pinecone/core-2020-05-10-deduplication huggingface dataset:
Timing properties and correctness for structured parallel programs on x86-64 multicores

This paper determines correctness and timing properties for structured parallel programs on x86-64 multicores. Multicore architectures are increasingly common, but real architectures have unpredictable timing properties, and even correctness is not obvious above the relaxed-memory concurrency models that are enforced by commonly-used hardware. This paper takes a rigorous approach to correctness and timing properties, examining common locking protocols from first principles, and extending this through queues to structured parallel constructs. We prove functional correctness and derive simple timing models, and both extend for the first time from low-level primitives to high-level parallel patterns. Our derived high-level timing models for structured parallel programs allow us to accurately predict upper bounds on program execution times on x86-64 multicores.Postprin
Cognitive and neuromuscular robotic rehabilitation framework

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-46562-3_21.© Springer International Publishing AG 2017.This paper presents a cognitive and neuromuscular robotic rehabilitation framework to support enhanced control of arm movement for humans with muscular control impairment, typically with some level of memory deficiency due to, for example, suffering from a stroke. It describes the design, development and integration of the framework architecture as well as a Baxter robot based demonstration platform. Three key elements of the proposed framework (rehabilitation module, workspace and rehabilitation scenarios) have been described in detail. In the rehabilitation sessions, the users and the robot are asked to work together to place cubes so as to form a predefined shape. The robot and the user hold the same object in order to move it to a particular destination according to a rehabilitation scenario. If the robot detects a force from the user directed in the wrong direction during the navigation then it resists and corrects the movement in order to assist the user towards the right direction. The assistive support scenarios were designed to evaluate the achieved enhancement of precision, efficiency and dexterity of arm movements. The proposed rehabilitation framework provides a modular, automated and open-source platform for researchers and practitioners in neuromuscular rehabilitation applications
Classification of human hand movements using surface EMG for myoelectric control

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-46562-3_22.© Springer International Publishing AG 2017.Surface electromyogram (sEMG) is a bioelectric signal that can be captured non-invasively by placing electrodes on the human skin. The sEMG is capable of representing the action intent of nearby muscles. The research of myoelectric control using sEMG has been primarily driven by the potential to create humanmachine interfaces which respond to users intentions intuitively. However, it is one of the major gaps between research and commercial applications that there are rarely robust simultaneous control schemes. This paper proposes one classification method and a potential real-time control scheme. Four machine learning classifiers have been tested and compared to find the best configuration for different potential applications, and non-negative matrix factorisation has been used as a pre-processing tool for performance improvement. This control scheme achieves its highest accuracy when it is adapted to a single user at a time. It can identify intact subjects hand movements with above 98% precision and 91% upwards for amputees but takes double the amount of time for decision-making
A New Multi-objective Model for Constrained Optimisation

Multi-objective optimization evolutionary algorithms have becoming a promising approach for solving constrained optimization problems in the last decade. Standard two-objective schemes aim at minimising the objective function and the degrees of violating constraints (the degrees of violating each constraint or their sum) simultaneously. This paper proposes a new multi-objective model for constrained optimization. The model keeps the standard objectives: the original objective function and the sum of the degrees of constraint violation. Besides them, other helper objectives are constructed, which are inspired from MOEA/D or Tchebycheff method for multi-objective optimization. The new helper objectives are weighted sums of the normalized original objective function and normalized degrees of constraint violation. The normalization is a major improvement. Unlike our previous model without the normalization, experimental results demonstrate that the new model is completely superior to the standard model with two objectives. This confirms our expectation that adding more help objectives may improve the solution quality significantly
Aspect-Based Sentiment Analysis Using a Two-Step Neural Network
  Architecture

The World Wide Web holds a wealth of information in the form of unstructured
texts such as customer reviews for products, events and more. By extracting and
analyzing the expressed opinions in customer reviews in a fine-grained way,
valuable opportunities and insights for customers and businesses can be gained.
We propose a neural network based system to address the task of Aspect-Based
Sentiment Analysis to compete in Task 2 of the ESWC-2016 Challenge on Semantic
Sentiment Analysis. Our proposed architecture divides the task in two subtasks:
aspect term extraction and aspect-specific sentiment extraction. This approach
is flexible in that it allows to address each subtask independently. As a first
step, a recurrent neural network is used to extract aspects from a text by
framing the problem as a sequence labeling task. In a second step, a recurrent
network processes each extracted aspect with respect to its context and
predicts a sentiment label. The system uses pretrained semantic word embedding
features which we experimentally enhance with semantic knowledge extracted from
WordNet. Further features extracted from SenticNet prove to be beneficial for
the extraction of sentiment labels. As the best performing system in its
category, our proposed system proves to be an effective approach for the
Aspect-Based Sentiment Analysis
The Automatic Blind Spot Camera: A Vision-Based Active Alarm System

In this paper we present a vision-based active safety system targeting the blind spot zone of trucks. Each year, these blind spot accidents are responsible for numerous fatalities and heavily injured. Existing commercial systems seem not to be able to cope with this problem completely. Therefore, we propose a vision-based safety system relying solely on the blind spot camera images. Our system is able to detect all vulnerable road users (VRUs) in the blind spot zone, and automatically generates an alarm towards the truck driver. This inherently is a challenging task. Indeed, such active safety system implicitly requires extremely high accuracy demands at very low latency. These two demands are contradictory, and thus very difficult to unite. However, our real-life experiments show that our proposed active alarm system achieves excellent accuracy results while meeting these stringent requirements.status: publishe
From on-road to off : transfer learning within a deep convolutional neural network for segmentation and classification of off-road scenes.

Real-time road-scene understanding is a challenging computer vision task with recent advances in convolutional neural networks (CNN) achieving results that notably surpass prior traditional feature driven approaches. Here, we take an existing CNN architecture, pre-trained for urban road-scene understanding, and retrain it towards the task of classifying off-road scenes, assessing the network performance within the training cycle. Within the paradigm of transfer learning we analyse the effects on CNN classification, by training and assessing varying levels of prior training on varying sub-sets of our off-road training data. For each of these configurations, we evaluate the network at multiple points during its training cycle, allowing us to analyse in depth exactly how the training process is affected by these variations. Finally, we compare this CNN to a more traditional approach using a feature-driven Support Vector Machine (SVM) classifier and demonstrate state-of-the-art results in this particularly challenging problem of off-road scene understanding
Dendritic spine shape analysis: a clustering perspective

Functional properties of neurons are strongly coupled with their morphology. Changes in neuronal activity alter morphological characteristics of dendritic spines. First step towards understanding the structure-function relationship is to group spines into main spine classes reported in the literature. Shape analysis of dendritic spines can help neuroscientists understand the underlying relationships. Due to unavailability of reliable automated tools, this analysis is currently performed manually which is a time-intensive and subjective task. Several studies on spine shape classification have been reported in the literature, however, there is an on-going debate on whether distinct spine shape classes exist or whether spines should be modeled through a continuum of shape variations. Another challenge is the subjectivity and bias that is introduced due to the supervised nature of classification approaches. In this paper, we aim to address these issues by presenting a clustering perspective. In this context, clustering may serve both confirmation of known patterns and discovery of new ones. We perform cluster analysis on two-photon microscopic images of spines using morphological, shape, and appearance based features and gain insights into the spine shape analysis problem. We use histogram of oriented gradients (HOG), disjunctive normal shape models (DNSM), morphological features, and intensity profile based features for cluster analysis. We use x-means to perform cluster analysis that selects the number of clusters automatically using the Bayesian information criterion (BIC). For all features, this analysis produces 4 clusters and we observe the formation of at least one cluster consisting of spines which are difficult to be assigned to a known class. This observation supports the argument of intermediate shape types
Towards Semantic Fast-Forward and Stabilized Egocentric Videos

The emergence of low-cost personal mobiles devices and wearable cameras and
the increasing storage capacity of video-sharing websites have pushed forward a
growing interest towards first-person videos. Since most of the recorded videos
compose long-running streams with unedited content, they are tedious and
unpleasant to watch. The fast-forward state-of-the-art methods are facing
challenges of balancing the smoothness of the video and the emphasis in the
relevant frames given a speed-up rate. In this work, we present a methodology
capable of summarizing and stabilizing egocentric videos by extracting the
semantic information from the frames. This paper also describes a dataset
collection with several semantically labeled videos and introduces a new
smoothness evaluation metric for egocentric videos that is used to test our
method.Comment: Accepted for publication and presented in the First International
  Workshop on Egocentric Perception, Interaction and Computing at European
  Conference on Computer Vision (EPIC@ECCV) 201
Context Change Detection for an Ultra-Low Power Low-Resolution Ego-Vision Imager

With the increasing popularity of wearable cameras, such as GoPro or Narrative Clip, research on continuous activity monitoring from egocentric cameras has received a lot of attention. Research in hardware and software is devoted to find new efficient, stable and long-time running solutions; \ud
however, devices are too power-hungry for truly always-on operation, and are aggressively duty-cycled to achieve acceptable lifetimes. In this paper we present a wearable system for context change detection based on an egocentric camera with ultra-low power consumption that can collect data 24/7. Although the resolution of the captured images is low, experimental results in real scenarios demonstrate how our approach, based on Siamese Neural Networks, can achieve visual context awareness. In particular, we  compare our solution with hand-crafted features and with state of art technique and propose a novel and challenging dataset composed of roughly 30000  low-resolution images
Adversarial Training For Sketch Retrieval

Generative Adversarial Networks (GAN) are able to learn excellent
representations for unlabelled data which can be applied to image generation
and scene classification. Representations learned by GANs have not yet been
applied to retrieval. In this paper, we show that the representations learned
by GANs can indeed be used for retrieval. We consider heritage documents that
contain unlabelled Merchant Marks, sketch-like symbols that are similar to
hieroglyphs. We introduce a novel GAN architecture with design features that
make it suitable for sketch retrieval. The performance of this sketch-GAN is
compared to a modified version of the original GAN architecture with respect to
simple invariance properties. Experiments suggest that sketch-GANs learn
representations that are suitable for retrieval and which also have increased
stability to rotation, scale and translation compared to the standard GAN
architecture.Comment: Accepted to ECCV2016 VisArt Worksho
Convolutional Sketch Inversion

In this paper, we use deep neural networks for inverting face sketches to
synthesize photorealistic face images. We first construct a semi-simulated
dataset containing a very large number of computer-generated face sketches with
different styles and corresponding face images by expanding existing
unconstrained face data sets. We then train models achieving state-of-the-art
results on both computer-generated sketches and hand-drawn sketches by
leveraging recent advances in deep learning such as batch normalization, deep
residual learning, perceptual losses and stochastic optimization in combination
with our new dataset. We finally demonstrate potential applications of our
models in fine arts and forensic arts. In contrast to existing patch-based
approaches, our deep-neural-network-based approach can be used for synthesizing
photorealistic face images by inverting face sketches in the wild
Detecting People in Artwork with CNNs

CNNs have massively improved performance in object detection in photographs.
However research into object detection in artwork remains limited. We show
state-of-the-art performance on a challenging dataset, People-Art, which
contains people from photos, cartoons and 41 different artwork movements. We
achieve this high performance by fine-tuning a CNN for this task, thus also
demonstrating that training CNNs on photos results in overfitting for photos:
only the first three or four layers transfer from photos to artwork. Although
the CNN's performance is the highest yet, it remains less than 60\% AP,
suggesting further work is needed for the cross-depiction problem. The final
publication is available at Springer via
http://dx.doi.org/10.1007/978-3-319-46604-0_57Comment: 14 pages, plus 3 pages of references; 7 figures in ECCV 2016
  Workshop
Industrial robot ethics: facing the challenges of human-robot collaboration in future manufacturing systems

As a result of significant advances in information and communications technology the manufacturing industry is facing revolutionary changes whereby production processes will become increasingly digitised and interconnected cyber-physical systems. A key component of these new complex systems will be intelligent automation and human-robot collaboration. Industrial robots have traditionally been segregated from people in manufacturing systems because of the dangers posed by their operational speeds and heavy payloads. However, advances in technology mean that we will soon see large-scale robots being deployed to work more closely and collaboratively with people in monitored manufacturing sytems and widespread introduction of small-scale robots and assistive robotic devices. This will not only transform the way people are expected to work and interact with automation but will also involve much more data provision and capture for performance monitoring. This paper discusses the background to these developments and the anticipated ethical issues that we now face as people and robots become able to work collaboratively in industry
Unsupervised video hashing by exploiting spatio-temporal feature

© Springer International Publishing AG 2016.Video hashing is a common solution for content-based video retrieval by encoding high-dimensional feature vectors into short binary codes. Videos not only have spatial structure inside each frame but also have temporal correlation structure between frames, while the latter has been largely neglected by many existing methods. Therefore, in this paper we propose to perform video hashing by incorporating the temporal structure as well as the conventional spatial structure. Specifically, the spatial features of videos are obtained by utilizing Convolutional Neural Network (CNN), and the temporal features are established via Long-Short Term Memory (LSTM). The proposed spatio-temporal feature learning framework can be applied to many existing unsupervised hashing methods such as Iterative Quantization (ITQ), Spectral Hashing (SH), and others. Experimental results on the UCF-101 dataset indicate that by simultaneously employing the temporal features and spatial features, our hashing method is able to significantly improve the performance of existing methods which only deploy the spatial feature
Selective dropout for deep neural networks.

Dropout has been proven to be an effective method for reducing overfitting in deep artificial neural networks. We present 3 new alternative methods for performing dropout on a deep neural network which improves the effectiveness of the dropout method over the same training period. These methods select neurons to be dropped through statistical values calculated using a neurons change in weight, the average size of a neuron's weights, and the output variance of a neuron. We found that increasing the probability of dropping neurons with smaller values of these statistics and decreasing the probability of those with larger statistics gave an improved result in training over 10,000 epochs. The most effective of these was found to be the Output Variance method, giving an average improvement of 1.17 % accuracy over traditional dropout methods
Combining Deep Learning and Preference Learning for Object Tracking

International Conference on Neural Information Processing, ICONIP 2016 (23th. 2016. Kyoto, Japan)Object tracking is nowadays a hot topic in computer vision. Generally speaking, its aim is to find a target object in every frame of a video sequence. In order to build a tracking system, this paper proposes to combine two different learning frameworks: deep learning and preference learning. On the one hand, deep learning is used to automatically extract latent features for describing the multi-dimensional raw images. Previous research has shown that deep learning has been successfully applied in different computer vision applications. On the other hand, object tracking can be seen as a ranking problem, in the sense that the regions of an image can be ranked according to their level of overlapping with the target object. Preference learning is used to build the ranking function. The experimental results of our method, called DPL2DPL2(Deep & Preference Learning), are competitive with respect to the state-of-the-art algorithm
Implementation of a Modular Growing When Required Neural Gas Architecture for Recognition of Falls

In this paper we aim for the replication of a state of the art architecture for recognition of human actions using skeleton poses obtained from a depth sensor. We review the usefulness of accurate human action recognition in the field of robotic elderly care, focusing on fall detection. We attempt fall recognition using a chained Growing When Required neural gas classifier that is fed only skeleton joints data. We test this architecture against Recurrent SOMs (RSOMs) to classify the TST Fall detection database ver. 2, a specialised dataset for fall sequences. We also introduce a simplified mathematical model of falls for easier and faster bench-testing of classification algorithms for fall detection. The outcome of classifying falls from our mathematical model was successful with an accuracy of 97.12±1.65% and from the TST Fall detection database ver. 2 with an accuracy of 90.2±2.68% when a filter was added.CNP
Real-time 3D Tracking of Articulated Tools for Robotic Surgery

In robotic surgery, tool tracking is important for providing safe tool-tissue
interaction and facilitating surgical skills assessment. Despite recent
advances in tool tracking, existing approaches are faced with major
difficulties in real-time tracking of articulated tools. Most algorithms are
tailored for offline processing with pre-recorded videos. In this paper, we
propose a real-time 3D tracking method for articulated tools in robotic
surgery. The proposed method is based on the CAD model of the tools as well as
robot kinematics to generate online part-based templates for efficient 2D
matching and 3D pose estimation. A robust verification approach is incorporated
to reject outliers in 2D detections, which is then followed by fusing inliers
with robot kinematic readings for 3D pose estimation of the tool. The proposed
method has been validated with phantom data, as well as ex vivo and in vivo
experiments. The results derived clearly demonstrate the performance advantage
of the proposed method when compared to the state-of-the-art.Comment: This paper was presented in MICCAI 2016 conference, and a DOI was
  linked to the publisher's versio
Robust Image Descriptors for Real-Time Inter-Examination Retargeting in
  Gastrointestinal Endoscopy

For early diagnosis of malignancies in the gastrointestinal tract,
surveillance endoscopy is increasingly used to monitor abnormal tissue changes
in serial examinations of the same patient. Despite successes with optical
biopsy for in vivo and in situ tissue characterisation, biopsy retargeting for
serial examinations is challenging because tissue may change in appearance
between examinations. In this paper, we propose an inter-examination
retargeting framework for optical biopsy, based on an image descriptor designed
for matching between endoscopic scenes over significant time intervals. Each
scene is described by a hierarchy of regional intensity comparisons at various
scales, offering tolerance to long-term change in tissue appearance whilst
remaining discriminative. Binary coding is then used to compress the descriptor
via a novel random forests approach, providing fast comparisons in Hamming
space and real-time retargeting. Extensive validation conducted on 13 in vivo
gastrointestinal videos, collected from six patients, show that our approach
outperforms state-of-the-art methods.Comment: This paper was presented in MICCAI 2016 conference, and a DOI was
  linked to the publisher's versio