Reviewerly/exHarmony huggingface dataset:
360-DEGREE VIRTUAL REALITY

Virtual Reality (VR) is a technology that enables users to experience a computer-generated environment as if it were real. This is achieved by using head-mounted displays, tracking systems, and other input devices to create an immersive, three- dimensional world that users can interact with. VR is being used in industries, like gaming, entertainment, education, healthcare etc,... In gaming, VR provides a new level of immersion and interactivity, allowing players to experience new worlds and adventures in a way that was previously impossible Virtual reality (VR) technology has rapidly evolved in recent years, with significant advancements in hardware, software, and content development. VR offers users an immersive experience that can simulate real-world scenarios, environments, and interactions. This technology has been applied to a wide range of fields, including entertainment, education, healthcare, and training. In the entertainment industry, VR provides users with a unique and interactive way to consume media, from gaming and movies to virtual tourism and social experiences. In education, VR allows students to experience immersive learning environments that can enhance their understanding of complex concepts. In healthcare, VR is used for therapy and rehabilitation, allowing patients to simulate real-life scenarios in a safe and controlled environment. Finally, in the field of training, VR provides a cost-effective and efficient way to train employees in various industries, such as aviation, military, and manufacturing despite its potential, there are still challenges to overcome, such as high costs, technical limitations, and the need for more compelling and engaging content. Nonetheless, the future of VR looks promising, with further advancements and new applications yet to be discovered. KEYWORDS VR headset, VR games, VR videos, VR education, Augmented Reality(AR),360 degree video.
M-SAN: a patch-based transferable adversarial attack using the multi-stack adversarial network

In recent times, deep neural networks (DNN) have been extensively used in several areas and have achieved great success. State-of-the-art face recognition (FR) systems have gained high accuracy using DNN. However, researchers have found that DNN-based systems fail when facing an adversarial attack on images. In adversarial attacks, the adversary modifies the face images in a manner that the human does not perceive the changes in the generated image but FR systems are unable to recognize the faces correctly. We proposed a method to generate an adversarial attack. A multistack adversarial network (M-SAN) patch-based attack is generated using the generative adversarial network under the black-box settings. The M-SAN attack targets the features of the face images using a patch to fool the FR model as targeted and untargeted attacks. In the past, several attack generation methods have been presented under white-box settings. However, white-box settings need a model architecture and information about its parameters. Due to this, a single white-box attack is not able to fool different FR models. We propose an attack generation approach that is based on black-box settings in which an attacker does not have access to the target model parameters. The attack is generated with the help of the surrogate model and then transferred to the various target models. The proposed M-SAN attack is applied to FR models including FaceNet, ArcFace, and CosFace on the labeled face in the wild dataset.
Generative Adversarial Networks with Data Augmentation and Multiple Penalty Areas for Image Synthesis

The quality of generated images is one of the significant criteria for Generative Adversarial Networks (GANs) evaluation in image synthesis research. Previous researches proposed a great many tricks to modify the model structure or loss functions. However, seldom of them consider the effect of combination of data augmentation and multiple penalty areas on image quality improvement. This research introduces a GAN architecture based on data augmentation, in order to make the model fulfill 1-Lipschitz constraints, it proposes to consider these additional data included into the penalty areas which can improve ability of discriminator and generator. With the help of these techniques, compared with previous model Deep Convolutional GAN (DCGAN) and Wasserstein GAN with gradient penalty (WGAN-GP), the model proposed in this research can get lower Frechet Inception Distance score (FID) score 2.973 and 2.941 on celebA and LSUN towers at 64×64 resolution respectively which proves that this model can produce high visual quality results.
Exp function for Edwards curves over local fields

The difficulty of solving the ECDLP is the foundation of the security of elliptic curve cryptography (ECC).A connection between the lifting problem on a curve in Weierstrass form and the ECDLP has been stressed by Silverman.Based on this, in order to study the ECDLP, we compute the exponential map for Edwards curves, which are more efficient for cryptographic use, as proved by Bernstein and Lange.In this paper, using the birational equivalence between Edwards curves and elliptic curves in Weierstrass form, we extend the map Exp to Edwards curves over local fields.Furthermore, we compute the map Exp for the specific case of p-adic numbers.More recently, curves over local fields have been investigated by Tang, Xu, and Qi, who introduced a cryptosystem based on their quotient groups.
BDA: Bandit-based Transferable AutoAugment

AutoAugment is an automatic method to design data augmentation policies for deep learning, and has achieved significant improvements on computer vision tasks. However, since early AutoAugment approaches cost thousands of GPU hours, there is a recent demand to investigate low-cost search methods that can still find effective augmentation policies. In this paper, we propose a multi-armed bandit algorithm, named Bandit Data Augment (BDA), to efficiently search for optimal and transferable data augmentation policies. We leverage Successive Halving to make the bandit model progressively focus on more promising augmentation operations during the search, leading to sparse selection of operations and more generalizable augmentation policies. We also propose a computationally efficient rewarding scheme to reduce the evaluation cost of augmentation policies. Extensive experiments demonstrate that BDA can achieve comparable or better performance than prior Auto Augment methods on a wide range of models on CIFAR-10/100 and ImageNet benchmarks. Besides, BDA is 555 times and 536 times faster than AutoAugment on CIFAR-10 and ImageNet, respectively. In addition, BDA is 16 times faster than Fast Auto Augment on ImageNet. More importantly, BDA can discover policies that are transferable across datasets and models, and achieve similar performance to policies found directly on the target dataset.KeywordsData AugmentationDeep LearningRepresentation LearningBandit
Boosting Graph Convolutional Networks with Semi-supervised Training

Graph convolutional networks (GCN) suffer from the over-smoothing problem, which causes most of the current GCN models to be shallow. Shallow GCN can only use a very small part of nodes and edges in the graph, which leads to over-fitting. In this paper, we propose a semi-supervised training method to solve this problem, and greatly improve the performance of GCN. Firstly, we propose an integrated data augmentation framework to conduct effective data augmentations for graph-structured data. Then consistency loss, entropy minimization loss, and graph loss are introduced to help GCN make full use of unlabeled nodes and edges, which alleviates the excessive dependence of the model on labeled nodes. Extensive experiments on three widely-used citation datasets demonstrate our method can achieve state-of-the-art performance in solving the semi-supervised node classification problem. Especially, we get $$85.52\%$$ accuracy on Cora with the public split.
Research on face occlusion recognition algorithm based on capsule network

Capsule network has shown excellent performance in many fields, but there is not much exploration of capsule network in face detection and recognition. For traditional CNN on face detection identification problem such as characteristic information lost and too dependent on sample, a capsule network with an iterative routing mechanism is used and improved upon it. Based on this, we introduce a two-layer feature extraction layer, and then add a spatial attention mechanism in the pooling process, so that the network pays more attention to the contour information of the face and retains the features of key areas of the face to the greatest extent. In order to increase the accuracy of the recognition algorithm, the open-source tool called “MaskTheFace” is employed to create a dataset of one-to-one correspondence between faces and occluded faces. Through the experiment, it can be seen that the improved algorithm has better performance than the traditional algorithm. Although the study is exploratory, it provides some insights into masked face identification.
Different Preferences for Emojis in the Aspect of Age

It is well-established that emojis play an essential part when communicating with others online. This study investigates the different preferences of emojis among different age groups. The interview is done to support the investigation. Respondents are divided by age into two groups. (respondents aged from 10-30 and respondents aged from 31-50) They are asked to answer several questions which are designed based on the theory of conversational implicature. Respondents' answers are collected and analyzed. The result showed that there are differences in emoji preferences between age groups. The result suggests that there is a distinguished difference between young people and older people in the choice of emojis. Older people tend not to use emojis that convey negative emotions, and the younger people can feel the emojis' implications hidden in it through the context.
Context Matters: Distilling Knowledge Graph for Enhanced Object Detection

The human visual system is capable of not only recognizing individual objects but also comprehending the contextual relationship between them in real-world scenarios, making it highly advantageous for object detection. However, in practical applications, such contextual information is often not available. Previous attempts to compensate for this by utilizing cross-modal data such as language and statistics to obtain contextual priors have been deemed sub-optimal due to a semantic gap. To overcome this challenge, we present a seamless integration of context into an object detector through Knowledge Distillation. Our approach intuitively represents context as a knowledge graph, describing the relative location and semantic relevance of different visual concepts. Leveraging recent advancements in graph representation learning with Transformer, we exploit the contextual information among objects using edge encoding and graph attention. Specifically, each image region propagates and aggregates the representation from its highly similar neighbors to form the knowledge graph in the Transformer encoder. Extensive experiments and a thorough ablation study conducted on challenging benchmarks MS-COCO, Pascal VOC and LVIS demonstrate the superiority of our method.
Investigating Effective Domain Adaptation Method for Speaker Verification Task

CN-Celeb data set is a popular Chinese data set used for speaker recognition. In order to improve the speaker recognition performance on CN-Celeb, this paper mainly makes efforts in following two aspects: applying scenario information and using the self-supervised learning (SSL) model to the speaker recognition system. Compared to the SSL model, scenario information, named domain embedding, is more effective. We integrate speaker embedding and speaker scenario information abstracted as word embedding and puts forward several back-end processing methods. These methods are more suitable for each registered speaker with multiple enroll utterances. Moreover, data augmentation is also investigated. Experiments show that concatenating and averaging each utterance’s domain embedding dimensions of multiple registered speakers with data augmentation shows better performance. Our proposed method effectively reduces 6.0%, 21.7% and 13.5% on EER, minDCF (P = 0.01) and minDCF (P = 0.001), respectively, compared with the baseline system. We did not observe improvement by using SSL as reported in previous works, and the possible reasons and analysis are also given in this paper.
CLNIE: A Contrastive Learning Based Node Importance Evaluation Method for Knowledge Graphs with Few Labels

Graph node importance estimation, which evaluates the importance of graph nodes, is an important graph mining problem and has been widely used in many fields such as search engines and recommender systems. Different from ordinary graphs, there are various types of nodes and relationships in knowledge graphs. The relationships in knowledge graphs encode different information, so the information of nodes in a knowledge graph is richer, which leads to the evaluation of the importance of nodes in knowledge graphs being more complicated. The existing research on the importance of nodes in knowledge graphs is mainly based on the assumption that there are enough labels, which even reach 70% of the dataset. However, there are usually few labels in reality. To better study the importance estimation of nodes in a knowledge graph when labels are sparse, we propose a node importance evaluation algorithm based on contrastive learning. First, an unsupervised contrastive loss is designed to generate rich node representations by maximizing the consistency of representations under different views of the same node. To utilize scarce but valuable labeled data for learning node importance, we design a semi-supervised contrastive loss, which solves the problem of failing to determine positive and negative examples in the task of node importance evaluation. In order to improve the effectiveness of contrastive learning, we propose a negative sampling strategy based on label similarity. Negative samples are constructed according to the label difference. Finally, the experimental results on real-world datasets confirm the effectiveness of CLNIE, which achieves a significant performance improvement over the state-of-the-art solutions.
Raven: Benchmarking Monetary Expense and Query Efficiency of OLAP Engines on the Cloud

Nowadays, it is prevalent to build OLAP services on cloud platforms. Cloud OLAP adopters are eager to understand and characterize the performance of OLAP engines on the cloud. However, traditional OLAP benchmarks are usually designed for on-premise environments. When evaluating cloud OLAP engines, they have limitations on cloud environment adaption and cloud scenario benchmark execution. To address these issues, this paper proposes Raven, a cloud-oriented OLAP benchmark with flexible system architecture and diversified workloads. Raven supports cloud service deployment and various cloud OLAP engine integration. In addition, to simulate complex cloud query scenarios, we design a group of timeline-based and service-oriented workloads. We implement Raven on the Amazon AWS cloud platform and use it to evaluate typical types of widely-used OLAP engines, including Presto, SparkSQL, Kylin, and Athena. Experimental results show that Raven can effectively benchmark diversified OLAP engines. Besides, Raven can benchmark various configuration settings of an identical OLAP engine. We also explore an OLAP case study on the cloud using Raven.
Graph Attention Transformer Network for Robust Visual Tracking

Visual tracking aims to estimate the state of an arbitrary object in a video frame only when the bounding box is given in the first frame. However, the existing trackers still struggle to adapt to complex environments due to the lack of adaptive appearance features. In this paper, we propose a graph attention transformer network, termed GATransT, to improve the robustness of visual tracking. Specifically, we design an adaptive graph attention module to enrich the embedding information extracted by the transformer backbone, which establishes the part-to-part correspondences between the template and search nodes. Extensive experimental results demonstrate that the proposed tracker outperforms the state-of-the-art methods on five challenging datasets, including OTB100, UAV123, LaSOT, GOT-10k, and TrackingNet.
Predictive Model with Twitter Data for Predicting the Price Volatility of Cryptocurrencies Using Machine Learning Algorithms

Cryptocurrency trading is gaining momentum and is one of the fastest growing trading segments in the world. But cryptocurrency prices are highly volatile. This study aims to develop a predictive model to predict the price of cryptocurrency using machine learning algorithms by analyzing Twitter sentiments. This study differs from other studies by providing the tweets as input to the machine learning algorithms and analysing the important words in the tweets that influence the price volatility of cryptocurrencies. Since Bitcoin is the most widely used cryptocurrency in the market, the price of bitcoin is considered for the study. The study makes use of cryptocurrency price and Twitter data from April 4th, 2021 to May 3rd, 2021. The Twitter data is extracted and converted to a document term matrix and is used as predictor variables. Price volatility is the response variable. Three machine learning algorithms, such as support vector machine, decision tree, and random forest, were used for model building. The hyperparameters of the algorithms were tuned to enhance the accuracy. The three models were evaluated based on accuracy, sensitivity, specificity, area under the curve, and the Kolmogorov–Smirnov test. It was found that random forest provided the best accuracy compared to others. Further, the predictor variables were analysed to understand the important words in the tweets that influenced the price volatility. Due to price volatility, there is uncertainty for investors who want to use cryptocurrency as an investment opportunity. This study is a contribution towards using investor sentiment to predict the price of cryptocurrency.
Thinking Outside the Pattern-Based Box in Jazz Improvisation

Jazz improvisation has long been considered an art form centered around creativity: To successfully improvise in the genre, one must play creatively, while adhering to the stylistic traits of the jazz genre. The classic methods of teaching jazz improvisation lean, in one way or another, on pattern-based learning. Relying on patterns ensures “appropriate” improvisation but is insufficient for creating a successful solo. In this research, we conducted semi-structured interviews with jazz educators to understand how they address the tension between pattern-based teaching and cultivating improvisational creativity.
CONSTRUCTION OF UML DIAGRAM IN DESIGNING PROGRAMS

When designing a software product, it is necessary to take into account its specificity and purpose, in this case, we will consider the construction of UML diagrams for a training program for children with autism spectrum disorders.
Proposing Improvement to Communication through Animated Infographics by Observing Cultur al Allocation of Attention to Animation

The number of foreigners visiting and residing in Korea is steadily increasing, therefore new research on how to deliver information in a multicultural environment is needed. The research presented in this paper is part of a current trend in Korea which focuses on the improvement of the country’s multicultural competence. From a designer
Trusted Access Authentication Technology for Large-Scale Heterogeneous Terminals in a Vehicle Charging Network System

A vehicle charging network system has to access large-scale heterogeneous terminals to collect charging pile status information, which may also give malicious terminals an opportunity to access. Though some general access authentication solutions aimed at only allowing trusted terminals have been proposed, they are difficult to work with in a vehicle charging network system. First, among various heterogeneous terminals with significant differences in computing capabilities, there are inevitably terminals that cannot support computations required for cryptography-based access authentication schemes. Second, though access authentication schemes based on device fingerprints are independent of terminal computing capabilities, their authentication performance is weak in robustness and high in overhead. Third, the access authentication delay is huge since the system cannot withstand heavy concurrent access requests from large-scale terminals. To address the above problems, we propose a reliable and lightweight trusted access authentication solution for terminals in the vehicle charging network system. By cloud, edge, and local servers cooperating to execute authentication tasks, our Cloud-Edge-End Collaborative architecture effectively alleviates the authentication delay caused by high concurrent requests. Each server in the architecture deploys our well-designed unified trusted access authentication (UATT) model based on device fingerprints. With ingenious data construction and the powerful swin-transformer network, the UATT model can provide robust and low-overhead authentication services for heterogeneous terminals. To minimize authentication latency, we further design an A2C-based authentication task scheduling scheme to decide which server executes the current task. Comprehensive experiments demonstrate our solution can authenticate terminals with an accuracy higher than 98% while reducing the required data packets by two orders of magnitude, and it can effectively reduce authentication latency.
The Development of the Competency Model for Integrated Education Curriculum for Child Welfare Workers

본 연구는 아동복지종사자 통합교육과정 개발을 위한 기초자료를 마련하기 위한 목적으로 수행하였다. 이를 위해 최근 아동복지종사자의 교육 현황을 분석하였으며, 문헌연구와 전문가 델파이 조사를 바탕으로 아동복지종사자 역량모델을 설계하고, AHP 분석을 통해 역량의 우선순위를 제시하였다. 전문가 델파이 조사 결과, 8개의 역량군과 각각의 역량군에 따른 35개의 세부 역량군을 도출하였다. 리더십 영역은 크게 조직관리 역량과 자기관리 역량으로, 직무공통 영역은 아동복지 기본 역량, 실천 역량, 행정 역량으로 구분하였으며, 전문 영역은 아동돌봄 역량, 아동보호 역량, 아동학대개입 역량으로 구분하였다. 아울러 도출한 역량을 AHP 조사를 통해 가중치를 부여하여 우선순위를 확인하였으며, 그 결과를 토대로 아동복지종사자 통합교육과정 설계를 위한 방안을 논의하였다. This study was conducted to develop of a competency model for child welfare worker’s curriculum. In pursuance of the study’s objective, the current educational status of child workers was analyzed, the competency model for child welfare workers was developed the basic of a literature review and the expert Delphi survey, and the priority competencies were derived by conducting an Analytic Hierarchical Process(AHP) analysis. The expert Delphi survey results yielded, 8 competency groups and 35 sub-competency groups for each competency group. Leadership competency was largely classified into organizational management competency and self-management competency, and general job competency was classified into basic competency for child welfare practice, practical competency, and administrative competency. Job-specific competency was classified into child care competency, child protection competency, and child abuse intervention competency. In addition, priority competencies were obtained by assigning weights to the derived competencies through the AHP analysis. The competency model and educational proposal developed through the study can be used as preliminary data in designing an integrated education curriculum for child welfare workers.