nils-herrmann/clean_scirepeval huggingface dataset:
Learning state representations with robotic priors

Robot learning is critically enabled by the availability of appropriate state representations. We propose a robotics-specific approach to learning such state representations. As robots accomplish tasks by interacting with the physical world, we can facilitate representation learning by considering the structure imposed by physics; this structure is reflected in the changes that occur in the world and in the way a robot can effect them. By exploiting this structure in learning, robots can obtain state representations consistent with the aspects of physics relevant to the learning task. We name this prior knowledge about the structure of interactions with the physical world robotic priors. We identify five robotic priors and explain how they can be used to learn pertinent state representations. We demonstrate the effectiveness of this approach in simulated and real robotic experiments with distracting moving objects. We show that our method extracts task-relevant state representations from high-dimensional observations, even in the presence of taskirrelevant distractions. We also show that the state representations learned by our method greatly improve generalization in reinforcement learning.
Design of 56 Gb/s NRZ and PAM4 SerDes Transceivers in CMOS Technologies

This paper presents two ultra-high-speed SerDes dedicated for PAM4 and NRZ data. The PAM4 TX incorporates an output driver with 3-tap FFE and adjustable weighting to deliver clean outputs at 4 levels, and the PAM4 RX employs a purely linear full-rate CDR and CTLE/1-tap DFE combination to recover and demultiplex the data. NRZ TX includes a tree-structure MUX with built-in PLL and phase aligner. NRZ RX adopts linear PD with special vernier technique to handle the 56 Gb/s input data. All chips have been verified in silicon with reasonable performance, providing prospective design examples for next-generation 400 GbE.
A Design Theme for Tangible Interaction: Embodied Facilitation

This paper presents parts of a design framework for collaboratively used tangible interaction systems, focusing on the theme of Embodied Facilitation. Systems can be interpreted as spaces/structures to act and move in, facilitating some movements and hindering others. Thus they shape the ways we collaborate, induce collaboration or make us refrain from it. Tangible interaction systems provide virtual and physical structure they truly embody facilitation. Three concepts further refine the theme: Embodied Constraints, Multiple Access Points and Tailored Representations. These are broken down into design guidelines and each illustrated with examples.
Energy Aware Consolidation Algorithm Based on K-Nearest Neighbor Regression for Cloud Data Centers

In this paper, we propose a dynamic virtual machine consolidation algorithm to minimize the number of active physical servers on a data center in order to reduce energy cost. The proposed dynamic consolidation method uses the k-nearest neighbor regression algorithm to predict resource usage in each host. Based on prediction utilization, the consolidation method can determine (i) when a host becomes over-utilized (ii) when a host becomes under-utilized. Experimental results on the real workload traces from more than a thousand Planet Lab virtual machines show that the proposed technique minimizes energy consumption and maintains required performance levels.
SIW Pillbox Antenna for Monopulse Radar Applications

A multibeam pillbox antenna system incorporating monopulse phase comparison technique is proposed in the 24-GHz band. This low-profile antenna architecture combines the scanning capabilities of pillbox configurations and enhanced resolution of two-quadrant monopulse technique; this approach avoids mechanical orientation of the antenna system for tracking applications. The radiating panel consists of two subarrays of 23 slotted waveguides, with 8 slots per waveguide. The beam is scanned in E-plane over a field of view of ±40°, and sum/difference patterns are generated in H-plane. Around the design frequency, f = 24.15 GHz, the antenna gain varies between 25 and 21.5 dBi for the central and extreme beams, respectively. The measured null depth is better than -16 dB for all difference beam patterns. The antenna bandwidth for VSWR <; 2 (Sii <; -10 dB) and isolation better than 10 dB (Sij <; -10 dB) is equal to 4.5%.
Psychopathic personality traits and environmental contexts: Differential correlates, gender differences, and genetic mediation.

Theorists have speculated that primary psychopathy (or Factor 1 affective-interpersonal features) is prominently heritable whereas secondary psychopathy (or Factor 2 social deviance) is more environmentally determined. We tested this differential heritability hypothesis using a large adolescent twin sample. Trait-based proxies of primary and secondary psychopathic tendencies were assessed using Multidimensional Personality Questionnaire (MPQ) estimates of Fearless Dominance and Impulsive Antisociality, respectively. The environmental contexts of family, school, peers, and stressful life events were assessed using multiple raters and methods. Consistent with prior research, MPQ Impulsive Antisociality was robustly associated with each environmental risk factor, and these associations were significantly greater than those for MPQ Fearless Dominance. However, MPQ Fearless Dominance and Impulsive Antisociality exhibited similar heritability, and genetic effects mediated the associations between MPQ Impulsive Antisociality and the environmental measures. Results were largely consistent across male and female twins. We conclude that gene-environment correlations rather than main effects of genes and environments account for the differential environmental correlates of primary and secondary psychopathy.
Optimizing the Marketing Interventions Mix in Intermediate-Term CRM

W provide a fully personalized model for optimizing multiple marketing interventions in intermediateterm customer relationship management (CRM). We derive theoretically based propositions on the moderating effects of past customer behavior and conduct a longitudinal validation test to compare the performance of our model with that of commonly used segmentation models in predicting intermediate-term, customerspecific gross profit change. Our findings show that response to marketing interventions is highly heterogeneous, that heterogeneity of response varies across different marketing interventions, and that the heterogeneity of response to marketing interventions may be partially explained by customer-specific variables related to customer characteristics and the customer’s past interactions with the company. One important result from these moderating effects is that relationship-oriented interventions are more effective with loyal customers, while action-oriented interventions are more effective with nonloyal customers. We show that our proposed model outperformed models based on demographics, recency-frequency-monetary value (RFM), or finite mixture segmentation in predicting the effectiveness of intermediate-term CRM. The empirical results project a significant increase in intermediate-term profitability over all of the competing segmentation approaches and a significant increase in intermediate-term profitability over current practice.
Review and Evaluation of Feature Selection Algorithms in Synthetic Problems

The main purpose of Feature Subset Selection is to find a reduced subset of attributes from a data set described by a feature set. The task of a feature selection algorithm (FSA) is to provide with a computational solution motivated by a certain definition of relevance or by a reliable evaluation measure. In this paper several fundamental algorithms are studied to assess their performance in a controlled experimental scenario. A measure to evaluate FSAs is devised that computes the degree of matching between the output given by a FSA and the known optimal solutions. An extensive experimental study on synthetic problems is carried out to assess the behaviour of the algorithms in terms of solution accuracy and size as a function of the relevance, irrelevance, redundancy and size of the data samples. The controlled experimental conditions facilitate the derivation of better-supported and meaningful conclusions.
A review of power converter topologies for wind generators

Wind energy conversion systems have become a focal point in the research of renewable energy sources. This is in no small part due to the rapid advances in the size of wind generators as well as the development of power electronics and their applicability in wind energy extraction. This paper provides a comprehensive review of past and present converter topologies applicable to permanent magnet generators, induction generators, synchronous generators and doubly fed induction generators. The many different generator-converter combinations are compared on the basis of topology, cost, efficiency, power consumption and control complexity. The features of each generator-converter configuration are considered in the context of wind turbine systems
An inventory for measuring depression.

PHILADELPHIA The difficulties inherent in obtaining consistent and adequate diagnoses for the purposes of research and therapy have been pointed out by a number of authors. Pasamanick12 in a recent article viewed the low interclinician agreement on diagnosis as an indictment of the present state of psychiatry and called for "the development of objective, measurable and verifiable criteria of classification based not on personal or parochial considerations, buton behavioral and other objectively measurable manifestations." Attempts by other investigators to subject clinical observations and judgments to objective measurement have resulted in a wide variety of psychiatric rating ~ c a l e s . ~ J ~ These have been well summarized in a review article by Lorr l1 on "Rating Scales and Check Lists for the E v a 1 u a t i o n of Psychopathology." In the area of psychological testing, a variety of paper-andpencil tests have been devised for the purpose of measuring specific personality traits; for example, the Depression-Elation Test, devised by Jasper in 1930. This report describes the development of an instrument designed to measure the behavioral manifestations of depression. In the planning of the research design of a project aimed at testing certain psychoanalytic formulations of depression, the necessity for establishing an appropriate system for identifying depression was recognized. Because of the reports on the low degree of interclinician agreement on diagnosis,13 we could not depend on the clinical diagnosis, but had to formulate a method of defining depression that would be reliable and valid. The available instruments were not considered adequate for our purposes. The Minnesota Multiphasic Personality Inventory, for example, was not specifically designed
Understanding knowledge sharing activities in free/open source software projects: An empirical study

Free/Open Source Software (F/OSS) projects are people-oriented and knowledge intensive software development environments. Many researchers focused on mailing lists to study coding activities of software developers. How expert software developers interact with each other and with non-developers in the use of community products have received little attention. This paper discusses the altruistic sharing of knowledge between knowledge providers and knowledge seekers in the Developer and User mailing lists of the Debian project. We analyze the posting and replying activities of the participants by counting the number of email messages they posted to the lists and the number of replies they made to questions others posted. We found out that participants interact and share their knowledge a lot, their positing activity is fairly highly correlated with their replying activity, the characteristics of posting and replying activities are different for different kinds of lists, and the knowledge sharing activity of self-organizing Free/Open Source communities could best be explained in terms of what we called ‘‘Fractal Cubic Distribution’’ rather than the power-law distribution mostly reported in the literature. The paper also proposes what could be researched in knowledge sharing activities in F/OSS projects mailing list and for what purpose. The research findings add to our understanding of knowledge sharing activities in F/OSS projects.
Facilitating Preservice Teachers' Development of Technological, Pedagogical, and Content Knowledge (TPACK)

Preparing preservice teachers for ICT integration in the classrooms is a key focus for many teacher education institutes. This paper examines the perceived development of preservice teachers in terms of their technological knowledge, pedagogical knowledge, content knowledge and the synthesis of such knowledge, i.e., the technological, pedagogical, and content knowledge (TPACK). A questionnaire adapted from Schmidt, Baran, Thompson, Mishra, Koehler, and Shin (2009) was validated using factor analyses and the preservice teachers’ TPACK perceptions before and after their ICT course were examined. The results reveal statistical significant gains with good effect sizes. Regression analysis further reveals that technological knowledge, pedagogical knowledge and content knowledge are all significant predictors of preservice teachers’ TPACK, with pedagogical knowledge having the largest impact. Implications for designing the ICT instruction of preservice teachers are discussed.
Joint visual denoising and classification using deep learning

Visual restoration and recognition are traditionally addressed in pipeline fashion, i.e. denoising followed by classification. Instead, observing correlations between the two tasks, for example clearer image will lead to better categorization and vice visa, we propose a joint framework for visual restoration and recognition for handwritten images, inspired by advances in deep autoencoder and multi-modality learning. Our model is a 3-pathway deep architecture with a hidden-layer representation which is shared by multi-inputs and outputs, and each branch can be composed of a multi-layer deep model. Thus, visual restoration and classification can be unified using shared representation via non-linear mapping, and model parameters can be learnt via backpropagation. Using MNIST and USPS data corrupted with structured noise, the proposed framework performs at least 20% better in classification than separate pipelines, as well as clearer recovered images.
Marginalized Denoising Auto-encoders for Nonlinear Representations

Denoising auto-encoders (DAEs) have been successfully used to learn new representations for a wide range of machine learning tasks. During training, DAEs make many passes over the training dataset and reconstruct it from partial corruption generated from a pre-specified corrupting distribution. This process learns robust representation, though at the expense of requiring many training epochs, in which the data is explicitly corrupted. In this paper we present the marginalized Denoising Auto-encoder (mDAE), which (approximately) marginalizes out the corruption during training. Effectively, the mDAE takes into account infinitely many corrupted copies of the training data in every epoch, and therefore is able to match or outperform the DAE with much fewer training epochs. We analyze our proposed algorithm and show that it can be understood as a classic auto-encoder with a special form of regularization. In empirical evaluations we show that it attains 1-2 order-of-magnitude speedup in training time over other competing approaches.
Attention Networks for Weakly Supervised Object Localization

We consider the problem of weakly supervised learning for object localization. Given a collection of images with image-level annotations indicating the presence/absence of an object, our goal is to localize the object in each image. We propose a neural network architecture called the attention network for this problem. Given a set of candidate regions in an image, the attention network first computes an attention score on each candidate region in the image. Then these candidate regions are combined together with their attention scores to form a whole-image feature vector. This feature vector is used for classifying the image. The object localization is implicitly achieved via the attention scores on candidate regions. We demonstrate that our approach achieves superior performance on several benchmark datasets.
Stronger Password Authentication Using Browser Extensions

We describe a browser extension, PwdHash, that transparently produces a different password for each site, improving web password security and defending against password phishing and other attacks. Since the browser extension applies a cryptographic hash function to a combination of the plaintext password entered by the user, data associated with the web site, and (optionally) a private salt stored on the client machine, theft of the password received at one site will not yield a password that is useful at another site. While the scheme requires no changes on the server side, implementing this password method securely and transparently in a web browser extension turns out to be quite difficult. We describe the challenges we faced in implementing PwdHash and some techniques that may be useful to anyone facing similar security issues in a browser environment.
Gaining insights from social media language: Methodologies and challenges.

Language data available through social media provide opportunities to study people at an unprecedented scale. However, little guidance is available to psychologists who want to enter this area of research. Drawing on tools and techniques developed in natural language processing, we first introduce psychologists to social media language research, identifying descriptive and predictive analyses that language data allow. Second, we describe how raw language data can be accessed and quantified for inclusion in subsequent analyses, exploring personality as expressed on Facebook to illustrate. Third, we highlight challenges and issues to be considered, including accessing and processing the data, interpreting effects, and ethical issues. Social media has become a valuable part of social life, and there is much we can learn by bringing together the tools of computer science with the theories and insights of psychology.
Gamification - Design of IT-Based Enhancing Services for Motivational Support and Behavioral Change

2013-04-15 Accepted: 2013-05-13 Accepted after one revision by Prof. Dr. Sinz.  This article is also available in German in print and via http://www. wirtschaftsinformatik.de: Blohm I, Leimeister JM (2013) Gamification. Gestaltung IT-basierter Zusatzdienstleistungen zur Motivationsunterstützung und Verhaltensänderung. WIRTSCHAFTSINFORMATIK.
Studying the impact of dependency network measures on software quality

Dependency network measures capture various facets of the dependencies among software modules. For example, betweenness centrality measures how much information flows through a module compared to the rest of the network. Prior studies have shown that these measures are good predictors of post-release failures. However, these studies did not explore the causes for such good performance and did not provide guidance for practitioners to avoid future bugs. In this paper, we closely examine the causes for such performance by replicating prior studies using data from the Eclipse project. Our study shows that a small subset of dependency network measures have a large impact on post-release failure, while other network measures have a very limited impact. We also analyze the benefit of bug prediction in reducing testing cost. Finally, we explore the practical implications of the important network measures.
LoDPD: A Location Difference-Based Proximity Detection Protocol for Fog Computing

Proximity detection is one of the most common location-based applications in daily life when users intent to find their friends who get into their proximity. Studies on protecting user privacy information during the detection process have been widely concerned. In this paper, we first analyze a theoretical and experimental analysis of existing solutions for proximity detection, and then demonstrate that these solutions either provide a weak privacy preserving or result in a high communication and computational complexity. Accordingly, a location difference-based proximity detection protocol is proposed based on the Paillier cryptosystem for the purpose of dealing with the above shortcomings. The analysis results through an extensive simulation illustrate that our protocol outperforms traditional protocols in terms of communication and computation cost.
Cross-Lingual Type Inference

Entity typing is an essential task for constructing a knowledge base. However, many non-English knowledge bases fail to type their entities due to the absence of a reasonable local hierarchical taxonomy. Since constructing a widely accepted taxonomy is a hard problem, we propose to type these non-English entities with some widely accepted taxonomies in English, such as DBpedia, Yago and Freebase. We define this problem as cross-lingual type inference. In this paper, we present CUTE to type Chinese entities with DBpedia types. First we exploit the cross-lingual entity linking between Chinese and English entities to construct the training data. Then we propose a multi-label hierarchical classification algorithm to type these Chinese entities. Experimental results show the effectiveness and efficiency of our method.
Bayesian Density Estimation and Inference Using Mixtures

Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/about/terms.html. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use. Please contact the publisher regarding any further use of this work. Publisher contact information may be obtained at http://www.jstor.org/journals/astata.html. Each copy of any part of a JSTOR transmission must contain the same copyright notice that appears on the screen or printed page of such transmission.
Novel CPW-Fed Planar Monopole Antenna for WiMAX/WLAN Applications

A coplanar waveguide (CPW)-fed planar monopole antenna with triple-band operation for WiMAX and WLAN applications is presented. The antenna, which occupies a small size of 25(L) × 25(W) × 0.8(H) mm3, is simply composed of a pentagonal radiating patch with two bent slots. By carefully selecting the positions and lengths of these slots, good dual stopband rejection characteristic of the antenna can be obtained so that three operating bands covering 2.14-2.85, 3.29-4.08, and 5.02-6.09 GHz can be achieved. The measured results also demonstrate that the proposed antenna has good omnidirectional radiation patterns with appreciable gain across the operating bands and is thus suitable to be integrated within the portable devices for WiMAX/WLAN applications.