OpenRaiser/Intern-Atlas huggingface dataset:
The Autonomic Architecture of the Licas System
Venue: arXiv cs.SE 2019
Year: 2019

Licas (lightweight internet-based communication for autonomic services) is a distributed framework for building service-based systems. The framework provides a p2p server and more intelligent processing of information through its AI algorithms. Distributed communication includes XML-RPC, REST, HTTP and Web Services. It can now provide a robust platform for building different types of system, where Microservices or SOA would be possible. However, the system may be equally suited for the IoT, as it provides classes to connect with external sources and has an optional Autonomic Manager with a MAPE control loop integrated into the communication process. The system is also mobile-compatible with Android. This paper focuses in particular on the autonomic setup and how that might be used. A novel linking mechanism has been described previously and is considered again, as part of the autonomous framework.
Socio-technical Smart Grid Optimization via Decentralized Charge Control of Electric Vehicles
Venue: arXiv cs.SY 2019
Year: 2019

The penetration of electric vehicles becomes a catalyst for the sustainability of Smart Cities. However, unregulated battery charging remains a challenge causing high energy costs, power peaks or even blackouts. This paper studies this challenge from a socio-technical perspective: social dynamics such as the participation in demand-response programs, the discomfort experienced by alternative suggested vehicle usage times and even the fairness in terms of how equally discomfort is experienced among the population are highly intertwined with Smart Grid reliability. To address challenges of such a socio-technical nature, this paper introduces a fully decentralized and participatory learning mechanism for privacy-preserving coordinated charging control of electric vehicles that regulates three Smart Grid socio-technical aspects: (i) reliability, (ii) discomfort and (iii) fairness. In contrast to related work, a novel autonomous software agent exclusively uses local knowledge to generate energy demand plans for its vehicle that encode different battery charging regimes. Agents interact to learn and make collective decisions of which plan to execute so that power peaks and energy cost are reduced system-wide. Evaluation with real-world data confirms the improvement of drivers' comfort and fairness using the proposed planning method, while this improvement is assessed in terms of reliability and cost reduction under a varying number of participating vehicles. These findings have a significant relevance and impact for power utilities and system operator on designing more reliable and socially responsible Smart Grids with high penetration of electric vehicles.
Matrix Completion Based Localization in the Internet of Things Network
Venue: arXiv cs.IT 2019
Year: 2019

In order to make a proper reaction to the collected information from internet of things (IoT) devices, location information of things should be available at the data center. One challenge for the massive IoT networks is to identify the location map of whole sensor nodes from partially observed distance information. In this paper, we propose a matrix completion based localization algorithm to reconstruct the location map of sensors using partially observed distance information. From the numerical experiments, we show that the proposed method based on the modified conjugate gradient is effective in recovering the Euclidean distance matrix.
Cograph Editing: Merging Modules is equivalent to Editing P4's
Venue: arXiv cs.DM 2019
Year: 2019

The modular decomposition of a graph $G=(V,E)$ does not contain prime modules if and only if $G$ is a cograph, that is, if no quadruple of vertices induces a simple connected path $P_4$. The cograph editing problem consists in inserting into and deleting from $G$ a set $F$ of edges so that $H=(V,EΔF)$ is a cograph and $|F|$ is minimum. This NP-hard combinatorial optimization problem has recently found applications, e.g., in the context of phylogenetics. Efficient heuristics are hence of practical importance. The simple characterization of cographs in terms of their modular decomposition suggests that instead of editing $G$ one could operate directly on the modular decomposition. We show here that editing the induced $P_4$s is equivalent to resolving prime modules by means of a suitable defined merge operation on the submodules. Moreover, we characterize so-called module-preserving edit sets and demonstrate that optimal pairwise sequences of module-preserving edit sets exist for every non-cograph. This eventually leads to an exact algorithm for the cograph editing problem as well as fixed-parameter tractable (FPT) results when cograph editing is parameterized by the so-called modular-width. In addition, we provide two heuristics with time complexity $O(|V|^3)$, resp., $O(|V|^2)$.
Dropping Convexity for More Efficient and Scalable Online Multiview Learning
Venue: arXiv cs.LG 2019
Year: 2019

Multiview representation learning is very popular for latent factor analysis. It naturally arises in many data analysis, machine learning, and information retrieval applications to model dependent structures among multiple data sources. For computational convenience, existing approaches usually formulate the multiview representation learning as convex optimization problems, where global optima can be obtained by certain algorithms in polynomial time. However, many pieces of evidence have corroborated that heuristic nonconvex approaches also have good empirical computational performance and convergence to the global optima, although there is a lack of theoretical justification. Such a gap between theory and practice motivates us to study a nonconvex formulation for multiview representation learning, which can be efficiently solved by a simple stochastic gradient descent (SGD) algorithm. We first illustrate the geometry of the nonconvex formulation; Then, we establish asymptotic global rates of convergence to the global optima by diffusion approximations. Numerical experiments are provided to support our theory.
Adaptive Ensemble Prediction for Deep Neural Networks based on Confidence Level
Venue: arXiv cs.LG 2019
Year: 2019

Ensembling multiple predictions is a widely used technique for improving the accuracy of various machine learning tasks. One obvious drawback of ensembling is its higher execution cost during inference. In this paper, we first describe our insights on the relationship between the probability of prediction and the effect of ensembling with current deep neural networks; ensembling does not help mispredictions for inputs predicted with a high probability even when there is a non-negligible number of mispredicted inputs. This finding motivated us to develop a way to adaptively control the ensembling. If the prediction for an input reaches a high enough probability, i.e., the output from the softmax function, on the basis of the confidence level, we stop ensembling for this input to avoid wasting computation power. We evaluated the adaptive ensembling by using various datasets and showed that it reduces the computation cost significantly while achieving accuracy similar to that of static ensembling using a pre-defined number of local predictions. We also show that our statistically rigorous confidence-level-based early-exit condition reduces the burden of task-dependent threshold tuning better compared with naive early exit based on a pre-defined threshold in addition to yielding a better accuracy with the same cost.
A Unifying View of Explicit and Implicit Feature Maps of Graph Kernels
Venue: arXiv cs.LG 2019
Year: 2019

Non-linear kernel methods can be approximated by fast linear ones using suitable explicit feature maps allowing their application to large scale problems. We investigate how convolution kernels for structured data are composed from base kernels and construct corresponding feature maps. On this basis we propose exact and approximative feature maps for widely used graph kernels based on the kernel trick. We analyze for which kernels and graph properties computation by explicit feature maps is feasible and actually more efficient. In particular, we derive approximative, explicit feature maps for state-of-the-art kernels supporting real-valued attributes including the GraphHopper and graph invariant kernels. In extensive experiments we show that our approaches often achieve a classification accuracy close to the exact methods based on the kernel trick, but require only a fraction of their running time. Moreover, we propose and analyze algorithms for computing random walk, shortest-path and subgraph matching kernels by explicit and implicit feature maps. Our theoretical results are confirmed experimentally by observing a phase transition when comparing running time with respect to label diversity, walk lengths and subgraph size, respectively.
A Correlative Denoising Autoencoder to Model Social Influence for Top-N Recommender System
Venue: arXiv cs.IR 2019
Year: 2019

In recent years, there are numerous works been proposed to leverage the techniques of deep learning to improve social-aware recommendation performance. In most cases, it requires a larger number of data to train a robust deep learning model, which contains a lot of parameters to fit training data. However, both data of user ratings and social networks are facing critical sparse problem, which makes it not easy to train a robust deep neural network model. Towards this problem, we propose a novel Correlative Denoising Autoencoder (CoDAE) method by taking correlations between users with multiple roles into account to learn robust representations from sparse inputs of ratings and social networks for recommendation. We develop the CoDAE model by utilizing three separated autoencoders to learn user features with roles of rater, truster and trustee, respectively. Especially, on account of that each input unit of user vectors with roles of truster and trustee is corresponding to a particular user, we propose to utilize shared parameters to learn common information of the units that corresponding to same users. Moreover, we propose a related regularization term to learn correlations between user features that learnt by the three subnetworks of CoDAE model. We further conduct a series of experiments to evaluate the proposed method on two public datasets for Top-N recommendation task. The experimental results demonstrate that the proposed model outperforms state-of-the-art algorithms on rank-sensitive metrics of MAP and NDCG.
Task-based End-to-end Model Learning in Stochastic Optimization
Venue: arXiv cs.LG 2019
Year: 2019

With the increasing popularity of machine learning techniques, it has become common to see prediction algorithms operating within some larger process. However, the criteria by which we train these algorithms often differ from the ultimate criteria on which we evaluate them. This paper proposes an end-to-end approach for learning probabilistic machine learning models in a manner that directly captures the ultimate task-based objective for which they will be used, within the context of stochastic programming. We present three experimental evaluations of the proposed approach: a classical inventory stock problem, a real-world electrical grid scheduling task, and a real-world energy storage arbitrage task. We show that the proposed approach can outperform both traditional modeling and purely black-box policy optimization approaches in these applications.
Not All Browsers Are Created Equal: Comparing Web Browser Fingerprintability
Venue: arXiv cs.CR 2019
Year: 2019

Browsers and their users can be tracked even in the absence of a persistent IP address or cookie. Unique and hence identifying pieces of information, making up what is known as a fingerprint, can be collected from browsers by a visited website, e.g. using JavaScript. However, browsers vary in precisely what information they make available, and hence their fingerprintability may also vary. In this paper, we report on the results of experiments examining the fingerprintable attributes made available by a range of modern browsers. We tested the most widely used browsers for both desktop and mobile platforms. The results reveal significant differences between browsers in terms of their fingerprinting potential, meaning that the choice of browser has significant privacy implications.
Steganographic Generative Adversarial Networks
Venue: arXiv cs.MM 2019
Year: 2019

Steganography is collection of methods to hide secret information ("payload") within non-secret information "container"). Its counterpart, Steganalysis, is the practice of determining if a message contains a hidden payload, and recovering it if possible. Presence of hidden payloads is typically detected by a binary classifier. In the present study, we propose a new model for generating image-like containers based on Deep Convolutional Generative Adversarial Networks (DCGAN). This approach allows to generate more setganalysis-secure message embedding using standard steganography algorithms. Experiment results demonstrate that the new model successfully deceives the steganography analyzer, and for this reason, can be used in steganographic applications.
Cartoonish sketch-based face editing in videos using identity deformation transfer
Venue: arXiv cs.CV 2019
Year: 2019

We address the problem of using hand-drawn sketches to create exaggerated deformations to faces in videos, such as enlarging the shape or modifying the position of eyes or mouth. This task is formulated as a 3D face model reconstruction and deformation problem. We first recover the facial identity and expressions from the video by fitting a face morphable model for each frame. At the same time, user's editing intention is recognized from input sketches as a set of facial modifications. Then a novel identity deformation algorithm is proposed to transfer these facial deformations from 2D space to the 3D facial identity directly while preserving the facial expressions. After an optional stage for further refining the 3D face model, these changes are propagated to the whole video with the modified identity. Both the user study and experimental results demonstrate that our sketching framework can help users effectively edit facial identities in videos, while high consistency and fidelity are ensured at the same time.
Randomness extraction via a quantum generalization of the conditional collision entropy
Venue: arXiv cs.IT 2019
Year: 2019

Randomness extraction against side information is the art of distilling from a given source a key which is almost uniform conditioned on the side information. This paper provides randomness extraction against quantum side information whose extractable key length is given by a quantum generalization of the collision entropy, which is smoothed and conditioned differently from how this is done in existing schemes. Based on the fact that the collision entropy is not subadditive, its optimization with respect to additional side information is introduced, and is shown to be asymptotically optimal. The lower bound derived there for general states is expressed as the difference between two unconditional entropies and its evaluation reduces to an eigenvalue problem of two states, which are the entire state and the marginal state of side information.
Diabetic Retinopathy Detection via Deep Convolutional Networks for Discriminative Localization and Visual Explanation
Venue: arXiv cs.CV 2019
Year: 2019

We proposed a deep learning method for interpretable diabetic retinopathy (DR) detection. The visual-interpretable feature of the proposed method is achieved by adding the regression activation map (RAM) after the global averaging pooling layer of the convolutional networks (CNN). With RAM, the proposed model can localize the discriminative regions of an retina image to show the specific region of interest in terms of its severity level. We believe this advantage of the proposed deep learning model is highly desired for DR detection because in practice, users are not only interested with high prediction performance, but also keen to understand the insights of DR detection and why the adopted learning model works. In the experiments conducted on a large scale of retina image dataset, we show that the proposed CNN model can achieve high performance on DR detection compared with the state-of-the-art while achieving the merits of providing the RAM to highlight the salient regions of the input image.
Moderately Complex Paxos Made Simple: High-Level Executable Specification of Distributed Algorithms
Venue: arXiv cs.DC 2019
Year: 2019

This paper describes the application of a high-level language and method in developing simpler specifications of more complex variants of the Paxos algorithm for distributed consensus. The specifications are for Multi-Paxos with preemption, replicated state machine, and reconfiguration and optimized with state reduction and failure detection. The language is DistAlgo. The key is to express complex control flows and synchronization conditions precisely at a high level, using nondeterministic waits and message-history queries. We obtain complete executable specifications that are almost completely declarative---updating only a number for the protocol round besides the sets of messages sent and received. We show the following results: 1.English and pseudocode descriptions of distributed algorithms can be captured completely and precisely at a high level, without adding, removing, or reformulating algorithm details to fit lower-level, more abstract, or less direct languages. 2.We created higher-level control flows and synchronization conditions than all previous specifications, and obtained specifications that are much simpler and smaller, even matching or smaller than abstract specifications that omit many algorithm details. 3.The simpler specifications led us to easily discover useless replies, unnecessary delays, and liveness violations (if messages can be lost) in previous published specifications, by just following the simplified algorithm flows. 4.The resulting specifications can be executed directly, and we can express optimizations cleanly, yielding drastic performance improvement over naive execution and facilitating a general method for merging processes. 5.We systematically translated the resulting specifications into TLA+ and developed machine-checked safety proofs, which also allowed us to detect and fix a subtle safety violation in an earlier unpublished specification.
A GRU-Gated Attention Model for Neural Machine Translation
Venue: arXiv cs.CL 2019
Year: 2019

Neural machine translation (NMT) heavily relies on an attention network to produce a context vector for each target word prediction. In practice, we find that context vectors for different target words are quite similar to one another and therefore are insufficient in discriminatively predicting target words. The reason for this might be that context vectors produced by the vanilla attention network are just a weighted sum of source representations that are invariant to decoder states. In this paper, we propose a novel GRU-gated attention model (GAtt) for NMT which enhances the degree of discrimination of context vectors by enabling source representations to be sensitive to the partial translation generated by the decoder. GAtt uses a gated recurrent unit (GRU) to combine two types of information: treating a source annotation vector originally produced by the bidirectional encoder as the history state while the corresponding previous decoder state as the input to the GRU. The GRU-combined information forms a new source annotation vector. In this way, we can obtain translation-sensitive source representations which are then feed into the attention network to generate discriminative context vectors. We further propose a variant that regards a source annotation vector as the current input while the previous decoder state as the history. Experiments on NIST Chinese-English translation tasks show that both GAtt-based models achieve significant improvements over the vanilla attentionbased NMT. Further analyses on attention weights and context vectors demonstrate the effectiveness of GAtt in improving the discrimination power of representations and handling the challenging issue of over-translation.
Understanding People Flow in Transportation Hubs
Venue: arXiv cs.CV 2019
Year: 2019

In this paper, we aim to monitor the flow of people in large public infrastructures. We propose an unsupervised methodology to cluster people flow patterns into the most typical and meaningful configurations. By processing 3D images from a network of depth cameras, we build a descriptor for the flow pattern. We define a data-irregularity measure that assesses how well each descriptor fits a data model. This allows us to rank flow patterns from highly distinctive (outliers) to very common ones. By discarding outliers, we obtain more reliable key configurations (classes). Synthetic experiments show that the proposed method is superior to standard clustering methods. We applied it in an operational scenario during 14 days in the X-ray screening area of an international airport. Results show that our methodology is able to successfully summarize the representative patterns for such a long observation period, providing relevant information for airport management. Beyond regular flows, our method identifies a set of rare events corresponding to uncommon activities (cleaning, special security and circulating staff).
Relational $\star$-Liftings for Differential Privacy
Venue: arXiv cs.LO 2019
Year: 2019

Recent developments in formal verification have identified approximate liftings (also known as approximate couplings) as a clean, compositional abstraction for proving differential privacy. This construction can be defined in two styles. Earlier definitions require the existence of one or more witness distributions, while a recent definition by Sato uses universal quantification over all sets of samples. These notions have each have their own strengths: the universal version is more general than the existential ones, while existential liftings are known to satisfy more precise composition principles. We propose a novel, existential version of approximate lifting, called $\star$-lifting, and show that it is equivalent to Sato's construction for discrete probability measures. Our work unifies all known notions of approximate lifting, yielding cleaner properties, more general constructions, and more precise composition theorems for both styles of lifting, enabling richer proofs of differential privacy. We also clarify the relation between existing definitions of approximate lifting, and consider more general approximate liftings based on $f$-divergences.
BLENDER: Enabling Local Search with a Hybrid Differential Privacy Model
Venue: arXiv cs.CR 2019
Year: 2019

We propose a hybrid model of differential privacy that considers a combination of regular and opt-in users who desire the differential privacy guarantees of the local privacy model and the trusted curator model, respectively. We demonstrate that within this model, it is possible to design a new type of blended algorithm for the task of privately computing the head of a search log. This blended approach provides significant improvements in the utility of obtained data compared to related work while providing users with their desired privacy guarantees. Specifically, on two large search click data sets, comprising 1.75 and 16 GB respectively, our approach attains NDCG values exceeding 95% across a range of privacy budget values.