nils-herrmann/clean_scirepeval huggingface dataset:
Probabilistic Contact Estimation and Impact Detection for State Estimation of Quadruped Robots

Reliable state estimation is crucial for stable planning and control of legged locomotion. A fundamental component of a state estimator in legged platforms is Leg Odometry, which only requires information about kinematics and contacts. Many legged robots use dedicated sensors on each foot to detect ground contacts. However, this choice is impractical for many agile legged robots in field operations, as these sensors often degrade and break. Instead, this paper focuses on the development of a robust Leg Odometry module, which does not require contact sensors. The module estimates the probability of reliable contact and detects foot impacts using internal force sensing. This knowledge is then used to improve the kinematics-inertial state estimate of the robot's base. We show how our approach can reach comparable performance to systems with foot sensors. Extensive experimental results lasting over 1 h are presented on our 85 $\text{kg}$ quadrupedal robot HyQ carrying out a variety of gaits.
Populism : Demand and Supply ∗

Using individual data on voting and political parties manifestos in European countries, we empirically characterize the drivers of voting for populist parties (the demand side) as well as the presence of populist parties (the supply side). We show that the economic insecurity drivers of the demand of populism are significant, especially when considering the key interactions with turnout incentives, neglected in previous studies. Once turnout effects are taken into account, economic insecurity drives consensus to populist policies directly and through indirect negative effects on trust and attitudes towards immigrants. On the supply side, populist parties are more likely to emerge when countries are faced with a systemic crisis of economic security. The orientation choice of populist parties, i.e., whether they arise on left or right of the political spectrum, is determined by the availability of political space. The typical mainstream parties response is to reduce the distance of their platform from that of successful populist entrants, amplifying the aggregate supply of populist policies.
Human-Computer Interaction

HCI ( Human Computer Interaction ) studies the ways humans use digital or computational machines, systems or infrastructures. The study of the barriers encountered when users interact with the various interfaces is critical to improving their use, as well as their experience. Access and information processing is carried out today from multiple devices (computers, tablets, phones... ) which is essential to maintain a multichannel consistency. This complexity increases with environments in which we do not have much experience as users, where interaction with the machine is a challenge even in phases of research: virtual reality environments, augmented reality, or viewing and handling of large amounts of data, where the simplicity and ease of use are critical.
DOM-based content extraction of HTML documents

Web pages often contain clutter (such as pop-up ads, unnecessary images and extraneous links) around the body of an article that distracts a user from actual content. Extraction of "useful and relevant" content from web pages has many applications, including cell phone and PDA browsing, speech rendering for the visually impaired, and text summarization. Most approaches to removing clutter or making content more readable involve changing font size or removing HTML and data components such as images, which takes away from a webpage's inherent look and feel. Unlike "Content Reformatting", which aims to reproduce the entire webpage in a more convenient form, our solution directly addresses "Content Extraction". We have developed a framework that employs easily extensible set of techniques that incorporate advantages of previous work on content extraction. Our key insight is to work with the DOM trees, rather than with raw HTML markup. We have implemented our approach in a publicly available Web proxy to extract content from HTML web pages.
Feature-Fused SSD: Fast Detection for Small Objects

Small objects detection is a challenging task in computer vision due to its limited resolution and information. In order to solve this problem, the majority of existing methods sacrifice speed for improvement in accuracy. In this paper, we aim to detect small objects at a fast speed, using the best object detector Single Shot Multibox Detector (SSD) with respect to accuracy-vs-speed trade-off as base architecture. We propose a multi-level feature fusion method for introducing contextual information in SSD, in order to improve the accuracy for small objects. In detailed fusion operation, we design two feature fusion modules, concatenation module and element-sum module, different in the way of adding contextual information. Experimental results show that these two fusion modules obtain higher mAP on PASCAL VOC2007 than baseline SSD by 1.6 and 1.7 points respectively, especially with 2-3 points improvement on some small objects categories. The testing speed of them is 43 and 40 FPS respectively, superior to the state of the art Deconvolutional single shot detector (DSSD) by 29.4 and 26.4 FPS.
A hybrid framework combining background subtraction and deep neural networks for rapid person detection

Currently, the number of surveillance cameras is rapidly increasing responding to security issues. But constructing an intelligent detection system is not easy because it needs high computing performance. This study aims to construct a real-world video surveillance system that can effectively detect moving person using limited resources. To this end, we propose a simple framework to detect and recognize moving objects using outdoor CCTV video footages by combining background subtraction and Convolutional Neural Networks (CNNs). A background subtraction algorithm is first applied to each video frame to find the regions of interest (ROIs). A CNN classification is then carried out to classify the obtained ROIs into one of the predefined classes. Our approach much reduces the computation complexity in comparison to other object detection algorithms. For the experiments, new datasets are constructed by filming alleys and playgrounds, places where crimes are likely to occur. Different image sizes and experimental settings are tested to construct the best classifier for detecting people. The best classification accuracy of 0.85 was obtained for a test set from the same camera with training set and 0.82 with different cameras.
The Horizontal / Vertical Distinction in Cross-Cultural Consumer ResearchTHE

We argue for the importance of a relatively new cultural distinction in the horizontal (valuing equality) or vertical (emphasizing hierarchy) nature of cultures and cultural orientations. A review of the existing cross-cultural literature is presented suggesting that, although the contribution of the horizontal/vertical distinction is sometimes obscured by methods that conflate it with other dimensions, its impact is distinct from that associated with individualism– collectivism. We present studies that highlight several sources of value for the horizontal/vertical distinction—as a predictor of new consumer psychology phenomena and as a basis for refining the understanding of known phenomena. Results support the utility of examining this distinction for the understanding of personal values, advertising and consumer persuasion, self-presentational patterns, and gender differences. Methodological issues in studying the horizontal/vertical distinction are also discussed.
CONTRIBUTIONS OF BANKING SECTOR IN ECONOMIC GROWTH : A Case of Pakistan

This study investigates the contributions of banking sector in economic growth of Pakistan. The data used in this study were collected from the period of 1981 to 2010 o 10 banks. Augmented Dickey Fuller (ADF) and Philip Perron unit root test, ordinary least square and granger causality test have been used. Unit root test confirms the stationary of all variables at first difference. Regression results indicate that deposits, investments, advances, profitability and interest earnings have significant positive impact on economic growth of Pakistan. The Granger-Causality test confirms the bidirectional causal relationship of deposits, advances and profitability with economic growth. On the other side we found unidirectional causal relationship of investments and interest earnings with economic growth runs from investments and interest earnings to economic growth. It is recommended that the policy makers should make policies to enhance the banking sector in Pakistan because banking sector is significantly contributing in the economic growth of Pakistan.
A Novel Approach for the Detection & Analysis of Brain Tumor

Medical image processing is the most challenging and emerging field now a day’s. In this field, detection of brain tumor from MRI brain scan has become one of the most challenging problems, due to complex structure of brain. The quantitative analysis of MRI brain tumor allows obtaining useful key indicators of disease progression. A computer aided diagnostic system has been proposed here for detecting the tumor texture in biological study. This is an attempt made which describes the proposed strategy for detection of tumor with the help of segmentation techniques in MATLAB; which incorporates preprocessing stages of noise removal, image enhancement and edge detection. Processing stages includes segmentation like intensity and watershed based segmentation, thresholding to extract the area of unwanted cells from the whole image. Here algorithms are proposed to calculate area and percentage of the tumor.  MRI, FCM, MKFCM, SVM, Otsu, threshold, fudge factor
Learning visual similarity for product design with convolutional neural networks

Popular sites like Houzz, Pinterest, and LikeThatDecor, have communities of users helping each other answer questions about products in images. In this paper we learn an embedding for visual search in interior design. Our embedding contains two different domains of product images: products cropped from internet scenes, and products in their iconic form. With such a multi-domain embedding, we demonstrate several applications of visual search including identifying products in scenes and finding stylistically similar products. To obtain the embedding, we train a convolutional neural network on pairs of images. We explore several training architectures including re-purposing object classifiers, using siamese networks, and using multitask learning. We evaluate our search quantitatively and qualitatively and demonstrate high quality results for search across multiple visual domains, enabling new applications in interior design.
CERES: Distantly Supervised Relation Extraction from the Semi-Structured Web

The web contains countless semi-structured websites, which can be a rich source of information for populating knowledge bases. Existing methods for extracting relations from the DOM trees of semi-structured webpages can achieve high precision and recall only when manual annotations for each website are available. Although there have been efforts to learn extractors from automatically generated labels, these methods are not sufficiently robust to succeed in settings with complex schemas and information-rich websites. In this paper we present a new method for automatic extraction from semi-structured websites based on distant supervision. We automatically generate training labels by aligning an existing knowledge base with a website and leveraging the unique structural characteristics of semi-structured websites. We then train a classifier based on the potentially noisy and incomplete labels to predict new relation instances. Our method can compete with annotationbased techniques in the literature in terms of extraction quality. A large-scale experiment on over 400,000 pages from dozens of multi-lingual long-tail websites harvested 1.25 million facts at a precision of 90%. PVLDB Reference Format: Colin Lockard, Xin Luna Dong, Arash Einolghozati, Prashant Shiralkar.  Distantly Supervised Relation Extraction from the Semi-Structured Web. PVLDB, 11(10): 1084-1096, 2018.
Sentiment Analysis of Big Data: Methods, Applications, and Open Challenges

The development of IoT technologies and the massive admiration and acceptance of social media tools and applications, new doors of opportunity have been opened for using data analytics in gaining meaningful insights from unstructured information. The application of opinion mining and sentiment analysis (OMSA) in the era of big data have been used a useful way in categorizing the opinion into different sentiment and in general evaluating the mood of the public. Moreover, different techniques of OMSA have been developed over the years in different data sets and applied to various experimental settings. In this regard, this paper presents a comprehensive systematic literature review, aims to discuss both technical aspect of OMSA (techniques and types) and non-technical aspect in the form of application areas are discussed. Furthermore, this paper also highlighted both technical aspects of OMSA in the form of challenges in the development of its technique and non-technical challenges mainly based on its application. These challenges are presented as a future direction for research.
On Ideal Binary Mask As the Computational Goal of Auditory Scene Analysis

What is the computational goal of auditory scene analysis? This is a key issue to address in the Marrian information-processing framework. It is also an important question for researchers in computational auditory scene analysis (CASA) because it bears directly on how a CASA system should be evaluated. In this chapter I discuss different objectives used in CASA. I suggest as a main CASA goal the use of the ideal time-frequency (T-F) binary mask whose value is one for a T-F unit where the target energy is greater than the interference energy and is zero otherwise. The notion of the ideal binary mask is motivated by the auditory masking phenomenon. Properties of the ideal binary mask are discussed, including their relationship to automatic speech recognition and human speech intelligibility. This CASA goal has led to algorithms that directly estimate the ideal binary mask in monaural and binaural conditions, and these algorithms have substantially advanced the state-of-the-art performance in speech separation.
Real time News Story Detection and Tracking with Hashtags

Topic Detection and Tracking (TDT) is an important research topic in data mining and information retrieval and has been explored for many years. Most of the studies have approached the problem from the event tracking point of view. We argue that the definition of stories as events is not reflecting the full picture. In this work we propose a story tracking method built on crowd-tagging in social media, where news articles are labeled with hashtags in real-time. The social tags act as rich meta-data for news articles, with the advantage that, if carefully employed, they can capture emerging concepts and address concept drift in a story. We present an approach for employing social tags for the purpose of story detection and tracking and show initial empirical results. We compare our method to classic keyword query retrieval and discuss an example of story tracking over time.
A model of aesthetic appreciation and aesthetic judgments.

Although aesthetic experiences are frequent in modern life, there is as of yet no scientifically comprehensive theory that explains what psychologically constitutes such experiences. These experiences are particularly interesting because of their hedonic properties and the possibility to provide self-rewarding cognitive operations. We shall explain why modern art's large number of individualized styles, innovativeness and conceptuality offer positive aesthetic experiences. Moreover, the challenge of art is mainly driven by a need for understanding. Cognitive challenges of both abstract art and other conceptual, complex and multidimensional stimuli require an extension of previous approaches to empirical aesthetics. We present an information-processing stage model of aesthetic processing. According to the model, aesthetic experiences involve five stages: perception, explicit classification, implicit classification, cognitive mastering and evaluation. The model differentiates between aesthetic emotion and aesthetic judgments as two types of output.
Multichannel Texture Analysis Using Localized Spatial Filters

A computational approach for analyzing visible textures is described. Textures are modeled as irradiance patterns containing a limited range of spatial frequencies, where mutually distinct textures differ significantly in their dominant characterizing frequencies. By encoding images into multiple narrow spatial frequency and orientation channels, the slowly-varying channel envelopes (amplitude and phase) are used to segregate textural regions of different spatial frequency, orientation, or phase characteristics. Thus, an interpretation of image texture as a region code, or currier of region information, is
Deep Density Destructors

We propose a unified framework for deep density models by formally defining density destructors. A density destructor is an invertible function that transforms a given density to the uniform density—essentially destroying any structure in the original density. This destructive transformation generalizes Gaussianization via ICA and more recent autoregressive models such as MAF and Real NVP. Informally, this transformation can be seen as a generalized whitening procedure or a multivariate generalization of the univariate CDF function. Unlike Gaussianization, our destructive transformation has the elegant property that the density function is equal to the absolute value of the Jacobian determinant. Thus, each layer of a deep density can be seen as a shallow density—uncovering a fundamental connection between shallow and deep densities. In addition, our framework provides a common interface for all previous methods enabling them to be systematically combined, evaluated and improved. Leveraging the connection to shallow densities, we also propose a novel tree destructor based on tree densities and an image-specific destructor based on pixel locality. We illustrate our framework on a 2D dataset, MNIST, and CIFAR-10. Code is available on first author’s website.
Mobile Based Secure Digital Wallet for Peer to Peer Payment System

E-commerce in today's conditions has the highest dependence on network infrastructure of banking. However, when the possibility of communicating with the Banking network is not provided, business activities will suffer. This paper proposes a new approach of digital wallet based on mobile devices without the need to exchange physical money or communicate with banking network. A digital wallet is a software component that allows a user to make an electronic payment in cash (such as a credit card or a digital coin), and hides the low-level details of executing the payment protocol that is used to make the payment. The main features of proposed architecture are secure awareness, fault tolerance, and infrastructure-less protocol.
Selenium: from cancer prevention to DNA damage.

Selenium (Se) is a dietary essential trace element with important biological roles. Accumulating evidence indicates that Se compounds possess anticancer properties. Se is specifically incorporated into proteins in the form of selenocysteine and non-specifically incorporated as selenomethionine in place of methionine. The effects of Se compounds on cells are strictly compositional and concentration-dependent. At supranutritional dietary levels, Se can prevent the development of many types of cancer. At higher concentrations, Se compounds can be either cytotoxic or possibly carcinogenic. The cytotoxicity of Se is suggested to be associated with oxidative stress. Accordingly, sodium selenite, an inorganic Se compound, was reported to induce DNA damage, particularly DNA strand breaks and base damage. In this review we summarize the various activities of Se compounds and focus on their relation to DNA damage and repair. We discuss the use of Saccharomyces cerevisiae for identification of the genes involved in Se toxicity and resistance.
DEVELOPMENT OF A ROBOTIC WHEELCHAIR

ii Development of a Robotic Wheelchair Abstract Robotic wheelchairs extend the capabilities of traditional powered devices by introducing control and navigational intelligence. These devices can ease the lives of many disabled people, particularly those with severe impairments, by increasing their range of mobility. A robotic wheelchair has been under development at the University of Wollongong for some years. This thesis describes ongoing work towards the ultimate aim of an intelligent and useful device.
Dynamic control flow in large-scale machine learning

Many recent machine learning models rely on fine-grained dynamic control flow for training and inference. In particular, models based on recurrent neural networks and on reinforcement learning depend on recurrence relations, data-dependent conditional execution, and other features that call for dynamic control flow. These applications benefit from the ability to make rapid control-flow decisions across a set of computing devices in a distributed system. For performance, scalability, and expressiveness, a machine learning system must support dynamic control flow in distributed and heterogeneous environments.
 This paper presents a programming model for distributed machine learning that supports dynamic control flow. We describe the design of the programming model, and its implementation in TensorFlow, a distributed machine learning system. Our approach extends the use of dataflow graphs to represent machine learning models, offering several distinctive features. First, the branches of conditionals and bodies of loops can be partitioned across many machines to run on a set of heterogeneous devices, including CPUs, GPUs, and custom ASICs. Second, programs written in our model support automatic differentiation and distributed gradient computations, which are necessary for training machine learning models that use control flow. Third, our choice of non-strict semantics enables multiple loop iterations to execute in parallel across machines, and to overlap compute and I/O operations.
 We have done our work in the context of TensorFlow, and it has been used extensively in research and production. We evaluate it using several real-world applications, and demonstrate its performance and scalability.
Big Data: The NoSQL and RDBMS review

The quantity of data transmitted in the network intensified rapidly with the increased dependency on social media applications, sensors for data acquisitions and smartphones utilizations. Typically, such data is unstructured and originates from multiple sources in different format. Consequently, the abstraction of data for rendering is difficult, that lead to the development of a computing system that is able to store data in unstructured format and support distributed parallel computing. To data, there exist approaches to handle big data using NoSQL. This paper provides a review and the comparison between NoSQL and Relational Database Management System (RDBMS). By reviewing each approach, the mechanics of NoSQL systems can be clearly distinguished from the RDBMS. Basically, such systems rely on multiple factors, that include the query language, architecture, data model and consumer API. This paper also defines the application that matches the system and subsequently able to accurately correlates to a specific NoSQL system.