Reviewerly/exHarmony huggingface dataset:
A Study on Morphological Analyser for Indian Languages: A Literature Perspective

India is the home to a very large number of languages. The Indian languages are rich in literature and has been studied by native and foreign Linguists. Unlike English, Indian languages are Morphologically rich and follows free word-order. Even though there have been efforts towards building morphological analyser for Malayalam and Sanskrit, until now an efficient one is not available. In order to solve this problem we come up with the study on morphological analyzer in Indian languages. Morphological analyser is a linguistic tool that would generate the morphemes of a given word. These rules are based on Indian language linguistics. This paper gives a brief description of the approach used for morphological analyser. With the development of a Python Package that make use of Rule Based Approach for developing Morphological Analyzer. It mainly focusing on noun and this analyzer can be used for Information Retrieval, search engines, Machine Translation, speech recognizer, Text Processing etc.
The realization and optimization of path planning algorithm for autonomous mobile robot

research-article The realization and optimization of path planning algorithm for autonomous mobile robot Share on Authors: Yong Liang Guilin University of Technology, China Guilin University of Technology, ChinaView Profile , Xing Luo Guilin University of Technology, China Guilin University of Technology, ChinaView Profile , Zhisong Xie Guilin University of Technology, China Guilin University of Technology, ChinaView Profile , Qi Cui Guilin University of Technology, China Guilin University of Technology, ChinaView Profile , Lifu Gan Guilin University of Technology, China Guilin University of Technology, ChinaView Profile Authors Info & Claims ICIIP 2021: 2021 6th International Conference on Intelligent Information ProcessingJuly 2021 Pages 109–114https://doi.org/10.1145/3480571.3480589Online:29 October 2021Publication History 0citation22DownloadsMetricsTotal Citations0Total Downloads22Last 12 Months22Last 6 weeks7 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Analysis on the Application of Intelligent Data Analysis Technology of Internet of Things in E-Commerce System

The traditional Internet of Things model has been unable to meet the expanding needs of the current e-commerce platform, and it is urgent to upgrade to the direction of intelligence. With the continuous development of technologies such as artificial intelligence and big data analysis, IoT intelligent data analysis technologies are increasingly used in e-commerce systems to optimize and upgrade e-commerce systems. The intelligent data analysis technology of the Internet of Things enables the entire logistics system to collect and process information in real time like a human brain, filter out invalid information, and achieve the optimal layout of the entire industry, ultimately enabling all participants in the logistics system to perform high-quality, high-efficiency, and low-cost Cooperation. This article first reviews the application history of IoT intelligent data analysis technology in e-commerce systems. This development trend has gone through a period of initial contact, a period of gradual penetration, and a period of widespread application, supporting the upgrade of traditional logistics to smart logistics in the e-commerce system, and fulfilling the fundamental requirements of the e-commerce model to reduce costs and increase efficiency. Secondly, from the perspective of the supply chain, comparing the traditional e-commerce system with the e-commerce system after applying the intelligent data analysis technology of the Internet of Things, it demonstrates the positive effect of the intelligent data analysis technology of the Internet of Things. In each link of the e-commerce supply chain process, the core modules of the intelligent data analysis technology of the Internet of Things are introduced in detail. Finally, from the perspective of the specific connection between e-commerce supply chain logistics and big data systems, this article analyzes how the intelligent data analysis technology of the Internet of Things matches the various needs generated in the process of e-commerce supply chain, and realizes the overall optimal allocation of resources.
Approximate R-Calculus

In R-calculus for first-order logic, there is a rule $$\dfrac{\Delta \not \vdash \lnot A}{\Delta |A\Rightarrow \Delta ,A},$$which cannot be reduced to the atomic form: $$\dfrac{\Delta \not \vdash \lnot l}{\Delta |l\Rightarrow \Delta ,l},$$because \(\Delta \not \vdash \lnot l\) is undecidable and \(\Delta \vdash \lnot l\) is semi-decidable. A set A is decidable [, ] if there is an algorithm to decide whether a given x is in A, such that (i) if \(x\in A\) then the algorithm gives yes, and (ii) if \(x\not \in A\) then the algorithm gives no. A set A is semi-decidable if there is an algorithm to decide whether a given x is in A, such that (i) if \(x\in A\) then the algorithm gives yes, and (ii) if \(x\not \in A\) then the algorithm may not terminate.
TACL: Trust-Based and Scalable Access Control for IoT Using Blockchain

Internet of Things (IoT) security, privacy and trust remain the major challenges, mainly due to the massive scale and distributed nature of IoT networks. Access control systems are used in security to control access to valuable resources. This paper proposes a novel trust-based access control (TACL) model for IoT. Trust provides IoT devices with a natural mechanism to judge other devices, similarly to how we tackle security in our human society. Trust relationship among IoT devices provides a means to influence the future behaviours of their communication. Services and resources should be shared with a requesting device only if other devices trust that device. An access control system equipped with a trust management allows the computation of trust to make efficient decision in controlling access to resources. TACL is an advanced access control system that takes trust information into consideration before allowing subjects to perform operations on resources. The EOS blockchain is used as a tool to publish and evaluate the performance of the proposed model. The results demonstrate that TACL is a lightweight and scalable protocol designed to achieve fine-grained access control.
A Voice Based Assistant Using Google Dialogflow and Machine Learning

Virtual Personal Assistant (VPA) is one of the most successful results of Artificial Intelligence, which has given a new way for the human to have its work done from a machine, just like a normal person. This paper provides methodologies and concepts used in making of a Virtual Personal Assistant (VPA) and thereby going on to use it in different software applications. In this project, we are trying to make a Virtual Personal Assistant ERAA which will include the important features that could help in assisting ones’ needs. Keeping in mind the user experience, we will make it as appealing as possible, just like other VPAs. Various Natural Language Understanding Platforms like IBM Watson and Google Dialogflow were studied for the same. In our project, we have used Google Dialogflow as the NLU Platform for the implementation of the software application. The User-Interface for the application is designed with the help of Flutter software platform. All the models used for this VPA will be designed in a way to work as efficiently as possible. Some of the common features which are available in most of the VPAs will be added. We will be implementing ERAA via a smartphone application, and for future scope, our aim will be to implement it on the desktop environment. The following Paper ensures to provide the methodologies used for development of the application. It provides the obtained outcomes of the features developed within the application. It shows how the available natural language understanding platforms can reduce the burden of the user, and therefore going on to develop a robust software application.
Training Neural Networks with Random Noise Images for Adversarial Robustness

Despite their high accuracy, deep neural networks (DNNs) are vulnerable to adversarial examples. Currently, adversarial training is the mainstream defense approach against adversarial examples. However, given the unknown nature of adversarial attacks in real life, this approach has fundamental limitations in practical use, as it is impossible to obtain sufficient adversarial examples for the training. In this paper, we propose RanTrain, a simple training approach which employs a background class with random noise images to augment the original DNN model and training data, without requiring any adversarial examples. Experiments have shown that RanTrain works effectively with different datasets and various DNN structures, and it significantly increases the robustness of DNNs to adversarial examples.
Simultaneously Predicting Video Object Segmentation and Optical Flow Without Motion Annotations

Optical flow information is one of the most commonly used temporal cues in video object segmentation algorithms. However, as it is difficult to label real-world video data with motion annotations, video segmentation methods are often forced to use external optical flow datasets and additional flow prediction models. In this paper, we propose an optical flow synthesizing approach which can generate artificial object flow from video segmentation masks, reliving the constraint of manual motion annotations for joint learning of video segmentation and optical flow prediction tasks. Extensive experiments and analysis are carried out on the DAVIS video segmentation datasets and the self-constructed synthetic flow database, demonstrating that the proposed synthetic flow has a better training effect compared with external flow datasets, and that this target-specific flow synthesizing training scheme can help video segmentation networks to better distinguish the motion patterns of certain targets in multiple-instance video segmentation scenes.
The Research and Application of the Improved Prediction Model for Big Data

With the development of big data technology, the application of data fusion in different disciplines and fields is the hot spot of big data research and application in the future, and more industries are facing the problem of massive data prediction. In this paper, a new improved prediction model is proposed. In the prediction process, PCA algorithm is used to process the data. Gaussian process regression model is used to fit the basic prediction data and residual series. The residual series modified by BP neural network is combined with the basic prediction data to obtain the prediction results. Compared with the single model, this model is more applicable and accurate.
Self-Supervised Learning based on Sentiment Analysis with Word Weight Calculation

Learning domain information for a downstream task is important to improve the performance of sentiment analysis. However, the labeling task to obtain a sufficient amount of training data in an application domain tends to be highly time-consuming and tedious. To solve this problem, we propose a novel method to effectively learn domain information and improve sentiment analysis performance with a small amount of training data. We use the masked language model (MLM), which is a self-supervised learning model, to calculate word weights and improve a downstream fine-tuning task for sentiment analysis. In particular, the MLM with the calculated word weights is executed simultaneously with the fine-tuning task. The results show that the proposed model achieves better performances than previous models in four different datasets for sentiment analysis.
A Cooperative Optimal Mining Model for Bitcoin

We analyze Bitcoin mining from the perspective of a game and propose an optimal mining model that maximizes profits of pools and miners. The model is a two-stage Stackelberg game in which each stage forms a sub-game. In stage I, pools are the leaders who assign a computing power to be consumed by miners. In stage II, miners decide of their power consumption and distribution. They find themselves in a social dilemma in which they must choose between mining in solo, therefore prioritizing their individual preferences, and participating in a pool for the collective interest. The model relies on a pool protocol based on a simulated game in which the miners compete for the reward won by the pool. The solutions for the stage I sub-game and the simulated protocol game are unique and stable Nash equilibriums while the stage II sub-game leads to a stable cooperative equilibrium only when miners choose their strategies according to certain criteria. We conclude that the cooperative optimal mining model has the potential to favor Bitcoin decentralization and stability. Mainly, the social dilemma faced by miners together with the balance of incentives ensure a certain distribution of the network computing power between pools and solo miners, while equilibriums in the game solutions provide stability to the system.
The Nexus between Remittances and Mobile Technology: Evidence from Southern Africa

The main purpose of our study is to establish the causal linkages between remittances and mobile technology. The study drew from the fact that remittances are critical to the livelihoods of hundreds of millions of people in the developing world, and mobile technology is an emerging force shaping how people send and receive money. Evidence from the literature demonstrates that empirical findings on the nature of the relationship between remittances and mobile technology are scant or non-existent. This study, motivated by the scarcity of literature on the direction of causality between remittances and mobile technology investigated the nature of the relationship between remittances and mobile technology in selected SADC states. The study employed some panel cointegration techniques and the Dumitrescu & Hurlin (2012) to test the relationship between remittances and mobile technology in selected SADC states. Results showed that causality runs from remittances to mobile subscriptions. This shows that remittances have an impact on mobile subscriptions but not vice versa. This result shows the development impact of remittances. The analysis also showed that there is no causal relationships between unemployment and remittances. The relationship between economic development and remittances seen to be running from economic development to remittances. The study recommended that the expansion of mobile telecom infrastructure should precede any effort to expand mobile money adoption in these countries.
Finding Misdetections in Unlabeled Image Database: Toward Reliable Object Detection

Object detection from images plays an important role in ITS applications, such as autonomous-driving systems. A concern for recent object detection models based on neural networks is the black box nature of the neural networks, which should be addressed in mission critical ITS applications. In this paper, we propose a framework of partial image retrieval to analyze and debug a target detection model. Given an image with incorrectly detected objects, our method discovers similar images that also contain incorrectly detected objects from a large unlabeled image database, which is relatively easier to collect than annotating all images. These similar images provide us with insights into the misdetection by object detection models, which will be useful in resolving the issue. We qualitatively and quantitatively demonstrate that the proposed framework effectively outputs similar doubtful images using real-world datasets. Specifically, we show that, by querying an image with a false positive detection, we can efficiently find images with false positives from a large unlabeled image database using the proposed framework.
A closed form solution for viewing graph construction in uncalibrated vision

This paper presents a closed form solution for the problem of computing a set of projective cameras from the fundamental matrices of a given viewing graph. The approach is incremental, exploits trifocal constraints, and does not rely on either image or structure points. Represented by a vector of four parameters that uniquely ensure its consistency with the local trifocal geometry, each newly computed camera is automatically coherent with the projective frame chosen as global reference, thus not needing any a posteriori synchronization. Results of experiments made under controlled conditions show that the proposed approach is relatively resilient to noise, and faster by three orders of magnitude than classical camera resectioning solutions, while reaching a comparable accuracy. This makes our closed form approach a good candidate for camera initialization in scenarios involving large-scale viewing graphs.
Secure Computation of Shared Secrets and Its Applications

There has been renewed attention to threshold signature in recent years as the threshold version of the ECDSA and SM2 Elliptic Curve Cryptographic Algorithm (SM2) could be used in Bitcoin as an underlying digital signature scheme to protect users’ private keys that guarantees transactions. A (t, n) threshold signature scheme means in a set of n parties, at least t players can exercise the right of generating signatures on behalf of the group, and any less than t of the players’ cooperation cannot generate a valid signature for the message nor obtain any information about the shared secret key. Thus, it is meaningful to construct a purely (t, n) threshold SM2 signature scheme (purely (t, n) means in the whole signature scheme, the threshold value is fixed to t). We propose a robust multiplication protocol of shared secrets to resolve the “multiplication of shared secrets” problem in existing threshold signature schemes. Using the proposed multiplication protocol, we improve the existing secret reciprocal computation protocol and show how to get a purely (t, n) threshold SM2 signature scheme.
Uncertainty to Avoid Entrapment: Comparing Internet Stings to Real Victim Conversations

Uncertainty can be viewed as a mechanism by which researchers can examine speaker commitment in both bona-fide and deceptive communication. We apply the examination of varying levels of uncertainty in text to the online grooming domain. Within online grooming literature, researchers use data sets consisting of real victims, vigilantes, and law enforcement officers. However, psychology researchers question whether internet stings involving law enforcement officers (LEO) and vigilantes can be used to approximate the grooming process of children. In this paper, we examine differences between the three groups within the stages of the grooming process. We approach the problem through the lens of uncertainty language. We find the use of uncertain language differs the most between victims, vigilantes, and LEO in the meeting, sexual, and risk assessment stages.
Enhancing Adversarial Examples Transferability via Ensemble Feature Manifolds

The adversarial attack is a technique that causes intended misclassification by adding imperceptible perturbations to benign inputs. It provides a way to evaluate the robustness of models. Many existing adversarial attacks have achieved good performance in the white-box settings. However, these adversarial examples generated by various attacks typically overfit the particular architecture of the source model, resulting in low transferability in the black-box scenarios. In this work, we propose a novel feature attack method called Features-Ensemble Generative Adversarial Network (FEGAN), which ensembles multiple feature manifolds to capture intrinsic adversarial information that is most likely to cause misclassification of many models, thereby improving the transferability of adversarial examples. Accordingly, a generator trained based on various latent feature vectors of benign inputs can produce adversarial examples containing this adversarial information. Extensive experiments on the MNIST and CIFAR10 datasets demonstrate that the proposed method improves the transferability of adversarial examples while ensuring the attack success rate in the white-box scenario. In addition, the generated adversarial examples are more realistic with distribution close to that of the actual data.
Significance of Creative Commons Licenses for the Evolution of Copyright in Cross-Border Relationships

The paper analyzes the evolution of copyright and the main events that led to a change in the concept of determining the author’s rights and their protection. The main legal acts regulating copyright relations from the stage of their inception (from the moment of the invention of printing in Europe) to the present time are considered. The creation of the Internet became the starting point for rethinking the foundations of copyright law to adapt it to modern needs. Thus, treaties were adopted, the norms of which extended traditional law to the digital environment, securing the rights of authors on the Internet. Developing a licensing approach under the Creative Commons licensing concept and the “some rights reserved” principle has been an important step in the enforcement and protection of cross-border copyright rights on the Internet. This is a new stage in the development of licensing, due to technological progress and the introduction of artificial intelligence into human life.
Universal Adversarial Perturbation Generated by Using Attention Information

Since the concept of adversarial attack was proposed, the vulnerability of Deep Neural Networks (DNNs) has attracted the interest of artificial intelligence researchers. Many state-of-the-art DNNs suffer the risk of adversarial perturbations, which can be divided into image-dependent and universal. In this paper, we review some traditional algorithms developed by others and introduce a novel method to create universal adversarial perturbations by attention. Our approach is the first to generate universal perturbations by attacking the attention heat maps with the interpretation method, Layer-wise Relevance Propagation. Our experiment results, when compared with traditional ones, showing an improvement both on fooling ratios and transferability with the ImageNet dataset. The goal of our research is to create less vulnerable DNNs to adversarial attacks.
The Application of Smart Phone APP Mobile Platform in College English Online and Offline Learning

With the development of information technology, mobile terminals such as smart phones and tablet computers have been integrated into people’s lives and studies, bringing students the good visual and auditory experience, and becoming the important path to enhance students’ interest. In order to meet the needs of online and offline mixed teaching of college English, this paper is based on the mobile learning platform of the smart phone App. First, recommended to develop the architecture of the smart phone App mobile learning platform; then, design the smart phone App mobile learning platform software architecture to improve the quality and efficiency of software development; finally, purpose specific application strategies: improve the usability of smart phone App mobile learning platform, improve the interactivity of the smart phone App mobile learning platform, innovate teachers’ deep participation in the new model of English teaching based on smart phone APP, and improve students’ autonomous learning ability based on smart phone APP.