nils-herrmann/clean_scirepeval huggingface dataset:
What can I say?: addressing user experience challenges of a mobile voice user interface for accessibility

Voice interactions on mobile phones are most often used to augment or supplement touch based interactions for users' convenience. However, for people with limited hand dexterity caused by various forms of motor-impairments voice interactions can have a significant impact and in some cases even enable independent interaction with a mobile device for the first time. For these users, a Mobile Voice User Interface (M-VUI), which allows for completely hands-free, voice only interaction would provide a high level of accessibility and independence. Implementing such a system requires research to address long standing usability challenges introduced by voice interactions that negatively affect user experience due to difficulty learning and discovering voice commands.
 In this paper we address these concerns reporting on research conducted to improve the visibility and learnability of voice commands of a M-VUI application being developed on the Android platform. Our research confirmed long standing challenges with voice interactions while exploring several methods for improving the onboarding and learning experience. Based on our findings we offer a set of implications for the design of M-VUIs.
Automatic Calibration of Digital Cameras Using Planar Chessboard Patterns

A variety of methods for camera calibration, relying on different camera models, algorithms and a priori object information, have been reported and reviewed in literature. Use of simple 2D patterns of the chess-board type represents an interesting approach, for which several ‘calibration toolboxes’ are available on the Internet, requiring varying degrees of human interaction. This paper presents an automatic multi-image approach exclusively for camera calibration purposes on the assumption that the imaged pattern consists of adjacent light and dark squares of equal size. Calibration results, also based on image sets from Internet sources, are viewed as satisfactory and comparable to those from other approaches. Questions regarding the role of image configuration need further investigation.
Crowdsourcing in Software Engineering: Models, Motivations, and Challenges

Almost surreptitiously, crowdsourcing has entered software engineering practice. In-house development, contracting, and outsourcing still dominate, but many development projects use crowdsourcing-for example, to squash bugs, test software, or gather alternative UI designs. Although the overall impact has been mundane so far, crowdsourcing could lead to fundamental, disruptive changes in how software is developed. Various crowdsourcing models have been applied to software development. Such changes offer exciting opportunities, but several challenges must be met for crowdsourcing software development to reach its potential.
Dual-Band Microstrip Circular Patch Antenna With Monopolar Radiation Pattern

A novel dual-band microstrip monopolar patch antenna with zeroth-order resonance is proposed and analyzed in this letter. The antenna is a combination of circular patch antenna with center-fed and circularly periodic mushroom units. With the zeroth-order resonant (ZOR) mode and TM02 mode, the proposed antenna can create horizontal magnetic loop currents on the patch, which lead to a low profile of 0.02λ at the low frequency band. The radiation mechanism of the proposed antenna is discussed. The antenna is fabricated on a double-layered printed circuit board (PCB) and center-fed by a 50- Ω SMA connector. Simulated and measured results show that the proposed antenna produces a stable monopole-like radiation pattern in two working frequency bands. The corresponding impedance bandwidths and gains are 0.75% and 5.1 dBi for the low frequency band and 20% and a range of 5.8-8 dBi for the high frequency band.
Learning Dynamic Siamese Network for Visual Object Tracking

How to effectively learn temporal variation of target appearance, to exclude the interference of cluttered background, while maintaining real-time response, is an essential problem of visual object tracking. Recently, Siamese networks have shown great potentials of matching based trackers in achieving balanced accuracy and beyond realtime speed. However, they still have a big gap to classification & updating based trackers in tolerating the temporal changes of objects and imaging conditions. In this paper, we propose dynamic Siamese network, via a fast transformation learning model that enables effective online learning of target appearance variation and background suppression from previous frames. We then present elementwise multi-layer fusion to adaptively integrate the network outputs using multi-level deep features. Unlike state-of-theart trackers, our approach allows the usage of any feasible generally- or particularly-trained features, such as SiamFC and VGG. More importantly, the proposed dynamic Siamese network can be jointly trained as a whole directly on the labeled video sequences, thus can take full advantage of the rich spatial temporal information of moving objects. As a result, our approach achieves state-of-the-art performance on OTB-2013 and VOT-2015 benchmarks, while exhibits superiorly balanced accuracy and real-time response over state-of-the-art competitors.
Sensing WiFi packets in the air: practicality and implications in urban mobility monitoring

Mobile sensing systems employ various sensors in smartphones to extract human-related information. As the demand for sensing systems increases, a more effective mechanism is required to sense information about human life. In this paper, we present a systematic study on the feasibility and gaining properties of a crowdsensing system that primarily concerns sensing WiFi packets in the air. We propose that this method is effective for estimating urban mobility by using only a small number of participants. During a seven-week deployment, we collected smartphone sensor data, including approximately four million WiFi packets from more than 130,000 unique devices in a city. Our analysis of this dataset examines core issues in urban mobility monitoring, including feasibility, spatio-temporal coverage, scalability, and threats to privacy. Collectively, our findings provide valuable insights to guide the development of new mobile sensing systems for urban life monitoring.
Exploring the Impact of Social Media on Hotel Service Performance : A Sentimental Analysis Approach

Online user-generated content in various social media websites, such as consumer experiences, user feedback, and product reviews, has increasingly become the primary information source for both consumers and businesses. In this study, we aim to look beyond the quantitative summary and unidimensional interpretation of online user reviews to provide a more comprehensive view of online user-generated content. Moreover, we would like to extend the current literature to the more customer-driven service industries, particularly the hotel industry. We obtain a unique and extensive dataset of online user reviews for hotels across various review sites and over long time periods. We use the sentiment analysis technique to decompose user reviews into different dimensions to measure hotel service quality and performance based on the SERVPERF model. Those dimensions are then incorporated into econometrics models to examine their effect in shaping users’ overall evaluation and content-generating behavior. The results suggest that different dimensions of user reviews have significantly different effects in forming user evaluation and driving content generation. This paper demonstrates the importance of using textual data to measure consumers’ relative preferences for service quality and evaluate service performance.
Lung nodule segmentation and recognition using SVM classifier and active contour modeling: A complete intelligent system

In this paper, a novel method for lung nodule detection, segmentation and recognition using computed tomography (CT) images is presented. Our contribution consists of several steps. First, the lung area is segmented by active contour modeling followed by some masking techniques to transfer non-isolated nodules into isolated ones. Then, nodules are detected by the support vector machine (SVM) classifier using efficient 2D stochastic and 3D anatomical features. Contours of detected nodules are then extracted by active contour modeling. In this step all solid and cavitary nodules are accurately segmented. Finally, lung tissues are classified into four classes: namely lung wall, parenchyma, bronchioles and nodules. This classification helps us to distinguish a nodule connected to the lung wall and/or bronchioles (attached nodule) from the one covered by parenchyma (solitary nodule). At the end, performance of our proposed method is examined and compared with other efficient methods through experiments using clinical CT images and two groups of public datasets from Lung Image Database Consortium (LIDC) and ANODE09. Solid, non-solid and cavitary nodules are detected with an overall detection rate of 89%; the number of false positive is 7.3/scan and the location of all detected nodules are recognized correctly.
Real Robots Don't Drive Straight

Over last fifteen years, robot technology has become popular in classrooms across our whole educational system. Both engineering and AI educators have developed ways to integrate robots into their teaching. Engineering educators are primarily concerned with engineering science (e.g., feedback control) and process (e.g., design skills). AI educators have different goals—namely, AI educators want students to learn AI concepts. Both agree that students are enthusiastic about working with robots, and in both cases, the pedagogical challenge is to develop robotics technology and provide classroom assignments that highlight key ideas in the respective field. Mobile robots are particularly intriguing because of their dual nature as both deterministic machines and unpredictable entities. This paper explores challenges for both engineering and AI educators as robot toolkits
Markov chain Monte Carlo ( MCMC ) sampling methods to determine optimal models , model resolution and model choice for Earth Science problems

We present an overview of Markov chain Monte Carlo, a sampling method for model inference and uncertainty quantification. We focus on the Bayesian approach to MCMC, which allows us to estimate the posterior distribution of model parameters, without needing to know the normalising constant in Bayes’ theorem. Given an estimate of the posterior, we can then determine representative models (such as the expected model, and the maximum posterior probability model), the probability distributions for individual parameters, and the uncertainty about the predictions from these models. We also consider variable dimensional problems in which the number of model parameters is unknown and needs to be inferred. Such problems can be addressed with reversible jump (RJ) MCMC. This leads us to model choice, where we may want to discriminate between models or theories of differing complexity. For problems where the models are hierarchical (e.g. similar structure but with a different number of parameters), the Bayesian approach naturally selects the simpler models. More complex problems require an estimate of the normalising constant in Bayes’ theorem (also known as the evidence) and this is difficult to do reliably for high dimensional problems. We illustrate the applications of RJMCMC with 3 examples from our earlier working involving modelling distributions of geochronological age data, inference of sea-level and sediment supply histories from 2D stratigraphic cross-sections, and identification of spatially discontinuous thermal histories from a suite of apatite fission track samples distributed in 3D. 2009 Elsevier Ltd. All rights reserved.
Foliage Plant Retrieval using Polar Fourier Transform, Color Moments and Vein Features

This paper proposed a method that combines Polar Fourier Transform, color moments, and vein features to retrieve leaf images based on a leaf image. The method is very useful to help people in recognizing foliage plants. Foliage plants are plants that have various colors and unique patterns in the leaf. Therefore, the colors and its patterns are information that should be counted on in the processing of plant identification. To compare the performance of retrieving system to other result, the experiments used Flavia dataset, which is very popular in recognizing plants. The result shows that the method gave better performance than PNN, SVM, and Fourier Transform. The method was also tested using foliage plants with various colors. The accuracy was 90.80% for 50 kinds of plants.
A self-awareness approach to computer-mediated communication

Many explanations of both proand anti-social behaviors in computer-mediated communication (CMC) appear to hinge on changes in individual self-awareness. In spite of this, little research has been devoted to understanding the effects of self-awareness in CMC. To fill this void, this study examined the effects of individuals public and private self-awareness in anonymous, time-restricted, and synchronous CMC. Two experiments were conducted. A pilot experiment tested and confirmed the effectiveness of using a Web camera combined with an alleged online audience to enhance users public self-awareness. In the main study users private and public self-awareness were manipulated in a crossed 2 · 2 factorial design. Pairs of participants completed a Desert Survival Problem via a synchronous, text-only chat program. After the task, they evaluated each other on intimacy, task/social orientation, formality, politeness, attraction, and group identification. The results suggest that a lack of private and public self-awareness does not automatically lead to impersonal tendencies in CMC as deindividuation perspectives of CMC would argue. Moreover, participants in this study were able to form favorable impressions in a completely anonymous environment based on brief interaction, which lends strong support to the idealization proposed by hyperpersonal theory. Findings are used to modify and extend current theoretical perspectives on CMC.
Combining Global Models for Parsing Universal Dependencies

We describe our entry, C2L2, to the CoNLL 2017 shared task on parsing Universal Dependencies from raw text. Our system features an ensemble of three global parsing paradigms, one graph-based and two transition-based. Each model leverages character-level bidirectional LSTMs as lexical feature extractors to encode morphological information. Though relying on baseline tokenizers and focusing only on parsing, our system ranked second in the official end-toend evaluation with a macro-average of 75.00 LAS F1 score over 81 test treebanks. In addition, we had the top average performance on the four surprise languages and on the small treebank subset.
What Test Oracle Should I Use for Effective GUI Testing?

Test designers widely believe that the overall effectiveness and cost of software testing depends largely on the type and number of test cases executed on the software. In this paper we show that the test oracle used during testing also contributes significantly to test effectiveness and cost. A test oracle is a mechanism that determines whether a software executed correctly for a test case. We define a test oracle to contain two essential parts: oracle information that represents expected output, and an oracle procedure that compares the oracle information with the actual output. By varying the level of detail of oracle information and changing the oracle procedure, a test designer can create different types of test oracles. We design 11 types of test oracles and empirically compare them on four software systems. We seed faults in each software to create 100 faulty versions, execute 600 test cases on each version, for all 11 types of oracles. In all, we report results of 660,000 test runs on each software. We show (1) the time and space requirements of the oracles, (2) that faults are detected early in the testing process when using detailed oracle information and complex oracle procedures, although at a higher cost per test case, and (3) that employing expensive oracles results in detecting a large number of faults using relatively smaller number of test cases.
Managing RFID Data: Challenges, Opportunities and Solutions

The advances of Radio-Frequency Identification (RFID) technology have significantly enhanced the capability of capturing data from pervasive space. It becomes a great challenge in the information era to effectively understand human behavior, mobility and activity through the perceived RFID data. Focusing on RFID data management, this article provides an overview of current challenges, emerging opportunities and recent progresses in RFID. In particular, this article has described and analyzed the research work on three aspects: algorithm, protocol and performance evaluation. We investigate the research progress in RFID with anti-collision algorithms, authentication and privacy protection protocols, localization and activity sensing, as well as performance tuning in realistic settings. We emphasize the basic principles of RFID data management to understand the state-of-the-art and to address directions of future research in RFID.
Balanced\/Unbalanced Wideband: A Wideband, Bifilar Transmission-Line Balun

This article presents the design details of a wideband, bifilar transmission-line balun submitted for the student design competition for wideband baluns held during the 2015 IEEE Microwave Theory and Techniques Society (MTT-S) International Microwave Symposium (IMS 2015) in Phoenix, Arizona, and sponsored by the MTT-17 Technical Committee on HF-VHF-UHF Technology.
An Automatic Graph-Based Approach for Artery/Vein Classification in Retinal Images

The classification of retinal vessels into artery/vein (A/V) is an important phase for automating the detection of vascular changes, and for the calculation of characteristic signs associated with several systemic diseases such as diabetes, hypertension, and other cardiovascular conditions. This paper presents an automatic approach for A/V classification based on the analysis of a graph extracted from the retinal vasculature. The proposed method classifies the entire vascular tree deciding on the type of each intersection point (graph nodes) and assigning one of two labels to each vessel segment (graph links). Final classification of a vessel segment as A/V is performed through the combination of the graph-based labeling results with a set of intensity features. The results of this proposed method are compared with manual labeling for three public databases. Accuracy values of 88.3%, 87.4%, and 89.8% are obtained for the images of the INSPIRE-AVR, DRIVE, and VICAVR databases, respectively. These results demonstrate that our method outperforms recent approaches for A/V classification.
Brain-Inspired Cognitive Model With Attention for Self-Driving Cars

The perception-driven approach and end-to-end system are two major vision-based frameworks for self-driving cars. However, it is difficult to introduce attention and historical information into the autonomous driving process, which are essential for achieving human-like driving in these two methods. In this paper, we propose a novel model for self-driving cars called the brain-inspired cognitive model with attention. This model comprises three parts: 1) a convolutional neural network for simulating the human visual cortex; 2) a cognitive map to describe the relationships between objects in a complex traffic scene; and 3) a recurrent neural network, which is combined with the real-time updated cognitive map to implement the attention mechanism and long-short term memory. An advantage of our model is that it can accurately solve three tasks simultaneously: 1) detecting the free space and boundaries for the current and adjacent lanes; 2) estimating the distances to obstacles and vehicle attitude; and 3) learning the driving behavior and decision-making process of a human driver. Importantly, the proposed model can accept external navigation instructions during an end-to-end driving process. To evaluate the model, we built a large-scale road-vehicle dataset containing over 40 000 labeled road images captured by three cameras placed on our self-driving car. Moreover, human driving activities and vehicle states were recorded at the same time.
Lateral NWFET optimization for beyond 7nm nodes

In this study, different S/D contacting options for lateral NWFET devices are benchmarked at 7nm node dimensions and beyond. Comparison is done at both DC and ring oscillator levels. It is demonstrated that implementing a direct contact to a fin made of Si/SiGe super-lattice results in 13% performance improvement. Also, we conclude that the integration of internal spacers between the NWs is a must for lateral NWFETs in order to reduce device parasitic capacitance.
The Fixed-Size Ordinally-Forgetting Encoding Method for Neural Network Language Models

In this paper, we propose the new fixedsize ordinally-forgetting encoding (FOFE) method, which can almost uniquely encode any variable-length sequence of words into a fixed-size representation. FOFE can model the word order in a sequence using a simple ordinally-forgetting mechanism according to the positions of words. In this work, we have applied FOFE to feedforward neural network language models (FNN-LMs). Experimental results have shown that without using any recurrent feedbacks, FOFE based FNNLMs can significantly outperform not only the standard fixed-input FNN-LMs but also the popular recurrent neural network (RNN) LMs.
Learning Lightprobes for Mixed Reality Illumination

This paper presents the first photometric registration pipeline for Mixed Reality based on high quality illumination estimation using convolutional neural networks (CNNs). For easy adaptation and deployment of the system, we train the CNNs using purely synthetic images and apply them to real image data. To keep the pipeline accurate and efficient, we propose to fuse the light estimation results from multiple CNN instances and show an approach for caching estimates over time. For optimal performance, we furthermore explore multiple strategies for the CNN training. Experimental results show that the proposed method yields highly accurate estimates for photo-realistic augmentations.
Complex signal demodulation and random body movement cancellation techniques for non-contact vital sign detection

A complex signal demodulation technique is proposed to eliminate the null detection point problem in non-contact vital sign detection. This technique is robust against DC offset in direct conversion system. Based on the complex signal demodulation, a random body movement cancellation technique is developed to cancel out strong noise caused by random body movement in non-contact vital sign monitoring. Multiple transceivers and antennas with polarization and frequency multiplexing are used to detect signals from different body orientations. The noise due to random body movement is cancelled out based on different patterns of the desired and undesired signals. Experiments by highly compact 5–6 GHz portable radar systems have been performed to verify these two techniques.