pwc-archive/papers-with-abstracts huggingface dataset:
Comparison of State-of-the-Art Deep Learning APIs for Image Multi-Label Classification using Semantic Metrics
Authors: Adam Kubany, Shimon Ben Ishay, Ruben-sacha Ohayon, Armin Shmilovici, Lior Rokach, Tomer Doitshman
Date: 2019-03-21 00:00:00
Tasks: General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Semantic Similarity, Semantic Textual Similarity

Image understanding heavily relies on accurate multi-label classification. In recent years, deep learning algorithms have become very successful for such tasks, and various commercial and open-source APIs have been released for public use. However, these APIs are often trained on different datasets, which, besides affecting their performance, might pose a challenge to their performance evaluation. This challenge concerns the different object-class dictionaries of the APIs' training dataset and the benchmark dataset, in which the predicted labels are semantically similar to the benchmark labels but considered different simply because they have different wording in the dictionaries. To face this challenge, we propose semantic similarity metrics to obtain richer understating of the APIs predicted labels and thus their performance. In this study, we evaluate and compare the performance of 13 of the most prominent commercial and open-source APIs in a best-of-breed challenge on the Visual Genome and Open Images benchmark datasets. Our findings demonstrate that, while using traditional metrics, the Microsoft Computer Vision, Imagga, and IBM APIs performed better than others. However, applying semantic metrics also unveil the InceptionResNet-v2, Inception-v3, and ResNet50 APIs, which are trained only with the simple ImageNet dataset, as challengers for top semantic performers.
Towards automatic construction of multi-network models for heterogeneous multi-task learning
Authors: Unai Garciarena, Alexander Mendiburu, Roberto Santana
Date: 2019-03-21 00:00:00
Tasks: Atari Games, Multi-Task Learning, Reinforcement Learning

Multi-task learning, as it is understood nowadays, consists of using one
single model to carry out several similar tasks. From classifying hand-written
characters of different alphabets to figuring out how to play several Atari
games using reinforcement learning, multi-task models have been able to widen
their performance range across different tasks, although these tasks are
usually of a similar nature. In this work, we attempt to widen this range even
further, by including heterogeneous tasks in a single learning procedure. To do
so, we firstly formally define a multi-network model, identifying the necessary
components and characteristics to allow different adaptations of said model
depending on the tasks it is required to fulfill. Secondly, employing the
formal definition as a starting point, we develop an illustrative model example
consisting of three different tasks (classification, regression and data
sampling). The performance of this model implementation is then analyzed,
showing its capabilities. Motivated by the results of the analysis, we
enumerate a set of open challenges and future research lines over which the
full potential of the proposed model definition can be exploited.
Quantitative Depth Quality Assessment of RGBD Cameras At Close Range Using 3D Printed Fixtures
Authors: Michele Pratusevich, Jason Chrisos, Shreyas Aditya
Date: 2019-03-21 00:00:00
Tasks: Scene Understanding

Mobile robots that manipulate their environments require high-accuracy scene
understanding at close range. Typically this understanding is achieved with
RGBD cameras, but the evaluation process for selecting an appropriate RGBD
camera for the application is minimally quantitative. Limited
manufacturer-published metrics do not translate to observed quality in
real-world cluttered environments, since quality is application-specific. To
bridge the gap, we present a method for quantitatively measuring depth quality
using a set of extendable 3D printed fixtures that approximate real-world
conditions. By framing depth quality as point cloud density and root mean
square error (RMSE) from a known geometry, we present a method that is
extendable by other system integrators for custom environments. We show a
comparison of 3 cameras and present a case study for camera selection, provide
reference meshes and analysis code, and discuss further extensions.
Are We Ready for Driver-less Vehicles? Security vs. Privacy- A Social Perspective
Authors: Anish Acharya
Date: 2014-12-16 00:00:00
Tasks: Autonomous Driving

At this moment Autonomous cars are probably the biggest and most talked about
technology in the Robotics Research Community. In spite of great technological
advances over past few years a full edged autonomous car is still far from
reality. This article talks about the existing system and discusses the
possibility of a Computer Vision enabled driving being superior than the LiDar
based system. A detailed overview of privacy violations that might arise from
autonomous driving has been discussed in detail both from a technical as well
as legal perspective. It has been proved through evidence and arguments that
efficient and accurate estimation and efficient solution of the constraint
satisfaction problem addressed in the case of autonomous cars are negatively
correlated with the preserving the privacy of the user. It is a very difficult
trade-off since both are very important aspects and has to be taken into
account. The fact that one cannot compromise with the safety issues of the car
makes it inevitable to run into serious privacy concerns that might have
adverse social and political effects.
Transparent Machine Education of Neural Networks for Swarm Shepherding Using Curriculum Design
Authors: Alexander Gee, Hussein Abbass
Date: 2019-01-04 00:00:00

Swarm control is a difficult problem due to the need to guide a large number
of agents simultaneously. We cast the problem as a shepherding problem, similar
to biological dogs guiding a group of sheep towards a goal. The shepherd needs
to deal with complex and dynamic environments and make decisions in order to
direct the swarm from one location to another. In this paper, we design a novel
curriculum to teach an artificial intelligence empowered agent to shepherd in
the presence of the large state space associated with the shepherding problem
and in a transparent manner. The results show that a properly designed
curriculum could indeed enhance the speed of learning and the complexity of
learnt behaviours.
Was ist eine Professur fuer Kuenstliche Intelligenz?
Authors: Kristian Kersting, Jan Peters, Constantin Rothkopf
Date: 2019-02-17 00:00:00

The Federal Government of Germany aims to boost the research in the field of Artificial Intelligence (AI). For instance, 100 new professorships are said to be established. However, the white paper of the government does not answer what an AI professorship is at all. In order to give colleagues, politicians, and citizens an idea, we present a view that is often followed when appointing professors for AI at German and international universities. We hope that it will help to establish a guideline with internationally accepted measures and thus make the public debate more informed.
Trial of an AI: Empowering people to explore law and science challenges
Authors: Gaudron Arthur
Date: 2019-03-05 00:00:00

Artificial Intelligence represents many things: a new market to conquer or a quality label for tech companies, a threat for traditional industries, a menace for democracy, or a blessing for our busy everyday life. The press abounds in examples illustrating these aspects, but one should draw not hasty and premature conclusions. The first successes in AI have been a surprise for society at large-including researchers in the field. Today, after the initial stupefaction, we have examples of the system reactions: traditional companies are heavily investing in AI, social platforms are monitored during elections, data collection is more and more regulated, etc. The resilience of an organization (i.e. its capacity to resist to a shock) relies deeply on the perception of its environment. Future problems have to be anticipated, while unforeseen events occurring have to be quickly identified in order to be mitigated as fast as possible. The author states that this clear perception starts with a common definition of AI in terms of capacities and limits. AI practitioners should make notions and concepts accessible to the general public and the impacted fields (e.g. industries, law, education). It is a truism that only law experts would have the potential to estimate IA impacts on judicial system. However, questions remain on how to connect different kind of expertise and what is the appropriate level of detail required for the knowledge exchanges. And the same consideration is true for dissemination towards society. Ultimately, society will live with decisions made by the "experts". It sounds wise to involve society in the decision process rather than risking to pay consequences later. Therefore, society also needs the key concepts to understand AI impact on their life. This was the purpose of the trial of an IA that took place in October 2018 at the Court of Appeal of Paris: gathering experts from various fields to expose challenges in law and science towards a general public.
High-Dimensional Bernoulli Autoregressive Process with Long-Range Dependence
Authors: Parthe Pandit, Mojtaba Sahraee-Ardakan, Arash A. Amini, Sundeep Rangan, Alyson K. Fletcher
Date: 2019-03-19 00:00:00
Tasks: Gaussian Processes, Vocal Bursts Intensity Prediction

We consider the problem of estimating the parameters of a multivariate
Bernoulli process with auto-regressive feedback in the high-dimensional setting
where the number of samples available is much less than the number of
parameters. This problem arises in learning interconnections of networks of
dynamical systems with spiking or binary-valued data. We allow the process to
depend on its past up to a lag $p$, for a general $p \ge 1$, allowing for more
realistic modeling in many applications. We propose and analyze an
$\ell_1$-regularized maximum likelihood estimator (MLE) under the assumption
that the parameter tensor is approximately sparse. Rigorous analysis of such
estimators is made challenging by the dependent and non-Gaussian nature of the
process as well as the presence of the nonlinearities and multi-level feedback.
We derive precise upper bounds on the mean-squared estimation error in terms of
the number of samples, dimensions of the process, the lag $p$ and other key
statistical properties of the model. The ideas presented can be used in the
high-dimensional analysis of regularized $M$-estimators for other sparse
nonlinear and non-Gaussian processes with long-range dependence.
Tackling Unit Commitment and Load Dispatch Problems Considering All Constraints with Evolutionary Computation
Authors: Danilo Vasconcellos Vargas, Junichi Murata, Hirotaka Takano
Date: 2019-03-06 00:00:00
Tasks: All

Unit commitment and load dispatch problems are important and complex problems
in power system operations that have being traditionally solved separately. In
this paper, both problems are solved together without approximations or
simplifications. In fact, the problem solved has a massive amount of
grid-connected photovoltaic units, four pump-storage hydro plants as energy
storage units and ten thermal power plants, each with its own set of operation
requirements that need to be satisfied. To face such a complex constrained
optimization problem an adaptive repair method is proposed. By including a
given repair method itself as a parameter to be optimized, the proposed
adaptive repair method avoid any bias in repair choices. Moreover, this results
in a repair method that adapt to the problem and will improve together with the
solution during optimization. Experiments are conducted revealing that the
proposed method is capable of surpassing exact method solutions on a simplified
version of the problem with approximations as well as solve the otherwise
intractable complete problem without simplifications. Moreover, since the
proposed approach can be applied to other problems in general and it may not be
obvious how to choose the constraint handling for a certain constraint, a
guideline is provided explaining the reasoning behind. Thus, this paper open
further possibilities to deal with the ever changing types of generation units
and other similarly complex operation/schedule optimization problems with many
difficult constraints.
Enabling Efficient Verifiable Fuzzy Keyword Search Over Encrypted Data in Cloud Computing
Authors: XINRUI GE, JIA YU, Chengyu Hu, HANLIN ZHANG, AND RONG HAO
Date: 2018-08-17 00:00:00
Proceeding: journal 2018 8
Tasks: Cloud Computing

Searchable encryption can support data user to selectively retrieve the cipher documents over
encrypted cloud data by keyword-based search. Most of the existing searchable encryption schemes only
focus on the exact keyword search. When data user makes spelling errors, these schemes fail to return
the result of interest. In searchable encryption, the cloud server might return the invalid result to data
user for saving the computation cost or other reasons. Therefore, these exact keyword search schemes
find little practical significance in real-world applications. In order to address these issues, we propose a
novel verifiable fuzzy keyword search scheme over encrypted cloud data. For the purpose of introducing
this scheme, we first propose a verifiable exact keyword search scheme and then extend this scheme to the
fuzzy keyword search scheme. In the fuzzy keyword search scheme, we employ the linked list as our secure
index to achieve the efficient storage. We construct a linked list with three nodes for each exact keyword
and generate a fuzzy keyword set for it. To reduce the computation cost and the storage space, we generate
one index vector for each fuzzy keyword set, rather than each fuzzy keyword. To resist malicious behaviors
of the cloud server, we generate an authentication label for each fuzzy keyword to verify the authenticity of
the returned ciphertexts. Through security analysis and experiment evaluation, we show that our proposed
schemes are secure and efficient.
An Adaptive Locally Connected Neuron Model: Focusing Neuron
Authors: F. Boray Tek
Date: 2018-08-31 00:00:00
Tasks: model

This paper presents a new artificial neuron model capable of learning its receptive field in the topological domain of inputs. The model provides adaptive and differentiable local connectivity (plasticity) applicable to any domain. It requires no other tool than the backpropagation algorithm to learn its parameters which control the receptive field locations and apertures. This research explores whether this ability makes the neuron focus on informative inputs and yields any advantage over fully connected neurons. The experiments include tests of focusing neuron networks of one or two hidden layers on synthetic and well-known image recognition data sets. The results demonstrated that the focusing neurons can move their receptive fields towards more informative inputs. In the simple two-hidden layer networks, the focusing layers outperformed the dense layers in the classification of the 2D spatial data sets. Moreover, the focusing networks performed better than the dense networks even when 70$\%$ of the weights were pruned. The tests on convolutional networks revealed that using focusing layers instead of dense layers for the classification of convolutional features may work better in some data sets.
Modeling Text with Graph Convolutional Network for Cross-Modal Information Retrieval
Authors: Jing Yu, Yuhang Lu, Zengchang Qin, Yanbing Liu, Jianlong Tan, Li Guo, Weifeng Zhang
Date: 2018-02-03 00:00:00
Tasks: Cross-Modal Information Retrieval, Information Retrieval, Retrieval

Cross-modal information retrieval aims to find heterogeneous data of various
modalities from a given query of one modality. The main challenge is to map
different modalities into a common semantic space, in which distance between
concepts in different modalities can be well modeled. For cross-modal
information retrieval between images and texts, existing work mostly uses
off-the-shelf Convolutional Neural Network (CNN) for image feature extraction.
For texts, word-level features such as bag-of-words or word2vec are employed to
build deep learning models to represent texts. Besides word-level semantics,
the semantic relations between words are also informative but less explored. In
this paper, we model texts by graphs using similarity measure based on
word2vec. A dual-path neural network model is proposed for couple feature
learning in cross-modal information retrieval. One path utilizes Graph
Convolutional Network (GCN) for text modeling based on graph representations.
The other path uses a neural network with layers of nonlinearities for image
modeling based on off-the-shelf features. The model is trained by a pairwise
similarity loss function to maximize the similarity of relevant text-image
pairs and minimize the similarity of irrelevant pairs. Experimental results
show that the proposed model outperforms the state-of-the-art methods
significantly, with 17% improvement on accuracy for the best case.
Learning to Match Using Local and Distributed Representations of Text for Web Search
Authors: Bhaskar Mitra, Fernando Diaz, Nick Craswell
Date: 2016-10-26 00:00:00
Proceeding: Proceedings of the 26th International Conference on World Wide Web, WWW '17 2017 4
Tasks: Document Ranking, Information Retrieval, Retrieval

Models such as latent semantic analysis and those based on neural embeddings
learn distributed representations of text, and match the query against the
document in the latent semantic space. In traditional information retrieval
models, on the other hand, terms have discrete or local representations, and
the relevance of a document is determined by the exact matches of query terms
in the body text. We hypothesize that matching with distributed representations
complements matching with traditional local representations, and that a
combination of the two is favorable. We propose a novel document ranking model
composed of two separate deep neural networks, one that matches the query and
the document using a local representation, and another that matches the query
and the document using learned distributed representations. The two networks
are jointly trained as part of a single neural network. We show that this
combination or `duet' performs significantly better than either neural network
individually on a Web page ranking task, and also significantly outperforms
traditional baselines and other recently proposed models based on neural
networks.
Parser Extraction of Triples in Unstructured Text
Authors: Shaun D'Souza
Date: 2018-11-06 00:00:00
Tasks: Object, POS, Question Answering

The web contains vast repositories of unstructured text. We investigate the
opportunity for building a knowledge graph from these text sources. We generate
a set of triples which can be used in knowledge gathering and integration. We
define the architecture of a language compiler for processing
subject-predicate-object triples using the OpenNLP parser. We implement a
depth-first search traversal on the POS tagged syntactic tree appending
predicate and object information. A parser enables higher precision and higher
recall extractions of syntactic relationships across conjunction boundaries. We
are able to extract 2-2.5 times the correct extractions of ReVerb. The
extractions are used in a variety of semantic web applications and question
answering. We verify extraction of 50,000 triples on the ClueWeb dataset.
PI-REC: Progressive Image Reconstruction Network With Edge and Color Domain
Authors: Sheng You, Ning You, Minxue Pan
Date: 2019-03-25 00:00:00
Proceeding: arXiv 2019 3
Tasks: Generative Adversarial Network, Image Reconstruction, Image-to-Image Translation, Translation

We propose a universal image reconstruction method to represent detailed
images purely from binary sparse edge and flat color domain. Inspired by the
procedures of painting, our framework, based on generative adversarial network,
consists of three phases: Imitation Phase aims at initializing networks,
followed by Generating Phase to reconstruct preliminary images. Moreover,
Refinement Phase is utilized to fine-tune preliminary images into final outputs
with details. This framework allows our model generating abundant high
frequency details from sparse input information. We also explore the defects of
disentangling style latent space implicitly from images, and demonstrate that
explicit color domain in our model performs better on controllability and
interpretability. In our experiments, we achieve outstanding results on
reconstructing realistic images and translating hand drawn drafts into
satisfactory paintings. Besides, within the domain of edge-to-image
translation, our model PI-REC outperforms existing state-of-the-art methods on
evaluations of realism and accuracy, both quantitatively and qualitatively.
Jet grooming through reinforcement learning
Authors: Stefano Carrazza, Frédéric A. Dreyer
Date: 2019-03-22 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We introduce a novel implementation of a reinforcement learning (RL) algorithm which is designed to find an optimal jet grooming strategy, a critical tool for collider experiments. The RL agent is trained with a reward function constructed to optimize the resulting jet properties, using both signal and background samples in a simultaneous multi-level training. We show that the grooming algorithm derived from the deep RL agent can match state-of-the-art techniques used at the Large Hadron Collider, resulting in improved mass resolution for boosted objects. Given a suitable reward function, the agent learns how to train a policy which optimally removes soft wide-angle radiation, allowing for a modular grooming technique that can be applied in a wide range of contexts. These results are accessible through the corresponding GroomRL framework.
Exploiting Excessive Invariance caused by Norm-Bounded Adversarial Robustness
Authors: Jörn-Henrik Jacobsen, Jens Behrmannn, Nicholas Carlini, Florian Tramèr, Nicolas Papernot
Date: 2019-03-25 00:00:00
Tasks: Adversarial Robustness

Adversarial examples are malicious inputs crafted to cause a model to
misclassify them. Their most common instantiation, "perturbation-based"
adversarial examples introduce changes to the input that leave its true label
unchanged, yet result in a different model prediction. Conversely,
"invariance-based" adversarial examples insert changes to the input that leave
the model's prediction unaffected despite the underlying input's label having
changed.
  In this paper, we demonstrate that robustness to perturbation-based
adversarial examples is not only insufficient for general robustness, but
worse, it can also increase vulnerability of the model to invariance-based
adversarial examples. In addition to analytical constructions, we empirically
study vision classifiers with state-of-the-art robustness to perturbation-based
adversaries constrained by an $\ell_p$ norm. We mount attacks that exploit
excessive model invariance in directions relevant to the task, which are able
to find adversarial examples within the $\ell_p$ ball. In fact, we find that
classifiers trained to be $\ell_p$-norm robust are more vulnerable to
invariance-based adversarial examples than their undefended counterparts.
  Excessive invariance is not limited to models trained to be robust to
perturbation-based $\ell_p$-norm adversaries. In fact, we argue that the term
adversarial example is used to capture a series of model limitations, some of
which may not have been discovered yet. Accordingly, we call for a set of
precise definitions that taxonomize and address each of these shortcomings in
learning.