nils-herrmann/clean_scirepeval huggingface dataset:
The minds of gods: A comparative study of supernatural agency

The present work is the first study to systematically compare the minds of gods by examining some of the intuitive processes that guide how people reason about them. By examining the Christian god and the spirit-masters of the Tyva Republic, it first confirms that the consensus view of the Christian god's mind is one of omniscience with acute concern for interpersonal social behavior (i.e., moral behaviors) and that Tyvan spirit-masters are not as readily attributed with knowledge or concern of moral information. Then, it reports evidence of a moralization bias of gods' minds; American Christians who believe that God is omniscient rate God as more knowledgeable of moral behaviors than nonmoral information. Additionally, Tyvans who do not readily report pro- or antisocial behavior among the things that spirit-masters care about will nevertheless rate spirit-masters' knowledge and concern of moral information higher than nonmoral information. However, this knowledge is distributed spatially; the farther away from spirits' place of governance a moral behavior takes place, the less they know and care about it. Finally, the wider the breadth of knowledge Tyvans attribute to spirit-masters, the more they attribute moral concern for behaviors that transpire beyond their jurisdiction. These results further demonstrate that there is a significant gulf between expressed beliefs and intuitive religious cognition and provides evidence for a moralization bias of gods' minds.
f3.js: A Parametric Design Tool for Physical Computing Devices for Both Interaction Designers and End-users

Although the exploration of design alternatives is crucial for interaction designers and customization is required for end-users, the current development tools for physical computing devices have focused on single versions of an artifact. We propose the parametric design of devices including their enclosure layouts and programs to address this issue. A Web-based design tool called f3.js is presented as an example implementation, which allows devices assembled from laser-cut panels with sensors and actuator modules to be parametrically created and customized. It enables interaction designers to write code with dedicated APIs, declare parameters, and interactively tune them to produce the enclosure layouts and programs. It also provides a separate user interface for end-users that allows parameter tuning and dynamically generates instructions for device assembly. The parametric design approach and the tool were evaluated through two user studies with interaction designers, university students, and end-users.
Bimodal biometric system for hand shape and palmprint recognition based on SIFT sparse representation

Biometric-based hand modality is considered as one of the most popular biometric technologies especially in forensic applications. In this paper, a bimodal hand identification system was proposed based on Scale Invariant Feature Transform (SIFT) descriptors, extracted from hand shape and palmprint modalities. A local sparse representation method was adopted in order to represent images with high discrimination. Moreover, fusion was performed at feature and decision levels using a cascade fusion in order to generate the final identification rate of our bimodal system. Our experiments were applied on two hand databases: Indian Institute of Technology of Delhi (IITD) hand database and Bosphorus hand database containing, respectively, 230 and 615 subjects. The results show that the proposed method offers high accuracies compared to other popular bimodal hand biometric methods over the two hand databases. The correct identification rate reaches 99.57 % which is competitive compared to systems existing in the literature.
Unsupervised prediction of citation influences

Publication repositories contain an abundance of information about the evolution of scientific research areas. We address the problem of creating a visualization of a research area that describes the flow of topics between papers, quantifies the impact that papers have on each other, and helps to identify key contributions. To this end, we devise a probabilistic topic model that explains the generation of documents; the model incorporates the aspects of topical innovation and topical inheritance via citations. We evaluate the model's ability to predict the strength of influence of citations against manually rated citations.
Motion Planning : The Essentials

This is the first installment of a two-part tutorial. The goal of the first part is to give the reader a basic understanding of the technical issues and types of approaches to solving the basic path planning or obstacle avoidance problem. The second installment will cover more advanced issues, including feedback, differential constraints, and uncert ainty. Note that is a brieftutorial, rather than a comprehensive surveyof methods. For the latter, consult recent textbooks , .
Traffic sign detection and recognition for intelligent vehicle

In this paper, we propose a computer vision based system for real-time robust traffic sign detection and recognition, especially developed for intelligent vehicle. In detection phase, a color-based segmentation method is used to scan the scene in order to quickly establish regions of interest (ROI). Sign candidates within ROIs are detected by a set of Haar wavelet features obtained from AdaBoost training. Then, the Speeded Up Robust Features (SURF) is applied for the sign recognition. SURF finds local invariant features in a candidate sign and matches these features to the features of template images that exist in data set. The recognition is performed by finding out the template image that gives the maximum number of matches. We have evaluated the proposed system on our intelligent vehicle SmartVII. A recognition accuracy of over 90% in real-time has been achieved.
A robust adaptive mixing control for improved forward flight of a tilt-rotor UAV

This work presents the modeling and control of a tilt-rotor UAV, with tail controlled surfaces, for path tracking with improved forward flight. The dynamic model is obtained using the Euler-Lagrange formulation considering the aerodynamic forces and torques exerted on the horizontal and vertical stabilizers, and fuselage. For control design purposes, the equations of motion are linearized around different operation points to cover a large range of forward velocity. Based on these linearized dynamic models, a mixed H2/H∞ robust controller is designed for each operation point. Therefore, an adaptive mixing scheme is used to perform an on-line smooth gain-scheduling between them. Simulation results show the control strategy efficiency when the UAV is designated to have a forward acceleration and perform a circular trajectory subject to a wind disturbance.
Container-based operating system virtualization: a scalable, high-performance alternative to hypervisors

Hypervisors, popularized by Xen and VMware, are quickly becoming commodity. They are appropriate for many usage scenarios, but there are scenarios that require system virtualization with high degrees of both isolation and efficiency. Examples include HPC clusters, the Grid, hosting centers, and PlanetLab. We present an alternative to hypervisors that is better suited to such scenarios. The approach is a synthesis of prior work on resource containers and security containers applied to general-purpose, time-shared operating systems. Examples of such container-based systems include Solaris 10, Virtuozzo for Linux, and Linux-VServer. As a representative instance of container-based systems, this paper describes the design and implementation of Linux-VServer. In addition, it contrasts the architecture of Linux-VServer with current generations of Xen, and shows how Linux-VServer provides comparable support for isolation and superior system efficiency.
APAC: Augmented PAttern Classification with Neural Networks

Deep neural networks have been exhibiting splendid accuracies in many of visual pattern classification problems. Many of the state-of-the-art methods employ a technique known as data augmentation at the training stage. This paper addresses an issue of decision rule for classifiers trained with augmented data. Our method is named as APAC: the Augmented PAttern Classification, which is a way of classification using the optimal decision rule for augmented data learning. Discussion of methods of data augmentation is not our primary focus. We show clear evidences that APAC gives far better generalization performance than the traditional way of class prediction in several experiments. Our convolutional neural network model with APAC achieved a state-of-the-art accuracy on the MNIST dataset among non-ensemble classifiers. Even our multilayer perceptron model beats some of the convolutional models with recentlyinvented stochastic regularization techniques on the CIFAR10 dataset.
Visualizing Topic Models

Managing large collections of documents is an important problem for many areas of science, industry, and culture. Probabilistic topic modeling offers a promising solution. Topic modeling is an unsupervised machine learning method that learns the underlying themes in a large collection of otherwise unorganized documents. This discovered structure summarizes and organizes the documents. However, topic models are high-level statistical tools—a user must scrutinize numerical distributions to understand and explore their results. In this paper, we present a method for visualizing topic models. Our method creates a navigator of the documents, allowing users to explore the hidden structure that a topic model discovers. These browsing interfaces reveal meaningful patterns in a collection, helping end-users explore and understand its contents in new ways. We provide open source software of our method. Understanding and navigating large collections of documents has become an important activity in many spheres. However, many document collections are not coherently organized and organizing them by hand is impractical. We need automated ways to discover and visualize the structure of a collection in order to more easily explore its contents. Probabilistic topic modeling is a set of machine learning tools that may provide a solution (Blei and Lafferty 2009). Topic modeling algorithms discover a hidden thematic structure in a collection of documents; they find salient themes and represent each document as a combination of themes. However, topic models are high-level statistical tools. A user must scrutinize numerical distributions to understand and explore their results; the raw output of the model is not enough to create an easily explored corpus. We propose a method for using a fitted topic model to organize, summarize, visualize, and interact with a corpus. With our method, users can explore the corpus, moving between high level discovered summaries (the “topics”) and the documents themselves, as Figure 1 illustrates. Our design is centered around the idea that the model both summarizes and organizes the collection. Our method translates these representations into a visual system for exploring a collection, but visualizing this structure is not enough. The discovered structure induces relationships—between topics and articles, and between articles and articles—which lead to interactions in the visualization.  Thus, we have three main goals in designing the visualization: summarize the corpus for the user; reveal the relationships between the content and summaries; and, reveal the relationships across content. We aim to present these in a ways that are accessible and useful to a spectrum of users, not just machine learning experts.
Recognizing Contextual Polarity: An Exploration of Features for Phrase-Level Sentiment Analysis

Many approaches to automatic sentiment analysis begin with a large lexicon of words marked with their prior polarity (also called semantic orientation). However, the contextual polarity of the phrase in which a particular instance of a word appears may be quite different from the word's prior polarity. Positive words are used in phrases expressing negative sentiments, or vice versa. Also, quite often words that are positive or negative out of context are neutral in context, meaning they are not even being used to express a sentiment. The goal of this work is to automatically distinguish between prior and contextual polarity, with a focus on understanding which features are important for this task. Because an important aspect of the problem is identifying when polar terms are being used in neutral contexts, features for distinguishing between neutral and polar instances are evaluated, as well as features for distinguishing between positive and negative contextual polarity. The evaluation includes assessing the performance of features across multiple machine learning algorithms. For all learning algorithms except one, the combination of all features together gives the best performance. Another facet of the evaluation considers how the presence of neutral instances affects the performance of features for distinguishing between positive and negative polarity. These experiments show that the presence of neutral instances greatly degrades the performance of these features, and that perhaps the best way to improve performance across all polarity classes is to improve the system's ability to identify when an instance is neutral.
Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL

This paper presents a simple unsupervised learning algorithm for recognizing synonyms, based on statistical data acquir ed by querying a Web search engine. The algorithm, called PMI-IR, uses Pointwis e Mutual Information (PMI) and Information Retrieval (IR) to measure the similarity of pairs of words. PMI-IR is empirically evaluated using 80 syn onym test questions from the Test of English as a Foreign Language (TOEFL) a nd 50 synonym test questions from a collection of tests for students of En glish as a Second Language (ESL). On both tests, the algorithm obtains a score of 74%. PMI-IR is contrasted with Latent Semantic Analysis (LSA), which achieves a score of 64% on the same 80 TOEFL questions. The paper discusses po t ntial applications of the new unsupervised learning algorithm and some implic ations of the results for LSA and LSI (Latent Semantic Indexing).
Active ESD protection for input transistors in a 40-nm CMOS process

This work presents a novel design for input ESD protection. By replacing the protection resistor with an active switch that isolates the input transistors from the pad under ESD stress, the ESD robustness can be greatly improved. The proposed designs were designed and verified in a 40-nm CMOS process using only thin oxide devices, which can successfully pass the typical industry ESD-protection specifications of 2-kV HBM and 200-V MM ESD tests.
Application of Data Mining Classification Algorithms for Breast Cancer Diagnosis

Breast cancer is one of the diseases that represent a large number of incidence and mortality in the world. Data mining classifications techniques will be effective tools for classifying data of cancer to facilitate decision-making.
 The objective of this paper is to compare the performance of different machine learning algorithms in the diagnosis of breast cancer, to define exactly if this type of cancer is a benign or malignant tumor.
 Six machine learning algorithms were evaluated in this research Bayes Network (BN), Support Vector Machine (SVM), k-nearest neighbors algorithm (Knn), Artificial Neural Network (ANN), Decision Tree (C4.5) and Logistic Regression. The simulation of the algorithms is done using the WEKA tool (The Waikato Environment for Knowledge Analysis) on the Wisconsin breast cancer dataset available in UCI machine learning repository.
Collaborative video reindexing via matrix factorization

Concept-based video indexing generates a matrix of scores predicting the possibilities of concepts occurring in video shots. Based on the idea of collaborative filtering, this article presents unsupervised methods to refine the initial scores generated by concept classifiers by taking into account the concept-to-concept correlation and shot-to-shot similarity embedded within the score matrix. Given a noisy matrix, we refine the inaccurate scores via matrix factorization. This method is further improved by learning multiple local models and incorporating contextual-temporal structures. Experiments on the TRECVID 2006--2008 datasets demonstrate relative performance gains ranging from 13% to 52% without using any user annotations or external knowledge resources.
An intrusion detection system against malicious attacks on the communication network of driverless cars

Vehicular ad hoc networking (VANET) have become a significant technology in the current years because of the emerging generation of self-driving cars such as Google driverless cars. VANET have more vulnerabilities compared to other networks such as wired networks, because these networks are an autonomous collection of mobile vehicles and there is no fixed security infrastructure, no high dynamic topology and the open wireless medium makes them more vulnerable to attacks. It is important to design new approaches and mechanisms to rise the security these networks and protect them from attacks. In this paper, we design an intrusion detection mechanism for the VANETs using Artificial Neural Networks (ANNs) to detect Denial of Service (DoS) attacks. The main role of IDS is to detect the attack using a data generated from the network behavior such as a trace file. The IDSs use the features extracted from the trace file as auditable data. In this paper, we propose anomaly and misuse detection to detect the malicious attack.
Broadband digital frontends as enabler for software defined Radar

In this paper technical advances towards future broadband digital frontends are presented and discussed. Technical challenges on this path, such as ultra-wideband digital sampling, architectures for wideband frequency coverage, System on Chip technologies for digital beamforming, broadband antenna apertures including GaN / SiGe TR-Module technologies are addressed. Hensoldt's technological progress with respect to these challenges is shown. The operational benefits of future broadband digital frontends are outlined and even greater advantages in combining Radar, EW and Data Link capabilities with digital broadband AESAs are pointed out.
Clothing and People - A Social Signal Processing Perspective

In our society and century, clothing is not anymore used only as a means for body protection. Our paper builds upon the evidence, studied within the social sciences, that clothing brings a clear communicative message in terms of social signals, influencing the impression and behaviour of others towards a person. In fact, clothing correlates with personality traits, both in terms of self-assessment and assessments that unacquainted people give to an individual. The consequences of these facts are important: the influence of clothing on the decision making of individuals has been investigated in the literature, showing that it represents a discriminative factor to differentiate among diverse groups of people. Unfortunately, this has been observed after cumbersome and expensive manual annotations, on very restricted populations, limiting the scope of the resulting claims. With this position paper, we want to sketch the main steps of the very first systematic analysis, driven by social signal processing techniques, of the relationship between clothing and social signals, both sent and perceived. Thanks to human parsing technologies, which exhibit high robustness owing to deep learning architectures, we are now capable to isolate visual patterns characterising a large types of garments. These algorithms will be used to capture statistical relations on a large corpus of evidence to confirm the sociological findings and to go beyond the state of the art.
Evaluating Frameworks for Creating Mobile Web Apps

Mobile Web apps are an alternative to native apps when developing mobile applications for more than one platform. They can be accessed from almost any current smartphone and tablet using a browser. However, developing a Web app adapted to the particularities of mobile devices such as limited screen size and touch-based interaction requires consideration and effort. Several frameworks with different strengths and weaknesses support creating mobile Web apps. In this paper, we develop a set of criteria to be met by mobile Web frameworks of high quality. Based on these criteria, we evaluate jQuery Mobile, Sencha Touch, The-M-Project, and Google Web Toolkit combined with mgwt, thereby assessing their suitability for certain situations. We find Sencha Touch suited for mobile Web apps of moderate and increased complexity, while jQuery Mobile is a good starting point for apps primarily concerned with a mobile user interface.
Injectable treatments for the aging face.

The use of injectable agents, specifically soft tissue fillers and botulinum toxin type A, has risen dramatically over recent years, due to the increased demand for minimally invasive techniques. In fact, today they represent the most commonly performed cosmetic procedures in the United States, with botulinum type A injections topping the list. In the treatment of the aging face, these agents, when used individually or in combination, can effectively decrease rhytids and restore lost volume. The result is a fuller, smoother, more youthful appearance. This article provides an overview of botulinum type A (Botox Cosmetic; Allergan, Inc., Irvine, CA) and the two injectable fillers most commonly used in our practice, namely hyaluronic acid (Restalyne; Medicis Aesthetics, Inc., Scottsdale, AZ) and human-derived collagen (Cosmoderm and Cosmoplast; Inamed Aesthetics, Inc., Santa Barbara, CA). Although we commonly use autologous fat as an injectable filler for facial-volume augmentation, its discussion is beyond the scope of this article. Conceptually, the aging face can be divided into upper, middle, and lower thirds. Using this framework, we will discuss our treatment strategies for addressing each facial region. General principles, preinjection evaluation, and specific approaches and techniques for each anatomic region will be discussed, with particular emphasis on the benefit of using dermal fillers in conjunction with botulinum toxin type A to achieve optimal aesthetic results for facial rejuvenation.
p-Cycle-based node failure protection for survivable virtual network embedding

Network Virtualization offers to Physical Infrastructure Provider (PIP) the possibility to smoothly roll out multiple isolated networks on top of his infrastructure. A major challenge in this respect is the embedding problem which deals with the mapping of Virtual Networks (VNs) resources on PIP network. In literature, a number of research proposals have been focused on providing heuristic approaches to solve this NP-hard problem while most often assuming that PIP infrastructure is operational at all time. In virtualization environment a single physical node failure can result in single/multi logical link failure(s) as it effects all VNs with a mapping that spans over. Setup a dedicated backup for each VN embedding, i.e., no sharing, is inefficient in terms of resource usage. To address these concerns, we propose in this work an approach for VN mapping with combined physical node and multiple logical links protection mechanism (VNM-CNLP) that: (a) uses a large scale optimization technique, developed in our previous work, to calculate in a first stage a cost-efficient VN mapping while minimizing the effects of a single node failure in VN layer, and (b) proposes in a second stage link p-Cycle based protection techniques that minimize the backup resources while providing a full VN protection scheme against a single physical node failure and a multiple logical links failure. Our simulations show a clear advantage of VNM-CNLP approach over benchmark in terms of VN backup cost and resources utilization.