Reviewerly/exHarmony huggingface dataset:
DMS: An Architecture of a Decentralized-based Music Streaming Platform using Blockchain

Managing royalty fees in the music business is the task that is done by record labels, music retailers, and performance-right organization firms. Royalty fees are mostly distributed in a centralized manner. A centralized-based approach may be straightforward for managing a music business. However, a centralized-based approach prevents artists to be able to set their revenue model for their music and getting paid promptly. To allow an artist to have freedom in setting their revenue model, a decentralized-based approach may need to be proposed. In this work, we propose to apply Blockchain and Smart Contract in the music business to propose a decentralized-based music streaming service that allows artists can independently design their royalty fee model and get paid promptly. Our proposal is based on an extension of ERC20 and ERC721 standards on writing a smart contract. Our proposal is deployed and tested in a private Ethereum-based blockchain in a laboratory-controlled environment to determine the feasibility and gas fee required. The result suggests that the business logic required in our contract does not consume a high amount of gas fee. Hence, it can be deemed that our contract design is suitable for deploying in an actual decentralized-based music streaming service.
A Collaborative Framework for Ad Click-Through Rate Prediction in Mobile App Services

Recent embedding techniques have shown remarkable effectiveness in CTR prediction. However, such methods heavily rely on centralized storage and work poorly on cold-start users and items. In reality, historical data of users or ads are heterogeneously distributed across multiple platforms and not directly accessible due to business competition and privacy issues, leading to cold start problems with CTR predictions for each application server. In this paper, we learn federated meta embedding (FME) based on the cooperation of the user-server-advertiser (H-USA), which completes the CTR prediction task in two stages. In the federated phase, we learn richer semantic information about hot IDs under the collaboration of the USA. We treat each cold id in the meta phase as a learning task. And then, the application server learns embedding for cold IDs using federated embeddings through gradient-based meta-learning. Extensive experiments on real-world datasets show that FME learned in H-USA can significantly improve the prediction performance of cold IDs.
Few-Shot Object Detection Based on Latent Knowledge Representation

Few-shot object detection (FSOD) aims to achieve excellent novel category object detection accuracy with few samples. Most existing researches address this problem by fine-tuning Faster R-CNN, where the model is first trained on the base class set with abundant samples, and then fine-tuned on the novel class set with scarce samples. But in the fine-tuning stage, the connection between the base class set and the novel class set is ignored, which makes it difficult to learn novel classes with scarce samples. To solve this issue, we propose a latent knowledge-based FSOD method, which aims to utilize latent knowledge to build connections between categories. Specifically, first we propose a latent knowledge classifier (LK-Classifier), which realizes object recognition by splitting features through latent knowledge. Then a guidance module is designed to constrain latent knowledge with semantic expression, so as to realize the bridge between base class set and novel class set through latent knowledge. Experimental results show that our method achieves promising results on the FSOD task on the PASCAL VOC and COCO datasets, especially when the number of samples is extremely scarce.
Chinese-English Contrastive Translation System Based on Lagrangian Search Mathematical Algorithm Model

Abstract The English-Chinese machine translation system is a current research topic. This paper designs an English-Chinese comparison system based on Lagrangian search mathematical algorithm. This paper proposes a Lagrangian algorithm to describe the English short syntax and improves the English-Chinese translation algorithm. When expanding nodes, the algorithm expands on the inspiration node and expands on other nodes. The experimental results show that the improved Lagrangian search algorithm can obtain better translation results and search efficiency in the system.
Explicate Toxicity By eXplainable Artificial Intelligence

Chemical exposure can cause formative neurotoxicity, which requires fast and exact testing techniques. Current methods, notably in vivo research on animal models and assessments of primary cell cultures derived from animal and humans, have difficulties in terms of time, expense, and application to human physiology. For example, in vivo animal studies may take years to complete. In this study, eXplainable Artificial Intelligence (XAI) was combined with XGBoost machine learning (ML) models that were prepared to utilize binary classification as a potent mix of datasets to identify genes that may be associated with neurotoxicity. Significant genes were found and connected to the progression of neurotoxicity after SHAP values were effectively integrated into the ML models.
C4.5 and ID3 Comparison to Classify Credit Issue on Indonesian National Health Insurance

Indonesian citizens are required to become participants of the national health insurance in accordance with applicable laws. Some time ago, namely in 2014, the government implemented an independent system for participants who were classified as non-workers. However, it causes many Indonesian citizens to ignore the payment. This causes various financial instability from related parties such as companies and various blood health facilities to hospitals as the final reference for the guarantee claim process. In this study, we summarize and categorize insured data from mandatory health insurance and make predictions about insured delays and smooth payments. Classification is a data mining technique that classifies data based on the attachment of the data to the sample data. The classification process begins by preprocessing the data, eliminating missing values, and selecting features in the dataset so that it can classify certain data into certain classes. In the evaluation phase, the accuracy rate of C4.5 is higher than the ID3 algorithm, namely 89.7% and 88.48%. C4.5 has an efficient and simple process performance, while ID3 has a more complex process performance.
An efficient Synergic Model with Contrast Limited Adaptive Histogram Equalization model for Object Classification in Ship Detection

For public seas security and public protection, ship detection and identification are essential. It is true that a few SAR (Synthetic Aperture Radar) picture-based ship detection systems have been developed and used, but they cannot meet the requirement of certifiable applications due to the limited number of SAR sensors available, a modest target and a delayed return to cycle. When large optical images of high quality are available, ship recognition and classification on such images is becoming a promising technique, and has attracted considerable attention on applications like marine security and traffic light. A number of computerized picture preparation strategies have been presented to recognize ships in optical far-off detecting images, however the great majority of them have difficulties in terms of exactness, execution, and unpredictability. An auto encoder-based deep neural organization with outrageous learning machine was recently presented, however it cannot match the requirements of certifiable applications since it only works with simple and small sized informative sets. Rough deep convolutional neural network (CNN) is used as the ship classifier in this study to provide a new method of ship identification and classification. In this paper, the different machine learning techniques are examined for ship detection. Based on the examination this paper proposed a Synergic Convolution Neural Network (SYCNN) model for the detection and classification of the ship in the images. The proposed SYCNN involved in computation of the image features with Contrast Limited Adaptive Histogram Equalization (CLAHE) model for the ship detection and classification. The simulation analysis of the proposed SYNCNN expressed that higher classification accuracy of 92% which is significantly higher than the existing techniques.
Pitch in: A Secure Extension Signature Based on SM9

SM9 is the identity-based cryptography (IBC) promulgated by the State Cryptography Administration of China. SM9-IBS has become an international standard in 2017 (the main part of ISO/IEC 14888-3/AMD1 standard). SM9-IBS reduces the certificate application and verification phases, which has obvious advantages in identity authentication in mobile Internet, Internet of things (IoT), big data, and other fields. Many researchers use SM9 as the underlying cryptography and have successively proposed extended signatures such as group, ring, and two-party cooperation. In order to further promote the application of SM9, reduce the pressure of root KGC and improve the reliability of the system, we present a secure and feasible hierarchical SM9 signature scheme which is the first SM9-IBS (Identity-based Signature) extension. Also, we prove its security is existentially unforgeable under adaptive chosen message and identity attacks (EUF-CMIA) in the standard model. At the same time, the implementation of our scheme using Java does not lead to an additional performance burden based on the SM9 algorithm.
PUSHO, a pulse-level variational quantum algorithm for Rydberg atom platforms

The manipulation of neutral atoms by light is at the heart of countless scientific discoveries in the field of quantum physics in the last three decades. The level of control that has been achieved at the single particle level within arrays of optical traps, while preserving the fundamental properties of quantum matter (coherence, entanglement, superposition), makes these technologies prime candidates to implement disruptive computation paradigms. The current quantum processing units (QPU) are nothing close to universal computers but can already target specific combinatorial problems, on which many industrial issues can be mapped. Problems, whose classical complexity scales exponentially with their size, such as finding the Maximum Independent Set (MIS) of a Unit-Disk (UD) graph or approximating the solution of a Quadratic Unconstrained Binary Optimisation (QUBO) can be mapped to Rydberg platforms. It enables to solve them by approximating their cost function with the Hamiltonian of the atomic array. Then the atoms are optimally driven with implementable laser pulses shaped by Bayesian Optimisation (BO) to a solution state which minimises the previous cost. Initially, BO performs well with fewer iterations than others classical algorithms and additionally can maintain this level with noisier estimations, especially when the number of shots is limited for instance. Pulse Shaping Optimisation (PUSHO) provides a straightforward but powerful Variational Quantum Algorithm (VQA), taking advantage of our ability to precisely control the shape of the driving fields.
Egocentric Scene Understanding via Multimodal Spatial Rectifier

In this paper, we study a problem of egocentric scene understanding, i.e., predicting depths and surface normals from an egocentric image. Egocentric scene understanding poses unprecedented challenges: (1) due to large head movements, the images are taken from non-canonical viewpoints (i.e., tilted images) where existing models of geometry prediction do not apply; (2) dynamic foreground objects including hands constitute a large proportion of visual scenes. These challenges limit the performance of the existing models learned from large indoor datasets, such as ScanNet [6] and NYUv2 [36], which comprise predominantly upright images of static scenes. We present a multimodal spatial rectifier that stabilizes the egocentric images to a set of reference directions, which allows learning a coherent visual representation. Unlike unimodal spatial rectifier that often produces excessive perspective warp for egocentric images, the multimodal spatial rectifier learns from multiple directions that can minimize the impact of the perspective warp. To learn visual representations of the dynamic foreground objects, we present a new dataset called EDINA (Egocentric Depth on everyday INdoor Activities) that comprises more than 500K synchronized RGBD frames and gravity directions. Equipped with the multimodal spatial rectifier and the EDINA dataset, our proposed method on single-view depth and surface normal estimation significantly outperforms the baselines not only on our ED-INA dataset, but also on other popular egocentric datasets, such as First Person Hand Action (FPHA) [18] and EPIC-KITCHENS [7].
A Trifacacking System for Dynamic Subset Targets using Probability Hypothesis Filtering

In this paper we present a novel formulation of the multi-target tracking problem in which the target represents a variable shape and size subset of the state space. Due to the nonlinear nature of the system and the inherent multi-target nature of the problem, we provide a solution based on the particle implementation of the PHD filter. Testing in simulations yields promising results allowing a significant reduction of the errors with respect to measurements.
CMNet: Cross-Aggregation Multi-branch Network for Salient Object Detection

Fully convolutional networks (FCNs) have shown extraordinary performance in salient object detection (SOD). However, when faced with complex and variable salient objects in terms of types and sizes, FCNs-based methods may still generate some under-segmentation saliency maps, such as inaccurate or incomplete object information, which is mainly caused by sub-optimal multi-scale context features and inadequate interaction of complementary information. In this paper, we devote to exploring an effective structure to capture contextual information and interact with complementary information for an accurate SOD task. Specifically, we first design a multi-source contextual cue extraction (MCCE) module to effectively capture context information with different receptive fields, and further aggregate the information to improve the expressive ability of the initial input features. Furthermore, we build a dual-residual adjacent feature interaction (DAFI) module to increase the exchange of high-level semantic and low-level space details information from multi-level features for better saliency prediction. Finally, extensive experimental results convincingly demonstrate that our method achieves more favorably against 15 state-of-the-art methods on five public SOD datasets.
Analytical Use of IHC Dataset By Using Segmentation And Classification Techniques

This paper provides the applicability of a dataset of breast cancer samples prepared in immunohistochemistry staining and applies on it common methods in the literature including image enhancement, segmentation, and classification techniques as the proof. Segmentation techniques such as watershed and highpass are tested for solving the problem of overlapping and classification methods including SVM, K-NN, and random forest are applied and the results are compared. The results show that the accuracy of RF is the best among the three methods which is 90% without segmentation methods and 93% with segmentation methods added. a precision of 95.5%. In addition the computational time for all the methods involved is acquired which shows that RF has the highest computational time but the high accuracy is worth the added extra seconds.
Digital Yoga Game with Enhanced Pose Grading Model

Today is the era of a digital world. The need for another person to be present around for a simple task is getting slim to none. Computer Vision and Machine Learning are the basis of this modern era. With cutting-edge technologies in computer vision, we can detect, monitor, and even control the results as we desire. They are ruling in every other industry, so why not in Yoga Industry. Yoga is not a new practice. It has been in existence since ancient times. Yoga is something that adds years to your life and automatically life to your years. One of the crucial parts of computer vision is the human body pose estimation. Pose estimation techniques can be further extended to grade or compare human poses as part of the yoga practice sessions. There are different pose grading models. This paper goes through the existing pose grading model and proposes a new computer vision based transformation model for human pose grading. This paper also proposes an implementation technique which is in the form of web based yoga game that uses the proposed model and let the users learn and enjoy yoga practices.
User-Centered Prototyping for Single-User Cross-Reality Virtual Object Transitions

Cross-reality is a newly emerged research field that studies the transition and current usage of multiple systems along Milgram's Reality-Virtuality Continuum(RVC). Our research currently focuses on studying how the user could interact with CR applications and move virtual objects along Milgram's RVC. The prototype is an embodiment of an application and can be used to gain insights and knowledge. We build low-fidelity and high-fidelity prototypes to study the transition of 3D virtual objects. However, We face challenges since cross-reality involves more than one space on the RVC, so when designing the prototype, researchers and designers can not refer to their previous experience and interaction metaphor from a space they are familiar with, such as physical, AR, or VR space. Thus we plan to conduct elicitation studies to gain more knowledge and insights to serve as a benchmark to guide the design of the prototypes.
Augmented Reality in Collaborative Learning

A fascinating, intense, and critical challenge is the new technological revolution, the so-called Fourth Industrial Revolution or Industry 4.0. This digital revolution represents new ways technology is integrated into societies and even the human body. Its arrival is undeniable, affecting all social and economic sectors, which must begin digital transformations regardless of their nature. Education has embraced this transformation early, but there is still a long way to go. Among the most significant trends in digital education, augmented reality (AR) probably occupies the most prominent position due to the tremendous didactic advantages provided by this immersive tool. Augmented reality offers powerful visualization to support learning scientific concepts. Currently, collaborative group work is essential in all learning activities. However, creating teams and performance expectations is one of the most complex tasks faced in the classroom and work–life. So in this work, we applied augmented reality to strengthen students’ disciplinary and transversal competencies in an initiative to transform work groups into collaborative teams and achieve meaningful learning. Educational resources with augmented reality technology were designed with the blender tool and made compatible with mobile devices. We observed students’ performance in the work team. We assessed qualitative aspects such as acceptance, interest, and mastery of the didactic tools and conducted quantitative and comparative analyses of their grades. The results showed a significant positive impact, indicating that interactive lessons using augmented reality increase student collaboration and meaningful learning of scientific concepts.
Robust Liver Segmentation Using Boundary Preserving Dual Attention Network

Accurate liver segmentation is essential for the diagnosis and treatment planning of liver diseases. Recently, deep-learning-based segmentation methods have achieved state-of-the-art performance in the medical image domain. However, due to low soft tissue contrast between the liver and its surrounding organs, most existing segmentation methods are difficult to capture the boundary information of the liver well. Considering boundary information is necessary for liver edge localization in liver segmentation, we propose a novel deep-learning-based segmentation network, called Boundary Preserving Dual Attention network (BPDA). In the proposed BPDA, the boundary is being concerned with the dual attention module which consists of the spatial attention module and the semantic attention module at each layer of U-Net with the feature maps of current and its neighboring layer as inputs. The spatial attention module enhances the perception of target boundaries by focusing on salient intensity changes. Meanwhile, the semantic attention module highlights the contribution of different filters in semantic feature extraction by weighting important channels. Moreover, to overcome the phenomena that the segmentation of left lobe performance bad especially in new datasets from different sources in practical use, we analyze the attribution of the liver, we devise a data augmentation strategy to expand the data of the left lobe of the liver under the guidance of professional radiologists. The comparative experiments have done on the self-build clinic liver dataset, which includes 156 clinic case images with Iterative Decomposition of Water and Fat with Echo Asymmetry and Least-squares Estimation Magnetic Resonance Imaging(IDEAL-IQ MRI). Experiments show that our proposed method outperforms other state-of-the-art segmentation methods.
Deep sparse representation for Super-Resolution Image Reconstruction

Image reconstruction is an important research direction in computer vision. In this paper, a deep sparse representation model is proposed for super-resolution image reconstruction. We firstly study the decomposition of sparse coefficients and the construction of over-complete dictionary, and then use the K- VSD algorithm to extract the image sparse feature. Finally the deep feature migration model is designed to refine image features with deep convolutional neural network (CNN). The experiments carry out on the perspective single-channel, multi-channel and pixel-wise amplitude reconstruction. Both subjective assessments and objective metrics demonstrate that the proposed method has a good reconstruction effect.
Local POI matching based on KNN and LightGBM Method

High-quality POI data is of great significance to both businesses and individuals. For businesses, POI data is advantageous for management to analyze the competitive landscape and make decisions. For individuals, the accurate location of unknown places will significantly improve the travel experience. Impressive POI matching methods assist to improve the quality of POI data. This paper proposes a novel POI matching approach, which firstly utilzes the KNN algorithm to construct local candidate sets for the problem of high global matching complexity, which significantly improves the optimization efficiency. In addition, our approach leveraging TF-IDF and BERT to re-encode the original features to improve the feature representation. Finally, we build a binary classification model based on LightGBM to improve the matching performance. The experimental results demonstrate that the approach proposed in this paper has achieved effective improvement compared to the DNN and SVM.