Reviewerly/exHarmony huggingface dataset:
Investigating the transferring capability of capsule networks for text classification

Text classification has been attracting increasing attention with the growth of textual data created on the Internet. Great progress has been made by deep neural networks for domains where a large amount of labeled training data is available. However, providing sufficient data is time-consuming and labor-intensive, establishing substantial obstacles for expanding the learned models to new domains or new tasks. In this paper, we investigate the transferring capability of capsule networks for text classification. Capsule networks are able to capture the intrinsic spatial part-whole relationship constituting domain invariant knowledge that bridges the knowledge gap between the source and target domains (or tasks). We propose an iterative adaptation strategy for cross-domain text classification, which adapts the source domain to the target domain. A fast training method with capsule compression and class-guided routing is designed to make the capsule network more efficient in computation for cross-domain text classification. We first conduct experiments to evaluate the performance of the capsule network on six benchmark datasets for generic text classification. The capsule networks outperform the compared models on 4 out of 6 datasets, suggesting the effectiveness of the capsule networks for text classification. More importantly, we demonstrate the transferring capability of the proposed cross-domain capsule network (TL-Capsule) by applying it to two transfer learning applications: single-label to multi-label text classification and cross-domain sentiment classification. The experimental results show that capsule networks consistently and substantially outperform the compared methods for both tasks. To the best of our knowledge, this is the first work that empirically investigates the transferring capability of capsule networks for text modeling.
A New Secure Data Dissemination Model in Internet of Drones

Data Dissemination is the distribution of data/statistics to the end users. With the adoption of Internet of Drones (IoD) environment for data dissemination, an efficient scheme is proposed which provides data integrity, identity anonymity, authentication, authorization, accountability (AAA) to the system model. We propose a system model having Ethereum based public blockchain distributed network in order to secure drone communication for the data collection and transmission. The proposed model provides secure communication between the drones and the users in a decentralized way. In this paper, blockchain technology is used for the storage of collected data from the drones and update the information into the distributed ledgers to reduce the burden of drones. It also provides integrity, authentication, and authorization to the collected data by the drones in the system model. Motivated by this consideration, the goal of this paper is threefold. First, we select a forger node from the number of drones. Second, we create blocks and validate their processes. Third, we provide secure data dissemination by applying Proof-of-Stake consensus mechanism. Afterward, we evaluate the security of the presented system model compared against the corresponding ones of some state-of-the-art in terms of communication time/cost. The results confirm that our system model is reliable and scalable for data dissemination in the IoD environment.
Enhanced embedding capacity for the SMSD-based data-hiding method

The embedding capacity and stego-image quality are two important features in data-hiding schemes. In 2016, Kuo et al. introduced sparse modified signed-digit (SMSD)-based data hiding that could embed secret data into a group of n cover-pixels. Compared with previous exploiting modification direction (EMD) and generalized EMD (GEMD)-based data hiding, SMSD data hiding has higher embedding capacity and better stego-image quality. In this paper, we introduce a new definition of the enhanced MSD (EMSD) representation and propose an EMSD-based data-hiding scheme that improves the embedding capacity of SMSD data hiding and maintains equal quality of the stego-image. In addition, the embedding capacity in the proposed scheme is further improved using the section-wise approach in EMSD data hiding.
Blockchain-Based Scalable and Tamper-Evident Solution for Registering Energy Data

Nowadays, it has been recognized that blockchain can provide the technological infrastructure for developing decentralized, secure, and reliable smart energy grid management systems. However, an open issue that slows the adoption of blockchain technology in the energy sector is the low scalability and high processing overhead when dealing with the real-time energy data collected by smart energy meters. Thus, in this paper, we propose a scalable second tier solution which combines the blockchain ledger with distributed queuing systems and NoSQL (Not Only SQL database) databases to allow the registration of energy transactions less frequently on the chain without losing the tamper-evident benefits brought by the blockchain technology. At the same time, we propose a technique for tamper-evident registration of smart meters' energy data and associated energy transactions using digital fingerprinting which allows the energy transaction to be linked hashed-back on-chain, while the sensors data is stored off-chain. A prototype was implemented using Ethereum and smart contracts for the on-chain components while for the off-chain components we used Cassandra database and RabbitMQ messaging broker. The prototype proved to be effective in managing a settlement of energy imbalances use-case and during the evaluation conducted in simulated environment shows promising results in terms of scalability, throughput, and tampering of energy data sampled by smart energy meters.
Unsupervised pre-trained filter learning approach for efficient convolution neural network

The concept of Convolution Neural Network (ConvNet or CNN) is evaluated from the animal visual cortex. Since humans can learn through experience, similarly, ConvNet changes its weight accordingly to accomplish the desired output through backpropagation. In this paper, we provide a comprehensive survey of the relationship between ConvNet with different pre-trained learning methodologies and its optimization effects. These hybrid networks further develop the state-of-the-art algorithms in recognition, classification, and detection of images, speeches, texts, and videos. Furthermore, some task-specific applications of ConvNet have been introduced in computer vision. To validate the survey, we also perform some experiments on a public face and skin detection dataset to provide an authentic solution. The experimental results on the benchmark dataset highlight the merit of efficient pre-trained learning algorithms for optimized ConvNet. To motivate the follow-up research, we identify open problems and present future directions with regards to optimized ConvNet system design parameters and unsupervised learning.
Deep Representation Learning for Social Network Analysis

Social network analysis is an important problem in data mining. A fundamental step for analyzing3social networks is to encode network data into low-dimensional representations, i.e., network4embeddings, so that the network topology structure and other attribute information can be5effectively preserved. Network representation leaning facilitates further applications such as6classification, link prediction, anomaly detection and clustering. In addition, techniques based on7deep neural networks have attracted great interests over the past a few years. In this survey, we8conduct a comprehensive review of current literature in network representation learning utilizing9neural network models. First, we introduce the basic models for learning node representations in10homogeneous networks. Meanwhile, we will also introduce some extensions of the base models11in tackling more complex scenarios, such as analyzing attributed networks, heterogeneous12networks and dynamic networks. Then, we introduce the techniques for embedding subgraphs.13After that, we present the applications of network representation learning. At the end, we discuss14some promising research directions for future work
Deep Convolutional Compressed Sensing for LiDAR Depth Completion

In this paper we consider the problem of estimating a dense depth map from a set of sparse LiDAR points. We use techniques from compressed sensing and the recently developed Alternating Direction Neural Networks (ADNNs) to create a deep network which performs multi-layer convolutional compressed sensing. Our architecture internally performs the optimization for extracting convolutional sparse codes from the input which are then used to make a prediction. Our results demonstrate that with only three layers and 1800 parameters we achieve performance which is competitive with the state of the art, including deep networks with orders of magnitude more parameters and layers.
Learning Spatial Common Sense With Geometry-Aware Recurrent Networks

We integrate two powerful ideas, geometry and deep visual representation learning, into recurrent network architectures for mobile visual scene understanding. The proposed networks learn to “lift” 2D visual features and integrate them over time into latent 3D feature maps of the scene. They are equipped with differentiable geometric operations, such as projection, unprojection, egomotion estimation and stabilization, in order to compute a geometrically-consistent mapping between the world scene and their 3D latent feature space. We train the proposed architectures to predict novel image views given short frame sequences as input. Their predictions strongly generalize to scenes with a novel number of objects, appearances and configurations, and greatly outperform predictions of previous works that do not consider egomotion stabilization or a space-aware latent feature space. We train the proposed architectures to detect and segment objects in 3D, using the latent 3D feature map as input—as opposed to 2D feature maps computed from video frames. The resulting detections are permanent: they continue to exist even when an object gets occluded or leaves the field of view. Our experiments suggest the proposed space-aware latent feature arrangement and egomotion-stabilized convolutions are essential architectural choices for spatial common sense to emerge in artificial embodied visual agents.
Salient Object Detection via Multiple Instance Joint Re-Learning

In recent years deep neural networks have been widely applied to visual saliency detection tasks with remarkable detection performance improvements. As for the salient object detection in single image, the automatically computed convolutional features frequently demonstrate high discriminative power to distinguish salient foregrounds from its non-salient surroundings in most cases. Yet, the obstinate feature conflicts still persist, which naturally gives rise to the learning ambiguity, arriving at massive failure detections. To solve such problem, we propose to jointly re-learn common consistency of inter-image saliency and then use it to boost the detection performance. Its core rationale is to utilize the easy-to-detect cases to re-boost much harder ones. Compared with the conventional methods, which focus on their problem domain within the single image scope, our method attempts to utilize those beyond-scope information to facilitate the current salient object detection. To validate our new approach, we have conducted a comprehensive quantitative comparisons between our approach and 13 state-of-the-art methods over 5 publicly available benchmarks, and all the results suggest the advantage of our approach in terms of accuracy, reliability, and versatility.
Graph Representation Learning via Hard and Channel-Wise Attention Networks

Attention operators have been widely applied in various fields, including computer vision, natural language processing, and network embedding learning. Attention operators on graph data enables learnable weights when aggregating information from neighboring nodes. However, graph attention operators (GAOs) consume excessive computational resources, preventing their applications on large graphs. In addition, GAOs belong to the family of soft attention, instead of hard attention, which has been shown to yield better performance. In this work, we propose novel hard graph attention operator~(hGAO) and channel-wise graph attention operator~(cGAO). hGAO uses the hard attention mechanism by attending to only important nodes. Compared to GAO, hGAO improves performance and saves computational cost by only attending to important nodes. To further reduce the requirements on computational resources, we propose the cGAO that performs attention operations along channels. cGAO avoids the dependency on the adjacency matrix, leading to dramatic reductions in computational resource requirements. Experimental results demonstrate that our proposed deep models with the new operators achieve consistently better performance. Comparison results also indicates that hGAO achieves significantly better performance than GAO on both node and graph embedding tasks. Efficiency comparison shows that our cGAO leads to dramatic savings in computational resources, making them applicable to large graphs.
Efficient and robust Pedestrian Detection using Deep Learning for Human-Aware Navigation

This paper addresses the problem of Human-Aware Navigation (HAN), using multi camera sensors to implement a vision-based person tracking system. The main contributions of this paper are as follows: a novel and efficient Deep Learning person detection and a standardization of human-aware constraints. In the first stage of the approach, we propose to cascade the Aggregate Channel Features (ACF) detector with a deep Convolutional Neural Network (CNN) to achieve fast and accurate Pedestrian Detection (PD). Regarding the human awareness (that can be defined as constraints associated with the robot’s motion), we use a mixture of asymmetric Gaussian functions, to define the cost functions associated to each constraint. Both methods proposed herein are evaluated individually to measure the impact of each of the components. The final solution (including both the proposed pedestrian detection and the human-aware constraints) is tested in a typical domestic indoor scenario, in four distinct experiments. The results show that the robot is able to cope with human-aware constraints, defined after common proxemics and social rules.
Solving the Buyer and Seller’s Dilemma: A Dual-Deposit Escrow Smart Contract for Provably Cheat-Proof Delivery and Payment for a Digital Good without a Trusted Mediator

A fundamental problem for electronic commerce is the buying and selling of digital goods between individuals that may not know or trust each other. Traditionally, this problem has been addressed by the use of trusted third-parties such as credit-card companies, mediated escrows, legal adjudication, or reputation systems. Despite the rise of blockchain protocols as a way to send payments without trusted third parties, the important problem of exchanging a digital good for payment without trusted third parties has been paid much less attention. We refer to this problem as the Buyer and Seller's Dilemma and present for it a dual-deposit escrow trade protocol which uses double-sided payment deposits in conjunction with simple cryptographic primitives, and that can be implemented using a blockchain-based smart contract. We analyze our protocol as an extensive-form game and prove that the Sub-game Perfect Nash Equilibrium for this game is for both the buyer and seller to cooperate and behave honestly. We address this problem under the assumption that the digital good being traded is known and verifiable, with a fixed price known to both parties.
Learning Privacy Preserving Encodings Through Adversarial Training

We present a framework to learn privacy-preserving encodings of images that inhibit inference of chosen private attributes, while allowing recovery of other desirable information. Rather than simply inhibiting a given fixed pre-trained estimator, our goal is that an estimator be unable to learn to accurately predict the private attributes even with knowledge of the encoding function. We use a natural adversarial optimization-based formulation for this-training the encoding function against a classifier for the private attribute, with both modeled as deep neural networks. The key contribution of our work is a stable and convergent optimization approach that is successful at learning an encoder with our desired properties-maintaining utility while inhibiting inference of private attributes, not just within the adversarial optimization, but also by classifiers that are trained after the encoder is fixed. We adopt a rigorous experimental protocol for verification wherein classifiers are trained exhaustively till saturation on the fixed encoders. We evaluate our approach on tasks of real-world complexity-learning high-dimensional encodings that inhibit detection of different scene categories-and find that it yields encoders that are resilient at maintaining privacy.
Compact and Computationally Efficient Representation of Deep Neural Networks

At the core of any inference procedure, deep neural networks are dot product operations, which are the component that requires the highest computational resources. For instance, deep neural networks, such as VGG-16, require up to 15-G operations in order to perform the dot products present in a single forward pass, which results in significant energy consumption and thus limits their use in resource-limited environments, e.g., on embedded devices or smartphones. One common approach to reduce the complexity of the inference is to prune and quantize the weight matrices of the neural network. Usually, this results in matrices whose entropy values are low, as measured relative to the empirical probability mass distribution of its elements. In order to efficiently exploit such matrices, one usually relies on, inter alia, sparse matrix representations. However, most of these common matrix storage formats make strong statistical assumptions about the distribution of the elements; therefore, cannot efficiently represent the entire set of matrices that exhibit low-entropy statistics (thus, the entire set of compressed neural network weight matrices). In this paper, we address this issue and present new efficient representations for matrices with low-entropy statistics. Alike sparse matrix data structures, these formats exploit the statistical properties of the data in order to reduce the size and execution complexity. Moreover, we show that the proposed data structures can not only be regarded as a generalization of sparse formats but are also more energy and time efficient under practically relevant assumptions. Finally, we test the storage requirements and execution performance of the proposed formats on compressed neural networks and compare them to dense and sparse representations. We experimentally show that we are able to attain up to ×42 compression ratios, ×5 speed ups, and ×90 energy savings when we lossless convert the state-of-the-art networks, such as AlexNet, VGG-16, ResNet152, and DenseNet, into the new data structures and benchmark their respective dot product.
Opinion spam detection by incorporating multimodal embedded representation into a probabilistic review graph

Spam reviews typically appear perfectly normal until examined in a large context. The standard approach to classifying reviews independently ignores these relations. In this study, we propose a complex probabilistic graph classification approach to address the problem of opinion spam detection. To obtain an initial effective spamicity estimation for the nodes (reviews, authors, and products) in the graph, we first train a neural network with attention mechanism to learn the multimodal embedded representation of nodes by leveraging both textual and rich features. Then based on the node prior computation, a heterogeneous graph is constructed to capture the relationships among different kinds of nodes, and the beliefs are further updated through iterative message propagation. To support this work, we collect two kinds of real-life datasets, which are separately composed of 97,839 restaurant reviews and 31,317 hotel reviews. The evaluation of the two datasets demonstrates the effectiveness of the proposed approach. We further analyze several salient rich features and the intermediate component of our model, thereby revealing that their states capture certain statistical characteristics of the datasets.
RI-GAN: An End-To-End Network for Single Image Haze Removal

The presence of the haze or fog particles in the atmosphere causes visibility degradation in the captured scene. Most of the initial approaches anticipate the transmission map of the hazy scene, airlight component and make use of an atmospheric scattering model to reduce the effect of haze and to recover the haze-free scene. In spite of the remarkable progress of these approaches, they propagate cascaded error upstretched due to the employed priors. We embrace this observation and designed an end-to-end generative adversarial network (GAN) for single image haze removal. Proposed network bypasses the intermediate stages and directly recovers the haze-free scene. Generator architecture of the proposed network is designed using a novel residual inception (RI) module. Proposed RI module comprises of dense connections within the multi-scale convolution layers which allows it to learn the integrated flavors of the haze-related features. Discriminator of the proposed network is built using the dense residual module. Further, to preserve the edge and the structural details in the recovered haze-free scene, structural similarity index and edge loss along with the L1 loss are incorporated in the GAN loss. Experimental analysis has been carried out on NTIRE2019 dehazing challenge dataset, D-Hazy [1] and indoor SOTS [22] databases. Experiments on the publically available datasets show that the proposed method outperforms the existing methods for image de-hazing.
Code coverage at Google

Code coverage is a measure of the degree to which a test suite exercises a software system. Although coverage is well established in software engineering research, deployment in industry is often inhibited by the perceived usefulness and the computational costs of analyzing coverage at scale. At Google, coverage information is computed for one billion lines of code daily, for seven programming languages. A key aspect of making coverage information actionable is to apply it at the level of changesets and code review. This paper describes Google’s code coverage infrastructure and how the computed code coverage information is visualized and used. It also describes the challenges and solutions for adopting code coverage at scale. To study how code coverage is adopted and perceived by developers, this paper analyzes adoption rates, error rates, and average code coverage ratios over a five-year period, and it reports on 512 responses, received from surveying 3000 developers. Finally, this paper provides concrete suggestions for how to implement and use code coverage in an industrial setting.