nils-herrmann/clean_scirepeval huggingface dataset:
A Context-Aware Security Architecture for Emerging Applications

We describe an approach to building security services for context-aware environments. Specifically, we focus on the design of security services that incorporate the use of security-relevant “context” to provide flexible access control and policy enforcement. We previously presented a generalized access control model that makes significant use of contextual information in policy definition. This document provides a concrete realization of such a model by presenting a system-level service architecture, as well as early implementation experience with the framework. Through our context-aware security services, our system architecture offers enhanced authentication services, more flexible access control and a security subsystem that can adapt itself based on current conditions in the environment. We discuss our architecture and implementation and show how it can be used to secure several sample applications.
AZIMUT, a leg-track-wheel robot

AZIMUT is a mobile robotic platform that combines wheels, legs and tracks to move in three-dimensional environments. The robot is symmetrical and is made of four independent leg-track-wheel articulations. It can move with its articulations up, down or straight, or to move sideways without changing the robot's orientation. To validate the concept, the rst prototype developed measures 70.5 cm 70.5 cm with the articulations up. It has a body clearance of 8.4 cm to 40.6 cm depending on the position of the articulations. The design of the robot is highly modular, with distributed embedded systems to control the di erent components of the robot.
Inferring user knowledge level from eye movement patterns

The acquisition of information and the search interaction process is influenced strongly by a person’s use of their knowledge of the domain and the task. In this paper we show that a user’s level of domain knowledge can be inferred from their interactive search behaviors without considering the content of queries or documents. A technique is presented to model a user’s information acquisition process during search using only measurements of eye movement patterns. In a user study (n=40) of search in the domain of genomics, a representation of the participant’s domain knowledge was constructed using self-ratings of knowledge of genomics-related terms (n=409). Cognitive effort features associated with reading eye movement patterns were calculated for each reading instance during the search tasks. The results show correlations between the cognitive effort due to reading and an individual’s level of domain knowledge. We construct exploratory regression models that suggest it is possible to build models that can make predictions of the user’s level of knowledge based on real-time measurements of eye movement patterns during a task session.
Facial Expression Recognition with deep age

This paper presents a novel deep learning framework for facial expression recognition (FER). Our framework is derived from Convolutional Neural Networks (CNNs), adopting outline, texture, and angle raw data to generate 3 different convolutional feature maps for deep learning. In so doing, the proposed method is cable of robustly classifying expressions, by emphasizing the facial deformation, muscle movement, and outline feature descriptions. Therefore, our method makes the facial expression task not only more tractable, but also more reliable in the case of expression images, which leads to an overall improvement of classification performance compared to conventional methods. The proposed method is valid for the Static Facial Expression Recognition (SFEW) database, improving the baseline results by 6.98%.
Online Learning for Time Series Prediction

In this paper we address the problem of predicting a time seri e using the ARMA (autoregressive moving average) model, under minimal assumptions on the noi se terms. Using regret minimization techniques, we develop effective online learning algorith ms for the prediction problem, withoutassuming that the noise terms are Gaussian, identically distribu ted or even independent. Furthermore, we show that our algorithm’s performances asymptotically app roaches the performance of the best ARMA model in hindsight.
Robust moving objects detection in lidar data exploiting visual cues

Detecting moving objects in dynamic scenes from sequences of lidar scans is an important task in object tracking, mapping, localization, and navigation. Many works focus on changes detection in previously observed scenes, while a very limited amount of literature addresses moving objects detection. The state-of-the-art method exploits Dempster-Shafer Theory to evaluate the occupancy of a lidar scan and to discriminate points belonging to the static scene from moving ones. In this paper we improve both speed and accuracy of this method by discretizing the occupancy representation, and by removing false positives through visual cues. Many false positives lying on the ground plane are also removed thanks to a novel ground plane removal algorithm. Efficiency is improved through an octree indexing strategy. Experimental evaluation against the KITTI public dataset shows the effectiveness of our approach, both qualitatively and quantitatively with respect to the state-of-the-art.
Augmented Reality Tracking in Natural Environments

Tracking, or camera pose determination, is the main technical challenge in creating augmented realities. Constraining the degree to which the environment may be altered to support tracking heightens the challenge. This paper describes several years of work at the USC Computer Graphics and Immersive Technologies (CGIT) laboratory to develop self-contained, minimally intrusive tracking systems for use in both indoor and outdoor settings. These hybrid-technology tracking systems combine vision and inertial sensing with research in fiducial design, feature detection, motion estimation, recursive filters, and pragmatic engineering to satisfy realistic application requirements.
60 GHz Fermi tapered slot antenna with sin-corrugation

A low-cost, high gain and broadband 60 GHz Fermi tapered slot antenna with a sin-corrugation is presented. The novel corrugation design parameters provide a high flexibility to enhance antenna characteristics. The proposed antenna provides a gain of 18.8 dB at 60 GHz, a high total radiation efficiency of ~90%, and an impedance bandwidth covers the 50-70 GHz band. With these features, this antenna is suitable for unlicensed millimeter-wave ISM-band, wireless communications, and imaging applications.
A Method to Generate a Modular ifcOWL Ontology

Building Information Modeling (BIM) and Semantic Web technologies are becoming more and more popular in the Architecture Engineering Construction (AEC) and Facilities Management (FM) industry to support information management, information exchange and data interoperability. One of the key integration gateways between BIM and Semantic Web is represented by the ifcOWL ontology, i.e. the Web Ontology Language (OWL) version of the IFC standard, being one of reference technical standard for AEC/FM. Previous studies have shown how a recommended ifcOWL ontology can be automatically generated by converting the IFC standard from the official EXPRESS schema. However, the resulting ifcOWL is a large monolithic ontology that presents serious limitations for real industrial applications in terms of usability and performance (i.e. querying and reasoning). Possible enhancements to reduce the complexity and the data size consist in (1) modularization of ifcOWL making it easier to use subsets of the entire ontology, and (2) rethinking the contents and structure of an ontology for AEC/FM to better fit in the semantic web scope and make its usage more efficient. The second approach can be enabled by the first one, since it would make it easier to replace some of the ifcOWL modules with new optimized ontologies for the AEC-FM industry. This paper focuses on the first approach presenting a method to automatically generate a modular ifcOWL ontology. The method aims at minimizing the dependencies between modules to better exploit the modularization. The results are compared with simpler and more straight-forward solutions.
Micro bubble manipulation towards single cell handling tool

We report a series of micro air bubble manipulations (transporting, merging and eliminating of air bubbles) in two-dimensional microchannels filled with a water solution. Air bubbles (~500 nano liters in volume) are driven by electrowetting-on-dielectric (EWOD) principle. By sequentially energizing an array of electrodes covered with dielectric layers, air bubbles can be transported along a programmed path, merged into a single larger one, and eliminated out of a bulk water solution. This bubble manipulation technique can be applied to efficiently handle micro objects. As a proof of concept, we successfully demonstrate in a millimeter scale that a fish egg and a sesame husk are attached to an air bubble and then driven by the bubble in a programmed way. The moving air bubble can push and pull the attached objects of which surface is hydrophilic or hydrophobic. This handling technique will bring a novel, versatile, cost-effective, high throughput tool, enabling us to efficiently transport, trap, and isolate individual micro bio-entities such as cells, functional particles and possibly molecules
System Software for Ubiquitous Computing

U biquitous computing, or ubicomp, systems designers embed devices in various physical objects and places. Frequently mobile, these devices— such as those we carry with us and embed in cars—are typically wirelessly networked. Some 30 years of research have gone into creating distributed computing systems, and we've invested nearly 20 years of experience in mobile computing. With this background, and with today's developments in miniaturization and wireless operation, our community seems poised to realize the ubicomp vision. However, we aren't there yet. Ubicomp software must deliver functionality in our everyday world. It must do so on failure-prone hardware with limited resources. Additionally, ubicomp software must operate in conditions of radical change. Varying physical circumstances cause components routinely to make and break associations with peers of a new degree of functional heterogeneity. Mobile and distributed computing research has already addressed parts of these requirements, but a qualitative difference remains between the requirements and the achievements. In this article, we examine today's ubiquitous systems, focusing on software infrastructure, and discuss the road that lies ahead. We base our analysis on physical integration and spontaneous interoperation, two main characteristics of ubicomp systems, because much of the ubicomp vision, as expounded by Mark Weiser and others, 1 either deals directly with or is predicated on them. Physical integration A ubicomp system involves some integration between computing nodes and the physical world. For example, a smart coffee cup, such as a Media-Cup, 2 serves as a coffee cup in the usual way but also contains sensing, processing, and networking elements that let it communicate its state (full or empty, held or put down). So, the cup can give colleagues a hint about the state of the cup's owner. Or consider a smart meeting room that senses the presence of users in meetings, records their actions, 3 and provides services as they sit at a table or talk at a whiteboard. 4 The room contains digital furniture such as chairs with sensors, whiteboards that record what's written on them, and projectors that you can activate from anywhere in the room using a PDA (personal digital assistant). Human administrative, territorial, and cultural considerations mean that ubicomp takes place in more or less discrete environments based, for example , on homes, rooms, or airport lounges. In other words, the world consists of ubiquitous systems rather than " the ubiquitous system. " So, from physical integration, we draw our …
Adaptive Color Attributes for Real-Time Visual Tracking

Visual tracking is a challenging problem in computer vision. Most state-of-the-art visual trackers either rely on luminance information or use simple color representations for image description. Contrary to visual tracking, for object recognition and detection, sophisticated color features when combined with luminance have shown to provide excellent performance. Due to the complexity of the tracking problem, the desired color feature should be computationally efficient, and possess a certain amount of photometric invariance while maintaining high discriminative power. This paper investigates the contribution of color in a tracking-by-detection framework. Our results suggest that color attributes provides superior performance for visual tracking. We further propose an adaptive low-dimensional variant of color attributes. Both quantitative and attribute-based evaluations are performed on 41 challenging benchmark color sequences. The proposed approach improves the baseline intensity-based tracker by 24 % in median distance precision. Furthermore, we show that our approach outperforms state-of-the-art tracking methods while running at more than 100 frames per second.
Data Loss Prevention Based on Text Classification in Controlled Environments

Loss of sensitive data is a common problem with potentially severe consequences. By categorizing documents according to their sensitivity, security controls can be performed based on this classification. However, errors in the classification process may effectively result in information leakage. While automated classification techniques can be used to mitigate this risk, little work has been done to evaluate the effectiveness of such techniques when sensitive content has been transformed (e.g., a document can be summarized, rewritten, or have paragraphs copy-pasted into a new one). To better handle these more difficult data leaks, this paper proposes the use of controlled environments to detect misclassification. By monitoring the incoming information flow, the documents imported into a controlled environment can be used to better determine the sensitivity of the document(s) created within the same environment. Our evaluation results show that this approach, using techniques from machine learning and information retrieval, provides improved detection of incorrectly classified documents that have been subject to more complex data transformations.
A conceptual model of client-driven agile requirements prioritization: results of a case study

Requirements (re)prioritization is an essential mechanism of agile development approaches to maximize the value for the clients and to accommodate changing requirements. Yet, in the agile Requirements Engineering (RE) literature, very little is known about how agile (re)prioritization happens in practice. Conceptual models about this process are missing, which, in turn, makes it difficult for both practitioners and researchers to reason about requirements decision-making at inter-iteration time. We did a multiple case study on agile requirements prioritization methods to yield a conceptual model for understanding the inter-iteration prioritization process. The model is derived by using interview data from practitioners in 8 development organizations. Such a model makes explicit the concepts that are used tacitly in the agile requirements prioritization practice and can be used for structuring future empirical investigations about this topic, and for analyzing, supporting, and improving the process in real-life projects.
Generative Adversarial Networks for Parallel Vision

Video image dataset is playing an essential role in design and evaluation of traffic vision methods. However, there is a longstanding difficulty that manually collecting and annotating large-scale diversified dataset from real scenes is time-consuming and prone to error. In 2016, we proposed the parallel vision methodology to tackle the issues of conventional vision computing approach in data collection, model learning and evaluation. We built the ParallelEye dataset with virtual reality and the scene-specific virtual pedestrian dataset with augmented reality. In the dataset compiling process, the graphics rendering engine was used to render the artificial scenes and generate virtual images. However, the fidelity of virtual images is not satisfactory due to limitation of rendering engine, so that there is a distribution gap between virtual data and real data. In our opinion, Generative Adversarial Networks (GANs) can generate more realistic images for parallel vision research. We introduce some GANs and explain their utility in parallel vision.
Predicting Song Popularity

Future Work Dataset and Features Music has been an integral part of our culture all throughout human history. In 2012 alone, the U.S. music industry generated $15 billion. Having a fundamental understanding of what makes a song popular has major implications to businesses that thrive on popular music, namely radio stations, record labels, and digital and physical music market places. Many private companies in these industries have solutions for this problem, but details have been kept private for competitive reasons. For our project, we will predict song popularity based on an song’s audio features and metadata.
All robots are not created equal: the design and perception of humanoid robot heads

This paper presents design research conducted as part of a larger project on human-robot interaction. The primary goal of this study was to come to an initial understanding of what features and dimensions of a humanoid robot's face most dramatically contribute to people's perception of its humanness. To answer this question we analyzed 48 robots and conducted surveys to measure people's perception of each robot's humanness. Through our research we found that the presence of certain features, the dimensions of the head, and the total number of facial features heavily influence the perception of humanness in robot heads. This paper presents our findings and initial guidelines for the design of humanoid robot heads.
Knowledge Extraction From Trained Neural Networks

Received Jul 16 th , 2012 Revised Aug 01 th , 2012 Accepted Sept 02 th , 2012 Artificial neural networks (ANN) are very efficient in solving various kinds of problems But Lack of explanation capability (Black box nature of Neural Networks) is one of the most important reasons why artificial neural networks do not get necessary interest in some parts of industry. In this work artificial neural networks first trained and then combined with decision trees in order to fetch knowledge learnt in the training process. After successful training, knowledge is extracted from these trained neural networks using decision trees in the forms of IF THEN Rules which we can easily understand as compare to direct neural network outputs. We use decision trees to train on the results set of trained neural network and compare the performance of neural networks and decision trees in knowledge extraction from neural networks. Weka machine learning simulator with version 3.7.5 is used for research purpose. The experimental study is done on bank customers‟ data which have 12 attributes and 600 instances. The results study show that although neural networks takes much time in training and testing but are more accurate in classification then decision trees.
Storage-less and converter-less maximum power point tracking of photovoltaic cells for a nonvolatile microprocessor

This paper pioneers the maximum power point tracking (MPPT) of photovoltaic (PV) cells that directly supply power to a microprocessor without an energy storage element (a battery or a large-size capacitor) nor power converters. The maximum power point tracking is conventionally performed by an MPPT charger that stores in the energy storage element, and a voltage regulator (typically a DC-DC converter) produces a proper voltage level for the microprocessor. The energy storage element is an energy buffer and makes it possible to perform MPPT of the PV cells and power management of the microprocessor independently. However, the energy storage element, MPPT charger and DC-DC converter cause seriously limited lifetime (when a typical battery is adopted), significant energy loss (typically over 20%), increased weight/volume and high cost, etc. The proposed method enables extremely fine-grain dynamic power management (DPM) in every a few hundred microseconds and performs the MPPT without using an MPPT charger and a DC-DC converter as well as an energy storage element. We achieve 84.5% of energy harvesting efficiency using the proposed setup with huge reduction in cost, weight and volume, and extended lifetime, which is not even numerically comparable with conventional MPPT methods.
The Development and Comparison of Robust Methods for Estimating the Fundamental Matrix

This paper has two goals. The first is to develop a variety of robust methods for the computation of the Fundamental Matrix, the calibration-free representation of camera motion. The methods are drawn from the principal categories of robust estimators, viz. case deletion diagnostics, M-estimators and random sampling, and the paper develops the theory required to apply them to non-linear orthogonal regression problems. Although a considerable amount of interest has focussed on the application of robust estimation in computer vision, the relative merits of the many individual methods are unknown, leaving the potential practitioner to guess at their value. The second goal is therefore to compare and judge the methods. Comparative tests are carried out using correspondences generated both synthetically in a statistically controlled fashion and from feature matching in real imagery. In contrast with previously reported methods the goodness of fit to the synthetic observations is judged not in terms of the fit to the observations per se but in terms of fit to the ground truth. A variety of error measures are examined. The experiments allow a statistically satisfying and quasi-optimal method to be synthesized, which is shown to be stable with up to 50 percent outlier contamination, and may still be used if there are more than 50 percent outliers. Performance bounds are established for the method, and a variety of robust methods to estimate the standard deviation of the error and covariance matrix of the parameters are examined. The results of the comparison have broad applicability to vision algorithms where the input data are corrupted not only by noise but also by gross outliers.
Recursive Neural Conditional Random Fields for Aspect-based Sentiment Analysis

In aspect-based sentiment analysis, extracting aspect terms along with the opinions being expressed from user-generated content is one of the most important subtasks. Previous studies have shown that exploiting connections between aspect and opinion terms is promising for this task. In this paper, we propose a novel joint model that integrates recursive neural networks and conditional random fields into a unified framework for explicit aspect and opinion terms co-extraction. The proposed model learns high-level discriminative features and double propagates information between aspect and opinion terms, simultaneously. Moreover, it is flexible to incorporate hand-crafted features into the proposed model to further boost its information extraction performance. Experimental results on the dataset from SemEval Challenge 2014 task 4 show the superiority of our proposed model over several baseline methods as well as the winning systems of the challenge.