Reviewerly/exHarmony huggingface dataset:
Realistic Crying Simulation of 3D virtual human: A survey

Recent advancements in technology, PC facial animation is utilized in a critically large number of fields that carried human and social to contemplate PC games, films, and intuitive sight and sound reality development. this study aims to pro-vide a survey of efficient computer facial animation techniques. The proposed approach consists of two basic techniques for the generation of extreme 3D facial animation expression; facial expressions were created and described using FACS. This system disintegrates facial expressions into minor units referred to as Action Units (AUs). The generation of the emotional facial expressions relied on the combination of the independent AUs. Then, crying teardrops were created following the rules of physics by using the SPH method. The SPH rule was used to simulate and control the particles in the tears drop. Expressions such as happiness and sadness were created with FACS, followed by the addition of the tears based on the SPH rules. This provides the avatar tears that portrayed the desired expression. The study succeeded in combining the FACS & SPH approaches in creating a system that can express and implement extreme facial animation expressions. there are still areas that need to be furthered, such as modeling of muscle movement in certain expressions like crying.
Robust Fractional Nonlinear State Estimation Against Random Incomplete Measurements and Unknown Noise Statistics

Focusing on the challenge of the measurement of random packet loss and unknown or inaccurate noise characteristics in lossy networks for engineering practice, this article investigates the state estimation problem for increasingly widely used nonlinear fractional-order stochastic systems. To tackle this issue, a modified uncertainty measurement equation with random packet loss is provided. Within the central difference Kalman filter (CDKF) framework, the matrix diagonalization technique is first adopted and it is combined with the Stirling interpolation formulation to ensure the adequate stability and wide practicality of the proposed algorithms. A robust fractional CDKF (RFCDKF) is presented to address the challenge of measurement loss. Integrating the scenarios of lossy networks and unknown noise characteristics, a recursive algorithm, namely, RFCDKF with unknown noise characteristics (RFCDKF-UN) is proposed based on the maximum likelihood criterion. This filter allows parallel estimation of state and noise parameters, where an adaptive sliding window is introduced to reduce redundancy. Last, use is made of a lithium-ion battery system and a nonlinear system to verify the effectiveness, practicality, and superiority of the proposed algorithms.
Multi-view Based Entity Frequency-Aware Graph Neural Network for Temporal Knowledge Graph Link Prediction

Inferring missing facts in temporal knowledge graph (TKG) is a fundamental and challenging task. Existing models typically use representation learning to solve this problem. However, most of these models fall short of capturing multi-hop structural information and general preferences for future emerging facts when implementing the representation of target nodes. In addition, most of them use recurrent neural networks to achieve the aggregation of temporal information, which is not only less scalable as the time step increases, but also fails to explicitly address the problem of temporal sparsity of entity distribution in TKG. To address the above problems, we present a MEFGNN (Multi-view based Entity Frequency-aware Graph Neural Network) framework that learns node embedding to capture structural evolution of TKG by combining Multi-view Graph Neural Network (MGNN) and Entity Frequency-aware Attention Network (EFAN). Experiments on three real datasets show that MEFGNN outperforms state-of-the-art methods, our ablation study also validates the effectiveness of MGNN and EFAN.
A Study on the Collision of Artificial Intelligence and Art Based on Generative Adversarial Networks (GAN)

The cross-collision of artificial intelligence and art has attracted significant attention in related fields, such as the migration and integration of painting styles. Due to the inherent differences of different painting techniques, direct application of existing methods does not bring satisfactory results. This paper proposes a multi-layer Pyramid Generative Adversarial Network (MPGAN), an end-to-end Generative Adversarial Network(GAN) based architecture, whose advantages are in image quality and better-scaled fusion of painting styles compared with state-of-the-art algorithms demonstrate the effectiveness of the method. The method's effectiveness is demonstrated by its advantages in image quality compared to the state-of-the-art algorithms and its better scaling fusion of painting styles.
Credit Card Fraud Detection Using Deep Learning Based on Auto-Encoder

Fraudulent activities in financial fields are continuously rising. The fraud patterns tend to vary with time, and no consistency can be observed in this regard. The incorporation of new technology by fraudsters is the reason for the execution of online fraud transactions. Given the volatility of the fraud patterns, a good fraud detection model must be able to evolve and update itself to the changing patterns. Thus we aim in this paper to analyze the fraud cases that are unable to be detected based on supervised learning or previous history, create an Auto-encoder model based on deep learning and Compare and assess the performance of the model based on data from different parts of the world and check for the demographic diversity of fraud patterns thereby inferring that the data from which part of the world the model fits the best. The proposed algorithm, deep learning based on the auto-encoder (AE) network is an unsupervised learning algorithm that utilizes backpropagation by setting the inputs and outputs identical. In this research, the Tensorflow package from Google has been employed to implement AE by using deep learning. The accuracy, precision, recall, F1 score and area under the curve(AUC) are all executed to assess the performance of the model.
The Cryptocurrency Participation Puzzle

We show that ongoing zero portfolio weights in cryptocurrency are surprisingly difficult to generate in a standard Bayesian portfolio theory framework. With ten years of prior data, equity market investors would need very pessimistic priors on mean returns to justify never having bought cryptocurrency: -10.6% per month for Bitcoin, and -19.6% per month for a diversified portfolio of cryptocurrencies. Moreover, most priors that involve never purchasing cryptocurrency imply that investors should short cryptocurrency. Optimal absolute weights are generally small but non-trivial (1-5%), frequently positive, and fairly smooth despite returns being volatile. Under a wide range of priors, the certainty equivalent gains from cryptocurrency are comparable to international diversification and exceed the size anomaly. Trading costs (ambiguity aversion, storage, fees) would need to be enormous to justify non-investment, over 21% per year for Bitcoin and 39% for a diversified cryptocurrency portfolio.
Classification of (<i>q</i>, <i>q</i>)-Biprojective APN Functions

In this paper, we classify <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(q,q)$ </tex-math></inline-formula> -biprojective almost perfect nonlinear (APN) functions over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {L}\times \mathbb {L}$ </tex-math></inline-formula> under the natural left and right action of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathop {\mathrm {GL}}\nolimits (2, \mathbb {L})$ </tex-math></inline-formula> where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {L}$ </tex-math></inline-formula> is a finite field of characteristic 2. This shows in particular that the only quadratic APN functions (up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathsf {CCZ}}$ </tex-math></inline-formula> -equivalence) over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {L}\times \mathbb {L}$ </tex-math></inline-formula> that satisfy the so-called subfield property are the Gold functions and the function <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\kappa: \mathbb {F}_{64} \to \mathbb {F}_{64}$ </tex-math></inline-formula> which is the only known APN function that is equivalent to a permutation over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {L}\times \mathbb {L}$ </tex-math></inline-formula> up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathsf {CCZ}}$ </tex-math></inline-formula> -equivalence as shown in Browning et al. (2010). Deciding whether there exist other quadratic APN functions <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathsf {CCZ}}$ </tex-math></inline-formula> -equivalent to permutations that satisfy subfield property or equivalently, generalizing <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\kappa $ </tex-math></inline-formula> to higher dimensions was an open problem listed for instance in Carlet (2015) as one of the interesting open problems on cryptographic functions.
Usability Testing of MOOC Prototype Using SUS (System Usability Scale) Method

One way to improve civil servants' performance is competency development. Training is part of competency development that is widely used but still has limitations in implementation. There is a new idea with the use of e-learning in training that is expected to solve the various shortcomings of the classical training model. MOOC (Massive Open Online Course) is a currently popular e-learning model with an open and unlimited character expected to be applied in training with constraints on space and time limitations. This research discusses the usability test on the MOOC prototype that has been made using Axure software. Usability testing of the prototype uses the System Usability Scale (SUS) method to measure the usability level of a system on the user side. The result of the SUS score is 72.75, which means it has a suitable category for the system to be used as a reference to continue the implementation of application development.
Pedestrian Re-Identification Algorithm Based on Multivariate Manifold Metric-Anti-Noise Manifold Space Learning

Deep learning is widely used in pedestrian re-identification, but pedestrian images are susceptible to changes in illumination, environmental noise, and other factors. These factors have affected the in-depth promotion of this type of method. First, the existing models cannot effectively extract the structural information of low-light pedestrian samples. Second, environmental noise information will cause the model to fail to extract stable and reliable feature information. In this paper, to solve the problems as mentioned above, we propose a multi-group manifold metric learning model. Our method can obtain manifold conditions under different lighting conditions. It can retain the inherent structure of low-dimensional spatial data in high-dimensional space, and realize the extraction of features of pedestrians in low-light conditions. In addition, we proposed an anti-noise manifold space learning method and designed a manifold module network structure. The manifold modules are stacked layer by layer to construct a manifold convolutional neural network further. Our method can extract robust pedestrian image features and reduce noise disturbance to the model. We use noise and low-light images as the main experimental objects. The experimental results show that our proposed method can achieve the most advanced performance in multiple ReID data sets.
Phishing as a Cyber Security Threat

The aim of this article is to present the threat of phishing within the issue of cyber security and education as one of the tools to reduce it. The article presents procedures for verifying the level of awareness of this threat in a group of selected employees of medical facilities. These procedures consist of a theoretical and a practical part. The theoretical part defines the basic framework of phishing attacks. It describes the basic knowledge and definitions that need to be mastered to understand the given area. The practical part consists of testing the readiness of a specific reference group of users through phishing trainings and phishing tests. The data collected from the testing was further analyzed and compared to determine the potential use of phishing training and phishing testing in organizations.
Writer Identification using Handwriting Samples

The writer identification task has received a lot of interests due to its variety of applications in forensic analysis and security systems. However, research studies have focused on offline handwritten document analysis and mostly in English handwritten scripts. The research in the field of Indian Languages is very limited because of lack of suitable databases. The proposed methodology for writer identification is based on Bangla handwriting script and consists of two phases: Feature extraction using Local Binary Pattern and Machine Learning algorithms using Support Vector Machine and K-Nearest Neighbor classifiers. The average accuracy achieved through this method is 93.34% through Support Vector Machine and 90% through K-Nearest Neighbor algorithm. Different experiments have been carried out in terms of number of writers, types of scripts etc. to analyze the impact of various factors in the accuracy of the proposed algorithm.
Virtual Machine Placement Techniques Based on Biological Models

Cloud computing is a recent trend of managing virtual spaces for holding information, accessing them through different devices. With green computing as a predominant design approach, managing energy efficiently is the proven solution to reduce emission of greenhouse gases. Count of physical machines can be optimized into a considerable number of data centers through which dynamic migration of information can be regulated. Consolidation process, followed by effective placement of VMs, can further improve the quality of services offered by cloud service providers. In the same context, placing the virtual machines within a specific region in considerable proximity of physical machines, is a renowned strategy for achieving energy efficiency in virtual environments. Optimization algorithms are, at times, inspired from biological models to deliver quality of service parameters and refining cost of communications, energy utilizations, managing resources and hence meeting the user expectations in terms of deadlines. A detailed review of biological models for constructing the taxonomy of virtual machine placement techniques is presented in this literature survey. The fundamental ideologies of placing virtual machines, their pros and cons, achievement of tangible and intangible factors, meeting the requirements and expectations of end users, issues and challenges in the design and implementation are discussed in detail. Different strategies, their approaches and optimization algorithms, comparisons of performance in real time and simulated platforms are presented for better understanding of the models. The common list of parameters which have to be satisfied for efficient functioning and energy management are listed. Finally, the article concludes with future prospects of biological models.
UA-HGAT: Uncertainty-aware Heterogeneous Graph Attention Network for Short Text Classification

During the last decade, deep learning has played a significant role in improving text classification which is widely applied in question answering systems, comment extraction, sentiment analysis, etc. Considering that few labeled training data are available, semi-supervised short text classification needs to be studied urgently. Existing works focus on long texts and cannot achieve satisfactory performance on short texts due to the sparsity, non-standardization, and insufficient labeled data. In this paper, we propose a novel uncertainty-aware heterogeneous graph attention network for semi-supervised short text classification (UA-HGAT), which makes full use of limited labeled data and large amounts of unlabeled data via exploiting an uncertainty-aware mechanism. In the model, entity description from Wikipedia is introduced to empower short texts, and word-level information is acquired by Word2Vec. The entity description and word-level information are combined with topic information to address the issue of short text sparseness. In particular, we introduce uncertainty-aware mechanism and negative sample learning to relieve the erroneous high confidence predictions from poorly calibrated models, so that it can offer enough pseudo-label samples for the model training. Extensive experiments show that our model significantly outperforms the SOTA methods on AGNews and Snippets datasets.
From Ontology to Knowledge Graph Trend: Ontology as Foundation Layer for Knowledge Graph

Ontology and Knowledge Graph (KG) are two hotspot topics in Semantic Web and Artificial Intelligence (AI) fields. They have gained their importance due to the thriving development of the Internet in this century coupled with the explosive growth of data, which leads to increased demand for ontologies or KG to promote the Semantic Web and AI applications. Many researchers conflate ontology with KG, especially when even Wikipedia refers to both terms as synonymous; however, the difference is obvious. In this paper, we will highlight the differences between ontology and KG. Also, we have provided a redefinition for the ontology tuple and explained how the ontology is a foundation layer for KG.
L-FPN R-CNN: An Accurate Detector for Detecting Bird Nests in Aerial Power Tower Pictures

Today's power inspection bird's nest detection brings difficulties to object detection due to problems such as few object samples, similar backgrounds, and occlusion by towers. In this paper, an improved Sparse R-CNN model L-FPN R-CNN is proposed. Firstly, an ex-grid initialization method is proposed for learnable proposal boxes in Sparse R-CNN to improve the positioning accuracy of proposal boxes. Then, the SiLU function is introduced into the dynamic detection head of the original Sparse R-CNN network, which makes the network have stronger convergence ability. Finally, a feature pyramid structure L-FPN that fuses low-level features is proposed to improve the richness of feature maps. The experimental results show that the average accuracy of the algorithm is 2.9% higher than the original algorithm, and it has a good detection effect on the bird's nest in the aerial electric power tower image.
Efficient Data Encoding and Decoding for Quantum Computing

Noisy Intermediate-Scale Quantum (NISQ) devices face many critical challenges that limit their usefulness for practical applications. A primary challenge is classical-to-quantum (C2Q) data encoding, which requires specific circuits for quantum state initialization, particularly for I/O intensive applications. The required state initialization circuits are often complex, and violate decoherence constraints. Another critical challenge for quantum computers is quantum state readout or quantum-to-classical (Q2C) data decoding. The general approach for Q2C involves repeated sampling of the quantum circuit, which often incurs significant overhead in the overall execution time. In this paper, we propose time-efficient methods for C2Q data encoding and Q2C data decoding for quantum algorithms. Decoherence optimized circuits for C2Q data encoding are presented along with analysis of their circuit depths. For Q2C, a novel approach based on more efficient sampling of the output state using the Quantum Wavelet Transform is proposed. The proposed methods are experimentally evaluated on a state-of-the-art quantum computing device from IBM Quantum using realistic multi-spectral data. Experimental results are consistent with our theoretical expectations and confirm the efficiency of our proposed methods compared to existing techniques. More specifically, our proposed C2Q method demonstrates a theoretical 2x reduction in circuit depth which resulted in improving the experimental execution time compared to the state-of-the-art, while our Q2C method achieved a maximum of 89% reduction in circuit sampling time.
Generating Adaptive Targeted Adversarial Examples for Content-Based Image Retrieval

Massive accessible personal data on the Internet raises the risk of malicious retrieval. In this paper, we propose to conceal the images with the targeted adversarial attacks on content-based image retrieval. An imperceptible perturbation is added to the original image to generate adversarial examples, making the retrieval results similar to the target image but look completely different. Previous work on the targeted attack for image retrieval only introduces a target-specific model and needs to train the model each time for new targets. We extend the attack adaptability by exploiting the target images as conditional input for the generative model. The proposed Adaptive Targeted Attack Generative Adversarial Network (ATA-GAN) is a GAN-based model with a generator and discriminator. The generator extracts the features of origin and target, then uses the Feature Integration Module to explore the relation between the target and original image to ignore the origin feature while paying more attention to the target. Simultaneously, the discriminator distinguishes the realness and ensures the adversarial example is similar to the origin. We evaluate and analyze the performance of the adaptive targeted attack on popular retrieval benchmarks.
Persuasive Games for Physical Activity in App Stores: A Systematic Review

Persuasive games (PGs) are effective at motivating desired behaviour change using various strategies. Over the years, increasing research attention has been focused on developing PGs targeted at motivating behaviour change in various domains, including physical activity. This paper presents a systematic review of 45 PGs from app stores with the aim to: (1) deconstruct and highlight trends with respect to the persuasive strategies employed and the various ways the strategies were implemented; (2) examine for relationships between the strategies employed and games rating - effectiveness, (3) understand the effective genres for physical activity PGs and (3) uncover pitfalls of existing PGs. Our review showed that the liking strategy, which deals with making games visually attractive, is the most implemented persuasive strategy in physical activity PGs existing on app stores. We revealed that the three most popular genres for physical activity PGs are casual, simulation, and adventure. We found a negative correlation between the number of persuasive strategies employed and game effectiveness (app average rating) for PGs reviewed. Based on our findings, we offer suggestions for developing physical activity PGs that can effectively promote behaviour change.