Reviewerly/exHarmony huggingface dataset:
Dissipation-induced bistability in the two-photon Dicke model

Abstract The Dicke model is a paradigmatic quantum-optical model describing the interaction of a collection of two-level systems with a single bosonic mode. Effective implementations of this model made it possible to observe the emergence of superradiance, i.e., cooperative phenomena arising from the collective nature of light-matter interactions. Via reservoir engineering and analogue quantum simulation techniques, current experimental platforms allow us not only to implement the Dicke model but also to design more exotic interactions, such as the two-photon Dicke model. In the Hamiltonian case, this model presents an interesting phase diagram characterized by two quantum criticalities: a superradiant phase transition and a spectral collapse, that is, the coalescence of discrete energy levels into a continuous band. Here, we investigate the effects of both qubit and photon dissipation on the phase transition and on the instability induced by the spectral collapse. Using a mean-field decoupling approximation, we analytically obtain the steady-state expectation values of the observables signaling a symmetry breaking, identifying a first-order phase transition from the normal to the superradiant phase. Our stability analysis unveils a very rich phase diagram, which features stable, bistable, and unstable phases depending on the dissipation rate.
Language-Agnostic Twitter Bot Detection

In this paper we address the problem of detecting Twitter bots.We analyze a dataset of 8385 Twitter accounts and their tweets consisting of both humans and different kinds of bots.We use this data to train machine learning classifiers that distinguish between real and bot accounts.We identify features that are easy to extract while still providing good results.We analyze different feature groups based on account specific, tweet specific and behavioral specific features and measure their performance compared to other state of the art bot detection methods.For easy future portability of our work we focus on language-agnostic features.With Ad-aBoost, the best performing classifier, we achieve an accuracy of 0.988 and an AUC of 0.995.As the creation of good training data in machine learning is often difficult -especially in the domain of Twitter bot detection -we additionally analyze to what extent smaller amounts of training data lead to useful results by reviewing cross-validated learning curves.Our results indicate that using few but expressive features already has a good practical benefit for bot detection, especially if only a small amount of training data is available.
BIM Integration in Augmented Reality Model

Building information modeling (BIM) is one of the most popular technologies contributing to information handling.However, the technology's contribution to fieldwork is very limited due to the limited interaction between the real and virtual world.Integrating BIM with augmented reality (AR) is believed to highly increase BIM's applicability to fieldwork.Therefore, the aim of the study is to examine the credibility of BIM integration into AR (AR-BIM) in the construction industry.A case study is adopted, computing the AR-BIM system through Structure Sensor, Unity 3D, and C#.The results show that BIM is compatible for integration with the AR platform.Although the preliminary AR-BIM system is not perfect compared to the marketed product, the initial investment cost of this system is much cheaper, while conserving the benefits of BIM in AR.This study is important to raise awareness among construction stakeholders about the adoption of technology.The stakeholders left out of the industrial revolution (IR 4.0) will lose their competitiveness in both local and international markets.
Augmenting Encrypted Search: A Decentralized Service Realization with Enforced Execution

Searchable symmetric encryption (SSE) allows the data owner to outsource an encrypted database to a remote server in a private manner while maintaining the ability for selectively search. So far, most existing solutions focus on an honest-but-curious server, while security designs against a malicious server have not drawn enough attention. A few recent works have attempted to construct verifiable SSE that enables the data owner to verify the integrity of search results. Nevertheless, these verification mechanisms are highly dependent on specific SSE schemes, and fail to support complex queries. A general verification mechanism is desired that can be applied to all SSE schemes. In this work, instead of concentrating on a central server, we explore the potential of the smart contract, an emerging blockchain-based decentralized technology, and construct decentralized SSE schemes where the data owner can receive correct search results with assurance without worrying about potential wrongdoings of a malicious server. We study both public and private blockchain environments and propose two designs with a trade-off between security and efficiency. To better support practical applications, the multi-user setting of SSE is further investigated where the data owner allows authenticated users to search keywords in shared documents. We implement prototypes of our two designs and present experiments and evaluations to demonstrate the practicability of our decentralized SSE schemes.
An Artificial Bee Colony Algorithm for Data Replication Optimization in Cloud Environments

Cloud computing is a modern technology for dealing with large-scale data. The Cloud has been used to process the selection and placement of replications on a large scale. Most previous studies concerning replication used mathematical models, and few studies focused on artificial intelligence (AI). The Artificial Bee Colony (ABC) is a member of the family of swarm intelligence based algorithms. It simulates bee direction to the final route and has been proven to be effective for optimization. In this paper, we present the different costs and shortest route sides in the Cloud with regard to replication and its placement between data centers (DCs) through Multi-Objective Optimization (MOO) and evaluate the cost distance by using the knapsack problem. ABC has been used to solve shortest route and lower cost problems to identify the best selection for replication placement, according to the distance or shortest routes and lower costs that the knapsack approach has used to solve these problems. Multi-objective optimization with the artificial bee colony (MOABC) algorithm can be used to achieve highest efficiency and lowest costs in the proposed system. MOABC can find an optimal solution for the best placement of data replicas according to the minimum distance and the number of data transmissions, affording low cost with the knapsack approach and availability of data replication.Low cost and fast access are characteristics that guide the shortest route in the CloudSim implementation as well. The experimental results show that the proposed MOABC is more efficient and effective for the best placement of replications than compared algorithms.
Perceptual image hashing based on frequency dominant neighborhood structure applied to Tor domains recognition

Tor (The Onion Router) is one of the most famous anonymous networks in the Deep Web. It provides a wide range of legal and illegal hidden services to the user. Recognizing such illicit domains is a challenging task for Cyber Security and Law Enforcement Agencies. However, doing it manually, based only on the officers’ experience, is slow and prone to errors. Therefore, in this paper, we propose an automatic method based on perceptual hashing to recognize services on the Tor network only by means of their snapshots. Firstly, we introduce and make publicly available DUSI-2K (Darknet Usage Service Images-2K), an image dataset which contains snapshots from active Tor service domains. We also present a new, efficient, robust and discriminative image hashing method, named F-DNS, built by incorporating the Dominant Neighborhood Structure (DNS) map and the Global Neighborhood Structure (GNS) texture energy map extracted from the discrete cosine transform of the image. In order to evaluate the efficiency of our hashing method, we carry out intra- and inter- tests using images from some state-of-the-art datasets subject to various content-preserving operations. The high correlation coefficient values that our method obtains, indicates that F-DNS performs better than other state-of-the-art methods, especially in the case of rotation. Additionally, we assess F-DNS for recognizing the category of Tor domains based on their snapshots using the DUSI-2K dataset. We compare its performance with three typical image classification methods, i.e. Bag of Visual Words (BoVW) and features obtained from ResNet50 and Inception-ResNet-v2. F-DNS outperforms all of them, with an accuracy of 98.75%, against 31.39%, 82.70% and 85.19%, respectively. Fine-tuning ResNet50 and Inception-Resnet-v2 for DUSI-2K does not improve the result, attaining 37.12% and 79.15%, respectively.
Improved sequence generation model for multi-label classification via CNN and initialized fully connection

In multi-label text classification, considering the correlation between labels is an important yet challenging task due to the combination possibility in the label space increasing exponentially. In recent years, neural network models have been widely applied and gradually achieved satisfactory performance in this field. However, existing methods either not model the fully internal correlations among labels or not capture the local and global semantic information of text simultaneously, which somewhat affects the classification results finally. In this paper, we implement a novel model for multi-label classification based on sequence-to-sequence learning, in which two different neural network modules are employed, named encoder and decoder respectively. The encoder uses the convolutional neural network to extract the high-level local sequential semantic, which is combined with the word vector to generate the final text representation through the recurrent neuron network and attention mechanism. The decoder, besides using a recurrent neural network to capture the global label correlation, employs an initialized fully connection layer to capture the correlation between any two different labels. When trained on RCV1-v2, AAPD and Ren-CECps datasets, the proposed model outperforms previous work in main evaluation metrics of hamming loss and micro-F1 score.
SQIL: Imitation Learning via Reinforcement Learning with Sparse Rewards

Learning to imitate expert behavior from demonstrations can be challenging, especially in environments with high-dimensional, continuous observations and unknown dynamics. Supervised learning methods based on behavioral cloning (BC) suffer from distribution shift: because the agent greedily imitates demonstrated actions, it can drift away from demonstrated states due to error accumulation. Recent methods based on reinforcement learning (RL), such as inverse RL and generative adversarial imitation learning (GAIL), overcome this issue by training an RL agent to match the demonstrations over a long horizon. Since the true reward function for the task is unknown, these methods learn a reward function from the demonstrations, often using complex and brittle approximation techniques that involve adversarial training. We propose a simple alternative that still uses RL, but does not require learning a reward function. The key idea is to provide the agent with an incentive to match the demonstrations over a long horizon, by encouraging it to return to demonstrated states upon encountering new, out-of-distribution states. We accomplish this by giving the agent a constant reward of r=+1 for matching the demonstrated action in a demonstrated state, and a constant reward of r=0 for all other behavior. Our method, which we call soft Q imitation learning (SQIL), can be implemented with a handful of minor modifications to any standard Q-learning or off-policy actor-critic algorithm. Theoretically, we show that SQIL can be interpreted as a regularized variant of BC that uses a sparsity prior to encourage long-horizon imitation. Empirically, we show that SQIL outperforms BC and achieves competitive results compared to GAIL, on a variety of image-based and low-dimensional tasks in Box2D, Atari, and MuJoCo. This paper is a proof of concept that illustrates how a simple imitation method based on RL with constant rewards can be as effective as more complex methods that use learned rewards.
Building Detail-Sensitive Semantic Segmentation Networks With Polynomial Pooling

Semantic segmentation is an important computer vision task, which aims to allocate a semantic label to each pixel in an image. When training a segmentation model, it is common to fine-tune a classification network pre-trained on a large-scale dataset. However, as an intrinsic property of the classification model, invariance to spatial perturbation resulting from the lose of detail-sensitivity prevents segmentation networks from achieving high performance. The use of standard poolings is one of the key factors for this invariance. The most common standard poolings are max and average pooling. Max pooling can increase both the invariance to spatial perturbations and the non-linearity of the networks. Average pooling, on the other hand, is sensitive to spatial perturbations, but is a linear function. For semantic segmentation, we prefer both the preservation of detailed cues within a local feature region and non-linearity that increases a network's functional complexity. In this work, we propose a polynomial pooling (P-pooling) function that finds an intermediate form between max and average pooling to provide an optimally balanced and self-adjusted pooling strategy for semantic segmentation. The P-pooling is differentiable and can be applied into a variety of pre-trained networks. Extensive studies on the PASCAL VOC, Cityscapes and ADE20k datasets demonstrate the superiority of P-pooling over other poolings. Experiments on various network architectures and state-of-the-art training strategies also show that models with P-pooling layers consistently outperform those directly fine-tuned using pre-trained classification models.
V-MPO: On-Policy Maximum a Posteriori Policy Optimization for Discrete and Continuous Control

Some of the most successful applications of deep reinforcement learning to challenging domains in discrete and continuous control have used policy gradient methods in the on-policy setting. However, policy gradients can suffer from large variance that may limit performance, and in practice require carefully tuned entropy regularization to prevent policy collapse. As an alternative to policy gradient algorithms, we introduce V-MPO, an on-policy adaptation of Maximum a Posteriori Policy Optimization (MPO) that performs policy iteration based on a learned state-value function. We show that V-MPO surpasses previously reported scores for both the Atari-57 and DMLab-30 benchmark suites in the multi-task setting, and does so reliably without importance weighting, entropy regularization, or population-based tuning of hyperparameters. On individual DMLab and Atari levels, the proposed algorithm can achieve scores that are substantially higher than has previously been reported. V-MPO is also applicable to problems with high-dimensional, continuous action spaces, which we demonstrate in the context of learning to control simulated humanoids with 22 degrees of freedom from full state observations and 56 degrees of freedom from pixel observations, as well as example OpenAI Gym tasks where V-MPO achieves substantially higher asymptotic scores than previously reported.
Efficient Riemannian Optimization on the Stiefel Manifold via the Cayley Transform

Strictly enforcing orthonormality constraints on parameter matrices has been shown advantageous in deep learning. This amounts to Riemannian optimization on the Stiefel manifold, which, however, is computationally expensive. To address this challenge, we present two main contributions: (1) A new efficient retraction map based on an iterative Cayley transform for optimization updates, and (2) An implicit vector transport mechanism based on the combination of a projection of the momentum and the Cayley transform on the Stiefel manifold. We specify two new optimization algorithms: Cayley SGD with momentum, and Cayley ADAM on the Stiefel manifold. Convergence of the Cayley SGD is theoretically analyzed. Our experiments for CNN training demonstrate that both algorithms: (a) Use less running time per iteration relative to existing approaches which also enforce orthonormality of CNN parameters; and (b) Achieve faster convergence rates than the baseline SGD and ADAM algorithms without compromising the CNN's performance. The Cayley SGD and Cayley ADAM are also shown to reduce the training time for optimizing the unitary transition matrices in RNNs.
Traffic Sign Recognition and Classification Using YOLOv2, Faster RCNN and SSD

In the walk of Advanced Driving Assistance Systems (ADAS), Intelligent Driving and Traffic safety, Object detection plays a crucial role in the upcoming genesis of self-governing vehicles. Traditional computer vision and machine learning advances for object detection confront challenges against the difficult image backgrounds and environment conditions like sunlight effects, barricades and occlusions. In this paper, Single Shot Detector (SSD), Faster Region Convolutional Neural Network (Faster RCNN) and You Only Look Once (YOLOv2) deep learning architectures are compared by applying distinct pretrained Convolutional Neural Network (CNN) models. Experiments have been organized in a wide range to attain distinct models of Faster RCNN, SSD and YOLOv2 through appropriate modification in algorithms and parameters tuning. In this work, SSD, Faster RCNN and YOLOv2 are trained for 5 different object classes of traffic signs and their outcomes are evaluated. Traditional Evaluation parameters: mAp(mean Average precision-Precision, Recall and IoU) and FPS(Frames per second) are run-down to analyze the accuracy and speed of the algorithms. On analyzing, the accuracy of YOLOv2 outperforms Faster RCNN and SSD by 3.5% and 21% respectively. Also, YOLOv2 learned 3 times speedy than Faster RCNN with increased accuracy.
Realistic Face Reenactment via Self-Supervised Disentangling of Identity and Pose

Recent works have shown how realistic talking face images can be obtained under the supervision of geometry guidance, e.g., facial landmark or boundary. To alleviate the demand for manual annotations, in this paper, we propose a novel self-supervised hybrid model (DAE-GAN) that learns how to reenact face naturally given large amounts of unlabeled videos. Our approach combines two deforming autoencoders with the latest advances in the conditional generation. On the one hand, we adopt the deforming autoencoder to disentangle identity and pose representations. A strong prior in talking face videos is that each frame can be encoded as two parts: one for video-specific identity and the other for various poses. Inspired by that, we utilize a multi-frame deforming autoencoder to learn a pose-invariant embedded face for each video. Meanwhile, a multi-scale deforming autoencoder is proposed to extract pose-related information for each frame. On the other hand, the conditional generator allows for enhancing fine details and overall reality. It leverages the disentangled features to generate photo-realistic and pose-alike face images. We evaluate our model on VoxCeleb1 and RaFD dataset. Experiment results demonstrate the superior quality of reenacted images and the flexibility of transferring facial movements between identities.
Semi-Supervised Streaming Learning with Emerging New Labels

In many real-world applications, the modeling environment is usually dynamic and evolutionary, especially in a data stream where emerging new class often happens. Great efforts have been devoted to learning with novel concepts recently, which are typically in a supervised setting with completely supervised initialization. However, the data collected in the stream are often in a semi-supervised manner actually, which means only a few of them are labeled while the great majority miss ground-truth labels. Besides, new classes hidden in unlabeled instances bring more challenges for the learning task. In this paper, we tackle these issues by a new approach called SEEN which consists of three major components: an effective novel class detector based on clustering random trees, a robust classifier for predictions on the known classes, and an efficient updating process that ensures the whole framework adapts to the changing environment automatically. The classifier produces known labels via label propagation that utilizes all labeled and part unlabeled data in the past which naturally describe the entire stream seen so far. Empirical studies on several datasets validate that the algorithm can accurately classify points on a dynamic stream with a small number of labeled examples and emerging new classes.
Convolutional neural networks for multispectral pedestrian detection

In this paper, we present a pedestrian detection method by leveraging multispectral images which consist of color and thermal image information. Our method is based on the observation that a multispectral image enables us to overcome inherent limitations for pedestrian detection under challenging situations, e.g., insufficient illumination, small size pedestrian instances and occlusion. In order to detect pedestrian under such conditions, we apply deep convolutional neural networks (CNNs) for effectively combining color and thermal image information in multispectral images. We present a novel multispectral network that is built from the region-based fully convolutional networks (R-FCN) network model. A network-in-network (NIN) is employed to fuse these information across different modalities. Experimental results on KAIST benchmark demonstrate that our method surpasses the baseline method R-FCN and other proposed architectures.
Sentiment Classification in Customer Service Dialogue with Topic-Aware Multi-Task Learning

Sentiment analysis in dialogues plays a critical role in dialogue data analysis. However, previous studies on sentiment classification in dialogues largely ignore topic information, which is important for capturing overall information in some types of dialogues. In this study, we focus on the sentiment classification task in an important type of dialogue, namely customer service dialogue, and propose a novel approach which captures overall information to enhance the classification performance. Specifically, we propose a topic-aware multi-task learning (TML) approach which learns topic-enriched utterance representations in customer service dialogue by capturing various kinds of topic information. In the experiment, we propose a large-scale and high-quality annotated corpus for the sentiment classification task in customer service dialogue and empirical studies on the proposed corpus show that our approach significantly outperforms several strong baselines.