Reviewerly/exHarmony huggingface dataset:
Blockchain and IoT for Personal and Physical Security

The Internet of Things connects intelligent physical objects (things) and people. IoT enables every “Thing” to connect and interact, creating and transferring massive amounts of data. Because IoT de-vices handle so much data, it became necessary to incorporate Cloud Computing, Machine Learning, and Information Modelling. The significant rise in IoT is creating a surge in ICT business. By 2020, 95% of new goods will include IoT at their heart. As can be seen, IoT items will be more pre-sent, raising concerns about their visibility on the internet and legal access to resources. Innovating apps that improve a person's physical and personal life are enabled by IoT, but people's lack of security and susceptibility may lead to serious concerns like home security being penetrated and centralized organisations employing sensitive data being hacked. Blockchain technology is getting widespread interest and investigation because to its remarkable solutions to the challenges connected with the traditional centralized IoT architecture. Because there are so many IoT devices on the mar-ket for enhancing physical and mental health, and overall quality of life, a distributed trust platform that ensures scalability, privacy, and reliability is required. Integration of IoT and BC is a complex task. This research paper's goal is to create an IOT and Blockchain Application Environment that will improve a person's personal and physical existence. Thus, our main emphasis will be on developing a secure and safe Smart Home. Our suggested design uses a hierarchical and distributed blockchain platform to preserve security and privacy while meeting IoT needs. Thus, we have linked home automation with physical health, assuring safety, convenience, and health.
Task-Aware Zero-Shot Model for Dialog Generation

The dialog model is designed to communicate with users on the Internet. Due to the large number of users and the complexity and diversity of interaction scenarios, multi-dialogue domain adaptation is the key task of dialog model. At the same time, the model training data is related to real users, the cost of data collection is high and the balance of data in different fields cannot be guaranteed, which affects the overall generalization ability and performance of the model. Based on the above problems, we propose a task-aware zero-shot network for dialog generation, we extract semantic information according to different tasks each time and use it for language model parameters, making the model domain-specific to improve the overall performance of the model under multi-domain Settings. More importantly, our method does not rely on training data in the application stage, which makes the model has strong practical application. We evaluate our method on a simulated dialog dataset and get some improvement compared to traditional training methods.
Designing the human-centric IoT society: Cooperative industry-academic strategies for creative future connection

This paper summarizes the open panel discussions at the special session (SS) held on December 8th, 2021, entitled “Designing the human-centric IoT society: Cooperative industry-academic strategies for creative future connection” in the 2021 Nonlinear Science Workshop (NLSW2021). The SS was jointly organized by Tohoku Forum for Creativity(TFC) and the NOLTA society. Before the SS, an online advance questionnaire survey was conducted using Google Forms. After three introductory talks, 52 participants from academic and industry fields discussed, and shared their idea in the open panel for the future IoT society to enhance human well-being.
Target tracking algorithm of intermittently updated template based on reliability evaluation

Abstract In the siamese network tracking method based on the target matching idea, the tracking performance is closely related to the similarity between the tracking template and the tracking target. The UpdateNet algorithm updates the tracking results of each frame prediction into the template during tracking, which undoubtedly guarantees the real-time correlation between the template and the tracking target. However, tracking targets will encounter tracking challenges such as occlusion and short-term severe deformation. The tracking results predicted under this tracking challenge will be mixed with messy information that has nothing to do with the tracking target. If the UpdateNet algorithm updates the tracking results that are rich in messy information into the template, the purity of the template will decrease. This paper proposes a method of intermittently updating templates by proposing template update constraints, so that the UpdateNet algorithm discards the low-confidence tracking results. This guarantees the purity of the tracking template to a certain extent, which improves the relevance of the tracking template and the tracking target. In addition, this article also conducts optimal training on the UpdateNet algorithm.
Topological Evidence Logics: Multi-agent Setting

We introduce a multi-agent topological semantics for evidence-based belief and knowledge, which extends the dense interior semantics developed in [2]. We provide the complete logic of this multi-agent framework together with generic models for a fragment of the language. We also define a new notion of group knowledge which differs conceptually from previous approaches.
Use Machine Learning to Predict the Running Time of the Program

The prediction of program running time can be used to improve scheduling performance of distributed systems. In 2011, Google released a data set documenting the vast amount of information in the Google cluster. However, most of the existing running time prediction models only consider the coarse-grained characteristics of the running environment without considering the influence of the time series data of the running environment on the prediction results. Based on this, this paper innovatively proposes a model to predict the running time of the program, which predicts the future running time through historical information. At the same time, we also propose a new data processing and feature extraction scheme for Google cluster data sets. The results show that our model greatly outperforms the classical model on the Google cluster data set, and the root-mean-square error index of running time under different prediction modes is reduced by more than 60% and 40%, respectively. We hope that the model proposed in this paper can provide new research ideas for cloud computing system design.
Localisation of Persons by Means of Impulse-Radar Sensors: Advanced Methods

This chapter is devoted to the advanced methods for preprocessing of the measurement data from of the impulse-radar sensors; in particular: the methods for extraction of a useful signal from those measurement data, the methods for estimation of the selected parameters of the extracted impulse-radar signal, the methods for smoothing of the one-dimensional trajectories of the distance between a monitored person and the corresponding impulse-radar sensors, and the methods for conversion of two distance trajectories into the two-dimensional trajectory of the movement of the monitored person. These methods are based on the Bayesian inference enabling the inference about the cause, i.e. the measurand, on the basis of the effects, i.e. the raw result of measurement; the measurand, the raw result of measurement, and the uncontrollable disturbances affecting the measurement are modelled by random variables, while the full a priori information—summarising the experimenter’s knowledge on the measurement object and on the measuring system—as well as the final result of measurement—are represented by the probability density functions.
Computer Network Fault Diagnosis Based on Neural Network

In recent years, with the continuous development and innovation of fault diagnosis technology, network fault diagnosis technology has penetrated from traditional electronic and mechanical systems to dynamic systems such as industrial automation systems, electromechanical integration systems and computer systems. The progress of basic and applied disciplines such as mathematics, physics and informatics has provided various technical means for defect diagnosis technology and effectively promoted the rapid development of defect diagnosis technology. With the development of intelligent technology, introducing neural network (NN) technology into the field of computer network fault diagnosis has become a new type of computer network fault diagnosis technology. Considering the advantages of NN in network fault diagnosis and detection, the computer network fault detection technology based on NN has always been an important research topic. This paper introduces the basic principles and related algorithms of computer network fault diagnosis based on BPNN, and discusses how to apply NN to computer network fault detection.
Distant Supervised Relation Extraction with Hierarchical Attention Mechanism

Current distant supervised relation extraction algorithms based on Neural Networks mostly use long short-term memory networks and convolutional neural networks, which cannot capture long-distance features of sentences. This paper proposes a distant supervised relation extraction model based on hierarchical attention mechanism, which uses self-attention mechanism to calculate features between words, and sentence-level soft-attention mechanism to extract dimensionality of sentence features. Compared with the previous method, the proposed model can better capture sentence features and improve the effect of sentence relation classification. On the dataset NYT-10, compared with the PCNN_ATT algorithm, the [email protected], [email protected], and [email protected] indicators increase by 4.8%, 4.9% and 2.3%, respectively, and the AUC indicator increases by 1.1%.
Learning Image Representation via Attribute-Aware Attention Networks for Fashion Classification

Attribute descriptions enrich the characteristics of fashion products, and they play an essential role in fashion image research. We propose a fashion classification model (M2Fashion) based on multi-modal data (text + image). It uses the intra-modal and inter-modal data correlation to locate relevant image regions under the guidance of attributes and the attention mechanism. Compared with traditional single-modal feature representation, learning embedding from multi-modal features can better reflect fine-grained image features. We adopt a multi-task learning framework that combines category classification and attribute prediction tasks. The extensive experimental result on the public dataset DeepFashion shows the superiority of our proposed M2Fashion compared with state-of-the-art methods. It achieves +1.3% top-3 accuracy rate improvement in the category classification task and +5.6%/+3.7% top-3 recall rate improvement in the attribute prediction of part/shape, respectively. A supplementary experiment on attribute-specific image retrieval on the DARN dataset also demonstrates the effectiveness of M2Fashion.
Intelligent Face Recognition under Large Pose

Face recognition beneath pose changes, recently, is a very tough problem and key point in face recognition, a lot of research institutes have tried to figure out the best way to solve the tough problem by producing different method. Recently, as the success development of deep learning in many fields of computer vision, deep learning is also applied to this problem and obtains the state-of-the-art performance. This review is trying to analyze the basic idea and introduce the state-of-the-art algorithms in this field.
Online Education in Industry 5.0

Higher education in the modern era is gradually changing. Students from lecture rooms are rapidly volunteering into the digital world of the internet, embracing technologies like the distant learning with unique pedagogy. Education, to educate future citizens to operate in the field of resource sharing, will need to undergo further adaptive changes. Flexibility of location and time, personalization of the offer, collaboration, and adaptability are all trends of techniques and tools. This research work has been carried as a contribution towards the goals of National Education Policy 2020 framework proposed by Government of India. To equip citizens to engage in and benefit from the increasingly connected virtual and physical worlds, together with the rising need for information alongside limited time resources, higher education will be dynamic.
Implementation of Recruitment Website Data Analysis System Based on Web Crawler

With continuous development of science and technology, China is in the era of big data. In order to extract useful information from massive data, we must use data analysis system. The data analysis system greatly improves the use efficiency of data, and can get the hidden information in the data through the analysis of some complex data. This topic designs a recruitment website data analysis system based on web crawler, uses web crawler to collect the recruitment information in the recruitment website, analyzes the recruitment information, and finally displays the relevant information. The system uses the proceptron algorithm in deep learning to realize the salary prediction function for job seekers. In this system, the crawler function is written in Python language, and multi-threaded, anti-crawler and other technologies are used to crawl the data of the recruitment website. The application of this system can extract, store, analyze and display the complex and diverse recruitment information in the recruitment website, so that job seekers can more comprehensively understand the post information and find the most suitable job.
CNN-Based Intelligent 3D Path Planning Algorithm in the Framework of the Improved Lazy Theta*

In this paper, a CNN-based intelligent path planning algorithm in the framework of the improved Lazy theta* is proposed to solve the problem of path planning in the 3D terrain environment. The key point of the proposed algorithm is that the safety factor and the total length of the path will be comprehensively considered. By considering these two factors, a short, safe and smooth path can be planned efficiently and automatically in a 3D terrain environment. In order to solve the problem of the path moving close to the edge of the obstacles and passing dangerously between multiple obstacles, CNN is used to create a continuous and safe 3D topographic map and improve the ways of node expansion to ensure the safety of the path. Moreover, a weight self-adjustment strategy is introduced to optimize the path cost function, which solves the problem of the low search efficiency. The simulation results show that compared with the ordinary A* algorithm and Lazy theta* algorithm, the path planned by the improved intelligent Lazy theta* algorithm proposed in this paper is safer and smoother, and the search efficiency is higher, which can be applied to different planning objects according to different task scenarios.
Integration of University Digital Resources Based on Cloud Computing and Electronics Systems

Integration of university digital resources based on the cloud computing and electronics systems is studied in this manuscript. Coordinated computing objects occupy a lot of system resources and have high requirements for network resources after creation. The activation module can control the creation and release time of coordinated computing objects, and hence, the novel computing method is considered. With the wide application of the Internet, digital resources can integrate information through the network with high-speed and large-capacity computer and network system, hence, the integration model is designed. To verify the results of the proposed model, we use the public database with the integration of different parameters to verify the model. The comparison results have proven that the designed model is efficient.
Generic object detection using deep learning

Object discovery is one of the very important and challenging problems in computer computing, seeks to obtain the properties of objects from a large number of categories previously described in natural images. In particular, image classification that aims to see the semantic categories of objects in a given image. Object discovery is not only identifies item categories but also predicts the location of each item with a mandatory box. Semantic separation process aims to predict intelligent pixel separation to assign a specific category label to each pixel, thus providing a richer and more comprehensive understanding of the image. In depth learning strategies have emerged as a powerful strategy for reading presentations directly from the data and have led to significant success in the field of general acquisition. Given this period of rapid evolution, the aim of this paper is to provide a comprehensive analysis of the latest achievements in this field brought about in-depth learning strategies. Here, the deep learning object detection algorithm is implemented for accuracy and time effectiveness to show the correct label for a character of interest in a movie which will be more useful in surveillance of building, ATM centres etc. Finally, it concludes with a promising indication for future research.
A Tibetan Error Correction Method Based on Syllable-Based Levenshtein Distance

Accurately and effectively find and correct errors in Tibetan is of great significance to Tibet's informatization construction. However, due to the linguistic specificity of Tibetan, previous methods proposed for general Language were unable to accurately correct Tibetan errors. Consequently, in this paper, we present TEC_SLD, a Tibetan error correction method which is able to accurately identify incorrect syllables in Tibetan and provide candidate words to correct the errors. Exploiting syllable-based Levenshtein distances method, TEC_SLD calculate the edit distance between two strings with syllable as the smallest unit. Experimental results demonstrate that, compared with the traditional Levenshtein distance method, the average hit rate of TEC_SLD proposed in this paper is 70.9% higher. And the first hit rate of TEC_SLD achieves up to 18x increment.
College Badminton Multimedia Video Image Teaching System Based on Heap Sort Algorithm

The application of network information technology and the popularization of multimedia teaching facilities have further enriched school education methods. Badminton is a net-resistant sport that requires very strict requirements for body, technology, wisdom, etc., and the standardization of technical actions is also quite high. Therefore, multimedia video or image courses optimized by the Heap sort algorithm can be used. As an auxiliary means to enhance the teaching effect. This article investigated 48 students’ interest in learning badminton in college badminton optional courses. 50% of the students are interested in playing badminton, 29.17% of the students are generally interested in playing badminton, and 20.83% of the students are not interested in playing badminton. Compared with traditional teaching, the actual use of video and image teaching methods in badminton courses can more arouse students' enthusiasm, promote teacher-student exchanges, and improve teaching quality.
Neural approach-based quality estimation in improving translation of English to Hindi using machine translation under data science

Quality estimation could be a method for automatically indicating the standard of computational linguistics results without looking forward to human reference translations, i.e. determining how a machine translation system can produce good or bad translations without need human intervention. The main aim of this paper is to demonstrate the concept used to deal with the problem of estimating on a Hindi and English combined language pairs. To perform the proposed technique, we are using DL based quality estimation feature extraction and therefore, in this paper, we perform experiments on a set of multiple features along with the various Neural RNN models. The first part of implementing the recurrent neural networks generates the quality information about whether each word in translation is properly translated. The second part is that using the accuracy and loss of all the RNN models predicted is used to compare the quality of translation of each of the models. We apply these models on the English to Hindi taken from manythings.org website.
Improved Multi-modal Image Registration Using Geometric Edge-Oriented Histogram Feature Descriptor: G-EOH

Sirisha, B. Sandhya, B. Kumar, Prasanna J.Feature descriptors intended for multi-modal images in particular log-Gabor histogram, edge-oriented histogram and phase congruency edge-oriented histogram descriptor can handle the nonlinear intensity deformation between the infrared and visual images but fail to handle geometric deformations like rotation and scale. This paper exhibits a robust feature descriptor algorithm to the task of registering corresponding features points between multi-modal images with nonlinear geometric and intensity deformations. In this approach, an $$ M * N $$ image region around the detected feature point is re-sized using the scale $$\sigma _{i} $$ and rotated by an angle $$\theta _{i} $$ . The scale and orientation information are extracted from the feature detector corresponding to that feature point. From this preprocessed image region, histogram of local and global contour orientations is acquired to describe spatial distribution of edges. The proposed geometric EOH algorithm adapt several steps of the EOH to multi-modal images. We study the performance enhancement brought by this new algorithm, with state-of-the-art algorithms. We present an application of G-EOH for the registration of LWIR-VS images in different configurations, especially with geometric and photometric deformations.
AI-Driven Selective Protection of Multimedia Data

Volume of multimedia data such as videos are on exponential rise. Sensitive data is often embedded in such multimodal data. Moreover, the data object maybe a tampered one or a fake one. Protection of multimedia data thus suffers from several challenges. In this paper, we have described and analyzed this problem and have proposed a set of techniques. In this paper, we described the problem of how to partially and selectively protect multimodal data present in multimedia files/streams such as images and videos. Policy-driven protection of confidentiality and privacy by encrypting or anonymizing different objects and object types organized in a hierarchical manner is described and implemented in the prototype. Further redactable signature scheme using Merkle Hash Technique for the hierarchical data organization in the multimedia data has also been proposed. We have also presented our preliminary results on this work <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> This work was carried out when Ashish Kundu was working for Graphalgo Inc..