OpenRaiser/Intern-Atlas huggingface dataset:
Constrained Joint Cascade Regression Framework for Simultaneous Facial Action Unit Recognition and Facial Landmark Detection
Venue: arXiv cs.CV 2017
Year: 2017

Cascade regression framework has been shown to be effective for facial landmark detection. It starts from an initial face shape and gradually predicts the face shape update from the local appearance features to generate the facial landmark locations in the next iteration until convergence. In this paper, we improve upon the cascade regression framework and propose the Constrained Joint Cascade Regression Framework (CJCRF) for simultaneous facial action unit recognition and facial landmark detection, which are two related face analysis tasks, but are seldomly exploited together. In particular, we first learn the relationships among facial action units and face shapes as a constraint. Then, in the proposed constrained joint cascade regression framework, with the help from the constraint, we iteratively update the facial landmark locations and the action unit activation probabilities until convergence. Experimental results demonstrate that the intertwined relationships of facial action units and face shapes boost the performances of both facial action unit recognition and facial landmark detection. The experimental results also demonstrate the effectiveness of the proposed method comparing to the state-of-the-art works.
Disabling External Influence in Social Networks via Edge Recommendation
Venue: arXiv cs.SI 2017
Year: 2017

Existing socio-psychological studies suggest that users of a social network form their opinions relying on the opinions of their neighbors. According to DeGroot opinion formation model, one value of particular importance is the asymptotic consensus value---the sum of user opinions weighted by the users' eigenvector centralities. This value plays the role of an attractor for the opinions in the network and is a lucrative target for external influence. However, since any potentially malicious control of the opinion distribution in a social network is clearly undesirable, it is important to design methods to prevent the external attempts to strategically change the asymptotic consensus value. In this work, we assume that the adversary wants to maximize the asymptotic consensus value by altering the opinions of some users in a network; we, then, state DIVER---an NP-hard problem of disabling such external influence attempts by strategically adding a limited number of edges to the network. Relying on the theory of Markov chains, we provide perturbation analysis that shows how eigenvector centrality and, hence, DIVER's objective function change in response to an edge's addition to the network. The latter leads to the design of a pseudo-linear-time heuristic for DIVER, whose computation relies on efficient estimation of mean first passage times in a Markov chain. We confirm our theoretical findings in experiments.
Comparison of Batch Normalization and Weight Normalization Algorithms for the Large-scale Image Classification
Venue: arXiv cs.CV 2017
Year: 2017

Batch normalization (BN) has become a de facto standard for training deep convolutional networks. However, BN accounts for a significant fraction of training run-time and is difficult to accelerate, since it is a memory-bandwidth bounded operation. Such a drawback of BN motivates us to explore recently proposed weight normalization algorithms (WN algorithms), i.e. weight normalization, normalization propagation and weight normalization with translated ReLU. These algorithms don't slow-down training iterations and were experimentally shown to outperform BN on relatively small networks and datasets. However, it is not clear if these algorithms could replace BN in practical, large-scale applications. We answer this question by providing a detailed comparison of BN and WN algorithms using ResNet-50 network trained on ImageNet. We found that although WN achieves better training accuracy, the final test accuracy is significantly lower ($\approx 6\%$) than that of BN. This result demonstrates the surprising strength of the BN regularization effect which we were unable to compensate for using standard regularization techniques like dropout and weight decay. We also found that training of deep networks with WN algorithms is significantly less stable compared to BN, limiting their practical applications.
Can Image Retrieval help Visual Saliency Detection?
Venue: arXiv cs.CV 2017
Year: 2017

We propose a novel image retrieval framework for visual saliency detection using information about salient objects contained within bounding box annotations for similar images. For each test image, we train a customized SVM from similar example images to predict the saliency values of its object proposals and generate an external saliency map (ES) by aggregating the regional scores. To overcome limitations caused by the size of the training dataset, we also propose an internal optimization module which computes an internal saliency map (IS) by measuring the low-level contrast information of the test image. The two maps, ES and IS, have complementary properties so we take a weighted combination to further improve the detection performance. Experimental results on several challenging datasets demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods.
Performance Characterization of Image Feature Detectors in Relation to the Scene Content Utilizing a Large Image Database
Venue: arXiv cs.CV 2017
Year: 2017

Selecting the most suitable local invariant feature detector for a particular application has rendered the task of evaluating feature detectors a critical issue in vision research. Although the literature offers a variety of comparison works focusing on performance evaluation of image feature detectors under several types of image transformations, the influence of the scene content on the performance of local feature detectors has received little attention so far. This paper aims to bridge this gap with a new framework for determining the type of scenes which maximize and minimize the performance of detectors in terms of repeatability rate. The results are presented for several state-of-the-art feature detectors that have been obtained using a large image database of 20482 images under JPEG compression, uniform light and blur changes with 539 different scenes captured from real-world scenarios. These results provide new insights into the behavior of feature detectors.
MDS Code Constructions with Small Sub-packetization and Near-optimal Repair Bandwidth
Venue: arXiv cs.IT 2017
Year: 2017

This paper addresses the problem of constructing MDS codes that enable exact repair of each code block with small repair bandwidth, which refers to the total amount of information flow from the remaining code blocks during the repair process. This problem naturally arises in the context of distributed storage systems as the node repair problem [7]. The constructions of exact-repairable MDS codes with optimal repair-bandwidth require working with large sub-packetization levels, which restricts their employment in practice. This paper presents constructions for MDS codes that simultaneously provide both small repair bandwidth and small sub-packetization level. In particular, this paper presents two general approaches to construct exact-repairable MDS codes that aim at significantly reducing the required sub-packetization level at the cost of slightly sub-optimal repair bandwidth. The first approach gives MDS codes that have repair bandwidth at most twice the optimal repair-bandwidth. Additionally, these codes also have the smallest possible sub-packetization level $\ell = O(r)$, where $r$ denotes the number of parity blocks. This approach is then generalized to design codes that have their repair bandwidth approaching the optimal repair-bandwidth at the cost of graceful increment in the required sub-packetization level. The second approach transforms an MDS code with optimal repair-bandwidth and large sub-packetization level into a longer MDS code with small sub-packetization level and near-optimal repair bandwidth. For a given $r$, the obtained codes have their sub-packetization level scaling logarithmically with the code length. In addition, the obtained codes require field size only linear in the code length and ensure load balancing among the intact code blocks in terms of the information downloaded from these blocks during the exact reconstruction of a code block.
On improved bound for measure of cluster structure in compact metric spaces
Venue: arXiv cs.DM 2017
Year: 2017

A compact metric space $(X, ρ)$ is given. Let $μ$ be a Borel measure on $X$. By $r$-cluster we mean a measurable subset of $X$ with diameter at most $r$. A family of $k$ $2r$-clusters is called a $r$-cluster structure of order $k$ if any two clusters from the family are separated by a distance at least $r$. By measure of a cluster structure we mean a sum of clusters measures from the cluster structure. In our previous work we showed that under some parametric restrictions for distance distribution measure of maximal cluster structure $μ(\mathcal{X})^*$ is close $μ(X)$ and lower bound for $μ(\mathcal{X})^*$ converges to $μ(X)$ when corresponding parameters tend to 0. However, this bound asymptotically unimprovable. We propose an additional restriction for distance distribution that is responsible for balance of cluster's measure in cluster structure. This restriction allows to significantly improve previous bound in asymptotic sense.
Using Conservative Estimation for Conditional Probability instead of Ignoring Infrequent Case
Venue: arXiv cs.IT 2017
Year: 2017

There are several estimators of conditional probability from observed frequencies of features. In this paper, we propose using the lower limit of confidence interval on posterior distribution determined by the observed frequencies to ascertain conditional probability. In our experiments, this method outperformed other popular estimators.
Best Rank-One Tensor Approximation and Parallel Update Algorithm for CPD
Venue: arXiv cs.NA 2017
Year: 2017

A novel algorithm is proposed for CANDECOMP/PARAFAC tensor decomposition to exploit best rank-1 tensor approximation. Different from the existing algorithms, our algorithm updates rank-1 tensors simultaneously in parallel. In order to achieve this, we develop new all-at-once algorithms for best rank-1 tensor approximation based on the Levenberg-Marquardt method and the rotational update. We show that the LM algorithm has the same complexity of first-order optimisation algorithms, while the rotational method leads to solving the best rank-1 approximation of tensors of size $2 \times 2 \times \cdots \times 2$. We derive a closed-form expression of the best rank-1 tensor of $2\times 2 \times 2$ tensors and present an ALS algorithm which updates 3 component at a time for higher order tensors. The proposed algorithm is illustrated in decomposition of difficult tensors which are associated with multiplication of two matrices.
Machine Learning for Networking: Workflow, Advances and Opportunities
Venue: arXiv cs.NI 2017
Year: 2017

Recently, machine learning has been used in every possible field to leverage its amazing power. For a long time, the net-working and distributed computing system is the key infrastructure to provide efficient computational resource for machine learning. Networking itself can also benefit from this promising technology. This article focuses on the application of Machine Learning techniques for Networking (MLN), which can not only help solve the intractable old network questions but also stimulate new network applications. In this article, we summarize the basic workflow to explain how to apply the machine learning technology in the networking domain. Then we provide a selective survey of the latest representative advances with explanations on their design principles and benefits. These advances are divided into several network design objectives and the detailed information of how they perform in each step of MLN workflow is presented. Finally, we shed light on the new opportunities on networking design and community building of this new inter-discipline. Our goal is to provide a broad research guideline on networking with machine learning to help and motivate researchers to develop innovative algorithms, standards and frameworks.
Quantum Online Algorithms with Respect to Space Complexity
Venue: arXiv cs.CC 2017
Year: 2017

Online algorithm is a well-known computational model. We introduce quantum online algorithms and investigate them with respect to a competitive ratio in two points of view: space complexity and advice complexity. We start with exploring a model with restricted memory and show that quantum online algorithms can be better than classical ones (deterministic or randomized) for sublogarithmic space (memory), and they can be better than deterministic online algorithms without restriction for memory. Additionally, we consider polylogarithmic space case and show that in this case, quantum online algorithms can be better than deterministic ones as well.
House Price Prediction Using LSTM
Venue: arXiv cs.LG 2017
Year: 2017

In this paper, we use the house price data ranging from January 2004 to October 2016 to predict the average house price of November and December in 2016 for each district in Beijing, Shanghai, Guangzhou and Shenzhen. We apply Autoregressive Integrated Moving Average model to generate the baseline while LSTM networks to build prediction model. These algorithms are compared in terms of Mean Squared Error. The result shows that the LSTM model has excellent properties with respect to predict time series. Also, stateful LSTM networks and stack LSTM networks are employed to further study the improvement of accuracy of the house prediction model.
Spatial-Temporal Residue Network Based In-Loop Filter for Video Coding
Venue: arXiv cs.MM 2017
Year: 2017

Deep learning has demonstrated tremendous break through in the area of image/video processing. In this paper, a spatial-temporal residue network (STResNet) based in-loop filter is proposed to suppress visual artifacts such as blocking, ringing in video coding. Specifically, the spatial and temporal information is jointly exploited by taking both current block and co-located block in reference frame into consideration during the processing of in-loop filter. The architecture of STResNet only consists of four convolution layers which shows hospitality to memory and coding complexity. Moreover, to fully adapt the input content and improve the performance of the proposed in-loop filter, coding tree unit (CTU) level control flag is applied in the sense of rate-distortion optimization. Extensive experimental results show that our scheme provides up to 5.1% bit-rate reduction compared to the state-of-the-art video coding standard.
LADAR-Based Mover Detection from Moving Vehicles
Venue: arXiv cs.RO 2017
Year: 2017

Detecting moving vehicles and people is crucial for safe operation of UGVs but is challenging in cluttered, real world environments. We propose a registration technique that enables objects to be robustly matched and tracked, and hence movers to be detected even in high clutter. Range data are acquired using a 2D scanning Ladar from a moving platform. These are automatically clustered into objects and modeled using a surface density function. A Bhattacharya similarity is optimized to register subsequent views of each object enabling good discrimination and tracking, and hence mover detection.
Generative learning for deep networks
Venue: arXiv cs.LG 2017
Year: 2017

Learning, taking into account full distribution of the data, referred to as generative, is not feasible with deep neural networks (DNNs) because they model only the conditional distribution of the outputs given the inputs. Current solutions are either based on joint probability models facing difficult estimation problems or learn two separate networks, mapping inputs to outputs (recognition) and vice-versa (generation). We propose an intermediate approach. First, we show that forward computation in DNNs with logistic sigmoid activations corresponds to a simplified approximate Bayesian inference in a directed probabilistic multi-layer model. This connection allows to interpret DNN as a probabilistic model of the output and all hidden units given the input. Second, we propose that in order for the recognition and generation networks to be more consistent with the joint model of the data, weights of the recognition and generator network should be related by transposition. We demonstrate in a tentative experiment that such a coupled pair can be learned generatively, modelling the full distribution of the data, and has enough capacity to perform well in both recognition and generation.
Delay based Duplicate Transmission Avoid (DDA) Coordination Scheme for Opportunistic routing
Venue: arXiv cs.NI 2017
Year: 2017

Since the packet is transmitted to a set of relaying nodes in opportunistic routing strategy, so the transmission delay and the duplication transmission are serious. For reducing the transmission delay and the duplicate transmission, in this paper, we propose the delay based duplication transmission avoid (DDA) coordination scheme for opportunistic routing. In this coordination scheme, the candidate relaying nodes are divided into different fully connected relaying networks, so the duplicate transmission is avoided. Moreover, we propose the relaying network recognition algorithm which can be used to judge whether the sub-network is fully connected or not. The properties of the relaying networks are investigated in detail in this paper. When the fully connected relaying networks are got, they will be the basic units in the next hop relaying network selection. In this paper, we prove that the packet delivery ratio of the high priority relaying nodes in the relaying network has greater effection on the relaying delay than that of the low priority relaying nodes. According to this conclusion, in DDA, the relaying networks which the packet delivery ratios of the high priority relaying nodes are high have higher priority than that of the low one. During the next hop relaying network selection, the transmission delay, the network utility, and the packet delivery ratio are taken into account. By these innovations, the DDA can improve the network performance greatly than that of ExOR and SOAR.
A Secure Self-synchronized Stream Cipher
Venue: arXiv cs.CR 2017
Year: 2017

We follow two main objectives in this article. On the one hand, we introduce a security model called LORBACPA$^+$ for self-synchronized stream ciphers which is stronger than the blockwise LOR-IND-CPA, where we show that standard constructions as delayed CBC or similar existing self-synchronized modes of operation are not secure in this stronger model. Then, on the other hand, following contributions of G.~Millérioux et.al., we introduce a new self-synchronized stream cipher and prove its security in LORBACPA$^+$ model.
Fast Vehicle Detection in Aerial Imagery
Venue: arXiv cs.CV 2017
Year: 2017

In recent years, several real-time or near real-time object detectors have been developed. However these object detectors are typically designed for first-person view images where the subject is large in the image and do not directly apply well to detecting vehicles in aerial imagery. Though some detectors have been developed for aerial imagery, these are either slow or do not handle multi-scale imagery very well. Here the popular YOLOv2 detector is modified to vastly improve it's performance on aerial data. The modified detector is compared to Faster RCNN on several aerial imagery datasets. The proposed detector gives near state of the art performance at more than 4x the speed.
Methodology and Results for the Competition on Semantic Similarity Evaluation and Entailment Recognition for PROPOR 2016
Venue: arXiv cs.CL 2017
Year: 2017

In this paper, we present the methodology and the results obtained by our teams, dubbed Blue Man Group, in the ASSIN (from the Portuguese {\it Avaliação de Similaridade Semântica e Inferência Textual}) competition, held at PROPOR 2016\footnote{International Conference on the Computational Processing of the Portuguese Language - http://propor2016.di.fc.ul.pt/}. Our team's strategy consisted of evaluating methods based on semantic word vectors, following two distinct directions: 1) to make use of low-dimensional, compact, feature sets, and 2) deep learning-based strategies dealing with high-dimensional feature vectors. Evaluation results demonstrated that the first strategy was more promising, so that the results from the second strategy have been discarded. As a result, by considering the best run of each of the six teams, we have been able to achieve the best accuracy and F1 values in entailment recognition, in the Brazilian Portuguese set, and the best F1 score overall. In the semantic similarity task, our team was ranked second in the Brazilian Portuguese set, and third considering both sets.
Encoding Bitrate Optimization Using Playback Statistics for HTTP-based Adaptive Video Streaming
Venue: arXiv cs.MM 2017
Year: 2017

HTTP video streaming is in wide use to deliver video over the Internet. With HTTP adaptive steaming, a video playback dynamically selects a video stream from a pre-encoded representation based on available bandwidth and viewport (screen) size. The viewer's video quality is therefore influenced by the encoded bitrates. We minimize the average delivered bitrate subject to a quality lower bound on a per-chunk basis by modeling the probability that a player selects a particular encoding. Through simulation and real-world experiments, the proposed method saves 9.6% of bandwidth while average delivered video quality comparing with state of the art while keeping average delivered video quality.
Malware Detection Approach for Android systems Using System Call Logs
Venue: arXiv cs.CR 2017
Year: 2017

Static detection technologies based on signature-based approaches that are widely used in Android platform to detect malicious applications. It can accurately detect malware by extracting signatures from test data and then comparing the test data with the signature samples of virus and benign samples. However, this method is generally unable to detect unknown malware applications. This is because, sometimes, the machine code can be converted into assembly code, which can be easily read and understood by humans. Furthuremore, the attacker can then make sense of the assembly instructions and understand the functioning of the program from the same. Therefore we focus on observing the behaviour of the malicious software while it is actually running on a host system. The dynamic behaviours of an application are conducted by the system call sequences at the end. Hence, we observe the system call log of each application, use the same for the construction of our dataset, and finally use this dataset to classify an unknown application as malicious or benign.
Towards End-to-End Car License Plates Detection and Recognition with Deep Neural Networks
Venue: arXiv cs.CV 2017
Year: 2017

In this work, we tackle the problem of car license plate detection and recognition in natural scene images. We propose a unified deep neural network which can localize license plates and recognize the letters simultaneously in a single forward pass. The whole network can be trained end-to-end. In contrast to existing approaches which take license plate detection and recognition as two separate tasks and settle them step by step, our method jointly solves these two tasks by a single network. It not only avoids intermediate error accumulation, but also accelerates the processing speed. For performance evaluation, three datasets including images captured from various scenes under different conditions are tested. Extensive experiments show the effectiveness and efficiency of our proposed approach.