Reviewerly/exHarmony huggingface dataset:
An Insight Review on Jellyfish Optimization Algorithm and Its Application in Engineering

Present paper conducts the study on metaheuristic jellyfish optimization algorithm and reviews work done on it. This is a newly developed meta-heuristic optimization algorithm concentrates on movements of jellyfish in ocean such as search of food in sea current, motion inside swarm, bloom, etc. The optimization demonstrates higher and additional favourable results versus other bio-inspired optimization algorithms. Some of the work is discussed in this paper are: finding of unknown parameters of Photo Voltic models using premature convergence strategy, finding of optimal VAR coordination in Automated Delivery System, estimating the parameters of single-phase power transformers, overcoming the drawbacks and improving the quality of network service, tracking the overall maximum power point under partial shade conditions, improving MapReduce job performance and increasing resource usage in Hadoop yarn, solving engineering problems optimally with multiple objectives, dimensionally reducing humanoid application feature, solving the problems to identify the parameters of Polymer Exchange Membrane Fuel Cell models, watermark extraction process and preserving location confidentiality to avoid congestion with less guaranteed delay, OPNN network planning to categorise plant diseases, Brain image segmentation method and multiple radio access network. All the above work reviewed shows promising results from the algorithm. More work can be conducted using this algorithm in future.
FM2 Path Planner for UAV Applications with Curvature Constraints: A Comparative Analysis with Other Planning Approaches

This paper studies the Fast Marching Square (FM2) method as a competitive path planner for UAV applications. The approach fulfills trajectory curvature constraints together with a significantly reduced computation time, which makes it overperform with respect to other planning methods of the literature based on optimization. A comparative analysis is presented to demonstrate how the FM2 approach can easily adapt its performance thanks to the introduction of two parameters, saturation α and exponent β, that allow a flexible configuration of the paths in terms of curvature restrictions, among others. The main contributions of the method are twofold: first, a feasible path is directly obtained without the need of a later optimization process to accomplish curvature restrictions; second, the computation speed is significantly increased, up to 220 times faster than other optimization-based methods such as, for instance, Dubins, Euler-Mumford Elastica and Reeds-Shepp. Simulation results are given to demonstrate the superiority of the method when used for UAV applications in comparison with the three previously mentioned methods.
Verifiable privacy-preserving association rule mining using distributed decryption mechanism on the cloud

As one of the important ways in data mining, the association rule mining is to analyze the correlation of transactions based on massive data and mine the hidden valuable information. However, excessive data collection and analysis might lead to the privacy leakage of user data and the damage of data integrity. Meanwhile, in the existing privacy-preserving schemes, the cloud servers such as the evaluator have strong decryption capabilities, which causes active attacks easily, or data reliability in mining and analysis is not considered. In response to these problems, this paper proposes a verifiable privacy-preserving association rule mining scheme (VPPARM) using distributed decryption mechanism on the cloud. First, the scheme adopts distributed decryption to complete the data mining tasks through the dual-cloud servers, which weakens the decryption ability of the cloud servers and prevents the server from active attacks. Secondly, to protect the privacy of the association rule mining process, our scheme adopts adding virtual transactions, permutations, and random number masking collaboratively to hide the data in the whole mining process. In addition, to eliminate the hidden danger of illegal users, our scheme adopts a verifiable short digital signature scheme to verify data integrity and ensure data reliability, avoiding the poisoning attack. Finally, through the performance evaluation, the results demonstrate that our scheme realizes correctness, security, reliability with lower communication and computation costs and improves efficiency to a certain extent.
Tgnet: A Task-Guided Network Architecture for Multi-Organ and Tumour Segmentation from Partially Labelled Datasets

The cost of labour and expertise makes it challenging to collect a large amount of medical data and annotate 3D medical images at a voxel level. Most public medical datasets are only labelled with one type of organ or tumour. For example, in the liver segmentation task, only the liver is labelled while all the other organs, even when present, as well as irrelevant parts are annotated as background, resulting in partially labelled datasets. Current popular methods usually build multiple neural network models for different tasks that lead to great model redundancy, or design a unified network for all tasks which suffers from low ability to extract task-related features. In this paper, we propose a unified and task-guided network architecture to efficiently learn task-related features and avoid mixing representations of different organs and tumours from different tasks. Specifically, a novel residual block and attention module are devised in a task-guided way to fuse image features and task encoding constraints. Moreover, both designs significantly suppress task-unrelated features and highlight features related to the specific segmentation task. Experiments conducted on seven benchmark datasets illustrate that our task-guided model achieves more competitive performance compared with state-of-the-art approaches in segmenting multiple organs and tumours from partially labelled data.
Proof transfer for fast certification of multiple approximate neural networks

Developers of machine learning applications often apply post-training neural network optimizations, such as quantization and pruning, that approximate a neural network to speed up inference and reduce energy consumption, while maintaining high accuracy and robustness. Despite a recent surge in techniques for the robustness verification of neural networks, a major limitation of almost all state-of-the-art approaches is that the verification needs to be run from scratch every time the network is even slightly modified. Running precise end-to-end verification from scratch for every new network is expensive and impractical in many scenarios that use or compare multiple approximate network versions, and the robustness of all the networks needs to be verified efficiently. We present FANC, the first general technique for transferring proofs between a given network and its multiple approximate versions without compromising verifier precision. To reuse the proofs obtained when verifying the original network, FANC generates a set of templates – connected symbolic shapes at intermediate layers of the original network – that capture the proof of the property to be verified. We present novel algorithms for generating and transforming templates that generalize to a broad range of approximate networks and reduce the verification cost. We present a comprehensive evaluation demonstrating the effectiveness of our approach. We consider a diverse set of networks obtained by applying popular approximation techniques such as quantization and pruning on fully-connected and convolutional architectures and verify their robustness against different adversarial attacks such as adversarial patches, L 0 , rotation and brightening. Our results indicate that FANC can significantly speed up verification with state-of-the-art verifier, DeepZ by up to 4.1x.
Algorithmic QUBO formulations for <i>k</i> -SAT and hamiltonian cycles

Quadratic Unconstrained Binary Optimization (QUBO) can be seen as a generic language for optimization problems. QUBOs attract particular attention since they can be solved with quantum hardware, like quantum annealers or quantum gate computers running QAOA. In this paper, we present two novel QUBO formulations for k-SAT and Hamiltonian Cycles that scale significantly better than existing approaches. For k-SAT we reduce the growth of the QUBO matrix from O(k) to O(log(k)). For Hamiltonian Cycles the matrix no longer grows quadratically in the number of nodes, as currently, but linearly in the number of edges and logarithmically in the number of nodes.
An approach to analysis of arabic text documents into text lines, words, and characters

Text line extraction from a text document image and segmenting it into isolate words and segmenting these words into individual characters are considered as one of the most critical processes in OCR systems development and turning the document into a searchable electronic representation, this paper presents a new approach to analyze the Arabic text documents, the proposed approach contains four steps, preprocessing, text line segmentation, word segmentation, character segmentation. The horizontal projection method are used to detect and extract the text line from preprocessed text documents image, in word segmentation step The space threshold are computed to determine the spaces among connected components in text line as within-word space or between-words space for segmenting the text line into isolate words, finally thinning method applied to find the skeleton of segmented word and analyses geometric characteristics of the characters to detect ligatures and characters. The proposed approach was tested and evaluated on a set of 115 text images, this set contains images from the KFUPM Handwritten Arabic TexT (KHATT) database and some images produced by the authors. The experiment results are extremely encouraging, with a success rate of 98.6% for lines segmentation, 96% for words segmentation, and 87.1% for characters segmentation.
Enhancement: SiamFC Tracker Algorithm Performance Based on Convolutional Hyperparameters Optimization and Low Pass Filter

Over the past few decades, convolutional neural networks (CNNs) have achieved outstanding results in addressing a broad scope of computer vision problems. Despite these improvements, fully convolutional Siamese neural networks (FCSNN) still hardly adapt to complex scenes, such as appearance change, scale change, similar objects interference, etc. The present study focuses on an enhanced FCSNN based on convolutional block hyperparameters optimization, a new activation function (ModReLU) and Gaussian low pass filter. The optimization of hyperparameters is an important task, as it has a crucial ascendancy on the tracking process performance, especially when it comes to the initialization of weights and bias. They have to work efficiently with the following activation function layer. Inadequate initialization can result in vanishing or exploding gradients. In the first method, we propose an optimization strategy for initializing weights and bias in the convolutional block to ameliorate the learning of features so that each neuron learns as much as possible. Next, the activation function normalizes the output. We implement the convolutional block hyperparameters optimization by setting the convolutional weights initialization to constant, the bias initialization to zero and the Leaky ReLU activation function at the output. In the second method, we propose a new activation, ModReLU, in the activation layer of CNN. Additionally, we also introduce a Gaussian low pass filter to minimize image noise and improve the structures of images at distinct scales. Moreover, we add a pixel-domain-based color adjustment implementation to enhance the capacity of the proposed strategies. The proposed implementations handle better rotation, moving, occlusion and appearance change problems and improve tracking speed. Our experimental results clearly show a significant improvement in the overall performance compared to the original SiamFC tracker. The first proposed technique of this work surpasses the original fully convolutional Siamese networks (SiamFC) on the VOT 2016 dataset with an increase of 15.42% in precision, 16.79% in AUPC and 15.93% in IOU compared to the original SiamFC. Our second proposed technique also reveals remarkable advances over the original SiamFC with 18.07% precision increment, 17.01% AUPC improvement and an increase of 15.87% in IOU. We evaluate our methods on the Visual Object Tracking (VOT) Challenge 2016 dataset, and they both outperform the original SiamFC tracker performance and many other top performers.
In-Memory Annealing Unit (IMAU): Energy-Efficient (2000 TOPS/W) Combinatorial Optimizer for Solving Travelling Salesman Problem

An in-memory annealing unit (IMAU) as an energy-efficient combinatorial optimizer for solving the travelling salesman problem (TSP) has been demonstrated for the first time. A hardware-algorithm co-optimization approach is adopted to overcome the challenges of solving TSP using IMAU, such as large problem size, insufficient weight precision, and inaccurate analog computing. The high-capacity (1152x1024) binary RRAM-based IMAU with an embedded simulated annealing (SA) function achieves an extremely high throughput of 90 TOPS and energy efficiency of 2000 TOPS/W. A new multi-step SA algorithm is proposed to solve the otherwise floating-point TSP using merely 5-level (2.3 bit) weights and achieves the floating point-equivalent shortest route for the 10-city TSP in IMAU.
Improving Local Trajectory Optimization by Enhanced Initialization and Global Guidance

Trajectory optimization is a promising method for planning trajectories of robotic manipulators.With the increasing success of collaborative robots in dynamic environments, the demand for online planning methods grows and offers new opportunities as well as challenges for trajectory optimization.Special requirements in terms of real-time capabilities are one of the greatest difficulties.Optimizing a short planning horizon instead of an entire trajectory is one approach to reduce computation time, which nonetheless separates the optimality of local and global solutions.This contribution introduces, on the one hand, Extended Initialization as a new approach that reduces the risk of local minima and aims at improving the quality of the global trajectory.On the other hand, the particularly critical cases in which local solutions lead to standstills are mitigated by globally guiding local solutions.The evaluation performs four experiments with comparisons to Stochastic Trajectory Optimization for Motion Planning (STOMP) or Probabilistic Roadmap Method (PRM*) and demonstrates the effectiveness of both approaches.
View-Consistent Heterogeneous Network on Graphs With Few Labeled Nodes

Performing transductive learning on graphs with very few labeled data, that is, two or three samples for each category, is challenging due to the lack of supervision. In the existing work, self-supervised learning via a single view model is widely adopted to address the problem. However, recent observation shows multiview representations of an object share the same semantic information in high-level feature space. For each sample, we generate heterogeneous representations and use view-consistency loss to make their representations consistent with each other. Multiview representation also inspires to supervise the pseudolabels generation by the aid of mutual supervision between views. In this article, we thus propose a view-consistent heterogeneous network (VCHN) to learn better representations by aligning view-agnostic semantics. Specifically, VCHN is constructed by constraining the predictions between two views so that the view pairs can supervise each other. To make the best use of cross-view information, we further propose a novel training strategy to generate more reliable pseudolabels, which thus enhances predictions of the VCHN. Extensive experimental results on three benchmark datasets demonstrate that our method achieves superior performance over state-of-the-art methods under very low label rates.
SiamTrans: Zero-Shot Multi-Frame Image Restoration with Pre-trained Siamese Transformers

We propose a novel zero-shot multi-frame image restoration method for removing unwanted obstruction elements (such as rains, snow, and moire patterns) that vary in successive frames. It has three stages: transformer pre-training, zero-shot restoration, and hard patch refinement. Using the pre-trained transformers, our model is able to tell the motion difference between the true image information and the obstructing elements. For zero-shot image restoration, we design a novel model, termed SiamTrans, which is constructed by Siamese transformers, encoders, and decoders. Each transformer has a temporal attention layer and several self-attention layers, to capture both temporal and spatial information of multiple frames. Only self-supervisedly pre-trained on the denoising task, SiamTrans is tested on three different low-level vision tasks (deraining, demoireing, and desnowing). Compared with related methods, SiamTrans achieves the best performances, even outperforming those with supervised learning.
Diverse Preference Augmentation with Multiple Domains for Cold-start Recommendations

Cold-start issues have been more and more challenging for providing accurate recommendations with the fast increase of users and items. Most existing approaches attempt to solve the intractable problems via content-aware recommendations based on auxiliary information and/or cross-domain recommendations with transfer learning. Their performances are often constrained by the extremely sparse user-item interactions, unavailable side information, or very limited domain-shared users. Recently, meta-learners with meta-augmentation by adding noises to labels have been proven to be effective to avoid overfitting and shown good performance on new tasks. Motivated by the idea of meta-augmentation, in this paper, by treating a user's preference over items as a task, we propose a so-called Diverse Preference Augmentation framework with multiple source domains based on meta-learning (referred to as MetaDPA) to i) generate diverse ratings in a new domain of interest (known as target domain) to handle overfitting on the case of sparse interactions, and to ii) learn a preference model in the target domain via a meta-learning scheme to alleviate cold-start issues. Specifically, we first conduct multi-source domain adaptation by dual conditional variational autoencoders and impose a Multi-domain InfoMax (MDI) constraint on the latent representations to learn domain-shared and domain-specific preference properties. To avoid overfitting, we add a Mutually-Exclusive (ME) constraint on the output of decoders to generate diverse ratings given content data. Finally, these generated diverse ratings and the original ratings are introduced into the meta-training procedure to learn a preference meta-learner, which produces good generalization ability on cold-start recommendation tasks. Experiments on real-world datasets show our proposed MetaDPA clearly outperforms the current state-of-the-art baselines.
Cyber Security Maturity Model for Critical Infrastructures

Critical infrastructures have one of the most important roles for our economy, society and government. The information systems that are often controlling and managing processes from this type of organizations, must fulfill stringent safety and security requirements. As a result, cyber security management becomes a challenging task due to the number, interdependencies and role that information systems have. In this paper we will propose a model to evaluate the cyber security maturity in critical infrastructures, taking into account both technological and human dimension aspects. This will support decision makers in identifying priorities and gaps when it comes to cyber security. This model will be customized for the given context, however it can be adapted and used by any type of organizations.
Compression of Generative Pre-trained Language Models via Quantization

The increasing size of generative Pre-trained Language Models (PLMs) have greatly increased the demand for model compression.Despite various methods to compress BERT or its variants, there are few attempts to compress generative PLMs, and the underlying difficulty remains unclear.In this paper, we compress generative PLMs by quantization.We find that previous quantization methods fail on generative tasks due to the homogeneous word embeddings caused by reduced capacity, and varied distribution of weights.Correspondingly, we propose a token-level contrastive distillation to learn distinguishable word embeddings, and a module-wise dynamic scaling to make quantizers adaptive to different modules.Empirical results on various tasks show that our proposed method outperforms the stateof-the-art compression methods on generative PLMs by a clear margin.With comparable performance with the full-precision models, we achieve 14.4× and 13.4× compression rates on GPT-2 and BART, respectively.
Pose Adaptive Dual Mixup for Few-Shot Single-View 3D Reconstruction

We present a pose adaptive few-shot learning procedure and a two-stage data interpolation regularization, termed Pose Adaptive Dual Mixup (PADMix), for single-image 3D reconstruction. While augmentations via interpolating feature-label pairs are effective in classification tasks, they fall short in shape predictions potentially due to inconsistencies between interpolated products of two images and volumes when rendering viewpoints are unknown. PADMix targets this issue with two sets of mixup procedures performed sequentially. We first perform an input mixup which, combined with a pose adaptive learning procedure, is helpful in learning 2D feature extraction and pose adaptive latent encoding. The stagewise training allows us to build upon the pose invariant representations to perform a follow-up latent mixup under one-to-one correspondences between features and ground-truth volumes. PADMix significantly outperforms previous literature on few-shot settings over the ShapeNet dataset and sets new benchmarks on the more challenging real-world Pix3D dataset.
Secure wireless sensor network using cryptographic technique based hybrid genetic firefly algorithm

Wireless sensor networks (WSNs) are formed of self-contained nodes of sensors that are connected to one base station or more.WSNs have several primary aims one of them is to transport network node's trustworthy information to another one.As WSNs expand, they become more vulnerable to attacks, necessitating the implementation of strong security systems.The identification of effective cryptography for WSNs is a significant problem because of the limited energy of the sensor nodes, compute capability, and storage resources.Advanced Encryption Standard (AES) is an encryption technique implemented in this paper with three meta-heuristic algorithms which are called Hybrid Genetic Firefly algorithm, Firefly algorithm, and Genetic algorithm to ensure that the data in the WSNs is kept confidential by providing enough degrees of security.We have used hybrid Genetic firefly as a searching operator whose goal is to improve the searchability of the baseline genetic algorithm.The suggested framework's performance that based on the algorithm of hybrid genetic firefly is rated by using Convergence Graphs of the Benchmark Functions.From the graphs we have conclude that hybrid genetic firefly with AES is best from other algorithms.