nils-herrmann/clean_scirepeval huggingface dataset:
Fast learning of scale-free networks based on Cholesky factorization

School of Electrical Engineering, University of Belgrade, Belgrade, Serbia Mathematical Institute of the Serbian Academy of Sciences and Arts, Belgrade, Serbia Center for Data Analytics & Biomedical Informatics, Temple University, Philadelphia, USA Correspondence Vladisav Jelisavcic, School of Electrical Engineering, University of Belgrade, Belgrade, Serbia. E-mail: vladisav@mi.sanu.ac.rs Funding information NSF BIGDATA, Grant/Award Numbers: 1447670, 1659998, CNS-1625061; CURE Abstract Recovering network connectivity structure from highdimensional observations is of increasing importance in statistical learning applications. A prominent approach is to learn a Sparse Gaussian Markov Random Field by optimizing regularized maximum likelihood, where the sparsity is induced by imposing L1 norm on the entries of a precision matrix. In this article, we shed light on an alternative objective, where instead of precision, its Cholesky factor is penalized by the L1 norm. We show that such an objective criterion possesses attractive properties that allowed us to develop a very fast Scale-Free Networks Estimation Through Cholesky factorization (SNETCH) optimization algorithm based on coordinate descent, which is highly parallelizable and can exploit an active set approach. The approach is particularly suited for problems with structures that allow sparse Cholesky factor, an important example being scale-free networks. Evaluation on synthetically generated examples and high-impact applications from a biomedical domain of up to more than 900,000 variables provides evidence that for such tasks the SNETCH algorithm can learn the underlying structure more accurately, and an order of magnitude faster than state-of-the-art approaches based on the L1 penalized precision matrix.
When do Recommender Systems Work the Best?: The Moderating Effects of Product Attributes and Consumer Reviews on Recommender Performance

We investigate the moderating effect of product attributes and consumer reviews on the efficacy of a collaborative filtering recommender system on an e-commerce site. We run a randomized field experiment on a top North American retailer’s website with 184,375 users split into a recommendertreated group and a control group with 37,215 unique products in the dataset. By augmenting the dataset with Amazon Mechanical Turk tagged product attributes and consumer review data from the website, we study their moderating influence on recommenders in generating conversion. We first confirm that the use of recommenders increases the baseline conversion rate by 5.9%. We find that the recommenders act as substitutes for high average review ratings with the effect of using recommenders increasing the conversion rate as much as about 1.4 additional average star ratings. Additionally, we find that the positive impacts on conversion from recommenders are greater for hedonic products compared to utilitarian products while searchexperience quality did not have any impact. We also find that the higher the price, the lower the positive impact of recommenders, while having lengthier product descriptions and higher review volumes increased the recommender’s effectiveness. More findings are discussed in the Results. For managers, we 1) identify the products and product attributes for which the recommenders work well, 2) show how other product information sources on e-commerce sites interact with recommenders. Additionally, the insights from the results could inform novel recommender algorithm designs that are aware of strength and shortcomings. From an academic standpoint, we provide insight into the underlying mechanism behind how recommenders cause consumers to purchase. ∗The authors gratefully acknowledge the financial support from the Jay H. Baker Retailing Center, the Mack Institute for Innovation Management, and the Fishman-Davidson Center for Service and Operations Management. All errors are our own.  IW3C2 reserves the right to provide a hyperlink to the author’s site if the Material is used in electronic media. WWW 2016, April 11–15, 2016, Montréal, Québec, Canada. ACM 978-1-4503-4143-1/16/04.
Kernel Clustering: Density Biases and Solutions

Kernel methods are popular in clustering due to their generality and discriminating power. However, we show that many kernel clustering criteria have density biases theoretically explaining some practically significant artifacts empirically observed in the past. For example, we provide conditions and formally prove the density mode isolation bias in kernel K-means for a common class of kernels. We call it Breiman’s bias due to its similarity to the histogram mode isolation previously discovered by Breiman in decision tree learning with Gini impurity. We also extend our analysis to other popular kernel clustering methods, e.g., average/normalized cut or dominant sets, where density biases can take different forms. For example, splitting isolated points by cut-based criteria is essentially the sparsest subset bias, which is the opposite of the density mode bias. Our findings suggest that a principled solution for density biases in kernel clustering should directly address data inhomogeneity. We show that density equalization can be implicitly achieved using either locally adaptive weights or locally adaptive kernels. Moreover, density equalization makes many popular kernel clustering objectives equivalent. Our synthetic and real data experiments illustrate density biases and proposed solutions. We anticipate that theoretical understanding of kernel clustering limitations and their principled solutions will be important for a broad spectrum of data analysis applications across the disciplines.
Towards virtual honeynet based on LXC virtualization

Honeynet represents the new theory in network protection. Unlike purely defensive methods such as Intrusion Detection Systems (IDS), Data Encryption and Firewalls that try to avoid interaction with the attacker, Honeynet is based on the idea of interaction with the attacker. During this interaction, the Honeynet records all of the attacker's actions and tools used without the attacker knowing, representing them to the security analyzers to study and prepare defensive methods. A hybrid honeynet including both the high-interaction and low-interaction honeypots was developed and implemented utilizing virtualization technology, representing a typical laboratory environment.
Long-term electroencephalogram measurement using polymer-based dry microneedle electrode

This paper reports a successful electroencephalogram (EEG) measurement for hours using polymer-based microneedle electrodes. Needle electrodes can penetrate through the stratum corneum and therefore, do not require any skin treatment for high-quality EEG measurement. The tested needles consist of SU-8 needles, a silver film, and a nanoporous parylene protective film. In prior work, fabrication processes of polymer-based microneedles, which are considered to be more robust than silicon microneedles was developed. In this work, the electrical impedance was measured at the forehead and was verified to maintain 6 kΩ for 3 h without any skin treatment, which was low enough for EEG measurement. A headset was designed to keep the contact between the needles and skin and with its help, EEG was successfully measured from the frontal poles. The acquired signals were found to be as high quality as the standard wet electrode that required skin treatment and uncomfortable pasting of conductive gel. The developed electrodes are readily applicable to record brain activities for hours while applying little mental and physical stress to the users.
Design and implementation of home automation system

M2M wireless communication of various machines and devices in mobile networks is a fast growing business and application area in industry, maintenance business, customer service, security, and banking areas. This paper presents design and implementation of remote control system by means of GSM cellular communication network. The design integrates the device to be controlled, the microcontroller, and the GSM module so that it can be used for a wide range of applications. Detailed description and implementation of each design element are presented. To verify the principle operation of the M2M design, two home applications are experimentally tested using PC-based environment.
Autonomous ground vehicle control system for high-speed and safe operation

This paper describes a new trajectory tracking control system for autonomous ground vehicles (AGV) toward safe and high-speed operation enabled by incorporating vehicle dynamics control (VDC) into the AGV. The control system consists of two levels: an AGV desired yaw rate generator based on a kinematic model, and a yaw rate controller based on the vehicle/tire dynamic models. The separation between AGV trajectory tracking and low-level actuation allows the incorporation of the VDC into the AGV systems. Sliding mode control is utilized to handle the system uncertainties. The performance of the proposed control system is evaluated by using a high-fidelity (experimentally validated) full-vehicle sport utility vehicle (SUV) model (rear-drive and front-steer) provided by CarSimregon a race track. Compared with the results for typically-employed position error based AGV control, significant performance improvement is observed.
Does Size Matter ? Men ’ s and Women ’ s Views on Penis Size Across the Lifespan

The media equate a man’s penis size with his power and masculinity. Views about penis size were assessed in an Internet survey of 52,031 heterosexual men and women. Most men (66%) rated their penis as average, 22% as large, and 12% as small. Self-reported penis size was correlated positively with height and negatively with body fat level. Whereas 85% of women were satisfied with their partner’s penis size, only 55% of men were satisfied with their penis size, 45% wanted to be larger, and 0.2% wanted to be smaller. Satisfaction did not vary across age groups from 18 to 65. Men reporting a larger-than-average penis rated their appearance most favorably, suggesting a possible confidence effect of perceived large penis size.
Subdivision templates for converting a non-conformal hex-dominant mesh to a conformal hex-dominant mesh without pyramid elements

This paper presents a computational method for converting a non-conformal hex-dominant mesh to a conformal hex-dominant mesh without help of pyramid elements. During the conversion, the proposed method subdivides a non-conformal element by applying a subdivision template and conformal elements by a conventional subdivision scheme. Although many finite element solvers accept mixed elements, some of them require a mesh to be conformal without a pyramid element. None of the published automated methods could create a conformal hex-dominant mesh without help of pyramid elements, and therefore the applicability of the hex-dominant mesh has been significantly limited. The proposed method takes a non-conformal hex-dominant mesh as an input and converts it to a conformal hex-dominant mesh that consists only of hex, tet, and prism elements. No pyramid element will be introduced. The conversion thus considerably increases the applicability of the hex-dominant mesh in many finite element solvers.
Multispectral Pedestrian Detection using Deep Fusion Convolutional Neural Networks

Robust vision-based pedestrian detection is a crucial feature of future autonomous systems. Thermal cameras provide an additional input channel that helps solving this task and deep convolutional networks are the currently leading approach for many pattern recognition problems, including object detection. In this paper, we explore the potential of deep models for multispectral pedestrian detection. We investigate two deep fusion architectures and analyze their performance on multispectral data. Our results show that a pre-trained late-fusion architecture significantly outperforms the current state-of-the-art ACF+T+THOG solution.
Context counts: How learners' contexts influence learning in a MOOC

Massive Open Online Courses (MOOCs) require individual learners to self-regulate their own learning, determining when, how and with what content and activities they engage. However, MOOCs attract a diverse range of learners, from a variety of learning and professional contexts. This study examines how a learner's current role and context influences their ability to self-regulate their learning in a MOOC: Introduction to Data Science offered by Coursera. The study compared the self-reported self-regulated learning behaviour between learners from different contexts and with different roles. Significant differences were identified between learners who were working as data professionals or studying towards a higher education degree and other learners in the MOOC. The study provides an insight into how an individual's context and role may impact their learning behaviour in MOOCs.
Deep 3D Human Pose Estimation Under Partial Body Presence

This paper addresses the problem of 3D human pose estimation when not all body parts are present in the input image, i.e., when some body joints are present while other joints are fully absent (we exclude self-occlusion). State-of-the-art is not designed and thus not effective for such cases. We propose a deep CNN to regress the human pose directly from an input image; we design and train this network to work under partial body presence. Parallel to this, we train a detection network to classify the presence or absence of each of the main body joints in the input image. The outputs of our detection and regression networks are a) joints that are present and b) joints that are absent. With these outputs, our method reconstructs the full body skeleton. Evaluations on the Hu-man3.6M dataset yield promising results compared to related work.
Skating motion by a leg-wheeled robot with passive wheels

This paper presents a new type of mobile robot that was developed based on the idea of developing a robot with reduced number of Degree of Freedom (DOF). The higher the number of DOF implies that more actuators are being fixed onto the robot. This eventually results in a complicated control system, as well as influencing the energy efficiency of the robot. Besides that, more DOF will only add up the total weight of the robot, which is a disadvantage because lightweight robot is more likely to be applicable. In this study where a closed link mobile robot was made, number of active joints was lessened, while passive wheels were used for mobility to meet the objective of reducing the number of DOF. In this paper, we will discuss on the reasons behind the development of a reduced DOF robot, the mechanism of trajectory that allows the robot to move by skating, and finally the results to the experiments conducted.
An Ethics Evaluation Tool for Automating Ethical Decision-Making in Robots and Self-Driving Cars

As we march down the road of automation in robotics and artificial intelligence, we will need to automate an increasing amount of ethical decision-making in order for our devices to operate independently from us. But automating ethical decision-making raises novel questions for engineers and designers, who will have to make decisions about how to accomplish that task. For example, some ethical decisionmaking involves hard moral cases, which in turn requires user input if we are to respect established norms surrounding autonomy and informed consent. The author considers this and other ethical considerations that accompany the automation of ethical decision-making. He proposes some general ethical requirements that should be taken into account in the design room, and sketches a design tool that can be integrated into the design process to help engineers, designers, ethicists, and policymakers decide how best to automate certain forms of ethical decision-making.
A Swedish national twin study of lifetime major depression.

Substantial evidence supports the heritability of lifetime major depression. Less clear is whether genetic influences in major depression are more important in women than in men and whether genetic risk factors are the same in the two sexes. It is not known whether genetic effects on major depression are constant across historical cohorts.



Lifetime major depression was assessed at personal interview by modified DSM-IV criteria in 42,161 twins, including 15,493 complete pairs, from the national Swedish Twin Registry. Twin models were evaluated by using the program Mx.



Model fitting indicated that the heritability of liability to major depression was significantly higher in women (42%) than men (29%) and the genetic risk factors for major depression were moderately correlated in men and women. No significant differences were seen in the etiologic roles of genetic and environmental factors in major depression in three cohorts spanning birth years 1900-1958.



In the largest sample to date, lifetime major depression was moderately heritable, with estimates similar to those in prior studies. In accord with some but not other previous investigations, this study suggests both that the heritability of major depression is higher in women than in men and that some genetic risk factors for major depression are sex-specific in their effect. No evidence was found for differences in the roles of genetic and environmental risk factors in major depression in birth cohorts spanning nearly six decades.
The dynamics of organizational identity

While many organizational researchers make reference to Mead’s theory of social identity, none have explored how Mead’s ideas about the relationship between the “I” and the “me” might be extended to identity processes at the organizational level of analysis. In this paper we define organizational analogues for Mead’s “I” and “me” and explain how these two phases of organizational identity are related. In doing so we bring together existing theory concerning the links between organizational identities and images, with new theory concerning how reflection embeds identity in organizational culture and how identity expresses cultural understandings through symbols. We offer a model of organizational identity dynamics built on four processes linking organizational identity to culture and image. While the processes linking identity and image (mirroring and impressing) have been described in the literature before, the contribution of this paper lies in articulation of the processes linking identity and culture (reflecting and expressing), and of the interaction of all four processes working dynamically together to create, maintain and change organizational identity. We discuss the implications of our model in terms of two dysfunctions of organizational identity dynamics: narcissism and loss of culture.
Femtosecond-Long Pulse-Based Modulation for Terahertz Band Communication in Nanonetworks

Nanonetworks consist of nano-sized communicating devices which are able to perform simple tasks at the nanoscale. Nanonetworks are the enabling technology of long-awaited applications such as advanced health monitoring systems or high-performance distributed nano-computing architectures. The peculiarities of novel plasmonic nano-transceivers and nano-antennas, which operate in the Terahertz Band (0.1-10 THz), require the development of tailored communication schemes for nanonetworks. In this paper, a modulation and channel access scheme for nanonetworks in the Terahertz Band is developed. The proposed technique is based on the transmission of one-hundred-femtosecond-long pulses by following an asymmetric On-Off Keying modulation Spread in Time (TS-OOK). The performance of TS-OOK is evaluated in terms of the achievable information rate in the single-user and the multi-user cases. An accurate Terahertz Band channel model, validated by COMSOL simulation, is used, and novel stochastic models for the molecular absorption noise in the Terahertz Band and for the multi-user interference in TS-OOK are developed. The results show that the proposed modulation can support a very large number of nano-devices simultaneously transmitting at multiple Gigabits-per-second and up to Terabits-per-second, depending on the modulation parameters and the network conditions.
Towards Implicit Content-Introducing for Generative Short-Text Conversation Systems

The study on human-computer conversation systems is a hot research topic nowadays. One of the prevailing methods to build the system is using the generative Sequence-to-Sequence (Seq2Seq) model through neural networks. However, the standard Seq2Seq model is prone to generate trivial responses. In this paper, we aim to generate a more meaningful and informative reply when answering a given question. We propose an implicit content-introducing method which incorporates additional information into the Seq2Seq model in a flexible way. Specifically, we fuse the general decoding and the auxiliary cue word information through our proposed hierarchical gated fusion unit. Experiments on real-life data demonstrate that our model consistently outperforms a set of competitive baselines in terms of BLEU scores and human evaluation.
Position and Orientation Estimation Through Millimeter-Wave MIMO in 5G Systems

Millimeter-wave (mm-wave) signals and large antenna arrays are considered enabling technologies for future 5G networks. While their benefits for achieving high-data rate communications are well-known, their potential advantages for accurate positioning are largely undiscovered. We derive the Cramér-Rao bound (CRB) on position and rotation angle estimation uncertainty from mm-wave signals from a single transmitter, in the presence of scatterers. We also present a novel two-stage algorithm for position and rotation angle estimation that attains the CRB for average to high signal-to-noise ratio. The algorithm is based on multiple measurement vectors matching pursuit for coarse estimation, followed by a refinement stage based on the space-alternating generalized expectation maximization algorithm. We find that accurate position and rotation angle estimation is possible using signals from a single transmitter, in either line-of-sight, non-line-of-sight, or obstructed-line-of-sight conditions.
Quasi Frequency-Independent Increased Bandwidth Planar Log-Periodic Antenna

A novel antenna composite with greatly extended bandwidth of a baseline printed 4-arm log-periodic (LP) topology is introduced. Bandwidth increase by accomplishing a miniaturization factor >2 is achieved by coupling the currents from the booms of the LP to an annular ring, thereby providing an efficient well-matched radiator at frequencies below the cut-off frequency of the LP. The annulus, the turnstile (booms of the LP), and the LP form the radiators of this novel quasi frequency-independent (FI) ring-turnstile-LP antenna. A 160 mm diameter antenna is designed and fabricated to demonstrate the proposed concept. Measured VSWR and extensive far-field characterization verify multi-octave dual circular polarization (CP) operation and a size reduction factor of 2.25. Also, the ring-turnstile-LP configuration is integrated with two planar impedance transformers/feeds to demonstrate small size, simple beamformer and dual-CP operation over bandwidths as wide as those of more complex sinuous and singly polarized spiral antennas. Detailed computational studies are conducted for better understanding the impact of various structural parameters on antenna's unidirectional performance and relevant conclusions are provided.