Reviewerly/exHarmony huggingface dataset:
Sophisticated yet Convenient Information Encryption/Decryption Based on Synergistically Time‐/Temperature‐Resolved Photonic Inks

Exploring high-safety but convenient encryption and decryption technologies to combat threats of information leakage is urgently needed but remains a great challenge. Here, a synergistically time- and temperature-resolved information coding/decoding solution based on functional photonic inks is demonstrated. Encrypted messages can be stored into multiple channels with dynamic-color patterns, and information decryption is only enabled at appointed temperature and time points. Notably, the ink can be easily processed into quick-response codes and multipixel plates. With high transparency and responsive color variations controlled by ink compositions and ambient temperatures, advanced 3D stacking multichannel coding and Morse coding techniques can be applied for multi-information storage, complex anticounterfeiting, and information interference. This study paves an avenue for the design and development of dynamic photonic inks and complex encryption technologies for high-end anticounterfeiting applications.
3D Face Reconstruction with Dense Landmarks

Landmarks often play a key role in face analysis, but many aspects of identity or expression cannot be represented by sparse landmarks alone. Thus, in order to reconstruct faces more accurately, landmarks are often combined with additional signals like depth images or techniques like differentiable rendering. Can we keep things simple by just using more landmarks? In answer, we present the first method that accurately predicts 10 $$\times $$ as many landmarks as usual, covering the whole head, including the eyes and teeth. This is accomplished using synthetic training data, which guarantees perfect landmark annotations. By fitting a morphable model to these dense landmarks, we achieve state-of-the-art results for monocular 3D face reconstruction in the wild. We show that dense landmarks are an ideal signal for integrating face shape information across frames by demonstrating accurate and expressive facial performance capture in both monocular and multi-view scenarios. Finally, our method is highly efficient: we can predict dense landmarks and fit our 3D face model at over 150FPS on a single CPU thread. Please see our website: https://microsoft.github.io/DenseLandmarks/ .
Digital health in the era of COVID-19: Reshaping the next generation of healthcare

COVID-19 is one of the most deadly diseases to have stricken us in recent decades. In the fight against this disease, governments and stakeholders require all the assistance they can get from various systems, including digital health interventions. Digital health technologies are supporting the tracking of the COVID-19 outbreak, diagnosing patients, expediting the process of finding potential medicines and vaccines, and disinfecting the environment, The establishment of electronic medical and health records, computerized clinical decision support systems, telemedicine, and mobile health have shown the potential to strengthen the healthcare system. Recently, these technologies have aided the health sector in a variety of ways, including prevention, early diagnosis, treatment adherence, medication safety, care coordination, documentation, data management, outbreak tracking, and pandemic surveillance. On the other hand, implementation of such technologies has questions of cost, compatibility with existing systems, disruption in patient-provider interactions, and sustainability, calling for more evidence on clinical utility and economic evaluations to help shape the next generation of healthcare. This paper argues how digital health interventions assist in the fight against COVID-19 and their opportunities, implications, and limitations.
UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models

Structured knowledge grounding (SKG) leverages structured knowledge to complete user requests, such as semantic parsing over databases and question answering over knowledge bases.Since the inputs and outputs of SKG tasks are heterogeneous, they have been studied separately by different communities, which limits systematic and compatible research on SKG.In this paper, we overcome this limitation by proposing the UNIFIEDSKG framework, which unifies 21 SKG tasks into a text-to-text format, aiming to promote systematic SKG research, instead of being exclusive to a single task, domain, or dataset.We use UNIFIEDSKG to benchmark T5 with different sizes and show that T5, with simple modifications when necessary, achieves stateof-the-art performance on almost all of the 21 tasks.We further demonstrate that multi-task prefix-tuning improves the performance on most tasks, largely improving the overall performance.UNIFIEDSKG also facilitates the investigation of zero-shot and few-shot learning, and we show that T0, GPT-3, and Codex struggle in zero-shot and few-shot learning for SKG.We also use UNIFIEDSKG to conduct a series of controlled experiments on structured knowledge encoding variants across SKG tasks.UNIFIEDSKG is easily extensible to more tasks, and it is open-sourced at https: //github.com/hkunlp/unifiedskg.
Change Detection in Graph Streams by Learning Graph Embeddings on Constant-Curvature Manifolds

The space of graphs is often characterized by a nontrivial geometry, which complicates learning and inference in practical applications. A common approach is to use embedding techniques to represent graphs as points in a conventional Euclidean space, but non-Euclidean spaces have often been shown to be better suited for embedding graphs. Among these, constant-curvature Riemannian manifolds (CCMs) offer embedding spaces suitable for studying the statistical properties of a graph distribution, as they provide ways to easily compute metric geodesic distances. In this paper, we focus on the problem of detecting changes in stationarity in a stream of attributed graphs. To this end, we introduce a novel change detection framework based on neural networks and CCMs, which takes into account the non-Euclidean nature of graphs. Our contribution in this paper is twofold. First, via a novel approach based on adversarial learning, we compute graph embeddings by training an autoencoder to represent graphs on CCMs. Second, we introduce two novel change detection tests operating on CCMs. We perform experiments on synthetic data, as well as two real-world application scenarios: the detection of epileptic seizures using functional connectivity brain networks and the detection of hostility between two subjects, using human skeletal graphs. Results show that the proposed methods are able to detect even small changes in a graph-generating process, consistently outperforming approaches based on Euclidean embeddings.
Open Vocabulary Learning for Neural Chinese Pinyin IME

Pinyin-to-character (P2C) conversion is the core component of pinyin-based Chinese input method engine (IME).However, the conversion is seriously compromised by the ambiguities of Chinese characters corresponding to pinyin as well as the predefined fixed vocabularies.To alleviate such inconveniences, we propose a neural P2C conversion model augmented by an online updated vocabulary with a sampling mechanism to support open vocabulary learning during IME working.Our experiments show that the proposed method outperforms commercial IMEs and state-of-theart traditional models on standard corpus and true inputting history dataset in terms of multiple metrics and thus the online updated vocabulary indeed helps our IME effectively follows user inputting behavior.
CNN Model Design of Gesture Recognition Based on Tensorflow Framework

The paper uses Google newest open-source Tensorflow framework to build the model of gesture recognition, introduces the platform characteristics of Tensorflow, and puts forward a convolution network model based on Tensorflow framework. The experiment is designed with the combination of recognized dataset and self-collected dataset. The experimental results show that the model has high recognition accuracy, high computational efficiency, strong robustness, and can easily adjust the network structure, find the optimal model quickly, and accomplish the task of gesture recognition better.
Toward inverse generative social science using multi-objective genetic programming

Generative mechanism-based models of social systems, such as those represented by agent-based simulations, require that intra-agent equations (or rules) be specified. However there are often many different choices available for specifying these equations, which can still be interpreted as falling within a particular class of mechanisms. Whilst it is important for a generative model to reproduce historically observed dynamics, it is also important for the model to be theoretically enlightening. Genetic programs (our own included) often produce concatenations that are highly predictive but are complex and hard to interpret theoretically. Here, we develop a new method - based on multi-objective genetic programming - for automating the exploration of both objectives simultaneously. We demonstrate the method by evolving the equations for an existing agent-based simulation of alcohol use behaviors based on social norms theory, the initial model structure for which was developed by a team of human modelers. We discover a trade-off between empirical fit and theoretical interpretability that offers insight into the social norms processes that influence the change and stasis in alcohol use behaviors over time.
Pedestrian Attribute Recognition by Joint Visual-semantic Reasoning and Knowledge Distillation

Pedestrian attribute recognition in surveillance is a challenging task in computer vision due to significant pose variation, viewpoint change and poor image quality. To achieve effective recognition, this paper presents a graph-based global reasoning framework to jointly model potential visual-semantic relations of attributes and distill auxiliary human parsing knowledge to guide the relational learning. The reasoning framework models attribute groups on a graph and learns a projection function to adaptively assign local visual features to the nodes of the graph. After feature projection, graph convolution is utilized to perform global reasoning between the attribute groups to model their mutual dependencies. Then, the learned node features are projected back to visual space to facilitate knowledge transfer. An additional regularization term is proposed by distilling human parsing knowledge from a pre-trained teacher model to enhance feature representations. The proposed framework is verified on three large scale pedestrian attribute datasets including PETA, RAP, and PA-100k. Experiments show that our method achieves state-of-the-art results.
Learning Deep Landmarks for Imbalanced Classification

We introduce a deep imbalanced learning framework called learning DEep Landmarks in laTent spAce (DELTA). Our work is inspired by the shallow imbalanced learning approaches to rebalance imbalanced samples before feeding them to train a discriminative classifier. Our DELTA advances existing works by introducing the new concept of rebalancing samples in a deeply transformed latent space, where latent points exhibit several desired properties including compactness and separability. In general, DELTA simultaneously conducts feature learning, sample rebalancing, and discriminative learning in a joint, end-to-end framework. The framework is readily integrated with other sophisticated learning concepts including latent points oversampling and ensemble learning. More importantly, DELTA offers the possibility to conduct imbalanced learning with the assistancy of structured feature extractor. We verify the effectiveness of DELTA not only on several benchmark data sets but also on more challenging real-world tasks including click-through-rate (CTR) prediction, multi-class cell type classification, and sentiment analysis with sequential inputs.
D.C. motors

Despite the apparent complexity of the d.c. machine (with its commutator, interpoles, etc.) it is probably the easiest to understand, because the excitation or flux-producing part and the torque producing part are physically distinct. The performance can be studied with the aid of a very simple equivalent circuit, from which many aspects of behaviour can be predicted. The d.c. machine thus makes the ideal learning vehicle, because most of its properties are reflected in all other types.
Multi-Modal Deep Analysis for Multimedia

With the rapid development of Internet and multimedia services in the past decade, a huge amount of usergenerated and service provider-generated multimedia data become available.These data are heterogeneous and multi-modal in nature, imposing great challenges for processing and analyzing them.Multi-modal data consist of a mixture of various types of data from different modalities such as texts, images, videos, audios etc.In this article, we present a deep and comprehensive overview for multi-modal analysis in multimedia.We introduce two scientific research problems, data-driven correlational representation and knowledge-guided fusion for multimedia analysis.To address the two scientific problems, we investigate them from the following aspects: 1) multi-modal correlational representation: multi-modal fusion of data across different modalities, and 2)multi-modal data and knowledge fusion: multi-modal fusion of data with domain knowledge.More specifically, on datadriven correlational representation, we highlight three important categories of methods, such as multi-modal deep representation, multi-modal transfer learning, and multi-modal hashing.On knowledge-guided fusion, we discuss the approaches for fusing knowledge with data and four exemplar applications that require various kinds of domain knowledge, including multi-modal visual question answering, multi-modal video summarization, multimodal visual pattern mining and multi-modal recommendation.Finally, we bring forward our insights and future research directions.
High Throughput/Gate AES Hardware Architectures Based on Datapath Compression

This article proposes highly efficient Advanced Encryption Standard (AES) hardware architectures that support encryption and both encryption and decryption. New operation-reordering and register-retiming techniques presented in this article allow us to unify the inversion circuits in SubBytes and InvSubBytes without any delay overhead. In addition, a new optimization technique for minimizing linear mappings, named multiplicative-offset, further enhances the hardware efficiency. We also present a shared key scheduling datapath that can work on-the-fly in the proposed architecture. To the best of our knowledge, the proposed architecture has the shortest critical path delay and is the most efficient in terms of throughput per area among conventional AES encryption/decryption and encryption architectures with tower-field S-boxes. The proposed round-based architecture can perform AES encryption where block-wise parallelism is unavailable (e.g., cipher block chaining (CBC) mode); thus, our techniques can be globally applied to any type of architecture including pipelined ones. We evaluated the performance of the proposed and some conventional datapaths by logic synthesis with the NanGate 45-nm open-cell library. As a result, we can confirm that our proposed architectures achieve approximately 51-64 percent higher efficiency (i.e., higher bps/GE) and lower power/energy consumption than the other conventional counterparts.
ThinORAM: Towards Practical Oblivious Data Access in Fog Computing Environment

Oblivious RAM (ORAM) is important to applications that require hiding of access patterns.However, most of the existing implementations of ORAM are very expensive, which are infeasible to be deployed in lightweight devices, like the gateway devices as fog nodes.In this paper, we focus on how to apply the expensive ORAM to protect the access pattern of lightweight devices and propose an ORAM scheme supporting thin-client, called "ThinORAM", under non-colluding clouds.Our proposed scheme removes complicated computations in the client side and requires only O(1) communication cost with reasonable response time.We further show how to securely deploy ThinORAM in the fog computing environment to achieve oblivious data access to minimum client cost.Experiments show that our scheme can eliminate most of the client storage and reduce the cloud-cloud bandwidth by 2×, with 2× faster response time, when compared to the best scheme that aims at reducing client side overheads.
Learning a robust CNN-based rotation insensitive model for ship detection in VHR remote sensing images

Deep convolutional neural networks (CNN) have been widely applied in various fields, especially in the field of object detection. Deep CNN-based models showed great advantages over many traditional methods, even so, there are still many specific problems in the application of certain scenarios. In very high resolution (VHR) remote-sensing image datasets, the uncertainty of the object direction angle causes big trouble to the learning of the detector. Although the pooling operation can slightly alleviate the deviation caused by small angle, the feature learning of the objects with larger angle rotation still relies mainly on the sufficiency of sample data or effective data augmentation, which means the insufficiency of the training instances may cause serious performance degradation of the detector. In this paper, we propose a multi-angle box-based rotation insensitive object detection structure (MRI-CNN), which is an extended exploration for typical region-based CNN methods. On the one hand, we defined a set of directionally rotated bounding boxes before learning, and restricted the classification scene in a small angular range by rotated RoI (Region of Interest) pooling. On the other hand, we proposed a more effective screening method of bounding boxes, enabling the detector to adapt to diverse ground truth annotation methods and learn more accurate object localization. We trained our detector with different datasets containing different amount of training data, and the test results showed that the method proposed in this paper performs better than some mainstream detection methods when limited training data are provided in VHR remote-sensing datasets.
Data Center Power Oversubscription with a Medium Voltage Power Plane and Priority-Aware Capping

As major web and cloud service providers continue to accelerate the demand for new data center capacity worldwide, the importance of power oversubscription as a lever to reduce provisioning costs has never been greater. Building on insights from Google-scale deployments, we design and deploy a new architecture across hardware and software to improve power oversubscription significantly. Our design includes (1) a new \em medium voltage power plane to enable larger power sharing domains (across tens of MW of equipment) and (2) a \em scalable, fast, and robust power capping service coordinating multiple priorities of workload on every node. Over several years of production deployment, our co-design has enabled \em power oversubscription of 25% or higher, saving hundreds of millions of dollars of data center costs, while preserving the desired availability and performance of all workloads.
Blockchain Applications in Tourism and Tourism Marketing: A Short Review

Blockchain is one of the most prominent and discusses technologies of the twenty-first century that has the potential to change, the way that we are conducting business, payments, and the management processes in a big number of industries. Based on the Distributed Ledger Technology (DLT), blockchain applications provides a breakthrough and innovative solutions due to the security they offer to the participants of the network, the efficiency of record keeping and transaction execution, and the automation solutions in terms of executing contracts when certain conditions are met (smart contracts). Despite the fact that most literature concerning blockchain focuses on its use in the finance industry and cryptocurrencies, there is a growing interest in the ways that this technology could be applied in other industries and management processes as well, both in the private and the public sector. In this paper, we are going to review the main propositions and findings in the literature concerning the use of blockchain technology in tourism and tourism marketing. We identify potential uses of blockchain in the tourism and hospitality industry in general and outline the benefits derived, and the challenges that will arise from the adoption of this innovative technology. Finally, we point out some direction for future research on the topic.
The Discrete Gaussian for Differential Privacy

A key tool for building differentially private systems is adding Gaussian noise to the output of a function evaluated on a sensitive dataset. Unfortunately, using a continuous distribution presents several practical challenges. First and foremost, finite computers cannot exactly represent samples from continuous distributions, and previous work has demonstrated that seemingly innocuous numerical errors can entirely destroy privacy. Moreover, when the underlying data is itself discrete (e.g., population counts), adding continuous noise makes the result less interpretable. With these shortcomings in mind, we introduce and analyze the discrete Gaussian in the context of differential privacy. Specifically, we theoretically and experimentally show that adding discrete Gaussian noise provides essentially the same privacy and accuracy guarantees as the addition of continuous Gaussian noise. We also present an simple and efficient algorithm for exact sampling from this distribution. This demonstrates its applicability for privately answering counting queries, or more generally, low-sensitivity integer-valued queries.
Learning with Less Data Via Weakly Labeled Patch Classification in Digital Pathology

In Digital Pathology (DP), labeled data is generally very scarce due to the requirement that medical experts provide annotations. We address this issue by learning transferable features from weakly labeled data, which are collected from various parts of the body and are organized by non-medical experts. In this paper, we show that features learned from such weakly labeled datasets are indeed transferable and allow us to achieve highly competitive patch classification results on the colorectal cancer (CRC) dataset [1] and the PatchCamelyon (PCam) dataset [2] while using an order of magnitude less labeled data.
Identifying Unknown Instances for Autonomous Driving

In the past few years, we have seen great progress in perception algorithms, particular through the use of deep learning. However, most existing approaches focus on a few categories of interest, which represent only a small fraction of the potential categories that robots need to handle in the real-world. Thus, identifying objects from unknown classes remains a challenging yet crucial task. In this paper, we develop a novel open-set instance segmentation algorithm for point clouds which can segment objects from both known and unknown classes in a holistic way. Our method uses a deep convolutional neural network to project points into a category-agnostic embedding space in which they can be clustered into instances irrespective of their semantics. Experiments on two large-scale self-driving datasets validate the effectiveness of our proposed method.