Reviewerly/exHarmony huggingface dataset:
A Novel AlphaSRGAN for Underwater Image Super Resolution

Obtaining clear images of underwater scenes with descriptive details is an arduous task.Conventional imaging techniques fail to provide clear cut features and attributes that ultimately result in object recognition errors.Consequently, a need for a system that produces clear images for underwater image study has been necessitated.To overcome problems in resolution and to make better use of the Super-Resolution (SR) method, this paper introduces a novel method that has been derived from the Alpha Generative Adversarial Network (AlphaGAN) model, named Alpha Super Resolution Generative Adversarial Network (AlphaSRGAN).The model put forth in this paper helps in enhancing the quality of underwater imagery and yields images with greater resolution and more concise details.Images undergo pre-processing before they are fed into a generator network that optimizes and reforms the structure of the network while enhancing the stability of the network that acts as the generator.After the images are processed by the generator network, they are passed through an adversarial method for training models.The dataset used in this paper to learn Single Image Super Resolution (SISR) is the USR 248 dataset.Training supervision is performed by an unprejudiced function that simultaneously scrutinizes and improves the image quality.Appraisal of images is done with reference to factors like local style information, global content and color.The dataset USR 248 which has a huge collection of images has been used for the study is composed of three collections of images-high (640 × 480) and low (80 × 60, 160 × 120, and 320 × 240).Paired instances of different sizes-2×, 4× and 8×-are also present in the dataset.Parameters like Mean Opinion Score (MOS), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM) and Underwater Image Quality Measure (UIQM) scores have been compared to validate the improved efficiency of our model when compared to existing works.
Text Mining

This chapter provides a detailed look into the emerging area of text mining and text analytics. It starts with a background of the origins of text mining and provides the motivation for this fascinating topic using the example of IBM's Watson, the Jeopardy—the winning computer program that was built almost entirely using concepts from text and data science. This chapter introduces some key concepts important in the area of text analytics such as term frequency–inverse document frequency scores. Finally, it describes two hands-on case studies in which it is shown how to use RapidMiner to address problems like document clustering and automatic gender classification based on text content.
Gesture recognition based on improved VGGNET convolutional neural network

In this paper, based on the problem that the existing deep learning neural network relies on large-scale training data and powerful computing power, the convolutional neural network VGGNet is improved, and a modified gesture recognition algorithm based on VGGNet is proposed. Experiments show that under the condition of small training data size and limited computing power, the improved convolutional neural network algorithm has higher recognition rate and can meet the requirements of practical gesture recognition applications.
Scribble2D5: Weakly-Supervised Volumetric Image Segmentation via Scribble Annotations

Image segmentation using weak annotations like scribbles has gained great attention, since such annotations are easier to obtain compared to time-consuming and labor-intensive labeling at the pixel/voxel level. However, scribbles lack structure information of the region of interest (ROI), thus existing scribble-based methods suffer from poor boundary localization. Moreover, current methods are mostly designed for 2D image segmentation, which do not fully leverage volumetric information. In this paper, we propose a scribble-based volumetric image segmentation, Scribble2D5, which tackles 3D anisotropic image segmentation and improves boundary predictions. To achieve this, we augment a 2.5D attention UNet with a proposed label propagation module to extend semantic information from scribbles and a combination of static and active boundary prediction to learn ROI's boundaries and regularize its shape. Extensive experiments on three public datasets demonstrate Scribble2D5 significantly outperforms existing scribble-based methods and approaches the performance of fully-supervised ones. Our code is available at https://github.com/Qybc/Scribble2D5 .
Sender anonymity: Applying ring signature in gateway-based blockchain for IoT is not enough

Over the last decade, there has been extensive research into the security and privacy of blockchain technology, from the use of ring signatures to ring confidential transactions. Although existing cryptographic methods perform well on traditional blockchain architectures, they do not provide complete privacy in a hierarchical blockchain architecture for Internet of Things (IoT) devices. The blockchain technology is centered at the gateway layer in the hierarchical blockchain architecture, which can easily reveal the transaction sender using network traffic information, thereby violating the sender’s anonymity. Furthermore, the gateway can perform eclipse attacks against transaction senders on a case-by-case basis. To this end, this paper proposes a novel security and privacy blockchain protocol that is better suited to the hierarchical blockchain architecture and thus more appropriate for the IoT environment. Specifically, it leverages the relationship among IoT devices at the perception layer of the hierarchical blockchain architecture for IoT, and constructs an overlay social IoT network by exploiting devices’ multi-agent capabilities. Furthermore, circuit establishment based on multilayered encryption is applied to conceal the transaction propagation path through the overlay social IoT. Theoretical analysis shows that our novel protocol is flexible, evades a malicious gateway, and is secure under the decisional Diffie-Hellman assumption.
Person re-identification via semi-supervised adaptive graph embedding

Video surveillance is an indispensable part of the smart city for public safety and security. Person Re-Identification (Re-ID), as one of elementary learning tasks for video surveillance, is to track and identify a given pedestrian in a multi-camera scene. In general, most existing methods has firstly adopted a CNN based detector to obtain the cropped pedestrian image, it then aims to learn a specific distance metric for retrieval. However, unlabeled gallery images are generally overlooked and not utilized in the training. On the other hands, Manifold Embedding (ME) has well been applied to Person Re-ID as it is good to characterize the geometry of database associated with the query data. However, ME has its limitation to be scalable to large-scale data due to the huge computational complexity for graph construction and ranking. To handle this problem, we in this paper propose a novel scalable manifold embedding approach for Person Re-ID task. The new method is to incorporate both graph weight construction and manifold regularized term in the same framework. The graph we developed is discriminative and doubly-stochastic so that the side information has been considered so that it can enhance the clustering performances. The doubly-stochastic property can also guarantee the graph is highly robust and less sensitive to the parameters. Meriting from such a graph, we then incorporate the graph construction, the subspace learning method in the unified loss term. Therefore, the subspace results can be utilized into the graph construction, and the updated graph can in turn incorporate discriminative information for graph embedding. Extensive simulations is conducted based on three benchmark Person Re-ID datasets and the results verify that the proposed method can achieve better ranking performance compared with other state-of-the-art graph-based methods.
3D-DFM: Anchor-Free Multimodal 3-D Object Detection With Dynamic Fusion Module for Autonomous Driving

Recent advances in cross-modal 3D object detection rely heavily on anchor-based methods, and however, intractable anchor parameter tuning and computationally expensive postprocessing severely impede an embedded system application, such as autonomous driving. In this work, we develop an anchor-free architecture for efficient camera-light detection and ranging (LiDAR) 3D object detection. To highlight the effect of foreground information from different modalities, we propose a dynamic fusion module (DFM) to adaptively interact images with point features via learnable filters. In addition, the 3D distance intersection-over-union (3D-DIoU) loss is explicitly formulated as a supervision signal for 3D-oriented box regression and optimization. We integrate these components into an end-to-end multimodal 3D detector termed 3D-DFM. Comprehensive experimental results on the widely used KITTI dataset demonstrate the superiority and universality of 3D-DFM architecture, with competitive detection accuracy and real-time inference speed. To the best of our knowledge, this is the first work that incorporates an anchor-free pipeline with multimodal 3D object detection.
A Scalable Blockchain-Based Integrity Verification Scheme

Ensuring the integrity of remote data is the prerequisite for implementing cloud-edge computing. Traditional data integrity verification schemes make users spend a lot of time regularly checking their data, which is not suitable for large-scale IoT (Internet of Things) data. On the other hand, the introduction of a third-party auditor (TPA) may bring about greater privacy and security issues. We use blockchain to address the problem of TPA. However, implementing dynamic integrity verification with blockchain is a bigger challenge due to the low throughput and poor scalability of blockchain. More importantly, whether there is a security problem with blockchain-based integrity verification is not yet known. In this paper, we propose a scalable blockchain-based integrity verification scheme that implements fully dynamic operations and blockless verification. The scheme builds scalable homomorphic verification tags based on ZSS (Zhang-Safavi-Susilo) short signatures. We exploit smart contract technology to replace TPA for integrity verification tasks, which not only eliminates the risk of privacy leakage but also resists collusion attacks. Furthermore, we formally define a blockchain-based security model and prove that our scheme is secure under the security assumption of cryptographic primitives. Finally, the mathematical analysis of our scheme shows that both the communication complexity and the communication complexity of an audit are <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>O</mi> <mfenced open="(" close=")"> <mrow> <mi>c</mi> </mrow> </mfenced> </math> , in which <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>c</mi> </math> is the number of challenge blocks. We compare our scheme with other schemes, and the results show that our scheme has the lowest time consumption to complete an audit.
Distributed differentially-private learning with communication efficiency

In this paper, we propose a new algorithm for learning over distributed data such as in the IoT environment, in a privacy-preserving way. Our algorithm is a differentially private variant of distributed synchronous stochastic gradient descent method with multiple workers and one parameter server, and has the following two features: (1) each distributed worker only needs to send as small as O(1) gradients in each iteration, so that the communication from worker to server is modest; (2) the dataset of each worker is protected quantitatively by differential privacy. We mathematically prove the convergence of our algorithm, and experimentally verify that it converges and reaches standard testing results on a benchmark dataset, while simultaneously maintaining reasonable privacy budgets. Our results address two equally important issues in the IoT environment, communication efficiency and differential privacy, and potentially help reducing the tension caused by the issues.
Improved whale optimization algorithm and its application in vehicle structural crashworthiness

Whale optimization algorithm (WOA) is a novel-innovative swarm-based meta-heuristic algorithm with excellent performance, but it may still be trapped into local extremum for troublesome problems. To this end, an improved multi-objective whale optimization algorithm (IMOWOA) is proposed to cover the shortages. Firstly, in the search stage of WOA, individual difference is considered to strengthen the exploration ability, and evolution operators are introduced to regenerate the stagnated population to prevent premature convergence. Next, the performance of IMOWOA is compared with MOWOA and other classical optimization algorithms, and a series of multi-objective test functions are used. The results on the convergence and diversity of Pareto front confirm that IMOWOA has better feasibility and competitiveness. Finally, integrated with the least squares support vector regression (LSSVR) model, IMOWOA is applied to the deterministic optimization of vehicle structural crashworthiness. The conclusion testified the efficiency of IMOWOA in the field of vehicle crashworthiness.
LISSU: Continuous Monitoring of SOA Communication with Constraint-Based Validation

Abstract Service-oriented architectures (SOA) are becoming more widespread in the context of Industry 4.0, and their interface descriptions enable modular and scalable communication systems. Since syntactic checks such as data types are solved nowadays, the purpose of this work is to add semantic validation based on the idea of Semantic Web Services. This paper proposes Lightweight Semantic Web Services for Units (LISSU) and integrates promising concepts from the Semantic Web into SOA. We complement existing syntactic checks with semantic ones (e.g. for units), extend one-time initial checks with continuous monitoring, and include expressive constrain-based validations. LISSU can be integrated into any SOA and significantly increases the predictability of communications. Before components communicate, it checks their semantics via ontology URIs and automatically converts units if possible. Continuous monitoring at runtime extracts sent messages and guarantees flawless data quality via constraint-based validations. A real-world demonstrator setup in the manufacturing domain proves effectiveness and practicality. We present LISSU, which integrates concepts from the Semantic Web into SOA setups. It enables a wide range of semantic validations before and during communication, thereby increasing the quality and predictability of SOA communication.
Target-scale prospectivity modeling for gold mineralization within the Rajapalot Au-Co project area in northern Fennoscandian Shield, Finland. Part 1: Application of knowledge-driven- and machine learning-based-hybrid- expert systems for exploration targeting and addressing model-based uncertainties

This two-part paper reports results of data integration using different machine learning methods for target-scale prospectivity modeling of gold mineralization. The study area is the Rajapalot project area in the Northern Fennoscandian Shield in Finland. In this Part 1 paper, prospectivity modeling was implemented using knowledge-driven- and hybrid- expert systems. Prospectivity analysis-related systemic and model uncertainties were addressed using statistical and machine-learning based methods. The knowledge-driven Fuzzy Inference Systems (FISs) were optimized using Monte Carlo Simulations (MCS) to address and quantify mathematical model-related uncertainties and consequently present the prospectivity values at varying confidence levels. In the FIS results, as the confidence level increases, the high-prospectivity zones mapped by the models are spatially reduced and the mineralization trend becomes progressively well-defined. The resulting prospectivity map has 80% capture efficiency. The supplementary prospectivity map reporting prospectivity values at 90% confidence level has enhanced area-reduction capability. Machine-learning based Adaptive Neuro Fuzzy Inference System (ANFIS) was optimized under the neuro-fuzzy environment. This hybrid ANFIS-based result has 88% capture efficiency. Three exploration targets identified by the prospectivity maps together comprise about 1.5 km2 of the ∼20 km2 study area (i.e., 7.5% of the study area). This indicates considerable search-space reduction by the modeling results for follow-up ground exploration. Hence demonstrating that the prospectivity mapping methods, which are primarily 'predictive tools' at the regional scale, can be harnessed to serve as an aid to detection technologies at the target scale. Implementation of knowledge-driven and hybrid approaches deliver results that can (i) incorporate human cognition into prospectivity modeling, (ii) reduce bias of conceptual model, and (iii) are not overfitted to the training dataset. Statistical methods such as hypothesis testing and MCS, and machine learning methods such as ANFIS are therefore collectively beneficial for addressing both systemic and fuzzy systems-related modeling uncertainties.
Provider Fairness for Diversity and Coverage in Multi-Stakeholder Recommender Systems

Nowadays, recommender systems (RS) are no longer evaluated only for the accuracy of their recommendations. Instead, there is a requirement for other metrics (e.g., coverage, diversity, serendipity) to be taken into account as well. In this context, the multi-stakeholder RS paradigm (MSRS) has gained significant popularity, as it takes into consideration all beneficiaries involved, from item providers to simple users. In this paper, the goal is to provide fair recommendations across item providers in terms of diversity and coverage for users to whom each provider’s items are recommended. This is achieved by following the methodology provided by the literature for solving the recommendation problem as an optimization problem under constraints for coverage and diversity. As the constraints for diversity are quadratic and cannot be solved in sufficient time (NP-Hard problem), we propose a heuristic approach that provides solutions very close to the optimal one, as the proposed approach in the literature for solving diversity constraints was too generic. As a next step, we evaluate the results and identify several weaknesses in the problem formulation as provided in the literature. To this end, we introduce new formulations for diversity and provide a new heuristic approach for the solution of the new optimization problem.
Local Attention Graph-Based Transformer for Multi-target Genetic Alteration Prediction

Classical multiple instance learning (MIL) methods are often based on the identical and independent distributed assumption between instances, hence neglecting the potentially rich contextual information beyond individual entities. On the other hand, Transformers with global self-attention modules have been proposed to model the interdependencies among all instances. However, in this paper we question: Is global relation modeling using self-attention necessary, or can we appropriately restrict self-attention calculations to local regimes in large-scale whole slide images (WSIs)? We propose a general-purpose local attention graph-based Transformer for MIL (LA-MIL), introducing an inductive bias by explicitly contextualizing instances in adaptive local regimes of arbitrary size. Additionally, an efficiently adapted loss function enables our approach to learn expressive WSI embeddings for the joint analysis of multiple biomarkers. We demonstrate that LA-MIL achieves state-of-the-art results in mutation prediction for gastrointestinal cancer, outperforming existing models on important biomarkers such as microsatellite instability for colorectal cancer. Our findings suggest that local self-attention sufficiently models dependencies on par with global modules. Our LA-MIL implementation is available at https://github.com/agentdr1/LA_MIL .
Multivariate bounded support asymmetric generalized Gaussian mixture model with model selection using minimum message length

In this paper, the bounded support asymmetric generalized Gaussian mixture model (BAGGMM) is proposed for data modeling as an alternative to unbounded mixture models for the cases when the data lies in the bounded support region. The model parameters are learned through maximum likelihood estimation, and Expectation–Maximization (EM) with the Newton Raphson method is adopted for optimization of parameters. Model selection in mixtures is also considered an integral part of clustering; thus, we also have proposed a model selection criterion for BAGGMM through minimum message length. In order to validate the performance of the mixture model, it is applied to image spam detection, object clustering, and visual scene categorization. For the experiments, Spam Hunter, ETHZ, GHIM, and 15-Scene image datasets are adopted, and several clustering scenarios are developed to see the effectiveness of the proposed model. The clustering framework is also compared with AGGMM in a similar setting to all the experiments. In the next phase, the whole clustering framework is extended to examine the performance of the proposed model selection criterion and compared with different techniques to find the optimal mixture component, which further improves the clustering process. The experiments show that the proposed BAGGMM and model selection criterion has demonstrated its success in several learning applications.
Efficient Discovery of Partial Periodic Patterns in Large Temporal Databases

Periodic pattern mining is an emerging technique for knowledge discovery. Most previous approaches have aimed to find only those patterns that exhibit full (or perfect) periodic behavior in databases. Consequently, the existing approaches miss interesting patterns that exhibit partial periodic behavior in a database. With this motivation, this paper proposes a novel model for finding partial periodic patterns that may exist in temporal databases. An efficient pattern-growth algorithm, called Partial Periodic Pattern-growth (3P-growth), is also presented, which can effectively find all desired patterns within a database. Substantial experiments on both real-world and synthetic databases showed that our algorithm is not only efficient in terms of memory and runtime, but is also highly scalable. Finally, the effectiveness of our patterns is demonstrated using two case studies. In the first case study, our model was employed to identify the highly polluted areas in Japan. In the second case study, our model was employed to identify the road segments on which people regularly face traffic congestion.