nils-herrmann/clean_scirepeval huggingface dataset:
Comparison of different classification techniques in predicting a university course final grade

In this paper, we subject the education evaluation to different classification techniques. Then, we compare their results. The main idea is to check the suitability of different classification techniques to be used as prediction tools. After that, we conduct a comparison of the obtained results. Some classification techniques show better suitability to respond to the given assignment than the others. At the end, we show the classification techniques, which can be used in these cases as a prediction tool.
Building strong brands in a modern marketing communications environment

To help marketers to build and manage their brands in a dramatically changing marketing communications environment, the customer-based brand equity model that emphasizes the importance of understanding consumer brand knowledge structures is put forth. Specifically, the brand resonance pyramid is reviewed as a means to track how marketing communications can create intense, active loyalty relationships and affect brand equity. According to this model, integrating marketing communications involves mixing and matching different communication options to establish the desired awareness and image in the minds of consumers. The versatility of on-line, interactive marketing communications to marketers in brand building is also addressed.
Farmer-suicide in India: debating the role of biotechnology

Indian Biotech opponents have attributed the increase of suicides to the monopolization of GM seeds, centering on patent control, application of terminator technology, marketing strategy, and increased production costs. The contentions of the biotech opponents, however, have been criticized for a lack of transparency in their modus operandi i.e. the use of methodology in their argumentation. The fact is, however, that with the intention of getting the attention of those capable of determining the future of GM cotton in India, opponents resorted to generating controversies. Therefore, this article will review and evaluate the multifaceted contentions of both opponents and defenders. Although the association between seed monopolization and farmer-suicide is debatable, we will show that there is a link between the economic factors associated with Bt. cultivation and farmer suicide. The underlying thesis of biotech opponents becomes all the more significant when analysed vis-à-vis the contention of the globalization critics that there has been a political and economic marginalization of the Indian farmers. Their accusation assumes significance in the context of a fragile democracy like India where market forces are accorded precedence over farmers' needs until election time.
A brief survey of Cryptocurrency systems

Cryptocurrencies have emerged as important financial software systems. They rely on a secure distributed ledger data structure; mining is an integral part of such systems. Mining adds records of past transactions to the distributed ledger known as Blockchain, allowing users to reach secure, robust consensus for each transaction. Mining also introduces wealth in the form of new units of currency. Cryptocurrencies lack a central authority to mediate transactions because they were designed as peer-to-peer systems. They rely on miners to validate transactions. Cryptocurrencies require strong, secure mining algorithms. In this paper we survey and compare and contrast current mining techniques as used by major Cryptocurrencies. We evaluate the strengths, weaknesses, and possible threats to each mining strategy. Overall, a perspective on how Cryptocurrencies mine, where they have comparable performance and assurance, and where they have unique threats and strengths are outlined.
Embedding of Hierarchically Typed Knowledge Bases

Embedding has emerged as an important approach to prediction, inference, data mining and information retrieval based on knowledge bases and various embedding models have been presented. Most of these models are “typeless”, namely, treating a knowledge base solely as a collection of instances without considering the types of the entities therein. In this paper, we investigate the use of entity type information for knowledge base embedding. We present a framework that augments a generic “typeless” embedding model to a typed one. The framework interprets an entity type as a constraint on the set of all entities and let these type constraints induce isomorphically a set of subsets in the embedding space. Additional cost functions are then introduced to model the fitness between these constraints and the embedding of entities and relations. A concrete example scheme of the framework is proposed. We demonstrate experimentally that this framework offers improved embedding performance over the typeless models and other typed models.
Graph Matching With a Dual-Step EM Algorithm

This paper describes a new approach to matching geometric structure in 2D point-sets. The novel feature is to unify the tasks of estimating transformation geometry and identifying point-correspondence matches. Unification is realized by constructing a mixture model over the bipartite graph representing the correspondence match and by affecting optimization using the EM algorithm. According to our EM framework, the probabilities of structural correspondence gate contributions to the expected likelihood function used to estimate maximum likelihood transformation parameters. These gating probabilities measure the consistency of the matched neighborhoods in the graphs. The recovery of transformational geometry and hard correspondence matches are interleaved and are realized by applying coupled update operations to the expected log-likelihood function. In this way, the two processes bootstrap one another. This provides a means of rejecting structural outliers. We evaluate the technique on two real-world problems. The first involves the matching of different perspective views of 3.5-inch floppy discs. The second example is furnished by the matching of a digital map against aerial images that are subject to severe barrel distortion due to a line-scan sampling process. We complement these experiments with a sensitivity study based on synthetic data.
Being curious about the answers to questions: novelty search with learned attention

We investigate the use of attentional neural network layers in order to learn a ‘behavior characterization’ which can be used to drive novelty search and curiosity-based policies. The space is structured towards answering a particular distribution of questions, which are used in a supervised way to train the attentional neural network. We find that in a 2d exploration task, the structure of the space successfully encodes local sensory-motor contingencies such that even a greedy local ‘do the most novel action’ policy with no reinforcement learning or evolution can explore the space quickly. We also apply this to a high/low number guessing game task, and find that guessing according to the learned attention profile performs active inference and can discover the correct number more quickly than an exact but passive approach.
GPC Temperature Control of A Simulation Model Infant-Incubator and Practice with Arduino Board

The thermal environment surrounding preterm neonates in closed incubators is regulated via air temperature control mode. At present, these control modes do not take account of all the thermal parameters involved in a pattern of incubator such as the thermal parameters of preterm neonates (birth weight < 1000 grams). The objective of this work is to design and validate a generalized predictive control (GPC) that takes into account the closed incubator model as well as the newborn premature model. Then, we implemented this control law on a DRAGER neonatal incubator with and without newborn using microcontroller card.  The design of the predictive control law is based on a prediction model. The developed model allows us to take into account all the thermal exchanges (radioactive, conductive, convective and evaporative) and the various interactions between the environment of the incubator and the premature newborn.  The predictive control law and the simulation model developed in Matlab/Simulink environment make it possible to evaluate the quality of the mode of control of the air temperature to which newborn must be raised. The results of the simulation and implementation of the air temperature inside the incubator (with newborn and without newborn) prove the feasibility and effectiveness of the proposed GPC controller compared with a proportional–integral–derivative controller (PID controller). Incubator; neonatal; model; temperature; Arduino; GPC
Cognitive linguistics : current applications and future perspectives

1. General orientation of the volume: towards an empirical revolution The collective volume Cognitive Linguistics: Current Applications and Future Perspectives brings together specific case studies and critical overviews of work in a variety of CL strands. Written by prominent researchers, the chapters of the volume thus provide the scientific community with an updated survey of recent research in Cognitive Linguistics. Most authors furthermore go beyond the more immediate scope of describing or exemplifying state-of-the-art research (e.g. by providing the reader with a generally accessible synthesis or a specialized case study) and explicitly address a number of pressing questions pertaining to future perspectives and future research agendas. Together with its companion volume Cognitive Linguistics: Basic Readings (Cognitive Linguistics Research 34, edited by Dirk Geeraerts), it constitutes a highly informative resource for linguists and scholars in neighbouring disciplines, and in general for any scholar wishing to become familiar with what Cognitive Linguistics is all about. Whereas Cognitive Linguistics: Basic Readings offers an introductory survey of the foun-dational concepts of Cognitive Linguistics, the present volume focuses on more recent theoretical developments, illustrates the many fields of application that CL already covers (both within linguistics and in an interdis-ciplinary environment), and identifies the future research trends that CL is now heading for. At the same time, the present volume is the very first issue in the new book series Applications of Cognitive Linguistics (ACL). In collaboration with its sister series Cognitive Linguistics Research, ACL offers a platform for high quality work which applies the rich framework developed in Cog-nitive Linguistics to a wide range of different fields of application. These 2 and still many other fields, often within an interdisciplinary framework. The goals of ACL will be summarised in section 3 of this introduction. First and foremost, however, as the subtitle suggests, the volume overviews and explores the major avenues of the cognitive linguistic enterprise at present and towards the future. Over the last two, perhaps even three, decades, Cognitive Linguistics has gradually but firmly established itself as a complete and innovating discipline, but certainly not one which for these reasons has ceased to evolve, nor to expand. The contributions in this volume testify to the existence of a number of different strands, the most important of which may be summarised as follows. First, a number of basic concepts (cf. the twelve cornerstones of Cognitive Linguistics described and exemplified in Cognitive Linguistics: Basic Readings) …
The JEDI Event-Based Infrastructure and Its Application to the Development of the OPSS WFMS

The development of complex distributed systems demands for the creation of suitable architectural styles (or paradigms) and related run-time infrastructures. An emerging style that is receiving increasing attention is based on the notion of event. In an event-based architecture, distributed software components interact by generating and consuming events. An event is the occurrence of some state change in a component of a software system, made visible to the external world. The occurrence of an event in a component is asynchronously notified to any other component that has declared some interest in it. This paradigm (usually called “publish/subscribe” from the names of the two basic operations that regulate the communication) holds the promise of supporting a flexible and effective interaction among highly reconfigurable, distributed software components. In the past two years, we have developed an object-oriented infrastructure called JEDI (Java Event-based Distributed Infrastructure). JEDI supports the development and operation of event-based systems and has been used to implement a significant example of distributed system, namely, the OPSS workflow management system (WFMS). The paper illustrates JEDI main features and how we have used them to implement OPSS. Moreover, the paper provides an initial evaluation of our experiences in using the event-based architectural style and a classification of some of the event-based infrastructures presented in the literature.
Social Media Marketing and Advertising

Social media has recently gained tremendous fame as a highly impactful channel of communication in these modern times of digitized living. It has been put on a pedestal across varied streams for facilitating participatory interaction amongst businesses, groups, societies, organizations, consumers, communities, forums, and the like. This subject has received increased attention in the literature with many of its practical applications including social media marketing (SMM) being elaborated, analysed, and recorded by many studies. This study is aimed at collating the existing research on SMM to present a review of seventy one articles that will bring together the many facets of this rapidly blooming media marketing form. The surfacing limitations in the literature on social media have also been identified and potential research directions have been offered.
Electro-Culture ( The Electrical Tickle )

1 of 11 08/06/07 18:23 Original: http://thebirdman.org/Index/Others/Others-Doc-Environment&Ecology/ +Doc-Environment&Ecology-FoodMatters/StimulatingPlantGrowthWithElectricity&Magnetism&Sound.htm 2007-08-06 Link here: http://blog.lege.net/content/StimulatingPlantGrowthWithElectricityMagnetismSound.html PDF "printout": http://blog.lege.net/content/StimulatingPlantGrowthWithElectricityMagnetismSound.pdf
Phonologically Aware Neural Model for Named Entity Recognition in Low Resource Transfer Settings

Named Entity Recognition is a well established information extraction task with many state of the art systems existing for a variety of languages. Most systems rely on language specific resources, large annotated corpora, gazetteers and feature engineering to perform well monolingually. In this paper, we introduce an attentional neural model which only uses language universal phonological character representations with word embeddings to achieve state of the art performance in a monolingual setting using supervision and which can quickly adapt to a new language with minimal or no data. We demonstrate that phonological character representations facilitate cross-lingual transfer, outperform orthographic representations and incorporating both attention and phonological features improves statistical efficiency of the model in 0-shot and low data transfer settings with no task specific feature engineering in the source or target language.
Supervised machine learning for the detection of troll profiles in twitter social network: application to a real case of cyberbullying

The use of new technologies along with the popularity of social networks has given the power of anonymity to the users. The ability to create an alter-ego with no relation to the actual user, creates a situation in which no one can certify the match between a profile and a real person. This problem generates situations, repeated daily, in which users with fake accounts, or at least not related to their real identity, publish news, reviews or multimedia material trying to discredit or attack other people who may or may not be aware of the attack. These acts can have great impact on the affected victims’ environment generating situations in which virtual attacks escalate into fatal consequences in real life. In this paper, we present a methodology to detect and associate fake profiles on Twitter social network which are employed for defamatory activities to a real profile within the same network by analysing the content of comments generated by both profiles. Accompanying this approach we also present a successful real life use case in which this methodology was applied to detect and stop a cyberbullying situation in a real elementary school.
Convolutional Neural Pyramid for Image Processing

We propose a principled convolutional neural pyramid (CNP) framework for general low-level vision and image processing tasks. It is based on the essential finding that many applications require large receptive fields for structure understanding. But corresponding neural networks for regression either stack many layers or apply large kernels to achieve it, which is computationally very costly. Our pyramid structure can greatly enlarge the field while not sacrificing computation efficiency. Extra benefit includes adaptive network depth and progressive upsampling for quasirealtime testing on VGA-size input. Our method profits a broad set of applications, such as depth/RGB image restoration, completion, noise/artifact removal, edge refinement, image filtering, image enhancement and colorization.
Estimation of Axial and radial in-core power peaking in PWR plant using artificial neural network technique

Axial and radial power peaking factors (Fq, Fah) were estimated in Chashma Nuclear Power Plant Unit-1 (C-1) core using artificial Neural Network Technique (ANNT). Position of T4 control bank, axial offsets in four quadrants and quadrant power tilt ratios were taken as input variables in neural network designing. Power Peaking Factors (PPF) were calculated using computer codes FCXS, TWODFD and 3D-NB-2P for 52 core critical conditions made during C-1 fuel cycle-7. A multilayered Perceptron (MLP) neural network was trained by applying a set of measured input parameters and calculated output data for each core state. Training average relative errors between targets and ANNT estimated peaking factors were ranged from 0.018% to 0.054%, implies that ANNT introduces negligible error during training and exactly map the values. For validation process, PPF were estimated using ANNT for 36 cases devised at the time when power distribution measurement test and in-core/ex-core detectors calibration test were performed during fuel cycle. ANNT Results were compared with C-1 peaking factors measured with in-core flux mapping system and INCOPW computer code. Results showed that ANNT estimated PPF deviated from C-1 measured values within ±3%. The results of this study indicate that ANNT is an alternate technique for PPF measurement using only ex-core detectors signals data and independent of in-core flux mapping system. It might increase the time interval between in-core flux maps to 180 Effective Full Power Days (EFPDs) and reduce usage frequency of in-core flux mapping system during fuel cycle as present in Advanced Countries Nuclear Power Plants.
Learning Effective Embeddings from Medical Notes

With the large amount of available data and the variety of features they offer, electronic health records (EHR) have gotten a lot of interest over recent years, and start to be widely used by the machine learning and bioinformatics communities. While typical numerical fields such as demographics, vitals, lab measurements, diagnoses and procedures, are natural to use in machine learning models, there is no consensus yet on how to use the free-text clinical notes. We show how embeddings can be learned from patients’ history of notes, at the word, note and patient level, using simple neural and sequence models. We show on various relevant evaluation tasks that these embeddings are easily transferable to smaller problems, where they enable accurate predictions using only clinical notes.
Memory Bandwidth: The True Bottleneck of SIMD Multimedia Performance on a Superscalar Processor

This paper presents the performance of DSP, image and 3D applications on recent general-purpose microprocessors using streaming SIMD ISA extensions (integer and oating point). The 9 benchmarks benchmark we use for this evaluation have been optimized for DLP and caches use with SIMD extensions and data prefetch. The result of these cumulated optimizations is a speedup that ranges from 1.9 to 7.1. All the benchmarks were originaly computation bound and 7 becomes memory bandwidth bound with the addition of SIMD and data prefetch. Quadrupling the memory bandwidth has no eeect on original kernels but improves the performance of SIMD kernels by 15-55%.
A Cognitive Radio based Solution to Coexistence of FH and OFDM Signals Implemented on USRP N 210 Platform

A new concept development and practical implementation of an OFDM based secondary cognitive link are presented in this paper. Coexistence of a secondary user employing Orthogonal Frequency Division Multiplexing (OFDM) and a primary user employing Frequency Hopping (FH) is achieved. Secondary and primary links are realized using Universal Software Radio Peripheral (USRP) N210 platforms. Cognitive features of spectrum sensing and changing transmission parameters are implemented. Some experimental results are presented.
Neural Language Modeling by Jointly Learning Syntax and Lexicon

We propose a neural language model capable of unsupervised syntactic structure induction. The model leverages the structure information to form better semantic representations and better language modeling. Standard recurrent neural networks are limited by their structure and fail to efficiently use syntactic information. On the other hand, tree-structured recursive networks usually require additional structural supervision at the cost of human expert annotation. In this paper, We propose a novel neural language model, called the Parsing-Reading-Predict Networks (PRPN), that can simultaneously induce the syntactic structure from unannotated sentences and leverage the inferred structure to learn a better language model. In our model, the gradient can be directly back-propagated from the language model loss into the neural parsing network. Experiments show that the proposed model can discover the underlying syntactic structure and achieve state-of-the-art performance on word/character-level language model tasks.