Reviewerly/exHarmony huggingface dataset:
RecipeMeta: Metapath-enhanced Recipe Recommendation on Heterogeneous Recipe Network

Recipe is a set of instructions that describes how to make food. It can help people from the preparation of ingredients, food cooking process, etc. to prepare the food, and increasingly in demand on the Web. To help users find the vast amount of recipes on the Web, we address the task of recipe recommendation. Due to multiple data types and relationships in a recipe, we can treat it as a heterogeneous network to describe its information more accurately. To effectively utilize the heterogeneous network, metapath was proposed to describe the higher-level semantic information between two entities by defining a compound path from peer entities. Therefore, we propose a metapath-enhanced recipe recommendation framework, RecipeMeta, that combines GNN (Graph Neural Network)-based representation learning and specific metapath-based information in a recipe to predict User-Recipe pairs for recommendation. Through extensive experiments, we demonstrate that the proposed model, RecipeMeta, outperforms state-of-the-art methods for recipe recommendation.
Vision-Language Navigation for Quadcopters with Conditional Transformer and Prompt-based Text Rephraser

Controlling drones with natural language instructions is an important topic in Vision-and-Language Navigation (VLN). However, previous models can not effectively guide drones with the integration of multimodal features, as few of them exploit the correlations between instructions and the environmental contexts and consider the model’s capacity to understand natural languages. Therefore, we propose a novel language-enhanced cross-modal model that has a conditional Transformer to effectively integrate the multimodal features, i.e., the textual instructions and visual contexts. To enhance the ability of language representation, we also employ SentenceBERT. In addition, to address the issue that users could provide various textual instructions even for the same navigation task, we propose a prompt-based approach by introducing an LLM-based intermediary component (LLMIR) for rephrasing users’ instructions. We evaluate our approaches with a quadcopter simulator. Our model improves the absolute task completion rate by 1.39%. To evaluate LLMIR, we create a new test set by extracting the essential and minimal instructions from the original test set. By using the LLM, the task completion rate improves by 1.51%. And it narrows the performance gap between new and original test set by 34.83%.
DiffuseGAE: Controllable and High-fidelity Image Manipulation from Disentangled Representation

Diffusion probabilistic models (DPMs) have shown remarkable results on various image synthesis tasks such as text-to-image generation and image inpainting. However, compared to other generative methods like VAEs and GANs, DPMs lack a low-dimensional, interpretable, and well-decoupled latent code. Recently, diffusion autoencoders (Diff-AE) were proposed to explore the potential of DPMs for representation learning via autoencoding. Diff-AE provides an accessible latent space that exhibits remarkable interpretability, allowing us to manipulate image attributes based on latent codes from the space. However, previous works are not generic as they only operated on a few limited attributes. To further explore the latent space of Diff-AE and achieve a generic editing pipeline, we proposed a module called Group-supervised AutoEncoder(dubbed GAE) for Diff-AE to achieve better disentanglement on the latent code. Our proposed GAE has trained via an attribute-swap strategy to acquire the latent codes for multi-attribute image manipulation based on examples. We empirically demonstrate that our method enables multiple-attributes manipulation and achieves convincing sample quality and attribute alignments, while significantly reducing computational requirements compared to pixel-based approaches for representational decoupling.
Global-Local GraphFormer: Towards Better Understanding of User Intentions in Sequential Recommendation

Transformer-based model has gained great success in the multimedia sequential recommendation task due to its strong ability to handle sequential data. However, existing Transformer-based models regard the items in the sequential data as a user-specific fully-connected graph (local graph) and only explicitly consider the temporal information in the local graph to capture the users’ intentions, ignoring the fact that the user-item bipartite graph (global graph) may carry important relation patterns to the sequential items. Additionally, it is still unclear whether (and how) the information hidden in the global graphs can help the Transformer-based models better understand the users’ sequential behavior according to the current literature. To investigate this important problem, we propose to utilize the global graph information to help the Transformer-based sequential recommendation, where the information from different modalities, i.e., user-item interactions in the global graph and the temporal patterns in the historical sequences, are taken into account jointly. In concrete, we propose two Global-Local (GL) GraphFormer models for utilizing both the global graph and local temporal information. One GL-GraphFormer is able to gift the Transformer-based model with both first- and second-order graph information through two specifically designed encodings. The other GL-GraphFormer transfers higher-order graph information into the local Transformer with pretrained Graph Neural Networks (GNNs). Extensive experiments on several real-world datasets demonstrate that i) our proposed GL-GraphFormers can bring substantial improvement over baseline methods, and ii) the benefits of different orders of global graph information vary with the dataset sparsity.
Multi-Task Self-Blended Images for Face Forgery Detection

Deepfake detection has attracted extensive attention due to widespread forged images on social media. Recently, self-supervised learning (SSL) based Deepfake detection approaches have outperformed supervised methods in terms of model generalization. However, we notice that most SSL-based methods do not take the manipulation strength levels of synthesized forgery samples into consideration according to different synthesis parameters and result in suboptimal detection performances. To address this issue, we introduce several auxiliary losses to the state-of-the-art SSL-based method based on different synthesis sub-tasks during data generation by inferring their synthesis parameters where the ground-truth labels are obtained from the synthesis pipeline for free. With comprehensive evaluations on various benchmarks, our approach has achieved noticeable performance improvement. Specifically, for the cross-dataset evaluation, the proposed approach outperforms the state-of-the-art method in terms of AUC on various datasets with improvements of 3.4%, 1.47%, 1.56%, and 1.3% on the CDF, DFDC, DFDCP, and FFIW datasets and achieves competitive performance on the DFD dataset. This further demonstrates the effectiveness of the proposed approach in its generalization ability.
Multi-region CNN-Transformer for Micro-gesture Recognition in Face and Upper Body

This paper presents a novel task that recognizes from a video unintentional micro-gestures (UMGs), which are movements made by people unconsciously and unintentionally. Recognizing UMGs is crucial because they reveal a person’s underlying psychological state. Since a UMG is composed of subtle sequential movements, the recognition model must be able to capture accurate information in both the spatial and temporal directions. Therefore, we utilize a convolutional neural network (CNN) to capture information in the spatial direction and a Transformer to merge the features extracted by the CNN in the temporal direction. However, this model often misrecognizes UMGs because it is not possible to capture slight differences in movements, such as in the face and mouth regions. To address this issue, we propose a novel model for UMG recognition, the Multi-Region CNN-Transformer model, that inputs cropped videos from multiple upper body regions simultaneously. The key advance of our method is to capture subtle changes in regions such as the upper body, face, head, and mouth for recognizing UMGs. We demonstrate the effectiveness of the proposed method through experiments using our newly created UMG dataset for this task.
STUDY OF TEACHERS’ AND STUDENTS’ ATTITUDES TO DIGITAL TRANSFORMATION OF EDUCATION ENVIRONMENT

The article examines the problem of digital transformation of the education environment of organizations of general secondary, vocational and higher education. The relevance of the digitalization of education is determined at the state level and at the level of education management. The success of solving the problem largely depends on two factors. The first one is the attitudes of subjects of the education environment to the digitalization of education and the second factor is understanding the essence of digital transformation. The authors’ approach is to recognize the innovativeness of the activities of teachers and students employing the potential of digital transformation to ensure the intellectual development of students. The article aims to define attitudes of teachers and students to digital transformation of education environment. To achieve the aim a large-scale survey of subjects of the educational environment was carried out. The results of the survey show the predominance of a cautious, but generally positive attitudes of teachers and students to the digitalization of education. There is a high level of uncertainty in understanding the purpose of digital transformation and its essence among respondents. The results show that digital transformation of the education environment is considered to be an innovative process of improving the content, methods and technologies of all levels of education. At the same time, the majority of teaching staff believe that digital transformation in education is carried out externally. The attitudes of teaching staff, students and other subjects of education to the digitalization of education is a contradictory one. On the one hand, digitalization is considered as a universal means of solving education tasks, on the other hand, digitalization is perceived as the measures imposed from the outside, leading to excessive workload for teachers in their work. A productive solution to this contradiction is the creation of a complex of organizational and pedagogical conditions for the digital transformation of the education environment.
GADNN: Gender and Age Detector Neural Network

Automatic prediction of age group and gender from face images has shown good attention, due to the various applications in the domain of recommendation systems and human categorization. The large intra-class variations of the face images due to lightening, scale, occlusion, pose, and angles, made the problem challenging in terms of accuracy. The recent work in the domain of age and gender detection shows better detection accuracy using neural network-based approaches. In this work, a deep learning-based algorithm is proposed for the detection of age groups and gender. The usage of age in most cases is required to identify the age group, so the age detection network detects the age from one of the eight age groups. Both the age group and gender detection neural networks are applied to the benchmark datasets of Adience, UKTFace, and a custom-generated dataset of Pakistani faces. The algorithms resulted in an accuracy ranging from 0.91 to 0.99 on the datasets of gender detection while the accuracies of 0.58 to 0.85 on age detection on benchmark datasets.
Melanoma Detection Using Channel and Block Attention Module

Early Melanoma detection is a challenging task due to an imbalanced dataset and less number of labeled images. There are many methods based on different Deep CNN architectures however as the interclass similarity is very low, more focus on the target area is highly required. In this paper, a dual Attention module is proposed at the skip connection part of ResNet101 after the convolution block. The proposed model used a channel attention module to increase the weight of the channel that contains more relevant information. The model also used a spatial attention module to highlight local features in the spatial domain. Proposed model score classification accuracy 95.1%, sensitivity 87.6%, and specificity 95.8%. The proposed model achieves better classification accuracy, sensitivity, and specificity than other state-of-the-art methods.
AI-BASED PERSONALIZED E-LEARNING OF UNIVERSITY STUDENTS: CURRENT STATE OF THE PROBLEM

Personalization of student learning is an important direction in the development of a student-centered education model. It can be implemented both through direct interaction and in a mediated format within an electronic information-and-educational environment. One way to personalize e-learning is using artificial intelligence (AI) technologies. Machine learning algorithms, chatbots and voice assistants, virtual and augmented reality technologies enable the automation of data collection and data analysis, provide instant feedback and recommendations, create authentic learning environments, determine optimal learning strategies. However, despite all the advantages, the implementation of AI-based personalized e-learning faces technical and financial difficulties, lack of necessary digital competencies among educators, as well as insufficient theoretical foundations. The aim of this article is to identify and describe the characteristics of the current state of the problem of AI-based personalized e-learning for university students. The article defines the normative and legal guidelines for the use of AI in personalization of university students’ learning; examines the current state of the problem in the theory and practice of education; conducts a comprehensive assessment of factors directly and indirectly influencing the AI-based personalized of e-learning for university students; taking into account the identified factors, outlines the perspectives for the application of AI in personalized e-learning for students. To achieve the obtained results, different theoretical and empirical methods such as literature and electronic source analysis, content analysis, documentation analysis, SWOT analysis are applied.
CULTURAL COMPONENT IN INTERACTION AT LEVEL A1 IN TERMS OF COMMON EUROPEAN FRAMEWORK OF REFERENCE

The authors identified a cultural component for teaching English primary schoolchildren at level A1 according to the Common European Framework of Reference (CEFR). The purpose of the study was to identify and describe what knowledge, skills and abilities primary schoolchildren must have to interact effectively within the framework of CEFR speech interaction for level A1. During the analysis, interaction types were identified and described. Then the intercultural component was identified within each type of interaction at level A1, and on this basis, author’s educational materials were developed. The authors identified three types of communication, namely, oral, written and online interaction. Within each type of interaction the authors identified and described the intercultural component. The cultural component was identified and described as a set of knowledge, skills and abilities that primary schoolchildren must use in each method of interaction. For further research more cultures can become the focus of the analysis. This would help to identify cultural similarities, differences, and characteristics of communication types between the representatives of different cultures. The results of the study can be used to teach the cultural component to younger schoolchildren in the process of interaction at level A1.
Rule Based System Pemilihan Alternatif Merek Pembelian Notebook

With dozens of brands on the market, designed sustainably with the latest technology, making notebooks a complex product. Buyers use different notebook attributes in comparing alternative notebook brands with processor, memory system, screen, graphics, overall performance as determining attributes. Forward chaining is a strategy for drawing conclusions that starts from a number of known facts, to obtain a new fact by using rules that have premises that match the facts and continues until the goal is reached or until there are no rules that have suitable premises. or until you get the facts. Expert systems act as smart advisors or consultants by retrieving knowledge stored in a knowledge base. Even an inexperienced user, as long as they have a general understanding of how diagnostic equipment works, can solve complex problems and can make appropriate and accurate decisions like an expert would.
RİSK MANAGEMENT IN İNFORMATİON EXCHANGE

The field of risk management is one of today's most main fields. Risk-free business is not possible. It is necessary to calculate the risks that arise in every business so that it does not create any problems for us later. Although it is not possible to reduce the risk to zero, it is possible to reduce the risk. One of the most utilised areas of the risk term is the risk of informatisation. Ensuring the confidentiality, integrity and transparency of information is one of today's main problems. For this purpose, some standards and standards have been prepared. Some of these standards are ISO 27001 and ISO 31000. With the widespread use of computer networks and the Internet, Information Security has become very important. Since organisations are mostly dependent on information, technology and systems, Information Security is of vital importance and the need to protect information assets from damage arises from this. On the other hand, many companies still do not take adequate and necessary measures in information security. As a result, many companies, including many large and international organisations, are under serious threat. In order to be able to recognise these threats in advance and to reduce the severity of threats, it is necessary to comply with the Risk Management and the whole of the ISO 27001:2022 standard. Today, organisations face a risk in almost every transaction. It is necessary to identify and evaluate the risks that may arise during the functions of the institutions carefully and in detail in advance and to take measures to minimise or completely eliminate these risks. In this article, the steps to be taken to meet the requirements of ISO 27001:2022 Information Security Management System Risk Management are analysed step by step and a software has been developed to enter the data containing these requirements and receive the relevant reports. In this context, the ISO 27000:2018 family referenced by the ISO 27001:2022 Standard has been examined. The ways in which risk analyses can be performed and how risk improvement can be achieved have been investigated. As a result of the study, all these have been brought together in a software and made reportable. The security structure should be established by taking into account the differences of the organisation and the system. Afterwards information security risk management and methods that are not fully detailed in the standard. A documentation by analysing assets under a corporate information processing structure has been created. To create this structure and at the same time to create a dynamic a basic level of information security control software has been produced to provide control. Keywords: Information, information security, risk, risk management, risk analysis.
Opinion Classifier Transfer Learning from Review Data

Companies use users' opinions to improve their products and marketing activities. In recent years, the development of Internet technology has made it possible to extract users' opinions from text on the Web. There are many ways for users to post their opinions on the Internet, and Twitter is considered to be a platform that allows users to easily tweet their opinions. However, manually extracting opinions from Twitter is time-consuming, costly, and labor-intensive due to the relatively low percentage of opinions. Therefore, some companies aim to efficiently extract user opinions from Twitter using machine learning. However, the attempt to create a dataset for building a machine learning system produced an unbalanced dataset that does not extract opinions with sufficient accuracy because the proportion of views on Twitter is small. There are solutions to this problem of insufficient teacher data, such as utilizing knowledge from other domains through transfer learning. Although transfer learning is sometimes used to solve such problems, accuracy cannot be improved if the knowledge domains are far apart before and after the transfer. Therefore, we proposed a new method called OTR, which stands for Opinion classifier Transferred from Review data. OTR transfers knowledge of review submissions that are considered to be close in domain to opinion extraction. However, since the phrasing of review sentences and that of Social Networking Service (SNS) such as Twitter are different, there is a possibility that sufficient knowledge transfer cannot be achieved. In order to address this problem, we proposed an Opinion classifier Transferred from Review data with Pseudo-labels (OTR-P), a method that brings the domains of the source and target tasks closer. Here, the target task discriminated opinions regarding leisure facilities, and the source task estimated review ratings using Rakuten travel review data. And while performing these tasks, we attempted to bring the domains closer by attaching pseudo-labels to the tweet data. This approach improved accuracy compared to the conventional method of shifting Bidirectional Encoder Representations from Transformers (BERT).
A Preliminary Analysis of Software Metrics in Decentralised Applications

This study examines software metrics in decentralized applications (dApps) to analyze their structural and behavioral characteristics as they grow in complexity. Sixty dApps were categorized into Small (3 to 29 contracts), Medium (30 to 46 contracts), and Large (47 to 206 contracts) based on their contract count. Initial analysis showed a non-normal data distribution, leading to the use of Spearman's correlation method. Findings revealed that Medium dApps have strong correlations between metrics like 'Average Local Variables' and 'Maximum Local Variables', while Large dApps show higher correlations between 'Number of Functions' and 'State Variable Count', indicating more complex contract structures. The higher Coupling Between Objects (CBO) in large dApps suggests increased interactions with other contracts or libraries, potentially elevating security risks. These insights are valuable for developers and stakeholders in the blockchain and IoT sectors, aiding in understanding how dApps evolve with increasing complexity and the implications on software metric relationships.
Blockchain enabled Shared Mobility for Sustainable Transportation

Traffic congestion and air pollution have become critical challenges in modern urban areas, necessitating innovative solutions to alleviate these issues. The Shared Mobility or Carpooling concept has emerged as a promising approach to address heavy traffic while reducing per capita carbon footprint. However, conventional car-pooling systems pose significant security and privacy risks, including vulnerability to cyber-attacks and potential data misuse by car owners or the carpooling platform. This paper proposes a paradigm shift in the transportation sector by integrating blockchain technology into the carpooling network. As a decentralised and tamper-proof ledger, blockchain offers a robust solution to enhance security and data integrity in carpooling networks. It also enables more efficient ride-sharing operations and cost-sharing mechanisms through Smart Contracts. This paper discusses the system architecture for a blockchain-enabled shared mobility network. We envision that the proposed system addresses the issues in the current carpooling network and targets to achieve the United Nations Sustainable Development Goals 11, 12 and 13.