pinecone/core-2020-05-10-deduplication huggingface dataset:
Pay dispersion, culture and bank performance

This chapter examines the impact of executive pay dispersion on firm performance and valuation in a global sample of banks. Controlling for cultural differences across countries, we test whether the equity fairness theory (favoring smaller pay dispersion) or tournament theory (arguing for greater pay dispersion) is a better description of the relationship between pay dispersion and performance. We find that the equity fairness theory prevails in most sub-groups of our sample, with the exception of Common Law developed country banks. We also find for our sample banks in Developed Countries that Individualism is positively associated with market valuation, while Uncertainty Avoidance has a negative effect
Multi-Class Multi-Object Tracking using Changing Point Detection

This paper presents a robust multi-class multi-object tracking (MCMOT)
formulated by a Bayesian filtering framework. Multi-object tracking for
unlimited object classes is conducted by combining detection responses and
changing point detection (CPD) algorithm. The CPD model is used to observe
abrupt or abnormal changes due to a drift and an occlusion based spatiotemporal
characteristics of track states. The ensemble of convolutional neural network
(CNN) based object detector and Lucas-Kanede Tracker (KLT) based motion
detector is employed to compute the likelihoods of foreground regions as the
detection responses of different object classes. Extensive experiments are
performed using lately introduced challenging benchmark videos; ImageNet VID
and MOT benchmark dataset. The comparison to state-of-the-art video tracking
techniques shows very encouraging results
Multi-Level Net: a Visual Saliency Prediction Model

State of the art approaches for saliency prediction are based on Full Convolutional Networks, in which saliency maps are built using the last layer. In contrast, we here present a novel model that predicts saliency maps exploiting a non-linear combination of features coming from different layers of the network. We also present a new loss function to deal with the imbalance issue on saliency masks. Extensive results on three public datasets demonstrate the robustness of our solution. Our model outperforms the state of the art on SALICON, which is the largest and unconstrained dataset available, and obtains competitive results on MIT300 and CAT2000 benchmarks
A 3D Human Posture Approach for Activity Recognition Based on Depth Camera

Human activity recognition plays an important role in the context of Ambient Assisted Living (AAL), providing useful tools to improve people quality of life. This work presents an activity recognition algorithm based on the extraction of skeleton joints from a depth camera. The system describes an activity using a set of few and basic postures extracted by means of the X-means clustering algorithm. A multi-class Support Vector Machine, trained with the Sequential Minimal Optimization is employed to perform the classification. The system is evaluated on two public datasets for activity recognition which have different skeleton models, the CAD-60 with 15 joints and the TST with 25 joints. The proposed approach achieves precision/recall performances of 99.8 % on CAD-60 and 97.2 %/91.7 % on TST. The results are promising for an applied use in the context of AAL
Two-stage Convolutional Part Heatmap Regression for the 1st 3D Face
  Alignment in the Wild (3DFAW) Challenge

This paper describes our submission to the 1st 3D Face Alignment in the Wild
(3DFAW) Challenge. Our method builds upon the idea of convolutional part
heatmap regression [1], extending it for 3D face alignment. Our method
decomposes the problem into two parts: (a) X,Y (2D) estimation and (b) Z
(depth) estimation. At the first stage, our method estimates the X,Y
coordinates of the facial landmarks by producing a set of 2D heatmaps, one for
each landmark, using convolutional part heatmap regression. Then, these
heatmaps, alongside the input RGB image, are used as input to a very deep
subnetwork trained via residual learning for regressing the Z coordinate. Our
method ranked 1st in the 3DFAW Challenge, surpassing the second best result by
more than 22%.Comment: Winner of 3D Face Alignment in the Wild (3DFAW) Challenge, ECCV 201
Tracking Multiple Persons Based on a Variational Bayesian Model

International audienceObject tracking is an ubiquitous problem in computer vision with many applications in human-machine and human-robot interaction, augmented reality, driving assistance, surveillance, etc. Although thoroughly investigated, tracking multiple persons remains a challenging and an open problem. In this paper, an online variational Bayesian model for multiple-person tracking is proposed. This yields a variational expectation-maximization (VEM) algorithm. The computational efficiency of the proposed method is due to closed-form expressions for both the posterior distributions of the latent variables and for the estimation of the model parameters. A stochastic process that handles person birth and person death enables the tracker to handle a varying number of persons over long periods of time. The proposed method is benchmarked using the MOT 2016 dataset
A Relational Event Approach to Modeling Behavioral Dynamics

This chapter provides an introduction to the analysis of relational event
data (i.e., actions, interactions, or other events involving multiple actors
that occur over time) within the R/statnet platform. We begin by reviewing the
basics of relational event modeling, with an emphasis on models with piecewise
constant hazards. We then discuss estimation for dyadic and more general
relational event models using the relevent package, with an emphasis on
hands-on applications of the methods and interpretation of results. Statnet is
a collection of packages for the R statistical computing system that supports
the representation, manipulation, visualization, modeling, simulation, and
analysis of relational data. Statnet packages are contributed by a team of
volunteer developers, and are made freely available under the GNU Public
License. These packages are written for the R statistical computing
environment, and can be used with any computing platform that supports R
(including Windows, Linux, and Mac).
Efficient, XOR-based ideal (t, n)-threshold schemes

We propose a new, lightweight (t; n) threshold secret shar- ing scheme that can be implemented using only XOR operations. Our scheme is based on an idea extracted from a patent application by Hewlett Packard that utilises error correction codes. Our scheme im- proves on the patent by requiring fewer randomly generated bits and by reducing the size of shares given to each player, thereby making the scheme ideal. We provide a security proof and e ciency analysis. We compare our scheme to existing schemes in the literature and show that our scheme is more e cient than other schemes, especially when t is large
Towards Learning and Verifying Invariants of Cyber-Physical Systems by
  Code Mutation

Cyber-physical systems (CPS), which integrate algorithmic control with
physical processes, often consist of physically distributed components
communicating over a network. A malfunctioning or compromised component in such
a CPS can lead to costly consequences, especially in the context of public
infrastructure. In this short paper, we argue for the importance of
constructing invariants (or models) of the physical behaviour exhibited by CPS,
motivated by their applications to the control, monitoring, and attestation of
components. To achieve this despite the inherent complexity of CPS, we propose
a new technique for learning invariants that combines machine learning with
ideas from mutation testing. We present a preliminary study on a water
treatment system that suggests the efficacy of this approach, propose
strategies for establishing confidence in the correctness of invariants, then
summarise some research questions and the steps we are taking to investigate
them.Comment: Short paper accepted by the 21st International Symposium on Formal
  Methods (FM 2016
Equivalence Checking a Floating-point Unit against a High-level C Model

Semiconductor companies have increasingly adopted a methodology that starts with a system-level design specification in C/C++/SystemC. This model is extensively simulated to ensure correct functionality and performance. Later, a Register Transfer Level (RTL) implementation is created in Verilog, either manually by a designer or automatically by a high-level synthesis tool. It is essential to check that the C and Verilog programs are consistent. In this paper, we present a two-step approach, embodied in two equivalence checking tools, VERIFOX and HW-CBMC, to validate designs at the software and RTL levels, respectively. VERIFOX is used for equivalence checking of an untimed software model in C against a high-level reference model in C. HW-CBMC verifies the equivalence of a Verilog RTL implementation against an untimed software model in C. To evaluate our tools, we applied them to a commercial floating-point arithmetic unit (FPU) from ARM and an open-source dual-path floating-point adder
Diversifying Software Architecture for Sustainability: A Value-based Perspective

We use real options theory to evaluate the options of diversity\ud
in design by looking at the trade-offs between the cost and long-term\ud
value of different architectural strategies under uncertainty, given a set\ud
of scenarios of interest. As part of our approach, we extend one of the\ud
widely used architecture trade-offs analysis methods (Cost-Benefit Analysis\ud
Method) to incorporate diversification. We also use a case study\ud
to demonstrate how decision makers and architects can reason about\ud
sustainability using a diversified cost-value approach.Peer-reviewedPost-printECSA 2016, 10th European Conference on Software Architecture, University of Copenhagen, Denmar
An Evolutionary Algorithm to Learn SPARQL Queries for
  Source-Target-Pairs: Finding Patterns for Human Associations in DBpedia

Efficient usage of the knowledge provided by the Linked Data community is
often hindered by the need for domain experts to formulate the right SPARQL
queries to answer questions. For new questions they have to decide which
datasets are suitable and in which terminology and modelling style to phrase
the SPARQL query.
  In this work we present an evolutionary algorithm to help with this
challenging task. Given a training list of source-target node-pair examples our
algorithm can learn patterns (SPARQL queries) from a SPARQL endpoint. The
learned patterns can be visualised to form the basis for further investigation,
or they can be used to predict target nodes for new source nodes.
  Amongst others, we apply our algorithm to a dataset of several hundred human
associations (such as "circle - square") to find patterns for them in DBpedia.
We show the scalability of the algorithm by running it against a SPARQL
endpoint loaded with > 7.9 billion triples. Further, we use the resulting
SPARQL queries to mimic human associations with a Mean Average Precision (MAP)
of 39.9 % and a Recall@10 of 63.9 %.Comment: 15 pages, 2 figures, as of 2016-09-13
  6a19d5d7020770dc0711081ce2c1e52f71bf4b8
IoT-O, a core-domain IoT ontology to represent connected devices networks

Smart objects are now present in our everyday lives, and the Internet of Things is expanding both in number of devices and in volume of produced data. These devices are deployed in dynamic ecosys- tems, with spatial mobility constraints, intermittent network availability depending on many parameters (e.g. battery level or duty cycle), etc. To capture knowledge describing such evolving systems, open, shared and dynamic knowledge representations are required. These representations should also have the ability to adapt over time to the changing state of the world. That is why we propose IoT-O, a core-domain modular IoT ontology proposing a vocabulary to describe connected devices and their relation with their environment. First, existing IoT ontologies are described and compared to requirements an IoT ontology should be com- pliant with. Then, after a detailed description of its modules, IoT-O is instantiated in a home automation use case to illustrate how it supports the description of evolving systems
Active Integrity Constraints for Multi-Context Systems

We introduce a formalism to couple integrity constraints over general-purpose
knowledge bases with actions that can be executed to restore consistency. This
formalism generalizes active integrity constraints over databases. In the more
general setting of multi-context systems, adding repair suggestions to
integrity constraints allows defining simple iterative algorithms to find all
possible grounded repairs - repairs for the global system that follow the
suggestions given by the actions in the individual rules. We apply our
methodology to ontologies, and show that it can express most relevant types of
integrity constraints in this domain
Walker-Independent Features for Gait Recognition from Motion Capture
  Data

MoCap-based human identification, as a pattern recognition discipline, can be
optimized using a machine learning approach. Yet in some applications such as
video surveillance new identities can appear on the fly and labeled data for
all encountered people may not always be available. This work introduces the
concept of learning walker-independent gait features directly from raw joint
coordinates by a modification of the Fisher Linear Discriminant Analysis with
Maximum Margin Criterion. Our new approach shows not only that these features
can discriminate different people than who they are learned on, but also that
the number of learning identities can be much smaller than the number of
walkers encountered in the real operation.Comment: Preprint. Full paper published at the Joint IAPR International
  Workshops on Structural and Syntactic Pattern Recognition and Statistical
  Techniques in Pattern Recognition (S+SSPR), Merida, Mexico. pp. 310--321,
  Springer LNCS volume 10029, Nov 2016. DOI: 10.1007/978-3-319-49055-7_28.
  arXiv admin note: substantial text overlap with arXiv:1609.0439
On Security and Sparsity of Linear Classifiers for Adversarial Settings

Machine-learning techniques are widely used in security-related applications,
like spam and malware detection. However, in such settings, they have been
shown to be vulnerable to adversarial attacks, including the deliberate
manipulation of data at test time to evade detection. In this work, we focus on
the vulnerability of linear classifiers to evasion attacks. This can be
considered a relevant problem, as linear classifiers have been increasingly
used in embedded systems and mobile devices for their low processing time and
memory requirements. We exploit recent findings in robust optimization to
investigate the link between regularization and security of linear classifiers,
depending on the type of attack. We also analyze the relationship between the
sparsity of feature weights, which is desirable for reducing processing cost,
and the security of linear classifiers. We further propose a novel octagonal
regularizer that allows us to achieve a proper trade-off between them. Finally,
we empirically show how this regularizer can improve classifier security and
sparsity in real-world application examples including spam and malware
detection
On the Distinction of Functional and Quality Requirements in Practice

Requirements are often divided into functional requirements (FRs) and quality
requirements (QRs). However, we still have little knowledge about to which
extent this distinction makes sense from a practical perspective. In this
paper, we report on a survey we conducted with 103 practitioners to explore
whether and, if so, why they handle requirements labeled as FRs differently
from those labeled as QRs. We additionally asked for consequences of this
distinction w.r.t. the development process. Our results indicate that the
development process for requirements of the two classes strongly differs (e.g.,
in testing). We identified a number of reasons why practitioners do (or do not)
distinguish between QRs and FRs in their documentation and we analyzed both
problems and benefits that arise from that. We found, for instance, that many
reasons are based on expectations rather than on evidence. Those expectations
are, in fact, not reflected in specific negative or positive consequences per
se. It therefore seems more important that the decision whether to make an
explicit distinction or not should be made consciously such that people are
also aware of the risks that this distinction bears so that they may take
appropriate countermeasures.Comment: Proceedings of the 17th International Conference on Product-Focused
  Software Process Improvement, 201
A survey on software release planning models

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-49094-6_4Software release planning (SRP) is the problem of selecting which features or requirements will be included in the next release or releases. It is a crucial step in software development, which happens to be extremely complex given the need to reconcile multiple decision making criteria, (e.g., business value, effort and cost), while considering several constraints (e.g., feature precedencies, resource availability). For this reason, several SRP models have been proposed in the literature. The objective of this paper is to provide an updated review of SRP approaches reported in the literature.Peer ReviewedPostprint (published version
Algorithm agility – discussion on TPM 2.0 ECC functionalities.

The TPM 2.0 specification has been designed to support a number of Elliptic Curve Cryptographic (ECC) primitives, such as key exchange, digital signatures and Direct Anonymous Attestation (DAA). In order to meet the requirement that di↵erent TPM users may favor di↵erent cryptographic algorithms, each primitive can be implemented from multiple algorithms. This feature is called Algorithm Agility. For the purpose of performance efficiency, multiple algorithms share a small set of TPM commands. In this paper, we review all the TPM 2.0 ECC functionalities, and discuss on whether the existing TPM commands can be used to implement new cryptographic algorithms which have not yet been addressed in the specification. We demonstrate that four asymmetric encryption schemes specified in ISO/IEC 18033-2 can be implemented using a TPM 2.0 chip, and we also show on some ECDSA variants that the coverage of algorithm agility from TPM 2.0 is limited. Security analysis of algorithm agility is a challenge, which is not responded in this paper. However, we believe that this paper will help future researchers analyze TPM 2.0 in more comprehensive methods than it has been done so far
Assessment of Resilience in Desalination Infrastructure Using Semi-Markov Models

As the supply of desalinated water becomes significant in many countries, the reliable long-term operation of desalination infrastructure becomes paramount. As it is not realistic to build desalination systems with components that never fail, instead the system should be designed with more resilience. To answer the question how resilient the system should be, we present in this paper a quantitative approach to measure system resilience using semi-Markov models. This approach allows to probabilistically represent the resilience of a desalination system, considering the functional or failed states of its components, as well as the probability of failure and repair rates. As the desalination plants are connected with the end-user through water transportation and distribution networks, this approach also enables an evaluation of various network configurations and resilience strategies. A case study addressing a segment of the water system in Saudi Arabia is given with the results, benefits, and limitations of the technique discussed.Center for Complex Engineering Systems at MIT and KACSTUnited States. National Aeronautics and Space Administration (Space Technology Research Fellowship, grant number NNX14AM42H
On-line dynamic station redeployments in bike-sharing systems

Bike-sharing has seen great development during recent years, both in Europe and globally. However, these systems are far from perfect. The uncertainty of the customer demand often leads to an unbalanced distribution of bicycles over the time and space (congestion and/or starvation), resulting both in a loss of customers and a poor customer experience. In order to improve those aspects, we propose a dynamic bike-sharing system, which combines the standard fixed base stations with movable stations (using trucks), which will able to be dynamically re-allocated according to the upcoming forecasted customer demand during the day in real-time. The purpose of this paper is to investigate whether using moveable stations in designing the bike-sharing system has a significant positive effect on the system performance. To that end, we contribute an on-line stochastic optimization formulation to address the redeployment of the moveable stations during the day, to better match the upcoming customer demand. Finally, we demonstrate the utility of our approach with numerical experiments using data provided by bike-sharing companies
Automatic speech recognition with deep neural networks for impaired speech

The final publication is available at https://link.springer.com/chapter/10.1007%2F978-3-319-49169-1_10Automatic Speech Recognition has reached almost human performance in some controlled scenarios. However, recognition of impaired speech is a difficult task for two main reasons: data is (i) scarce and (ii) heterogeneous. In this work we train different architectures on a database of dysarthric speech. A comparison between architectures shows that, even with a small database, hybrid DNN-HMM models outperform classical GMM-HMM according to word error rate measures. A DNN is able to improve the recognition word error rate a 13% for subjects with dysarthria with respect to the best classical architecture. This improvement is higher than the one given by other deep neural networks such as CNNs, TDNNs and LSTMs. All the experiments have been done with the Kaldi toolkit for speech recognition for which we have adapted several recipes to deal with dysarthric speech and work on the TORGO database. These recipes are publicly available.Peer ReviewedPostprint (author's final draft
Assessing User Expertise in Spoken Dialog System Interactions

Identifying the level of expertise of its users is important for a system
since it can lead to a better interaction through adaptation techniques.
Furthermore, this information can be used in offline processes of root cause
analysis. However, not much effort has been put into automatically identifying
the level of expertise of an user, especially in dialog-based interactions. In
this paper we present an approach based on a specific set of task related
features. Based on the distribution of the features among the two classes -
Novice and Expert - we used Random Forests as a classification approach.
Furthermore, we used a Support Vector Machine classifier, in order to perform a
result comparison. By applying these approaches on data from a real system,
Let's Go, we obtained preliminary results that we consider positive, given the
difficulty of the task and the lack of competing approaches for comparison.Comment: 10 page