Reviewerly/exHarmony huggingface dataset:
An application of Dingo Optimization Algorithm (DOA) for solving continuous engineering problems

In the current research, problems in engineering are becoming more and more prominent. One of the classes of engineering problems in engineering design problems, where a set of variables is calibrated in order for the optimization function to have a minimal or maximal value. This function considers energy efficiency, cost efficiency, and production efficiency in engineering design. One of the ways such problems are solved is metaheuristics. In this paper, we demonstrate how Dingo Optimization Algorithm (DOA) can be used to solve certain optimization problems in mechanical engineering. Firstly, a brief review of the DOA and its biological inspiration is given, along with the most important formulae. The pseudo-code for this algorithm was written using MATLAB R2020a software suite. Dingo Optimization Algorithm (DOA) was used to optimize engineering problems, such as pressure vessel optimization, stepped cantilever beam, car side-impact, and cone clutch optimization. The results presented in this paper show that the DOA can produce relevant results in engineering design problems.
Blockchain-Enabled Digital Rights Management for Museum-Digital Property Rights

With the rapid development of digitization technology, digital copyright of museum has become more and more valuable. Its collections can be opened to and shared with the people through the Internet. However, centralized authorization, untransparent transaction information and risk of tampering data in traditional digital rights management have a strong impact on system normal operation. In this paper, we proposed a blockchain-based digital rights management scheme (BMDRM) that realizes a distributed digital rights management and authorization system by introducing non-fungible tokens (NFTs) and smart contracts. To ensure the security and efficiency of transactions and authorization, we store all processing data in a high-security distributed ledger based on cryptographic signatures. We test our scheme on Ethereum private network and the experimental results show that BMDRM is feasible and secure for digital rights management in museums.
Towards cross-chain access control model for medical data sharing

Secure and scalable dynamic data interaction is significant to improve the level of medical collaboration and facilitate patients' medical treatment. The consortium blockchain provides integrity and traceability management for medical data through preset authorized access and consensus mechanisms. However, how to realize the secure sharing of data among patients in the heterogeneous consortium blockchains, enabling users to own medical data ownership, and to share medical data securely and dynamically between different medical institutions is still challenging. To this end, we propose a cross-chain based access control model for medical data sharing, which model is patient-centered and solves the contradiction between medical data sharing and user privacy. Then, a dynamic access control strategy based on identity and role is designed. The strategy is written by smart contracts and can provide security for medical data access. Finally, in order to reduce the cost of storage on the blockchain, we adopted (InterPlanetary File System) IPFS as the storage system for medical data. Experimental results show that our model can complete the autonomous and secure authorization of medical data in milliseconds, and can realize the secure sharing of medical data onto heterogeneous consortium blockchains.
Big Data Analysis Using Hybrid Meta-Heuristic Optimization Algorithm and MapReduce Framework

Clustering large data is a recent and popular challenge that is used in various applications, including social networking, bioinformatics, and many others. In order to manage the rapidly growing data sizes, traditional clustering algorithms must be improved. In this research, a hybrid Harris Hawks Optimizer (HHHO) with K-mean clustering and MapReduce framework is proposed to solve the various data clustering problem. The proposed scheme uses the K-means' ability to solve the various clustering problems. More specifically, the K-means are utilized as initial solutions to the traditional Harris Hawks Optimizer (HHO). In general, HHO tries to refine the candidate solutions to find the best one. MapReduce is a distributed processing computing paradigm that produces datasets using a parallel program on a cluster. In particular, it is adopted in the developed HHHO for parallelization since it offers fault tolerance, load balancing, and data locality. The performance of the presented methodology has been evaluated by means of numerical comparisons which proved the efficiency of the proposed HHHO, where it produces better results than other existing computation methods. Moreover, it has a very good ability in improving and finding optimal and converging sets of data. In addition, the accuracy and error rate of the obtained results are assessed. The proposed method is implemented and evaluated using PYTHON simulation settings.
An Analysis of Links in Wikidata

Wikidata has become one of the most prominent open knowledge graphs (KGs) on the Web. Relying on a community of users with different expertise, this cross-domain KG is directly related to other data sources. This paper investigates how Wikidata is linked to other data sources in the Linked Data ecosystem. To this end, we adapt previous definitions of ontology links and instance links to the terminological part of the Wikidata vocabulary and perform an analysis of the links in Wikidata to external datasets and ontologies from the Linked Data ecosystem. As a side effect, this reveals insights on the ontological expressiveness of meta-properties used in Wikidata. The results of this analysis show that while Wikidata defines a large number of individuals, classes and properties within its own namespace, they are not (yet) extensively linked. We discuss reasons for this and conclude with some suggestions to increase the interconnectedness of Wikidata with other KGs.
Nanophotonic Cavity Based Synapse for Scalable Photonic Neural Networks

The bandwidth and energy demands of neural networks has spurred tremendous interest in developing novel neuromorphic hardware, including photonic integrated circuits. Although an optical waveguide can accommodate hundreds of channels with THz bandwidth, the channel count of photonic systems is always bottlenecked by the devices within. In WDM-based photonic neural networks, the synapses, i.e. network interconnections, are typically realized by microring resonators (MRRs), where the WDM channel count ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N$</tex-math></inline-formula> ) is bounded by the free-spectral range of the MRRs. For typical Si MRRs, we estimate <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N \leq 30$</tex-math></inline-formula> within the C-band. This not only restrains the aggregate throughput of the neural network but also makes applications with high input dimensions unfeasible. We experimentally demonstrate that photonic crystal nanobeam based synapses can be FSR-free within C-band, eliminating the bound on channel count. This increases data throughput as well as enables applications with high-dimensional inputs like natural language processing and high resolution image processing. In addition, the smaller physical footprint of photonic crystal nanobeam cavities offers higher tuning energy efficiency and a higher compute density than MRRs. Nanophotonic cavity based synapse thus offers a path towards realizing highly scalable photonic neural networks.
AceFL: Federated Learning Accelerating in 6G-Enabled Mobile Edge Computing Networks

6G is envisioned to achieve ubiquitous Artificial Intelligence (AI) in heterogeneous and massive-scale networks, where FEderated Edge Learning (FEEL) is an effective way to assist AI technology to be implemented at the network edge. FEEL enables training a machine learning model through communication among multiple edge devices based on data from a large number of users. However, the heterogeneity among distributed edge devices and the limitation of resources may degrade the training efficiency of FEEL over 6G-enabled mobile edge computing (MEC) networks. Taking this challenge into account, a novel federated learning scheme is proposed in this paper to accelerate the training process. In particular, we formulate a training efficiency maximization problem, where a novel analytical relation among the training loss, resource consumption and heterogeneity is inferred by convergence analysis. Then, we propose a searching algorithm named IFBA to obtain the optimal inexactness of local models and frequency band allocation for edge devices in order to mitigate the straggler effect caused by the heterogeneity and resource limitation of devices. As a result, the training efficiency can be improved by adapting the inexactness of local models and frequency band allocation for edge devices on-demand. Simulation results validate that AceFL has advantages in training efficiency improvement and heterogeneity adaption, e.g., accelerating the training by 5% - 15% under different heterogeneity.
Detecting Islamic Radicalism Arabic Tweets Using Natural Language Processing

The image of the tolerant religion of Islam has been distorted by extremists in the last two decades in many ways, such as luring teenagers into terrorist acts.Nowadays, millions of users socialize and share ideas using social media platforms such as Twitter.Typically, the ideas shared on Twitter (tweets) reach and influence many people who could simply retweet them and make them even spread faster.Unfortunately, some of these ideas are posted by extremists who share hateful Arabic content.Thus, it is very important to automate the process of controlling and monitoring hateful Arabic tweets, given that Arabic is the most widely used language in the Islamic world.In this paper, we provide a manually labeled and curated dataset of 3,000 Arabic tweets that contain hateful and non-hateful tweets.To automate the process of detecting hateful tweets, we utilize advanced Machine Learning (ML) techniques and perform sentiment analysis to capture the meaning of the Arabic words in a proper word embedding.Also, we used the proposed model to classify and analyze 100,000 tweets of the last decade.The outcome of this work promotes future research on analyzing Arabic hateful speech by providing a manually labeled Arabic dataset, and the trained model (achieved 92% accuracy) which can be used as an underlying tool by governments, Internet service providers, and social media applications to detect any inflammatory tweets before they spread to a wider audience.
Alexa to Support Patients with Dementia and Family Caregivers in Challenging Behaviors

Patients with Dementia (PwD) present psychological and behavioral alterations, such as short-term memory loss, anxiety, depression, repetitive questions/behaviors. The repetitive questions/behaviors are one of the most common symptoms of PwD, and these challenging behaviors are also one of the most reported by the caregivers because this problem could exasperate caregivers. There is an increasing interest in creating strategies or training to support PwD and caregivers to deal with these problems. In the case of dementia care and caregiving, information technologies have been identified as a possible way of support. Specifically, Intelligent voice assistants have been suggested as a potential source of assistance for older adults in general. In this study, we conducted semi-structured interviews with family caregivers and one expert. As a result, we present a model for attending challenging behaviors in PwD using the benefits of voice assistants as Alexa. This study also present a pilot study were a user interacted with Alexa and the results were favorable.
Deep Texton-Coherence Network for Camouflaged Object Detection

Camouflaged object detection is a challenging visual task since the appearance and morphology of foreground objects and background regions are highly similar in nature. Recent CNN-based studies gradually integrated the high-level semantic information and the low-level local features of images through hierarchical and progressive structures to achieve camouflaged object detection. However, these methods ignore the <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">spatial statistical properties</b> of the local context, which is a critical cue for distinguishing and describing camouflaged objects. To address this problem, we propose a novel Deep Texton-Coherence Network (DTC-Net) that leverages the spatial organization of textons in the foreground and background regions as discriminative cues for camouflaged object detection. Specifically, a Local Bilinear module (LB) is devised to obtain the robust representation of texton to trivial details and illumination changes, by replacing the classic first-order linearization operations with bilinear second-order statistical operations in the convolution process. Next, these texton representations are associated with a Spatial Coherence Organization module (SCO) to capture irregular spatial coherence via a deformable convolutional strategy, and then the descriptions of the textons extracted by the LB module are used as weights to suppress features that are spatially adjacent but have different representations. Finally, the texton-coherence representation is integrated with the original features at different levels to achieve camouflaged object detection. Evaluation on the three most challenging camouflaged object detection datasets demonstrats the superiority of the proposed model when compared to the state-of-the-art methods. Furthermore, our ablation studies and performance analyses demonstrate the effectiveness of the texton-coherence module.
Feature-Selection High-Resolution Network With Hypersphere Embedding for Semantic Segmentation of VHR Remote Sensing Images

Very high resolution (VHR) remote sensing images contain various multi-scale objects, such as large-scale buildings and small-scale cars. However, these multi-scale objects cannot be considered simultaneously in the widely used backbones with large downsampling factor (e.g. VGG-like and ResNet-like), resulting in the appearance of various context aggregation approaches such as fusing low-level features and attention-based modules. To alleviate this problem caused by backbones with large downsampling factor, we propose a feature-selection high-resolution network (FSHRNet) based on an observation: if the features maintain high resolution throughout the network, a high precision segmentation result can be obtained by only using a 1&#x00D7;1 convolution layer with no need for complex context aggregation modules. Specifically, the backbone of FSHRNet is a multi-branch structure similar to HRNet where the high-resolution branch is the principal line. Then, a lightweight dynamic weight module, named feature-selection convolution layer (FSConv), is presented to fuse multi-resolution features, allowing adaptively feature selection based on the characteristic of objects. Finally, a specially designed 1&#x00D7;1 convolution layer derived from hypersphere embedding is used to produce the segmentation result. Experiments with other widely used methods show that the proposed FSHRNet obtains competitive performance on ISPRS Vaihingen dataset, ISPRS Potsdam dataset and iSAID dataset.
An Ontological Characterization of a Conceptual Model of the Human Genome

The ability to sequence the human genome is a scientific, historical breakthrough. Although the human genome mapping is available to all scientists, information about it can be difficult to share. The Conceptual Schema of the Human Genome represents the concepts required to holistically understand the human genome. We report on our continued efforts to ensure that the human genome can be meaningfully shared by conducting an ontological analysis and enrichment of the conceptual model to facilitate domain understanding and data exchange among heterogeneous systems. The analysis and enrichment process is supported by the ontology-driven conceptual modeling language, OntoUML, to gain ontological clarity and demonstrated on a relevant section of the Pathways view of the schema. Consistent with the overall objective of designing a sound genomics information system, the results lead to major modeling implications for the: characterization of biological entities; changes in biological entities over time; and representation of chemical compounds. Our research shows that the inclusion of a strong ontological foundation in a conceptual model contributes to the design of complex systems.
Conservative Linear Unbiased Estimation Under Partially Known Covariances

Mean square error optimal estimation requires the full correlation structure to be available. Unfortunately, it is not always possible to maintain full knowledge about the correlations. One example is decentralized data fusion where the cross-correlations between estimates are unknown, partly due to information sharing. To avoid underestimating the covariance of an estimate in such situations, conservative estimation is one option. In this paper the conservative linear unbiased estimator is formalized including optimality criteria. Fundamental bounds of the optimal conservative linear unbiased estimator are derived. A main contribution is a general approach for computing the proposed estimator based on robust optimization. Furthermore, it is shown that several existing estimation algorithms are special cases of the optimal conservative linear unbiased estimator. An evaluation verifies the theoretical considerations and shows that the optimization based approach performs better than existing conservative estimation methods in certain cases.
GNNer: Reducing Overlapping in Span-based NER Using Graph Neural Networks

There are two main paradigms for Named Entity Recognition (NER): sequence labelling and span classification.Sequence labelling aims to assign a label to each word in an input text using, for example, BIO (Begin, Inside and Outside) tagging, while span classification involves enumerating all possible spans in a text and classifying them into their labels.In contrast to sequence labelling, unconstrained span-based methods tend to assign entity labels to overlapping spans, which is generally undesirable, especially for NER tasks without nested entities.Accordingly, we propose GN-Ner, a framework that uses Graph Neural Networks to enrich the span representation to reduce the number of overlapping spans during prediction.Our approach reduces the number of overlapping spans compared to strong baseline while maintaining competitive metric performance.Code is available at https: //github.com/urchade/GNNer.
Medicinal Boxes Recognition on a Deep Transfer Learning Augmented Reality Mobile Application

Taking medicines is a fundamental aspect to cure illnesses. However, studies have shown that it can be hard for patients to remember the correct posology. More aggravating, a wrong dosage generally causes the disease to worsen. Although, all relevant instructions for a medicine are summarized in the corresponding patient information leaflet, the latter is generally difficult to navigate and understand. To address this problem and help patients with their medication, in this paper we introduce an augmented reality mobile application that can present to the user important details on the framed medicine. In particular, the app implements an inference engine based on a deep neural network, i.e., a densenet, fine-tuned to recognize a medicinal from its package. Subsequently, relevant information, such as posology or a simplified leaflet, is overlaid on the camera feed to help a patient when taking a medicine. Extensive experiments to select the best hyperparameters were performed on a dataset specifically collected to address this task; ultimately obtaining up to 91.30% accuracy as well as real-time capabilities.
Briefly Analysis about CNN Accelerator based on FPGA

Since convolutional computation in deep learning is a large and time-consuming computation, researchers often use GPU or FPGA to accelerate these computation. This paper illustrates the advantages of convolutional computation by using FPGA accelerators. In detail, the paper presents some research results of convolutional computation based on FPGA, and explains the current common way of FPGA accelerator design, which uses high-level synthesis and Vitis AI. Finally, we deploy and run YOLOv4 model on the ZCU102 evaluation board using Vitis AI, and perform object detection with the tableware dataset, achieving a recognition result of 96.2%, and 72.5 times higher performance than CPU.
Correlation Filters Based on Multi-Expert and Game Theory for Visual Object Tracking

Most existing correlation filters based tracking algorithms only use the linear additive features or simple fusion features, which are unable to fully learn the appearance information of the target. In this paper, a multi-expert and game theory correlation filter (MEGTCF) algorithm is proposed to treat the aforementioned problems. The algorithm extracts a variety of handcrafted features and deep features of the target and forms multiple experts in different combinations to track the target in parallel. In addition, the game theory strategy is introduced to obtain more reliable decisions. The expert evaluation mechanism evaluates and selects the most discriminative experts as game participants and fully explores the complementary information among different expert features through the iterative game, so as to realize the information interaction between expert features. We also propose a confidence indicator to calculate the weights of the experts with higher scores and adaptively update the expert model to avoid expert corruption. Extensive experiments are conducted on five popular benchmarks, including OTB2013, OTB2015, VOT2018, LaSOT, and the UAV123 tracking benchmark. The success rate and precision of the MEGTCF tracker on the OTB2015 reach 67.8 % and 91.4 %, which outperforms other state-of-the-art trackers based on correlation filters. In addition, the MEGTCF tracker is also highly suitable for challenging scenarios such as object deformation, background clutter, and occlusion.
Radio Frequency Fingerprinting Improved by Statistical Noise Reduction

Radio frequency (RF) fingerprinting as an important non-password authentication technology has received extensive attention in recent years. It can be used as an underlying intrusion detection component for various wireless networks to enhance their wireless security further. In order to achieve more robust RF fingerprinting with deep learning, this paper develops a practical signal data augmentation solution consisting of two steps: 1) construction of sampling points 2) smooth filtering, which enables to enhance discriminability for individual radio examples. The underlying idea is that using a relatively long signal can implicitly perform noise reduction in a statistical sense since noise is often uncorrelated or partially correlated. As more sampling points accumulated, RF fingerprints could be more discernible. Experimental results have demonstrated that our proposed data augmentation can significantly improve recognition accuracy, especially in low signal-to-noise ratio (SNR) and small example conditions.