nils-herrmann/clean_scirepeval huggingface dataset:
The influence of eWOM in social media on consumers' purchase intentions: An extended approach to information adoption

Social media websites have created valuable opportunities for electronic word of mouth (eWOM) conversations. People are now able to discuss products and services of brands with their friends and acquaintances. The aim of this study is to examine the influence of these conversations in social media on consumers' purchase intentions. For this purpose, a conceptual model was developed based on the integration of Information Adoption Model (IAM) and related components of Theory of Reasoned Action (TRA). The new model, which is named as Information Acceptance Model (IACM), was validated through structural equation modelling (SEM) based on surveys of 384 university students who use social media websites. The results confirm that quality, credibility, usefulness and adoption of information, needs of information and attitude towards information are the key factors of eWOM in social media that influence consumers’ purchase intentions. Theoretical and practical implications are discussed as well as recommendations for further research.
Sequential Convolutional Neural Networks for Slot Filling in Spoken Language Understanding

We investigate the usage of convolutional neural networks (CNNs) for the slot filling task in spoken language understanding. We propose a novel CNN architecture for sequence labeling which takes into account the previous context words with preserved order information and pays special attention to the current word with its surrounding context. Moreover, it combines the information from the past and the future words for classification. Our proposed CNN architecture outperforms even the previously best ensembling recurrent neural network model and achieves state-of-the-art results with an F1-score of 95.61% on the ATIS benchmark dataset without using any additional linguistic knowledge and resources.
A Review on Support Vector Machine for Data Classification

185 All Rights Reserved With increasing amounts of data being generated by businesses and researchers there is a need for fast, accurate and robust algorithms for data analysis. Improvements in databases technology, computing performance and artificial intelligence have contributed to the development of intelligent data analysis. Support vector machines are a specific type of machine learning algorithm that are among the most widelyused for many statistical learning problems, such as spam filtering, text classification, handwriting analysis, face and object recognition, and countless others. Support vector machines have also come into widespread use in practically every area of bioinformatics within the last ten years, and their area of influence continues to expand today. The support vector machine has been developed as robust tool for classification and regression in noisy, complex domains. The two key features of support vector machines are generalization theory, which leads to a principled way to choose an hypothesis; and, kernel functions, which introduce nonlinearity in the hypothesis space without explicitly requiring a non-linear algorithm.
Exploring the Impact of Pragmatic Phenomena on Irony Detection in Tweets: A Multilingual Corpus Study

This paper provides a linguistic and pragmatic analysis of the phenomenon of irony in order to represent how Twitter’s users exploit irony devices within their communication strategies for generating textual contents. We aim to measure the impact of a wide-range of pragmatic phenomena in the interpretation of irony, and to investigate how these phenomena interact with contexts local to the tweet. Informed by linguistic theories, we propose for the first time a multi-layered annotation schema for irony and its application to a corpus of French, English and Italian tweets.We detail each layer, explore their interactions, and discuss our results according to a qualitative and quantitative perspective.
Various Image Segmentation Techniques: A Review

The Image segmentation is referred to as one of the most important processes of image processing. Image segmentation is the technique of dividing or partitioning an image into parts, called segments. It is mostly useful for applications like image compression or object recognition, because for these types of applications, it is inefficient to process the whole image. So, image segmentation is used to segment the parts from image for further processing. There exist several image segmentation techniques, which partition the image into several parts based on certain image features like pixel intensity value, color, texture, etc. These all techniques are categorized based on the segmentation method used. In this paper the various image segmentation techniques are reviewed, discussed and finally a comparison of their advantages and disadvantages is listed.
Performance evaluation of diodes in 27.12 MHz Class-D resonant rectifiers under high voltage and high slew rate conditions

This paper provides a performance review of select diodes for use in high frequency resonant rectifiers at modest power levels. Specifically, we evaluate the performance of several leading edge diodes for use in a 27.12 MHz Class-D type rectifier for output voltages from 170 V to 1000 V dc, and corresponding power levels between 8.5 W and 100 W. Previous work on resonant rectifiers at frequencies > 10 MHz  showed higher-than-expected losses in the diodes. These losses increased with increased output voltage and led to significant de-rating and poor utilization of the semiconductors. The authors suspect these losses are due in part to the high equation experienced by the Silicon Carbide (SiC) Schottky diodes used in that design. This paper provides an in depth comparison of select diodes to evaluate their performance for use at elevated voltages and frequencies. Further understanding of the losses involved in the design of high equation resonant rectifiers will lead to better de-rating guidelines for component selection of high frequency high voltage converters.
Estimation of Energy Balance Components over a Drip-Irrigated Olive Orchard Using Thermal and Multispectral Cameras Placed on a Helicopter-Based Unmanned Aerial Vehicle (UAV)

A field experiment was carried out to implement a remote sensing energy balance (RSEB) algorithm for estimating the incoming solar radiation (Rsi), net radiation (Rn), sensible heat flux (H), soil heat flux (G) and latent heat flux (LE) over a drip-irrigated olive (cv. Arbequina) orchard located in the Pencahue Valley, Maule Region, Chile (35 ̋251S; 71 ̋441W; 90 m above sea level). For this study, a helicopter-based unmanned aerial vehicle (UAV) was equipped with multispectral and infrared thermal cameras to obtain simultaneously the normalized difference vegetation index (NDVI) and surface temperature (Tsurface) at very high resolution (6 cm ˆ 6 cm). Meteorological variables and surface energy balance components were measured at the time of the UAV overpass (near solar noon). The performance of the RSEB algorithm was evaluated using measurements of H and LE obtained from an eddy correlation system. In addition, estimated values of Rsi and Rn were compared with ground-truth measurements from a four-way net radiometer while those of G were compared with soil heat flux based on flux plates. Results indicated that RSEB algorithm estimated LE and H with errors of 7% and 5%, respectively. Values of the root mean squared error (RMSE) and mean absolute error (MAE) for LE were 50 and 43 W m ́2 while those for H were 56 and 46 W m ́2, respectively. Finally, the RSEB algorithm computed Rsi, Rn and G with error less than 5% and with values of RMSE and MAE less than 38 W m ́2. Results demonstrated that multispectral and thermal cameras placed on an UAV could provide an excellent tool to evaluate the intra-orchard spatial variability of Rn, G, H, LE, NDVI and Tsurface over the tree canopy and soil surface between rows.
Code Smell Detection in Eclipse

Refactoring is a software engineering technique that, by applying a series of small behavior-preserving transformations, can improve a software system’s design, readability and extensibility. Code smells are signs that indicate that source code might need refactoring. The goal of this thesis project was to develop a prototype of a code smell detection plug-in for the Eclipse IDE framework. In earlier research by Van Emden and Moonen, a tool was developed to detect code smells in Java source code and visualize them in graph views. CodeNose, the plug-in prototype created in this thesis project, presents code smells in the Tasks View in Eclipse, similar to the way compiler errors and warnings are presented. These code smell reports provide feedback about the quality of a software system. CodeNose uses the Eclipse JDT parser to build abstract syntax trees that represent the source code. A tree visitor detects primitive code smells and collects derived smell aspects, which are written to a fact database and passed to a relational algebra calculator, the Grok tool. The results of the calculations on these facts can be used to infer more complex code smells. In a case study, the plug-in was tested by performing the code smell detection process on an existing software system. We present the case study results, focusing at performance of the plug-in and usefulness of the code smells that were detected.
Broadside Radiation From a Planar 2-D Leaky-Wave Antenna by Practical Surface-Wave Launching

A planar 2-D leaky-wave (LW) antenna, capable of broadside radiation, is presented for millimeter wave applications. A directive surface-wave launcher (SWL) is utilized as the antenna feed exciting cylindrical surface-waves (SWs) on a grounded dielectric slab (GDS). With the addition of a segmented circular strip grating, cylindrical LWs can be excited on the antenna aperture. Measurements illustrate maximum gain at broadside at 19.48 GHz in both the E and H planes with a 10deg half power beamwidth. Specifically, a directive pencil beam is observed just at the edge of the TE1 SW mode cuttoff frequency of the slab (19.47 GHz), suggesting maximum radiation at the edge of a TE stopband.
Discovering data quality rules

Dirty data is a serious problem for businesses leading to incorrect decision making, inefficient daily operations, and ultimately wasting both time and money. Dirty data often arises when domain constraints and business rules, meant to preserve data consistency and accuracy, are enforced incompletely or not at all in application code. In this work, we propose a new data-driven tool that can be used within an organization’s data quality management process to suggest possible rules, and to identify conformant and non-conformant records. Data quality rules are known to be contextual, so we focus on the discovery of context-dependent rules. Specifically, we search for conditional functional dependencies (CFDs), that is, functional dependencies that hold only over a portion of the data. The output of our tool is a set of functional dependencies together with the context in which they hold (for example, a rule that states for CS graduate courses, the course number and term functionally determines the room and instructor). Since the input to our tool will likely be a dirty database, we also search for CFDs that almost hold. We return these rules together with the non-conformant records (as these are potentially dirty records). We present effective algorithms for discovering CFDs and dirty values in a data instance. Our discovery algorithm searches for minimal CFDs among the data values and prunes redundant candidates. No universal objective measures of data quality or data quality rules are known. Hence, to avoid returning an unnecessarily large number of CFDs and only those that are most interesting, we evaluate a set of interest metrics and present comparative results using real datasets. We also present an experimental study showing the scalability of our techniques.
Exploring the role of customer relationship management (CRM) systems in customer knowledge creation

This study explores how customer relationship management (CRM) systems support customer knowledge creation processes [48], including socialization, externalization, combination and internalization. CRM systems are categorized as collaborative, operational and analytical. An analysis of CRM applications in three organizations reveals that analytical systems strongly support the combination process. Collaborative systems provide the greatest support for externalization. Operational systems facilitate socialization with customers, while collaborative systems are used for socialization within an organization. Collaborative and analytical systems both support the internalization process by providing learning opportunities. Three-way interactions among CRM systems, types of customer knowledge, and knowledge creation processes are explored.
The dawn of Big Data - Hbase

HBase is a distributed column-oriented database built on top of HDFS. HBase is the Hadoop application to use when you require real-time read/write random access to very large datasets. HBase is a scalable data store targeted at random read and write access of (fairly-) structured data. It's modeled after Google's Big table and targeted to support large tables, on the order of billions of rows and millions of columns. It uses HDFS as the underlying file system and is designed to be fully distributed and highly available. Version 0.20 introduces significant performance improvement.  HBase has different storage characteristics than HDFS, such as the ability to do row updates and column indexing, so we can expect to see these features used by Hive in future releases. It is already possible to access HBase tables from Hive. This paper includes the step by step introduction to the HBase, Identify differences between apache HBase and a traditional RDBMS, The Problem with Relational Database Systems, Relation between the Hadoop and HBase, How an Apache HBase table is physically stored on disk. Later part of this paper introduces Map Reduce, HBase table and how Apache HBase Cells stores data, what happens to data when it is deleted. Last part explains difference between Big Data and HBase, Conclusion followed with the References.
Automated Diagnosis for UMTS Networks Using Bayesian Network Approach

This paper presents an automated diagnosis in troubleshooting (TS) for Universal Mobile Telecommunications System (UMTS) networks using a Bayesian network (BN) approach. An automated diagnosis model is first described using the Naive Bayesian Classifier. To increase the performance of the diagnosis model, the entropy minimization discretization (EMD) method is incorporated into the model to select optimal segments for the discretization of the input symptoms. In the first phase, the diagnosis model is constructed using a dynamic simulator. The simulator TS platform allows generation of a large amount of data required to study the relations between faults and symptoms. In the second phase, the diagnosis model is adapted to a real UMTS network using counters and key performance indicators (KPIs) recovered from an Operations and Maintenance Center (OMC). Results for the automated diagnosis using both network simulator and real UMTS network measurements illustrate the efficiency of the proposed TS approach and its importance to mobile network operators.
Improving malware classification: bridging the static/dynamic gap

Malware classification systems have typically used some machine learning algorithm in conjunction with either static or dynamic features collected from the binary. Recently, more advanced malware has introduced mechanisms to avoid detection in these views by using obfuscation techniques to avoid static detection and execution-stalling techniques to avoid dynamic detection. In this paper we construct a classification framework that is able to incorporate both static and dynamic views into a unified framework in the hopes that, while a malicious executable can disguise itself in some views, disguising itself in every view while maintaining malicious intent will prove to be substantially more difficult. Our method uses kernels to place a similarity metric on each distinct view and then employs multiple kernel learning to find a weighted combination of the data sources which yields the best classification accuracy in a support vector machine classifier. Our approach opens up new avenues of malware research which will allow the research community to elegantly look at multiple facets of malware simultaneously, and which can easily be extended to integrate any new data sources that may become popular in the future.
Forgetting all else: on the antecedents and consequences of goal shielding.

Six studies explore the role of goal shielding in self-regulation by examining how the activation of focal goals to which the individual is committed inhibits the accessibility of alternative goals. Consistent evidence was found for such goal shielding, and a number of its moderators were identified: Individuals' level of commitment to the focal goal, their degree of anxiety and depression, their need for cognitive closure, and differences in their goal-related tenacity. Moreover, inhibition of alternative goals was found to be more pronounced when they serve the same overarching purpose as the focal goal, but lessened when the alternative goals facilitate focal goal attainment. Finally, goal shielding was shown to have beneficial consequences for goal pursuit and attainment.
Recent Advances in Zero-Shot Recognition: Toward Data-Efficient Understanding of Visual Content

With the recent renaissance of deep convolutional neural networks (CNNs), encouraging breakthroughs have been achieved on the supervised recognition tasks, where each class has sufficient and fully annotated training data. However, to scale the recognition to a large number of classes with few or no training samples for each class remains an unsolved problem. One approach is to develop models capable of recognizing unseen categories without any training instances, or zero-shot recognition/learning. This article provides a comprehensive review of existing zero-shot recognition techniques covering various aspects ranging from representations of models, data sets, and evaluation settings. We also overview related recognition tasks including one-shot and open-set recognition, which can be used as natural extensions of zero-shot recognition when a limited number of class samples become available or when zero-shot recognition is implemented in a real-world setting. We highlight the limitations of existing approaches and point out future research directions in this existing new research area.
Scanning Spaceborne Synthetic Aperture Radar with Integrated Radiometer

Spaceborne synthetic aperture radar systems are severely constrained to a narrow swath by ambiguity limitations. Here a vertically scanned-beam synthetic aperture system (SCANSAR) is proposed as a solution to this problem. The potential length of synthetic aperture must be shared between beam positions, so the along-track resolution is poorer; a direct tradeoff exists between resolution and swath width. The length of the real aperture is independently traded against the number of scanning positions. Design curves and equations are presented for spaceborne SCANSARs for altitudes between 400 and 1400 km and inner angles of incidence between 20° and 40°. When the real antenna is approximately square, it may also be used for a microwave radiometer. The combined radiometer and synthetic-aperture (RADISAR) should be useful for those applications where the poorer resolution of the radiometer is useful for some purposes, but the finer resolution of the radar is needed for others.
Avatar digitization from a single image for real-time rendering

We present a fully automatic framework that digitizes a complete 3D head with hair from a single unconstrained image. Our system offers a practical and consumer-friendly end-to-end solution for avatar personalization in gaming and social VR applications. The reconstructed models include secondary components (eyes, teeth, tongue, and gums) and provide animation-friendly blendshapes and joint-based rigs. While the generated face is a high-quality textured mesh, we propose a versatile and efficient polygonal strips (polystrips) representation for the hair. Polystrips are suitable for an extremely wide range of hairstyles and textures and are compatible with existing game engines for real-time rendering. In addition to integrating state-of-the-art advances in facial shape modeling and appearance inference, we propose a novel single-view hair generation pipeline, based on 3D-model and texture retrieval, shape refinement, and polystrip patching optimization. The performance of our hairstyle retrieval is enhanced using a deep convolutional neural network for semantic hair attribute classification. Our generated models are visually comparable to state-of-the-art game characters designed by professional artists. For real-time settings, we demonstrate the flexibility of polystrips in handling hairstyle variations, as opposed to conventional strand-based representations. We further show the effectiveness of our approach on a large number of images taken in the wild, and how compelling avatars can be easily created by anyone.
Decomposition by Partial Linearization: Parallel Optimization of Multi-Agent Systems

We propose a novel decomposition framework for the distributed optimization of general nonconvex sum-utility functions arising naturally in the system design of wireless multi-user interfering systems. Our main contributions are i) the development of the first class of (inexact) Jacobi best-response algorithms with provable convergence, where all the users simultaneously and iteratively solve a suitably convexified version of the original sum-utility optimization problem; ii) the derivation of a general dynamic pricing mechanism that provides a unified view of existing pricing schemes that are based, instead, on heuristics; and iii) a framework that can be easily particularized to well-known applications, giving rise to very efficient practical (Jacobi or Gauss-Seidel) algorithms that outperform existing ad hoc methods proposed for very specific problems. Interestingly, our framework contains as special cases well-known gradient algorithms for nonconvex sum-utility problems, and many block-coordinate descent schemes for convex functions.