Miracle-dz/UniFAR-FTUs huggingface dataset:
No Need to Stop : Menu Techniques for Passing by Public Displays

Although public displays are increasingly prevalent in public spaces, they are generally not interactive. Menu techniques can enable users to select what is interesting to them. Current touch screen techniques are unsuitable, because for many public displays, users merely pass by and rarely stop. We investigate command selection in this new context of passing-by interaction, in which users only have a few seconds to interact. We present six hands-free gestural techniques and evaluate them in a Wizard-of-Oz experiment. Based on the results of this study, we provide design recommendations for menu selection in passing-by situations. Introduction In this paper, we present and evaluate six hands-free gestural menu techniques for a new context of use for public displays: passing-by interaction (Fig. 2). Users do not need to stop to interact, but can interact while they keep on walking. This context of use is especially relevant in the case of frequent passers-by, who pass by the same interactive public display every day (e.g., in the subway station) on their way to work. Our contributions are: (1) to identify a new, important context of use of public displays: passing-by interaction, (2) to propose and evaluate six gestural interacCopyright is held by the author/owner(s). CHI 2011, May 7–12, 2011, Vancouver, BC, Canada. ACM 978-1-4503-0268-5/11/05. Chat Wacharamanotham RWTH Aachen University chat@cs.rwth-aachen.de Florian Alt University of Duisburg-Essen florian.alt@uni-due.de Jan Borchers RWTH Aachen University borchers@cs.rwth-aachen.de Tongyan Ning Deutsche Telekom Laboratories RWTH Aachen University tongyan.ning@telekom.de Jörg Müller Deutsche Telekom Laboratories joerg.mueller@tu-berlin.de Robert Walter Deutsche Telekom Laboratories r.walter@telekom.de Gilles Bailly Telecom ParisTech, CNRS, LTCI, UMR 5141 gilles.bailly@telecom-paristech.fr Fig. 1. Passing-by interaction at the exit of a subway station. Fig. 2. Passing-by interaction model.
2022 Galois hulls of linear codes and new EAQECCs with arbitrary lengths

The hull of linear codes is an important and hot issue in coding theory. In this paper, we ﬁrst show that the dimensions of Galois hulls of linear codes have symmetry and then some suﬃcient and necessary conditions for a linear code being an e -Galois self-orthogonal or LCD code are given. We generalize previous conclusions and prove that e -Galois self-orthogonal codes with length n + 2 i ( i ≥ 0) and n + 2 i + 1 ( i ≥ 1) can be constructed from e -Galois self-orthogonal codes with length n , which implies that arbitrary dimension e -Galois hull linear codes with lengths n +2 i and n +2 i +1 exist if an e -Galois self-orthogonal code with length n exists. Two classes of new Hermitian self-orthogonal MDS codes are also constructed via (extended) GRS codes by a new method. Finally, all results are applied to the construction of EAQECCs, and new EAQECCs with lengths n + 2 i and n + 2 i + 1 are obtained.
A robust and computationally efficient motion detection algorithm based on �-background estimation

This paper presents a new algorithm to detect moving objects within a scene acquired by a stationary camera. A simple recursive non linear operator, the filter, is used to estimate two orders of temporal statistics for every pixel of the image. The output data provide a scene characterization allowing a simple and efficient pixel-level change detection framework. For a more suitable detection, exploiting spatial correlation in these data is necessary. We use them as a multiple observation field in a Markov model, leading to a spatiotemporal regularization of the pixel-level solution. This method yields a good trade-off in terms of robustness and accuracy, with a minimal cost in memory and a low computational complexity.
Pattern Recognition for Neuroimaging Toolbox

In the past years, mass univariate statistical analyses of neuroimaging data have been complemented by the use of multivariate pattern analyses, especially based on machine learning models. While these allow an increased sensitivity for the detection of spatially distributed eects compared to univariate techniques, they lack an established and accessible software framework. Here we introduce the \Pattern Recognition for Neuroimaging Toolbox" (PRoNTo), an open-source, cross-platform and MATLAB-based software comprising many necessary functionalities for machine learning modelling of neuroimaging data.
ISPRS Hannover Workshop 2017

THIS REPORT is a personal account of the ISPRS Hannover Workshop, held at the Institute for Photogrammetry and GeoInformation (IPI) at Leibniz Universit€at Hannover (or Hanover, if you prefer the rather unnecessary English spelling) in June 2017. The workshop was a joint meeting of: High-Resolution Earth Imaging for Geospatial Information (HRIGI17); City Models, Roads and Traffic (CMRT17); Image Sequence Analysis (ISA17); and the European Calibration and Orientation Workshop (EuroCOW). That is a rather difficult list to remember, so everyone refers to it simply as the Hannover Workshop (although this proved difficult last time round in 2015, when it was held in Munich).
Information Processing and Management

Stock prediction via market data analysis is an attractive research topic. Both stock prices and news articles have been employed in the prediction processes. However, how to combine technical indicators from stock prices and news sentiments from textual news articles, and make the prediction model be able to learn sequential information within time series in an intelligent way, is still an unsolved problem. In this paper, we build up a stock prediction system and propose an approach that 1) represents numerical price data by technical indicators via technical analysis, and represents textual news articles by sentiment vectors via sentiment analysis, 2) setup a layered deep learning model to learn the sequential information within market snapshot series which is constructed by the technical indicators and news sentiments, 3) setup a fully connected neural network to make stock predictions. Experiments have been conducted on more than five years of Hong Kong Stock Exchange data using four different sentiment dictionaries, and results show that 1) the proposed approach outperforms the baselines in both validation and test sets using two different evaluation metrics, 2) models incorporating prices and news sentiments outperform models that only use either technical indicators or news sentiments, in both individual stock level and sector level, 3) among the four sentiment dictionaries, finance domain-specific sentiment dictionary (Loughran–McDonald Financial Dictionary) models the news sen-timents better, which brings more prediction performance improvements than the other three dictionaries.
FROM LOW LEVEL FEATURES TO HIGH LEVEL SEMANTICS

A typical content-based information retrieval (CBIR) system, e.g., an image or video retrieval system, includes three major aspects: feature extraction, high dimensional indexing and system design [1]. Among the three aspects, high dimensional indexing is important for speed performance; system design is critical for appearance performance; and feature extraction is the key to accuracy performance. In this chapter, we will discuss various ways people have tried to increase the accuracy of retrieval systems.
Developing a simulation framework for safe and optimal trajectories considering drivers’ driving style

Advanced Driving Assistance Systems (ADAS) have huge potential for improving road safety and travel times. However, their take-up in the market is very slow; and these systems should consider driver’s preferences to increase adoption rates. The aim of this paper is to develop a model providing drivers with the optimal trajectory considering the motorist’s driving style in real time. Travel duration and safety are the main parameters used to find the optimal trajectory. A simulation framework to determine the optimal trajectory was developed in which the ego car travels in a highway environment scenario, using an agent-oriented approach. The performance of the algorithm was compared against optimal trajectories computed offline with the hybrid A* algorithm. The new framework provides trajectories close to the optimal trajectory and is computationally achievable. The agents were shown to follow safe and fast trajectories in three tests scenarios: emergency braking, overtaking, and a complex situation with multiple vehicles around the ego vehicle. Different driver profiles were then tested in the complex scenario, showing that the proposed approach can adapt to driver preferences and provide a solution close to the optimal solution given the defined safety constraints.
Object Detection for Remote Sensing Images Based on Polar Coordinates

Arbitrary-oriented object detection is an important task in the field of remote sensing object detection. Existing studies have shown that the polar coordinate system has obvious advantages in dealing with the problem of rotating object modeling, that is, using fewer parameters to achieve more accurate rotating object detection. However, present state-of-the-art detectors based on deep learning are all modeled in Cartesian coordinates. In this article, we introduce the polar coordinate system to the deep learning detector for the first time, and propose an anchor free Polar Remote Sensing Object Detector (P-RSDet), which can achieve competitive detection accuracy via uses simpler object representation model and less regression parameters. In P-RSDet method, arbitrary-oriented object detection can be achieved by predicting the center point and regressing one polar radius and two polar angles. Besides, in order to express the geometric constraint relationship between the polar radius and the polar angle, a Polar Ring Area Loss function is proposed to improve the prediction accuracy of the corner position. Experiments on DOTA, UCAS-AOD and NWPU VHR-10 datasets show that our P-RSDet achieves state-of-the-art performances with simpler model and less regression parameters. INDEX TERMS Remote sensing images, Oriented detection, Polar coordinates, Anchor free
/ TP-2002-211932 Workload-Matched Adaptive Automation Support of Air Traffic Controller Information Processing Stages

Since its founding, NASA has been dedicated to the advancement of aeronautics and space science. The NASA Scientific and Technical Information (STI) Program Office plays a key part in helping NASA maintain this important role. The NASA STI Program Office is operated by Langley Research Center, the lead center for NASA's scientific and technical information. The NASA STI Program Office provides access to the NASA STI Database, the largest collection of aeronautical and space science STI in the world. The Program Office is also NASA's institutional mechanism for disseminating the results of its research and development activities. These results are published by NASA in the NASA STI Report Series, which includes the following report types: • TECHNICAL PUBLICATION. Reports of completed research or a major significant phase of research that present the results of NASA programs and include extensive data or theoretical analysis. Includes compilations of significant scientific and technical data and information deemed to be of continuing reference value. NASA counterpart of peer-reviewed formal professional papers, but having less stringent limitations on manuscript length and extent of graphic presentations. • TECHNICAL MEMORANDUM. Scientific and technical findings that are preliminary or of specialized interest, e.g., quick release reports, working papers, and bibliographies that contain minimal annotation. Does not contain extensive analysis.
Ieee Transactions on Communications, Accepted for Publication 1 Efficient Linear Programming Decoding of Hdpc Codes

—We propose several improvements for Linear Programming (LP) decoding algorithms for High Density Parity Check (HDPC) codes. First, we use the automorphism groups of a code to create parity check matrix diversity and to generate valid cuts from redundant parity checks. Second, we propose an efficient mixed integer decoder utilizing the branch and bound method. We further enhance the proposed decoders by removing inactive constraints and by adapting the parity check matrix prior to decoding according to the channel observations. Based on simulation results the proposed decoders achieve near-ML performance with reasonable complexity.
On handovers in Uplink/Downlink decoupled LTE HetNets

Cellular heterogeneous networks (HetNets) are going to be one of the key enablers for 5G. Downlink/Uplink decoupling (DUDe) is a concept in which a mobile device is connected with Macro cell for downlink communication and with small cell for uplink communication in LTE/LTE-A HetNets. It improves uplink data rate, reduces power consumption of devices, balances load between Macro cell and small cells. Due to incorporation of DUDe, a mobile device has to perform separate uplink and downlink handovers unlike traditional handovers in coupled LTE networks. In this paper, we propose various handover schemes for DUDe LTE networks. Apart from this, we have mathematically analysed the received SINR by small cells taken part in decoupling, with respect to a device moving in decoupling regions of these small cells, in multiple cell interference scenario. Simulation results show the signaling impact of DUDe in handovers, increased uplink SINR, decreased power consumption of devices in both single small cell and multiple small cell scenarios.
ExpM+NF: Differentially Private Machine Learning that Surpasses DPSGD

—In this pioneering work we formulate ExpM+NF, a method for training machine learning (ML) on private data with pre-specified differentially privacy guarantee ε > 0 , δ = 0 , by using the Exponential Mechanism (ExpM) and an auxiliary Normalizing Flow (NF). We articulate theoretical benefits of ExpM+NF over Differentially Private Stochastic Gradient Descent (DPSGD), the state-of-the-art (SOTA) and de facto method for differentially private ML, and we empirically test ExpM+NF against DPSGD using the SOTA implementation (Opacus [1] with PRV accounting) in multiple classification tasks on the Adult Dataset (census data) and MIMIC-III Dataset (electronic healthcare records) using Logistic Regression and GRU-D, a deep learning recurrent neural network with ∼ 20K-100K parameters. In all experiments, ExpM+NF achieves greater than 93% of the non-private training accuracy (AUC) for ε ∈ [1e − 3 , 1] , exhibiting greater accuracy (higher AUC) and privacy (lower ε with δ = 0 ) than DPSGD. Differentially private ML generally considers ε ∈ [1 , 10] to maintain reasonable accuracy; hence, ExpM+NF’s ability to provide strong accuracy for orders of magnitude better privacy (smaller ε ) substantially pushes what is currently possible in differentially private ML. Training time results are presented showing ExpM+NF is comparable to (slightly faster) than DPSGD. Code for these experiments will be provided after review. Limitations and future directions are provided.
JAMIA Focus on

The authors organized a Natural Language Processing (NLP) challenge on automatically determining the smoking status of patients from information found in their discharge records. This challenge was issued as a part of the i2b2 (Informatics for Integrating Biology to the Bedside) project, to survey, facilitate, and examine studies in medical language understanding for clinical narratives. This article describes the smoking challenge, details the data and the annotation process, explains the evaluation metrics, discusses the characteristics of the systems developed for the challenge, presents an analysis of the results of received system runs, draws conclusions about the state of the art, and identiﬁes directions for future research. A total of 11 teams participated in the smoking challenge. Each team submitted up to three system runs, providing a total of 23 submissions. The submitted system runs were evaluated with microaveraged and macroaveraged precision, recall, and F-measure. The systems submitted to the smoking challenge represented a variety of machine learning and rule-based algorithms. Despite the differences in their approaches to smoking status identiﬁcation, many of these systems provided good results. There were 12 system runs with microaveraged F-measures above 0.84. Analysis of the results highlighted the fact that discharge summaries express smoking status using a limited number of textual features (e.g., “smok”, “tobac”, “cigar”, Social History, etc.). Many of the effective smoking status identiﬁers beneﬁt from these features. (cid:1) J Am Med Inform Assoc. 2008;15:14–24. DOI 10.1197/jamia.M2408.
A COMPARATIVE STUDY OF ADAPTATION METH Johnny Mariéthoz S

Real-life speaker verification systems are often implemented using client model adaptation methods, since the amount of data available for each client is often too low to consider plain Maximum Likelihood methods. While the Bayesian Maximum A Posteriori (MAP) adaptation method is commonly used in speaker verification, other methods have proven to be successful in related domains such as speech recognition. This paper reports on experimental comparison between three well-known adaptation methods, namely MAP, Maximum Likelihood Linear Regression, and finally EigenVoices. All three methods are compared to the more classical Maximum Likelihood method, and results are given for a subset of the 1999 NIST Speaker Recognition Evaluation database.
The role of Music IR in the New Zealand Digital Music Library project.

Introduction MELDEX—the name given to our digital music library project—is a Web-based system that supports searching through text and sung queries, and browsing through automatically compiled lists of metadata, such as titles. Sample collections include 1,000 popular tunes derived from sheet music using Optical Music Recognition (OMR) software, 10,000 folksongs donated by two other computer music projects, and 100,000 MIDI files gathered from the Web at large.
Information Design Workbook: Graphic approaches, solutions, and inspiration + 30 case studies

New in paperback, this workbook is a methodical yet comprehensive approach to conveying the fundamentals of avant-garde, innovative, information design by examining history, theory, criticism, technology and media, process, method, and practice. Opening with a very brief history followed by an instructive breakdown of the discipline, readers get an intimate understanding of the complexities of crafting information design to effectively improve communication both functionally and aesthetically. The back half of the book contains a wide range of case studies from design firms around the world so designers can see the techniques previously outlined in the first half of the book. The author also critiques and explains why the design is successful in terms of formal quality (Aesthetics) and function (How does it improve communication? ).
Gammatone Auditory Filterbank and Independent Component Analysis for Speaker Identification systems School of Engineering Report No . 622

ABSTACT Speaker identification is the process of recognizing who is speaking on the basis of information extracted from the speech signal. It has a number of applications in security and voice controlled service area. However, the most commonly used speaker recognition techniques work successfully only in clean or matched environment. Accurate speaker identification is made difficult due to a number of factors, with handset/channel mismatch and environmental noise being two of the most prominent. This paper presents a new novel technique which is based on the Gammatone filterbank (GTF) and independent component analysis (ICA). Compared with some other standard techniques, the new technique holds the best performance for a speaker text-independent identification system with mismatched environment and low environmental SNR level (less than 20dB).
REAL-SPACE RENORMALIZATION-GROUP APPROACH TO THE INTEGER QUANTUM HALL EFFECT

We review recent results based on an application of the real-space renormalization group (RG) approach to a network model for the integer quantum Hall (QH) transition. We demonstrate that this RG approach reproduces the critical distribution of the power transmission coefficients, i.e., two-terminal conductances, Pc(G), with very high accuracy. The RG flow of P(G) at energies away from the transition yields a value of the critical exponent ν that agrees with most accurate large-size lattice simulations. A description of how to obtain other relevant transport coefficients such as RL and RH is given. From the non-trivial fixed point of the RG flow we extract the critical level-spacing distribution (LSD). This distribution is close, but distinctively different from the earlier large-scale simulations. We find that the LSD obeys scaling behavior around the QH transition with ν = 2.37±0.02. Away from the transition it crosses over towards the Poisson distribution. We next investigate the plateau-to-insulator transition at strong magnetic fields. For a fully quantum coherent situation, we find a quantized Hall insulator with RH≈h/e2 up to RL~20h/e2 when interpreting the results in terms of most probable value of the distribution function P(RH). Upon further increasing RL→∞, the Hall insulator with diverging Hall resistance is seen. The crossover between these two regimes depends on the precise nature of the averaging procedure for the distributions P(RL) and P(RH). We also study the effect of long-ranged inhomogeneities on the critical properties of the QH transition. Inhomogeneities are modeled by a smooth random potential with a correlator which falls off with distance as a power law r-α. Similar to the classical percolation, we observe an enhancement of ν with decreasing α. These results exemplify the surprising fact that a small RG unit, containing only five nodes, accurately captures most of the correlations responsible for the localization-delocalization transition.
Learning to Control Renewal Processes with Bandit Feedback

We consider a bandit problem with K task types from which the controller activates one task at a time. Each task takes a random and possibly heavy-tailed completion time, and a reward is obtained only after the task is completed. The task types are independent from each other, and have distinct and unknown distributions for completion time and reward. For a given time horizon τ, the goal of the controller is to schedule tasks adaptively so as to maximize the reward collected until τ expires. In addition, we allow the controller to interrupt a task and initiate a new one. In addition to the traditional exploration-exploitation dilemma, this interrupt mechanism introduces a new one: should the controller complete the task and get the reward, or interrupt the task for a possibly shorter and more rewarding alternative? We show that for all heavy-tailed and some light-tailed completion time distributions, this interruption mechanism improves the reward linearly over time. From a learning perspective, the interrupt mechanism necessitates implicitly learning statistics beyond the mean from truncated observations. For this purpose, we propose a robust learning algorithm named UCB-BwI based on the median-of-means estimator for possibly heavy-tailed reward and completion time distributions. We show that, in a K-armed bandit setting with an arbitrary set of L possible interrupt times, UCB-BwI achieves O(Kłog(τ)+KL) regret. We also prove that the regret under any admissible policy is Ømega(Kłog(τ)), which implies that UCB-BwI is order optimal.