Reviewerly/exHarmony huggingface dataset:
A New Training Data Organization Form and Training Mode for Unbiased Scene Graph Generation

The current mainstream studies on Scene Graph Generation (SGG) devote to the long-tailed predicate distribution problem to generate unbiased scene graph. The long-tailed predicate distribution exists in VG dataset and is more severe during the SGG network training process. Most existing de-biasing methods solve the problem by applying re-sampling or re-weighting in a mini-batch, with the main idea being to provide unbiased attention to different predicate categories based on prior predicate distributions. During the training process of SGG models, existing training mode samples several images into a mini-batch to obtain training data, thus providing sparse and scattered predicate instances for training. However, sampling predicate instances from a limited set of predicate samples in terms of quantity and category poses difficulties in training unbiased SGG models. In order to provide a wider range for sampling predicate instances, this paper reorganizes the images in VG training set with a new form, i.e. object-pairs, and constructs VG-OP (VG Object-Pair) training set to save object-pairs. Meanwhile, this paper introduces a new SGG network training mode, which can realize unbiased SGG without resampling or re-weighting. In particular, a Predicate-balanced Sampling Network (PS-Net) is designed to validate the new training mode. Extensive experiments on VG test set demonstrate that our method achieves competitive or state-of-the-art unbiased SGG performance.
A Semi-supervised Approach for Chinese Noun Phrase Chunking

This chapter addresses Chinese noun phrase chunking with special reference to nominalizations based on a semi-supervised approach. It uses YamCha, a support vector machine (SVM) toolkit, to train the model. In addition to the IOB scheme and the two words before and after the target word, we experimented with new features and exploited unlabeled data from web pages to enhance the performance of the model. The result of our experiments showed that our proposed method of semi-supervised learning is effective in tackling a variety of complex Chinese noun phrases that have been largely unexplored in previous research. An important bi-product of our approach is the identification of Chinese nominalized verbs, which are indistinguishable from verbs in terms of their morphology and part-of-speech tags. The findings of this research may shed light on more recent approaches to similar problems.
Posing biases in portraits of people that do not exist

We report posing biases in portraits of people that do not exist. Studies of painted or photographed portraiture have often reported such biases. However, whether these truly exist or are mere sampling artifacts remains open to question. A novel approach to such a question is provided by contemporary applications generating photo-realistic virtual portraits. Such applications are exposed to large datasets of portraits of real people. A neural network then maps the variation of the original input set to a huge-dimensional generative model capturing the variation in the original data, which is then used to synthesize the virtual portraits. We reasoned that, if posing biases exist in the original input, they should also be observable in the network output, and they did. This finding provides novel support for the reality of posing biases in portraiture—and helps us better understand what generative networks actually do.
IQNet: Image Quality Assessment Guided Just Noticeable Difference Prefiltering For Versatile Video Coding

Image prefiltering with just noticeable distortion (JND) improves coding efficiency in a visual lossless way by filtering the perceptually redundant information prior to compression. However, real JND cannot be well modeled with inaccurate masking equations in traditional approaches or image-level subject tests in deep learning approaches. Thus, this paper proposes a fine-grained JND prefiltering dataset guided by image quality assessment for accurate block-level JND modeling. The dataset is constructed from decoded images to include coding effects and is also perceptually enhanced with block overlap and edge preservation. Furthermore, based on this dataset, we propose a lightweight JND prefiltering network, IQNet, which can be applied directly to different quantization cases with the same model and only needs 3K parameters. The experimental results show that the proposed approach to Versatile Video Coding could yield maximum/average bitrate savings of 41\%/15\% and 53\%/19\% for all-intra and low-delay P configurations, respectively, with negligible subjective quality loss. Our method demonstrates higher perceptual quality and a model size that is an order of magnitude smaller than previous deep learning methods.
Cyber Crime Prevention Model Using Artificial Intelligence

The Cyber Crime Prevention Model using Artificial Intelligence (AI) is a framework designed to help prevent and mitigate the impact of cybercrime. The model integrates AI technology into the cyber security measures and processes to identify, prevent, and respond to cyber threats. AI algorithms are used to analyze large amounts of data to detect patterns and anomalies, which can indicate potential security breaches. The model also includes features such as automated threat response and real-time monitoring, which can improve incident response times and reduce the impact of cyber-attacks. Additionally, the model incorporates ethical considerations and human oversight to ensure that the AI-powered system operates in a responsible and transparent manner. Overall, the Cyber Crime Prevention Model using AI can provide organizations with a more proactive and effective approach to cyber security.
Named Entity Recognition on Medical text by using Deep Nueral Networks

Technological advancements have caused widespread changes in the medical industry. The development of new tools has made it possible to gain useful information from massive amounts of unstructured data. The medical literature publications issued by researchers include a vast amount of information. There are a lot of groups working on deciphering the papers of literature to find the hidden information. Thanks to advancements in Natural Language Processing technology, identifying drug names, ailments, symptoms, routes of administration, species, and dosage forms inside a textual material is a breeze. In this work, we offer a novel hybrid-based method for extracting named entities from papers in the medical literature. A brand-new vocabulary was developed to annotate the entities in the medical records with information about the route of administration, dose forms, and symptoms. Spacy is a blank machine learning model that trains the annotated entities. The trained model outperforms the baseline model in terms of accuracy. The confusion matrix is determined by comparing the results of the hybrid model’s dictionary- and human-validation processes.
Predicting the Acceptability of Atomic Candidate OWL Class Axioms

The task of evaluating the fitness of a candidate axiom against known facts or data is known as candidate axiom scoring. Being able to accurately score candidate axioms is a prerequisite for automatic schema or ontology induction, but can also be useful for ontology and/or knowledge graph validation. Accurate axiom scoring heuristics are often heavy to compute, which is a big problem if one wants to exploit them in iterative search methods like level-wise generate-and-test or evolutionary algorithms, where large numbers of candidate axioms need to be scored. We tackle the challenge of learning a predictive model as a surrogate to reasoning, that predicts the acceptability of candidate class axioms, that is fast to execute yet accurate enough to be used in such settings. For this purpose, we leverage a semantic similarity measure extracted from the subsumption hierarchy of an ontology. We prove that the method proposed in this paper is able to learn the acceptability labels of candidate OWL class axioms with high accuracy and that it can do so for multiple types of OWL class axioms.
HGExplainer: Explainable Heterogeneous Graph Neural Network

Graph Neural Networks (GNNs) are an effective framework for graph representation learning in real-world applications. However, despite their increasing success, they remain notoriously challenging to interpret, and their predictions are hard to explain. Nowadays, several recent works have proposed methods to explain the decisions made by GNNs. However, they only aggregate information from the same type of neighbors or indiscriminately treat homogeneous and heterogeneous neighbors similarly. Based on these observations, we propose HGExplainer, an explainer for heterogeneous GNNs to comprehensively capture structural, semantic, and attribute information from homogeneous and heterogeneous neighbors. We first train the GNN model to represent the predictions on a heterogeneous network. To make the explainable predictions, we design the model to capture heterogeneity information in calculating the joint mutual information maximization, extracting the meta-path-based graph sampling to generate more prosperous and more accurate explanations. Finally, we evaluate our explainable method on synthetic and real-life datasets and perform concrete case studies. Extensive results show that HGExplainer can provide inherent explanations while achieving high accuracy.
Wall-surface Running Control of a Tandem Type UAV with Tiltable Rotors

This study shows how to prevent the rank deficiency in the thrust allocation matrix by giving each rotor of a 2-DOF tiltable coaxial rotor a difference in rotational speed to generate counter-torque. In addition, in order to avoid a singular attitude at a pitch angle of 90 degrees when moving along a wall, we propose a method of expressing the attitude angle of the UAV as a quaternion and controlling translational movement while maintaining the singular attitude and controlling the force through impedance control. The usefulness of this method is verified by simulating wall running in an environment that includes a wall model.
TWITTER DATA SENTIMENT ANALYSIS FORSTOCK MARKET PREDICTION USING MACHINE LEARNING

: Recent outrageous posts on social media have taken the globe by storm and have led to diverse views and views of the general public. Social media plays a significant act for or against a government or a corporation that simply can’t decide the movement of market but to grasp the sentiment of twitter data that are posted on social media with good method could be a supreme necessity. It will analyse some twitter postings to grasp human semantic. In any tweet intended posting there are some downgraded keyword. At last, a data-set is ready that consists of unique words collected from twitter posts or comments and so the data-set is trained using Naive Bayes algorithm supported with applied mathematics to spot the sentiment given during a new call and comment . They are going to extract each word of the posting and so it'll be matched by virtue with the data-set words for dilution. Finally, it will be tested to their algorithm using numerous posts from twitter that can deliver the result with good accuracy.
CAREER ATTITUDES AND PROACTIVE BEHAVIOR OF EMPLOYEE

Proactivity today is one of the concepts actively developed in applied psychology and is considered as a key feature of job crafting or constructive design by a person of his work.&#x0D; The purpose of this article is to study personal proactivity in mediating the career orientations of working professionals. The study involved 40 sales managers. Data collection methods: questionnaire “Career Anchors” by E. Shane and “PVQ self-rating” (author’s development). The construct of proactivity was presented in the self-assessment methodology through the qualities of initiative, independence, perseverance, responsibility.&#x0D; Conclusions. Initiative, as the central link of proactivity, is associated with a whole range of properties that are responsible both for the general self-confidence of the individual and with specific skills to express and implement their goals. Factor analysis showed that initiative and responsibility are different aspects of personal proactivity. Proactive personal qualities indirectly mediate success in the implementation of career strategies and subjective satisfaction with professional self-realization.
Mind, body, intelligence amd language in the era of cognitive technologies. Brief overview of the MBIL 2023 conference

Science as a social institution today is experiencing a phase of profound transformation. Objects, methods, research technological tools, methods of institutional communication and mechanisms for commercializing new knowledge are changing. The creation of new interdisciplinary communication platforms is more relevant today than ever before. This review pro[1]vides key information about the First Conference «Mind, Body, Intelligence, Language in the Age of Cognitive Technologies». The organizers created an event that brought together IT developers, academic researchers, and business representatives.
Augmented Reality (AR): An Assistive Technology for Special Education Needs

Since 2017, the Ministry of Education has introduced Basic Vocational Skills subjects that able special students to master basic living skills in their schooling years underlying Secondary School Standard Curriculum – Special Education (KSSM –PK). The biggest challenge for individuals with ASD is to be independent and get jobs after schooling. Hence, beginning in 2017, Secondary School Standard Curriculum – Special Education (KSSM –PK) has introduced the Basic Vocational Skills subject, which students must master during their schooling years. From the preliminary study, many teachers revealed that they face difficulty delivering lessons primarily related to teaching and learning materials to enhance ASD students mastering living and vocational skills. Designing and developing effective teaching and learning aids, especially for children with special education needs are in alarming stages. Parents and caregivers indicated they face insufficient enhancement and practice learning tools for their children, especially after school hours. The guidebook and extra exercise books are essential for them to master their learning while at home. In response to the gap mentioned earlier and decipher the myriad of potential uses of Augmented Reality (AR) as an assistive educational technology, the current study aimed to design and develop a differentiated instructional pedagogical kit (Kit-MASAK) with AR to assist children with special education needs especially ASD students master in basic vocational skills Preparing and cooking skills). Methodologically, a total 3 students were involved in the phenomenological study. The preliminary analysis of the data summary across the case studies revealed that the Kit MASAK successfully brought contemporary content into the classroom, leading to an exciting teaching and learning environment among students. Furthermore, this study also provides several significant implications for the research and practice.
TEACHER TRAINING: MODEL OF HERBARTINIANISM

The article analyzes Herbartians pedagogy and the activities of educational institutions in the light of Herbartianism; retraces the main factors of training teachers of relevant qualifications at higher educational institutions, design and development of curricula and textbooks in pedagogy, publication of research results of pedagogues-Herbartians’ research work in scientific journals; points out the Herbartians’ traditions in the symbiotic training of a teacher run both by the state and by the church. The central idea of Herbartianism was the conviction that the purpose of education was the development of “ethical character”. Herbartianism pedagogy was never a monolith pedagogic theory, and the attitude to Herbartianism, criticised for schematism, formalism and dogmaticism, has always been ambiguous. Different schools within Herbartianism had strong disagreements, which were hearkened back to different attitudes towards Herbart and his theory.
A Study on Continuous Teacher Professional Development of University

The teacher’s professional development at the university has been highlighted in theory and practice circles. In terms of continuing teacher professional development methods, university models of continuing teacher professional development. The study included three aspects: the education management department, universities, and teachers themselves. This is a mixed-methods approach (qualitative and quantitative), based on the qualitative analysis of the system analysis of the professional development in colleges and universities as well as the model of the professional development system for college teachers. To make the analysis and conclusions of this study more practical and scientific, techniques like path analysis, descriptive analysis, and network analytic hierarchy process are used. Additionally, this study integrates the normative and empirical research. to evaluate the continuing teacher professional development capability in universities, the continuing teacher professional development capability model and the continuing teacher professional development capability assessment system were developed. And, combined with three Henan universities, further addressed the issue of relatively weightiness of assessment indices by applying the Flexible negotiation method and the Analytic Network Process. The results indicate that two components can be used to build a system for continuing teacher professional development: creating a professional learning organization and establishing a growth mode for individual teacher reflection. This research can also be used to summarize the limitations and future directions for further investigation. Conclusion: The path to the personal growth and development of college teachers is built through the creation of professional learning organizations and the reflective teacher personal growth model.
Black Box Adversarial Defense Based on Image Denoising and Pix2Pix

Deep Neural Networks (DNN) are widely utilized due to their outstanding performance, but the susceptibility to adversarial attacks poses significant security risks, making adversarial defense research crucial in the field of AI security. Currently, robustness defense techniques for models often rely on adversarial training, a method that tends to only defend against specific types of attacks and lacks strong generalization. In response to this challenge, this paper proposes a black-box defense method based on Image Denoising and Pix2Pix (IDP) technology. This method does not require prior knowledge of the specific attack type and eliminates the need for cumbersome adversarial training. When making predictions on unknown samples, the IDP method first undergoes denoising processing, followed by inputting the processed image into a trained Pix2Pix model for image transformation. Finally, the image generated by Pix2Pix is input into the classification model for prediction. This versatile defense approach demonstrates excellent defensive performance against common attack methods such as FGSM, I-FGSM, DeepFool, and UPSET, showcasing high flexibility and transferability. In summary, the IDP method introduces new perspectives and possibilities for adversarial sample defense, alleviating the limitations of traditional adversarial training methods and enhancing the overall robustness of models.
Optimal paths for UAV multi-express full delivery in 3D modeling

The use of drones in logistics is steadily becoming more common as drone technology advances. As a result, logistics path planning has become a viable research topic. The majority of previous research has concentrated on low-load vehicle-borne UAVs. DJI recently produced a high payload civilian delivery UAV with a maximum capacity of 30 to 40 kg, paving the path for high payload UAVs and providing the criteria for this research. The goal of this research is to increase the efficiency of UAV delivery while also reducing energy consumption, as well as to investigate the path planning problem for large load UAVs in batches. Prior to choosing the ACO method to determine the best route for a single batch, the genetic algorithm is used to achieve the entire load for express delivery in batches. After this, the whole path is determined and supported. The output of the program is the correlation between the number of iterations and the single optimal route, which is then added to produce the overall path. After showing this, we infer that the sum of single ideal paths is the best total path, which maximizes efficiency and saves energy. The significance of this research is to provide some directions for thinking and help for the research of UAV logistics, and we hope to further develop UAV logistics technology to some extent under the premise of energy saving and time saving.
A Study of Artificial Intelligence-Assisted Teaching on the Cultivation of Students’ Creative Ability

Abstract This paper first studies the use of artificial intelligence technology in the field of education and explores the realization process of “artificial intelligence + education”. Then, the teaching model of “AI+Education” is constructed from three perspectives: knowledge space, creation space and thinking space. Then, based on the logistic regression method, an analytical model of students’ innovation ability was established, and the parameters of the model were solved according to the data to analyze the causal relationship between the variables. Finally, the regression analysis model was used to analyze the relationship between intelligent assisted teaching and students’ innovative ability, innovative knowledgeability, innovative thinking ability, new learning ability and innovative practice ability. The results show that the correlation coefficients of intelligent-assisted teaching and innovative ability and innovative self-efficacy are 0.703 and 0.639, respectively, and all of them are positively correlated at the level of 10%, which indicates that intelligent-assisted teaching promotes the cultivation of students’ innovative ability.
Learning Hypergraphs Tensor Representations from Data via t-HGSP

Representation learning considering high-order relationships in data has recently shown to be advantageous in many applications. The construction of a meaningful hypergraph plays a crucial role in the success of hypergraph-based representation learning methods, which is particularly useful in hypergraph neural networks and hypergraph signal processing. However, a meaningful hypergraph may only be available in specific cases. This paper addresses the challenge of learning the underlying hypergraph topology from the data itself. As in graph signal processing applications, we consider the case in which the data possesses certain regularity or smoothness on the hypergraph. To this end, our method builds on the novel tensor-based hypergraph signal processing framework (t-HGSP) that has recently emerged as a powerful tool for preserving the intrinsic high-order structure of data on hypergraphs. Given the hypergraph spectrum and frequency coefficient definitions within the t-HGSP framework, we propose a method to learn the hypergraph Laplacian from data by minimizing the total variation on the hypergraph (TVL-HGSP). Additionally, we introduce an alternative approach (PDL-HGSP) that improves the connectivity of the learned hypergraph without compromising sparsity and use primal-dual-based algorithms to reduce the computational complexity. Finally, we combine the proposed learning algorithms with novel tensor-based hypergraph convolutional neural networks to propose hypergraph learning-convolutional neural networks (t-HyperGLNN).