nils-herrmann/clean_scirepeval huggingface dataset:
ParamILS: An Automatic Algorithm Configuration Framework

The identification of performance-optimizing parameter settings is an important part of the development and application of parameterized algorithms. We propose an automatic algorithm configuration framework in which the settings of discrete parameters are optimized to yield maximal performance of a target algorithm for a given class of problem instances. We begin with a thorough experimental analysis of this algorithm configuration problem, using a simple random search procedure to explore the parameter configuration spaces of high-performance algorithms for SAT and mixed integer programming. Next, we present a family of local-search-based algorithm configuration procedures, along with adaptive capping techniques for accelerating them. Finally, we describe the results of a comprehensive experimental evaluation of our methods, based on the configuration of prominent complete and incomplete algorithms for SAT, and the commercial software CPLEX for mixed integer programming. In all cases, these algorithms’ default parameter settings had previously been manually optimized with considerable effort; nevertheless, using our automated algorithm configuration procedures, we achieved substantial performance improvements.
Elevated Blood Lead Levels of Children in Guiyu, an Electronic Waste Recycling Town in China

Electronic waste (e-waste) recycling has remained primitive in Guiyu, China, and thus may contribute to the elevation of blood lead levels (BLLs) in children living in the local environment.



We compared the BLLs in children living in the e-waste recycling town of Guiyu with those living in the neighboring town of Chendian.



We observed the processing of e-waste recycling in Guiyu and studied BLLs in a cluster sample of 226 children < 6 years of age who lived in Guiyu and Chendian. BLLs were determined with atomic absorption spectrophotometry. Hemoglobin (Hgb) and physical indexes (height and weight, head and chest circumferences) were also measured.



BLLs in 165 children of Guiyu ranged from 4.40 to 32.67 microg/dL with a mean of 15.3 microg/dL, whereas BLLs in 61 children of Chendian were from 4.09 to 23.10 microg/dL with a mean of 9.94 microg/dL. Statistical analyses showed that children living in Guiyu had significantly higher BLLs compared with those living in Chendian (p < 0.01). Of children in Guiyu, 81.8% (135 of 165) had BLLs > 10 microg/dL, compared with 37.7% of children (23 of 61) in Chendian (p < 0.01). In addition, we observed a significant increasing trend in BLLs with increasing age in Guiyu (p < 0.01). It appeared that there was correlation between the BLLs in children and numbers of e-waste workshops. However, no significant difference in Hgb level or physical indexes was found between the two towns.



The primitive e-waste recycling activities may contribute to the elevated BLLs in children living in Guiyu.
LLVM-based code clone detection framework

Existed methods of code clones detection have some restrictions. Textual and lexical approaches cannot detect strongly modified fragments of code. Syntactic and metrics based approaches detect strong modifications with low accuracy. On the contrary, semantic approach accurately detects the cloned fragments of code with small changes as well as the strongly modified ones. Methods based on this approach are not scalable for analysis of large projects. This paper describes LLVM-based code clone detection framework, which uses program semantic analysis. It has high accuracy and is scalable for analysis million lines of source code. The tool embeds a testing system, which allows generating code clones for the project automatically. It is used for determining the developed algorithms accuracy. The instrument is applicable for all languages that can be compiled to LLVM bitcode. Proposed method was compared with two widely used tools MOSS and CloneDR. Results show that it has higher accuracy. The tool is scalable for analysis of Linux-2.6 kernel, which has about fourteen millions lines of source code.
Are the Drivers and Role of Online Trust the Same for All Web Sites and Consumers ? A Large-Scale Exploratory Empirical Study

The authors develop a conceptual model that links Web site and consumer characteristics, online trust, and behavioral intent. They estimate this model on data from 6831 consumers across 25 sites from eight Web site categories, using structural equation analysis with a priori and post hoc segmentation. The results show that the influences of the determinants of online trust are different across site categories and consumers. Privacy and order fulfillment are the most influential determinants of trust for sites in which both information risk and involvement are high, such as travel sites. Navigation is strongest for information-intensive sites, such as sports, portal, and community sites. Brand strength is critical for high-involvement categories, such as automobile and financial services sites. Online trust partially mediates the relationships between Web site and consumer characteristics and behavioral intent, and this mediation is strongest (weakest) for sites oriented toward infrequently (frequently) purchased, highinvolvement items, such as computers (financial services).
Integrated Communication and Control Systems: Part I—Analysis

Computer networking is a reliable and efficient means for communications between disparate and distributed components in complex dynamical processes like advanced aircraft, spacecraft, and autonomous manufacturing plants. The role of Integrated Communication and Control Systems (ICCS) is to coordinate and perform interrelated functions, ranging from real-time multi-loop control to information display and routine maintenance support. In ICCS, a feedback control loop is closed via the common communication channel which multiplexes digital data from the sensor to the controller and from the controller to the actuator along with the data traffic from other loops and management functions. Due to the asynchronous timedivision multiplexing of the network protocol, time-varying and possibly stochastic delays are introduced in the control system, which degrade the system dynamic performance and are a source of potential instability. The paper is divided into two parts. In the first part, the delayed control system is represented by a finitedimensional, time-varying, discrete-time model which is less complex than the existing continuous-time models for time-varying delays; this approach allows for simpler schemes for analysis and simulation of ICCS. The second part of the paper addresses ICCS design considerations and presents simulation results for certain operational scenarios of ICCS.
The Myth of the "Biphasic" Hyaluronic Acid Filler.

The terms "biphasic" and "monophasic" have been used frequently as a means of differentiating hyaluronic acid (HA) fillers. This type of categorization is based on misinterpretations of the term "phase" and provides no help to the practitioner when selecting the most appropriate product for each indication, patient, and injection technique.



The purpose of this study was to analyze the properties of 2 HA filler families; Juvederm (JUV) (Allergan), often stated to be monophasic and Restylane (RES) (Galderma), often stated to be biphasic, and discuss what properties may have led to the use of the terms monophasic and biphasic.



Three different methods were used for JUV and RES: determination of extractable HA; determination of water uptake; and microscopy.



The analyzed products were shown to contain both observable gel particles and extractable HA and have the ability to absorb added water.



The categorization of HA fillers as biphasic or monophasic was shown to be scientifically incorrect and should therefore be avoided. Further analytical measurement of the properties leading to this misinterpretation can provide information to discriminate and categorize HA fillers on a sounder scientific basis.
Using Augmented Reality to Visualise Architecture Designs in an Outdoor Environment

This paper presents the use of a wearable computer system to visualise outdoor architectural features using augmented reality. The paper examines the question How does one visualise a design for a building, modification to a building, or extension to an existing building relative to its physical surroundings? The solution presented to this problem is to use a mobile augmented reality platform to visualise the design in spatial context of its final physical surroundings. The paper describes the mobile augmented reality platform TINMITH2 used in the investigation. The operation of the system is described through a detailed example of the system in operation. The system was used to visualise a simple extension to a building on one of the University of South Australia campuses.
Recurrent Gaussian Processes

We define Recurrent Gaussian Processes (RGP) models, a general family of Bayesian nonparametric models with recurrent GP priors which are able to learn dynamical patterns from sequential data. Similar to Recurrent Neural Networks (RNNs), RGPs can have different formulations for their internal states, distinct inference methods and be extended with deep structures. In such context, we propose a novel deep RGP model whose autoregressive states are latent, thereby performing representation and dynamical learning simultaneously. To fully exploit the Bayesian nature of the RGP model we develop the Recurrent Variational Bayes (REVARB) framework, which enables efficient inference and strong regularization through coherent propagation of uncertainty across the RGP layers and states. We also introduce a RGP extension where variational parameters are greatly reduced by being reparametrized through RNN-based sequential recognition models. We apply our model to the tasks of nonlinear system identification and human motion modeling. The promising obtained results indicate that our RGP model maintains its highly flexibility while being able to avoid overfitting and being applicable even when larger datasets are not available.
Phishing Detection in IMs using Domain Ontology and CBA - An innovative Rule Generation Approach

User ignorance towards the use of communication services like Instant Messengers, emails, websites, social networks etc. is becoming the biggest advantage for phishers. It is required to create technical awareness in users by educating them to create a phishing detection application which would generate phishing alerts for the user so that phishing messages are not ignored. The lack of basic security features to detect and prevent phishing has had a profound effect on the IM clients, as they lose their faith in e-banking and e-commerce transactions, which will have a disastrous impact on the corporate and banking sectors and businesses which rely heavily on the internet. Very little research contributions were available in for phishing detection in Instant messengers. A context based, dynamic and intelligent phishing detection methodology in IMs is proposed, to analyze and detect phishing in Instant Messages with relevance to domain ontology (OBIE) and utilizes the Classification based on Association (CBA) for generating phishing rules and alerting the victims. A PDS Monitoring system algorithm is used to identify the phishing activity during exchange of messages in IMs, with high ratio of precision and recall. The results have shown improvement by the increased percentage of precision and recall when compared to the existing methods.
Design of 4-Bit Universal Shift Register Using Reversible Gates

Power dissipation is one of the important factor in digital circuit design. Landauer's principle states that logic computations which are not reversible necessarily generate KT*ln2 Joules of heat energy for every bit of information that is lost, where k is Boltzmann's constant and T the absolute temperature at which computation is performed. Reversibility is the property of digital circuits in which there is one-to-one mapping between the inputs and the output vectors that is for each input vector there is a unique output vector and vice-versa. Thus one of the primary motivations for adopting reversible logic lies in the fact that it can provide a logic design methodologies for designing low power dissipating circuits. Fredkin gate, Feynman Gate is popularly used reversible logic gate which are used to implement various sequential circuits which are basic for design of digital circuits.
Importance of audio feature reduction in automatic music genre classification

Multimedia database retrieval is rapidly growing and its popularity in online retrieval systems is consequently increasing. Large datasets are major challenges for searching, retrieving, and organizing the music content. Therefore, a robust automatic music-genre classification method is needed for organizing this music data into different classes according to specific viable information. Two fundamental components are to be considered for genre classification: audio feature extraction and classifier design. In this paper, we propose diverse audio features to precisely characterize the music content. The feature sets belong to four groups: dynamic, rhythmic, spectral, and harmonic. From the features, five statistical parameters are considered as representatives, including the fourth-order central moments of each feature as well as covariance components. Ultimately, insignificant representative parameters are controlled by minimum redundancy and maximum relevance. This algorithm calculates the score level of all feature attributes and orders them. Only high-score audio features are considered for genre classification. Moreover, we can recognize those audio features and distinguish which of the different statistical parameters derived from them are important for genre classification. Among them, mel frequency cepstral coefficient statistical parameters, such as covariance components and variance, are more frequently selected than the feature attributes of other groups. This approach does not transform the original features as do principal component analysis and linear discriminant analysis. In addition, other feature reduction methodologies, such as locality-preserving projection and non-negative matrix factorization are considered. The performance of the proposed system is measured based on the reduced features from the feature pool using different feature reduction techniques. The results indicate that the overall classification is competitive with existing state-of-the-art frame-based methodologies.
Linear vs Nonlinear MPC for Trajectory Tracking Applied to Rotary Wing Micro Aerial Vehicles

Precise trajectory tracking is a crucial property for Micro Air Vehicles (MAVs) to operate in cluttered environment or under disturbances. In this paper we present a detailed comparison between two state-of-the-art model-based control techniques for MAV trajectory tracking. A classical Linear Model Predictive Controller (LMPC) is presented and compared against a more advanced Nonlinear Model Predictive Controller (NMPC) that considers the full system model. In a careful analysis we show the advantages and disadvantages of the two implementations in terms of speed and tracking performance. This is achieved by evaluating hovering performance, step response, and aggressive trajectory tracking under nominal conditions and under external wind disturbances.
A Guided Genetic Algorithm for the Planning in Lunar Lander Games

We propose a guided genetic algorithm (GA) for planning in games. In guided GA, an extra reinforcement component is inserted into the evolution procedure of GA. During each evolution procedure, the reinforcement component will simulate the execution of a series of actions of an individual before the real trial and adjust the series of actions according to the reinforcement thus try to improve the performance. We then apply it to a Lunar Lander game in which the falling lunar module needs to learn to land on a platform safely. We compare the performance of guided GA and general GA as well as Q-Learning on the game. The result shows that the guided GA could guarantee to reach the goal and achieve much higher performance than general GA and Q-Learning.
Rethinking Visualization: A High-Level Taxonomy

We present the novel high-level visualization taxonomy. Our taxonomy classifies visualization algorithms rather than data. Algorithms are categorized based on the assumptions they make about the data being visualized; we call this set of assumptions the design model. Because our taxonomy is based on design models, it is more flexible than existing taxonomies and considers the user's conceptual model, emphasizing the human aspect of visualization. Design models are classified according to whether they are discrete or continuous and by how much the algorithm designer chooses display attributes such as spatialization, timing, colour, and transparency. This novel approach provides an alternative view of the visualization field that helps explain how traditional divisions (e.g., information and scientific visualization) relates and overlap, and that may inspire research ideas in hybrid visualization areas
Computable Elastic Distances Between Shapes

We define distances between geometric curves by the square root of the minimal energy required to transform one curve into the other. The energy is formally defined from a left invariant Riemannian distance on an infinite dimensional group acting on the curves, which can be explicitly computed. The obtained distance boils down to a variational problem for which an optimal matching between the curves has to be computed. An analysis of the distance when the curves are polygonal leads to a numerical procedure for the solution of the variational problem, which can efficiently be implemented, as illustrated by experiments.
Hadoop Performance Tuning - A Pragmatic & Iterative Approach

Hadoop represents a Java-based distributed computing framework that is designed to support applications that are implemented via the MapReduce programming model. In general, workload dependent Hadoop performance optimization efforts have to focus on 3 major categories: the systems HW, the systems SW, and the configuration and tuning/optimization of the Hadoop infrastructure components. From a systems HW perspective, it is paramount to balance the appropriate HW components in regards to performance, scalability, and cost. It has to be pointed out that Hadoop is classified as a highly-scalable, but not necessarily as a high-performance cluster solution. From a SW perspective, the choice of the OS, the JVM, the specific Hadoop version, as well as other SW components necessary to run the Hadoop setup do have a profound impact on performance and stability of the environment. The design, setup, configuration, and tuning phase of any Hadoop project is paramount to fully benefit from the distributed Hadoop HW and SW solution stack.
BreadCrumbs: forecasting mobile connectivity

Mobile devices cannot rely on a single managed network, but must exploit a wide variety of connectivity options as they travel. We argue that such systems must consider the derivative of connectivity--the changes inherent in movement between separately managed networks, with widely varying capabilities. With predictive knowledge of such changes, devices can more intelligently schedule network usage.
 To exploit the derivative of connectivity, we observe that people are creatures of habit; they take similar paths every day. Our system, BreadCrumbs, tracks the movement of the device's owner, and customizes a predictive mobility model for that specific user. Combined with past observations of wireless network capabilities, BreadCrumbs generates connectivity forecasts. We have built a BreadCrumbs prototype, and demonstrated its potential with several weeks of real-world usage. Our results show that these forecasts are sufficiently accurate, even with as little as one week of training, to provide improved performance with reduced power consumption for several applications.
Social Network Monetization via Sponsored Viral Marketing

Viral marketing is a powerful tool for online advertising and sales because it exploits the influence people have on one another. While this marketing technique has been beneficial for advertisers, it has not been shown how the social network providers such as Facebook and Twitter can benefit from it. In this paper, we initiate the study of sponsored viral marketing where a social network provider that has complete knowledge of its network is hired by several advertisers to provide viral marketing. Each advertiser has its own advertising budget and a fixed amount they are willing to pay for each user that adopts their product or shares their ads. The goal of the social network provider is to gain the most revenue from the advertisers. Since the products or ads from different advertisers may compete with each other in getting users' attention, and advertisers pay differently per share and have different budgets, it is very important that the social network providers start the "seeds" of the viral marketing of each product at the right places in order to gain the most benefit.
 We study both when advertisers have limited and unlimited budgets. In the unlimited budget setting, we give a tight approximation algorithm for the above task: we present a polynomial-time O(log n)-approximation algorithm for maximizing the expected revenue, where n is the number of nodes (i.e., users) in the social network, and show that no polynomial-time O(log1-ε n)-approximation algorithm exists, unless NP ⊆ DTIME}(npoly log n). In the limited budget setting, we show that it is hopeless to solve the problem (even approximately): unless P = NP, there is no polynomial-time O(n1-ε)-approximation algorithm. We perform experiments on several data sets to compare our provable algorithms to several heuristic baselines.
Vehicle Detection Techniques for Collision Avoidance Systems: A Review

Over the past decade, vision-based vehicle detection techniques for road safety improvement have gained an increasing amount of attention. Unfortunately, the techniques suffer from robustness due to huge variability in vehicle shape (particularly for motorcycles), cluttered environment, various illumination conditions, and driving behavior. In this paper, we provide a comprehensive survey in a systematic approach about the state-of-the-art on-road vision-based vehicle detection and tracking systems for collision avoidance systems (CASs). This paper is structured based on a vehicle detection processes starting from sensor selection to vehicle detection and tracking. Techniques in each process/step are reviewed and analyzed individually. Two main contributions in this paper are the following: survey on motorcycle detection techniques and the sensor comparison in terms of cost and range parameters. Finally, the survey provides an optimal choice with a low cost and reliable CAS design in vehicle industries.
Control of permanent-magnet generators applied to variable-speed wind-energy systems connected to the grid

Wind energy is a prominent area of application of variable-speed generators operating on the constant grid frequency. This paper describes the operation and control of one of these variable-speed wind generators: the direct driven permanent magnet synchronous generator (PMSG). This generator is connected to the power network by means of a fully controlled frequency converter, which consists of a pulsewidth-modulation (PWM) rectifier, an intermediate dc circuit, and a PWM inverter. The generator is controlled to obtain maximum power from the incident wind with maximum efficiency under different load conditions. Vector control of the grid-side inverter allows power factor regulation of the windmill. This paper shows the dynamic performance of the complete system. Different experimental tests in a 3-kW prototype have been carried out to verify the benefits of the proposed system.