nils-herrmann/clean_scirepeval huggingface dataset:
Context-Aware Sensor Search, Selection and Ranking Model for Internet of Things Middleware

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a substantial acceleration of the growth rate in the future. It is also evident that the increasing number of IoT middleware solutions are developed in both research and commercial environments. However, sensor search and selection remain a critical requirement and a challenge. In this paper, we present CASSARAM, a context-aware sensor search, selection, and ranking model for Internet of Things to address the research challenges of selecting sensors when large numbers of sensors with overlapping and sometimes redundant functionality are available. CASSARAM proposes the search and selection of sensors based on user priorities. CASSARAM considers a broad range of characteristics of sensors for search such as reliability, accuracy, battery life just to name a few. Our approach utilises both semantic querying and quantitative reasoning techniques. User priority based weighted Euclidean distance comparison in multidimensional space technique is used to index and rank sensors. Our objectives are to highlight the importance of sensor search in IoT paradigm, identify important characteristics of both sensors and data acquisition processes which help to select sensors, understand how semantic and statistical reasoning can be combined together to address this problem in an efficient manner. We developed a tool called CASSARA to evaluate the proposed model in terms of resource consumption and response time.
16 Real-Time Control System for a Two-Wheeled Inverted Pendulum Mobile Robot

The research on two-wheeled inverted pendulum (T-WIP) mobile robots or commonly known as balancing robots have gained momentum over the last decade in a number of robotic laboratories around the world (Solerno & Angeles, 2003;Grasser et al., 2002; Solerno & Angeles, 2007;Koyanagi, Lida & Yuta, 1992;Ha & Yuta, 1996; Kim, Kim & Kwak, 2003). This chapter describes the hardware design of such a robot. The objective of the design is to develop a T-WIP mobile robot as well as MATLABTM interfacing configuration to be used as flexible platform which comprises of embedded unstable linear plant intended for research and teaching purposes. Issues such as selection of actuators and sensors, signal processing units, MATLABTM Real Time Workshop coding, modeling and control scheme is addressed and discussed. The system is then tested using a well-known state feedback controller to verify its functionality.
MAC Protocols for Wireless Sensor Networks : A Survey

Mobile Ad Hoc networks are autonomously self-organized networks without infrastructure support. Wireless sensor networks are appealing to researchers due to their wide range of application potential in areas such as target detection and tracking, environmental monitoring, industrial process monitoring, and tactical systems. Highly dynamic topology and bandwidth constraint in dense networks, brings the necessity to achieve an efficient medium access protocol subject to power constraints. Various MAC protocols with different objectives were proposed for wireless sensor networks. The aim of this paper is to outline the significance of various MAC protocols along with their merits and demerits.
A Performance Study on the VM Startup Time in the Cloud

One of many advantages of the cloud is the elasticity, the ability to dynamically acquire or release computing resources in response to demand. However, this elasticity is only meaningful to the cloud users when the acquired Virtual Machines (VMs) can be provisioned in time and be ready to use within the user expectation. The long unexpected VM startup time could result in resource under-provisioning, which will inevitably hurt the application performance. A better understanding of the VM startup time is therefore needed to help cloud users to plan ahead and make in-time resource provisioning decisions. In this paper, we study the startup time of cloud VMs across three real-world cloud providers -- Amazon EC2, Windows Azure and Rackspace. We analyze the relationship between the VM startup time and different factors, such as time of the day, OS image size, instance type, data center location and the number of instances acquired at the same time. We also study the VM startup time of spot instances in EC2, which show a longer waiting time and greater variance compared to on-demand instances.
Urban Air Pollution Monitoring System With Forecasting Models

A system for monitoring and forecasting urban air pollution is presented in this paper. The system uses low-cost air-quality monitoring motes that are equipped with an array of gaseous and meteorological sensors. These motes wirelessly communicate to an intelligent sensing platform that consists of several modules. The modules are responsible for receiving and storing the data, preprocessing and converting the data into useful information, forecasting the pollutants based on historical information, and finally presenting the acquired information through different channels, such as mobile application, Web portal, and short message service. The focus of this paper is on the monitoring system and its forecasting module. Three machine learning (ML) algorithms are investigated to build accurate forecasting models for one-step and multi-step ahead of concentrations of ground-level ozone (O3), nitrogen dioxide (NO2), and sulfur dioxide (SO2). These ML algorithms are support vector machines, M5P model trees, and artificial neural networks (ANN). Two types of modeling are pursued: 1) univariate and 2) multivariate. The performance evaluation measures used are prediction trend accuracy and root mean square error (RMSE). The results show that using different features in multivariate modeling with M5P algorithm yields the best forecasting performances. For example, using M5P, RMSE is at its lowest, reaching 31.4, when hydrogen sulfide (H2S) is used to predict SO2. Contrarily, the worst performance, i.e., RMSE of 62.4, for SO2 is when using ANN in univariate modeling. The outcome of this paper can be significantly useful for alarming applications in areas with high air pollution levels.
Challenges for MapReduce in Big Data

In the Big Data community, MapReduce has been seen as one of the key enabling approaches for meeting continuously increasing demands on computing resources imposed by massive data sets. The reason for this is the high scalability of the MapReduce paradigm which allows for massively parallel and distributed execution over a large number of computing nodes. This paper identifies MapReduce issues and challenges in handling Big Data with the objective of providing an overview of the field, facilitating better planning and management of Big Data projects, and identifying opportunities for future research in this field. The identified challenges are grouped into four main categories corresponding to Big Data tasks types: data storage (relational databases and NoSQL stores), Big Data analytics (machine learning and interactive analytics), online processing, and security and privacy. Moreover, current efforts aimed at improving and extending MapReduce to address identified challenges are presented. Consequently, by identifying issues and challenges MapReduce faces when handling Big Data, this study encourages future Big Data research.
Trust management system design for the Internet of Things: A context-aware and multi-service approach

This work proposes a new trust management system (TMS) for the Internet of Things (IoT). The wide majority of these systems are today bound to the assessment of trustworthiness with respect to a single function. As such, they cannot use past experiences related to other functions. Even those that support multiple functions hide this heterogeneity by regrouping all past experiences into a single metric. These restrictions are detrimental to the adaptation of TMSs to today’s emerging M2M and IoT architectures, which are characterized with heterogeneity in nodes, capabilities and services. To overcome these limitations, we design a context-aware and multi-service trust management system fitting the new requirements of the IoT. Simulation results show the good performance of the proposed system and especially highlight its ability to deter a class of common attacks designed to target trust management systems. a 2013 Elsevier Ltd. All rights reserved.
User Conditional Hashtag Prediction for Images

Understanding the content of user's image posts is a particularly interesting problem in social networks and web settings. Current machine learning techniques focus mostly on curated training sets of image-label pairs, and perform image classification given the pixels within the image. In this work we instead leverage the wealth of information available from users: firstly, we employ user hashtags to capture the description of image content; and secondly, we make use of valuable contextual information about the user. We show how user metadata (age, gender, etc.) combined with image features derived from a convolutional neural network can be used to perform hashtag prediction. We explore two ways of combining these heterogeneous features into a learning framework: (i) simple concatenation; and (ii) a 3-way multiplicative gating, where the image model is conditioned on the user metadata. We apply these models to a large dataset of de-identified Facebook posts and demonstrate that modeling the user can significantly improve the tag prediction quality over current state-of-the-art methods.
Pearl's Calculus of Intervention Is Complete

This paper is concerned with graphical criteria that can be used to solve the problem of identifying casual effects from nonexperimental data in a causal Bayesian network structure, i.e., a directed acyclic graph that represents causal relationships. We first review Pearl’s work on this topic [Pearl, 1995], in which several useful graphical criteria are presented. Then we present a complete algorithm [Huang and Valtorta, 2006b] for the identifiability problem. By exploiting the completeness of this algorithm, we prove that the three basicdo-calculus rulesthat Pearl presents are complete, in the sense that, if a causal effect is identifiable, there exists a sequence of applications of the rules of the do-calculus that transforms the causal effect formula into a formula that only includes observational quantities.
Modeling the Dynamic Characteristics of Pneumatic Muscle

A pneumatic muscle (PM) system was studied to determine whether a three-element model could describe its dynamics. As far as the authors are aware, this model has not been used to describe the dynamics of PM. A new phenomenological model consists of a contractile (force-generating) element, spring element, and damping element in parallel. The PM system was investigated using an apparatus that allowed precise and accurate actuation pressure (P) control by a linear servovalve. Length change of the PM was measured by a linear potentiometer. Spring and damping element functions of P were determined by a static perturbation method at several constant P values. These results indicate that at constant P, PM behaves as a spring and damper in parallel. The contractile element function of P was determined by the response to a step input in P, using values of spring and damping elements from the perturbation study. The study showed that the resulting coefficient functions of the three-element model describe the dynamic response to the step input of P accurately, indicating that the static perturbation results can be applied to the dynamic case. This model is further validated by accurately predicting the contraction response to a triangular P waveform. All three elements have pressure-dependent coefficients for pressure P in the range 207 ⩽ P⩽ 621 kPa (30⩽ P⩽ 90 psi). Studies with a step decrease in P (relaxation of the PM) indicate that the damping element coefficient is smaller during relaxation than contraction.
Lateral power MOSFET for megahertz-frequency, high-density DC/DC converters

DC/DC converters to power future CPU cores mandate low-voltage power metal-oxide semiconductor field-effect transistors (MOSFETs) with ultra low on-resistance and gate charge. Conventional vertical trench MOSFETs cannot meet the challenge. In this paper, we introduce an alternative device solution, the large-area lateral power MOSFET with a unique metal interconnect scheme and a chip-scale package. We have designed and fabricated a family of lateral power MOSFETs including a sub-10 V class power MOSFET with a record-low R/sub DS(ON)/ of 1m/spl Omega/ at a gate voltage of 6V, approximately 50% of the lowest R/sub DS(ON)/ previously reported. The new device has a total gate charge Q/sub g/ of 22nC at 4.5V and a performance figures of merit of less than 30m/spl Omega/-nC, a 3/spl times/ improvement over the state of the art trench MOSFETs. This new MOSFET was used in a 100-W dc/dc converter as the synchronous rectifiers to achieve a 3.5-MHz pulse-width modulation switching frequency, 97%-99% efficiency, and a power density of 970W/in/sup 3/. The new lateral MOSEFT technology offers a viable solution for the next-generation, multimegahertz, high-density dc/dc converters for future CPU cores and many other high-performance power management applications.
Mining Large Graphs

Graphs provide a general representation or data model for many types of data where pair-wise relationships are known or thought to be particularly important.1 Thus, it should not be surprising that interest in graph mining has grown with the recent interest in “big data.” Much of the big data generated and analyzed involves pair-wise relationships among a set of entities. For example, in e-commerce applications such as with Amazon’s product database, customers are related to products through their purchasing activities; on the web, web pages are related through hypertext linking relationships; on social networks such as Facebook, individuals are related through their friendships; and so on. Similarly, in scientific applications, research articles are related through citations; proteins are related via metabolic pathways, co-expression, and regulatory network effects within a cell; materials are related through models of their crystalline structure; and so on. While many graphs are small, many large graphs are now extremely LARGE. For example, in early 2008, Google announced that it had indexed over 1 trillion URLs on the internet, corresponding to a graph with over 1 trillion nodes [Alpert and Hajaj, 2008]; in 2012, the Facebook friendship network spanned 721 million individuals and had 137 billion links [Backstrom et al., 2012]; phone companies process a few trillion calls a year [Strohm and Homan, 2013]; the human brain has around 100 billion neurons and 100 trillion neuronal connections [Zimmer, 2011]; one of the largest reported graph experiments involved 4.4 trillion nodes and around 70 trillion edges in a synthetic experiment that required one petabyte of storage [Burkhardt and Waring, 2013]; and one of the largest reported experiments with a real-world graph involved over 1.5 trillion edges [Fleury et al., 2015]. Given the ubiquity, size, and importance of graphs in many application areas, it should come as no surprise that large graph mining serves numerous roles within the large-scale data analysis ecosystem. For example, it can help us learn new things about the world, including both the chemical and biological sciences [Martin et al., 2012; Stelzl et al., 2005] as well as results in the social and economic sciences such as the Facebook study that showed that any two people in the(ir) world can be connected through approximately four intermediate individuals [Backstrom et al., 2012]. Alternatively, large graph mining produces similarity information for recommendation, suggestion, and prediction from messy data [Boldi et al., 2008; Epasto et al., 2014]; it can also tell us how to optimize a data infrastructure to improve response time [Ugander and Backstrom, 2013]; and it can tell us when and how our data are anomalous [Akoglu et al., 2010].
Deep Feature Interpolation for Image Content Changes

We propose Deep Feature Interpolation (DFI), a new data-driven baseline for automatic high-resolution image transformation. As the name suggests, DFI relies only on simple linear interpolation of deep convolutional features from pre-trained convnets. We show that despite its simplicity, DFI can perform high-level semantic transformations like make older/younger, make bespectacled, add smile, among others, surprisingly well&#x2013;sometimes even matching or outperforming the state-of-the-art. This is particularly unexpected as DFI requires no specialized network architecture or even any deep network to be trained for these tasks. DFI therefore can be used as a new baseline to evaluate more complex algorithms and provides a practical answer to the question of which image transformation tasks are still challenging after the advent of deep learning.
Robust 2D/3D face mask presentation attack detection scheme by exploring multiple features and comparison score level fusion

The face mask presentation attack introduces a greater threat to the face recognition system. With the evolving technology in generating both 2D and 3D masks in a more sophisticated, realistic and cost effective manner encloses the face recognition system to more challenging vulnerabilities. In this paper, we present a novel Presentation Attack Detection (PAD) scheme that explores both global (i.e. face) and local (i.e. periocular or eye) region to accurately identify the presence of both 2D and 3D face masks. The proposed PAD algorithm is based on both Binarized Statistical Image Features (BSIF) and Local Binary Patterns (LBP) that can capture a prominent micro-texture features. The linear Support Vector Machine (SVM) is then trained independently on these two features that are applied on both local and global region to obtain the comparison scores. We then combine these scores using the weighted sum rule before making the decision about a normal (or real or live) or an artefact (or spoof) face. Extensive experiments are carried out on two publicly available databases for 2D and 3D face masks namely: CASIA face spoof database and 3DMAD shows the efficacy of the proposed scheme when compared with well-established state-of-the-art techniques.
Designing an Adjustable Wide Range Regulated Current Source

LLC resonant converter has been used widely as dc-dc converter for achieving constant dc voltage. In this paper, an LLC resonant converter, by adding an inductance to its conventional topology and considering the rectifying stage stray inductances, is proposed for an adjustable wide range regulated current source (20-200 Adc) for using as ion implanter's filament power supply. The additional inductor increases output current adjustment range and efficiency, especially at light loads. Transformer's leakage inductances and rectifying stage stray inductances have been considered. Because of these inductances, the rectifier stage always works in continuous conduction mode, and its conduction angle is forced to be larger than ¿, and peak current of the rectifier stage and the output capacitor have been reduced effectively. Switching losses and electromagnetic interference noises have been reduced as well due to zero-voltage switching at the primary and secondary sides of the converter, and zero-current switching at its secondary side. Soft switching is achieved for all power devices under all operating conditions. A developed prototype of the converter has been tested under different load (2.5-12.5 m¿) and input voltage conditions (320-370 V dc) with maximum efficiency of 87%. Experimental results confirm high performance of the designed adjustable and regulated current source even under the worst-case conditions.
A 28GHz quadrature fractional-N synthesizer for 5G mobile communication with less than 100fs jitter in 65nm CMOS

A 26-32GHz quadrature cascaded phase locked loop (PLL) is presented. The PLL is implemented in 65nm bulk CMOS, consuming 27mW and has less than 100fsec integrated jitter with -114.4 and -112.6dBc/Hz phase noise at 1MHz offset for integer and fractional modes, respectively.
Neural Networks and Wavelet De-Noising for Stock Trading and Prediction

In this chapter, neural networks are used to predict the future stock prices and develop a suitable trading system. Wavelet analysis is used to de-noise the time series and the results are compared with the raw time series prediction without wavelet de-noising. Standard and Poor 500 (S&P 500) is used in experiments. We use a gradual data sub-sampling technique, i.e., training the network mostly with recent data, but without neglecting past data. In addition, effects of NASDAQ 100 are studied on prediction of S&P 500. A daily trading strategy is employed to buy/sell according to the predicted prices and to calculate the directional efficiency and the rate of returns for different periods. There are numerous exchange traded funds (ETF’s), which attempt to replicate the performance of S&P 500 by holding the same stocks in the same proportions as the index, and therefore, giving the same percentage returns as S&P 500. Therefore, this study can be used to help invest in any of the various ETFs, which replicates the performance of S&P 500. The experimental results show that neural networks, with appropriate training and input data, can be used to achieve high profits by investing in ETFs based on S&P 500.
Sentiment, emotion, purpose, and style in electoral tweets

Social media is playing a growing role in elections world-wide. Thus, automatically analyzing electoral tweets has applications in understanding how public sentiment is shaped, tracking public sentiment and polarization with respect to candidates and issues, understanding the impact of tweets from various entities, etc. Here, for the first time, we automatically annotate a set of 2012 US presidential election tweets for a number of attributes pertaining to sentiment, emotion, purpose, and style by crowdsourcing. Overall, more than 100,000 crowdsourced responses were obtained for 13 questions on emotions, style, and purpose. Additionally, we show through an analysis of these annotations that purpose, even though correlated with emotions, is significantly different. Finally, we describe how we developed automatic classifiers, using features from state-of-the-art sentiment analysis systems, to predict emotion and purpose labels, respectively, in new unseen tweets. These experiments establish baseline results for automatic systems on this new data.