nils-herrmann/clean_scirepeval huggingface dataset:
Solving Systems of Random Quadratic Equations via Truncated Amplitude Flow

This paper presents a new algorithm, termed <italic>truncated amplitude flow</italic> (TAF), to recover an unknown vector <inline-formula> <tex-math notation="LaTeX">$ {x}$ </tex-math></inline-formula> from a system of quadratic equations of the form <inline-formula> <tex-math notation="LaTeX">$y_{i}=|\langle {a}_{i}, {x}\rangle |^{2}$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$ {a}_{i}$ </tex-math></inline-formula>’s are given random measurement vectors. This problem is known to be <italic>NP-hard</italic> in general. We prove that as soon as the number of equations is on the order of the number of unknowns, TAF recovers the solution exactly (up to a global unimodular constant) with high probability and complexity growing linearly with both the number of unknowns and the number of equations. Our TAF approach adapts the <italic>amplitude-based</italic> empirical loss function and proceeds in two stages. In the first stage, we introduce an <italic>orthogonality-promoting</italic> initialization that can be obtained with a few power iterations. Stage two refines the initial estimate by successive updates of scalable <italic>truncated generalized gradient iterations</italic>, which are able to handle the rather challenging nonconvex and nonsmooth amplitude-based objective function. In particular, when vectors <inline-formula> <tex-math notation="LaTeX">$ {x}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${a}_{i}$ </tex-math></inline-formula>’s are real valued, our gradient truncation rule provably eliminates erroneously estimated signs with high probability to markedly improve upon its untruncated version. Numerical tests using synthetic data and real images demonstrate that our initialization returns more accurate and robust estimates relative to spectral initializations. Furthermore, even under the same initialization, the proposed amplitude-based refinement outperforms existing Wirtinger flow variants, corroborating the superior performance of TAF over state-of-the-art algorithms.
Generalization in Reinforcement Learning: Safely Approximating the Value Function

To appear in G Tesauro D S Touretzky and T K Leen eds Advances in Neural Information Processing Systems MIT Press Cambridge MA A straightforward approach to the curse of dimensionality in re inforcement learning and dynamic programming is to replace the lookup table with a generalizing function approximator such as a neu ral net Although this has been successful in the domain of backgam mon there is no guarantee of convergence In this paper we show that the combination of dynamic programming and function approx imation is not robust and in even very benign cases may produce an entirely wrong policy We then introduce Grow Support a new algorithm which is safe from divergence yet can still reap the bene ts of successful generalization
On the analysis of the Zeus botnet crimeware toolkit

In this paper, we present our reverse engineering results for the Zeus crimeware toolkit which is one of the recent and powerful crimeware tools that emerged in the Internet underground community to control botnets. Zeus has reportedly infected over 3.6 million computers in the United States. Our analysis aims at uncovering the various obfuscation levels and shedding the light on the resulting code. Accordingly, we explain the bot building and installation/infection processes. In addition, we detail a method to extract the encryption key from the malware binary and use that to decrypt the network communications and the botnet configuration information. The reverse engineering insights, together with network traffic analysis, allow for a better understanding of the technologies and behaviors of such modern HTTP botnet crimeware toolkits and opens an opportunity to inject falsified information into the botnet communications which can be used to defame this crimeware toolkit.
Automatic Road Anomaly Detection Using Smart Mobile Device

Maintaining the quality of roadways is a major challenge for governments around the world. In particular, poor road surfaces pose a significant safety threat to motorists, especially when motorbikes make up a significant portion of roadway traffic. According to the statistics of the Ministry of Justice in Taiwan, there were 220 claims for state compensation caused by road quality problems between 2005 to 2007, and the government paid a total of 113 million NTD in compensation. This research explores utilizing a mobile phone with a tri-axial accelerometer to collect acceleration data while riding a motorcycle. The data is analyzed to detect road anomalies and to evaluate road quality. Motorcycle-based acceleration data is collected on twelve stretches of road, with a data log spanning approximately three hours, and a total road length of about 60 kilometers. Both supervised and unsupervised machine learning methods are used to recognize road conditions. SVM learning is used to detect road anomalies and to identify their corresponding positions from labeled acceleration data. This method of road anomaly detection achieves a precision of 78.5%. Furthermore, to construct a model of smooth roads, unsupervised learning is used to learn anomaly thresholds by clustering data collected from the accelerometer. The results are used to rank the quality of the road segments in the experiment. We compare the ranked list from the learned evaluator with the ranked list from human evaluators who rode along the same roadways during the test phase. Based on the Kendall tau rank correlation coefficient, the automatically ranked result exhibited excellent performance. Keywords-mobile device; machine learning; accelerometer; road surface anomaly; pothole;
Generic Pattern Trees for Exhaustive Exceptional Model Mining

Exceptional model mining has been proposed as a variant of subgroup discovery especially focusing on complex target concepts. Currently, efficient mining algorithms are limited to heuristic (non exhaustive) methods. In this paper, we propose a novel approach for fast exhaustive exceptional model mining: We introduce the concept of valuation bases as an intermediate condensed data representation, and present the general GP-growth algorithm based on FP-growth. Furthermore, we discuss the scope of the proposed approach by drawing an analogy to data stream mining and provide examples for several different model classes. Runtime experiments show improvements of more than an order of magnitude in comparison to a naive exhaustive depth-first search.
Deep Variational Information Bottleneck

We present a variational approximation to the information bottleneck of Tishby et al. (1999). This variational approach allows us to parameterize the information bottleneck model using a neural network and leverage the reparameterization trick for efficient training. We call this method “Deep Variational Information Bottleneck”, or Deep VIB. We show that models trained with the VIB objective outperform those that are trained with other forms of regularization, in terms of generalization performance and robustness to adversarial attack.
Study of Mathematically Precocious Youth After 35 Years: Uncovering Antecedents for the Development of Math-Science Expertise.

This review provides an account of the Study of Mathematically Precocious Youth (SMPY) after 35 years of longitudinal research. Findings from recent 20-year follow-ups from three cohorts, plus 5- or 10-year findings from all five SMPY cohorts (totaling more than 5,000 participants), are presented. SMPY has devoted particular attention to uncovering personal antecedents necessary for the development of exceptional math-science careers and to developing educational interventions to facilitate learning among intellectually precocious youth. Along with mathematical gifts, high levels of spatial ability, investigative interests, and theoretical values form a particularly promising aptitude complex indicative of potential for developing scientific expertise and of sustained commitment to scientific pursuits. Special educational opportunities, however, can markedly enhance the development of talent. Moreover, extraordinary scientific accomplishments require extraordinary commitment both in and outside of school. The theory of work adjustment (TWA) is useful in conceptualizing talent identification and development and bridging interconnections among educational, counseling, and industrial psychology. The lens of TWA can clarify how some sex differences emerge in educational settings and the world of work. For example, in the SMPY cohorts, although more mathematically precocious males than females entered math-science careers, this does not necessarily imply a loss of talent because the women secured similar proportions of advanced degrees and high-level careers in areas more correspondent with the multidimensionality of their ability-preference pattern (e.g., administration, law, medicine, and the social sciences). By their mid-30s, the men and women appeared to be happy with their life choices and viewed themselves as equally successful (and objective measures support these subjective impressions). Given the ever-increasing importance of quantitative and scientific reasoning skills in modern cultures, when mathematically gifted individuals choose to pursue careers outside engineering and the physical sciences, it should be seen as a contribution to society, not a loss of talent.
Fast running experiments involving a humanoid robot

The present paper describes an implementation of fast running motions involving a humanoid robot. Two important technologies are described: a motion generation and a balance control. The motion generation is a unified way to design both walking and running and can generate the trajectory with the vertical conditions of the Center Of Mass (COM) in short calculation time. The balance control enables a robot to maintain balance by changing the positions of the contact foot dynamically when the robot is disturbed. This control consists of 1) compliance control without force sensors, in which the joints are made compliant by feed-forward torques and adjustment of gains of position control, and 2) feedback control, which uses the measured orientation of the robot's torso used in the motion generation as an initial condition to decide the foot positions. Finally, a human-sized humanoid robot that can run forward at 7.0 [km/h] is presented.
Extinction-Based Shading and Illumination in GPU Volume Ray-Casting

Direct volume rendering has become a popular method for visualizing volumetric datasets. Even though computers are continually getting faster, it remains a challenge to incorporate sophisticated illumination models into direct volume rendering while maintaining interactive frame rates. In this paper, we present a novel approach for advanced illumination in direct volume rendering based on GPU ray-casting. Our approach features directional soft shadows taking scattering into account, ambient occlusion and color bleeding effects while achieving very competitive frame rates. In particular, multiple dynamic lights and interactive transfer function changes are fully supported. Commonly, direct volume rendering is based on a very simplified discrete version of the original volume rendering integral, including the development of the original exponential extinction into a-blending. In contrast to a-blending forming a product when sampling along a ray, the original exponential extinction coefficient is an integral and its discretization a Riemann sum. The fact that it is a sum can cleverly be exploited to implement volume lighting effects, i.e. soft directional shadows, ambient occlusion and color bleeding. We will show how this can be achieved and how it can be implemented on the GPU.
Smart city as urban innovation: focusing on management, policy, and context

This paper sees a smart city not as a status of how smart a city is but as a city's effort to make itself smart. The connotation of a smart city represents city innovation in management and policy as well as technology. Since the unique context of each city shapes the technological, organizational and policy aspects of that city, a smart city can be considered a contextualized interplay among technological innovation, managerial and organizational innovation, and policy innovation. However, only little research discusses innovation in management and policy while the literature of technology innovation is abundant. This paper aims to fill the research gap by building a comprehensive framework to view the smart city movement as innovation comprised of technology, management and policy. We also discuss inevitable risks from innovation, strategies to innovate while avoiding risks, and contexts underlying innovation and risks.
A Multiscale Image Representation Using Hierarchical (BV, L2 ) Decompositions

We propose a new multiscale image decomposition which offers a hierarchical, adaptive representation for the different features in general images. The starting point is a variational decomposition of an image, f = u0 + v0, where [u0, v0] is the minimizer of a J-functional, J(f, λ0;X,Y ) = infu+v=f { ‖u‖X + λ0‖v‖pY } . Such minimizers are standard tools for image manipulations (e.g., denoising, deblurring, compression); see, for example, [M. Mumford and J. Shah,  1985] and [L. Rudin, S. Osher, and E. Fatemi,  (1992), ]. Here, u0 should capture “essential features” of f which are to be separated from the spurious components absorbed by v0, and λ0 is a fixed threshold which dictates separation of scales. To proceed, we iterate the refinement step [uj+1, vj+1] = arginf J(vj , λ02 j), leading to the hierarchical decomposition, f = ∑k j=0 uj + vk. We focus our attention on the particular case of (X,Y ) = (BV,L2) decomposition. The resulting hierarchical decomposition, f ∼ ∑ j uj , is essentially nonlinear. The questions of convergence, energy decomposition, localization, and adaptivity are discussed. The decomposition is constructed by numerical solution of successive Euler–Lagrange equations. Numerical results illustrate applications of the new decomposition to synthetic and real images. Both greyscale and color images are considered.
A Nonparametric Mixture Model for Topic Modeling over Time

A single, stationary topic model such as latent Dirichlet allocation is inappropriate for modeling corpora that span long time periods, as the popularity of topics is likely to change over time. A number of models that incorporate time have been proposed, but in general they either exhibit limited forms of temporal variation, or require computationally expensive inference methods. In this paper we propose non-parametric Topics over Time (npTOT), a model for time-varying topics that allows an unbounded number of topics and flexible distribution over the temporal variations in those topics’ popularity. We develop a collapsed Gibbs sampler for the proposed model and compare against existing models on synthetic and real document sets.
Lisbon Emoji and Emoticon Database (LEED): Norms for emoji and emoticons in seven evaluative dimensions.

The use of emoticons and emoji is increasingly popular across a variety of new platforms of online communication. They have also become popular as stimulus materials in scientific research. However, the assumption that emoji/emoticon users' interpretations always correspond to the developers'/researchers' intended meanings might be misleading. This article presents subjective norms of emoji and emoticons provided by everyday users. The Lisbon Emoji and Emoticon Database (LEED) comprises 238 stimuli: 85 emoticons and 153 emoji (collected from iOS, Android, Facebook, and Emojipedia). The sample included 505 Portuguese participants recruited online. Each participant evaluated a random subset of 20 stimuli for seven dimensions: aesthetic appeal, familiarity, visual complexity, concreteness, valence, arousal, and meaningfulness. Participants were additionally asked to attribute a meaning to each stimulus. The norms obtained include quantitative descriptive results (means, standard deviations, and confidence intervals) and a meaning analysis for each stimulus. We also examined the correlations between the dimensions and tested for differences between emoticons and emoji, as well as between the two major operating systems-Android and iOS. The LEED constitutes a readily available normative database (available at www.osf.io/nua4x ) with potential applications to different research domains.
Barriers to the Successful Integration of ICT in Teaching and Learning Environments : A Review of the Literature

The use of ICT in the classroom is very important for providing opportunities for students to learn to operate in an information age. Studying the obstacles to the use of ICT in education may assist educators to overcome these barriers and become successful technology adopters in the future. This paper provides a meta-analysis of the relevant literature that aims to present the perceived barriers to technology integration in science education. The findings indicate that teachers had a strong desire for to integrate ICT into education; but that, they encountered many barriers. The major barriers were lack of confidence, lack of competence, and lack of access to resources. Since confidence, competence and accessibility have been found to be the critical components of technology integration in schools, ICT resources including software and hardware, effective professional development, sufficient time, and technical support need to be provided to teachers. No one component in itself is sufficient to provide good teaching. However, the presence of all components increases the possibility of excellent integration of ICT in learning and teaching opportunities. Generally, this paper provides information and recommendation to those responsible for the integration of new technologies into science education.
Investigation of the CasCor Family of Learning Algorithms

Six learning algorithms are investigated and compared empirically. All of them are based on variants of the candidate training idea of the Cascade Correlation method. The comparison was performed using 42 different datasets from the PROBEN1 benchmark collection. The results indicate: (1) for these problems it is slightly better not to cascade the hidden units; (2) error minimization candidate training is better than covariance maximization for regression problems but may be a little worse for classification problems; (3) for most learning tasks, considering validation set errors during the selection of the best candidate will not lead to improved networks, but for a few tasks it will.
Automatic solar photovoltaic panel detection in satellite imagery

The quantity of rooftop solar photovoltaic (PV) installations has grown rapidly in the US in recent years. There is a strong interest among decision makers in obtaining high quality information about rooftop PV, such as the locations, power capacity, and energy production of existing rooftop PV installations. Solar PV installations are typically connected directly to local power distribution grids, and therefore it is important for the reliable integration of solar energy to have information at high geospatial resolutions: by county, zip code, or even by neighborhood. Unfortunately, traditional means of obtaining this information, such as surveys and utility interconnection filings, are limited in availability and geospatial resolution. In this work a new approach is investigated where a computer vision algorithm is used to detect rooftop PV installations in high resolution color satellite imagery and aerial photography. It may then be possible to use the identified PV images to estimate power capacity and energy production for each array of panels, yielding a fast, scalable, and inexpensive method to obtain rooftop PV estimates for regions of any size. The aim of this work is to investigate the feasibility of the first step of the proposed approach: detecting rooftop PV in satellite imagery. Towards this goal, a collection of satellite rooftop images is used to develop and evaluate a detection algorithm. The results show excellent detection performance on the testing dataset and that, with further development, the proposed approach may be an effective solution for fast and scalable rooftop PV information collection.
Collaboration-Based Cloud Computing Security Management Framework

Although the cloud computing model is considered to be a very promising internet-based computing platform, it results in a loss of security control over the cloud-hosted assets. This is due to the outsourcing of enterprise IT assets hosted on third-party cloud computing platforms. Moreover, the lack of security constraints in the Service Level Agreements between the cloud providers and consumers results in a loss of trust as well. Obtaining a security certificate such as ISO 27000 or NIST-FISMA would help cloud providers improve consumers trust in their cloud platforms' security. However, such standards are still far from covering the full complexity of the cloud computing model. We introduce a new cloud security management framework based on aligning the FISMA standard to fit with the cloud computing model, enabling cloud providers and consumers to be security certified. Our framework is based on improving collaboration between cloud providers, service providers and service consumers in managing the security of the cloud platform and the hosted services. It is built on top of a number of security standards that assist in automating the security management process. We have developed a proof of concept of our framework using. NET and deployed it on a test bed cloud platform. We evaluated the framework by managing the security of a multi-tenant SaaS application exemplar.
CHECK: a document plagiarism detection system

Digital documents are vulnerable to being copied. Most existing copy detection prototypes employ an exhaustive sentence-based comparison method in comparing a potential plagiarized document against a repository of legal or original documents to identify plagiarism activities. This approach is not scalable due to the potentially large number of original documents and the large number of sentences in each document . Furthermore, the security level of existing mechanisms is quite weak; a plagiarized document could simply by-pass the detection mechanisms by performing a minor modification on each sentence. In this paper, we propose a copy detection mechanism that will el iminate unnecessary comparisons. This is based on the observation that comparisons between two documents addressing different subjects are not necessary. We describe the design and implementation of our exper imental proto type called CHECK. The results of some exploratory experiments will be illust rated and the security level of our mechanism will be discussed.
Detecting user engagement with a robot companion using task and social interaction-based features

Affect sensitivity is of the utmost importance for a robot companion to be able to display socially intelligent behaviour, a key requirement for sustaining long-term interactions with humans. This paper explores a naturalistic scenario in which children play chess with the iCat, a robot companion. A person-independent, Bayesian approach to detect the user's engagement with the iCat robot is presented. Our framework models both causes and effects of engagement: features related to the user's non-verbal behaviour, the task and the companion's affective reactions are identified to predict the children's level of engagement. An experiment was carried out to train and validate our model. Results show that our approach based on multimodal integration of task and social interaction-based features outperforms those based solely on non-verbal behaviour or contextual information (94.79 % vs. 93.75 % and 78.13 %).