OpenRaiser/Intern-Atlas huggingface dataset:
Discriminative Cross-View Binary Representation Learning
Venue: arXiv cs.CV 2018
Year: 2018

Learning compact representation is vital and challenging for large scale multimedia data. Cross-view/cross-modal hashing for effective binary representation learning has received significant attention with exponentially growing availability of multimedia content. Most existing cross-view hashing algorithms emphasize the similarities in individual views, which are then connected via cross-view similarities. In this work, we focus on the exploitation of the discriminative information from different views, and propose an end-to-end method to learn semantic-preserving and discriminative binary representation, dubbed Discriminative Cross-View Hashing (DCVH), in light of learning multitasking binary representation for various tasks including cross-view retrieval, image-to-image retrieval, and image annotation/tagging. The proposed DCVH has the following key components. First, it uses convolutional neural network (CNN) based nonlinear hashing functions and multilabel classification for both images and texts simultaneously. Such hashing functions achieve effective continuous relaxation during training without explicit quantization loss by using Direct Binary Embedding (DBE) layers. Second, we propose an effective view alignment via Hamming distance minimization, which is efficiently accomplished by bit-wise XOR operation. Extensive experiments on two image-text benchmark datasets demonstrate that DCVH outperforms state-of-the-art cross-view hashing algorithms as well as single-view image hashing algorithms. In addition, DCVH can provide competitive performance for image annotation/tagging.
A Fog-based Architecture and Programming Model for IoT Applications in the Smart Grid
Venue: arXiv cs.NI 2018
Year: 2018

The smart grid utilizes many Internet of Things (IoT) applications to support its intelligent grid monitoring and control. The requirements of the IoT applications vary due to different tasks in the smart grid. In this paper, we propose a new computing paradigm to offer location-aware, latencysensitive monitoring and intelligent control for IoT applications in the smart grid. In particular, a new fog-based architecture and programming model is designed. Fog computing extends computing to the edge of a network, which has a perfect match to IoT applications. However, existing schemes can hardly satisfy the distributed coordination within fog computing nodes in the smart grid. In the proposed model, we introduce a new distributed fog computing coordinator, which periodically gathers information of fog computing nodes, e.g., remaining resources, tasks, etc. Moreover, the fog computing coordinator also manages jobs so that all computing nodes can collaborate on complex tasks. In addition, we construct a working prototype of intelligent electric vehicle service to evaluate the proposed model. Experiment results are also presented to demonstrate that our proposed model exceed the traditional fog computing schemes for IoT applications in the smart grid.
How could we ignore the lens and pupils of eyeballs: Metamaterial optics for retinal projection
Venue: arXiv cs.GR 2018
Year: 2018

Retinal projection is required for xR applications that can deliver immersive visual experience throughout the day. If general-purpose retinal projection methods can be realized at a low cost, not only could the image be displayed on the retina using less energy, but there is also the possibility of cutting off the weight of projection unit itself from the AR goggles. Several retinal projection methods have been previously proposed; however, as the lenses and iris of the eyeball are in front of the retina, which is a limitation of the eyeball, the proposal of retinal projection is generally fraught with narrow viewing angles and small eyebox problems. In this short technical report, we introduce ideas and samples of an optical system for solving the common problems of retinal projection by using the metamaterial mirror (plane symmetric transfer optical system). Using this projection method, the designing of retinal projection can becomes easy, and if appropriate optics are available, it would be possible to construct an optical system that allows the quick follow-up of retinal projection hardware.
Unsupervised Semantic-based Aggregation of Deep Convolutional Features
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we propose a simple but effective semantic-based aggregation (SBA) method. The proposed SBA utilizes the discriminative filters of deep convolutional layers as semantic detectors. Moreover, we propose the effective unsupervised strategy to select some semantic detectors to generate the "probabilistic proposals", which highlight certain discriminative pattern of objects and suppress the noise of background. The final global SBA representation could then be acquired by aggregating the regional representations weighted by the selected "probabilistic proposals" corresponding to various semantic content. Our unsupervised SBA is easy to generalize and achieves excellent performance on various tasks. We conduct comprehensive experiments and show that our unsupervised SBA outperforms the state-of-the-art unsupervised and supervised aggregation methods on image retrieval, place recognition and cloud classification.
Predicting interactions between individuals with structural and dynamical information
Venue: arXiv cs.SI 2018
Year: 2018

Capturing both the structural and temporal aspects of interactions is crucial for many real world datasets like contact between individuals. Using the link stream formalism to capture the dynamic of the systems, we tackle the issue of activity prediction in link streams, that is to say predicting the number of links occurring during a given period of time and we present a protocol that takes advantage of the temporal and structural information contained in the link stream. Using a supervised learning method, we are able to model the dynamic of our system to improve the prediction. We investigate the behavior of our algorithm and crucial elements affecting the prediction. By introducing different categories of pair of nodes, we are able to improve the quality as well as increase the diversity of our prediction.
On-line Chain Partitioning Approach to Scheduling
Venue: arXiv cs.DS 2018
Year: 2018

An on-line chain partitioning algorithm receives the points of the poset from some externally determined list. Being presented with a new point the algorithm learns the comparability status of this new point to all previously presented ones. As each point is received, the algorithm assigns this new point to a chain in an irrevocable manner and this assignment is made without knowledge of future points. Kierstead presented an algorithm using $(5^w-1)/4$ chains to cover each poset of width $w$. Felsner proved that width $2$ posets can be partitioned on-line into $5$ chains. We present an algorithm using $16$ chains on posets of width $3$. This result significantly narrows down the previous bound of $31$. Moreover, we address the on-line chain partitioning problem for interval orders. Kierstead and Trotter presented an algorithm using $3w-2$ chains. We deal with an up-growing version of an on-line chain partition of interval orders, i.e. we restrict possible inputs by the rule that each new point is maximal at the moment of its arrival. We present an algorithm using $2w-1$ chains and show that there is no better one. These problems come from a need for better algorithms that can be applied to scheduling. Each on-line chain partitioning algorithm schedules tasks in a multiprocessor environment, and therefore can be applied in order to minimize number of processors.
Learnable Exposure Fusion for Dynamic Scenes
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we focus on Exposure Fusion (EF) [ExposFusi2] for dynamic scenes. The task is to fuse multiple images obtained by exposure bracketing to create an image which comprises a high level of details. Typically, such images are not possible to obtain directly from a camera due to hardware limitations, e.g., a limited dynamic range of the sensor. A major problem of such tasks is that the images may not be spatially aligned due to scene motion or camera motion. It is known that the required alignment by image registration problems is ill-posed. In this case, the images to be aligned vary in their intensity range, which makes the problem even more difficult. To address the mentioned problems, we propose an end-to-end \emph{Convolutional Neural Network} (CNN) based approach to learn to estimate exposure fusion from $2$ and $3$ Low Dynamic Range (LDR) images depicting different scene contents. To the best of our knowledge, no efficient and robust CNN-based end-to-end approach can be found in the literature for this kind of problem. The idea is to create a dataset with perfectly aligned LDR images to obtain ground-truth exposure fusion images. At the same time, we obtain additional LDR images with some motion, having the same exposure fusion ground-truth as the perfectly aligned LDR images. This way, we can train an end-to-end CNN having misaligned LDR input images, but with a proper ground truth exposure fusion image. We propose a specific CNN-architecture to solve this problem. In various experiments, we show that the proposed approach yields excellent results.
A Pyramid CNN for Dense-Leaves Segmentation
Venue: arXiv cs.CV 2018
Year: 2018

Automatic detection and segmentation of overlapping leaves in dense foliage can be a difficult task, particularly for leaves with strong textures and high occlusions. We present Dense-Leaves, an image dataset with ground truth segmentation labels that can be used to train and quantify algorithms for leaf segmentation in the wild. We also propose a pyramid convolutional neural network with multi-scale predictions that detects and discriminates leaf boundaries from interior textures. Using these detected boundaries, closed-contour boundaries around individual leaves are estimated with a watershed-based algorithm. The result is an instance segmenter for dense leaves. Promising segmentation results for leaves in dense foliage are obtained.
Cancelable Indexing Based on Low-rank Approximation of Correlation-invariant Random Filtering for Fast and Secure Biometric Identification
Venue: arXiv cs.CV 2018
Year: 2018

A cancelable biometric scheme called correlation-invariant random filtering (CIRF) is known as a promising template protection scheme. This scheme transforms a biometric feature represented as an image via the 2D number theoretic transform (NTT) and random filtering. CIRF has perfect secrecy in that the transformed feature leaks no information about the original feature. However, CIRF cannot be applied to large-scale biometric identification, since the 2D inverse NTT in the matching phase requires high computational time. Furthermore, existing biometric indexing schemes cannot be used in conjunction with template protection schemes to speed up biometric identification, since a biometric index leaks some information about the original feature. In this paper, we propose a novel indexing scheme called "cancelable indexing" to speed up CIRF without losing its security properties. The proposed scheme is based on fast computation of CIRF via low-rank approximation of biometric images and via a minimum spanning tree representation of low-rank matrices in the Fourier domain. We prove that the transformed index leaks no information about the original index and the original biometric feature (i.e., perfect secrecy), and thoroughly discuss the security of the proposed scheme. We also demonstrate that it significantly reduces the one-to-many matching time using a finger-vein dataset that includes six fingers from 505 subjects.
Using a Classifier Ensemble for Proactive Quality Monitoring and Control: the impact of the choice of classifiers types, selection criterion, and fusion process
Venue: arXiv cs.LG 2018
Year: 2018

In recent times, the manufacturing processes are faced with many external or internal (the increase of customized product rescheduling , process reliability,..) changes. Therefore, monitoring and quality management activities for these manufacturing processes are difficult. Thus, the managers need more proactive approaches to deal with this variability. In this study, a proactive quality monitoring and control approach based on classifiers to predict defect occurrences and provide optimal values for factors critical to the quality processes is proposed. In a previous work (Noyel et al. 2013), the classification approach had been used in order to improve the quality of a lacquering process at a company plant; the results obtained are promising, but the accuracy of the classification model used needs to be improved. One way to achieve this is to construct a committee of classifiers (referred to as an ensemble) to obtain a better predictive model than its constituent models. However, the selection of the best classification methods and the construction of the final ensemble still poses a challenging issue. In this study, we focus and analyze the impact of the choice of classifier types on the accuracy of the classifier ensemble; in addition, we explore the effects of the selection criterion and fusion process on the ensemble accuracy as well. Several fusion scenarios were tested and compared based on a real-world case. Our results show that using an ensemble classification leads to an increase in the accuracy of the classifier models. Consequently, the monitoring and control of the considered real-world case can be improved.
Combining STPA and BDD for Safety Analysis and Verification in Agile Development: A Controlled Experiment
Venue: arXiv cs.SE 2018
Year: 2018

Context: Agile development is in widespread use, even in safety-critical domains. Motivation: However, there is a lack of an ap- propriate safety analysis and verification method in agile development. Objective: In this paper, we investigate the use of Behavior Driven De- velopment (BDD) instead of standard User Acceptance Testing (UAT) for safety verification with System-Theoretic Process Analysis (STPA) for safety analysis in agile development. Method: We evaluate the effect of this combination in a controlled experiment with 44 students in terms of productivity, test thoroughness, fault detection effectiveness and com- munication effectiveness. Results: The results show that BDD is more effective for safety verification regarding the impact on communication effectiveness than standard UAT, whereas productivity, test thorough- ness and fault detection effectiveness show no statistically significant difference in our controlled experiment. Conclusion: The combination of BDD and STPA seems promising with an enhancement on communica- tion, but its impact needs more research.
Bringing Cartoons to Life: Towards Improved Cartoon Face Detection and Recognition Systems
Venue: arXiv cs.CV 2018
Year: 2018

Given the recent deep learning advancements in face detection and recognition techniques for human faces, this paper answers the question "how well would they work for cartoons'?" - a domain that remains largely unexplored until recently, mainly due to the unavailability of large scale datasets and the failure of traditional methods on these. Our work studies and extends multiple frameworks for the aforementioned tasks. For face detection, we incorporate the Multi-task Cascaded Convolutional Network (MTCNN) architecture and contrast it with conventional methods. For face recognition, our two-fold contributions include: (i) an inductive transfer learning approach combining the feature learning capability of the Inception v3 network and the feature recognizing capability of Support Vector Machines (SVMs), (ii) a proposed Hybrid Convolutional Neural Network (HCNN) framework trained over a fusion of pixel values and 15 manually located facial keypoints. All the methods are evaluated on the Cartoon Faces in the Wild (IIIT-CFW) database. We demonstrate that the HCNN model offers stability superior to that of Inception+SVM over larger input variations, and explore the plausible architectural principles. We show that the Inception+SVM model establishes a state-of-the-art F1 score on the task of gender recognition of cartoon faces. Further, we introduce a small database hosting location coordinates of 15 points on the cartoon faces belonging to 50 public figures of the IIIT-CFW database.
Domain Adaptation for Statistical Machine Translation
Venue: arXiv cs.CL 2018
Year: 2018

Statistical machine translation (SMT) systems perform poorly when it is applied to new target domains. Our goal is to explore domain adaptation approaches and techniques for improving the translation quality of domain-specific SMT systems. However, translating texts from a specific domain (e.g., medicine) is full of challenges. The first challenge is ambiguity. Words or phrases contain different meanings in different contexts. The second one is language style due to the fact that texts from different genres are always presented in different syntax, length and structural organization. The third one is the out-of-vocabulary words (OOVs) problem. In-domain training data are often scarce with low terminology coverage. In this thesis, we explore the state-of-the-art domain adaptation approaches and propose effective solutions to address those problems.
Not just about size - A Study on the Role of Distributed Word Representations in the Analysis of Scientific Publications
Venue: arXiv cs.CL 2018
Year: 2018

The emergence of knowledge graphs in the scholarly communication domain and recent advances in artificial intelligence and natural language processing bring us closer to a scenario where intelligent systems can assist scientists over a range of knowledge-intensive tasks. In this paper we present experimental results about the generation of word embeddings from scholarly publications for the intelligent processing of scientific texts extracted from SciGraph. We compare the performance of domain-specific embeddings with existing pre-trained vectors generated from very large and general purpose corpora. Our results suggest that there is a trade-off between corpus specificity and volume. Embeddings from domain-specific scientific corpora effectively capture the semantics of the domain. On the other hand, obtaining comparable results through general corpora can also be achieved, but only in the presence of very large corpora of well formed text. Furthermore, We also show that the degree of overlapping between knowledge areas is directly related to the performance of embeddings in domain evaluation tasks.
Word Segmentation as Graph Partition
Venue: arXiv cs.CL 2018
Year: 2018

We propose a new approach to the Chinese word segmentation problem that considers the sentence as an undirected graph, whose nodes are the characters. One can use various techniques to compute the edge weights that measure the connection strength between characters. Spectral graph partition algorithms are used to group the characters and achieve word segmentation. We follow the graph partition approach and design several unsupervised algorithms, and we show their inspiring segmentation results on two corpora: (1) electronic health records in Chinese, and (2) benchmark data from the Second International Chinese Word Segmentation Bakeoff.
End-to-End Saliency Mapping via Probability Distribution Prediction
Venue: arXiv cs.CV 2018
Year: 2018

Most saliency estimation methods aim to explicitly model low-level conspicuity cues such as edges or blobs and may additionally incorporate top-down cues using face or text detection. Data-driven methods for training saliency models using eye-fixation data are increasingly popular, particularly with the introduction of large-scale datasets and deep architectures. However, current methods in this latter paradigm use loss functions designed for classification or regression tasks whereas saliency estimation is evaluated on topographical maps. In this work, we introduce a new saliency map model which formulates a map as a generalized Bernoulli distribution. We then train a deep architecture to predict such maps using novel loss functions which pair the softmax activation function with measures designed to compute distances between probability distributions. We show in extensive experiments the effectiveness of such loss functions over standard ones on four public benchmark datasets, and demonstrate improved performance over state-of-the-art saliency methods.
Composable, Unconditionally Secure Message Authentication without any Secret Key
Venue: arXiv cs.IT 2018
Year: 2018

We consider a setup in which the channel from Alice to Bob is less noisy than the channel from Eve to Bob. We show that there exist encoding and decoding which accomplish error correction and authentication simultaneously; that is, Bob is able to correctly decode a message coming from Alice and reject a message coming from Eve with high probability. The system does not require any secret key shared between Alice and Bob, provides information theoretic security, and can safely be composed with other protocols in an arbitrary context.
Higher-Order Bounded Model Checking
Venue: arXiv cs.PL 2018
Year: 2018

We present a Bounded Model Checking technique for higher-order programs. The vehicle of our study is a higher-order calculus with general references. Our technique is a symbolic state syntactical translation based on SMT solvers, adapted to a setting where the values passed and stored during computation can be functions of arbitrary order. We prove that our algorithm is sound, and devise an optimisation based on points-to analysis to improve scalability. We moreover provide a prototype implementation of the algorithm with experimental results showcasing its performance.
Incremental Verification of Parametric and Reconfigurable Markov Chains
Venue: arXiv cs.LO 2018
Year: 2018

The analysis of parametrised systems is a growing field in verification, but the analysis of parametrised probabilistic systems is still in its infancy. This is partly because it is much harder: while there are beautiful cut-off results for non-stochastic systems that allow to focus only on small instances, there is little hope that such approaches extend to the quantitative analysis of probabilistic systems, as the probabilities depend on the size of a system. The unicorn would be an automatic transformation of a parametrised system into a formula, which allows to plot, say, the likelihood to reach a goal or the expected costs to do so, against the parameters of a system. While such analysis exists for narrow classes of systems, such as waiting queues, we aim both lower---stepwise exploring the parameter space---and higher---considering general systems. The novelty is to heavily exploit the similarity between instances of parametrised systems. When the parameter grows, the system for the smaller parameter is, broadly speaking, present in the larger system. We use this observation to guide the elegant state-elimination method for parametric Markov chains in such a way, that the model transformations will start with those parts of the system that are stable under increasing the parameter. We argue that this can lead to a very cheap iterative way to analyse parametric systems, show how this approach extends to reconfigurable systems, and demonstrate on two benchmarks that this approach scales.