nils-herrmann/clean_scirepeval huggingface dataset:
Differentially Private Stochastic Gradient Descent for in-RDBMS Analytics

This paper studies differential privacy for stochastic gradient descent (SGD) in an in-RDBMS system. While significant progress has been made separately on in-RDBMS SGD and private SGD, none of the major in-RDBMS machine learning frameworks have incorporated differentially private SGD. There are two inter-related issues for this disconnect between research and practice: (1) low model accuracy due to added noise to guarantee privacy, and (2) high development and runtime overhead of the private algorithms. This paper takes a first step to remedy this disconnect and proposes a private SGD algorithm to address both issues in an integrated manner. In contrast to the white-box approach adopted by previous algorithms, we revisit and use the classical technique of output perturbation. While using output perturbation trivially addresses (2), it gives rise to challenges in addressing (1). We address this challenge by providing a novel analysis of the L2-sensitivity of SGD, which allows, under the same privacy guarantees, better convergence of SGD when only a constant number of passes can be made over the data. We then integrate this algorithm, along with the state-of-the-art differentially private SGD, into Bismarck, an inRDBMS analytics system. Extensive experiments demonstrate that our algorithm can be easily integrated, incurs virtually no overhead, scales well, and most importantly, for a number of datasets yields substantially better (up to 4X) test accuracy than the state-of-the-art algorithms.
The power of gifts: organizing social relationships in open source communities

In writings on the open source software development model, it is often argued that it is successful as a result of the gift economy that embraces activities in online communities. However, the theoretical foundations for this argument are seldom discussed and empirically tested. Starting with the ‘classic’ theories of gift giving, we discuss how they need to be developed in order to explain giftgiving practices in digital domains. In this paper, we argue that the gift economy is important, not only because it creates openness, but also because it organizes relationships between people in a certain way. Open source software development relies on gift giving as a way of getting new ideas and prototypes out into circulation. This also implies that the giver gets power from giving away. This power is used as a way of guaranteeing the quality of the code. We relate this practice to how gifts, in the form of new scientific knowledge, are given to the research community, and how this is done through peer review processes.
A 24-GHz high-gain Yagi-Uda antenna array

A compact 24-GHz Yagi-Uda antenna has been developed using standard design tables and simple scaling to take into account the added capacitance due to the supporting dielectric substrate. The antenna results in a directivity of 9.3 dB, a front-to-back ratio of 11 dB, and a bandwidth of 2.5-3%. The Yagi-Uda antenna has been implemented in an 11-beam system using a planar array and a 2-inch Teflon spherical lens. The measured patterns show a 22 dB gain beam, a cross-polarization level of -24 dB, and a crossover level of -6 dB. The design method presented in this paper is quite straightforward, and can be used to develop low-, medium-, and even high-gain endfire Yagi-Uda antennas.
Impacts of IoT and big data to automotive industry

With the recent advancement in technologies such as embedded system, wireless distributed sensor, light weight material, smart cognitive radio networks, cloud computing, higher efficiency and ultra-low emission internal combustion engines, intelligent converter, high performance battery and fuel cell technology, the production of smarter, safer, energy efficient and zero emission vehicles is possible in near future. Apart from vehicle technologies, other factors such as road users', well maintained road infrastructure, well maintained vehicles, drivers' attitudes and law and enforcement are also important to be considered and they should work together in order to make our world natural resources can be preserved and maintain cleaner environment and produce sustainable mobility. This paper will discuss the impacts of IoT and Big Data and other emerging technologies mentioned above to the automotive industry. It will include discussion on education, economy, advanced technology, environment, safety and energy.
Development and Validation of the Smartphone Addiction Inventory (SPAI)

The aim of this study was to develop a self-administered scale based on the special features of smartphone. The reliability and validity of the Smartphone Addiction Inventory (SPAI) was demonstrated.



A total of 283 participants were recruited from Dec. 2012 to Jul. 2013 to complete a set of questionnaires, including a 26-item SPAI modified from the Chinese Internet Addiction Scale and phantom vibration and ringing syndrome questionnaire. There were 260 males and 23 females, with ages 22.9 ± 2.0 years. Exploratory factor analysis, internal-consistency test, test-retest, and correlation analysis were conducted to verify the reliability and validity of the SPAI. Correlations between each subscale and phantom vibration and ringing were also explored.



Exploratory factor analysis yielded four factors: compulsive behavior, functional impairment, withdrawal and tolerance. Test-retest reliabilities (intraclass correlations  = 0.74-0.91) and internal consistency (Cronbach's α = 0.94) were all satisfactory. The four subscales had moderate to high correlations (0.56-0.78), but had no or very low correlation to phantom vibration/ringing syndrome.



This study provides evidence that the SPAI is a valid and reliable, self-administered screening tool to investigate smartphone addiction. Phantom vibration and ringing might be independent entities of smartphone addiction.
Anterior prefrontal function and the limits of human decision-making.

The frontopolar cortex (FPC), the most anterior part of the frontal lobes, forms the apex of the executive system underlying decision-making. Here, we review empirical evidence showing that the FPC function enables contingent interposition of two concurrent behavioral plans or mental tasks according to respective reward expectations, overcoming the serial constraint that bears upon the control of task execution in the prefrontal cortex. This function is mechanistically explained by interactions between FPC and neighboring prefrontal regions. However, its capacity appears highly limited, which suggests that the FPC is efficient for protecting the execution of long-term mental plans from immediate environmental demands and for generating new, possibly more rewarding, behavioral or cognitive sequences, rather than for complex decision-making and reasoning.
A Practical Method for Modeling PCB Transmission Lines with Conductor Surface Roughness and Wideband Dielectric Properties

This paper discusses the modeling techniques to account for transmission line high frequency effects, and proposes a method to integrate these techniques into a practical and design-worthy model generation flow. The frequency tabulated transmission line models are capable of predicting wideband dielectric characteristics and high frequency conductor losses due to skin effect and surface roughness. They can be used to accurately model high volume, low cost printed circuit board traces which often have roughened trace surfaces and pronounced frequency variation in dielectric properties. The model accuracy is verified up to 20 GHz in frequency domain, thus suitable for multi-gigabit signaling analysis
Achieving efficient and privacy-preserving truth discovery in crowd sensing systems

Benefiting from the development of network and mobile communication technologies, crowd sensing systems have emerged as new technology to sense and collect data via mobile devices. However, aggregated results of the collected data may not be accurate since information provided by devices may not be reliable. To tackle this problem, approaches based on truth discovery have been proposed to improve accuracy of data aggregation operations in crowd sensing systems. Nevertheless, it should be noted that most existing truth discovery proposals failed to consider user’s data privacy. In this paper, we propose an Efficient and Privacy-preserving Truth Discovery (EPTD) scheme in crowd sensing systems. Specifically, we utilize the additive homomorphic privacy-preserving data aggregation and super-increasing sequence techniques to achieve both high performance and strong privacy protection. Security analysis indicates that the EPTD can achieve confidentiality of observed values and privacy protection of users’ weights. Furthermore, extensive experiments demonstrate that the EPTD is better than existing proposals in terms of communication and computation overhead.
TOLDI: An effective and robust approach for 3D local shape description

Feature description for the 3D local shape in the presence of noise, varying mesh resolutions, clutter and occlusion is a quite challenging task in 3D computer vision. This paper tackles the problem by proposing a new local reference frame (LRF) together with a novel triple orthogonal local depth images (TOLDI) representation, forming the TOLDI method for local shape description. Compared with previous methods, TOLDI manages to perform efficient, distinctive and robust description for the 3D local surface simultaneously under various feature matching contexts. The proposed LRF differs from many prior ones in its calculation of the z-axis and x-axis, the z-axis is calculated using the normal of the keypoint and the x-axis is computed by aggregating the weighted projection vectors of the radius neighbors. TOLDI feature descriptors are then obtained by concatenating three local depth images (LDI) captured from three orthogonal view planes in the LRF into feature vectors. The performance of our TOLDI approach is rigorously evaluated on several public datasets, which contain three major surface matching scenarios, namely shape retrieval, object recognition and 3D registration. Experimental results and comparisons with the state-of-the-arts validate the effectiveness, robustness, high efficiency, and overall superiority of our method. Our method is also applied to aligning 3D object ∗Corresponding author Email addresses: jqyang@hust.edu.cn (Jiaqi Yang), hangfanzq@163.com (Qian Zhang), Yang Xiao@hust.edu.cn (Yang Xiao), zgcao@hust.edu.cn (Zhiguo Cao)  2016 and indoor scene point clouds obtained by different devices (i.e., LiDAR and Kinect), the accurate outcomes further confirm the effectiveness of our method.
Neural Text Generation: A Practical Guide

Deep learning methods have recently achieved great empirical success on machine translation, dialogue response generation, summarization, and other text generation tasks. At a high level, the technique has been to train end-to-end neural network models consisting of an encoder model to produce a hidden representation of the source text, followed by a decoder model to generate the target. While such models have significantly fewer pieces than earlier systems, significant tuning is still required to achieve good performance. For text generation models in particular, the decoder can behave in undesired ways, such as by generating truncated or repetitive outputs, outputting bland and generic responses, or in some cases producing ungrammatical gibberish.
A Case for Protecting Computer Games With SGX

The integrity and confidentiality of computer games has long been a concern of game developers, both in preventing players from cheating and from obtaining unlicensed copies of the software. Recently, Intel released SGX, which can provide new security guarantees for software developers to achieve an unprecedented level of software integrity and confidentiality. To explore how SGX can protect a computer game in practice, in this paper we make a first step of exploring new ways to protect the integrity and confidentiality of game code and data, and in doing so we have developed a framework and design principles for integrating games with SGX. We have applied our framework to demonstrate how it can be used to protect a real world computer game.
Transition flight modeling and control of a novel tilt tri-rotor UAV

A novel tilt tri-rotor unmanned aerial vehicle (UAV) which combines the vertical flight capability of a helicopter and forward flight performance of an airplane is proposed. The two front rotors are able to tilt from the vertical to the horizontal position, however, the third rotor is fixed in the aft fuselage with a small angle. The nonlinear dynamic model of tilt tri-rotor UAV is given by the equations of Newton-Euler. By banlancing flight conditions in transition mode where there are redundant controls, the transition corridor is obtained. To resolve the attitude control problem of tilt tri-rotor UAV, we proposed an improved control allocation based on the rotational speeds of three propellers and the tilt angles of two front rotors. Besides, the structure of PID controller including inter loop and outer loop is developted. Simulation results during full envelope flight are presented, and the success of the modeling carried out as well as the control strategy designed are demonstrated.
Collision-free object movement using vector fields

The topics of computer animation and object collision detection and resolution have become increasingly desirable in a wide range of applications including autonomous vehicle navigation, robotics, and the movie industry. However, the techniques currently available are typically difficult to use or are restricted to a particular domain. This paper presents a technique for providing automatic animation and collision avoidance of arbitrary objects in a computer graphics system. The underlying construct used in this process is a surrounding volume octree vector field. The system automatically generates these structures around objects in the scene. By judicious creation and use of these vector fields, objects in the scene move and interact, but do not collide. The manner in which these vector fields are created is given. Two applications cloud movement over terrain and autonomous aircraft navigation are presented which show typical usage of this technique.
ltm : An R Package for Latent Variable Modeling and Item Response Theory Analyses

The R package ltm has been developed for the analysis of multivariate dichotomous and polytomous data using latent variable models, under the Item Response Theory approach. For dichotomous data the Rasch, the Two-Parameter Logistic, and Birnbaum’s Three-Parameter models have been implemented, whereas for polytomous data Semejima’s Graded Response model is available. Parameter estimates are obtained under marginal maximum likelihood using the Gauss-Hermite quadrature rule. The capabilities and features of the package are illustrated using two real data examples.
Classifier cascades and trees for minimizing feature evaluation cost

Machine learning algorithms have successfully entered industry through many real-world applications (e.g. , search engines and product recommendations). In these applications, the test-time CPU cost must be budgeted and accounted for. In this paper, we examine two main components of the test-time CPU cost, classifier evaluation cost and feature extraction cost, and show how to balance these costs with the classifier accuracy. Since the computation required for feature extraction dominates the test-time cost of a classifier in these settings, we develop two algorithms to efficiently balance the performance with the test-time cost. Our first contribution describes how to construct and optimize a tree of classifiers, through which test inputs traverse along individual paths. Each path extracts different features and is optimized for a specific sub-partition of the input space. Our second contribution is a natural reduction of the tree of classifiers into a cascade. The cascade is particularly useful for class-imbalanced data sets as the majority of instances can be early-exited out of the cascade when the algorithm is sufficiently confident in its prediction. Because both approaches only compute features for inputs that benefit from them the most, we find our trained classifiers lead to high accuracies at a small fraction of the computational cost.
Introduction to planning in multiagent systems

In most multiagent systems planning on forehand can help to seriously improve the efficiency of executing actions. The main difference between centrally creating a plan and constructing a plan for a system of agents lies in the fact that in the latter coordination plays the main part. This introduces a number of additional difficulties. This special issue discusses some of these difficulties in detail. To place these in a context, this introduction gives a brief overview of multiagent planning problems, and most multiagent planning techniques.
Internet of Things: A review to support IoT architecture's design

Internet of Things (IoT) is a state-of-the-art field that is affecting every aspect of human life. This paper presents a state-of-the-art overview of IoT challenges, available standards, and popular architectures. The aim is to guide future researchers in the field of IoT by highlighting the main research gaps. Moreover, it provides research recommendations to help in designing future IoT architectures that fit IoT environment characteristics.
Elderly people at home: technological help in everyday activities

The aim of this paper is to understand to what extent elderly people are likely to accept a technological aid in performing everyday activities. In this perspective, the present research focused on elderly people's strategies in performing everyday activities at home, in order to understand in what domestic domains technology can be considered an acceptable help. We administered a questionnaire focusing on preferred strategies in carrying out common domestic tasks, and on attitudes towards new technologies and home modification to a sample of 123 elderly people living in Rome. Results show that the adoption of a strategy, including the introduction of technological devices, is highly problem-specific, while personal factors are relevant only in particular situations. With increasing age, people are more inclined to give up, and higher educational levels correspond to more frequent technological solutions.
A Tutorial on Runtime Verification

This tutorial presents an overview of the field referred as to runtime verification. Runtime Verification is the study of algorithms, data structures, and tools focused on analyzing executions of systems. The performed analysis aims at improving the confidence in systems behavior, either by improving program understanding, or by checking conformance to specifications or algorithms. This chapter focuses specifically on checking execution traces against requirements formalized in terms of monitors. It is first shown on examples how such monitors can be written using aspect-oriented programming, exemplified by ASPECTJ. Subsequently four monitoring systems are illustrated on the same examples. The systems cover such formalisms as regular expressions, temporal logics, state machines, and rule-based programming, as well as the distinction between external and internal DSLs.
Production of first and second generation biofuels : A comprehensive review

Sustainable economic and industrial growth requires safe, sustainable resources of energy. For the future re-arrangement of a sustainable economy to biological raw materials, completely new approaches in research and development, production, and economy are necessary. The ‘first-generation’ biofuels appear unsustainable because of the potential stress that their production places on food commodities. For organic chemicals and materials these needs to follow a biorefinery model under environmentally sustainable conditions. Where these operate at present, their product range is largely limited to simple materials (i.e. cellulose, ethanol, and biofuels). Second generation biorefineries need to build on the need for sustainable chemical products through modern and proven green chemical technologies such as bioprocessing including pyrolysis, Fisher Tropsch, and other catalytic processes in order to make more complex molecules and materials on which a future sustainable society will be based. This review focus on cost effective technologies and the processes to convert biomass into useful liquid biofuels and bioproducts, with particular focus on some biorefinery concepts based on different feedstocks aiming at the integral utilization of these feedstocks for the production of value added chemicals.
Battery balancing methods: A comprehensive review

The trend toward more electric vehicles has demanded the need for high voltage, high efficiency and long life battery systems. A complete battery system consists of the following parts: protection, management and balancing. Of the three parts, balancing is the most important concerning the life of the battery system because without the balancing system, the individual cell voltages will drift apart over time. The capacity of the total pack will also decrease more quickly during operation which will result in the fail of the total battery system. This condition is especially severe when the battery has a long string of cells (high voltage battery systems) and frequent regenerative braking (charging) is done via the battery pack. This paper presents the theory behind the proposed balancing methods for battery systems within the past twenty years. Comparison between the methods is carried out and different balancing methods are grouped by their nature of balancing.
Application of ordinal logistic regression analysis in determining risk factors of child malnutrition in Bangladesh

The study attempts to develop an ordinal logistic regression (OLR) model to identify the determinants of child malnutrition instead of developing traditional binary logistic regression (BLR) model using the data of Bangladesh Demographic and Health Survey 2004.



Based on weight-for-age anthropometric index (Z-score) child nutrition status is categorized into three groups-severely undernourished (< -3.0), moderately undernourished (-3.0 to -2.01) and nourished (≥-2.0). Since nutrition status is ordinal, an OLR model-proportional odds model (POM) can be developed instead of two separate BLR models to find predictors of both malnutrition and severe malnutrition if the proportional odds assumption satisfies. The assumption is satisfied with low p-value (0.144) due to violation of the assumption for one co-variate. So partial proportional odds model (PPOM) and two BLR models have also been developed to check the applicability of the OLR model. Graphical test has also been adopted for checking the proportional odds assumption.



All the models determine that age of child, birth interval, mothers' education, maternal nutrition, household wealth status, child feeding index, and incidence of fever, ARI & diarrhoea were the significant predictors of child malnutrition; however, results of PPOM were more precise than those of other models.



These findings clearly justify that OLR models (POM and PPOM) are appropriate to find predictors of malnutrition instead of BLR models.