Reviewerly/exHarmony huggingface dataset:
Few-shot named entity recognition with hybrid multi-prototype learning

Information extraction provides the basic technical support for knowledge graph construction and Web applications. Named entity recognition (NER) is one of the fundamental tasks of information extraction. Recognizing unseen entities from numerous contents with the support of only a few labeled samples, also termed as few-shot learning, is a crucial issue to be studied. Few-shot NER aims at identifying emerging named entities from the context with the support of a few labeled samples. Existing methods mainly use the same strategy to construct a single prototype for each entity or non-entity class, which has limited expressiveness power and even biased representation. In this work, we propose a novel hybrid multi-prototype class representation approach. Specifically, for entity classes, we first insert labels after entities in support sentences to enrich the learned token and label embeddings with more contextual information. Then, for each entity span, the contextual token embeddings are averaged to form its entity-level prototype, while the contextual label embedding is considered as its label-level prototype. The set of prototypes for all entities in a class constitutes the multi-prototype of this entity class. For non-entity class, we directly use the set of token embeddings to represent it, where multi-prototype refers to the multiple token embeddings. By treating the entity and non-entity classes differently, our hybrid strategy can extract more precise class representations from the support examples. Furthermore, we establish a harder and more reasonable experimental setting of few-shot NER by offering a rigorous sampling strategy. Extensive empirical results show that our proposal improves performance over prior models on popular benchmark Few-NERD under both loose and our proposed rigorous sampling constraints, achieving comparable performance to current state-of-the-arts.
Blockchain-Based Pension System Ensuring Security, Provenance and Efficiency

The literature presents a digitized pension system based on a consortium blockchain, with the aim of overcoming existing pension system challenges such as multiparty collaboration, manual intervention, high turnaround time, cost transparency, auditability, etc. In addition, the adoption of hyperledger fabric and the introduction of smart contracts aim to transform multi-organizational workflow into a synchronized, automated, modular, and error-free procedure.
An Improved Insulator and Spacer Detection Algorithm Based on Dual Network and SSD

The traditional manual inspection is gradually replaced by the unmanned aerial vehicles (UAV) automatic inspection. However, due to the limited computational resources carried by the UAV, the existing deep learning-based algorithm needs a large amount of computational resources, which makes it impossible to realize the online detection. Moreover, there is no effective online detection system at present. To realize the high-precision online detection of electrical equipment, this paper proposes an SSD (Single Shot Multibox Detector) detection algorithm based on the improved Dual network for the images of insulators and spacers taken by UAVs. The proposed algorithm uses MnasNet and MobileNetv3 to form the Dual network to extract multi-level features, which overcomes the shortcoming of single convolutional network-based backbone for feature extraction. Then the features extracted from the two networks are fused together to obtain the features with high-level semantic information. Finally, the proposed algorithm is tested on the public dataset of the insulator and spacer. The experimental results show that the proposed algorithm can detect insulators and spacers efficiently. Compared with other methods, the proposed algorithm has the advantages of smaller model size and higher accuracy. The object detection accuracy of the proposed method is up to 95.1%.
A Novel SSD-Based Detection Algorithm Suitable for Small Object

The original single-shot multibox detector (SSD) algorithm has good detection accuracy and speed for regular object recognition. However, the SSD is not suitable for detecting small objects for two reasons: 1) the relationships among different feature layers with various scales are not considered, 2) the predicted results are solely determined by several independent feature layers. To enhance its detection capability for small objects, this study proposes an improved SSD-based algorithm called proportional channels' fusion SSD (PCF-SSD). Three enhancements are provided by this novel PCF-SSD algorithm. First, a fusion feature pyramid model is proposed by concatenating channels of certain key feature layers in a given proportion for object detection. Second, the default box sizes are adjusted properly for small object detection. Third, an improved loss function is suggested to train the above-proposed fusion model, which can further improve object detection performance. A series of experiments are conducted on the public database Pascal VOC to validate the PCF-SSD. On comparing with the original SSD algorithm, our algorithm improves the mean average precision and detection accuracy for small objects by 3.3% and 3.9%, respectively, with a detection speed of 40FPS. Furthermore, the proposed PCF-SSD can achieve a better balance of detection accuracy and efficiency than the original SSD algorithm, as demonstrated by a series of experimental results.
We're making a list and we're checking it twice, gonna find out what makes digital forensic examiners suffice

Abstract Digital forensic examinations have grown in breadth and depth at a currently unsustainable rate. Digital Investigations now feature in around 90% of criminal cases, demonstrating that digital evidence is crucial to forensic investigations. Due to the high number of cases, most law enforcement units have significant backlogs of devices waiting for analysis. As the field of Digital Investigation has grown, it is no longer solely related to criminal investigations, with the techniques also supporting civil, private, and corporate activities. Given the evident challenges, it is logical that more digital forensic experts are needed to keep pace with the field's complexities and demands. Identifying what characteristics and skills make a digital forensic expert enables an evaluation to ensure that any new staff are fit for purpose. There is a growth in academic, civil, corporate, and intelligence‐based activity within the field. Each area defines their standards, field scope, and expertise level. Still, as any case has the potential to become a matter of criminal investigation, surely the focus needs to be on the standards required to ensure evidence is admissible for that purpose. As expertise levels can vary, it is also necessary to challenge the level at which an expert is defined and the implications of this decision. By identifying what makes an expert in this unique forensic science area, it is possible to explore the potential challenges the field faces in obtaining, retaining, and training staff. This article is categorized under: Digital and Multimedia Science &gt; Cybercrime Investigation
Vibro-acoustic analysis of cellos using the finite and boundary element methods and its application to studies on the effects of endpin properties

In this paper, an improved vibro-acoustic analysis method for cellos is presented. The soundpost and endpin are physically modeled as ordinary beams. The cello box, bassbar, bridge, and neck are physically modeled as shells. Their governing equations are numerically analyzed by the finite element method, yielding an equation of motion of the total multi-degree-of-freedom system. The radiated sound field around the cello box is dealt with using the normally differentiated Kirchhoff–Huygens integral equation, which is numerically analyzed by the boundary element method. All vibration and sound fields are fully coupled. The proposed method is validated through measurements of a cello. As an application of the developed numerical method, the authors investigate the effects of endpin properties.
Deep Reinforcement Learning Based Ontology Meta-Matching Technique

Ontologies are regarded as the solution to data heterogeneity on the Semantic Web (SW), but they also suffer from the heterogeneity problem, which leads to the ambiguity of data information. Ontology Meta-Matching technique (OMM) is able to solve the ontology heterogeneity problem through aggregating various similarity measures to find the heterogeneous entities. Inspired by the success of Reinforcement Learning (RL) in solving complex optimization problems, this work proposes a RL-based OMM technique to address the ontology heterogeneity problem. First, we propose a novel RL-based OMM framework, and then, a neural network that is called evaluated network is proposed to replace the Q table when we choose the next action of the agent, which is able to reduce memory consumption and computing time. After that, to better guide the training of neural network and improve the accuracy of RL agent, we establish a memory bank to mine depth information during the evaluated network's training procedure, and we use another neural network that is called target network to save the historical parameters. The experiment uses the famous benchmark in ontology matching domain to test our approach's performance, and the comparisons among Deep Reinforcement Learning(DRL), RL and state-of-the-art ontology matching systems show that our approach is able to effectively determine high-quality alignments.
Construction of an Information Service Dialogue System in Collaboration with Local Governments using the Chatbot Platform of Role Play-based Question-Answering

Local governments have high expectations for dialogue systems that provide residents and visitors with dialogue on multiple tasks, such as tourist information, town office information, and daily life information. In addition, there is also an expectation for such dialogue systems to be given the personalities of characters possessed by local governments and to provide chat dialogues for residents. However, to give the personality of a character to the dialogue system, task dialogue data and dialogue manager that reflect the personality are essential. Based on the study of role play-based question-answering dialogue systems that reproduce characters, we propose a method for collecting dialogue data that enables task dialogue and a method of dialogue manager and response selection that facilitates task dialogue. We constructed a dialogue system that imitated a certain local government character using the proposed method and evaluated its effectiveness through a laboratory experiment and demonstration experiment. The results showed that our dialogue system had statistically better performance in both task-oriented dialogue and chat oriented one.
The Effectiveness of Data Augmentation for Mature White Blood Cell Image Classification in Deep Learning — Selection of an Optimal Technique for Hematological Morphology Recognition —

The data augmentation method is known as a helpful technique to generate a dataset with a large number of images from one with a small number of images for supervised training in deep learning. However, a low validity augmentation method for image recognition was reported in a recent study on artificial intelligence (AI). This study aimed to clarify the optimal data augmentation method in deep learning model generation for the recognition of white blood cells (WBCs). Study Design: We conducted three different data augmentation methods (rotation, scaling, and distortion) on original WBC images, with each AI model for WBC recognition generated by supervised training. The subjects of the clinical assessment were 51 healthy persons. Thin-layer blood smears were prepared from peripheral blood and subjected to May-Grünwald-Giemsa staining. Results: The only significantly effective technique among the AI models for WBC recognition was data augmentation with rotation. By contrast, the effectiveness of both image distortion and image scaling was poor, and improved accuracy was limited to a specific WBC subcategory. Conclusion: Although data augmentation methods are often used for achieving high accuracy in AI generation with supervised training, we consider that it is necessary to select the optimal data augmentation method for medical AI generation based on the characteristics of medical images.
The Implementation of Telegram as A Pedagogical Tool to Enhance Student Motivation and Interaction

Despite efforts to understand how social media platforms encourage student interaction, researchers have noted that there needs to be more research that focuses on using Telegram messenger as a means to encourage student motivation and interaction in academic writing. This study aimed to understand how Telegram groups, created for first-year students at ODeL universities, increase student motivation and interaction in the Academic Writing module. This type of research is qualitative. This study uses a qualitative interpretative approach. Data collection techniques using interviews and observation. The subjects of this study were 4 lecturers and 9 students. The data analysis technique used is descriptive qualitative analysis. The study results found that lecturers considered Telegram a useful pedagogical tool to increase student motivation and interaction. Further findings reveal that Telegram motivates students to interact in groups and with modules. It is suggested that stakeholders reconsider their pedagogy and encourage using Telegram to assist students' learning of academic writing.
A Novel Hybrid Compression Algorithm for Remote Sensing Imagery

The remote sensing images that are captured by satellites are very high in resolution and involve an enormous amount of memory for storage. These images must be compressed so that more images can be transmitted in the same bandwidth. Extensive study has taken place in the field of compression for remote sensing imagery. In this paper, a novel hybrid algorithm is proposed that increases the compression ratio while maintaining the quality of the image. This hybrid methodology comprises wavelet transform, Run-length, and Arithmetic coding to achieve higher PSNR with minimal MSE. The experimental results up to three levels of wavelet decomposition indicate the range of the obtained parameter values like compression ratio, PSNR, and MSE. The compression is implemented by MATLAB software tool.
Healthcare Data Security in Cloud Environment

As of late, the cloud offers the advantage of putting away the potential documents to a far-off database that can be recovered on-demand. The infrastructure of healthcare cloud is utilized to store the clinical records that thus help in overseeing and following the records of the client. The essential consideration suppliers utilize Electronic Medical Record (EMR) to do the diagnosis and also assess the medical information. Big data analytics is a significant innovation in tremendous business regions that incorporates social media, banking, medical welfare and machine-censored data. Healthcare cloud offers several storage options, for example, one drive, drop box business, Google drive where security requirements and privacy concerns such as data robbery attacks are considered as major breaches of security in the cloud. In this research paper, I have analyzed the feasibility for applying artificial intelligence techniques to improve the healthcare data security in cloud computing environment. A modified decoy strategy is proposed to guarantee enhanced security for client's Medical Big Data. The system identifies who the attacker is and reverts back the data, for example, access date and time, IP address to the client. The tri-party confirmation key purposes key arrangement convention which is demonstrated by cryptography which is of the form bilinear pairing. It likewise offers the facility to obstruct the account from additional access. The parameters such as computational complexity is diminished and throughput is enhanced when using the bilinear pairing's triple-des algorithm.
A Survey of the Implementations of Model Inversion Attacks

Attacks on machine learning systems are usually called special manipulations with data at different stages of the machine learning pipeline, which are designed to either prevent the normal operation of the attacked machine learning systems, or vice versa - to ensure their specific functioning, which is necessary for the attacker. There are attacks that allow you to extract non-public data from machine learning models. Model inversion attacks, first described in 2015, aim to expose the data used to train the model. Such attacks involve polling the model in a special pattern and represent a major threat to machine learning as a service (MLaaS) projects. In this article, we provide an overview of off-the-shelf software tools for carrying out model inversion attacks and possible protection against such attacks.
Single Under-Water Image Enhancement Using the Modified Transmission Map and Background Light Estimation

In under-water environment, sensors are used to explore marine resources, sea-bed, archaeology, and tracking. Due to that, vision sensor plays a vital role as it helps to obtain high-content information. The light attenuation and backscattering in under-water environment are dependent on the wavelength that leads to color distortion, low contrast, and blurry images. The under-water image formation model depends on mainly two factors: background light and transmission light. In this paper, a modified transmission light estimation method and a new attenuation coefficient (U-TMBL) are proposed that enhance the quality of under-water images. Also, novel background light estimation method is proposed. The performance of the proposed method is quantitatively and qualitatively analyzed. The quantitative comparison is based on NIQE, BRISQUE, and UIQM evaluation metrics. The quantitative and qualitative results demonstrate that the proposed U-TMBL overcomes the existing under-water image enhancement approaches.
Binary Pattern for Copy-Move Image Forgery Detection

To identify copy-move image counterfeiting, this work proposes a new feature extraction approach based on a modified Gabor filter and a Center Symmetric Local Binary Pattern (CSLBP). The input image is first pre-processed, and then Gabor filter and CSLBP feature extraction are performed to the image with various scales and orientations. The Manhattan distance is used to detect forged regions by comparing the critical spots. To classify the counterfeit photos, Hybrid Neural Networks with Decision Tree (HNN-DT) is used on feature extraction. The performance of the proposed Modified Gabor filter with CSLBP is compared to that of existing feature extraction methods such as the Improved Speeded-up Robust Features (SURF) algorithm with PCA and the existing Improved Speeded-up Robust Features (SURF) algorithm with PCA. Using the Gabor filter and CSLBP, the expected results demonstrate efficient classification.
Authentication Schemes for Healthcare Data Using Emerging Computing Technologies

People these days need a stronger, more efficient healthcare system, one that is more individualized and responsive to the demands of modern living. An AI-driven computing environment, when paired with the current computing techniques, provides a solution for data authentication requirements for real-time collection and analysis of health data. The primary goal of this study is to provide an overview of present and emerging computing architectures and approaches for healthcare applications. A total of 21 past studies were considered for detailed review-based analysis. It has been found that researchers have proposed a framework and architecture for data operations to authenticate the data using the applications of various computing technologies such as Fog computing, IoT, Cloud computing, Blockchain, Edge AI, and EoT. These data operations include authentication, transmission, protocol, encryption-decryption, prediction, and classification of healthcare data. To accomplish this goal, a review study has been carried out in the present study. Additionally, the selection of the right literature for the review study is further validated using Word cloud analysis, where results show the main interests of researchers while presenting data security and verification techniques as well as validating the focus of past studies toward authentication schemes using the applications of emerging computing techniques.
Artificial Intelligence and Machine Learning Techniques for Analysis of Yoga Pose

Many individuals all across the world have embraced yoga as a way of life. As a result, yoga poses must be studied scientifically. Pose recognition approaches have been found to be effective in both identifying postures and assisting people in more properly performing yoga. Due to a lack of information and the need to identify posture in real time, posture recognition is a difficult problem. In this research, we carried out extensive trials with popular state-of-the-art CNN architectures and provided benchmark results for the yoga pose detection dataset. To use the hierarchical labels and achieve a performance improvement, we implemented VGG16, VGG19, and Inception v3 pre-trained architecture. In addition, multiple machine learning techniques are utilised to classify the various yoga positions.
Hybrid Attention Module Based on YOLOv5 for Foreign Object Debris Detection

Foreign object debris detection plays an important role in aircraft safety. Because the detection target is tiny and has limited feature information, foreign object debris detection is quite challenging. In this paper, we propose a new model called HAM-YOLOv5 for foreign object debris detection. Specifically, HAM-YOLOv5 is based on YOLOv5 to design hybrid attention module (HAM) for learning contextual information. For foreign object debris detection task, we add one prediction head for tiny object detection. Furthermore, we release Donghua University Foreign Object Debris Detection Dataset (DHFOD) to supplement the lack of current data samples. We conduct a serial of experiments on DHFOD. The experimental results show that our method outperforms other methods, which demonstrates the effectiveness of the proposed HAM-YOLOv5.
Temporal-Relational Matching Network for Few-Shot Temporal Knowledge Graph Completion

Temporal knowledge graph completion (TKGC) is an important research task due to the incompleteness of temporal knowledge graphs. However, existing TKGC models face the following two issues: 1) these models cannot be directly applied to few-shot scenario where most relations have only few quadruples and new relations will be added; 2) these models cannot fully exploit the dynamic time and relation properties to generate discriminative embeddings of entities. In this paper, we propose a temporal-relational matching network, namely TR-Match, for few-shot temporal knowledge graph completion. Specifically, we design a multi-scale time-relation attention encoder to adaptively capture local and global information based on time and relation to tackle the dynamic properties problem. Then, we build a new matching processor to tackle the few-shot problem by mapping the query to few support quadruples in a relation-agnostic manner. Finally, we construct three new datasets for few-shot TKGC task based on benchmark datasets. Extensive experimental results demonstrate the superiority of our model over the state-of-the-art baselines.