Reviewerly/exHarmony huggingface dataset:
Graph Iso/Auto-morphism

The graph isomorphism is to determine whether two graphs are isomorphic. A closely related problem is graph automorphism (symmetry) detection, where an isomorphism between two graphs is a bijection between their vertex sets that preserves adjacency, and an automorphism is an isomorphism from a graph to itself. By graph automorphism, we deal with symmetric subgraph matching (SSM), which is to find all subgraphs in a graph G that are symmetric to a given subgraph q in G. To test two graphs for isomorphism, canonical labeling has been studied to relabel a graph in such a way that isomorphic graphs are identical after relabeling. Efficient canonical labeling algorithms are designed by individualization-refinement. They enumerate all permutations using a search tree, and select the minimum one as the canonical labeling. These algorithms face difficulties in handling massive graphs, and the search trees used are for pruning purposes which cannot answer symmetric subgraphs matching. In this paper, we design a new efficient canonical labeling algorithm DviCL based on the observation that we can use the k-th minimum permutation as the canonical labeling. Different from previous algorithms, we take a divide-and-conquer approach to partition a graph G. By partitioning G, an AutoTree is constructed, which preserves symmetric structures as well as the automorphism group of G. The canonical labeling for a tree node can be obtained by composing those of its child nodes, and the canonical labeling for the root is the one for G. Such AutoTree can also be effectively used to answer the automorphism group and symmetric subgraphs. We conducted extensive performance studies using 22 large graphs, and confirmed that DviCL is much more efficient and robust than the state-of-the-art.
Metrological characterization of different methods for recovering the optically sectioned image by means of structured light

Imaging confocal microscopy (ICM) and focus variation (FV) are two of the most used technologies for 3D surface metrology. Both methods rely on the depth of focus of the microscope objective, which depends on its numerical aperture and wavelength of the light source to compute an optical section. In this paper we study how several methods of structured illumination microscopy affect the metrological characteristics of an areal optical profiler. We study the effect of the projection of different structured patterns, the sectioning algorithms, and the use of high and low frequency components onto the optically sectioned image. We characterized their performance in terms of system noise, instrument transfer function and metrological characteristics such as roughness parameters and step height values.
KFU NLP Team at SMM4H 2021 Tasks: Cross-lingual and Cross-modal BERT-based Models for Adverse Drug Effects

This paper describes neural models developed for the Social Media Mining for Health (SMM4H) 2021 Shared Task.We participated in two tasks on classification of tweets that mention an adverse drug effect (ADE) (Tasks 1a & 2) and two tasks on extraction of ADE concepts (Tasks 1b & 1c).For classification, we investigate the impact of joint use of BERTbased language models and drug embeddings obtained by chemical structure BERT-based encoder.The BERT-based multimodal models ranked first and second on classification of Russian (Task 2) and English tweets (Task 1a) with the F1 scores of 57% and 61%, respectively.For Task 1b and 1c, we utilized the previous year's best solution based on the EnDR-BERT model with additional corpora.Our model achieved the best results in Task 1c, obtaining an F1 of 29%.
Motion Deblurring Analysis for Underwater Image Restoration

Abstract Exploration of Underwater Images will be a challenging task due to its degradations by haze, blur, colour cast and noise. In order to extract the latent information from the blurred portion, its parameters are estimated and various algorithms were analysed. Initially, length and angle of motion blurred image are estimated. Then, filters and restoration algorithms were implemented for controlling of ringing artifacts, removal of noise and latent information restoration. Some of the model based classical methods were implemented and analysed for underwater motion blurred image restoration.
Evoke, don’t show: Narration in cinematic virtual reality and the making of Entangled

Over the past three years, cinematic virtual reality (CVR) has emerged as a form of media storytelling that takes advantage of the immersive properties of VR technology. However, as a practice it poses a number of challenges for the writer–director used to controlling the frame through which the viewer experiences the narrative. This research outlines the making of Entangled (a live-action, stereoscopic, VR experience incorporating ambisonic audio) and reflects on concept development and production decision-making with reference to the emerging body of academic knowledge about cinematic VR, in particular ideas about the position of the viewer and the nature of narration. The research addresses some of the gaps in knowledge in these areas, reconciling theoretical positions with a deep understanding of the realities of production processes.
A Knowledge-Based Hybrid Approach on Particle Swarm Optimization Using Hidden Markov Models

Bio-inspired computing is an engaging area of artificial intelligence which studies how natural phenomena provide a rich source of inspiration in the design of smart procedures able to become powerful algorithms. Many of these procedures have been successfully used in classification, prediction, and optimization problems. Swarm intelligence methods are a kind of bio-inspired algorithm that have been shown to be impressive optimization solvers for a long time. However, for these algorithms to reach their maximum performance, the proper setting of the initial parameters by an expert user is required. This task is extremely comprehensive and it must be done in a previous phase of the search process. Different online methods have been developed to support swarm intelligence techniques, however, this issue remains an open challenge. In this paper, we propose a hybrid approach that allows adjusting the parameters based on a state deducted by the swarm intelligence algorithm. The state deduction is determined by the classification of a chain of observations using the hidden Markov model. The results show that our proposal exhibits good performance compared to the original version.
ParCourE: A Parallel Corpus Explorer for a Massively Multilingual Corpus

With more than 7000 languages worldwide, multilingual natural language processing (NLP) is essential both from an academic and commercial perspective.Researching typological properties of languages is fundamental for progress in multilingual NLP.Examples include assessing language similarity for effective transfer learning, injecting inductive biases into machine learning models or creating resources such as dictionaries and inflection tables.We provide ParCourE, an online tool that allows to browse a word-aligned parallel corpus, covering 1334 languages.We give evidence that this is useful for typological research.ParCourE can be set up for any parallel corpus and can thus be used for typological research on other corpora as well as for exploring their quality and properties.
Deep Implicit Coordination Graphs for Multi-agent Reinforcement Learning

Multi-agent reinforcement learning (MARL) requires coordination to efficiently solve certain tasks. Fully centralized control is often infeasible in such domains due to the size of joint action spaces. Coordination graph based formalization allows reasoning about the joint action based on the structure of interactions. However, they often require domain expertise in their design. This paper introduces the deep implicit coordination graph (DICG) architecture for such scenarios. DICG consists of a module for inferring the dynamic coordination graph structure which is then used by a graph neural network based module to learn to implicitly reason about the joint actions or values. DICG allows learning the tradeoff between full centralization and decentralization via standard actor-critic methods to significantly improve coordination for domains with large number of agents. We apply DICG to both centralized-training-centralized-execution and centralized-training-decentralized-execution regimes. We demonstrate that DICG solves the relative overgeneralization pathology in predatory-prey tasks as well as outperforms various MARL baselines on the challenging StarCraft II Multi-agent Challenge (SMAC) and traffic junction environments.
On the Use of Quality Models to Address Distinct Quality Views

Different software product quality models interpret different amounts of information, i.e., they can capture and address different manifestations of software quality. This characteristic can cause misleading statements and misunderstandings while explaining or comparing the results of software product quality assessments. A total of 23 previously identified distinct software product quality models are analysed on how they handle the abstract notion of quality, and a taxonomy on the quality manifestations that the individual software product quality models are able to capture is established. Quality models that are able to solely describe the quality manifestation of the source code are attractive due to their full automation potential through static code analysers, but their assessment results ignore a huge part of software product quality, which is the one that most impresses the end user. The manifestations of software product quality that address the behaviour of the software while it operates, or the perception of the end user with regard to the software in use, require human involvement in the quality assessment. The taxonomy contributes to interpreting the quality assessment results of different quality models by showing the possible quality manifestations that can be captured by the identified models; moreover, the taxonomy also provides assistance while selecting a quality model for a given project. The quality manifestations used for the quality measurement always need to be presented, otherwise the quality assessment results cannot be interpreted in an appropriate manner.
Write to know

The popularity of smart wrist wearable technology (e.g., smart-watches) has rejuvenated the exploration of dynamic biometric-based authentication techniques that employ sensor data from these devices. Despite the progress demonstrated by the scientific community, research in this area has not successfully transitioned to practice, and we are yet to see a mainstream user-authentication product based on a dynamic biometric such as handwriting/hand gestures captured using commercial wrist wearables. This work undertakes an investigative analysis to further explore why that is the case. We accomplish this by studying the feasibility and practical deployability of handwriting-based authentication techniques in the literature that utilize motion sensors on-board wrist wearables. We conduct this analysis by replicating four state-of-the-art and representative handwriting-based authentication schemes that employ wrist motion data, in order to test their viability in realistic hand-writing/gesture scenarios. By using data collected from actual human subjects in an unconstrained fashion, we comparatively evaluate the performance of these schemes with well-defined usability and security metrics. Our experimental results show that some of the tested schemes perform considerably well in practice, and are promising. However, they do suffer from several practical user-dependent and technique-specific challenges that act as roadblocks towards their wide-scale adoption in mainstream applications.
The Role of the Blockchain Technology in the Elderly Care Solutions: A Systematic Mapping Study

The incorporation of technology in the improvement of health care for the elderly is identified as a potential strategy that maximizes the effectiveness of the provision of health services, generating a higher quality of life for older adults. However, an important health general concern is the security and data privacy. Thus, blockchain technologies are an opportunity for supporting them in this kind of solutions. However, due to lack of an unified knowledge about how blockchain could be applied, we present a systematic mapping study (SMS), in order to understand from primary studies as blockchain technology is supporting care solutions for the elderly. We selected Springer, ACM Digital Library, Scopus, IEEE Explore, and Direct of Science databases, which are consistent with the discipline of study. Among the main findings are different types of Blockchain that are being applied in many ways to positively support aspects of the healthcare solutions, such as in the registration, consultation and transmission of sensitive data, time banks, smart home sensor, wireless Body Area Networks (WBAN), user login, Wireless Sensor Network (WSN), Ambient Assisted Living (AAL), telemedicine in solutions combined with IoT or supported cognitive, physical therapy and others.
Accelerating ODE-Based Neural Networks on Low-Cost FPGAs

ODENet is a deep neural network architecture in which a stacking structure of ResNet is implemented with an ordinary differential equation (ODE) solver. It can reduce the number of parameters and strike a balance between accuracy and performance by selecting a proper solver. It is also possible to improve the accuracy while keeping the same number of parameters on resource-limited edge devices. In this paper, using Euler method as an ODE solver, a part of ODENet is implemented as a dedicated logic on a low-cost FPGA (Field-Programmable Gate Array) board, such as PYNQ-Z2 board. As ODENet variants, reduced ODENets (rODENets) each of which heavily uses a part of ODENet layers and reduces/eliminates some layers differently are proposed and analyzed for low-cost FPGA implementation. They are evaluated in terms of parameter size, accuracy, execution time, and resource utilization on the FPGA. The results show that an overall execution time of an rODENet variant is improved by up to 2.66 times compared to a pure software execution while keeping a comparable accuracy to the original ODENet.
Dynamic Management of Identity Federations using Blockchain

Federated Identity Management (FIM) is a model of identity management in which different trusted organizations can provide secure online services to their uses. Security Assertion Markup Language (SAML) is one of the widely-used technologies for FIM. However, a SAML-based FIM has two significant issues: the metadata (a crucial component in SAML) has security issues, and federation management is hard to scale. The concept of dynamic identity federation has been introduced, enabling previously unknown entities to join in a new federation facilitating inter-organization service provisioning to address federation management's scalability issue. However, the existing dynamic federation approaches have security issues concerning confidentiality, integrity, authenticity, and transparency. In this paper, we present the idea of facilitating dynamic identity federations utilizing blockchain technology to improve the existing approaches' security issues. We demonstrate its architecture based on a rigorous threat model and requirement analysis. We also discuss its implementation details, current protocol flows and analyze its performance to underline its applicability.
Dual Adversarial Variational Embedding for Robust Recommendation

Robust recommendation aims at capturing true preference of users from noisy data, for which there are two lines of methods have been proposed. One is based on noise injection, and the other is to adopt the generative model Variational Auto-encoder (VAE). However, the existing works still face two challenges. First, the noise injection based methods often draw the noise from a fixed noise distribution given in advance, while in real world, the noise distributions of different users and items may differ from each other due to personal behaviors and item usage patterns. Second, the VAE based models are not expressive enough to capture the true preference since VAE often yields an embedding space of a single modal, while in real world, user-item interactions usually exhibit multi-modality on user preference distribution. In this paper, we propose a novel model called Dual Adversarial Variational Embedding (DAVE) for robust recommendation, which can provide personalized noise reduction for different users and items, and capture the multi-modality of the embedding space, by combining the advantages of VAE and adversarial training between the introduced auxiliary discriminators and the variational inference networks. The extensive experiments conducted on real datasets verify the effectiveness of DAVE on robust recommendation.
Determine the Shortest Path Problem Using Haversine Algorithm, A Case Study of SMA Zoning in Depok

The new student admission evolves with government rules which are described in the ministerial decrees. Zoning is suite of policies that are intact, integrated, and systematic in attempt to do a restoration in educational field. The new student admission is applied with zoning system, based on Depok Mayor Regulation No. 20 of 2020, Technical Instructions for Admission of New Students, 2020 that admission of new student from Depok with the nearest distance has fifty percent quota as evidenced by Family Registration Card and scoring based on the nearest sub-district. Problem that occurs is how to determine and count the domicile distance to destination school with straight line calculation and get the nearest distance that corresponding to the student school zone. This research begins by collecting spatial and non-spatial data from high school in Depok and continued with calculating the nearest distance from student's domicile to destination school using Haversine Algorithm. The Haversine Algorithm is used to calculate the distance between two points on earth based on the length of a straight line without ignore curvature that earth has. The result of this research shows recommendation about the destination school which corresponds with the nearest distance from user's location.
VRHI: Visibility Restoration for Hazy Images Using a Haze Density Model

In this paper, a image processing method called VRHI is developed to enhance single hazy images. More specifically, inspired by visual characteristics of haze, a haze density estimation model is designed to predict the haze distribution. According to this recognized haze distribution, a quadtree based recursive strategy is subsequently proposed to locate the atmospheric light. Finally, by combining a global-wise adjusting mechanism and atmospheric scattering model, the haze cover in an image can be easily excluded using the estimated parameters. It is worth mentioning that VRHI is based on whole image to search the unknown parameters, thereby avoiding some unfavorable phenomena, e.g., over-enhancement and color distortion. Extensive experiments on real-world images and well-known dehazing datasets show that VRHI outperforms state-of-the-art techniques in robustness and effectiveness.
Dynamic State Estimation for Power System Based on the Measurement Data Reconstructed by RGAN

Aiming at the problem of incomplete measurement data in the power system state estimation process, the missing data reconstructed by the residual generative adversarial network (RGAN) was introduced for the power system dynamic estimation. RGAN is a deep learning model based on the idea of a “two-player zero-sum” game, in which two deep neural networks compete with each other to mine the relevant features of the data. Distinguish from existing structure, skip connection and residual network (ResNet) were incorporated into two deep neural networks. And the incomplete measurement data were reconstructed accurately from the remaining measurement data. To lessen the impact of reconstruction errors, the unscented Kalman filter (UKF) method was used to estimate power system state. A case studied in the IEEE 30-bus shows that the UKF based RGAN dynamic estimation can maintain a high accuracy under different proportions of missing data.
DocOIE: A Document-level Context-Aware Dataset for OpenIE

Open Information Extraction (OpenIE) aims to extract structured relational tuples (subject, relation, object) from sentences, and plays a critical role in many NLP applications.Existing solutions perform extraction at sentence level, without referring to any additional contextual information.In reality, however, a sentence typically exists as part of a document rather than standalone; we often need to access relevant contextual information around the sentence before we can accurately interpret it.As there is no document-level context-aware Ope-nIE dataset available, we manually annotate 800 sentences from 80 documents in two domains (Healthcare and Transportation) to form a DocOIE dataset for evaluation.In addition, we propose DocIE, a document-level contextaware OpenIE model.Our experimental results demonstrate that incorporating documentlevel context is helpful in improving OpenIE performance.Both the DocOIE dataset and DocIE model are available online. 1
Semantic attention-based network for inshore SAR ship detection

The performance of Synthetic Aperture Radar (SAR) ship detector has been significantly improved with the development of convolutional neural network. However, the issue of effective detection of inshore ships is still a challenging problem. In this paper, we propose a novel one-stage SAR ship detector, called Semantic Attention-Based Network (SANet), which can largely improve the accuracy of ship detection in the inshore scenario without compromising the speed. Specifically, we introduce a semantic attention mechanism, which will highlight the features from the ships area and enhance the detector's classification ability. We train the proposed Semantic Attention Module with focal loss, and assign labels for the attention maps by center sampling. Combined with our anchor assign strategy, our SANet achieves state-of-the-art results on the open SAR Ship Detection Dataset (SSDD).
Uncertainty‐aware Visualization of Regional Time Series Correlation in Spatio‐temporal Ensembles

Abstract Given a time‐varying scalar field, the analysis of correlations between different spatial regions, i.e., the linear dependence of time series within these regions, provides insights into the structural properties of the data. In this context, regions are connected components of the spatial domain with high time series correlations. The detection and analysis of such regions is often performed globally, which requires pairwise correlation computations that are quadratic in the number of spatial data samples. Thus, operations based on all pairwise correlations are computationally demanding, especially when dealing with ensembles that model the uncertainty in the spatio‐temporal phenomena using multiple simulation runs. We propose a two‐step procedure: In a first step, we map the spatial samples to a 3D embedding based on a pairwise correlation matrix computed from the ensemble of time series. The 3D embedding allows for a one‐to‐one mapping to a 3D color space such that the outcome can be visually investigated by rendering the colors for all samples in the spatial domain. In a second step, we generate a hierarchical image segmentation based on the color images. From then on, we can visually analyze correlations of regions at all levels in the hierarchy within an interactive setting, which includes the uncertainty‐aware analysis of the region's time series correlation and respective time lags.