nils-herrmann/clean_scirepeval huggingface dataset:
Modeling movements in oil, gold, forex and market indices using search volume index and Twitter sentiments

Study of the forecasting models using large scale microblog discussions and the search behavior data can provide a good insight for better understanding the market movements. In this work we collected a dataset of 2 million tweets and search volume index (SVI from Google) for a period of June 2010 to September 2011. We model a set of comprehensive causative relationships over this dataset for various market securities like equity (Dow Jones Industrial Average-DJIA and NASDAQ-100), commodity markets (oil and gold) and Euro Forex rates. We also investigate the lagged and statistically causative relations of Twitter sentiments developed during active trading days and market inactive days in combination with the search behavior of public before any change in the prices/indices. Our results show extent of lagged significance with high correlation value upto 0.82 between search volumes and gold price in USD. We find weekly accuracy in direction (up and down prediction) uptil 94.3% for DJIA and 90% for NASDAQ-100 with significant reduction in mean average percentage error for all the forecasting models.
The Contradictory Influence of Social Media Affordances on Online Communal Knowledge Sharing

The use of social media creates the opportunity to turn organization-wide knowledge sharing in the workplace from an intermittent, centralized knowledge management process to a continuous online knowledge conversation of strangers, unexpected interpretations and re-uses, and dynamic emergence. We theorize four affordances of social media representing different ways to engage in this publicly visible knowledge conversations: metavoicing, triggered attending, network-informed associating, and generative role-taking. We further theorize mechanisms that affect how people engage in the knowledge conversation, finding that some mechanisms, when activated, will have positive effects on moving the knowledge conversation forward, but others will have adverse consequences not intended by the organization. These emergent tensions become the basis for the implications we draw.
Churn Prediction in Telecommunications Using MiningMart

This paper summarises a successful application of Knowledge Discovery in Databases (KDD) in an Italian telecommunications research lab. The aim of the application was to predict customer churn behaviour. A critical success factor for this application was clever preprocessing of the given data, in particular the construction of derived predictor features. The application was realised in the MiningMart KDD system, whose particular strength is data preprocessing on a conceptual level. Since MiningMart provides a declarative, yet executable model of the presented application, this model could be published in a central repository of KDD models, where it is publicly inspectable, which complements the descriptions in this paper. 1 A case study in churn prediction A major concern in customer relationship management in telecommunications companies is the ease with which customers can move to a competitor, a process called “churning”. Churning is a costly process for the company, as it is much cheaper to retain a customer than to acquire a new one . The objectives of the application to be presented here were to find out which types of customers of a telecommunications company are likely to churn, and when. The task was solved using decision trees which achieved a predictive accuracy of 82%. This good result was only possible due to the introduction of relevant derived features for prediction which were not available in the original data, and due to a rerepresentation of the data so that temporal aspects could be included. Thus data preprocessing was a key success factor in this application. During preprocessing, the available data tables were transformed so that a classification algorithm could be applied. In the resulting data set, each row (that is, each example for classification) corresponded to one customer of the company, and contained many features describing their telecommunication behaviour for each of five consecutive months. Whether or not the customer left the company in the sixth month determined the binary classification label or target. Once a learned classifier is available it can be applied every month on data from the current and past four months, to predict churn for the following month.
Comparing ICP variants on real-world data sets - Open-source library and experimental protocol

Many modern sensors used for mapping produce 3D point clouds, which are typically registered together using the iterative closest point (ICP) algorithm. Because ICP has many variants whose performances depend on the environment and the sensor, hundreds of variations have been published. However, no comparison frameworks are available, leading to an arduous selection of an appropriate variant for particular experimental conditions. The first contribution of this paper consists of a protocol that allows for a comparison between ICP variants, taking into account a broad range of inputs. The second contribution is an open-source ICP library, which is fast enough to be usable in multiple real-world applications, while being modular enough to ease comparison of multiple solutions. This paper presents two examples of these field applications. The last contribution is the comparison of two baseline ICP variants using data sets that cover a rich variety of environments. Besides demonstrating the need for improved ICP methods for natural, unstructured and information-deprived environments, these baseline variants also provide a solid basis to which novel solutions could be compared. The combination of our protocol, software, and baseline results demonstrate convincingly how open-source software can push forward the research in mapping and navigation. F. Pomerleau (B) · F. Colas · R. Siegwart · S. Magnenat Autonomous System Lab, ETH Zurich, Tannenstrasse 3, 8092 Zurich, Switzerland e-mail: f.pomerleau@gmail.com F. Colas e-mail: francis.colas@mavt.ethz.ch R. Siegwart e-mail: rsiegwart@ethz.ch S. Magnenat e-mail: stephane@magnenat.net
A Secure Routing Protocol for Ad Hoc Networks

Most recent ad hoc network research has focused on providing routing services without considering security. In this paper, we detail security threats against ad hoc routing protocols, specifically examining AODV and DSR. In light of these threats, we identify three different environments with distinct security requirements. We propose a solution to one, the managed-open scenario where no network infrastructure is pre-deployed, but a small amount of prior security coordination is expected. Our protocol, ARAN, is based on certificates and successfully defeats all identified attacks.
Forecast chaotic time series data by DBNs

Deep belief nets (DBNs) with multiple artificial neural networks (ANNs) have attracted many researchers recently. In this paper, we propose to compose restricted Boltzmann machine (RBM) and multi-layer perceptron (MLP) as a DBN to predict chaotic time series data, such as the Lorenz chaos and the Henon map. Experiment results showed that in the sense of prediction precision, the novel DBN performed better than the conventional DBN with RBMs.
A Comparison of Online Trust Building Factors between Potential Customers and Repeat Customers

While vendors on the Internet may have enjoyed an increase in the number of clicks on their Web sites, they have also faced disappointments in converting these clicks into purchases. Lack of trust is identified as one of the greatest barriers inhibiting Internet transactions. Thus, it is essential to understand how trust is created and how it evolves in the Electronic Commerce (EC) context throughout a customer’s purchase experience with an Internet store. As the first step in studying the dynamics of online trust building, this research aims to compare online trust-building factors between potential customers and repeat customers. For this purpose, we classify trust in an Internet store into potential customer trust and repeat customer trust, depending on the customer’s purchase experience with the store. We find that trust building differs between potential customers and repeat customers in terms of antecedents. We also compare the effects of shared antecedents on trust between potential customers and repeat customers. We find that customer satisfaction has a stronger effect on trust building for repeat ∗ Soon Ang was the accepting senior editor for this paper. Harrison McKnight and Suzanne Rivard were reviewers for this paper. Kim, Xu, and Koh/A Comparison of Online Trust Building Factors Journal of the Association for Information Systems Vol. 5 No. 10, pp.392-420/October 2004 393 customers than other antecedents. We discuss the theoretical reasons for the differences and the implications of our research.
A critical analysis of media discourse on information technology: preliminary results of a proposed method for critical discourse analysis

Since the 1980s, there has been a growing body of critical theory in information systems research. A central theoretical foundation of this research is Habermas’ theory of communicative action, which focuses on implications of speech and proposes general normative standards for communication. Habermas also places particular emphasis on the importance of the public sphere in a democratic society, critiquing the role of the media and other actors in shaping public discourse. While there has been growing emphasis on critical discourse analysis (CDA), there has been limited effort to systematically apply Habermas’ validity claims to empirical research. Moreover, while critical research in information systems has examined communication within the organizational context, public discourse on information technology has received little attention. The paper makes three primary contributions: (1) it responds to Habermas’ call for empirical research to ground and extend his theory of communication in every day critical practice; (2) it proposes an approach to applying Habermas’ theory of communication to CDA; and (3) it extends the reach of critical research in information systems beyond micro-level organizational concerns and opens up to critical reflection and debate on the impact of systematically distorted communication about technology in the public sphere.
Super-Resolution Reconstruction of High-Resolution Satellite ZY-3 TLC Images

Super-resolution (SR) image reconstruction is a technique used to recover a high-resolution image using the cumulative information provided by several low-resolution images. With the help of SR techniques, satellite remotely sensed images can be combined to achieve a higher-resolution image, which is especially useful for a two- or three-line camera satellite, e.g., the ZY-3 high-resolution Three Line Camera (TLC) satellite. In this paper, we introduce the application of the SR reconstruction method, including motion estimation and the robust super-resolution technique, to ZY-3 TLC images. The results show that SR reconstruction can significantly improve both the resolution and image quality of ZY-3 TLC images.
Hybrid computing using a neural network with dynamic external memory

Artificial neural networks are remarkably adept at sensory processing, sequence learning and reinforcement learning, but are limited in their ability to represent variables and data structures and to store data over long timescales, owing to the lack of an external memory. Here we introduce a machine learning model called a differentiable neural computer (DNC), which consists of a neural network that can read from and write to an external memory matrix, analogous to the random-access memory in a conventional computer. Like a conventional computer, it can use its memory to represent and manipulate complex data structures, but, like a neural network, it can learn to do so from data. When trained with supervised learning, we demonstrate that a DNC can successfully answer synthetic questions designed to emulate reasoning and inference problems in natural language. We show that it can learn tasks such as finding the shortest path between specified points and inferring the missing links in randomly generated graphs, and then generalize these tasks to specific graphs such as transport networks and family trees. When trained with reinforcement learning, a DNC can complete a moving blocks puzzle in which changing goals are specified by sequences of symbols. Taken together, our results demonstrate that DNCs have the capacity to solve complex, structured tasks that are inaccessible to neural networks without external read–write memory.
Resource Management in Cloud Networking Using Economic Analysis and Pricing Models: A Survey

This paper presents a comprehensive literature review on applications of economic and pricing models for resource management in cloud networking. To achieve sustainable profit advantage, cost reduction, and flexibility in provisioning of cloud resources, resource management in cloud networking requires adaptive and robust designs to address many issues, e.g., resource allocation, bandwidth reservation, request allocation, and workload allocation. Economic and pricing models have received a lot of attention as they can lead to desirable performance in terms of social welfare, fairness, truthfulness, profit, user satisfaction, and resource utilization. This paper reviews applications of the economic and pricing models to develop adaptive algorithms and protocols for resource management in cloud networking. Besides, we survey a variety of incentive mechanisms using the pricing strategies in sharing resources in edge computing. In addition, we consider using pricing models in cloud-based software defined wireless networking. Finally, we highlight important challenges, open issues and future research directions of applying economic and pricing models to cloud networking.
USC-HAD: a daily activity dataset for ubiquitous activity recognition using wearable sensors

Many ubiquitous computing applications involve human activity recognition based on wearable sensors. Although this problem has been studied for a decade, there are a limited number of publicly available datasets to use as standard benchmarks to compare the performance of activity models and recognition algorithms. In this paper, we describe the freely available USC human activity dataset (USC-HAD), consisting of well-defined low-level daily activities intended as a benchmark for algorithm comparison particularly for healthcare scenarios. We briefly review some existing publicly available datasets and compare them with USC-HAD. We describe the wearable sensors used and details of dataset construction. We use high-precision well-calibrated sensing hardware such that the collected data is accurate, reliable, and easy to interpret. The goal is to make the dataset and research based on it repeatable and extendible by others.
Lattice-based homomorphic encryption of vector spaces

In this paper we introduce a new probabilistic lattice-based bounded homomorphic encryption scheme. For this scheme the sum of two encrypted messages is the encryption of the sum of two messages and the scheme is able to preserve a vector spave structure of the message. The size of the public key is rather large ap 3 Mb but the encryption and the decryption operations are very fast (of the same speed order than NTRU). The homomorphic operation, i.e. the addition of ciphertexts is dramatically fast compared to homomorphic schemes based on group theory like Paillier or El Gamal.
Predicting Visual Exemplars of Unseen Classes for Zero-Shot Learning

Leveraging class semantic descriptions and examples of known objects, zero-shot learning makes it possible to train a recognition model for an object class whose examples are not available. In this paper, we propose a novel zero-shot learning model that takes advantage of clustering structures in the semantic embedding space. The key idea is to impose the structural constraint that semantic representations must be predictive of the locations of their corresponding visual exemplars. To this end, this reduces to training multiple kernel-based regressors from semantic representation-exemplar pairs from labeled data of the seen object categories. Despite its simplicity, our approach significantly outperforms existing zero-shot learning methods on standard benchmark datasets, including the ImageNet dataset with more than 20,000 unseen categories.
Automatic Query Expansion Using SMART: TREC 3

The Smart information retrieval project emphasizes completely automatic approaches to the understanding and retrieval of large quantities of text. We continue our work in TREC 3, performing runs in the routing, ad-hoc, and foreign language environments. Our major focus is massive query expansion: adding from 300 to 530 terms to each query. These terms come from known relevant documents in the case of routing, and from just the top retrieved documents in the case of ad-hoc and Spanish. This approach improves e ectiveness from 7% to 25% in the various experiments. Other ad-hoc work extends our investigations into combining global similarities, giving an overall indication of how a document matches a query, with local similarities identifying a smaller part of the document which matches the query. Using an overlapping text window de nition of \local", we achieve a 16% improvement.
Tag, you can see it!: using tags for access control in photo sharing

Users often have rich and complex photo-sharing preferences, but properly configuring access control can be difficult and time-consuming. In an 18-participant laboratory study, we explore whether the keywords and captions with which users tag their photos can be used to help users more intuitively create and maintain access-control policies. We find that (a) tags created for organizational purposes can be repurposed to create efficient and reasonably accurate access-control rules; (b) users tagging with access control in mind develop coherent strategies that lead to significantly more accurate rules than those associated with organizational tags alone; and (c) participants can understand and actively engage with the concept of tag-based access control.
Using SVM based method for equipment fault detection in a thermal power plant

Due to the growing demand on electricity, how to improve the efficiency of equipment in a thermal power plant has become one of the critical issues. Reports indicate that efficiency and availability are heavily dependant upon high reliability and maintainability. Recently, the concept of e-maintenance has been introduced to reduce the cost of maintenance. In e-maintenance systems, the intelligent fault detection system plays a crucial role for identifying failures. Data mining techniques are at the core of such intelligent systems and can greatly influence their performance. Applying these techniques to fault detection makes it possible to shorten shutdown maintenance and thus increase the capacity utilization rates of equipment. Therefore, this work proposes a support vector machines (SVM) based model which integrates a dimension reduction scheme to analyze the failures of turbines in thermal power facilities. Finally, a real case from a thermal power plant is provided to evaluate the effectiveness of the proposed SVM based model. Experimental results show that SVM outperforms linear discriminant analysis (LDA) and back-propagation neural networks (BPN) in classification performance.
FIRE DETECTION ALGORITHM USING IMAGE PROCESSING TECHNIQUES

Lately fire outbreak is common issue happening in Malays and the damage caused by these type of incidents is tremendous toward nature and human interest. Due to this the need for application for fire detection has increases in recent years. In this paper we proposed a fire detection algorithm based on image processing techniques which is compatible in surveillance devices like CCTV, wireless camera to UAVs. The algorithm uses RGB colour model to detect the colour of the fire which is mainly comprehended by the intensity of the component R which is red colour. The growth of fire is detected using sobel edge detection. Finally a colour based segmentation technique was applied based on the results from the first technique and second technique to identify the region of interest (ROI) of the fire. After analysing 50 different fire scenarios images, the final accuracy obtained from testing the algorithm was 93.61% and the efficiency was 80.64%.
Analysis of the Communication Traffic for Blockchain Synchronization of IoT Devices

Blockchain is a technology uniquely suited to support massive number of transactions and smart contracts within the Internet of Things (IoT) ecosystem, thanks to the decentralized accounting mechanism. In a blockchain network, the states of the accounts are stored and updated by the validator nodes, interconnected in a peer-to-peer fashion. IoT devices are characterized by relatively low computing capabilities and low power consumption, as well as sporadic and low-bandwidth wireless connectivity. An IoT device connects to one or more validator nodes to observe or modify the state of the accounts. In order to interact with the most recent state of accounts, a device needs to be synchronized with the blockchain copy stored by the validator nodes. In this work, we describe general architectures and synchronization protocols that enable synchronization of the IoT endpoints to the blockchain, with different communication costs and security levels. We model and analytically characterize the traffic generated by the synchronization protocols, and also investigate the power consumption and synchronization trade-off via numerical simulations. To the best of our knowledge, this is the first study that rigorously models the role of wireless connectivity in blockchain-powered IoT systems.
Understanding and evaluating qualitative research.

Qualitative research aims to address questions concerned with developing an understanding of the meaning and experience dimensions of humans' lives and social worlds. Central to good qualitative research is whether the research participants' subjective meanings, actions and social contexts, as understood by them, are illuminated. This paper aims to provide beginning researchers, and those unfamiliar with qualitative research, with an orientation to the principles that inform the evaluation of the design, conduct, findings and interpretation of qualitative research. It orients the reader to two philosophical perspectives, the interpretive and critical research paradigms, which underpin both the qualitative research methodologies most often used in mental health research, and how qualitative research is evaluated. Criteria for evaluating quality are interconnected with standards for ethics in qualitative research. They include principles for good practice in the conduct of qualitative research, and for trustworthiness in the interpretation of qualitative data. The paper reviews these criteria, and discusses how they may be used to evaluate qualitative research presented in research reports. These principles also offer some guidance about the conduct of sound qualitative research for the beginner qualitative researcher.
Progress in Tourism Management Event tourism : Definition , evolution , and research

This article reviews ‘event tourism’ as both professional practice and a field of academic study. The origins and evolution of research on event tourism are pinpointed through both chronological and thematic literature reviews. A conceptual model of the core phenomenon and key themes in event tourism studies is provided as a framework for spurring theoretical advancement, identifying research gaps, and assisting professional practice. Conclusions are in two parts: a discussion of implications for the practice of event management and tourism, and implications are drawn for advancing theory in event tourism. r 2007 Elsevier Ltd. All rights reserved.