Reviewerly/exHarmony huggingface dataset:
Efficient kNN Join over Dynamic High-Dimensional Data

Given a user dataset U and an object dataset I in high-dimensional space, a kNN join query retrieves each object in dataset U its k nearest neighbors from the dataset I. kNN join is a fundamental and essential operation in applications from many domains such as databases, computer vision, multi-media, machine learning, recommendation systems, and many more. The datasets in real world often update dynamically on insertion or deletion of objects. However, existing algorithms of dynamic kNN join lack support for deletion and batch update, which are important in real-life applications. In this paper, we propose a new method of kNN join over dynamic high-dimensional data. Specifically, our method features lazy updates, batch operations, and optimised deletions. Experiments on real-world datasets show that our method outperforms the existing algorithms of naive RkNN join and HDR Tree by up to 5 and 4 times, respectively.
Implementation of Shor's Algorithm and Reliability of Quantum Computing Devices

The process to find the prime factors of a large number, the "factoring problem" is believed to be a very hard problem. For this reason, it is the cornerstone of modern cryptographic schemes, like RSA cryptosystem. In 1994, Professor Peter Shor proposed a new polynomial-time quantum algorithm that finds the prime factors of a number with many digits. This was a bolt from the blue for the security of transactions and electronic communications and became an example of how quantum computing changes our perception of security and safety. In this paper Shor's Algorithm is presented and an implementation, a way to factor number 21 is described. In addition, some reliability issues of quantum devices were considered in order to explore the potentiality of Shor's algorithm.
Q-learning with Uniformly Bounded Variance

Sample complexity bounds are a common performance metric in the Reinforcement Learning literature. In the discounted cost, infinite horizon setting, all of the known bounds can be arbitrarily large, as the discount factor approaches unity. These results seem to imply that a very large number of samples is required to achieve an epsilon-optimal policy. The objective of the present work is to present algorithms that have sample complexity uniformly bounded over all discount factors.
Efficient Implementation of Dilithium Signature Scheme on FPGA SoC Platform

In the process of NIST postquantum cryptography standardization, module lattice-based Dilithium has been chosen as one of the three third-round finalists for digital signature schemes. More evaluations of its implementation efficiency on different platforms are required for further competition. In this article, we present an efficient implementation of Dilithium on a field-programmable gate array (FPGA) system-on-chip (SoC) platform. To achieve a high computation speed, we design a hardware architecture to perform the main body of the algorithm, and the preprocessing and postprocessing steps are accomplished by the processor. For the hardware architecture, we take some optimizations on the most time-consuming operations, that is, polynomial multiplication, hashing, and sampling. Polynomial multiplications are accelerated by the radix-4 number theoretic transform (NTT) architecture with a conflict-free memory mapping scheme. A fast modular multiplication on the Dilithium modulus is proposed to support the underlying calculations. For hashing and sampling, we design a multipurpose hashing unit and a compact sampling unit. The cooperative work of the two units accelerates the sampling process significantly. We implement the Key Generation, Signing, and Verification algorithms of the round-3 Dilithium at all three security levels on the Xilinx Zynq-7000 platform. Compared with existing software/hardware codesign for Dilithium on a similar platform, our design achieves about <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$17\times $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$40\times $ </tex-math></inline-formula> improvements in performance for the Signing and Verification algorithms, respectively, at the cost of about <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$7.8\times $ </tex-math></inline-formula> more look up table (LUT) resources.
Cx‐CP‐ABE: Context‐aware attribute‐based access control schema and blockchain technology to ensure scalable and efficient health data privacy

Abstract Nowadays, advancements in health technology have characterized modern illness diagnosis methods in the global healthcare system. The World Health Organization (WHO) has received a large number of respiratory disease patients in recent years, resulting in billions of personal health records (PHRs) being created and transmitted via cloud with the aim of taking precautions and restricting the disease. The transmission of PHR through Internet has generated significant security and privacy concerns for the global health care system. Furthermore, the potential change in the patient's health situation at any time needs immediate and secure healthcare access. For robust authentication and optimal confidentiality, it is necessary to reinforce the access control policies. As a result, various security schemes have been developed but lack efficiency while deploying cloud‐based e‐health applications, where the cloud providers are honest but curious and may obtain sensitive data without the consent of users. In this article, we present a new security scheme for e‐health mobile applications based on hybrid cipher text‐policy attribute access control, blockchain, and context‐awareness (Cx‐CP‐ABE). The approach is based on reinforcing security policies pertaining to the continuous evolution of the patient's state and context changes and the improvement of the patient's data confidentiality. The proposed security scheme has been evaluated under various patient contexts, and its performance has been measured in terms of sensitivity, number of health attributes, and execution time.
VoCSK: Verb-oriented commonsense knowledge mining with taxonomy-guided induction

Commonsense knowledge acquisition is one of the fundamental issues in realizing human-level AI. However, commonsense knowledge is difficult to obtain because it is a human consensus and rarely explicitly appears in texts or other data. In this paper, we focus on the automatic acquisition of a typical kind of implicit verb-oriented commonsense knowledge (e.g., "person eats food"), which is the concept-level knowledge of verb phrases. For this purpose, we propose a taxonomy-guided induction method to mine verb-oriented commonsense knowledge from verb phrases with the help of a probabilistic taxonomy. First, we design an entropy-based triplet filter to cope with noisy verb phrases. Then, we propose a joint model based on the minimum description length principle and a neural language model to generate verb-oriented commonsense knowledge. Besides, we introduce two strategies to accelerate the computation, including the simulated annealing-based approximate solution and the verb phrase clustering method. Finally, we conduct extensive experiments to prove that our solution is more effective than competitors in mining verb-oriented commonsense knowledge. We construct a commonsense knowledge base called VoCSK, containing 259 verbs and 18,406 verb-oriented commonsense knowledge. To verify the usefulness of VoCSK, we utilize the knowledge in this KB to improve the model performance on two downstream applications.
Semantic inpainting on segmentation map via multi-expansion loss

Semantic Inpainting on Segmentation Map (SISM) aims to manipulate segmentation maps by semantics. Recent works show SISM provides semantic-aware auxiliary information for better style or structure manipulations. Providing structural assistance, segmentation maps have been broadly used as an intermediate interface to achieve better image manipulation. Mainstream solutions of image manipulation use Generative Adversarial Net (GAN) globally, locally or jointly. It is also applicable to SISM. However, the discriminator of global GAN is easier fooled, because the majority of its input is the same as the ground-truth, which is hard to fully mitigate the inconsistency between inpainted areas and the context. The inconsistency is more difficult for local GAN to address, due to the lack of context in its input. To mitigate the inconsistency, we propose a novel Multi-Expansion (MEx) loss. It is implemented by the adversarial loss on MEx areas. Each MEx area has the inpainted area as dominance and keeps knowledge of the scene context, so the consistency of the SISM results can be boosted. We propose an approximation of MEx loss, i.e., A-MEx loss, to further enhance the stability and usability. Besides performing well on SISM tasks, MEx loss also performs impressively on natural image inpainting. Extensive experiments on the two tasks demonstrate the advantages of our model over existing methods on four challenging datasets, such as a 2.59% increase in Hamm on SISM in Cityscape and a decrease of 5.00% FID on natural image inpainting in CMP Facade. The code of our work is available at: https://github.com/he159ok/AMEx-MEx-Loss.
Informative Multiview Planning for Underwater Sensors

Many modern imaging sensors must obtain multiple looks or “views” of a target at different orientations to automatically classify it with high confidence. Therefore, when tasked with classifying many targets, a mobile sensor may need to travel a long distance to change its position and orientation relative to every target, resulting in costly and time-consuming operations. This article presents a novel and general approach, referred to as informative multiview planning (IMVP) that simultaneously determines the most informative sequence of views and the shortest path between them. The approach is demonstrated on an underwater multitarget classification problem in which a sidescan sonar installed on an unmanned underwater vehicle must classify all targets in minimum time. Simulation and experimental results show that IMVP can achieve the same, or better, classification performance in half the time of existing multiview path planning methods. Also, by determining the most informative views and the shortest path between them, IMVP significantly improves classification efficiency, classification confidence level, as well as performance robustness.
Environmental and life sciences observations in knowledge graphs using NLP techniques to support multidisciplinary studies

The understanding of environmental observations is a continuous challenge for environmental and life science investigations. The environmental data is complex as it involves its own features, methods, properties, systems, and spatio-temporal dimensions. The time granularity remains approximately the same for different environmental contexts but geographic and rest of the above-mentioned entities are defined using domain vocabularies that are specific for each discipline. It is time-consuming for the researchers of life sciences’ discipline to discover, access, and analyze relevant environmental observations as each discipline has its data formats, vocabularies, and metadata standards. These differences introduce structural and semantic heterogeneities, resulting in creating a barrier for reusing datasets generated by other disciplines. Existing dataset discovery platforms contain domain-specific metadata descriptions for explaining datasets which limits their usage. To overcome this knowledge barrier, this work reports the proof-of-concept implementation of a knowledge graph that is centered towards the oceanography use case scenario using NLP techniques (named entity recognition (NER) followed by text preprocessing). The constructed knowledge graph is a collection of subgraphs each representing the metadata of a dataset. It uses the geo-spatial and open semantic data standards that aim to provide enhanced metadata descriptions of datasets for enabling multidisciplinary research.
A comparative study on vectorization methods for non-functional requirements classification

Identifying non-functional requirements (NFRs) and their categories at the early phase is crucial for analysts to design software systems and recognize constraints. Automatic non-functional requirements classification methods have been studied for reducing the costs of that labor-intensive task. Our previous study focused on the differences among vectorization methods that converted requirements written in natural language into numerical vectors for classification. It had some limitations regarding the number of datasets used, the types of vectorization methods supporting pre-trained data, and the performance evaluation procedure. To examine whether different vectorization methods lead to differences in the classification performance of NFRs and their categories with extended settings. Comparative experiments were conducted with five open data. Nine vectorization methods, including ones with pre-trained data and four supervised classification methods, were supplied. Performance was evaluated with AUC and Scott-Knott ESD test. Some advanced methods could achieve better performance than traditional ones when combined with some classifiers. The use of pre-trained data was useful for some categories. It is beneficial to consider using some combinations of vectorization methods and classifiers for classifying non-functional requirements categories.
A novel level set model initialized with guided filter for automated PET-CT image segmentation

Positron emission tomography (PET) and computed tomography (CT) scanner image analysis plays an important role in clinical radiotherapy treatment. PET and CT images provide complementary cues for identifying tumor tissues. In specific, PET images can clearly denote the tumor tissue, whereas this source suffers from the problem of low spatial resolution. On the contrary, CT images have a high resolution, but they can not recognize the tumor from normal tissues. In this work, we firstly fuse PET and CT images by using the guided filter. Then, a region and edge-based level set model is proposed to segment PET-CT fusion images. At last, a normalization term is designed by combining length, distance and H1 terms with the aim to improve segmentation accuracy. The proposed method was validated in the robust delineation of lung tumor tissues on 20 PET-CT samples. Both qualitative and quantitative results demonstrate significant improvement compared to both the data-independent and deep learning based segmentation methods.
A metrics-based meta-learning model with meta-pretraining for industrial knowledge graph construction

Isolated data silos and domain-specific knowledge pose challenges for knowledge graph construction in the manufacturing industry, where heterogeneous storage leads to distributed databases with complex schemas. In this article, a resource-based industrial knowledge graph is developed using a few-shot classification algorithm to save on labor and other related costs in industrial knowledge graph construction, and an attribute-based fusion strategy for data fusion and alignment is designed. We also propose a novel metrics-based meta-learning model with meta-pretraining (MMM) to address the few-shot text classification problem. Experiment results show that MMM achieves 87.13% accuracy on the 5-shot text classification benchmark Amazon Review Sentiment Classification (ARSC), outperforming other baselines, such as Induction Networks (85.63%) and Distributional Signatures (81.16%). The MMM achieves a 34.6% accuracy improvement compared with Distributional Signatures (84.34% vs. 62.66%) on 1-shot problems of ARSC, hence highlighting the applicability of our model in low-resource conditions. Based on the proposed methods, we further develop an industrial knowledge graph platform with industrial applications, such as value chain analysis and collaboration, to improve knowledge reuse and service innovation. • We construct an industrial knowledge graph to integrate resources instead of complex schema migration for extensions. • We propose a Metric-based Meta-learning model with Meta-pretraining (MMM) to address few-shot text classification. • We develop an Angular Asymmetric Triplet-Center Loss (AATCL) to improve few-shot text classification. • We conduct a case that constructs an IKG with a prototype platform for cognitive intelligence.
Knowledge Representation and Reasoning for Complex Time Expression in Clinical Text

Abstract Temporal information is pervasive and crucial in medical records and other clinical text, as it formulates the development process of medical conditions and is vital for clinical decision making. However, providing a holistic knowledge representation and reasoning framework for various time expressions in the clinical text is challenging. In order to capture complex temporal semantics in clinical text, we propose a novel Clinical Time Ontology (CTO) as an extension from OWL framework. More specifically, we identified eight time-related problems in clinical text and created 11 core temporal classes to conceptualize the fuzzy time, cyclic time, irregular time, negations and other complex aspects of clinical time. Then, we extended Allen's and TEO's temporal relations and defined the relation concept description between complex and simple time. Simultaneously, we provided a formulaic and graphical presentation of complex time and complex time relationships. We carried out empirical study on the expressiveness and usability of CTO using real-world healthcare datasets. Finally, experiment results demonstrate that CTO could faithfully represent and reason over 93% of the temporal expressions, and it can cover a wider range of time-related classes in clinical domain.
JSL3d: Joint subspace learning with implicit structure supervision for 3D pose estimation

Estimating 3D human poses from a single image is an important task in computer graphics. Most model-based estimation methods represent the labeled/detected 2D poses and the projection of approximated 3D poses using vector representations of body joints. However, such lower-dimensional vector representations fail to maintain the spatial relations of original body joints, because the representations do not consider the inherent structure of body joints. In this paper, we propose JSL3d, a novel joint subspace learning approach with implicit structure supervision based on Sparse Representation (SR) model, capturing the latent spatial relations of 2D body joints by an end-to-end autoencoder network. JSL3djointly combines the learned latent spatial relations and 2D joints as inputs for the standard SR inference frame. The optimization is simultaneously processed via geometric priors in both latent and original feature spaces. We have evaluated JSL3dusing four large-scale and well-recognized benchmarks, including Human3.6M, HumanEva-I, CMU MoCap and MPII. The experiment results demonstrate the effectiveness of JSL3d.
Bridging the gap between academia and industry

In the last 30 years, many variability modeling approaches have been developed and new ones are still developed regularly. Most of them are only described in academic papers, only few come with tool support. The sheer plethora of approaches, all differing in terms of scope and expressiveness, makes it difficult to assess their properties, experiment with them and find the right approach for a specific use case. Implementing transformations between variability modeling approaches or importers/exporters for tools can help, but are hard to realize without information loss. In this paper, we describe how we derived and implemented transformations between the academically developed Universal Variability Language and the commercially developed pure::variants tool, with as little information loss as possible. Our approach can also be used to optimize constraints, e.g., reduce their number without an effect on the configuration space, using particular capabilities pure::variants provides. Also, via an existing variability model transformation approach, which uses UVL as a pivot language, we enable the transformation of FeatureIDE feature models, DOPLER decision models, and Orthogonal Variability Models into/from pure::variants and back. With our approach, we work towards bridging the gap between academic and industrial variability modeling tools and enable experiments with the different capabilities these tools provide.
Exact master equation for generalized quantum Brownian motion with momentum-dependent system-environment couplings

In this paper we generalize the quantum Brownian motion (QBM) to include the momentum-dependent system-environment couplings. The resulting Hamiltonian in the particle number representation is given by ${H}_{t}ot=\ensuremath{\hbar}{\ensuremath{\omega}}_{\mathrm{S}}{a}^{\ifmmode\dagger\else\textdagger\fi{}}a+{\ensuremath{\sum}}_{k}\ensuremath{\hbar}{\ensuremath{\omega}}_{k}{b}_{k}^{\ifmmode\dagger\else\textdagger\fi{}}{b}_{k}+{\ensuremath{\sum}}_{k}\ensuremath{\hbar}({V}_{k}{a}^{\ifmmode\dagger\else\textdagger\fi{}}{b}_{k}+{V}_{k}^{*}{b}_{k}^{\ifmmode\dagger\else\textdagger\fi{}}a)+{\ensuremath{\sum}}_{k}\ensuremath{\hbar}({W}_{k}{a}^{\ifmmode\dagger\else\textdagger\fi{}}{b}_{k}^{\ifmmode\dagger\else\textdagger\fi{}}+{W}_{k}^{*}{b}_{k}a)$. The conventional QBM model corresponds to the special case ${W}_{k}={V}_{k}$. The generalized QBM can capture more physical applications because the single-particle transition and the two-particle pair production between the system and the environment represent two very different physical processes that usually do not have the same coupling strengths. We discuss the physical realizations of the generalized QBM in different physical systems, and derive its exact master equation for both the initial decoupled states and initial correlated states. The Hu-Paz-Zhang master equation of the conventional QBM model is reproduced as a special case. We find that the renormalized system Hamiltonian after tracing out all the environmental states induced naturally a momentum-dependent potential, which shows the consistency of including the momentum-dependent coupling in the QBM Hamiltonian. In the Hu-Paz-Zhang master equation, such a renormalized potential is misplaced so that the correct renormalization Hamiltonian has not been found. With the exact master equation for both the initial decoupled and initial correlated states, the issues about the initial jolt are also reexamined. We find that the so-called ``initial jolt,'' which has been thought to be an artificial effect due to the use of the initial decoupled system-environment states, has nothing to do with the initial decoupled state. The exact master equation for the generalized QBM also has potential applications to photonics quantum computing.
Probability envelopes and their Dempster-Shafer approximations in statistical matching

Many economic applications require to integrate information coming from different data sources. In this work we consider a specific integration problem, called statistical matching, referring to integration of data sets where some variables are separately observed and some others are observed in all the data sets. This problem leads to the issue of non-uniqueness for the compatible (conditional) distributions and so it suggests to deal with sets of probabilities. For that we consider different strategies to get a (conditional) belief function that approximates the lower envelope of the class of compatible (conditional) probabilities. We first analyze the case without logical constraints among the variables and then generalize the obtained results by allowing for logical constraints. We finally show an application to real data.