Reviewerly/exHarmony huggingface dataset:
Cybersecurity threat perception technology based on knowledge graph

&lt;p class="Abstract"&gt;The issue of complex sources, difficult to understand and share security threat intelligence, this paper realizes deep learning of threat intelligence features based on Restricted Boltzmann Machine, which graphs the original threat intelligence features from high dimensional space to low dimensional space layer by layer, and constructs the cyberspace security threat knowledge graphs. The deep learning used to build a multi-level and structured knowledge graph of cyberspace security threats can reflect the structural characteristics of the knowledge graph, making the graph have a lower dimension and a higher level of abstraction. The experiment verifies the feasibility of constructing the cyberspace security threat knowledge graph, and verifies the security threat perception method based on the knowledge graph is more suitable for the perception of high-intensity security threats by comparing with traditional threat detection methods.&lt;strong&gt;&lt;/strong&gt;&lt;/p&gt;
Saliency Detection Algorithm for Foggy Images Based on Deep Learning

The detection of salient objects in foggy scenes is an important research component in many practical applications such as action recognition, target tracking and pedestrian re-identification. To facilitate saliency detection in foggy scenes, this paper explores two issues. The construction of dataset for foggy weather conditions and implementation scheme for foggy weather saliency detection. Firstly, a foggy sky image synthesis method is designed based on the atmospheric scattering model, and a saliency detection dataset applicable to foggy sky is constructed. Secondly, we compare the current classification networks and adopt resnet50, which has the highest classification accuracy, as the backbone network of the classification module, and classify the foggy sky images into three levels, namely fogless, light fog and dense fog, according to different concentrations. Then, Residual Refinement Network (R2Net) was selected to train and test the classified images. Horizontal and vertical flipping and image cropping were used to enhance the training set to relieve over-fitting. The accuracy of the network model was improved by using Adam as the optimizer. Experimental results show that for the detection of fogless images, our method is almost on par with state-of-the-art, and performs well for both light and dense fog images. Our method has good adaptability, accuracy and robustness.
The Effects of Practical TRIZ-based Classes on the Improvement of Creativity and Problem-Solving Ability of Invention Gifted Elementary Students

The purpose of this study is to develop an educational program using practical TRIZ for invention gifted elementary students and to apply it to the class to see if practical TRIZ can help students improve their creativity and problem solving ability. Kim (2015)’s Practical TRIZ stage 3, which is an effective theory for solving problems creatively, was reconstructed to a level that invention gifted elementary students can understand, and a 12-hour educational program was developed to solve students’ uncomfortable problems in daily life step by step, and it was applied to 42 elementary school students in grades 5 to 6. As a result of the study, it was confirmed that the education program using practical TRIZ was developed according to the level of the elementary invention gifted students, and the class using practical TRIZ had a significant result in improving the creativity and problem solving ability of invention gifted elementary students. It is necessary to develop a variety of teaching and learning programs that can enhance the creativity and problem-solving skills of gifted students, such as the practical TRIZ used in this study.
A Formal Ontology for Special Needs Conditions

Ontology deals with the conceptualization of domain concepts and the relationships among them while special needs conditions refer to any condition that requires assistance ranging from disability to giftedness and talentedness. Unified categorization of special needs conditions and the availability of comprehensive facts and knowledge about each category of special needs conditions have become major problems in the field of special education. It is, therefore, necessary to develop an ontology for special needs conditions that will unify the knowledge and fact about special needs conditions to support various decision-making in the special education domain. Hence, this research builds a formal ontology for special needs conditions that unifies the major categories of special needs conditions which are disability and giftedness, and talentedness, and includes various assistive technology that is applicable to each nature of special needs conditions. The knowledge engineering method was used to build the ontology while the language of formalization is Description logic. Web ontology language in protégé was used to implement the work and evaluated with competency questions in form of simple queries. This ontology can be adopted in all special education domains to forestall the problem of information overloading and inconsistency. Keywords: Special needs conditions, Assistive technology, Ontology, Protégé. CISDI Journal Reference Format Adebayo, E.O., Ayorinde, I.T &amp; Akinkunmi, B.O. (2023): A Formal Ontology for Special Needs Conditions. Computing, Information Systems, Development Informatics Journal. Vol 14 No 2, Pp 31-60. Available online at https://www.isteams.net/cisdijournal. dx.doi.org/10.22624/AIMS/CISDI/V14N2P4
Identification of Condition of Corn Plant Based on Leaf Image Features Using Gray Level Co-Occurrence Matrix and Backpropagation Neural Network

This study aims to identify the condition of corn plants based on imagery leaf using the gray level co-occurrence matrix (GLCM) method and artificial neural network (ANN) backpropagation. The GLCM method is used for extracting features from image leaf corn, whereas ANN backpropagation is used for classification condition plant corn based on features. The classification was done using a dataset of corn leaves with four conditions: healthy, leaf spot, blight, and leaf rust. Next, the leaf features are extracted using method GLCM and training on model ANN backpropagation to classify conditions of corn plants. After training on the model, the next step is model evaluation using the confusion matrix method. The research results show that the technique can produce accuracy, which is tall enough to identify condition corn plants, with an accuracy of 95%. This indicates that the use of GLCM and ANN backpropagation can be a good alternative in determining the condition of corn plants. This research provides benefits in facilitating the identification of the state of corn plants quickly and accurately.
Research on English Translation System Based on Computer Intelligent AI Assisted Technology

This paper proposes an AI machine translation multitask system for artificial intelligence. In the hardware design of the system, STM32 microcontroller is used as the main control module. It realizes synchronous information conversion between English and sign language automatic translation system. In the aspect of system software design, CNN is used for model training and recognition of feature images. Then, the ontology fusion method of semantic unit is used to automatically extract the semantic context of English and sign language input in the system. Structured two-way translation in the order generated by decoding. JSP technology is used to design the system page to realize the timely jump of each function module page. The test results show that the functions of the session module and the dialogue module meet the requirements of interactive design. It can effectively improve the quality of English machine translation.
Detection of Credit Card Fraud with Artificial Neural Networks

Along with the Internet, digital technologies are frequently used in every moment of our lives. Many transactions that we carry out in monetary terms such as shopping in our daily life are now done digitally. With the developing digitalization in the world, people's lives become easier and people can access different products in a short time. In particular, people can spend and shop quickly and easily without carrying cash in their pockets with a credit card. However, with the increase in the use of credit cards, there are also some security vulnerabilities. Fraudsters can gain unfair advantage by obtaining certain credit card information such as passwords. They can shop with someone else's credit card without permission. These transactions cause substantial financial damage to individuals and institutions. With the increase in the use of credit cards with the developing technology, such credit card fraud is also increasing rapidly. Taking precautions against credit card fraud is a very important issue in order to ensure the safety of people. For this reason, in order to ensure the security of both banks and financial institutions that provide credit card services, it is necessary to prevent credit card fraud and to detect fraud that may occur in credit cards within the scope of combating fraud. In our study, Artificial Neural Networks were used to detect credit card fraud transactions. A prediction model has been developed to detect fraud in credit card transactions with ANN. Using the Credit Card data set obtained from the Kaggle database, modeling was done with the Feed Forward Artificial Neural Network method. The aim of this study is to automatically detect abnormal behaviors made with credit cards. 98.44% success was achieved with feedforward artificial neural network.
Selective prediction for extracting unstructured clinical data

While there are currently approaches to handle unstructured clinical data, such as manual abstraction and structured proxy variables, these methods may be time-consuming, not scalable, and imprecise. This article aims to determine whether selective prediction, which gives a model the option to abstain from generating a prediction, can improve the accuracy and efficiency of unstructured clinical data abstraction.We trained selective classifiers (logistic regression, random forest, support vector machine) to extract 5 variables from clinical notes: depression (n = 1563), glioblastoma (GBM, n = 659), rectal adenocarcinoma (DRA, n = 601), and abdominoperineal resection (APR, n = 601) and low anterior resection (LAR, n = 601) of adenocarcinoma. We varied the cost of false positives (FP), false negatives (FN), and abstained notes and measured total misclassification cost.The depression selective classifiers abstained on anywhere from 0% to 97% of notes, and the change in total misclassification cost ranged from -58% to 9%. Selective classifiers abstained on 5%-43% of notes across the GBM and colorectal cancer models. The GBM selective classifier abstained on 43% of notes, which led to improvements in sensitivity (0.94 to 0.96), specificity (0.79 to 0.96), PPV (0.89 to 0.98), and NPV (0.88 to 0.91) when compared to a non-selective classifier and when compared to structured proxy variables.We showed that selective classifiers outperformed both non-selective classifiers and structured proxy variables for extracting data from unstructured clinical notes.Selective prediction should be considered when abstaining is preferable to making an incorrect prediction.
Making it easy to know how to self-archive a paper with a new API, tools, and new website.

There are major updates to our tools and data to make permissions checking easier, faster, and clearer for libraries on our new website.
SENTENCE STRUCTURES IN LEARNING ENGLISH

Learning sentence structure is an important element in mastering English, as it allows learners to convey thoughts and ideas in a meaningful way. However, mastering sentence structure in English can be difficult for English learners, as English has a unique sentence structure system that is different from other languages. This research aims to explore the role of sentence structure in learning English, the challenges that English learners face when mastering sentence structure and strategies to help English learners overcome these challenges. The research will focus on the challenges that English learners face when mastering sentence structure, such as the influence of native language, the complexity of English sentence structure and the impact of English proficiency. The results of this research will contribute to a deeper understanding of the role of sentence structure in English language learning and provide information for the development of more effective language teaching methods and strategies. The results of this research may be useful to English language lecturers and curriculum developers interested in improving the quality of English language education for English learners. The findings of this study will contribute to a deeper understanding of the role of sentence structures in learning English and inform the development of more effective language teaching methods and strategies. The results of this research can be useful for English language instructors and curriculum developers who are interested in improving the quality of English language education for ELLs. The study highlights the importance of sentence structures in learning English and offers insights into how ELLs can be supported in acquiring this crucial aspect of the English language.
Using the Open Access Button in EBSCO Discovery Service (EDS) and EBSCOHost Databases

&lt;em&gt;The below is a guest post written by: &lt;/em&gt;&lt;em&gt;Sharon Whitfield and Melissa A. Hofmann at Rider University Libraries&lt;/em&gt;&lt;em&gt;. We’re incredibly grateful for their leadership in producing a guide for putting the Open Access Button into another Library Discovery System. If you’d like to see Open Access content included in your systems, head to our&lt;/em&gt;&lt;em&gt; Integrations page&lt;/em&gt;&lt;em&gt; for instructions for &lt;/em&gt;Primo, 360 Link, and SFX&lt;em&gt;.&lt;/em&gt;
InstantILL is being rolled out at IUPUI. Here’s how it works.

In March, we announced InstantILL, a new, powerfully simple library tool that delivers articles — no subscription needed. Since then, over 250 libraries, of all sizes have joined the waiting list to save money, improve services, and advance Open. Today, we’re debuting the first iteration with our partner, IUPUI University Library.
Brain-inspired nanophotonic spike-based devices for neuromorphic light-emitting and sensing AI applications

Despite remarkable advances in neuromorphic photonics, nanophotonic devices enabling neuron-like spiking emission and detection—needed for spike-based computation—are still at an early stage of development. Here, we present recent achievements on a photonic neuro-architecture based on III–V integrated spiking circuit nodes using nanoscale quantum resonant tunneling diodes (nanoRTDs) with negative differential resistance. We discuss the integration of nanoRTDs with nano-photodetectors (nanoPDs), nano-light-emitting diodes (nanoLEDs) and other light sources (e.g., VCSELs), to realize on-chip sensory neuron emitter and receiver spiking nodes. The RTD-based nanophotonic neuron devices and circuits for sensing and spike processing multi-functionalities pave the way for energy-efficient signal processing and sensory neuromorphic systems in edge computing AI applications.
AR-Driven Industrial Metaverse for the Auxiliary Maintenance of Machine Tools in IoT-Enabled Manufacturing Workshop

Owing to complex structural machine tools in the Internet of Things (IoT)-enabled manufacturing workshop, personnel lack intuitive and visual auxiliary maintenance methods, significantly resulting in improper maintenance with low maintenance efficiency and poor reliability. To tackle these problems, an Augmented Reality (AR)-driven industrial metaverse for the auxiliary maintenance of machine tools in the IoT-enabled manufacturing workshop is proposed. Through the fusion of adaptive information entropy and sharpening adjustment algorithm, the proposed method can solve the problem that the traditional ORB-SLAM2 (Oriented Brief - Simultaneous Localization and Mapping) cannot obtain sufficiently stable matching point pairs when running in a maintenance scene with rich textures. Hence, the loss of position tracking and registration can be avoided, and visible maintenance can be provided with the help of the AR-driven industrial metaverse. The comparative experiments of actual maintenance cases in the workshop show that the proposed method can effectively improve the maintenance efficiency and reliability of the maintenance personnel.
Deep Interactions-Boosted Embeddings for Link Prediction on Knowledge Graph

Link prediction for Knowledge Graphs (KGs) aims to predict missing links between entities. Previous works have utilized Graph Neural Networks (GNNs) to learn specific embeddings of entities and relations. However, these works only consider the linear aggregation of neighbors and do not consider interactions among neighbors, resulting in the neglect of partial indicating information. To address this issue, we propose Deep Interactions-boosted Embeddings (DInBE) which encodes interaction information to enrich the entity representations. To obtain interaction information, we disentangle the representation behind entities to learn diverse disentangled representations for each entity. Then, we learn intra-interactions among neighboring entities in the same component and inter-interactions among different components based on these disentangled representations. With the help of interaction information, our model generates more expressive representations. In addition, we propose a relation-aware scoring mechanism to select useful components based on the given query. Our experiments demonstrate that our proposed model outperforms existing state-of-the-art methods by a large margin in the link prediction task, and this verifies the effectiveness of exploring interactions and adaptive scoring.
Boosting Visual Question Answering Through Geometric Perception and Region Features

Visual question answering (VQA) is a crucial yet challenging task in multimodal understanding. To correctly answer questions about an image, VQA models are required to comprehend the fine-grained semantics of both the image and the question. Recent advances have shown that both grid and region features contribute to improving the VQA performance, while grid features surprisingly outperform region features. However, grid features will inevitably induce visual semantic noise due to fine granularity. Besides, the ignorance of geometric relationship makes VQA models difficult to understand the object relative positions in the image and answer questions accurately. In this paper, we propose a visual enhancement network for VQA that leverages region features and position information to enhance grid features, thus generating richer visual grid semantics. First, the grid enhancement multi-head guided-attention module utilizes regions around the grid to provide visual context, forming rich visual grid semantics and effectively compensating for the fine granularity of the grid. Second, a novel geometric perception multi-head self-attention is introduced to process two types of features, incorporating geometric relations such as relative direction between objects while exploring internal semantic interactions. Extensive experiments demonstrate that the proposed method can obtain competitive results over other strong baselines.
Antecedent Predictions Are More Important Than You Think: An Effective Method for Tree-Based Code Generation

Code generation focuses on automatically converting natural language (NL) utterances into code snippets. Sequence-to-tree (Seq2Tree) approaches are proposed for code generation with the aim of ensuring grammatical correctness of the generated code. These approaches generate subsequent Abstract Syntax Tree (AST) nodes based on the preceding predictions of AST nodes. However, existing Seq2Tree approaches tend to treat both antecedent predictions and subsequent predictions equally, which poses a challenge for models to produce accurate subsequent predictions if the antecedent predictions are incorrect under the constraints of the AST. Given this challenge, it is necessary to pay more attention to antecedent predictions compared to subsequent predictions. To this end, this paper proposes a novel and effective method, named Antecedent Prioritized (AP) Loss, which prioritizes antecedent predictions by leveraging the position information of the generated AST nodes. We design an AST-to-Vector (AST2Vec) method that maps AST node positions to two-dimensional vectors, thereby modeling the position information of AST nodes. To evaluate the effectiveness of our proposed loss, we implement and train an Antecedent Prioritized Tree-based code generation model called APT. Experiments on four benchmark datasets demonstrate that with better antecedent predictions and accompanying subsequent predictions, APT achieves significant improvements, indicating the superiority and generality of our proposed method.
Generalized aberration regularization in the single-lens computational imaging system design

The restoration algorithm can reduce the aberration blur in the single-lens computational imaging system. The single lens and the restoration algorithm should be co-designed to improve the imaging performance of the whole computational imaging system. By investigating the influence of aberrations on computational imaging quality, we find there are aberrations that cannot be removed by restoration algorithm, which cause the loss of high-resolution information. However, such aberrations are not constrained in existing co-design methods. To solve this problem, we propose the generalized aberration regularization (GAR), which mainly constrains the aberrations that reduce the modulation transfer function (MTF) at the high frequency. Experimental results demonstrate that our approach can significantly improve the quality of high-resolution information in the recovery image compared with the existing co-design methods.
ASR for Indian Regional Languages Using Fine-Tuned Wav2Vec2 Model

Recent technologies have pertained to huge success in the field of speech recognition and natural language processing. Numerous models are used for fine-tuning on large pre-trained models. Such models are of huge help when training languages with vast resources. This is not the case for languages that have comparatively fewer resources. In this paper, an automatic speech recognition model for low resourced Indian regional languages is proposed. The model used in this paper is the renowned Wav2Vec2 model. This paper will focus on the Tamil dataset. The objective of this paper is to minimize the word error rate (WER) metric when transcribing some audio file (e.g., a WAV file) containing speech. This is achieved by pre-training Wav2Vec2. Later, fine tuning it by the addition of a linear layer on top and feeding a custom tokenizer. This approach achieves 61.3% WER on the Mozilla Common Voice dataset. This outperforms previously gained WER of 69.76.