plants, Compounds, and Anticoccidial Actions

Coccidiosis is the bane of the poultry industry causing considerable economic loss. Eimeria species are known as protozoan parasites to cause morbidity and death in poultry. In addition to anticoccidial chemicals and vaccines, natural products are emerging as an alternative and complementary way to control avian coccidiosis. In this review, we update recent advances in the use of anticoccidial phytoextracts and phytocompounds, which cover 32 plants and 40 phytocompounds, following a database search in PubMed, Web of Science, and Google Scholar. Four plant products commercially available for coccidiosis are included and discussed. We also highlight the chemical and biological properties of the plants and compounds as related to coccidiosis control. Emphasis is placed on the modes of action of the anticoccidial plants and compounds such as interference with the life cycle of Eimeria, regulation of host immunity to Eimeria, growth regulation of gut bacteria, and/or multiple mechanisms. Biological actions, mechanisms, and prophylactic/therapeutic potential of the compounds and extracts of plant origin in coccidiosis are summarized and discussed.
Nipah virus infection in bats (order Chiroptera) in peninsular Malaysia.

Nipah virus, family Paramyxoviridae, caused disease in pigs and humans in peninsular Malaysia in 1998-99. Because Nipah virus appears closely related to Hendra virus, wildlife surveillance focused primarily on pteropid bats (suborder Megachiroptera), a natural host of Hendra virus in Australia. We collected 324 bats from 14 species on peninsular Malaysia. Neutralizing antibodies to Nipah virus were demonstrated in five species, suggesting widespread infection in bat populations in peninsular Malaysia.
Understanding Chatbot-mediated Task Management

Effective task management is essential to successful team collaboration. While the past decade has seen considerable innovation in systems that track and manage group tasks, these innovations have typically been outside of the principal communication channels: email, instant messenger, and group chat. Teams formulate, discuss, refine, assign, and track the progress of their collaborative tasks over electronic communication channels, yet they must leave these channels to update their task-tracking tools, creating a source of friction and inefficiency. To address this problem, we explore how bots might be used to mediate task management for individuals and teams. We deploy a prototype bot to eight different teams of information workers to help them create, assign, and keep track of tasks, all within their main communication channel. We derived seven insights for the design of future bots for coordinating work.
Big Data with Cloud Computing: an insight on the computing environment, MapReduce, and programming frameworks

The term ‘Big Data’ has spread rapidly in the framework of Data Mining and Business Intelligence. This new scenario can be defined by means of those problems that cannot be effectively or efficiently addressed using the standard computing resources that we currently have. We must emphasize that Big Data does not just imply large volumes of data but also the necessity for scalability, i.e., to ensure a response in an acceptable elapsed time. When the scalability term is considered, usually traditional parallel-type solutions are contemplated, such as the Message Passing Interface or high performance and distributed Database Management Systems. Nowadays there is a new paradigm that has gained popularity over the latter due to the number of benefits it offers. This model is Cloud Computing, and among its main features we has to stress its elasticity in the use of computing resources and space, less management effort, and flexible costs. In this article, we provide an overview on the topic of Big Data, and how the current problem can be addressed from the perspective of Cloud Computing and its programming frameworks. In particular, we focus on those systems for large-scale analytics based on the MapReduce scheme and Hadoop, its open-source implementation. We identify several libraries and software projects that have been developed for aiding practitioners to address this new programming model. We also analyze the advantages and disadvantages of MapReduce, in contrast to the classical solutions in this field. Finally, we present a number of programming frameworks that have been proposed as an alternative to MapReduce, developed under the premise of solving the shortcomings of this model in certain scenarios and platforms.
A Lexicon-based Approach for Hate Speech Detection

We explore the idea of creating a classifier that can be used to detect presence of hate speech in web discourses such as web forums and blogs. In this work, hate speech problem is abstracted into three main thematic areas of race, nationality and religion. The goal of our research is to create a model classifier that uses sentiment analysis techniques and in particular subjectivity detection to not only detect that a given sentence is subjective but also to identify and rate the polarity of sentiment expressions. We begin by whittling down the document size by removing objective sentences. Then, using subjectivity and semantic features related to hate speech, we create a lexicon that is employed to build a classifier for hate speech detection. Experiments with a hate corpus show significant practical application for a real-world web discourse.
Learning the Distribution Preserving Semantic Subspace for Clustering

This paper proposes a new clustering method for images called distribution preserving indexing (DPI). It aims to find a lower dimensional semantic space approximating the original image space in the sense of preserving the distribution of the data. In the theory, the intrinsic structure of the data clusters can be described by the distribution of the data effectively. Therefore, the cluster structure of the data in a lower dimensional semantic space derived by the DPI becomes clear. Unlike these distance-based clustering methods, which reveal the intrinsic Euclidean structure of data, our method attempts to discover the intrinsic cluster structure of the data space that actually is the union of some sub-manifolds. Moreover, we propose a revised kernel density estimator for the case of high-dimensional data, which is a crucial step in DPI. In addition, we provide a theoretical analysis of the bound of our method. Finally, the extensive experiments compared with other algorithms, on COIL20, CBCL, and MNIST demonstrate the effectiveness of our proposed approach.
SVD-Softmax: Fast Softmax Approximation on Large Vocabulary Neural Networks

We propose a fast approximation method of a softmax function with a very large vocabulary using singular value decomposition (SVD). SVD-softmax targets fast and accurate probability estimation of the topmost probable words during inference of neural network language models. The proposed method transforms the weight matrix used in the calculation of the output vector by using SVD. The approximate probability of each word can be estimated with only a small part of the weight matrix by using a few large singular values and the corresponding elements for most of the words. We applied the technique to language modeling and neural machine translation and present a guideline for good approximation. The algorithm requires only approximately 20% of arithmetic operations for an 800K vocabulary case and shows more than a three-fold speedup on a GPU.
Software & Systems Engineering Process and Tools for the Development of Autonomous Driving Intelligence

When a large number of people with heterogeneous knowledge and skills run a project together, it is important to use a sensible engineering process. This especially holds for a project building an intelligent autonomously driving car to participate in the 2007 DARPA Urban Challenge. In this article, we present essential elements of a software and systems engineering process for the development of artificial intelligence capable of driving autonomously in complex urban situations. The process includes agile concepts, like test first approach, continuous integration of every software module and a reliable release and configuration management assisted by software tools in integrated development environments. However, the most important ingredients for an efficient and stringent development are the ability to efficiently test the behavior of the developed system in a flexible and modular simulator for urban situations.
Board of Director Diversity and Firm Financial Performance

This study examines the relationship between demographic diversity on boards of directors with firm financial performance. This relationship is examined using 1993 and 1998 financial performance data (return on asset and investment) and the percentage of women and minorities on boards of directors for 127 large US companies. Correlation and regression analyses indicate board diversity is positively associated with these financial indicators of firm performance. Implications for both strategic human resource management and future research are discussed.
A Context-Aware User-Item Representation Learning for Item Recommendation

Both reviews and user-item interactions (i.e., rating scores) have been widely adopted for user rating prediction. However, these existing techniques mainly extract the latent representations for users and items in an independent and static manner. That is, a single static feature vector is derived to encode user preference without considering the particular characteristics of each candidate item. We argue that this static encoding scheme is incapable of fully capturing users’ preferences, because users usually exhibit different preferences when interacting with different items. In this article, we propose a novel context-aware user-item representation learning model for rating prediction, named CARL. CARL derives a joint representation for a given user-item pair based on their individual latent features and latent feature interactions. Then, CARL adopts Factorization Machines to further model higher order feature interactions on the basis of the user-item pair for rating prediction. Specifically, two separate learning components are devised in CARL to exploit review data and interaction data, respectively: review-based feature learning and interaction-based feature learning. In the review-based learning component, with convolution operations and attention mechanism, the pair-based relevant features for the given user-item pair are extracted by jointly considering their corresponding reviews. However, these features are only reivew-driven and may not be comprehensive. Hence, an interaction-based learning component further extracts complementary features from interaction data alone, also on the basis of user-item pairs. The final rating score is then derived with a dynamic linear fusion mechanism. Experiments on seven real-world datasets show that CARL achieves significantly better rating prediction accuracy than existing state-of-the-art alternatives. Also, with the attention mechanism, we show that the pair-based relevant information (i.e., context-aware information) in reviews can be highlighted to interpret the rating prediction for different user-item pairs.
Integrating Modeling Languages and Web Logs for Enhanced User Behavior Analytics

While basic Web analytics tools are widespread and provide statistics about Web site navigation, no approaches exist for merging such statistics with information about the Web application structure, content and semantics. We demonstrate the advantages of combining Web application models with runtime navigation logs, at the purpose of deepening the understanding of users behaviour. We propose a model-driven approach that combines user interaction modeling (based on the IFML standard), full code generation of the designed application, user tracking at runtime through logging of runtime component execution and user activities, integration with page content details, generation of integrated schemaless data streams, and application of large-scale analytics and visualization tools for big data, by applying both traditional data visualization techniques and direct representation of statistics on visual models of the Web application.
INDIAN SIGN LANGUAGE RECOGNITION USING NEURAL NETWORKS AND KNN CLASSIFIERS

Sign language recognition is helpful in communication between signing people and non-signing people. Various research projects are in progress on different sign language recognition systems worldwide. The research is limited to a particular country as there are country wide variations available. The idea of this project is to design a system that can interpret the Indian sign language in the domain of numerals accurately so that the less fortunate people will be able to communicate with the outside world without need of an interpreter in public places like railway stations, banks, etc. The research presented here describes a system for automatic recognition of Indian sign language of numeric signs which are in the form of isolated images, in which only a regular camera was used to acquire the signs. To use the project in real environment, first we created a numeric sign database containing 5000 signs, 500 images per numeral sign. Direct pixel value and hierarchical centroid techniques are used to extract desired features from sign images. After extracting features from images, neural network and kNN classification techniques were used to classify the signs. The result of these experiments is achieved up to 97.10% accuracy.
Stylistic Chinese Poetry Generation via Unsupervised Style Disentanglement

The ability to write diverse poems in different styles under the same poetic imagery is an important characteristic of human poetry writing. Most previous works on automatic Chinese poetry generation focused on improving the coherency among lines. Some work explored style transfer but suffered from expensive expert labeling of poem styles. In this paper, we target on stylistic poetry generation in a fully unsupervised manner for the first time. We propose a novel model which requires no supervised style labeling by incorporating mutual information, a concept in information theory, into modeling. Experimental results show that our model is able to generate stylistic poems without losing fluency and coherency.
Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges

While machine learning and artificial intelligence have long been applied in networking research, the bulk of such works has focused on supervised learning. Recently there has been a rising trend of employing unsupervised machine learning using unstructured raw network data to improve network performance and provide services such as traffic engineering, anomaly detection, Internet traffic classification, and quality of service optimization. The interest in applying unsupervised learning techniques in networking emerges from their great success in other fields such as computer vision, natural language processing, speech recognition, and optimal control (e.g., for developing autonomous self-driving cars). Unsupervised learning is interesting since it can unconstrain us from the need of labeled data and manual handcrafted feature engineering thereby facilitating flexible, general, and automated methods of machine learning. The focus of this survey paper is to provide an overview of the applications of unsupervised learning in the domain of networking. We provide a comprehensive survey highlighting the recent advancements in unsupervised learning techniques and describe their applications for various learning tasks in the context of networking. We also provide a discussion on future directions and open research issues, while also identifying potential pitfalls. While a few survey papers focusing on the applications of machine learning in networking have previously been published, a survey of similar scope and breadth is missing in literature. Through this paper, we advance the state of knowledge by carefully synthesizing the insights from these survey papers while also providing contemporary coverage of recent advances.
Internet of Things based Expert System for Smart Agriculture

Agriculture sector is evolving with the advent of the information and communication technology. Efforts are being made to enhance the productivity and reduce losses by using the state of the art technology and equipment. As most of the farmers are unaware of the technology and latest practices, many expert systems have been developed in the world to facilitate the farmers. However, these expert systems rely on the stored knowledge base. We propose an expert system based on the Internet of Things (IoT) that will use the input data collected in real time. It will help to take proactive and preventive actions to minimize the losses due to diseases and insects/pests. Internet of Things; Smart Agriculture; Cotton; Plant Diseases; Wireless Sensor Network
A Review of Studies on Neuromarketing : Practical Results , Techniques , Contributions and Limitations

This article surveyed the main neuromarketing techniques used in the world and the practical results obtained. Specifically, the objectives are (1) to identify the main existing definitions of neuromarketing; (2) to identify the importance and the potential contributions of neuromarketing; (3) to demonstrate the advantages of neuromarketing as a marketing research tool compared to traditional research methods; (4) to identify the ethical issues involved with neuromarketing research; (5) to present the main neuromarketing techniques that are being used in the development of marketing research; (6) to present studies in which neuromarketing research techniques were used; and (7) to identify the main limitations of neuromarketing. The results obtained allow an understanding of the ways to develop, store, Journal of Management Research ISSN 1941-899X 2014,  In addition to offering theoretical support for neuromarketing, this article discusses business cases, implementation and achievements.
Designing for video engagement on social networks: a video marketing case study

In this case study, we describe the design and execution of a video marketing campaign on Facebook. We present concrete recommendations for effective video persuasion design, focusing on a new kind of video player presentation, as well as one specific environment in which video content can be placed, seeded, and distributed. We present a number of findings that support our design recommendations. Finally, we acknowledge and discuss the limitations in our case study approach.
CCSP: A compressed certificate status protocol

Trust in SSL-based communications is provided by Certificate Authorities (CAs) in the form of signed certificates. Checking the validity of a certificate involves three steps: (i) checking its expiration date, (ii) verifying its signature, and (iii) ensuring that it is not revoked. Currently, such certificate revocation checks are done either via Certificate Revocation Lists (CRLs) or Online Certificate Status Protocol (OCSP) servers. Unfortunately, despite the existence of these revocation checks, sophisticated cyber-attackers, may trick web browsers to trust a revoked certificate, believing that it is still valid. Consequently, the web browser will communicate (over TLS) with web servers controlled by cyber-attackers. Although frequently updated, nonced, and timestamped certificates may reduce the frequency and impact of such cyber-attacks, they impose a very large overhead to the CAs and OCSP servers, which now need to timestamp and sign on a regular basis all the responses, for every certificate they have issued, resulting in a very high overhead. To mitigate this overhead and provide a solution to the described cyber-attacks, we present CCSP: a new approach to provide timely information regarding the status of certificates, which capitalizes on a newly introduced notion called signed collections. In this paper, we present the design, preliminary implementation, and evaluation of CCSP in general, and signed collections in particular. Our preliminary results suggest that CCSP (i) reduces space requirements by more than an order of magnitude, (ii) lowers the number of signatures required by 6 orders of magnitude compared to OCSP-based methods, and (iii) adds only a few milliseconds of overhead in the overall user latency.
War on Instagram: Framing conflict photojournalism with mobile photography apps

This paper examines recent acclaim for professional embedded photojournalists who visually document the experience of US soldiers in Afghanistan using the popular mobile photo application Hipstamatic. These photos have stirred controversy among fellow journalists and cultural critics regarding the use of photo filters in Hipstamatic and similar app Instagram, their contribution to the de-professionalization of photojournalism, and the depiction of war as stylishly vintage. The debates about Hipstamatic and Instagram in war photography open up a whole series of enduring questions about distinctions between photography and illustration, photography and photojournalism, professional and amateur, and reporting and editorializing. In consideration of the shifting nature of digital photography, photojournalism, and specifically war photojournalism, I argue that this discourse about the use of mobile apps overlooks another important ethical issue: the implications of non-soldiers mimicking the imagined hand of the modern smartphone-equipped US soldier, particularly in light of soldiers’ own complex mediamaking practices.
Predicting the Stock MarketThomas

This paper presents a tuturial introduction to predictions of stock time series. The various approaches of technical and fundamental analysis is presented and the prediction problem is formulated as a special case of inductive learning. The problems with performance evaluation of near-random-walk processes are illustrated with examples together with guidelines for avoiding the risk of data-snooping. The connections to concepts like "the bias/variance dilemma", overtraining and model complexity are further covered. Existing benchmarks and testing metrics are surveyed and some new measures are introduced.
Error-repair Dependency Parsing for Ungrammatical Texts

We propose a new dependency parsing scheme which jointly parses a sentence and repairs grammatical errors by extending the non-directional transitionbased formalism of Goldberg and Elhadad (2010) with three additional actions: SUBSTITUTE, DELETE, INSERT. Because these actions may cause an infinite loop in derivation, we also introduce simple constraints that ensure the parser termination. We evaluate our model with respect to dependency accuracy and grammaticality improvements for ungrammatical sentences, demonstrating the robustness and applicability of our scheme.