Reviewerly/exHarmony huggingface dataset:
Mirror-assisted panoramic-digital image correlation for full-surface 360-deg deformation measurement

A low-cost, easy-to-implement yet effective panoramic-digital image correlation (panoramic-DIC) system comprising a single binocular stereo-DIC system and two planar mirrors is proposed for full-surface 360-deg shape reconstruction and deformation measurement in material testing. With the aid of two mirrors, the rear portions of the object's full surface can be captured and reconstructed by the same stereo-DIC system that directly captures the front surface of the specimen. Two speckle patterns premade on the mirrors are used to transform all surface portions into a common global coordinate thus allowing a straightforward full-surface 360-deg profile and deformation measurements. For method validation, real experiments including full-surface 360-deg shape and displacement measurements of a cylinder and true stress-strain curves determination of a round low carbon steel specimen were performed. Results demonstrate that the proposed panoramic-DIC technique can provide accurate full-surface 360-deg shape and deformation data in standard material testing with important advantages such as low cost, compactness and easy implementation.
CycleEmotionGAN: Emotional Semantic Consistency Preserved CycleGAN for Adapting Image Emotions

Deep neural networks excel at learning from large-scale labeled training data, but cannot well generalize the learned knowledge to new domains or datasets. Domain adaptation studies how to transfer models trained on one labeled source domain to another sparsely labeled or unlabeled target domain. In this paper, we investigate the unsupervised domain adaptation (UDA) problem in image emotion classification. Specifically, we develop a novel cycle-consistent adversarial model, termed CycleEmotionGAN, by enforcing emotional semantic consistency while adapting images cycleconsistently. By alternately optimizing the CycleGAN loss, the emotional semantic consistency loss, and the target classification loss, CycleEmotionGAN can adapt source domain images to have similar distributions to the target domain without using aligned image pairs. Simultaneously, the annotation information of the source images is preserved. Extensive experiments are conducted on the ArtPhoto and FI datasets, and the results demonstrate that CycleEmotionGAN significantly outperforms the state-of-the-art UDA approaches.
Long-distance continuous-variable measurement-device-independent quantum key distribution with discrete modulation

We propose a long-distance continuous-variable measurement-device-independent quantum key distribution (CV-MDI-QKD) protocol with discrete modulation. This kind of discrete-modulated scheme has good compatibility with efficient error correction code, which leads to higher reconciliation efficiency even at low signal-to-noise ratio. Security analysis shows that the proposed protocol is secure against arbitrary collective attacks in the asymptotic limit with the proper use of decoy states. With the use of discrete modulation, the proposed CV-MDI-QKD protocol has simpler implementation and outperforms previous protocols in terms of achievable maximal transmission distance, which precisely solved the bottleneck of the original Gaussian-modulated CV-MDI-QKD protocol.
A Privacy-Preserving Image Retrieval Based on AC-Coefficients and Color Histograms in Cloud Environment

Content based image retrieval (CBIR) techniques have been widely deployed in many applications for seeking the abundant information existed in images. Due to large amounts of storage and computational requirements of CBIR, outsourcing image search work to the cloud provider becomes a very attractive option for many owners with small devices. However, owing to the private content contained in images, directly outsourcing retrieval work to the cloud provider apparently bring about privacy problem, so the images should be protected carefully before outsourcing. This paper presents a secure retrieval scheme for the encrypted images in the YUV color space. With this scheme, the discrete cosine transform (DCT) is performed on the Y component. The resulting DC coefficients are encrypted with stream cipher technology and the resulting AC coefficients as well as other two color components are encrypted with value permutation and position scrambling. Then the image owner transmits the encrypted images to the cloud server. When receiving a query trapdoor form on query user, the server extracts AC-coefficients histogram from the encrypted Y component and extracts two color histograms from the other two color components. The similarity between query trapdoor and database image is measured by calculating the Manhattan distance of their respective histograms. Finally, the encrypted images closest to the query image are returned to the query user.
Analysis on Mitigation of Visually Induced Motion Sickness by Applying Dynamical Blurring on a User's Retina

Visually induced motion sickness (MS) experienced in a 3D immersive virtual environment (VE) limits the widespread use of virtual reality (VR). This paper studies the effects of a saliency detection-based approach on the reduction of MS when the display on a user's retina is dynamic blurred. In the experiment, forty participants were exposed to a VR experience under a control condition without applying dynamic blurring, and an experimental condition applying dynamic blurring. The experimental results show that the participants under the experimental condition report a statistically significant reduction in the severity of MS symptoms on average during the VR experience compared to those under the control condition, which demonstrates that the proposed approach may alleviate visually induced MS in VR and enable users to remain in a VE for a longer period of time.
Exploiting effective facial patches for robust gender recognition

Gender classification is an important task in automated face analysis. Most existing approaches for gender classification use only raw/aligned face images after face detection as input. These methods exhibit fair classification ability under constrained conditions, in which face images are acquired under similar illumination with similar poses. The performances of these methods may deteriorate when face images show drastic variances in poses and occlusion as routinely encountered in real-world data. The reduction in the performances of current gender classification methods may be attributed to the sensitiveness of features to image translations. This work proposes to alleviate this sensitivity by introducing a majority voting procedure that involves multiple face patches. Specifically, this work utilizes a deep learning method based on multiple large patches. Several Convolutional Neural Networks (CNN) are trained on individual, predefined patches that reflect various image resolutions and partial cropping. The decisions of each CNN are aggregated through majority voting to obtain the final gender classification accurately. Extensive experiments are conducted on four gender classification databases, including Labeled Face in-the-Wild (LFW), CelebA, ColorFeret, and All-Age Faces database, a novel database collected by our group. Each individual patch is evaluated, and complementary patches are selected for voting. We show that the classification accuracy of our method is comparable with that of state-of-the-art systems. This characteristic validates the effectiveness of our proposed method.
Economic perspective analysis of protecting big data security and privacy

This paper investigates the economic perspective analysis of protecting security and privacy of big data. Traditionally, the pressing cyberthreats appear from emailed attachments. Recently, cyberattacks increasingly stealing or compromising data and are the potentials for physical damage to critical infrastructure. The risks of the data breach or compromised data collection are often favored by potential financial benefits (e.g., blackmail, fraud, false information, intellectual property thefts, business competition). That is, an important factor for current and future economical investments is due to the motivation of cybercrime activities. In this paper, we first analyze a question about our effort on security and privacy in terms of economic perspectives. That is, do we need to protect big data in a secure, private, and most effective manner, while the growing amount of security threats, attacks, and data breaches together with the increasing market for security products arises? Secondly, we perform the investigation from several perspectives: the economic perspective of big data security and privacy, investment decisions, fighting cybercrimes through big data, and cyberinsurance for big data. Our objective is to provide economic justification of technical decisions taken to protect the big data and the amount of costs that organizations often spend for it.
Fair and Dynamic Data Sharing Framework in Cloud-Assisted Internet of Everything

Cloud-assisted Internet of Things (IoT) is increasingly prevalent in our society, for example in home and office environment; hence, it is also known as cloud-assisted Internet of Everything (IoE). While in such a setup, data can be easily shared and disseminated (e.g., between a device, such as Amazon Echo and the cloud, such as Amazon AWS), there are potential security considerations that need to be addressed. Thus, a number of security solutions have been proposed. For example, searchable encryption (SE) has been extensively studied due to its capability to facilitate searching of encrypted data. However, threat models in most existing SE solutions rarely consider the malicious data owner and semi-trusted cloud server at the same time, particularly in dynamic applications. In a real-world deployment, disputes between above two parties may arise as either party will accuse the other of some misbehavior. Furthermore, efficient full-update operations (e.g., data modification, data insertion, and data deletion) are not typically supported in the cloud-assisted IoE deployment. Therefore, in this paper, we present a fair and dynamic data sharing framework (FairDynDSF) in the multiowner setting. Using FairDynDSF, one can check the correctness of search results, achieve fair arbitration, multikeyword search, and dynamic update. We also prove that FairDynDSF is secure against inside keyword guessing attack and demonstrate its efficiency by evaluating its performance using various datasets.
LoC — A new financial loan management system based on smart contracts

Current financial loan management systems are usually deployed in a single-service mode, also the transactions are not transparent and traceable to most of the roles participating in the process. Their data privacy protection mechanisms are not robust enough facing various cyber attacks. To overcome these challenges, we propose loan on blockchain (LoC), a novel financial loan management system based on smart contracts over permissioned blockchain Hyperledger Fabric. We use the Chinese poverty alleviation loan as the case study. We design a digital account model for the transfer of assets between centralized and decentralized ledgers; and propose locking and unlocking algorithms for smart contracts. We introduce digital signature and oracle to protect the data privacy. Performance evaluations on chaincode and unlocking codes show that our system is applicable in the real financial loan setting.
The use of TLS in Censorship Circumvention

TLS, the Transport Layer Security protocol, has quickly become the most popular protocol on the Internet, already used to load over 70% of web pages in Mozilla Firefox.Due to its ubiquity, TLS is also a popular protocol for censorship circumvention tools, including Tor and Signal, among others.However, the wide range of features supported in TLS makes it possible to distinguish implementations from one another by what set of cipher suites, elliptic curves, signature algorithms, and other extensions they support.Already, censors have used deep packet inspection (DPI) to identify and block popular circumvention tools based on the fingerprint of their TLS implementation.In response, many circumvention tools have attempted to mimic popular TLS implementations such as browsers, but this technique has several challenges.First, it is burdensome to keep up with the rapidly-changing browser TLS implementations, and know what fingerprints would be good candidates to mimic.Second, TLS implementations can be difficult to mimic correctly, as they offer many features that may not be supported by the relatively lightweight libraries used in typical circumvention tools.Finally, dependency changes and updates to the underlying libraries can silently impact what an application's TLS fingerprint looks like, making it difficult for tool maintainers to keep up.In this paper, we collect and analyze real-world TLS traffic from over 11.8 billion TLS connections over 9 months to identify a wide range of TLS client implementations actually used on the Internet.We use our data to analyze TLS implementations of several popular censorship circumvention tools, including Lantern, Psiphon, Signal, Outline, TapDance, and Tor (Snowflake and meek pluggable transports).We find that the many of these tools use TLS configurations that are easily distinguishable from the real-world traffic they attempt to mimic, even when these tools have put effort into parroting popular TLS implementations.To address this problem, we have developed a library, uTLS, that enables tool maintainers to automatically mimic other popular TLS implementations.Using our real-world traffic dataset, we observe many popular TLS implementations we are able to correctly mimic with uTLS, and we describe ways our tool can more flexibly adapt to the dynamic TLS ecosystem with minimal manual effort.
Riemannian Stochastic Variance Reduced Gradient Algorithm with Retraction and Vector Transport

In recent years, stochastic variance reduction algorithms have attracted considerable attention for minimizing the average of a large but finite number of loss functions.This paper proposes a novel Riemannian extension of the Euclidean stochastic variance reduced gradient (R-SVRG) algorithm to a manifold search space.The key challenges of averaging, adding, and subtracting multiple gradients are addressed with retraction and vector transport.For the proposed algorithm, we present a global convergence analysis with a decaying step size as well as a local convergence rate analysis with a fixed step size under some natural assumptions.In addition, the proposed algorithm is applied to the computation problem of the Riemannian centroid on the symmetric positive definite (SPD) manifold as well as the principal component analysis and low-rank matrix completion problems on the Grassmann manifold.The results show that the proposed algorithm outperforms the standard Riemannian stochastic gradient descent algorithm in each case 1 .
Photonic Computing With Single and Coupled Spiking Micropillar Lasers

We review experimental and theoretical results on the computing properties of single spiking micropillar lasers and present numerical studies of propagation and computing in chains of evanescently coupled micropillar lasers. Single micropillar lasers are shown to behave as ultrafast optical neurons with sub-nanosecond spike times. They also possess absolute and relative refractory times, spike latency, and show temporal summation. With delayed optical feedback, they emulate an autapse. These basic neural properties can be used for simple photonic computing. We show by numerical simulations of a chain of coupled spiking micropillar lasers that basic logical operations can be implemented in photonic circuits, as well as temporal pattern recognition based on the collision properties of pulses in this chain.
Y2Seq2Seq: Cross-Modal Representation Learning for 3D Shape and Text by Joint Reconstruction and Prediction of View and Word Sequences

Jointly learning representations of 3D shapes and text is crucial to support tasks such as cross-modal retrieval or shape captioning. A recent method employs 3D voxels to represent 3D shapes, but this limits the approach to low resolutions due to the computational cost caused by the cubic complexity of 3D voxels. Hence the method suffers from a lack of detailed geometry. To resolve this issue, we propose Y2Seq2Seq, a view-based model, to learn cross-modal representations by joint reconstruction and prediction of view and word sequences. Specifically, the network architecture of Y2Seq2Seq bridges the semantic meaning embedded in the two modalities by two coupled “Y” like sequence-tosequence (Seq2Seq) structures. In addition, our novel hierarchical constraints further increase the discriminability of the cross-modal representations by employing more detailed discriminative information. Experimental results on cross-modal retrieval and 3D shape captioning show that Y2Seq2Seq outperforms the state-of-the-art methods.
Unsupervised Cross-Lingual Representation Learning

In this tutorial, we provide a comprehensive survey of the exciting recent work on cutting-edge weakly-supervised and unsupervised cross-lingual word representations. After providing a brief history of supervised cross-lingual word representations, we focus on: 1) how to induce weakly-supervised and unsupervised cross-lingual word representations in truly resource-poor settings where bilingual supervision cannot be guaranteed; 2) critical examinations of different training conditions and requirements under which unsupervised algorithms can and cannot work effectively; 3) more robust methods for distant language pairs that can mitigate instability issues and low performance for distant language pairs; 4) how to comprehensively evaluate such representations; and 5) diverse applications that benefit from cross-lingual word representations (e.g., MT, dialogue, cross-lingual sequence labeling and structured prediction applications, cross-lingual IR).
Improved Heuristics for Multi-Agent Path Finding with Conflict-Based Search

Conflict-Based Search (CBS) and its enhancements are among the strongest algorithms for Multi-Agent Path Finding. Recent work introduced an admissible heuristic to guide the high-level search of CBS. In this work, we prove the limitation of this heuristic, as it is based on cardinal conflicts only. We then introduce two new admissible heuristics by reasoning about the pairwise dependencies between agents. Empirically, CBS with either new heuristic significantly improves the success rate over CBS with the recent heuristic and reduces the number of expanded nodes and runtime by up to a factor of 50.
Extracting Entities and Events as a Single Task Using a Transition-Based Neural Model

The task of event extraction contains subtasks including detections for entity mentions, event triggers and argument roles. Traditional methods solve them as a pipeline, which does not make use of task correlation for their mutual benefits. There have been recent efforts towards building a joint model for all tasks. However, due to technical challenges, there has not been work predicting the joint output structure as a single task. We build a first model to this end using a neural transition-based framework, incrementally predicting complex joint structures in a state-transition process. Results on standard benchmarks show the benefits of the joint model, which gives the best result in the literature.
On the Effectiveness of Low Frequency Perturbations

Carefully crafted, often imperceptible, adversarial perturbations have been shown to cause state-of-the-art models to yield extremely inaccurate outputs, rendering them unsuitable for safety-critical application domains. In addition, recent work has shown that constraining the attack space to a low frequency regime is particularly effective. Yet, it remains unclear whether this is due to generally constraining the attack search space or specifically removing high frequency components from consideration. By systematically controlling the frequency components of the perturbation, evaluating against the top-placing defense submissions in the NeurIPS 2017 competition, we empirically show that performance improvements in both the white-box and black-box transfer settings are yielded only when low frequency components are preserved. In fact, the defended models based on adversarial training are roughly as vulnerable to low frequency perturbations as undefended models, suggesting that the purported robustness of state-of-the-art ImageNet defenses is reliant upon adversarial perturbations being high frequency in nature. We do find that under L-inf-norm constraint 16/255, the competition distortion bound, low frequency perturbations are indeed perceptible. This questions the use of the L-inf-norm, in particular, as a distortion metric, and, in turn, suggests that explicitly considering the frequency space is promising for learning robust models which better align with human perception.
PD-GAN: Adversarial Learning for Personalized Diversity-Promoting Recommendation

This paper proposes Personalized Diversity-promoting GAN (PD-GAN), a novel recommendation model to generate diverse, yet relevant recommendations. Specifically, for each user, a generator recommends a set of diverse and relevant items by sequentially sampling from a personalized Determinantal Point Process (DPP) kernel matrix. This kernel matrix is constructed by two learnable components: the general co-occurrence of diverse items and the user's personal preference to items. To learn the first component, we propose a novel pairwise learning paradigm using training pairs, and each training pair consists of a set of diverse items and a set of similar items randomly sampled from the observed data of all users. The second component is learnt through adversarial training against a discriminator which strives to distinguish between recommended items and the ground-truth sets randomly sampled from the observed data of the target user. Experimental results show that PD-GAN is superior to generate recommendations that are both diverse and relevant.
Principles of feature modeling

Feature models are arguably one of the most intuitive and successful notations for modeling the features of a variant-rich software system. Feature models help developers to keep an overall understanding of the system, and also support scoping, planning, development, variant derivation, configuration, and maintenance activities that sustain the system's long-term success. Unfortunately, feature models are difficult to build and evolve. Features need to be identified, grouped, organized in a hierarchy, and mapped to software assets. Also, dependencies between features need to be declared. While feature models have been the subject of three decades of research, resulting in many feature-modeling notations together with automated analysis and configuration techniques, a generic set of principles for engineering feature models is still missing. It is not even clear whether feature models could be engineered using recurrent principles. Our work shows that such principles in fact exist. We analyzed feature-modeling practices elicited from ten interviews conducted with industrial practitioners and from 31 relevant papers. We synthesized a set of 34 principles covering eight different phases of feature modeling, from planning over model construction, to model maintenance and evolution. Grounded in empirical evidence, these principles provide practical, context-specific advice on how to perform feature modeling, describe what information sources to consider, and highlight common characteristics of feature models. We believe that our principles can support researchers and practitioners enhancing feature-modeling tooling, synthesis, and analyses techniques, as well as scope future research.