nils-herrmann/clean_scirepeval huggingface dataset:
Cobalt fill for advanced interconnects

Cobalt as a material promises to change the conductor landscape in many areas, particularly logic contact and interconnect. In this paper, we focus on Cobalt as the conductor for logic interconnect — a potential Cu replacement. We demonstrate Co fill capability down to 10nm CD, and show that Co-Cu resistivity cross-over will occur below 14nm CD. Single damascene Co-ULK structures are used to establish an optimized metallization stack with robust CMP performance that has electromigration and TDDB reliability better than copper interconnect.
1 . 1 Definition of adjustable autonomy and human-centered autonomous

We expect a variety of autonomous systems, from rovers to life-support systems, to play a critical role in the success of manned Mars missions. The crew and ground support personnel will want to control and be informed by these systems at varying levels of detail depending on the situation. Moreover, these systems will need to operate safely in the presence of people and cooperate with them effectively. We call such autonomous systems human-centered in contrast with traditional Òblack-boxÓ autonomous systems. Our goal is to design a framework for human-centered autonomous systems that enables users to interact with these systems at whatever level of control is most appropriate whenever they so choose, but minimize the necessity for such interaction. This paper discusses on-going research at the NASA Ames Research Center and the Johnson Space Center in developing human-centered autonomous systems that can be used for a manned Mars mission.
Movie recommendations based in explicit and implicit features extracted from the Filmtipset dataset

In this paper, we describe the experiments conducted by the Information Retrieval Group at the Universidad Autónoma de Madrid (Spain) in order to better recommend movies for the 2010 CAMRa Challenge edition. Experiments were carried out on the dataset corresponding to social Filmtipset track. To obtain the movies recommendations we have used different algorithms based on Random Walks, which are well documented in the literature of collaborative recommendation. We have also included a new proposal in one of the algorithms in order to get better results. The results obtained have been computed by means of the trec_eval standard NIST evaluation procedure.
Use of AI Techniques for Residential Fire Detection in Wireless Sensor Networks

Early residential fire detection is important for prompt extinguishing and reducing damages and life losses. To detect fire, one or a combination of sensors and a detection algorithm are needed. The sensors might be part of a wireless sensor network (WSN) or work independently. The previous research in the area of fire detection using WSN has paid little or no attention to investigate the optimal set of sensors as well as use of learning mechanisms and Artificial Intelligence (AI) techniques. They have only made some assumptions on what might be considered as appropriate sensor or an arbitrary AI technique has been used. By closing the gap between traditional fire detection techniques and modern wireless sensor network capabilities, in this paper we present a guideline on choosing the most optimal sensor combinations for accurate residential fire detection. Additionally, applicability of a feed forward neural network (FFNN) and Naïve Bayes Classifier is investigated and results in terms of detection rate and computational complexity are analyzed.
De-Anonymisierungsverfahren: Kategorisierung und Anwendung für Datenbankanfragen(De-Anonymization: Categorization and Use-Cases for Database Queries)

The project PArADISE deals with activity and intention recognition in smart environments. This can be used in apartments, for example, to recognize falls of elderly people. While doing this, the privacy concerns of the user should be kept. To reach this goal, the processing of the data is done as close as possible at those sensors collecting the data. Only in cases where the processing is not possible on local nodes the data will be transferred to the cloud. But before transferring, it is checked against the privacy concerns using some measures for the anonymity of the data. If the data is not valid against these checks, some additional anonymizations will be done. This anonymization of data must be done quite carefully. Mistakes might cause the problem that data can be reassigned to persons and anonymized data might be reproduced. This paper gives an overview about recent methods for anonymizing data while showing their weaknesses. How these weaknesses can be used to invert the anonymization (called de-anonymization) is shown as well. Our attacks representing the de-anonymization should help to find weaknesses in methods used to anonymize data and how these can be eliminated. Zusammenfassung: Im Projekt PArADISE sollen Aktivitätsund Intentionserkennungen in smarten Systemen, etwa Assistenzsystemen in Wohnungen, so durchgeführt werden, dass Privatheitsanforderungen des Nutzers gewahrt bleiben. Dazu werden einerseits Auswertungen der Daten sehr nah an den Sensoren, die die Daten erzeugen, vorgenommen. Eine Übertragung von Daten in die Cloud findet nur im Notfall statt. Zusätzlich werden aber vor der Übertragung der nicht vorausgewerteten Daten in die Cloud diese auf Privatheitsanforderungen hin geprüft, indem Anonymisierungsmaße getestet und eventuell weitere Anonymisierungen von Daten vorgenommen werden. Diese Anonymisierung von Datenbeständen muss mit großer Sorgfalt geschehen. Fehler können sehr schnell dazu führen, dass anonymisierte Datenbestände wieder personalisiert werden können und Daten, die eigentlich entfernt wurden, wieder zurückgewonnen werden können. Dieser Artikel betrachtet aktuelle Verfahren zur Anonymisierung und zeigt Schwachstellen auf, die zu Problemen oder gar der Umkehrung der Methoden führen können. Unsere künstlich erzeugten Angriffe durch De-Anonymisierungen sollen helfen, Schwachstellen in den Anonymisierungsverfahren zu entdecken und zu beheben.
Visual Roadmaps for Managed Enterprise Architecture Evolution

Managing the evolution of the Enterprise Architecture (EA) is a key challenge for modern enterprises. Current approaches to address this challenge focus on EA plans, indicating projected future states of the architecture. Nevertheless, these plans neglect the role of the information technology (IT) project, which actually performs the transformation of the current to a planned EA. In this paper, we account for the importance of IT projects as drivers of EA transformation by providing a viewpoint for roadmapping the development of the EA over time. Complementing, we further introduce a conceptual model, which explicates the information demands for such roadmap plans.
Rational approximations to rational models: alternative algorithms for category learning.

Rational models of cognition typically consider the abstract computational problems posed by the environment, assuming that people are capable of optimally solving those problems. This differs from more traditional formal models of cognition, which focus on the psychological processes responsible for behavior. A basic challenge for rational models is thus explaining how optimal solutions can be approximated by psychological processes. We outline a general strategy for answering this question, namely to explore the psychological plausibility of approximation algorithms developed in computer science and statistics. In particular, we argue that Monte Carlo methods provide a source of rational process models that connect optimal solutions to psychological processes. We support this argument through a detailed example, applying this approach to Anderson's (1990, 1991) rational model of categorization (RMC), which involves a particularly challenging computational problem. Drawing on a connection between the RMC and ideas from nonparametric Bayesian statistics, we propose 2 alternative algorithms for approximate inference in this model. The algorithms we consider include Gibbs sampling, a procedure appropriate when all stimuli are presented simultaneously, and particle filters, which sequentially approximate the posterior distribution with a small number of samples that are updated as new data become available. Applying these algorithms to several existing datasets shows that a particle filter with a single particle provides a good description of human inferences.
Performance analysis of two open source intrusion detection systems

Several studies have been conducted where authors compared the performance of open source Intrusion detection systems, namely Snort and Suricata. However, most studies were limited to either security indicators or performance measurements under the same operating system. The objective of this study is to give a comprehensive analysis of both products in terms of several security related and performance related indicators. In addition, we tested the products under two different operating systems. Several experiments were run to evaluate the effects of open source intrusion detection and prevention systems Snort and Suricata, operating systems Windows, Linux and various attack types on system resource usage, dropped packets rate and ability to detect intrusions. The results show that Suricata has a higher CPU and RAM utilization than Snort in all cases on both operating systems, but lower percentage of dropped packets when evaluated during five of six simulated attacks. Both products had the same number of correctly identified intrusions. The results show that Linux-based solutions consume more system resources, but Windows-based systems had a higher rate of dropped packets. This indicates that these two intrusion detection and prevention systems should be run on Linux. However, both systems are inappropriate for high volumes of traffic in single-server setting.
Swarm-Intelligence Tuned Current Reduction for Power-Assisted Steering Control in Electric Vehicles

In electric vehicle technology, battery energy conservation is paramount due to the dependency of all system operations on the available battery. The proportional, integral and derivative (PID) controller parameters in the electric power assisted steering system for electric vehicle need to be tuned with the optimal performance setting so that less current is needed for its operation. This proposed two methods under the umbrella of swarm-intelligence technique namely particle swarm optimization (PSO) and ant colony optimization (ACO) in order to reduce current consumption and to improve controller performance. The investigation involves an analysis on the convergence behavior of both techniques in search for accurate controller parameters. A comprehensive assessment on the assist current supplied to the assist motor of the system is also presented. Investigation reveals that the proposed controllers, PID-PSO and PID-ACO are able to reduce the assist current supplied to the assist motor as compared to the conventional PID controller. This study also demonstrate the feasibility of applying both swarm-intelligence tuning method in terms of reduced time taken to tune the PID controller as compared to the conventional tuning method.
Hand posture recognition based on bottom-up structured deep convolutional neural network with curriculum learning

Hand posture recognition has tremendous potential in the field of natural user interactions. There were many advances in research in recent years but there are still limitations regarding its usage in unfavorable live situations where hand posture variation, illumination change or background complexity are an issue. In cases like these, recognizing the hand posture is a difficult task. As such, we considered reducing the difficulty of the task by using curriculum learning with intermediate information. We proceeded to divide the complex architecture of the hand posture recognition task into two easier ones: 1) Extraction of the hand shape under clutter background with illumination change, 2) Recognition of the hand posture from a binary image. In order to do so, we propose here a bottom-up structured deep convolutional neural network incorporating a special layer for binary image extraction. Our proposed method also employs state-of-the art techniques for deep learning to obtain generalization. As a result, we achieved better recognition performances of the hand posture under clutter background compared to the baseline method.
Powering 3 Dimensional Microrobots : Power Density Limitations ∗

Many types of electrostatic and electromagnetic microactuators have been developed. It is important to understand the fundamental performance limitations of these actuators for use in micro-robotic systems. The most important consideration for micro mobile robots is the effective power density of the actuator. As inertia and gravitional forces become less significant for small robots, typical metrics for macro-robots, such as torque-to-weight ratio, are not appropriate. A very significant problem with micro-actuators for robotics is the need for efficient transmissions to obtain large forces and torques at low speeds from inherently high speed low force actuators.
Expressive Visual Text-to-Speech Using Active Appearance Models

This paper presents a complete system for expressive visual text-to-speech (VTTS), which is capable of producing expressive output, in the form of a 'talking head', given an input text and a set of continuous expression weights. The face is modeled using an active appearance model (AAM), and several extensions are proposed which make it more applicable to the task of VTTS. The model allows for normalization with respect to both pose and blink state which significantly reduces artifacts in the resulting synthesized sequences. We demonstrate quantitative improvements in terms of reconstruction error over a million frames, as well as in large-scale user studies, comparing the output of different systems.
Detection of cancer tumors in mammography images using support vector machine and mixed gravitational search algorithm

In this paper, support vector machine (SVM) and mixed gravitational search algorithm (MGSA) are utilized to detect the breast cancer tumors in mammography images. Sech template matching method is used to segment images and extract the regions of interest (ROIs). Gray-level co-occurrence matrix (GLCM) is used to extract features. The mixed GSA is used for optimization of the classifier parameters and selecting salient features. The main goal of using MGSA-SVM is to decrease the number of features and to improve the SVM classification accuracy. Finally, the selected features and the tuned SVM classifier are used for detecting tumors. The experimental results show that the proposed method is able to optimize both feature selection and the SVM parameters for the breast cancer tumor detection.
The role of civil society in protecting public health over commercial interests: lessons from Thailand

In October, 2002, two Thai people with HIV-1 won an important legal case to increase access to medicines. In its judgment in the didanosine patent case against Bristol-Myers Squibb, 1 the Thai Central Intellectual Property and International Trade Court ruled that, because pharmaceutical patents can lead to high prices and limit access to medicines, patients are injured by them and can challenge their legality. This ruling had great international implications for health and human rights, confirming that patients—whose health and lives can depend on being able to afford a medicine—can be considered as damaged parties and therefore have legal standing to sue. The complexities of pharmaceutical intellectual property law are most poorly understood by those most affected by their consequences—the patients who need the drugs. The Thai court case was the outcome of a learning process and years of networking between different civil society actors who joined forces to protect and promote the right of access to treatment. Our Viewpoint, based on key interviews and published reviews, summarises the efforts of civil society in Thailand to achieve a fair balance between international trade and public health. These efforts have focused on didanosine, an essential antiretroviral drug that in Thailand has become symbolic of how multinational companies and governments of industrialised countries protect their own interests at the expense of access to essential medicines for the poor. Thailand is a low-to-middle income country with a population of 63·5 million, of whom about 603 000 have HIV/AIDS (adult infection rate is 1·8%). The country is noted for an effective response to the epidemic. 2–4 The Thai Public Health Ministry began to provide antiretroviral monotherapy in 1992 and dual therapy in 1995 for an estimated 25% of symptomatic patients attending public hospitals. However, in 1995 specialists concluded that continuing the programme would be costly with minimum effectiveness, 5 although their analysis did not take into account the possibility of lower prices due to generic competition or of the greater effectiveness of triple therapy. In 2000, the Public Health Ministry began to promote triple therapy as the norm, using mostly brand name drugs. However, coverage was limited by the price of medicines. For several years, the only antiretroviral drugs commonly available in Thailand were zidovudine and didanosine. By 1996, generic zidovudine was available at a reasonable price, but generic production of didanosine was blocked by the patent holder Bristol-Myers Squibb (panel 1) 6–15 and …
Classifying Online Dating Profiles on Tinder using FaceNet Facial Embeddings

A method to produce personalized classification models to automatically review online dating profiles on Tinder, based on the user’s historical preference, is proposed. The method takes advantage of a FaceNet facial classification model to extract features which may be related to facial attractiveness. The embeddings from a FaceNet model were used as the features to describe an individual’s face. A user reviewed 8,545 online dating profiles. For each reviewed online dating profile, a feature set was constructed from the profile images which contained just one face. Two approaches are presented to go from the set of features for each face to a set of profile features. A simple logistic regression trained on the embeddings from just 20 profiles could obtain a 65% validation accuracy. A point of diminishing marginal returns was identified to occur around 80 profiles, at which the model accuracy of 73% would only improve marginally after reviewing a significant number of additional profiles.
Competition and Selection Among Conventions

In many domains, a latent competition among different conventions determines which one will come to dominate. One sees such effects in the success of community jargon, of competing frames in political rhetoric, or of terminology in technical contexts. These effects have become widespread in the on-line domain, where the ease of information transmission makes them particularly forceful, and where the available data offers the potential to study competition among conventions at a fine-grained level. In analyzing the dynamics of conventions over time, however, even with detailed on-line data, one encounters two significant challenges. First, as conventions evolve, the underlying substance of their meaning tends to change as well; and such substantive changes confound investigations of social effects. Second, the selection of a convention takes place through the complex interactions of individuals within a community, and contention between the users of competing conventions plays a key role in the convention’s evolution. Any analysis of the overall dynamics must take place in the presence of these two issues. In this work we study a setting in which we can cleanly track the competition among conventions while explicitly taking these sources of complexity into account. Our analysis is based on the spread of low-level authoring conventions in the e-print arXiv over 24 years and roughly a million posted papers: by tracking the spread of macros and other author-defined conventions, we are able to study conventions that vary even as the underlying meaning remains constant. We find that the interaction among co-authors over time plays a crucial role in the selection of conventions; the distinction between more and less experienced members of the community, and the distinction between conventions with visible versus invisible effects, are both central to the underlying processes. Through our analysis we make predictions at the population level about the ultimate success of different synonymous conventions over time — and at the individual level about the outcome of “fights” between people over convention choices.
Assessing dimensions of perceived visual aesthetics of web sites

Despite its centrality to human thought and practice, aesthetics has for the most part played a petty role in human–computer interaction research. Increasingly, however, researchers attempt to strike a balance between the traditional concerns of human–computer interaction and considerations of aesthetics. Thus, recent research suggests that the visual aesthetics of computer interfaces is a strong determinant of users’ satisfaction and pleasure. However, the lack of appropriate concepts and measures of aesthetics may severely constraint future research in this area. To address this issue, we conducted four studies in order to develop a measurement instrument of perceived web site aesthetics. Using exploratory and confirmatory factor analyses we found that users’ perceptions consist of two main dimensions, which we termed ‘‘classical aesthetics’’ and ‘‘expressive aesthetics’’. The classical aesthetics dimension pertains to aesthetic notions that presided from antiquity until the 18th century. These notions emphasize orderly and clear design and are closely related to many of the design rules advocated by usability experts. The expressive aesthetics dimension is manifested by the designers’ creativity and originality and by the ability to break design conventions. While both dimensions of perceived aesthetic are drawn from a pool of aesthetic judgments, they are clearly distinguishable from each other. Each of the aesthetic dimensions is measured by a fiveitem scale. The reliabilities, factor structure and validity tests indicate that these items reflect the two perceived aesthetics dimensions adequately. r 2003  This study was supported by a grant from the Burda Centre for Innovative Communications at BenGurion University of the Negev. Corresponding author.  +972-8-6472226; fax: +972-8-6477527. E-mail address: noamt@bgumail.bgu.ac.il (N. Tractinsky). 1071-5819/$ see front matter r 2003
Improving Sampling from Generative Autoencoders with Markov Chains

We focus on generative autoencoders, such as variational or adversarial autoencoders, which jointly learn a generative model alongside an inference model. Generative autoencoders are those which are trained to softly enforce a prior on the latent distribution learned by the inference model. We call the distribution to which the inference model maps observed samples, the learned latent distribution, which may not be consistent with the prior. We formulate a Markov chain Monte Carlo (MCMC) sampling process, equivalent to iteratively decoding and encoding, which allows us to sample from the learned latent distribution. Since, the generative model learns to map from the learned latent distribution, rather than the prior, we may use MCMC to improve the quality of samples drawn from the generative model, especially when the learned latent distribution is far from the prior. Using MCMC sampling, we are able to reveal previously unseen differences between generative autoencoders trained either with or without a denoising criterion.