Reviewerly/exHarmony huggingface dataset:
Adversarial Texture for Fooling Person Detectors in the Physical World

Nowadays, cameras equipped with AI systems can capture and analyze images to detect people automatically. However, the AI system can make mistakes when receiving deliberately designed patterns in the real world, i.e., physical adversarial examples. Prior works have shown that it is possible to print adversarial patches on clothes to evade DNN-based person detectors. However, these adversarial examples could have catastrophic drops in the attack success rate when the viewing angle (i.e., the camera's angle towards the object) changes. To perform a multi-angle attack, we propose Adversarial Texture (AdvTexture). AdvTexture can cover clothes with arbitrary shapes so that people wearing such clothes can hide from person detectors from different viewing angles. We propose a generative method, named Toroidal-Cropping-based Expandable Generative Attack (TC-EGA), to craft AdvTexture with repetitive structures. We printed several pieces of cloth with AdvTexure and then made T-shirts, skirts, and dresses in the physical world. Experiments showed that these clothes could fool person detectors in the physical world.
Coherent chaotic optical communication of 30 Gb/s over 340-km fiber transmission via deep learning

Chaotic optical communication has attracted much attention as a hardware encryption method in the physical layer. Limited by the requirements of chaotic hardware synchronization, fiber transmission impairments are restrictedly compensated in the optical domain. There has been little experimental demonstration of high-speed and long-distance chaotic optical communication systems. Here, we propose a method to overcome such limitations. Using a deep-learning model to realize chaotic synchronization in the digital domain, fiber transmission impairments can be compensated by digital-signal processing (DSP) algorithms with coherent detection. A successful transmission of 30 Gb/s quadrature phase-shift keying messages hidden in a 15 GHz wideband chaotic optical carrier was experimentally demonstrated over a 340-km fiber link. Meanwhile, the chaotic receiver can be significantly simplified without compromising security. The proposed method is a possible way to promote the practical application of chaotic optical communications.
MedCLIP: Contrastive Learning from Unpaired Medical Images and Text

Existing vision-text contrastive learning like CLIP (Radford et al., 2021) aims to match the paired image and caption embeddings while pushing others apart, which improves representation transferability and supports zero-shot prediction.However, medical image-text datasets are orders of magnitude below the general images and captions from the internet.Moreover, previous methods encounter many false negatives, i.e., images and reports from separate patients probably carry the same semantics but are wrongly treated as negatives.In this paper, we decouple images and texts for multimodal contrastive learning thus scaling the usable training data in a combinatorial magnitude with low cost.We also propose to replace the InfoNCE loss with semantic matching loss based on medical knowledge to eliminate false negatives in contrastive learning.We prove that MedCLIP is a simple yet effective framework: it outperforms state-of-the-art methods on zeroshot prediction, supervised classification, and image-text retrieval.Surprisingly, we observe that with only 20K pre-training data, MedCLIP wins over the state-of-the-art method (using ≈ 200K data) 1 .
Agencies in Technology Design: Feminist Reconfigurations*

In this paper the author considers some new resources for thinking about how capacities for action are configured at the human-machine interface, informed by developments in feminist science and technology studies. While not all of the authors and works cited would identify as feminist, they share commitments to a critical and generative interference in received conceptions of the human, the technological and the relations between them. The author interrogates the trope of innovation itself, to see how a fascination with change and transformation might be located, both culturally and historically, and in particular moments. He argues that through the figures of artificial intelligence we are witnessing a reiteration of traditional humanist notions of agency, at the same time - even through - the intra-actions of that notion with new computational media. The author also explores the question of what other directions our relations with machines, both conceptually and practically, might take.
Deep Cascade Multi-Task Learning for Slot Filling in Online Shopping Assistant

Slot filling is a critical task in natural language understanding (NLU) for dialog systems. State-of-the-art approaches treat it as a sequence labeling problem and adopt such models as BiLSTM-CRF. While these models work relatively well on standard benchmark datasets, they face challenges in the context of E-commerce where the slot labels are more informative and carry richer expressions. In this work, inspired by the unique structure of E-commerce knowledge base, we propose a novel multi-task model with cascade and residual connections, which jointly learns segment tagging, named entity tagging and slot filling. Experiments show the effectiveness of the proposed cascade and residual structures. Our model has a 14.6% advantage in F1 score over the strong baseline methods on a new Chinese E-commerce shopping assistant dataset, while achieving competitive accuracies on a standard dataset. Furthermore, online test deployed on such dominant E-commerce platform shows 130% improvement on accuracy of understanding user utterances. Our model has already gone into production in the E-commerce platform.
Smart technologies: perspectives of usage in higher education

Purpose The system of higher education is ineffective – it has to change the concept of educational process, which is peculiar for increase of the volume of education of labor resources. According to this, there is a necessity to pass to the system of higher education with elements of smart technologies. The purpose of this paper is to determine the role of smart technologies as an innovational and intellectual tool in development of the system of higher education and formation of actual skills with students. Design/methodology/approach The aspects of classical education in universities with elements of remote forms of implementation of smart technologies on IT platforms are studied; peculiarities of smart technology as intellectual tools of higher education are analyzed; perspectives of usage of smart technologies as innovational tools for development of higher education are determined. The research methods include analysis, synthesis, abstraction, comparison and logical method. Findings Information technologies become an inseparable part of life of society and human. A new network generation of people that cannot imagine life without new technological devices is growing. However, despite this, modern education does not sufficiently influence the development of human capital in the conditions of digital environment. Originality/value Scientific novelty consists in conducting the research in the sphere of significance and perspectives of implementing smart technologies into the systems of higher education of the Russian Federation. This paper could be interesting for public officers who form the program of development of higher education and academic staff of higher educational establishments.
Robust Inference via Generative Classifiers for Handling Noisy Labels

Large-scale datasets may contain significant proportions of noisy (incorrect) class labels, and it is well-known that modern deep neural networks (DNNs) poorly generalize from such noisy training datasets. To mitigate the issue, we propose a novel inference method, termed Robust Generative classifier (RoG), applicable to any discriminative (e.g., softmax) neural classifier pre-trained on noisy datasets. In particular, we induce a generative classifier on top of hidden feature spaces of the pre-trained DNNs, for obtaining a more robust decision boundary. By estimating the parameters of generative classifier using the minimum covariance determinant estimator, we significantly improve the classification accuracy with neither re-training of the deep model nor changing its architectures. With the assumption of Gaussian distribution for features, we prove that RoG generalizes better than baselines under noisy labels. Finally, we propose the ensemble version of RoG to improve its performance by investigating the layer-wise characteristics of DNNs. Our extensive experimental results demonstrate the superiority of RoG given different learning models optimized by several training techniques to handle diverse scenarios of noisy labels.
Efficient phase retrieval of two-directional phase-shifting fringe patterns using geometric constraints of deflectometry

In the phase measuring deflectometry, two groups of fringe patterns in orthogonal directions are usually applied to establish the correspondences between the pixel pairs on the screen and camera. Usually, 16 phase-shifting fringe patterns with different spatial frequencies are required in order to calculate the absolute phases in the conventional temporal phase unwrapping algorithms. This requirement makes the measurement inefficient and not robust against environmental noise. In this paper, an efficient phase retrieval strategy is developed, which requires only six fringe patterns. The modulating information in one-direction is obtained by first using four fringe patterns, and then it is applied to assist the phase calculation in the other direction, so that only two extra fringe patterns are needed. Subsequently the phases are unwrapped by using the geometric constraints of the software configurable optical test system without additional image acquisition. The measurement time is saved by 5/8, compared to the conventional methods. In this way, the influence of the low-frequency disturbances can be suppressed in the workshop condition. Experiments demonstrate that the proposed method can reliably retrieve the absolute phases, and it is of significance to improve the measuring efficiency and stability of in situ deflectometry.
An emoji-based metric for monitoring consumers’ emotions toward brands on social media

Purpose The purpose of this paper is to introduce and test a new emoji-based metric that could be used to monitor consumers’ emotions toward brands on social media. Design/methodology/approach To test this new metric, 720 consumer tweets were retrieved from official Twitter accounts of 18 leading global brands representing 6 product categories/markets. In order to check its validity, the emoji-based metric was correlated with two measures: the percentage of positive emojis from Brandwatch’s (2018) Emoji Report and the American Customer Satisfaction Index (ACSI) for 2017. Findings The findings of this paper indicate that consumers tend to use more (vs less) positive emojis when expressing their feelings toward Coca-Cola (vs Taco Bell). They also show that the new metric is highly and positively associated with the ACSI, hence supporting its validity. Research limitations/implications The new metric is only applicable to brands that have a social media presence. Practical implications The proposed metric is easy to implement and interpret by almost every researcher and manager. Originality/value While all extant brand sentiment analyses focus on analyzing the words in brand-related user-generated content, this paper considers an alternative source of information about emotions, that is, emojis. Beyond being valid, the proposed emoji-based metric is unique, easy to implement and interpret, and generalizable.
GLASS3: A Standalone Multiscale Seismic Detection Associator

Abstract The automated global real‐time association of phase picks into seismic sources comes with unique challenges when simultaneously monitoring at local, regional, and global scales. High spatial variability in seismic station density, transitory seismic data availability, and time‐varying noise characteristics of individual stations must be considered in the design of an associator that is fast and accurate with a low false association rate. These challenges are particularly apparent at the U.S. Geological Survey National Earthquake Information Center (NEIC), which monitors seismicity in near‐real time on local, regional, and global scales using seismic data from roughly 2100 real‐time seismic stations. To fully leverage this large dataset, NEIC developed a standalone self‐configuring seismic phase associator, GLobal ASSociator 3 (GLASS3) that simultaneously processes variably scaled 3D association webs, each with a unique set of nucleation criteria (e.g., nucleation stack threshold). GLASS3 has many useful features for real‐time monitoring including its computational efficiency, instantaneous pick processing, and on‐the‐fly configurability such as the creation and removal of targeted association webs and updates to supporting station metadata. GLASS3 runs both as part of a real‐time event processing system and as a configurable standalone associator that can be applied to a large variety of seismic problems. Here, we describe the GLASS3 algorithm and demonstrate (including input data and configuration files) its use in associating phase‐ambiguous picks on multiple scales.
High-Dimensional Robust Mean Estimation in Nearly-Linear Time

We study the fundamental problem of high-dimensional mean estimation in a robust model where a constant fraction of the samples are adversarially corrupted. Recent work gave the first polynomial time algorithms for this problem with dimension-independent error guarantees for several families of structured distributions.In this work, we give the first nearly-linear time algorithms for high-dimensional robust mean estimation. Specifically, we focus on distributions with (i) known covariance and sub-gaussian tails, and (ii) unknown bounded covariance. Given N samples on ℝd, an ∊-fraction of which may be arbitrarily corrupted, our algorithms run in time Õ(Nd)/poly(∊) and approximate the true mean within the information-theoretically optimal error, up to constant factors. Previous robust algorithms with comparable error guarantees have running times , for ∊ = Ω(1).Our algorithms rely on a natural family of SDPs parameterized by our current guess v for the unknown mean µ*. We give a win-win analysis establishing the following: either a near-optimal solution to the primal SDP yields a good candidate for µ* — independent of our current guess v — or a near-optimal solution to the dual SDP yields a new guess v' whose distance from µ* is smaller by a constant factor. We exploit the special structure of the corresponding SDPs to show that they are approximately solvable in nearly-linear time. Our approach is quite general, and we believe it can also be applied to obtain nearly-linear time algorithms for other high-dimensional robust learning problems.
Curriculum-guided Hindsight Experience Replay

In off-policy deep reinforcement learning, it is usually hard to collect sufficient successful experiences with sparse rewards to learn from. Hindsight experience replay (HER) enables an agent to learn from failures by treating the achieved state of a failed experience as a pseudo goal. However, not all the failed experiences are equally useful to different learning stages, so it is not efficient to replay all of them or uniform samples of them. In this paper, we propose to 1) adaptively select the failed experiences for replay according to the proximity to the true goals and the curiosity of exploration over diverse pseudo goals, and 2) gradually change the proportion of the goal-proximity and the diversity-based curiosity in the selection criteria: we adopt a human-like learning strategy that enforces more curiosity in earlier stages and changes to larger goal-proximity later. This ``Goal-and-Curiosity-driven Curriculum Learning'' leads to ``Curriculum-guided HER (CHER)'', which adaptively and dynamically controls the exploration-exploitation trade-off during the learning process via hindsight experience selection. We show that CHER improves the state of the art in challenging robotics environments.
Spherical Text Embedding

Unsupervised text embedding has shown great power in a wide range of NLP tasks. While text embeddings are typically learned in the Euclidean space, directional similarity is often more effective in tasks such as word similarity and document clustering, which creates a gap between the training stage and usage stage of text embedding. To close this gap, we propose a spherical generative model based on which unsupervised word and paragraph embeddings are jointly learned. To learn text embeddings in the spherical space, we develop an efficient optimization algorithm with convergence guarantee based on Riemannian optimization. Our model enjoys high efficiency and achieves state-of-the-art performances on various text embedding tasks including word similarity and document clustering.
Accelerated key generation and distribution using polarization scrambling in optical fiber

The physical-layer properties of the classical optical fiber channel provide an inherent, unique, random and reciprocal source for secure key generation and distribution (SKGD). However, the key generation rate (KGR) is generally less than kbit/s in the reported SKGD schemes. In this paper, an accelerated SKGD scheme based on active polarization scrambling is proposed in the classical optical fiber channel. A combination of unique birefringence distribution of optical fiber channel and active scrambling of instant state of polarization (SOP) enables a fast and random SOP fluctuation to be securely shared between the legitimate users for accelerated SKGD. The proposed SKGD scheme is experimentally demonstrated over 24-km standard single-mode fiber (SSMF), where a KGR of 200-kbit/s with an error-free operation is achieved after the post-processing procedure. Moreover, the possible fiber-tapping attacks are theoretically and experimentally analyzed for the security robustness of the proposed scheme. The results imply that a faster SKGD scheme could be achieved by incorporating an active polarization scrambling mechanism into the random properties of the fiber channel.
Multi-Instance Multi-Label Action Recognition and Localization Based on Spatio-Temporal Pre-Trimming for Untrimmed Videos

Weakly supervised action recognition and localization for untrimmed videos is a challenging problem with extensive applications. The overwhelming irrelevant background contents in untrimmed videos severely hamper effective identification of actions of interest. In this paper, we propose a novel multi-instance multi-label modeling network based on spatio-temporal pre-trimming to recognize actions and locate corresponding frames in untrimmed videos. Motivated by the fact that person is the key factor in a human action, we spatially and temporally segment each untrimmed video into person-centric clips with pose estimation and tracking techniques. Given the bag-of-instances structure associated with video-level labels, action recognition is naturally formulated as a multi-instance multi-label learning problem. The network is optimized iteratively with selective coarse-to-fine pre-trimming based on instance-label activation. After convergence, temporal localization is further achieved with local-global temporal class activation map. Extensive experiments are conducted on two benchmark datasets, i.e. THUMOS14 and ActivityNet1.3, and experimental results clearly corroborate the efficacy of our method when compared with the state-of-the-arts.
Iterative calibration method for measurement system having lens distortions in fringe projection profilometry

In fringe projection profilometry, system calibration is crucial for guaranteeing the measurement accuracies. Its difficulty lies in calibrating projector parameters, especially when the projector lens has distortions, since the projector, unlike a camera, cannot capture images, leading to an obstacle to knowing the correspondences between its pixels and object points. For solving this issue, this paper, exploiting the fact that the fringe phases on a plane board theoretically have a distribution of rational function, proposes an iterative calibration method based on phase measuring. Projecting fringes onto the calibration board and fitting the measured phases with a rational function allow us to determine projector pixels corresponding to the featured points on the calibration board. Using these correspondences, the projector parameters are easy to estimate. Noting that the projector lens distortions may deform the fitted phase map thus inducing errors in the estimates of the projector parameters, this paper suggests an iterative strategy to overcome this problem. By implementing the phase fitting and the parameter estimating alternately, the intrinsic and extrinsic parameters of the projector, as well as its lens distortion coefficients, are determined accurately. For compensating for the effects of the lens distortions on measurement, this paper gives two solutions. The pre-compensation actively curves the fringes in computer when generating them; whereas when using the post-compensation, the lens distortion correction is performed in the data processing stage. Both methods are experimentally verified to be effective in improving the measurement accuracies.
An authentication based scheme for applications using JSON web token

Information and Communication Technology (ICT) encompasses many devices and applications that are inter linked with each other on internet for communication. Development of client server applications on technologies, like IoT based, cloud based, and smart homes systems are growing rapidly in the current technology era. User authentication is important concern in these applications. REST (Representational State Transfer) based architecture with JWT emerged enormously in recent development of client server applications. JWTs (JSON Web Token) is used for authentication of subsequent client requests without making frequent calls to the resource server or database. In this paper, we present an authentication technique for regeneration of JWT on each client request based on truly random timestamp values to enhance the authenticity of client on server. The working of proposed technique is elaborated with a case study which indicates the stronger authentication and security.
Benchmarking GPU-Accelerated Edge Devices

We evaluate one of the state-of-the-art GPU-accelerated edge devices in this paper. We perform a set of deep learning benchmarks on the device to measure its performance. By comparing its performance with other GPU-accelerated systems in different computing platforms, we assess the computational capability of the modern edge devices equipped with a significant amount of hardware parallelism.