Reviewerly/exHarmony huggingface dataset:
SecDH: Security of COVID-19 images based on data hiding with PCA

Nowadays, image security and copyright protection become challenging, especially after the COVID-19 pandemic. In the paper, we develop SecDH as a medical data hiding scheme, which can guarantee the security and copyright protection of the COVID-19 images. Firstly, the cover image is normalized, which offers high resistance against the geometric attacks. Secondly, the normalized principal component as embedding factor is computed, which are calculated based on principal component analysis (PCA) between cover and mark image. Thirdly, the medical image is invisibly marked with secret mark based on normalized component, redundant discrete wavelet transform (RDWT) and randomized singular value decomposition (RSVD) is introduced. Finally, Arnold cat map scheme employed to ensure the security of the watermarking system. Under the experimental evaluation, our SecDH tool is not only imperceptible, but also has a satisfactory advantage in robustness and security compared with the traditional watermarking schemes.
Computational drug repurposing based on electronic health records: a scoping review

Computational drug repurposing methods adapt Artificial intelligence (AI) algorithms for the discovery of new applications of approved or investigational drugs. Among the heterogeneous datasets, electronic health records (EHRs) datasets provide rich longitudinal and pathophysiological data that facilitate the generation and validation of drug repurposing. Here, we present an appraisal of recently published research on computational drug repurposing utilizing the EHR. Thirty-three research articles, retrieved from Embase, Medline, Scopus, and Web of Science between January 2000 and January 2022, were included in the final review. Four themes, (1) publication venue, (2) data types and sources, (3) method for data processing and prediction, and (4) targeted disease, validation, and released tools were presented. The review summarized the contribution of EHR used in drug repurposing as well as revealed that the utilization is hindered by the validation, accessibility, and understanding of EHRs. These findings can support researchers in the utilization of medical data resources and the development of computational methods for drug repurposing.
Improved Hamiltonians for Quantum Simulations of Gauge Theories

Quantum simulations of lattice gauge theories for the foreseeable future will be hampered by limited resources. The historical success of improved lattice actions in classical simulations strongly suggests that Hamiltonians with improved discretization errors will reduce quantum resources, i.e., require $\ensuremath{\gtrsim}{2}^{d}$ fewer qubits in quantum simulations for lattices with $d$-spatial dimensions. In this work, we consider $\mathcal{O}({a}^{2})$-improved Hamiltonians for pure gauge theories and design the corresponding quantum circuits for its real-time evolution in terms of primitive gates. An explicit demonstration for ${\mathbb{Z}}_{2}$ gauge theory is presented including exploratory tests using the ibm_perth device.
Blockchain-Empowered Efficient Data Sharing in Internet of Things Settings

Sharing data across various Internet of Things (IoT) devices has been a common challenge due to efficiency, security, and stability issues. Blockchain, with security features, is considered to be a potential solution for data sharing in IoT settings. However, traditional blockchain-based solutions cannot satisfy the efficiency requirement of high-frequency data sharing among IoT devices. In this paper, we propose an efficient IoT data sharing approach by adopting the Payment Channel Network (PCN)-extended blockchain. Besides, we develop a homomorphic hashing-based transaction segmentation scheme to solve the issue of low transaction success ratio caused by channel deposit restrictions in PCN. In addition, a Multi-point Relay (MPR)-based multi-path routing scheme has been developed to ensure high-frequency transaction forwarding. The communication overhead of maintaining the routing table is reduced by our proposed Multi-point Relay Selection algorithm, and multiple alternate paths generated by Multiple Routing Path algorithm can improve the transaction success rate. Experiment evaluations have demonstrated that that our proposed approach outperforms the baseline approaches in terms of the transaction efficiency and success ratio.
Short-Form Videos for Public Library Marketing: Performance Analytics of Douyin in China

Short-form video platforms have become an important marketing channel for library resources and services. However, such promotions’ actual performance is not as good as expected. This research examined the performance of library marketing on the dominant short-form video platform in China, Douyin (aka TikTok worldwide), with social media analytics, including topic and correlation analysis. Results indicated that the number of fans of an account is positively correlated with the number of likes (p &lt; 0.001) and independent of the number of videos (p &gt; 0.05). Libraries post videos most often on the topic of “Reading Promotion”(31%), but the marketing performance on this topic is average (Mean DMI = 90.27). The most popular topics for patrons are “Hot Topics” and “Knowledge Quiz” (Mean DMI = 207.00). Using short-form videos for library marketing is a new practice, and scant studies have evaluated such performance, especially in Asia. Our results strengthen library practitioners’ awareness and reflections on conducting new media services and short-form video promotion, especially for the newer generation.
PolarFormer: Multi-Camera 3D Object Detection with Polar Transformer

3D object detection in autonomous driving aims to reason “what” and “where” the objects of interest present in a 3D world. Following the conventional wisdom of previous 2D object detection, existing methods often adopt the canonical Cartesian coordinate system with perpendicular axis. However, we conjugate that this does not fit the nature of the ego car’s perspective, as each onboard camera perceives the world in shape of wedge intrinsic to the imaging geometry with radical (non perpendicular) axis. Hence, in this paper we advocate the exploitation of the Polar coordinate system and propose a new Polar Transformer (PolarFormer) for more accurate 3D object detection in the bird’s-eye-view (BEV) taking as input only multi-camera 2D images. Specifically, we design a cross-attention based Polar detection head without restriction to the shape of input structure to deal with irregular Polar grids. For tackling the unconstrained object scale variations along Polar’s distance dimension, we further introduce a multi-scale Polar representation learning strategy. As a result, our model can make best use of the Polar representation rasterized via attending to the corresponding image observation in a sequence-to-sequence fashion subject to the geometric constraints. Thorough experiments on the nuScenes dataset demonstrate that our PolarFormer outperforms significantly state-of-the-art 3D object detection alternatives.
UniMSE: Towards Unified Multimodal Sentiment Analysis and Emotion Recognition

Multimodal sentiment analysis (MSA) and emotion recognition in conversation (ERC) are key research topics for computers to understand human behaviors.From a psychological perspective, emotions are the expression of affect or feelings during a short period, while sentiments are formed and held for a longer period.However, most existing works study sentiment and emotion separately and do not fully exploit the complementary knowledge behind the two.In this paper, we propose a multimodal sentiment knowledge-sharing framework (UniMSE) that unifies MSA and ERC tasks from features, labels, and models.We perform modality fusion at the syntactic and semantic levels and introduce contrastive learning between modalities and samples to better capture the difference and consistency between sentiments and emotions.Experiments on four public benchmark datasets, MOSI, MOSEI, MELD, and IEMO-CAP, demonstrate the effectiveness of the proposed method and achieve consistent improvements compared with state-of-the-art methods.
Kernel-Based Reinforcement Learning on Representative States

Markov decision processes (MDPs) are an established framework for solving sequential decision-making problems under uncertainty. In this work, we propose a new method for batch-mode reinforcement learning (RL) with continuous state variables. The method is an approximation to kernel-based RL on a set of k representative states. Similarly to kernel-based RL, our solution is a fixed point of a kernelized Bellman operator and can approximate the optimal solution to an arbitrary level of granularity. Unlike kernel-based RL, our method is fast. In particular, our policies can be computed in O(n) time, where n is the number of training examples. The time complexity of kernel-based RL is Ω(n2). We introduce our method, analyze its convergence, and compare it to existing work. The method is evaluated on two existing control problems with 2 to 4 continuous variables and a new problem with 64 variables. In all cases, we outperform state-of-the-art results and offer simpler solutions.
Fast Detection of Unsolvable Planning Instances Using Local Consistency

There has been a tremendous advance in domain-independent planning over the past decades, and planners have become increasingly efficient at finding plans. However, this has not been paired by any corresponding improvement in detecting unsolvable instances. Such instances are obviously important but largely neglected in planning. In other areas, such as constraint solving and model checking, much effort has been spent on devising methods for detecting unsolvability. We introduce a method for detecting unsolvable planning instances that is loosely based on consistency checking in constraint programming. Our method balances completeness against efficiency through a parameter k: the algorithm identifies more unsolvable instances but takes more time for increasing values of k. We present empirical data for our algorithm and some standard planners on a number of unsolvable instances, demonstrating that our method can be very efficient where the planners fail to detect unsolvability within reasonable resource bounds. We observe that planners based on the h^m heuristic or pattern databases are better than other planners for detecting unsolvability. This is not a coincidence since there are similarities (but also significant differences) between our algorithm and these two heuristic methods.
Crossing Media Streams with Sentiment: Domain Adaptation in Blogs, Reviews and Twitter

Most sentiment analysis studies address classification of a single source of data such as reviews or blog posts. However, the multitude of social media sources available for text analysis lends itself naturally to domain adaptation. In this study, we create a dataset spanning three social media sources -- blogs, reviews, and Twitter -- and a set of 37 common topics. We first examine sentiments expressed in these three sources while controlling for the change in topic. Then using this multi-dimensional data we show that when classifying documents in one source (a target source), models trained on other sources of data can be as good as or even better than those trained on the target data. That is, we show that models trained on some social media sources are generalizable to others. All source adaptation models we implement show reviews and Twitter to be the best sources of training data. It is especially useful to know that models trained on Twitter data are generalizable, since, unlike reviews, Twitter is more topically diverse.
Analyzing Political Trends in the Blogosphere

In the last years, the blogosphere has become a vital part of the web, covering a variety of different points of view and opinions on political and event-related topics such as immigration, election campaigns, or economic developments. Tracking the public opinion is usually done by conducting surveys resulting in significant costs both for interviewers and persons consulted. In this paper, we propose a method for extracting political trends in the blogosphere.To this end, we apply sentiment and time series analysis techniques in combination with aggregation methods on blog data to estimate the temporal development of opinions on politicians.
A Recommender System for Hero Line-Ups in MOBA Games

MOBA games are currently one the most popular online game genres. In their basic gameplay, two teams of multiple players compete against each other to destroy the enemy's base, controlling a powerful unit known as "hero". Each hero has different abilities, roles and strengths. Thus, choosing a good combination of heroes is fundamental for the success in the game. In this paper we propose a recommendation system for selecting heroes in a MOBA game. We develop a mechanism based on association rules that suggests the more suitable heroes for composing a team, using data collected from a large number of DOTA 2 matches. For evaluating the efficacy of the line-up, we trained a neural network capable of predicting the winner team with a 88.63% accuracy. The results of the recommendation system were very satisfactory with up to 74.9% success rate.
Efficient Trustworthiness Management for Malicious User Detection in Big Data Collection

Data collection in big data is an effective way to aggregate information that the collector is interested in. However, there is no assurance for the data that the users provide. Since collector does not have the ability to check the authenticity of every piece of information, the trustworthiness of users participated in the collection become important. In this paper, we design an efficient approach to calculate users’ trustworthiness in data collection for big data context. We divide the trustworthiness into familiarity trustworthiness and similarity trustworthiness, and study the influences of user actions on trustworthiness. To prevent malicious users from raising their trustworthiness and providing false information that may mislead final results, we also design a security queue to record users’ historical trust information, so that we can detect malicious users with high accuracy. Simulation results show that our model can sensitively resist the malicious actions of users.
Directional Statistics and Filtering Using <b>libDirectional</b>

In this paper, we present libDirectional, a MATLAB library for directional statistics and directional estimation.It supports a variety of commonly used distributions on the unit circle, such as the von Mises, wrapped normal, and wrapped Cauchy distributions.Furthermore, various distributions on higher-dimensional manifolds such as the unit hypersphere and the hypertorus are available.Based on these distributions, several recursive filtering algorithms in libDirectional allow estimation on these manifolds.The functionality is implemented in a clear, well-documented, and object-oriented structure that is both easy to use and easy to extend.
Adaptively Secure Proxy Re-encryption

A proxy re-encryption (PRE) scheme is a public-key encryption scheme that allows the holder of a key pk to derive a re-encryption key for any other key $$pk'$$ . This re-encryption key lets anyone transform ciphertexts under pk into ciphertexts under $$pk'$$ without having to know the underlying message, while transformations from $$pk'$$ to pk should not be possible (unidirectional). Security is defined in a multi-user setting against an adversary that gets the users’ public keys and can ask for re-encryption keys and can corrupt users by requesting their secret keys. Any ciphertext that the adversary cannot trivially decrypt given the obtained secret and re-encryption keys should be secure. All existing security proofs for PRE only show selective security, where the adversary must first declare the users it wants to corrupt. This can be lifted to more meaningful adaptive security by guessing the set of corrupted users among the n users, which loses a factor exponential in , rendering the result meaningless already for moderate . Jafargholi et al. (CRYPTO’17) proposed a framework that in some cases allows to give adaptive security proofs for schemes which were previously only known to be selectively secure, while avoiding the exponential loss that results from guessing the adaptive choices made by an adversary. We apply their framework to PREs that satisfy some natural additional properties. Concretely, we give a more fine-grained reduction for several unidirectional PREs, proving adaptive security at a much smaller loss. The loss depends on the graph of users whose edges represent the re-encryption keys queried by the adversary. For trees and chains the loss is quasi-polynomial in the size and for general graphs it is exponential in their depth and indegree (instead of their size as for previous reductions). Fortunately, trees and low-depth graphs cover many, if not most, interesting applications. Our results apply e.g. to the bilinear-map based PRE schemes by Ateniese et al. (NDSS’05 and CT-RSA’09), Gentry’s FHE-based scheme (STOC’09) and the LWE-based scheme by Chandran et al. (PKC’14).
Analysis of Evolutionary Algorithms in Dynamic and Stochastic Environments

Many real-world optimization problems occur in environments that change dynamically or involve stochastic components. Evolutionary algorithms and other bio-inspired algorithms have been widely applied to dynamic and stochastic problems. This survey gives an overview of major theoretical developments in the area of runtime analysis for these problems. We review recent theoretical studies of evolutionary algorithms and ant colony optimization for problems where the objective functions or the constraints change over time. Furthermore, we consider stochastic problems with various noise models and point out some directions for future research.
Dependency-Aware Network Adaptive Scheduling of Data-Intensive Parallel Jobs

Datacenter clusters often run data-intensive jobs in parallel for improving resource utilization and cost efficiency. The performance of parallel jobs is often constrained by the cluster's hard-to-scale network bisection bandwidth. Various solutions have been proposed to address the issue, however, most of them do not consider inter-job data dependencies and schedule jobs independently from one another. In this work, we find that aggregating and co-locating the data and tasks of dependent jobs offer an extra opportunity for data locality improvement that can help to greatly enhance the performance of jobs. We propose and design Dawn, a dependency-aware network-adaptive scheduler that includes an online plan and an adaptive task scheduler. The online plan, taking job dependencies into consideration, determines where (i.e., preferred racks) to place tasks in order to proactively aggregate dependent data. The task scheduler, based on the output of online plan and dynamic network status, adaptively schedules tasks to co-locate with the dependent data in order to take advantage of data locality. We implement Dawn on Apache Yarn and evaluate it on physical and virtual clusters using various machine learning and query workloads. Results show that Dawn effectively improves cluster throughput by up to 73 and 38 percent compared to Fair Scheduler and ShuffleWatcher, respectively. Dawn not only significantly enhances the performance of jobs with dependency, but also works well for jobs without dependency.
Local Hermite (LH) Method: An accurate and robust smooth technique for high-gradient strain reconstruction in digital image correlation

High-gradient strain field (HSF) measurement is attracting more and more attention from engineering and scientific fields, where digital image correlation (DIC) is widely adopted to calculate the dramatically changing displacement fields on stressed samples. Accurate reconstruction of reliable strain field from noisy displacement field is one of the key issues, since strain calculation is highly sensitive to displacement noise. The formally proposed improved Hermite finite element smoothing method (IHFESM) and fast Hermite element method (FHEM) are effective global smoothing techniques based on Tikhonov regularization. However, they involve complex meshing and global regularization matrix assembly processes. In this work, we present a local version of FHEM, called local Hermite (LH) method, in which one Hermite element is taken as a smoothing window centered on one displacement data, and the whole field is handled point-wisely. Two types of LH method are obtained by employing Hermite element with two types of continuities, i.e. C1 and C2. The smoothed displacement and strain are consequently computed from the regularized local surface as done in FHEM. Two kinds of simulated noisy displacement fields and one high-gradient displacement field obtained from shape memory alloy (SMA) tensile test are used to compare the performance of the proposed LH methods with the widely used point-wise least-squares (PLS) method. Experiments show that LH method with C2 element is nearly insensitive to window size, which is a quite useful property for practical engineering test without much prior knowledge. More importantly, it generally produces lower error than PLS in resolving local HSF. Thus, LH is a robust and accurate method which is highly recommended for practical HSF measurement.
Identifying Key Opinion Leaders in Social Media via Modality-Consistent Harmonized Discriminant Embedding

The digital age has empowered brands with new and more effective targeted marketing tools in the form of key opinion leaders (KOLs). Because of the KOLs' unique capability to draw specific types of audience and cultivate long-term relationship with them, correctly identifying the most suitable KOLs within a social network is of great importance, and sometimes could govern the success or failure of a brand's online marketing campaigns. However, given the high dimensionality of social media data, conducting effective KOL identification by means of data mining is especially challenging. Owing to the generally multiple modalities of the user profiles and user-generated content (UGC) over the social networks, we can approach the KOL identification process as a multimodal learning task, with KOLs as a rare yet far more important class over non-KOLs in our consideration. In this regard, learning the compact and informative representation from the high-dimensional multimodal space is crucial in KOL identification. To address this challenging problem, in this paper, we propose a novel subspace learning algorithm dubbed modality-consistent harmonized discriminant embedding (MCHDE) to uncover the low-dimensional discriminative representation from the social media data for identifying KOLs. Specifically, MCHDE aims to find a common subspace for multiple modalities, in which the local geometric structure, the harmonized discriminant information, and the modality consistency of the dataset could be preserved simultaneously. The above objective is then formulated as a generalized eigendecomposition problem and the closed-form solution is obtained. Experiments on both synthetic example and a realworld KOL dataset validate the effectiveness of the proposed method.