Reviewerly/exHarmony huggingface dataset:
Dual-grained global graph neural network for session-based recommendation

The session-based recommendation (SBR) is a task to predict user next action based on anonymous behavior sequences. Most existing studies utilize global context in conjunction with current session information to improve recommendation results. However, the local and global perspectives have an inherent conflict, and it has been a great challenge to integrate the two types of information. In this paper, we propose a novel recommendation method, namely, dual-grained global graph neural network (DG-GNN), to capture collaborative information from all sessions in a more subtle manner to model current user preferences. Specifically, we convert the local and global content into two graphs and process them with the corresponding encoders. The local section exploits the graph attention network to learn a set of item-level embeddings. The global section collects the pairwise item transitions relevant to the current session for constructing graphs and exploits a lightweight encoder to learn a single session-level embedding, which reduces the influence of irrelevant information from global content on the local section. Moreover, a new position embedding mechanism is proposed to generate different position embeddings depending on the length of sessions for enhancing the ability to capture position information. Experiments on real-world datasets demonstrate that DG-GNN outperforms other state-of-the-art methods.
Cost-sensitive random GBDT based anomaly detection method for cloud platform traffic data

In order to solve the problem of low classification accuracy of the cloud platform anomaly detection model, this paper proposes a cost-sensitive random GBDT anomaly detection method, considering the different classification costs of different types of data. In this paper, a cost- sensitive stochastic GBDT-based anomaly detection method is proposed based on considering the different classification costs of different categories of data. The method first introduces the cost matrix into the design of the loss function to establish a cost-sensitive loss function to reduce the classification cost of different categories of data; and in order to reduce the computational effort and improve the computational speed, the idea of stochastic gradient boosting is introduced into the GBDT method. And the effectiveness of the method proposed in this paper is proved by simulation experiments.
iCLAS: an improved secure and efficient certificateless aggregate signature scheme suitable for vehicular ad hoc networks

With the development of the Internet of Things (IoT) and the intersection of electrification, networking and intelligence, the risks of vehicle operational safety, data security and network security are intertwined and overlapping, and the security situation is more complex and severe. Thumbur propose a certificateless aggregated signature for vehicular ad hoc networks (VANETs) to solve the authentication and efficiency problems in vehicular networks, and prove the unforgeability of the scheme based on the elliptic curve discrete logarithm problem. In this paper, we propose a new certificateless aggregated signature scheme based on this scheme and prove its security under the random prediction model. Finally, we compare and analyse the computational and communication efficiency of the scheme and show that the computational and communication costs of the scheme are lower.
Multi-UAV trajectory planning based on differential evolution of Levy flights particle swarm optimization

Multi-UAV cooperative trajectory planning model with a complex battlefield environment is difficult to establish, and the solution is complex. Aiming at these problems, a more realistic optimal combination model is established, and a particle swarm optimization with based on levy flight and differential evolution (LFPSO-DE) algorithm based on embedded Levy flight strategy is proposed. First, this work establishes a multi-UAV cooperative trajectory planning problem model in a complex environment, and establishes a comprehensive cost function to represent the complex environment; Secondly, aiming at the problem that the PSO algorithm is easy to fall into the local extremum, the Levy flight strategy is introduced to improve the particle swarm position update formula, and the inertia weight and learning factor are optimized, and then combined with the improved DE algorithm, the convergence speed and accuracy of the particle swarm are improved , while enabling the algorithm to have a better ability to explore; Finally, LFPSO-DE algorithm is compared with other algorithms in a variety of test functions, and then applied to multi-UAV trajectory planning experiments. The experimental results show that the LFPSO-DE algorithm has a good convergence speed and can effectively deal with the problem of falling into a local extremum. The planned trajectory is better in terms of voyage, speed, and time-consuming voyage, which has practical application value.
Bi-directional LSTM-attention Chinese text classification model based on subsampling optimization

This paper proposes a text classification model based on a bi-directional LSTM network with attention mechanism and subsampling in the word vector stage. Firstly, we use the Skip-gram model in Word2Vec for feature extraction, and then combine the two-way LSTM network with subsampling to extract and classify the key semantic information in the text, and integrate the Attention mechanism to optimize the generation weights, enhance the feature transfer between layers, and focus attention on the high weight words. Experimental results on different categories of open datasets show that the proposed model improves the recognition and extraction of high-featured content compared to a single network, and can effectively improve classification accuracy.
Transductive few-shot image recognition with ranking-based multi-modal knowledge transfer

Few-shot image recognition is a challenging task that aims to train a model with limited labeled images for image classification. To overcome the issue of insufficient image information in few-shot learning (FSL), this paper proposes to incorporate textual semantic information for guiding knowledge transfer to achieve more robust models. However, existing multi-modal guidance methods often necessitate aligning feature spaces from different modalities, which not only adds extra model parameters and training costs but also increases overfitting risks in FSL. To address these issues, this paper proposes a multi-modal method based on ranking instead of training, called Ranking-Based Multi-Modal Knowledge Transfer (RMMKT). Specifically, our RMMKT method (1) measures inter-class correlations across different modalities and performs multi-modal fusion based on ranking to avoid the need for training multi-modal feature adapters, (2) utilizes the multi-modal metrics to transfer base classes information to novel classes, which improves the stability of model, and (3) introduces the maximum a posteriori estimation for prototype generation and image classification without training. The proposed method was evaluated on commonly used FSL datasets and achieved state-of-the-art performance. RMMKT provides a training-free transductive few-shot learning strategy that achieves multimodal fusion and knowledge transfer without introducing extra model parameters for novel tasks, thus enhancing model stability and accuracy.
Multi-granularity coal mine risk conceptual prediction based on time-varying cloud model

The big data of coal mine was characterized by large scale, many influencing factors and weak correlation. The existing big data mining based on quantitative data analysis usually adopts fixed framework processing, which is easy to fall into the dilemma of overfitting and cannot acquire elastic knowledge across layers. From the perspective of coal mine monitoring and management, a multi-granularity time-varying cloud model is proposed based on a multi-granularity semantic transformation model and a grey prediction model to achieve the conceptual prediction of coal mine gas concentration risk. The new model first converts the gas concentration data into semantic concepts using a semantic transformation model, then processes the sequence of semantic concept sets for randomness and oscillation, and builds a grey prediction model for them, which is used to project future semantic concepts. The new model is compared with the existing model, and the experimental results show that the new method can not only predict coal mine risk concepts at different granularities, but also has high prediction accuracy.
Research on low-pass filter and denoising autoencoder for side channel attack

The original energy trace dataset have low signal-to-noise ratio which extremely seriously affects the efficiency of side channel attack. For features in energy analysis attacks, this paper proposes a noise reduction method solving the problem that the traces mainly focus on desynchronization and gaussian noise. By combining the low-pass filtering and the DAE model used in this paper, the energy trace is reduced to achieve a high signal-to-noise ratio and expose the POI position of the leakage point clearly. As the image indicates, this work increases the signal to noise ratio of the noise energy trace by 75.9%.
A Background to the Information Design Library

Preview this article: A Background to the Information Design Library, Page 1 of 1 < Previous page | Next page > /docserver/preview/fulltext/idj.00016.pet-1.gif
Mutable order-preserving encryption scheme based on boundary search tree

The main task of a ciphertext database is to store ciphertext data and perform simple operations on ciphertext data while ensuring data security. In this paper, the problem that mutable order-preserving encryption(mOPE) scheme in the ciphertext database system requires frequent interaction between client and server to locate the ciphertext data location during ciphertext insertion and ciphertext retrieval is addressed, which leads to performance loss. B-mOPE scheme is proposed as an optimized mOPE scheme for ciphertext databases by designing a new data structure, a boundary search tree. Experimental results show that B-mOPE scheme has a significant improvement in execution performance compared with original mOPE scheme and satisfies the security criteria of the order-preserving encryption (OPE) scheme.
Embracing Open Relationships with Our Network Tech

In this article, I argue that in order to design for trust, we must first update our conceptions of trustworthiness.Networks are inherently poly-nodal.They are created by and are productive of many interconnected relations between people, energy, and machines.And yet, our conceptions of trust and fidelity are ill-fittingly monogamous.Drawing from feminist theorists-including Sue Campbell, Donna Haraway, and Alexis Shotwell-I propose an ethical non-fidelity for our networked technologies.Etymologically, fidelity shares a Latin root with fealty.As many feminist theorists have argued, the Euro-American version of marriage descends from feudal practices of property ownership.Current economic models for networks wed our digital doppels to platform and provider policies with no option for negotiation or even comprehension of the terms to which we will surely comply.This odd marriage of individual propriety to proprietary network is most obvious in the terms and conditions to which we all (coercively) agree in order to access everything from our email to social media.These unread, banal confirmations of contractual obligations create expectations that are then inevitably dashed.Every reported data leak feels like lipstick stains on a partner's collar; just more proof of ill-placed trust.Rather than chasing authentic(ated) relationships based on an outdated version of trust, it might be time to embrace the radical openness of relationality.Building from ethical non-monogamy, I propose a future network ethics based on responsibility, negotiation, and the inherent vulnerability of relationality.I close by exploring how a commitment to mutual flourishing and responsiveness would reconfigure networks and trust as we practice them.
Designing Integrative STEM Learning Materials for Junior High School Mathematics Classroom: What Works (and Not)?

The demand to make science and mathematics learning more relevant and meaningful leads to integrative stem learning, a teaching approach that integrates science, technology, engineering, and math (STEM) into a cohesive and interdisciplinary learning experience. Even though STEM has been consistently increasing in popularity in Indonesia, the lack of learning materials is persistent. This paper
Penerapan Metode Pengamatan Objek Langsung di Kelas dalam Meningkatkan Kemampuan Menulis Puisi Bebas di Sekolah Menengah Pertama

Abstract: Application of Direct Object Observation Method in the Classroom in Improving Free Poetry Writing Skills in Junior High School. This study aims to determine the implementation of the method of observing objects directly in class in improving the ability to write free poetry in junior high school. This research approach uses a qualitative approach with the type of classroom action research using research procedures including: planning, implementing actions, observation and reflection. The location of this research was carried out at SMP Negeri 5 Mandai. The results showed that there was an increase in student learning activities from cycle I to cycle II, namely the increase in the number of students in listening to teacher explanations, asking questions, answering questions, student learning outcomes also increased, namely the total score obtained in cycle I of 555 and in cycle II of 958. So that the application of the direct object method can improve student learning outcomes in junior high school.
A Novel Method for Colorizing Black-And-White Videos And Images Utilising Faster R-CNN

Black and white image & video colorization is a laborious and time-consuming manual procedure. Conventional techniques like Photoshop editing take a month to finish one photograph and need substantial study. Deep learning models can be used to implement enhanced picture colorization approaches to solve this issue. As it combines two obscure fields, deep learning and digital image processing, there has been an increase in interest in the literature on picture colorization in recent years. Researchers have taken advantage of the advantages of end-to-end deep learning models and transfer learning to automatically extract picture characteristics from the training data, which can speed up the colorization process with little human interaction. The colorization of CCTV images during unwelcome occurrences is one such use of this technology. By examining the colour of clothes, automobiles, or other things in CCTV footage of crimes or accidents, detectives might glean important details. Finding these nuances in the typical black and white film might be difficult, but colorization can aid with clarity and increase the investigation's accuracy. To do this, feature extraction in deep learning models like Faster R-CNN is employed, which improves performance by using previously trained models. The performance of many Faster R-CNN models is compared in order to choose the most effective model for the task. Moreover, a realistic and cohesive output image is produced using the Pix2Pix algorithm.
Clustering Food Security Levels based on Population, Area of Harvested Land, and Disaster Risk Index in Indonesia

Problems threatening food security are reduced agricultural land and decreased production caused by climate change. In 2 years, the rice harvested area in Indonesia has decreased by around 6.33%. This research aims to know how food security is in Indonesia.The secondary data is then processed using the Microsoft Excel application and analyzed using Machine Learning in Rstudio. Several variables were examined, Population, Area of Harvested Land, Productivity of Rice Commodity, and Level of Disaster Risk. Based on the literature, a food security ratio is calculated to get the index that will explain the deficit or surplus of food availability.This research shows that the model has an accuracy rate of 93.4%. Strengthening the results given by the elbow method, the gap statistical method ensures that the optimal number of clusters recommended by machine learning is k2, so the next process is to group districts/cities into 2 (two) clusters.a model can be formed that can be used to predict the condition of food security fairly accurately (accuracy level 90%). Cities in Indonesia when clustered based on this research can optimally be divided into two clusters which show the deficit and surplus of food.
Sustainability Reporting Practices in UAE Business Firms, the Society, and Stakeholders' Insights

Organizations are complex entities that operate with a set of internal regulations, instructions, procedures, and achievements that contribute to the successful execution of their goals and objectives. However, these accomplishments may not be widely known, even among most employees, since the effectiveness of organizations is largely dependent on the level of culture and engagement within the organization. In addition to internal operations, organizations must also consider their external relat
Unsupervised Super Resolution in X-ray Microscopy using a Cycle-consistent Generative Model

X-ray microscopy (XRM) is a tomographic imaging modality that has gained interest in the context of understanding bone-related diseases on the micro scale due to its high spatial resolution and strong bone to soft tissue contrast. Although in-vivo imaging of bone structures on the micro scale is desired from a medical perspective, high radiation dose so-far prohibits imaging living animals. Research has been focused on generating high-quality reconstructions while maintaining a low X-ray dosage. However, low dose acquisitions result in noisy images with a lower resolution. This study focuses on using an unsupervised deep-learning approach to accurately reconstruct high-resolution (HR) XRM images from their noisy low-resolution (LR) counterparts. We consider an unsupervised approach in a general case where paired data (low-/high resolution pairs) are unavailable. We propose the use of a cycle-consistent generative adversarial network (GAN) for this super resolution task which is to learn the mapping from noisy LR to HR images. Quantitative and qualitative assessments show that our method produces accurate high-resolution XRM reconstructions from their noisy low-resolution counterparts, increasing the peak signal-to-noise ratio (PSNR)/structural similarity index (SSIM) from 18.15/0.52 (baseline) to 31.94/0.73 (proposed method). We believe that our proposed XRM super resolution pipeline provides a valuable tool toward high-resolution in-vivo XRM imaging.
An Overview of Crime Scene Sketching

Crime scene sketching is a crucial component of forensic investigation, enabling investigators to accurately document and visually represent the spatial relationships and physical evidence present at a crime scene. This abstract explores the significance of crime scene sketching as a tool for capturing essential details, aiding in the reconstruction of events, and facilitating effective communication among investigators, legal professionals, and jurors. By employing established techniques and incorporating technological advancements, such as computer-aided drafting software and 3D modeling, crime scene sketching has evolved into a comprehensive process that enhances the accuracy and reliability of investigative analysis. This abstract further discusses the key elements of a crime scene sketch, including scale, measurements, and the inclusion of relevant objects, while highlighting the role of skilled forensic artists in producing detailed and informative sketches. Moreover, the abstract emphasizes the importance of timely and meticulous sketching, as it can help preserve perishable evidence and support the investigative process. Ultimately, crime scene sketching serves as a critical tool for forensic investigations, providing a visual representation that aids in understanding, analysis, and the pursuit of justice.
Prof. Nevzat Tarhan: “Artificial intelligence Technologies will eliminate the need for physician’s wisdom”

Prof. Nevzat Tarhan made remarkable predictions about the diagnosis and treatment process of diseases by computer technologies.
Innovative context of teaching and learning in modern educational institutions: the curriculum of the discipline for higher education applicants of the degree of Doctor of Philosophy in the specialty " 014 Secondary education "

The curriculum of the discipline “Innovative context of teaching and learning in modern educational institutions” is compiled in accordance with the educational and scientific program of the third level of higher education in the field of knowledge: 01 “Education/Pedagogy” for specialty 014 Secondary education. The purpose of the discipline “Innovative Context of Teaching and Learning in Modern Educational Institutions” is to form among PhD students a systematic understanding of the basics of organizing the educational process in modern educational institutions in the context of a competency-based paradigm, methodological foundations of teaching and learning in modern educational institutions, organizational and methodological support of the educational process in modern educational institutions; practical skills and abilities to apply the most effective traditional and innovative teaching and learning methods, as well as planning, organizing and analyzing various types of classes in modern educational institutions.
Manage Pictures with Firebase Storage

We are looking to include pictures in our posts. The problem is the following: Firestore doesn’t support such large documents. We can only add text, numbers, arrays, Booleans, timestamps, etc.
Introduction to SwiftUI

This first chapter is meant for beginners in iOS development. I will walk you through the main functionalities of Xcode, your tool to create projects and run code. Then, we will create a simple application: a map displaying a few places.
Psychology of educational management and conflict management: the curriculum of the discipline – 011 Educational, pedagogical sciences

The study of the discipline "Psychology of educational management and conflict management" is a discipline of the elective component of the curriculum. This discipline forms the professional competencies of applicants for higher education who study in the specialty that is part of the field of knowledge 01 "Education". As an optional component, the study of the discipline will allow applicants for higher education to form an individual educational trajectory. The purpose of studying the discipline is the formation of future specialists' systematic knowledge of the essence of the management process and the psychological nature of conflict confrontations; mastering the basic theoretical approaches to the study of communication and management; mastering the body of knowledge for the future implementation of effective social support for various categories of the population and achieving socially acceptable results; formation of skills of analysis of meanings and content of communication acts, preservation of communicative balance and efficiency of managerial practice; improvement of practical skills of elaboration of special professional literature for the purpose of their systematization and expedient application in the administrative sphere for the purpose of standardization and harmonization of social interaction.