Reviewerly/exHarmony huggingface dataset:
The InvokeAI Dream Engine: A Tool for Generating Visual Masterpieces

The InvokeAI Dream Engine is a powerful tool for generating visual masterpieces.It uses the latest AI-driven technologies to create stunning images, videos, and other visual media.The Dream Engine is easy to use, yet it offers a wide range of features and capabilities.It is a valuable tool for professional creatives, artists, and enthusiasts who are looking to create visually stunning works of art.
Figurative and visual transformation of personality in the Internet space

The relevance of the study is due to the fact that the influence of the Internet and media resources is growing and there is a tendency to develop virtual personalities that have little to do with reality and show the features of an antisocial person.In this regard, this article is aimed at identifying aspects and characteristics of a network personality that is influenced by modern media resources.The leading methods of studying this problem are methods of analysis, deduction and classification, which will help to recognize the most basic aspects of the figurative-visual transformation of a person on the Internet, show the results of the influence of social networks on a person and demonstrate the changes that a person is exposed to in the Internet space.The article reveals and substantiates the features and aspects that are distinctive for an individual in the Internet society, negative and positive characteristics of Internet users.The author substantiates the daily interactivity of social media.Also, the most relevant and influential forms of network interaction are shown and the frequency of their use is analysed.The materials of the article are of practical value for students, teenagers, youth, parents, future teachers who should know the aspect of the manifestation of personality transformation in the Internet space.
Language model-accelerated deep symbolic optimization

Symbolic optimization methods have been used to solve varied challenging and relevant problems such as symbolic regression and neural architecture search. However, the current state of the art typically learns each problem from scratch and is unable to leverage pre-existing knowledge and datasets that are available for many applications. Inspired by the similarity between sequence representations learned in natural language processing and the formulation of symbolic optimization as a discrete sequence optimization problem, we propose language model-accelerated deep symbolic optimization (LA-DSO), a method that leverages language models to learn symbolic optimization solutions more efficiently. We demonstrate LA-DSO in two tasks: symbolic regression, which allows us to perform extensive experimentation due to its low computation requirements, and computational antibody optimization, which shows that our proposal accelerates learning in challenging real-world problems.
A Markov random field based method for removing invalid unwrapping phase points in 3D reconstruction

Abstract Fringe projection profilometry is widely used in 3D structured light due to its fast speed and accuracy. However, in the process of phase unwrapping, it is easy to cause invalid points in the edges and shadows of objects, which leads to error points in 3D reconstruction. To solve this problem, we propose an invalid points removal method based on Markov random fields. Specifically, the proposed method formulates unwrapped phase and mask maps as energy functions and uses iterative methods to minimize them. Furthermore, we validate the proposed method in a monocular structured light system and compare it with existing algorithms. Results show that the proposed method effectively identifies edges and shadows while preserving valid points, and has strong robustness and correctness.
CLIP-guided StyleGAN Inversion for Text-driven Real Image Editing

Researchers have recently begun exploring the use of StyleGAN-based models for real image editing. One particularly interesting application is using natural language descriptions to guide the editing process. Existing approaches for editing images using language either resort to instance-level latent code optimization or map predefined text prompts to some editing directions in the latent space. However, these approaches have inherent limitations. The former is not very efficient, while the latter often struggles to effectively handle multi-attribute changes. To address these weaknesses, we present CLIPInverter, a new text-driven image editing approach that is able to efficiently and reliably perform multi-attribute changes. The core of our method is the use of novel, lightweight text-conditioned adapter layers integrated into pretrained GAN-inversion networks. We demonstrate that by conditioning the initial inversion step on the Contrastive Language-Image Pre-training (CLIP) embedding of the target description, we are able to obtain more successful edit directions. Additionally, we use a CLIP-guided refinement step to make corrections in the resulting residual latent codes, which further improves the alignment with the text prompt. Our method outperforms competing approaches in terms of manipulation accuracy and photo-realism on various domains including human faces, cats, and birds, as shown by our qualitative and quantitative results.
Two-Direction Prediction Method of Drilling Fluid Based on OS-ELM for Water Well Drilling

In this study, a drilling fluid prediction method based on an online sequential extreme learning machine (OS-ELM) is proposed, which is prepared for water well drilling on the muddy clay formation of Tarim Basin, Qinghai Province. First, we investigated the mechanism linking mix ratio to fluid performance, allowing us to employ an OS-ELM algorithm derived from the extreme learning machine. Particularly, the proposed prediction method is bidirectional to identify an appropriate slurry formulation. The forward prediction model is established to predict the fluid performance, where the mud additive contents are inputs, and the drilling fluid properties parameters are outputs. Correspondingly, the backward prediction model is established to modify the slurry formula, where differences in the drilling fluid properties are inputs and percentages of slurry additives amount are output. The simulation results show that the two-direction OS-ELM prediction model can better predict the drilling fluid properties in water well drilling.
Offline Handwritten Chinese Character Using Convolutional Neural Network: State-of-the-Art Methods

Given the presence of handwritten documents in human transactions, including email sorting, bank checks, and automating procedures, handwritten characters recognition (HCR) of documents has been invaluable to society. Handwritten Chinese characters (HCC) can be divided into offline and online categories. Online HCC recognition (HCCR) involves the trajectory movement of the pen tip for expressing linguistic content. In contrast, offline HCCR involves analyzing and categorizing the sample binary or grayscale images of characters. As recognition technology develops, academics’ interest in Chinese character recognition has continuously increased, as it significantly affects social and economic development. Recent development in this area is promising. However, the recognition accuracy of offline HCCR is still a sophisticated challenge owing to their complexity and variety of writing styles. With the advancement of deep learning, convolutional neural network (CNN)-based algorithms have demonstrated distinct benefits in offline HCCR and have achieved outstanding results. In this review, we aim to show the different HCCR methods for tackling the complexity and variability of offline HCC writing styles. This paper also reviews different activation functions used in offline HCCR and provides valuable assistance to new researchers in offline Chinese handwriting recognition by providing a succinct study of various methods for recognizing offline HCC.
A Systematic Literature Review on Vietnamese Aspect-based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) is one of the principal tasks in the automatic deep understanding of texts, widely applied in a broad range of real-world applications. Many studies have been performed on different tasks and datasets for other languages (e.g., English, Chinese) to address this topic. For Vietnamese language, this topic has been attracting considerable interest in recent years. However, we found that many studies tend to repeat the research instead of inheriting and extending the previous works. Moreover, previous studies’ methods of comparison or evaluation metrics have not shown consistency and connection. This might restrict the development of future studies on this research topic. To the best of our knowledge, no research has been conducted to overview the existing studies for the ABSA research in Vietnamese language. The primary objective of this study is to provide a systematic and comprehensive review of the current Vietnamese ABSA research. More specifically, we analyze the early approaches, evaluation metrics, and available published benchmark datasets used in the Vietnamese ABSA task. We also discuss the challenge and recommend potential future directions for Vietnamese ABSA. This work is expected to provide readers with a wealth of knowledge, the research gap, and the challenges in the Vietnamese ABSA field.
The cloud campus: imagining and investing in the digital future of higher education

Calculations and decisions about financial investment in educational technologies have become key ways through which the future of higher education is imagined. Focusing on the techno financial and imaginative work of investors in digital education platforms and infrastructures, this chapter examines the ‘futures in the making’ that emerge from edtech investment practices, and the ways investors’ funding decisions instantiate particular configurations of the future in platform and infrastructure developments. The ‘digital transformation’ of HE is thus intimately tied to the practices of both imaginative ‘futuring’ and ‘capitalization’, through which the value of technologies is calculated in terms of expected future monetary returns on investment. Empirically, the chapter explores the capitalization and futuring practices of an educational technology finance and investment firm and its role in imagining and investing in the future of HE, and how markets were made during the Covid-19 crisis for online learning platform providers to operate as large-scale infrastructures for university teaching and learning. The conclusion considers the implications of these investment futuring practices and, by contrast, the prospects for ‘defuturing’ and ‘refuturing’ post-pandemic higher education according to alternative, collective visions.
Visual Exploration of Repetitive Patterns on Ancient Peruvian Pottery

The analysis and understanding of artefact properties and their relationships is a key goal in archaeological analysis of cultural heritage objects. There are many aspects of concern, including shape properties of the objects, but also appearance properties stemming from paintings and ornamentations on the object surfaces. To date, these are considered by experts mostly holistically and on a per-object basis. We present an approach for the interactive visual exploration and correlation of shape- and ornament-based properties of a large collection of ancient vessels. Our approach allows to group objects by properties, and to relate them in side-by-side and bipartite graph displays. To this end, we define an encompassing set of feature descriptors, which are leveraged to cluster the objects by properties, selected by the user. Case studies show that a comparative overview of all objects effectively supports the discovery of interesting co-occurrences of shape and ornament properties. This way, our tool opens new possibilities for the domain analysis of cultural heritage object collections by data-driven visual exploration.
THE EFFECT OF ACCEPTANCE FACTORS ON THE QUALITY OF E-LEARNING SYSTEMS AMONG HEI STUDENTS IN MALAYSIA

This article aims to examine the effect of acceptance factors on the quality of e-learning systems among HEI students in Malaysia to meet the needs of teaching and learning in line with developed countries. This study is beneficial to the world of Education, especially at the level of Higher Education Institutions that make the e-learning system one of the main mediums in the teaching and learning system. This study uses a quantitative approach through a cross-sectional survey method. The findings of the study show the perception of ease of use (β=0.342, p&lt;.05; technical support (β=0.260, p&lt;.05); lecturer characteristics (β=0.203, p&lt;.05) and the perception of usefulness (β=0.181, p&lt;.05) has a positive effect on the quality of the e-learning system. This proves that the acceptance factors influence the quality of the e-learning system among HEI students in Malaysia. The advancement and use of this technology is important to enable the country to keep pace with the country-developed countries and can produce highly knowledgeable human capital to contribute to progress in the country's education and socio-economic systems.
Toward Mission-Critical AI: Interpretable, Actionable, and Resilient AI

Artificial intelligence (AI) is widely used in science and practice. However, its use in mission-critical contexts is limited due to the lack of appropriate methods for establishing confidence and trust in AI’s decisions. To bridge this gap, we argue that instead of aiming to achieve Explainable AI, we need to develop Interpretable, Actionable, and Resilient AI (AI3). Our position is that aiming to provide military commanders and decision-makers with an understanding of how AI models make decisions risks constraining AI capabilities to only those reconcilable with human cognition. Instead, complex systems should be designed with features that build trust by bringing decision-analytic perspectives and formal tools into the AI development and application process. AI3 incorporates explicit quantifications and visualizations of user confidence in AI decisions. In doing so, it makes examining and testing of AI predictions possible in order to establish a basis for trust in the systems’ decision-making and ensure broad benefits from deploying and advancing its computational capabilities. This presentation provides a methodological frame and practical examples of integrating AI into mission-critical use cases and decision-analytical tools.
3D CNN Acceleration using Block Circulant Matrix in Frequency Domain

Recently, 3D CNNs have proven to be outstanding in applications such as video analysis, 3D geometric data, and 3D medical image diagnosis. However, the forbidden storage and computational overhead of 3D CNNs hinder their deployment on edge devices. To tackle this challenge, we propose 3D FCirCNN, an algorithm-hardware co-design approach to realize efficient 3D CNNs acceleration. At the algorithm level, 3D FCirCNN applies block circulant matrix to compress 3D CNNs for the first time and further accelerates the computation with Fast Fourier Transform (FFT), significantly reducing the storage and computational overhead of 3D CNNs. Besides, to avoid the extra computation overhead caused by frequent spatial/frequency domain switching, we introduce several 3D CNN operators in the frequency domain, thus realizing the full frequency domain computation. At the hardware level, we design an FPGA-based dedicated hardware architecture to accelerate 3D FCirCNN. Experiments on the Xilinx ZCU102 demonstrate that with only an acceptable accuracy loss, 3D FCirCNN can achieve a performance of 2692.81 GOPS with a 3.349 GOPS/DSPs efficiency, which outperforms prior works significantly.
Using Virtual Reality to Help Chinese Students Learn English

At this stage in society, countries worldwide have realized the importance of technology and its effect of it on the development of countries. In China, instead of using immersive techniques in education systems, schools still choose to use textbooks and whiteboards to teach children. This results in a lack of focus from students and an incapability to study and learn effectively. To fix this problem, Virtual Reality is put forth as an immersive and engageable solution. To analyze how well VR is affecting students, this article discusses the importance of VR usage in China and how that will help improve the quality of education for students, highlighting different ways VR has been benefiting other education systems around the world and how more countries have been implementing this into their schools. This article also discusses the current implementations of VR in Chinese education systems and how different programs that have been developing are affecting the efficiency of learning and interactivity among students positively.
Which Matters Most in Making Fund Investment Decisions? A Multi-granularity Graph Disentangled Learning Framework

In this paper, we highlight that both conformity and risk preference matter in making fund investment decisions beyond personal interest and seek to jointly characterize these aspects in a disentangled manner. Consequently, we develop a novel Multi-granularity Graph Disentangled Learning framework named MGDL to effectively perform intelligent matching of fund investment products. Benefiting from the well-established fund graph and the attention module, multi-granularity user representations are derived from historical behaviors to separately express personal interest, conformity and risk preference in a fine-grained way. To attain stronger disentangled representations with specific semantics, MGDL explicitly involve two self-supervised signals, ie fund type based contrasts and fund popularity. Extensive experiments in offline and online environments verify the effectiveness of MGDL.
GammaGL: A Multi-Backend Library for Graph Neural Networks

Graph Neural Networks (GNNs) have shown their superiority in modeling graph-structured data, and gained much attention over the last five years. Though traditional deep learning frameworks such as TensorFlow and PyTorch provide convenient tools for implementing neural network algorithms, they do not support the key operations of GNNs well, e.g., the message passing computation based on sparse matrices. To address this issue, GNN libraries such as PyG are proposed by introducing rich Application Programming Interfaces (APIs) specialized for GNNs. However, most current GNN libraries only support a specific deep learning framework as the backend, e.g., PyG is tied up with PyTorch. In practice, users usually need to combine GNNs with other neural network components, which may come from their co-workers or open-source codes with different deep-learning backends. Consequently, users have to be familiar with various GNN libraries, and rewrite their GNNs with corresponding APIs. To provide a more convenient user experience, we present Gamma Graph Library (GammaGL), a GNN library that supports multiple deep learning frameworks as backends. GammaGL uses a framework-agnostic design that allows users to easily switch between deep learning backends on top of existing components with a single line of code change. Following the tensor-centric design idea, GammaGL splits the graph data into several key tensors, and abstracts GNN computational processes (such as message passing and graph mini-batch operations) into a few key functions. We develop many efficient operators in GammaGL for acceleration. So far, GammaGL has provided more than 40 GNN examples that can be applied to a variety of downstream tasks. GammaGL also provides tools for heterogeneous graph neural networks and recommendations to facilitate research in related fields. We present the performance of models implemented by GammaGL and the time consumption of our optimized operators to show the efficiency. Our library is available at https://github.com/BUPT-GAMMA/GammaGL.
GreenSeq: Automatic Design of Green Networks for Sequential Recommendation Systems

Transformer-based models have achieved tremendous success in sequential recommendation (SR), but they suffer from consuming excessive computational resources, particularly in the inference stage. Thus, developing lightweight yet effective SR models has become a frequent demand in industrial applications, which is also in line with the ideals of Green AI and Green IR. In this applied paper, we introduce GreenSeq deployed in Alipay to automatically design Green networks that can provide appropriate recommendations with lower computational consumption in SR. Specifically, GreenSeq uses a novel multi-layer search space that allows for flexible network design and a Greenness-aware loss term for balancing efficiency and effectiveness. Experiments on benchmark datasets and A/B testing show that GreenSeq performs well while using fewer resources. GreenSeq also reduces electricity and carbon emissions in Alipay.
Denoise to Protect: A Method to Robustify Visual Recommenders from Adversaries

While the integration of product images enhances the recommendation performance of visual-based recommender systems (VRSs), this can make the model vulnerable to adversaries that can produce noised images capable to alter the recommendation behavior. Recently, stronger and stronger adversarial attacks have emerged to raise awareness of these risks; however, effective defense methods are still an urgent open challenge. In this work, we propose "Adversarial Image Denoiser" (AiD), a novel defense method that cleans up the item images by malicious perturbations. In particular, we design a training strategy whose denoising objective is to minimize both the visual differences between clean and adversarial images and preserve the ranking performance in authentic settings. We perform experiments to evaluate the efficacy of AiD using three state-of-the-art adversarial attacks mounted against standard VRSs. Code and datasets at https://github.com/sisinflab/Denoise-to-protect-VRS.
INTERNET OF MEDICAL THINGS ENABLED CLOUD-BASED BREAST CANCER IDENTIFICATION WITH MACHINE LEARNING

Breast cancer occurs when cells in the breast grow out of control. Breast cancer canspread outside the breast through lymph vessels and blood vessels when it spreads to other parts of thebody, it is said to have metastasized. Most breast cancer cases are reported in women who are 50 yearsand/or o40 years older. According to facts and figures shared by WHO (World Health Organization), itimpacts 2.1 million women every year and also causes the greatest number of cancer-related deathsamongst women. Whilst breast cancer rates are higher among women in more developed regions, ratesare increasing in nearly every region globally. Different machine learning algorithms have beenapplied to the dataset like Naïve Bayes (NB), J48 Decision tree, K-Nearest Neighbor (KNN) and ANN(Gradient Descent) have been applied among them ANN (Gradient Descent) produces the optimalresults among these classification algorithms. The proposed Internet of Medical Things EnabledCloud-Based Breast Cancer Identification with Machine Learning system model with 98.07 %accuracy has been achieved. For the proposed model 97.64 % sensitivity and 98.32 % specificity havebeen recorded. From the results produced by the proposed expert system, it's satisfactory to utilize itfor breast cancer diagnosis. The Proposed system model will be helpful for the diagnosis of breastcancer.