Reviewerly/exHarmony huggingface dataset:
Privacy Protection Technology in IOT Data Storage Based on Blockchain

The so-called Internet of things refers to connecting many objects in reality and existing on the network structure in a specific form. With the development of the Internet of things technology, the types of data collected in the Internet of things are increasing and the data scale is expanding. The traditional data storage system is no longer applicable. In order to effectively store and use these data, a new data storage system is needed to manage the information collected in the Internet of things. This paper proposes a data storage system of the Internet of things based on the blockchain, then analyzes the functions of various privacy protection technologies and their protection effects on the system, and forecasts the development direction of privacy protection technologies in the future data storage system of the Internet of things.
Phase-MLP for Generalizable Implicit Neural Representations

Implicit neural representation(INR) has lifted a climax among deep learning researchers for its powerful continuous representation. With Sine activation or Fourier position embedding, INRs overcome the problem that could not reconstruct high-frequency signals in different domains, such as voice, image or 3D shape. However, many of INR researches can merely represent one object or one scene with high-frequency information, multiple instances will bring a sharp decline of performance. In this work, we propose a newly phase-MLP which can not only recover diverse instances but also keep high quality content. Our network takes phase information which corresponding to target signal as input and combine it with encoded position signals to reconstruct the original data. Moreover, we propose a multi-level phase-MLP base on the infrastructure to retain fidelity for bigger instance while limiting the phase information in the low amount. Experimental results on public images and videos demonstrate our proposed approach outperforms the state-of-the-art methods.
Improve GAN-based Neural Vocoder using Truncated Pointwise Relativistic Least Square GAN

Neural vocoders are widely utilized in modern text-to-speech (TTS) and voice conversion (VC) systems due to their high generation quality and fast inference speed. Recently, GAN-based neural vocoders have attracted great interest due to their lightweight and parallel structures, which enable them to generate a high-fidelity waveform in a real-time manner. Most existing GAN-based vocoders adopt the Least Square GAN (LSGAN) training framework. In this paper, we analyze the weaknesses of the LSGAN waveform synthesis framework and, inspired by Relativistic GAN, propose a simple yet effective variant of the LSGAN framework, named Truncated Pointwise Relativistic LSGAN (T-PRLSGAN). In this method, we consider the pointwise truism score distribution of real and fake wave segments and combine the Mean Squared error (MSE) loss with the proposed truncated pointwise relative discrepancy loss to increase the difficulty of the generator to fool the discriminator, leading to improved audio generation quality and stability. To demonstrate the effectiveness and generalization ability of our method, subjective and objective experiments have been conducted based on Avocodo, UnivNet, HiFiGAN, ParallelWaveGAN, and MelGAN vocoders, which show a consistent performance boost over those typical LSGAN-based vocoders. Moreover, our T-PRLSGAN can support multiple types of discriminators, i.e. multi-scale wave discriminator(MSD), multi-period discriminator(MPD), and multi-resolution spectrogram discriminator(MRD), without modifying their architecture or inference speed.
Comparisons of Classic and Quantum String Matching Algorithms✱

In this paper, we study the string matching problem. We design a quantum string-matching algorithm for noisy intermediate-scale quantum (NISQ) computers, given the current leading quantum processing units (QPUs) having no more than a few hundred qubits [16]. We also compare the performance of classic algorithms and quantum algorithms under various combinations. Our study provides a comprehensive and quantitative guide for users to choose appropriate classic or quantum algorithms for their string matching problems.
Machine Reading Comprehension Based on SpanBERT and Dynamic Convolutional Attention

Machine reading comprehension is a challenging task in the field of natural language processing. In this paper, we propose a new neural network structure, fused SpanBERT and Dynamic convolutional Attention Network (SDANet), for span-extracted question answering, aiming to better answer questions in a given text. the main contributions and originality of SDANet are as follows: 1) using a pre-trained language model–SpanBERT to obtain a sequential representation of the text. 2) Combining dynamic convolution with a self-attentive mechanism for capturing the local and global structure of the text during text feature interaction, with a residual mechanism to enrich the sequential information. Experimental validation on the Stanford datasets (SQuAD1.1 and SQuAD2.0) was conducted that our model made progress in span-extracted reading comprehension.
Overturning the Counting Cornerstone: Exploring Fine-Grained Adaptive Losses to Subvert the Conventional Density Estimation

Previous works of crowd counting prepossess the given label and convert it into a density map or count map used for learning. However, we revealed that density maps tend to have severe errors due to faulty occlusions, head size variation, and head shape variation. Directly learning the density map will often result in fatal over-fitting. On the other hand, Count-map did not fully utilize the detailed information of the image. These unsatisfactory preprocessing lead to the performance bottleneck despite recent advances in network architecture. To solve these problems, in this paper, we discovered that the distribution of errors throughout the density map is not uniform. Moreover, it is correlated with the distance to the nearest annotation point. Inspired by this finding, we introduce Fine-Grained Adaptive Losses to learn the density map differently in different regions of the density map. While our method is simple, it dictates that we should endeavor to obtain more supervision from the density map. Our effort subverts the traditional use of density maps and opens up a new vision for future counting research. Extensive experiments demonstrate that our approach significantly outperforms standard methods in crowd-counting datasets.
Layer-wise based Adabelief Optimization Algorithm for Deep Learning

For the optimization problem of deep learning, it is important to formulate a optimization method that can improve the convergence rate without sacrificing generalization ability. This paper proposes a layer-wise based Adabelief optimization algorithm to solve the deep learning optimization problems more efficiently. In the proposed algorithm, each layer of the deep neural network is set different learning rate appropriately in order to achieve a faster convergence rate. We also give the theorems that can guarantee the convergence property of Layer-wised AdaBelief method. Finally, we evaluate the effectiveness and efficiency of the proposed algorithm on experimental examples. Experimental results show that the converges speed of the layer-wised AdaBelief algorithm is the fastest compared with the mainstream algorithms. Besides, the new algorithm also maintaining an excellent convergence result in all numerical examples.
Gushing Resolvers: Measuring Open Resolvers’ Recursive Behavior

Open resolvers can be easily exploited by malicious actors to launch DDoS attacks against important services on the Internet, which has aroused much concern in the Internet community. Researchers have studied extensively the population, structure, and malicious behavior of open resolvers, while little has been done to reveal how open resolvers respond to non-recursion queries. In this paper, we conduct an Internet-wide measurement on the recursive behavior of open resolvers. We discover that more than 1 million gushing resolvers in the wild are more enthusiastic than needed to respond to non-recursive queries, either triggering a new recursive resolution process or replying with cached records. Furthermore, we discuss possible security implications posed by the massive gushing resolvers. Specifically, we show that gushing resolvers are prone to be targets of ranking manipulation attacks if they happen to be the data collection points of top lists. At last, we put forward suggestions for resolver operators to improve such a situation.
Social Recommendation System Based on Multi-agent Deep Reinforcement Learning

Currently, deep reinforcement learning is widely used to transform the recommendation process into a sequential task. Despite this, the recommendation system continues to be plagued by data sparsity and cold start issues as a result of high online costs and a sparse user-item rating matrix. To address the aforementioned issues, this article proposes a social multi-agent reinforcement learning framework (MATR). Two agents collaborate within the framework to enhance the performance of conventional solutions. Specifically, one agent is responsible for capturing the dynamic preferences of users based on their interaction with the system. The other agent leverages a ubiquitous social network to minimise cold starts and data sparsity for some users. In addition, we designed a module for learning state representations from social networks and user rating matrices. To optimise the use of social networks in the state representation module, in addition to first-order social neighbour features, we used trust inference to discover high-level social neighbour features, and we integrated Feature aggregation modelling state representation with graph convolutional neural networks and attention mechanisms. Our method has been shown to be effective and advantageous using actual data sets.
Securing QR Codes Using a Hybrid Pseudo Baker’s Mapped Cellular Automation

Since their advent in 1994, Quick Response (QR) codes have grown to become ubiquitous in wide-ranging applications covering marketing, healthcare, commerce, and many other areas. Increasingly, commerce, QR codes are taking up roles where confidentiality, privacy, and integrity of the information they transmit or and/or the identities of parties involved in the communication is vital. Meanwhile, cellular automation (CA) is veritable tool for abstract dynamical information processing with finite number of discrete states space, and time that can be updated synchronously contents and is steered. By employing a pseudo baker’s map, we design a template to scramble the QR code content whose evolution is steered using the dextral boundary condition (DBC) of cellular automation. The DBC combines a group of cells permeating contents of a QR code tile at state t to determine the left-most cell entry at state t+1. We evaluated our protocol by implementing both the encryption and recovery procedures on various QR codes, and our findings show that, our method can retain the physical appearance and heterogeneity of the QR code during both the encryption and decryption processes. This is manifest in 100% concordance between the original and recovered QR codes, which is better than what was reported in similar schemes.
SDW-DPC: An Advanced Clustering Algorithm by Searching Density Peaks using Standard Deviation Weighted Distance

DPC (clustering by fast search and find of density peaks) algorithm is an ingenious and efficient clustering algorithm that can discover cluster centers via its very novelty decision graph and subsequently achieve the clustering of a dataset efficiently via its innovative one-step assignment strategy. However, DPC algorithm has its inborn shortcomings, such as its “Domino Effect” that once a point is assigned to an error cluster, then there will be many subsequent points being assigned to error clusters, resulting in a poor clustering. This shortcoming in part due to its one-step assignment, and in part due to its distance metric. DPC uses the Euclidean distance to calculate the distance between points. The Euclidean distance takes it as default that each feature does equal contribute to the distance between points, but in practice, each feature does not make equal contribute to the distance. To address this shortcoming, this paper proposes a standard deviation weighted distance instead of the Euclidean distance used in DPC algorithm. This innovative distance weights a feature using the standard deviation of the feature on all points from a dataset, so that the distance between points embodies the specific contribution of the feature to the distance. The very efficient one-step assignment strategy is inherited. Therefore, we developed the advanced PDC clustering algorithm which is referred to as SDW-DPC (Standard Deviation Weighted Distance based Density Peaks Clustering) algorithm. Extensive experiments on synthetic datasets and real-world datasets from UCI machine learning repository demonstrate that our SDW-DPC outperforms the original DPC, and other famous benchmark clustering algorithms including AP, DBSCAN and K-means in terms of clustering accuracy (Acc), adjusted mutual information (AMI), and adjusted rand index (ARI).
U3E: Unsupervised and Erasure-based Evidence Extraction for Machine Reading Comprehension

More tasks in Machine Reading Comprehension (MRC) require predicting the answer and extracting supported evidence sentences simultaneously. However, the annotation of supporting evidence sentences is usually time-consuming and labor-intensive. In this paper, to address this issue and consider that most of the existing extraction methods are semi-supervised, we propose an unsupervised and erasure-based evidence extraction method named U3E, which takes the changes after sentence-level feature erasure in the document as input, simulating the decline in problem-solving ability caused by human memory decline. In order to make selections based on fully understanding the semantics of the context, we also propose metrics to quickly select the optimal memory model for these input changes. To compare U3E with typical evidence extraction methods and investigate its effectiveness in evidence, we conduct experiments on different datasets. Experimental results show that U3E is simple but effective, also improves model performance.
Metaverse for Learning Evaluation: Perspectives from Extended Reality and Blockchain

In this paper, we study the application of Metaverse in learning evaluation, focusing on in-depth exploration from the perspective of extended reality and blockchain. First, we present a metaverse exploration framework, summarizing the role of its technological advantages in learning evaluation. On this basis, we propose a solution scheme to the problems of reliability, scientificity, and credibility in learning evaluation, and discuss the key issues of the solution. We believe that this paper can provide useful insights into the field of metaverse exploration, and open up new ideas for learning evaluation.
Pedestrian Attribute Recognition Based on Association Rules

Over the past few years, deep learning has achieved impressive performance, and pedestrian attribute recognition has also been extensively widely studied. Pedestrian attribute recognition aims to predict a set of attributes from a predefined attributes list to describe the characteristics of the person. However, there are many different levels of attributes in the predefined attribute list, especially some high-level semantic information, so how to exploit the relationship between these attributes is an important challenge. We propose a flexible Association Rules Module(ARM), which can use association rules to express the relationship between attributes. Moreover, this module can work on different baselines. Extensive experiments show that the proposed method achieves excellent performance on two datasets and four baselines.
On the Use of a Semantic Segmentation Micro-Service in AR Devices for UI Placement

Augmented Reality (AR) technology is now increasingly applied in various fields, which can bring an unprecedented immersive experience and rich interaction to the application field. However, complex interactions and informative interfaces impose a long learning curve and burden on users. Making the AR experience more intelligent to reduce redundant operations is one solution to enhance the user experience. One potential research direction is seamlessly combining the two fields of machine learning and AR. This paper proposes using semantic segmentation to assist automatic information placement in AR using a case study within precision agriculture as an example. The precise location of the crop area in the user view is determined by semantic segmentation, which helps to place information in the AR environment automatically. The dataset used for machine learning model construction consists of 242 farmland images. Four semantic segmentation techniques are proposed and bench-marked against each other. The results show that the Attention U-Net deep neural network has the highest recognition accuracy, reaching 91.9%. An AR automatic information placement prototype using Attention U-Net has been developed to run on tablets utilising a micro-service approach. This work demonstrates how AR user interfaces could be placed correctly within the real world, which traditionally has been an understudied area of research within AR and is essential for future AR games and Enterprise applications. As such, this solution has potential usage in all areas of AR application.
Data Analysis and Friendship Prediction for Twitch Streamers

Designing recommendation systems for social networks is a common practice, and live-streaming platforms are not an exception. However, due to data and processing limitations not much work has been done to analyze these networks. In this paper, we analyzed a Twitch network gamers dataset and designed a new recommendation framework based on the specific characteristics of this dataset. The framework consists of three different layers: data, interest, and recommendation layer, each considering specific tasks. The results show the effectiveness of these friendship connection predictions among users.
Design Aspects of Dynamic Label Placement in Interactive 3D Information-Rich Virtual Environments Using Meta-Heuristics: Grey Wolf Optimizer and Simulated Annealing

Callout annotations are crucial elements in information-rich virtual environments such as Serious Games. However, their arrangement within the view management is challenging and can lead to occlusions of the annotated objects or overall visual clutter, also depending on the user's perspective and position in the virtual scene (e.g., in the medium of a Virtual Re-ality). Such issues can impair the overall quality of experience for users. In this paper, we introduce 20 design criteria for defining callout arrangements in interactive 3D information-rich virtual environments. We investigate the use of two meta-heuristics (the grey wolf optimizer (GWO) and simulated annealing (SA)) for handling the arrangement challenges by incorporating a sub-set of our criteria. Based on the results of our study, we conclude that SA's output was significantly preferred by our participants compared to two GWO versions, a heuristically altered SA, and also a random solution.
An Assistance System Framework for Virtual Reality Self-Service E-Learning Kiosks

Digital self-service kiosks are an essential element in various customer-oriented application areas such as self-check-ins at the airport, or self-ordering at fast-food restaurants. Be-sides, self-service kiosks are also used frequently for educational purposes, for example, to inform visitors at certain exhibits in museums. Furthermore, Virtual Reality (VR) technology has proven to be able to support learning experiences in such cases. However, being used unsupervised and by visitors novel to VR, the experience of these VR self-service E-learning kiosks can suffer greatly besides health- and safety-related challenges. In this paper, we explore the use of assistance systems to support users in the mentioned use-case. We propose a component-based assistance system design for VR. We divide the assistance functions within our system into direct and indirect assistance functions, depending on whether a user may actively trigger them or the functions trigger automatically based on given pre-conditions. We demonstrate the feasibility of our concepts by a reference implementation and discuss the limitations and possibilities of our resulted system. We show that our system meets the users' needs and can proactively support them before potential challenges occur.
The Effect of Attention Guidance and the Potential of Cinmatic Augmented Reality in Narrative Immersion

This paper discusses issues relate to narrative immersion in cinematic extended reality (CXR). System immersion built by reality technologies forms the fundamental feature of the new medium of storytelling. Attention guidance is widely applied in CXR to maintain the flow of narrative immersion. The authors review several attention guidance approaches and suggest diegetic cue is the preferred method in CXR since it embeds in the scene and less disturbs the storytelling. A framework is proposed in this paper to illustrate pathways of Cinematic Augmented Reality (CAR) and Cinematic Virtual Reality (CVR) to achieve narrative immersion. The authors conclude that CAR has more advantages in fulfilling narrative immersion than CVR due to viewers' reinforced involvement in CAR, suggesting the value of exploring the potential of CAR as a storytelling medium.
Prototyping a VR Sandbox for Scene Customization without 3D Authoring Skills

Virtual Reality (VR) and simulation continue po-sitioning as suitable tools for fine-tuning processes otherwise impossible in real life. Such is the case of Aether, a mobile service robot for elderly care developed during the COVID-19 pandemic. Aether's development was negatively impacted due to restrictions placed on accessing long-term care facilities that impeded testing object tracking, elderly tracking, fall detection, and human-robot interactions. Our efforts to maximize Aether's development led us to create a digital twin where the core functionality is replicated to train the machine learning modules to optimize the robot's responses before real-world deployment. However, the digital twin creation requires significant authoring to ensure the virtual environment matches the real one by employing 3D technical artistry skills, which demands a professional knowledgeable in this domain. This paper presents a sandbox prototype for scene customization that allows importing, positioning, scaling, and saving changes for mobile robot simulation. Our preliminary testing of the sandbox has focused on usability to understand how the setting up of the environment is perceived. Preliminary results indicate that the sandbox is usable with improvements pertaining to improving the manipulation of the objects.