Reviewerly/exHarmony huggingface dataset:
Neural Radiance Fields Methods and Improvement Approaches

With the increasing use of AR/VR technologies, how to quickly generate a usable three-dimensional (3D) model or scene has become an increasingly urgent problem to be solved. Currently, the common approach is to manually model from real objects or use 3D scanners and feature point snapping to generate models. Combined with machine learning techniques, the Neural Radiance Fields (NeRF) method enables the rapid generation of usable models from resources in a specific format or source. NeRF can use the 5D input volume containing color and position information in the algorithm to output color and transparency information that can be recognized by volume rendering, quickly generating a 3D model or scene from a 2D image collection. In addition, optimizations including costs, time usage, and generation rate can also be performed based on the initial NeRF method, as well as different release optimizations in applications. This paper gives an overview of existing research on NeRF methods, including the original model and improved approaches, as well as their applications and potential directions in view synthesis.
Fast Recommender System Combining Global and Local Information : Construction of large-scale commodity information recommendation system

Large-scale recommender systems have important applications in online shopping and other fields.. In this paper, we propose a system that can balance the speed and accuracy of large-scale personalized recommender systems. To improve speed, we use GCN instead of the previous dense operation to obtain global information. At the same time, we use an attention mechanism so that various information can be fully integrated. The experimental results show that the method has obvious advantages in speed and accuracy, and the inference speed of only 109ms can achieve an accuracy of 83.1%.
Improved CTPN Based Attention Mechanism for Scene Text Detection

Text, which can be seen everywhere in the natural environment, is one of the main ways to convey and communicate messages among people. In recent years, text detection and recognition techniques in natural scene images have become a hot research topic in the fields of computer vision, natural language processing, and instant translation. Global contextual information is crucial for natural scene text detection. However, most existing approaches rely on convolutional neural networks, and direct access to global contextual information is challenging due to the local nature of convolutional operations. Inspired by Swin-Transformer which has powerful global modeling capability, this paper proposes an improved CTPN model with Swin-Transformer instead of VGG16 as the backbone network for natural scene text detection, and uses FPN feature pyramids for feature fusion of different sizes to improve detection accuracy. On the ICDAR2015 dataset, the improved CTPN model incorporating Swin-Transformer improves the accuracy, recall, and F1 values by 1.3%, 3.3%, and 2.4%, respectively, compared with the CTPN model. The experimental results show that the improved CTPN algorithm incorporating Swin-Transformer can effectively and accurately detect the text location in natural scenes, laying a solid foundation for later text recognition and lab sheet interpretation.
Hot-Sim Supervised Contrastive Loss

In recent years, pre-training models using supervised contrastive loss have defeated the cross-entropy loss widely adopted to solve classification problems using deep learning. However, this approach is limited by its inability to directly train neural network models. To overcome this difficulty, we propose a novel loss function based on supervised contrastive loss, which can directly train deep models. Our extensive evaluations of the Cifar10 and the Mnist datasets show that our method achieves good performance on Cifar10 and the Mnist datasets.
Entity Linking Based on ERNIE for Chinese Knowledge Graph Question Answer

The existing entity link models have low ability to understand the context of short questions, and there are problems such as entity boundary recognition errors and entity semantic understanding errors, and there are few models that can handle Chinese. To solve the above problems, we propose a problem entity recognition method based on the ERNIE-BiLSTM-CRF model, which divides the entity link task into two parts: problem entity recognition and entity disambiguation. 2) The ERNIE model is used to calculate the similarity between the problem entity context information and the candidate entities, and the entity popularity is combined as the feature of entity disambiguation. Experiments demonstrate the method has a good entity linking effect for Chinese knowledge map question answering.
Visualization of Campus Card Consumption Data

The wide application of campus card in campus life has produced a lot of data. The data view expresses data in a simple and understandable way to provide users with the information they need. Based on the strong cognitive ability of human beings to detect, analyze and understand graphic patterns, compared with other traditional data analysis methods, users can more easily capture data features from data views. Based on the consumption data of a college canteen, this paper analyzes the composition dimensions of the data, preprocesses the consumption data, designs a data analysis model based on individual students, stalls and the restaurant as a whole, and realizes the visualization of campus card consumption data with the overall picture of the restaurant, the overall picture of the stalls and the personal consumption picture based on python language.
Research on Sports News Oriented Chinese NER

In the field of sports news, data analysis and named entity recognition(NER) around news content can provide a data basis for subsequent tasks such as building a sports knowledge database and recommendation system. Therefore, named entities in the field of sports news are of great value, but there are few studies in this field at present. This paper takes Chinese sports news NER as the primary research object, make an in-depth study on the entity characteristics, and designs the model structure of multi-feature fusion. As there are few data sets in this field, this paper also constructs a data set in the sports news field, and verifies the effectiveness of our model for Chinese named entity recognition. At the same time, it conducts comparative experiments with popular depth-learning methods. The main work of this paper is as follows: (1) A dataset for sports news Chinese NER is constructed, Includes 7,645 labeled samples and 14,258 entities in 6 categories (2) Aiming at the problems of low utilization rate of text features and insufficient semantic representation in the process of NER, this paper proposes a multi-feature fusion recognition model that integrates radical, character and word level features. The experimental results show that the recognition performance on our and other public datasets is better than the existing methods.
Research on Risk Assessment of 6G Network Construction for Mobile Network Operators Based on P-RAN

The rapid iterative development of network brings new opportunities as well as new risks to the network construction of telecom industry. This paper constructs the “7+N” network construction risk assessment system through the Analytic Hierarchy Process, and summarizes four kinds of “L-shaped”, “Inverse L-shaped”, “V-shaped” and “Inverse V-shaped” assessment results of network construction risk through Fuzzy Comprehensive Evaluation Model. On this basis, this paper takes the 6G decentralized network P-RAN as an example to verify the network construction risk assessment system model. Research shows that the risk of P-RAN network construction presents an “L-shaped” distribution, with low risk as the main risk. The application stage of future network construction is the key to the success of the project. Mobile network operators need to provide high-quality network services to P-RAN sharers and users, and do a good job of risk control when exploring new incentive mechanisms.
Dialogue Text Summarization Method Combined Self-supervised Learning and Neural Architecture Search

Dialogue summary generation task aims at generating summary for dialogue. Due to the changing topic and discrete semantic of dialogue, previous summarization methods on article cannot generate satisfactory summary for dialogue. To reduce the impact of discrete topics on dialogue summary generation, this work proposes a topic division method to form topic paragraph sets. In order to generate dialogue summaries according to the divided topic paragraphs, we further propose a dialogue text summarization model based on a Generative Adversarial framework. In this model, we use the differentiable Neural Architecture Search method to implement the entire search process of generative and discriminative network. The generative network is a BART network formed by sharing the Transformer structure obtained by the search, which we call EBART. The effectiveness of this work is verified by experiments on public dialogue datasets.
Non-random walk in cryptocurrency: An empirical analysis of bitcoin

The current study has examined the informational efficiency of market leader of cryptocurrency i.e, Bitcoin. The daily, weekly and monthly prices of Bitcoin have been used for analysis from 2013 to 2017. The information efficiency has been investigated by using different tests of random walk both parametric and non-parametric. The results indicate the Bitcoin returns are not weak form efficient and the element of random walk is not there. Hence, the investors have an opportunity to beat the market by using technical trading and get abnormal returns from the predictability of Bitcoin prices.
A Multi-Blockchain-Based Cross-Domain Authentication and Authorization Scheme for Energy Internet

The expansion of the scale of the Power Internet of Things stimulated by the development of the Energy Internet makes the growth in demand for the effective authentication and access control technologies in the cross-domain data exchange. Based on the cross-chain technology of the blockchain and the cuckoo filter, this paper proposes a cross-domain authentication scheme for Power Internet of Things. Firstly, a cross-chain authentication architecture is established. Combined with the existing authentication technologies used in intra-domain authentication, a cross-domain authentication process based on the cross-chain technology is proposed to realize the automatic transmission of the authentication credentials from application domain to authentication domain. The cuckoo filter is deployed on the blockchain as smart contracts, and the user certificate fingerprint is inserted into the filter to realize user registration, query, and revocation, which reduces the cost of the user certificate management. Experimental results show the effectiveness and feasibility of our scheme. Based on the proposed authentication scheme, a cross-domain access control scheme based on roles and object classes is presented, by treating the object classes as controlled objects and then applying the role-based access control to the object classes, on the condition that the heterogeneous domains in the Energy Internet have the same kinds of resources.
Online Platform for Reporting Suspicious Activities

Cybercrime Convention defines computer-related forgery as "intentional… and without right, the input, alteration, deletion, or suppression of computer data, resulting in inauthentic data with the intent that it be considered or acted upon for legal purposes as if it were authentic, regardless whether or not the data is directly readable and intelligible." Public procurement (or government crime to the authorities) is the single largest marketplace for government spending and the greatest source of official corruption worldwide. Various factors make this government process a hot bed of corruption in high-income and low-income countries alike. Vendor selection processes are complex and opaque, involving a high degree of human discretion. Not only do these vulnerabilities lead to massive financial waste, but they distort market prices, reduce healthy competition, and frequently result in substandard goods and ineffective services. To Overcome this we develop a platform where through a person can give a tip to crime authorities without showing their own information. Our platform includes 2 main user roles where first panel is government authorities panel where they can receive all the information and other panel is of person who is sending sensitive information to the authorities. Our platform is safe from malware, pishing attack and cross site scripting attacks and sql injection
Robot for the Treatment of Communicable Diseased Patients using AI &amp; ML

In recent years there has been significant research in the development of medical assistance autonomous robots these robots have the potential to revolutionize healthcare by improving efficiency reducing workload and improving patient outcomes one common theme in this research has been the desire for medical professionals to control their assistant robots remotely through the internet many advanced healthcare technologies have been developed such as smart healthcare systems digital thermometers non-contact infrared thermometers pulse oximeters heartbeat monitoring devices and iot devices for heart problem detection in addition temperature humidity measurement systems have been developed to monitor environmental conditions in healthcare facilities our work focuses on utilizing robotics to provide unique prescription medication care in the digital healthcare sector we have explored various methods of controlling these robots including pathfinding autonomous movement and obstacle avoidance additionally some research has focused on user end manual control through android apps however the development and implementation of medical assistant robots also raise important ethical legal and social implications patient privacy and data security must be protected and there must be regulations in place to ensure the safe and responsible use of these technologies.
FREELANCO: A Mobile Application for Freelancers

This paper has a main objective of Encouraging freelancers and creating employment and encouraging skillset of people around who are having skills but don’t have platform to show up. Many Freelancers struggle to find best place where they can create their own identity, and find potential customers for their product or service. As there is no specific platform for freelancers out there. so they all will get a specific platform where they can find their potential customers and network with the people in their niche. Which will help them all to grow fast and get explore to new vision. This application will make another world for freelancers where they can nurture their skills also and learn new also from other freelancers as there will be so many freelancers show casing their own skill set.
Intelligent invigilator system based on target detection

Affected by the COVID-19 epidemic, the final examinations at many universities and the recruitment interviews of enterprises were forced to be transferred to online remote video invigilation, which undoubtedly improves the space and possibility of cheating. To solve these problems, this paper proposes an intelligent invigilation system based on the EfficientDet target detection network model combined with a centroid tracking algorithm. Experiments show that cheating behavior detection model proposed in this paper has good detection, tracking and recognition effects in remote testing scenarios. Taking the EfficientDet network as the detection target, the average detection accuracy of the network is 81%. Experiments with real online test videos show that the cheating behavior detection accuracy can reach 83.1%. In addition, to compensate for the shortage of image detection, we also design an audio detection module to carry out auxiliary detection and forensics. The audio detection module is used to continuously detect the environmental sound of the examination room, save suspicious sounds and provide evidence for judging cheating behavior.
Large‐scale time‐lapse scanning electron microscopy image mosaic using a smooth stitching strategy

Due to the trade-off between the field of view and resolution of various microscopes, obtaining a wide-view panoramic image through high-resolution image tiles is frequently encountered and demanded in numerous applications. Here, we propose an automatic image mosaic strategy for sequential 2D time-lapse scanning electron microscopy (SEM) images. This method can accurately compute pairwise translations among serial image tiles with indeterminate overlapping areas. The detection and matching of feature points are limited by geographical coordinates, thus avoiding accidental mismatching. Moreover, the nonlinear deformation of the mosaic part is also taken into account. A smooth stitching field is utilized to gradually transform the perspective transformation in overlapping regions into the linear transformation in non-overlapping regions. Experimental results demonstrate that better image stitching accuracy can be achieved compared with some other image mosaic algorithms. Such a method has potential applications in high-resolution large-area analysis using serial microscopy images. RESEARCH HIGHLIGHTS: An automatic image mosaic strategy for processing sequential scanning electron microscopy images is proposed. A smooth stitching field is applied in the image mosaic. Improved stitching accuracy is achieved compared with other conventional mosaic methods.
Shape correspondence for cel animation based on a shape association graph and spectral matching

Abstract We present an effective spectral matching method based on a shape association graph for finding region correspondences between two cel animation keyframes. We formulate the correspondence problem as an adapted quadratic assignment problem, which comprehensively considers both the intrinsic geometric and topology of regions to find the globally optimal correspondence. To simultaneously represent the geometric and topological similarities between regions, we propose a shape association graph (SAG), whose node attributes indicate the geometric distance between regions, and whose edge attributes indicate the topological distance between combined region pairs. We convert topological distance to geometric distance between geometric objects with topological features of the pairs, and introduce Kendall shape space to calculate the intrinsic geometric distance. By utilizing the spectral properties of the affinity matrix induced by the SAG, our approach can efficiently extract globally optimal region correspondences, even if shapes have inconsistent topology and severe deformation. It is also robust to shapes undergoing similarity transformations, and compatible with parallel computing techniques.
Two faces of blindness

Abstract Blind signatures are a decades-old privacy enhancing technology. It is not always clearly understood that blind signatures actually possess two separate properties: the intuitive understanding that the message to be signed is hidden from the signer, and the fact that the resulting signature is unlinkable (meaning that the signer cannot later tell in which session it created a particular signature). The question is: how exactly should these properties be defined, and can they be defined in a natural way such that they are mutually independent yet together imply blindness? In this paper we study this question, present formal definitions for message indistinguishability and signature unlinkability (and a few more related ones), and study their relationships. We show that these two properties are indeed mutually independent. Unfortunately their union is not equivalent to blindness in what appear to be only pathological cases.
An online and highly-scalable streaming platform for filtering trolls with transfer learning

The internet has reached a mature stage of development, and Online Social Media (OSM) platforms such as Twitter and Facebook have become vital channels for public communication and discussion on matters of public interest. However, these platforms are often plagued by improper statements or content, propagated by anonymous users and trolls, which negatively impact both the platforms and their users. Existing methods for dealing with inappropriate information rely on (semi)-manual offline assessments, which do not fully account for the streaming nature of OSM feeds. In this paper, we implement a robust and decoupled system that considers social media data as streaming data. With a publisher and consumer model, our system can process more than 179 MB of data per second with only 166.3 ms latency using Apache Kafka. Accordingly, we deploy a well-trained transfer learning model to classify incoming data streams, with an accuracy of 0.836. Our proposed architecture has the potential to assist online communities in developing more constructive and flawless OSM platforms. We believe that our contribution will help address the challenges associated with improper content on OSM platforms and pave the way for the development of more effective and efficient solutions.
A decomposition of book structure through ousiometric fluctuations in cumulative word-time

While quantitative methods have been used to examine changes in word usage in books, studies have focused on overall trends, such as the shapes of narratives, which are independent of book length. We instead look at how words change over the course of a book as a function of the number of words, rather than the fraction of the book, completed at any given point; we define this measure as "cumulative word-time". Using ousiometrics, a reinterpretation of the valence-arousal-dominance framework of meaning obtained from semantic differentials, we convert text into time series of power and danger scores in cumulative word-time. Each time series is then decomposed using empirical mode decomposition into a sum of constituent oscillatory modes and a non-oscillatory trend. By comparing the decomposition of the original power and danger time series with those derived from shuffled text, we find that shorter books exhibit only a general trend, while longer books have fluctuations in addition to the general trend. These fluctuations typically have a period of a few thousand words regardless of the book length or library classification code, but vary depending on the content and structure of the book. Our findings suggest that, in the ousiometric sense, longer books are not expanded versions of shorter books, but are more similar in structure to a concatenation of shorter texts. Further, they are consistent with editorial practices that require longer texts to be broken down into sections, such as chapters. Our method also provides a data-driven denoising approach that works for texts of various lengths, in contrast to the more traditional approach of using large window sizes that may inadvertently smooth out relevant information, especially for shorter texts. These results open up avenues for future work in computational literary analysis, particularly the measurement of a basic unit of narrative.