Reviewerly/exHarmony huggingface dataset:
Object detection algorithm for handling sample imbalance problems based on dynamic weighted label assignment

The relatively large variation in samples from different categories in the object detection dataset leads to the problem of sample imbalance, which in turn affects the accuracy of the object detector. In this paper, a dynamically weighted approach to label assignment is proposed to enable model to suppress abnormal samples and mine hard samples during training, thus solving the problem of sample imbalance. Different dynamic weighting strategies are applied to positive and negative samples to calculate the classification loss, and a dynamically weighted EIOU method is used to calculate the localization loss. In addition, modifying the single convolutional prediction head to a dual prediction head structure enables the model to focus more on performing classification and regression tasks. This structure uses a fully-connected head to predict the classification results and a convolutional head to predict the localisation results. Experiments conducted on PASCAL VOC2007+12 show that the proposed method can help YOLOv5 to achieve better accuracy and reach 72.3 mAP in 100 epochs, an improvement of 2.4 mAP over the original model. Furthermore, we have done several experiments on MSCOCO to demonstrate the effectiveness of the proposed dynamic weighting method, which has implications for building faster and stronger object detectors.
MFFC-Net: multi-scale feature fusion-based coordination network for gaze estimation

Appearance-based methods with deep learning can predict the point of gaze by using a monocular camera, which requires a large sample to learn. However, existing appearance-based gaze estimation methods with deep learning mainly use face and eye images or only use a single face image, ignoring the correlation between face features and eye features In response to this issue, we propose a coordination model where face feature extraction is the gaze estimation network and eye feature extraction is the coordination network, which deeply fuses the eye-face feature relationships to perform the gaze estimation task. The model achieves good results on MPIIFaceGaze dataset and GazeCapture dataset.
17. Techno-Mischief

Here we have a micro analysis of a recorded online playdate between two families with children who live across the street from each other, during lockdown in Philadelphia, Pennsylvania. The playdate appears chaotic but upon detailed examination reflects many classic motifs in children’s playground lore, revealing cultural sophistication and subtle negotiation. The chapter utilizes tools of folklore study and sociolinguistics, and connects to the literature on the playful trickster.
ADeepCF: Deep Collaborative Recommendation Model Incorporating Attention Mechanisms

In real-life application scenarios there are often problems such as low user ratings of items and difficulties in collecting explicit user feedback. Since collaborative filtering algorithms that use only user and item ID information as input cannot perform its features well and the powerful feature learning ability of the attention mechanisms, this paper proposes a deep collaborative recommendation algorithm model incorporating an attention mechanism. The implicit feedback is integrated with the ID information as input, which avoids the user’s random scoring and alleviates the cold start problem. Introducing attention mechanisms to enhance the ability of deep neural networks to learn non-linear features of users and items. Finally, experiments are conducted on a publicly available dataset to verify the effectiveness of the algorithms in the paper.
Experiencing Rotation and Curvature Gain for Redirected Walking in Virtual Reality

The proliferation of virtual reality (VR) interaction in the wake of the Metaverse trend will place an increasing number of applications and services into virtual environments (VEs). Over the recent years, interactions with the VE have been studied intensely, but very frequently, such interactions are focused on stationary users or users who leverage specialized contraptions to act in the VE (e.g., omni-directional treadmills). The free movement in the VE tends to be achieved by controller input, which creates a huge hurdle to enter and act in it in a natural manner. The target of this study is the translation of the natural walking motion from the real environment (RE) into the VE. In particular, we aim to explore to which extent redirected walking (RW) is achievable without being noticed by the users. Towards this goal, we test two RW methods, i.e., rotation gain and curvature gain. According to the responses of the participants in our study, we find that there is a statistically significant difference with 90% confidence between the levels of gains for rotation gain. On the other hand, levels of gains for curvature gain are not noticeable (i.e., no statistically significant difference is observed).
QR code anti-counterfeiting technique with lattice-based cryptography

QR code is widely used in many scenarios including online transactions, warehousing, manufacturing and authentication. However, in the era of continuous development of quantum technology, the traditional QR code encryption method has low security in front of quantum technology, and information is easy to be stolen and forged. In response to the above problems, we consider combining post-quantum cryptography with QR code. In this work, we propose a quantum-safe, anti-counterfeiting and tamper-proof QR code based on lattice-based cryptography. The proposed QR code technique features two advantages: (a) it can be easily deployed on mobile phone apps and the verification is computationally efficient; (b) it is safe under quantum attacks and impossible to be forged and imitated in plausible quantum future.
Sentiment Analysis of Cryptocurrency Trading Platform Service Quality on Playstore Data: A Case of Indodax

Indodax is one of the cryptocurrency trading platforms in Indonesia that has the highest sentiment for the quality they provide, good quality on a platform is an important factor in obtaining user satisfaction and will have an impact on the long-term success of a company. The importance of user satisfaction on cryptocurrency online trading platforms is a significant factor in increasing user loyalty in today's competition. This research was conducted to analyze the quality of existing cryptocurrency trading platform services so that they can be input for cryptocurrency trading service providers to improve the quality of their services, this information can also be considered by prospective platform users in choosing a trading platform that has the best quality of service to minimize losses that may be caused by the platform. In this study, sentiment analysis was used for indodax play store platform users and then processed using the lexicon classification method to produce sentiment analysis for each significant factor of service quality. From the results of the classification carried out in this study, the results of the analysis show that most users are satisfied and give positive sentiments related to security, namely 87.63%, positive sentiments related to the interface design 88.46%, positive sentiments related to service &amp; convenience by 83%, but some users also gave a slightly positive sentiment related to administrative costs, namely 39%, and their negative sentiment was mostly related to the error &amp; failure system, which received more than 80% sentiment. While the recall value is 38.07%, the precision is 56.69% and the f1-score is 45.55%. The results of this study can be concluded that there are still many important points that must be improved in quality by the indodax platform service providers so that they can be more attractive and used by everyone.&#x0D;
The Analysis of Code Switching in EFL Students’ Online Presentation at University

Code-switching is a phenomenon of language exchange between one language and another which is often found in the communication of bilingual students, especially in EFL students. In this study, the researcher focuses to determine the types of code switching in EFL students’ online presentations, factors that influence code-switching in students’ online presentations and functions of code-switching in EFL students’ online presentations. This study applied a qualitative method by applying a case study. This research was conducted at Makassar State University. The subjects of this study were twenty-one students in the first semester of the postgraduate program whose selected by purposive sampling technique. This research was obtained by using observation and interviews as instruments. The results of this study indicate that three types of code-switching appear in EFL students’ online presentations, namely intra-sentential code-switching, extra-sentential or tag switching, and inter-sentential code-switching. Then, the factors that influence code-switching occur in students' online presentations are the interlocutor factor, physical situation, the topic of discourse, regional factor, and also lack of vocabulary. Furthermore, there are four functions of code-switching by EFL students during online presentations providing the keywords, explaining the material clearly, solving nervousness, and reducing tension.
Student-created media: Designing research, learning, and skill-building experiences

"Student-created media: Designing research, learning, and skill-building experiences." Journal of Web Librarianship, 17(1-2), pp. 35–36
Heavy Pose Empowered RGB Nets for Video Action Recognition

Recently, works related to video action recognition focus on using hybrid streams as input to get better results. Those streams usually are combinations of RGB channel with one additional feature stream such as audio, optical flow and pose information. Among those extra streams, posture as unstructured data is more difficult to fuse with RGB channel than the others. In this paper, we propose our Heavy Pose Empowered RGB Nets (HPER-Nets) ‐‐an end-to-end multitasking model‐‐based on the thorough investigation on how to fuse posture and RGB information. Given video frames as the only input, our model will reinforce it by merging the intrinsic posture information in the form of part affinity fields (PAFs), and use this hybrid stream to perform further video action recognition. Experimental results show that our model can outperform other different methods on UCF-101, UMDB and Kinetics datasets, and with only 16 frames, a 95.3% Top-1 accuracy on UCF101, a 69.6% on HMDB and a 41.0% on Kinetics have been recorded.
An image stabilization method based on adaptive ORB for offshore wind turbine logo image

For the problem of poor image quality captured with the camera on the telescopic arm of the offshore wind turbine boarding bridge due to the wave fluctuation, an image stabilization algorithm based on adaptive Oriented Fast and Rotated Brief (ORB) is proposed. The threshold of feature point decision function and the size of feature point searching box in ORB algorithm are updated in real time to reduce the impact of camera shake on the captured image, and the detection speed is improved by removing the invalid feature points in the image. The image stabilization algorithm proposed in this paper is more suitable for real-time detection of offshore wind turbine logo images than the ORB, SIFT and Harris-based algorithms with an average improvement of 25.9%.
Research on sentiment analysis of weibo based on Improving Capsule Network

When conducting text sentiment analysis based on traditional deep learning models, lack of extracting deep-seated characteristics and sentence system characteristics etc. The article propose a model based on improving capsule networks. This model uses Weibo text as the research object, and uses BERT to obtain the word vector; then use CNN to extract deep phrase characteristics, LSTM networks to model the dependencies of each word in the instance, and dig out its hidden semantics; then increase attention layer is screened with effective features; finally, the improved capsule network is used to express a richer expression of text sentiment information to enhance the overall abstraction capabilities of the model. After experimenting on the dataset, it achieved good results on Weibo's sentiment analysis. The result shows that the model is better than traditional models, and it also fully shows that the model can effectively improve the accuracy of sentiment analysis.
ILP-Based Approach for Cloud IaaS Composition

With its rapid evolution, cloud computing has become a complex ecosystem characterized by the diversity of service descriptions and prices, the heterogeneity of virtualization technologies and APIs used. It is increasingly difficult for users to compare and choose the services that best meet needs at optimal cost. In this paper, we model the user request as a graph, nodes describe virtual machines and edges represent network requirements. We propose a mathematical formulation of an integer linear program (ILP) to select a set of IaaS services, which define a cost-effective cloud resources allocation plan, according to the user request graph. A performance evaluation is reported to assess the efficiency and scalability of the proposed algorithm.
Comparing SVM and Random Forest in Patterned Gesture Phase Recognition in Visual Sequences

Recognizing the phases of patterned gestures in a visual sequence is essential for many tasks that may come later. Deep Learning represents the state of the art of performance in this task. However, the amount of labeled data needed to train deep models are only sometimes available, even for effective fine-tuning of pre-trained models. Several approaches have been evaluated for the segmentation of gesture phases; however, ensemble methods have yet to be considered. In this work, we apply ensemble classifiers to recognize gesture phases compared to the state-of-the-art classifier, SVM. Our results on a publicly available dataset show outperforming the published baseline, and we achieved good F-score results for each class, with the highest F-score reaching 0.96, 0.92, 0.9 for rest, stroke, and hold, respectively, and around 0.84 for retraction and preparation.
Pre-processing and Pre-trained Word Embedding Techniques for Arabic Machine Translation

In this paper, we aim to systematically compare the impact of different pre-processing techniques and different pre-trained word embeddings on the translation quality of a neural machine translation model that translates from the Arabic language to English. For the pre-processing, we compare Arabic segmentation, Arabic normalization, and English lower-casing. For the pre-trained embeddings, we use pre-trained models trained based on three context-independent models; Word2Vec, GloVe, and FastText. Our experimental results show that pre-processing techniques help to improve the translation quality with a gain of BLEU score up to $$+1.91$$ point. Furthermore, we find that the impact of pre-trained word embeddings strictly depends on the training data size.
CIWPR: A Strategic Framework for Collective Intelligence Encompassment for Web Page Recommendation

Web page recommendation is one of the vital strategies in the era of Web 3.0 due to the exponential increase of the contents over the World Wide Web (WWW). In this paper CIPWR framework for recommendation of web pages which incorporates upper ontology generation, ontology alignment using the Lin similarity with an appropriate threshold and SynSet generation has been put forth. The framework integrates two topic models or encopresis the concept of Bi-topic modelling with two distinct topic models namely the Latent Dirchlet Allocation (LDA) and Latent Semantic Indexing (LSI) for lateral enrichment of topics and also the NELL and YAGO knowledge stores are integrated for Query word enrichment which is further used for feature selection and a Random forest classifier which is a strong machine learning driven feature controlled classifier has been added for the classification of the dataset and semantic similarity is computed by amalgamating cosine similarity and Jaccard similarity with the differential thresholds in order to yield the best in class results of 97.81% of average accuracy, 96.18% of average precision, 99.43% of average recall, 97.78% of average F-measure and the lowest FDR of 0.04 has been accomplished by the proposed CIPWR framework.
Prediction Analytics of Hyperledger Blockchain Data in B2B Finance Applications

Hyperledger is a Blockchain architecture built to allow a flexible, robust ecosystem where individual modules can be used as required. In particular, Hyperledger is distributed because it is duplicated over the entire network so that all participants can work together safely. In this study, we examine the use of Hyperledgers in B2B Blockchain deployment, focusing on the integration of data analytics for forecasting.
Reason-able embeddings: Learning concept embeddings with a transferable neural reasoner

We present a novel approach for learning embeddings of ALC knowledge base concepts. The embeddings reflect the semantics of the concepts in such a way that it is possible to compute an embedding of a complex concept from the embeddings of its parts by using appropriate neural constructors. Embeddings for different knowledge bases are vectors in a shared vector space, shaped in such a way that approximate subsumption checking for arbitrarily complex concepts can be done by the same neural network, called a reasoner head, for all the knowledge bases. To underline this unique property of enabling reasoning directly on embeddings, we call them reason-able embeddings. We report the results of experimental evaluation showing that the difference in reasoning performance between training a separate reasoner head for each ontology and using a shared reasoner head, is negligible.
Research on dynamic artificial intelligence method for deep learning of big data

In the field of artificial intelligence research, deep learning as a very active research field, mainly reflected in natural language processing, computer vision, speech recognition and other aspects. After entering the era of big data, with the continuous expansion of data scale, technological innovation of all enterprises has ushered in new opportunities and challenges, and scientific researchers have begun to use deep learning to solve the problem of big data prediction and analysis. Therefore, on the basis of understanding the research status of artificial intelligence, this paper deeply discusses the application reliability of deep learning algorithms of artificial intelligence according to the dynamic changes of deep learning of big data. The final results show that the dynamic artificial intelligence method of deep learning with big data meets the needs of practical application.
Face detection and recognition technology based on EfficientNet and BNNeck

Face detection is the premise of face attribute recognition, and face attribute recognition is the deep understanding of the computer to face image features. In the construction and development of modern society, with the continuous development of computer vision technology, identity recognition technology with biometric recognition as the core has been paid attention to, and began to be widely used in face verification, interpersonal interaction, precision delivery and other fields. Therefore, on the basis of understanding the current research status of artificial intelligence technology theory and EfficientNet, BNNeck algorithm design, the paper deeply discusses the neural network as the core of the cascade face detection and recognition technology in the new era. The final results show that the improved cascaded face recognition method proposed in this paper has research value.
Preservation and enhancement of quantum correlations under Stark effect

We analyse the dynamics of quantum correlations by obtaining the exact expression of Bures distance entanglement, trace distance discord, and local quantum uncertainty of two two-level atoms. Here, the atoms undergo two-photon transitions mediated through an intermediate virtual state where each atom is separately coupled to a dissipative reservoir at zero temperature in the presence of the Stark shift effect. We have investigated the dynamics of this atomic system for two different initial conditions of the environment. In the first case, we have assumed the environment's state to be in ground state and in the other case, we have assumed the state to be in the first excited state. The second initial condition is significant as it shows the role played by both the Stark shift parameters in contrast to only one of the Stark shift parameters for the first initial condition. Our results demonstrate that quantum correlations can be sustained for an extended period in the presence of Stark shift effect in the case of both Markovian and non-Markovian reservoirs. The effect in the non-Markovian reservoir is more prominent than the Markovian reservoir, even for a very small value of the Stark shift parameter. We observe that among the correlation measures considered, only local quantum uncertainty is accompanied by a sudden change phenomenon, i.e. an abrupt change in the decay rate of a correlation measure. Our findings are significant as preserving quantum correlations is one of the essential aspects in attaining optimum performance in quantum information tasks.
Dokdo Tour: Augmented Reality Tour System Using Dioramas

Dokdo Tour is an augmented reality tour system developed using the Dokdo diorama. When the camera is pointed at the Dokdo diorama, virtual objects such as birds and ships are augmented, and the sounds of waves and seagulls are combined to create a sense of presence. Users can move their bodies to explore different parts of Dokdo, and interact with specific elements of the island, such as rocks and structures, to deepen their understanding of the island while hearing explanations of the elements they want to see. DokdoTour was developed using CoSpaces as the main development environment and MageCube for augmented reality implementation. In the future, if the DokdoTour is developed with 3D game engines such as Unity3D and Unreal by professional developers, it is expected to become a service with sufficient commercial value for education and non-face-to-face tourism. The system is also highly scalable, as it can be applied not only to Dokdo but also to various industries that can be made into dioramas. Although it was developed with CoSpaces, an educational platform, the DokdoTour is expected to be a prototype and a good reference for implementing an augmented reality system using dioramas in the future, including the process of directly generating voices for elements using artificial intelligence voice generation services and the process of implementing reality blending for realistic augmentation by scanning dioramas in three dimensions.
Artificial Intelligence Application Processing Method and System Based on Open Source Deep Learning Framework

In the field of artificial intelligence, deep learning is a very important research topic. At present, there are a large number of machine intelligence algorithms for processing and analyzing problems, solving complex data mining and other key technologies and related application requirements, as well as deep model processing methods to realize the huge knowledge value and potential commercial value and developability of massive data. And other characteristic information content. This paper mainly uses the time series analysis method and the experimental method to carry out the correlation research on the face recognition system, and compares and analyzes the open source deep learning framework to study its processing in artificial intelligence applications. Experimental results show that when the threshold is 0.74 and the side face angle is 0, the accuracy of the face recognition system is more credible.