Reviewerly/exHarmony huggingface dataset:
Multi-label Fine-Grained Entity Typing for Baidu Wikipedia Based on Pre-trained Model

It is a crucial premise for named entity recognition task to achieve high-accuracy entity extraction. CCKS-2021 held a Knowledge Graph Fine-grained Entity Typing competition, and 262 teams participated. What is challenging in the task is the extremely large amounts of unlabeled data and the multi-label entity typing. In our approach, a semi-supervised learning strategy is conducted to cope with the unlabeled data, and a multi-label loss is employed to recognize the multi-label entity. An F1-score of 0.85498 on the final testing data is achieved, which verifies the performance of our approach, and ranks the second place in the task.
Big Data: Finding Frequencies of Faulty Multimedia Data

In many health care domains, big data has arrived. How to manage and use big data better has become the focus of all walks of life. Many data sources provide the repeated fault data—the repeated fault data forming the delay of processing time and storage capacity. Big data includes properties like volume, velocity, variety, variability, value, complexity, and performance put forward more challenges. Most healthcare domains face the problem of testing for structured and unstructured data validation in big data. It provides low-quality data and delays in response. In testing process is delay and not provide the correct response. In Proposed, pre-testing and post-testing are used for big data testing. In pre-testing, classify fault data from different data sources. After Classification to group big data using SVM algorithms such as Text, Image, Audio, and Video file. In post-testing, to implement the pre-processing, remove the zero file size, unrelated file extension, and de-duplication after pre-processing to implement the Map-reduce algorithm to find out the big data efficiently. This process reduces the pre-processing time, reduces the server energy, and increases the processing time. To remove the fault data before pre-processing means to increase the processing time and data storage.
Preface to <i>JSSAM</i> Privacy, Confidentiality, and Disclosure Protection Special Issue

Abstract This article is an introduction to the 13 articles in the JSSAM special issue on Privacy, Confidentiality, and Disclosure Protection. We also provide background information to place the articles into context.
Poster: Towards Explaining the Effects of Contextual Influences on Cyber-Physical Systems

The increasing complexity of Cyber-Physical Systems (CPS) increases the difficulty for users to understand their behavior. Using existing Explainable Artificial Intelligence (XAI) methods, CPS can explain their behavior to the users. However, the input-output correlations used in XAI methods are not capable of explaining certain anomalies on CPS behavior caused by contextual influences (CIs) since they do not consider the context of the CPS. Some well-known techniques used for understanding such CIs on CPS are test chambers and the analysis of logged CPS data. However, test chambers are typically only available to the manufacturer of a CPS, thus not useful for understanding CIs on the shop floors. Data analysis methods focus on data correlations, which are insufficient to explain causal relationships without using expert (human) knowledge. Hence, we propose a context-aware log-based explanation system to explain the causal relationship between CIs and the behavior of a CPS. The proposed solution employs semantic technologies to access the context of the CPS. It demonstrates the causal relationship between the CPS and CIs through counterfactual explanation and abductive reasoning methods. The contextual explanations offered by the proposed system will assist users in visualizing diverse scenarios in order to improve the CPS’ behavior accordingly.
Editorial Illustration: An Exploration of Audience Interaction Through Time and Space

Editorial illustration is seeing a major shift in focus in terms of the presentation of content and the consumption of information. A rise in news sources, coupled with the plethora of platforms that present content for consumption, has increased exponentially over the last decade. This has created new opportunities for illustrators but must be seen against a complex context of disagreements about the provenance of factually accurate news and the voracity of literal and visual language employed to present ‘truth’. Crucially, editorial illustration can be a convenor of authenticity in a melting pot of viewpoints encompassing race, religion and gender. This makes the relationship between publisher, commissioner, illustrator and audience an ever-more important chain linking the consumption of news and portrayal of truth. This paper presents examples of how changing contextual environments such as augmented and virtual reality offer opportunities for editorial illustration to be seen and consumed differently. Challenges exist for illustrators to make work using AR and VR, posing questions about what we now understand as environment. The printed page, and the resulting distance between image and reception of audience, has been augmented by digital platforms. No longer can illustrators expect the closure of an image in published printed form, but rather a continued dialogue between creators and readers or audiences in emerging digital environments. Continued dialogues use sensory engagement with content that can be aural and haptic to envelop audiences, both through the image and the contextualizing environment, which creates an ongoing conversation. The paper draws examples from the most progressive creators and publishers of editorial content that push current boundaries of the form. The author explains changes in engagement with content, offers insights into the professional development of illustrators and suggests academic positions for critics to engage with these new editorial illustration outputs in interactive environments.
Graph Based Automatic Keyword Extraction from Odia Text Document

In natural language processing (NLP), keyword extraction is used as the mechanized cycle to recognize a set of terms that addresses the data being processed. Keywords are the collection of words that provide a compact representation of a document. These words are extensively used for automatic indexing. Since there is immense accessibility of Odia text, the importance of keyword identification from Odia script has been increased a lot. Realizing the importance of keyword extraction from Odia text this article has presented a simple unsupervised undirected weighted graph-based keyword extraction procedure for Odia text. The extracted keywords are analyzed by computing the weights of the nodes in the graph generated from the text. The performance of the proposed technique has been evaluated in terms of precision, recall, and F-measure. It is observed from the experimental result that the proposed graph-based technique can effectively extract keywords from Odia text with minimum computational complexity because of its implementation simplicity.
Application of New Media and Network Technology in the Teaching of Student Management in Colleges and Universities

With the progress of the Internet media era and the development of economy and society, NM has become an indispensable part of people's daily lives, especially among college students. New media(NM) and network technology have played a crucial role in the management and teaching of college students. Important help, NM is rapidly spreading in colleges and universities. For the cultivation of talents in colleges and universities, it is very necessary to train more high-level comprehensive talents through the use of multimedia network technology. NM network technology contains a large amount of network data information, while taking into account the two characteristics of immediacy and interactivity. As a brand-new technical means, NM network technology is a very meaningful. The main research content of this paper is the application of NM network technology in college student management teaching, the rational use of the power of network resources, the promotion of the rational use of network information, and the real understanding of NM network technology for college students. The final result of the research shows that the number of satisfied people in practical courses is the largest, 96 people, accounting for 26.96%. This is because under the situation of NM, various practical cases can be used to explain to students, and students can Faster integration into the curriculum system.
Extraction of Brain Tumour from MRI Images using Marker-Based Watershed Transform

A brain tumor is an abnormal tissue growth in the brain in which cells grow and multiply uncontrollably. The main aim of our task is to identify brain tumor from a particular MRI image and to extract the tumor region. The proposed method uses various digital image processing techniques such as noise removal, Otsu's thresholding, morphological operations etc. This paper focuses on the use of marker-based watershed transform for segmentation, to get near about accurate results.
Recognition of American Sign Language Using Modified Deep Residual Cnn with Modified Canny Edge Segmentation

American Sign Language (ASL) recognition system aims to recognise hand gestures’ meaningful motions, and it is a crucial solution to communicate between the deaf community and hearing people. However, existing sign language recognising algorithms still have some drawbacks, such as the difficulty of recognising hand movements low recognition accuracy for most of the sign language recognition. To address this problem, a Modified Convolutional Neural Network (MCNN) deep residual 101 classifier-based American Sign Language identification system is developed. There are three main parts presents in the method. The first part is preprocessing the images to remove the noise, enhance the contrast of the picture, adjust the contrast level and smoothen the picture using various filters. The second part is the segmentation, and it’s used to partition the image using a modified canny edge detection method by removing all weak edges present in the image. Finally, classification will be done using the Modified CNN deep residual 101 classifiers. By classifying the image, the American Sign Language is accurately identified. This process is conducted through images. The outcome shows that the suggested approach has a 0.95 per cent accuracy and a 0.05 per cent False Positive Rate. Other CNN such as resNet 50 and resNet 18 reached 0.90% and 0.80% accuracy, respectively, which is lower than our proposed method. In addition, the resNet 101 classifier effectively recognises the difficult hand gestures through the image data and obtain high recognition accuracy for 36 signs is 0 to 9 numbers and a to z alphabets from American Sign Language.
Study of Non Linear Design Approach for High Time Resolution Pulse Generation on FPGA

True Random Number Generator (TRNG) has vital role in data encryption for cyber security system. This paper proposes the TRNG circuit design and implementation to generate a pulse that has a high time resolution which is equal to the time delay of the buffer cell. The idea is to generate such a pulse without using a high frequency clock and meet the design into realising an effective, high speed, entropy source for cyber security systems. The randomness of TRNG is analysed in three approaches like Non-linear based Ring Oscillator (RO), Linear RO and DFF Jitter RO. The validation of the proposed design and implementation is performed using National Institute of Standards and Technology (NIST) test suite software.
Information Extraction from Social Media: A Hands-On Tutorial on Tasks, Data, and Open Source Tools

Information extraction (IE) is a common sub-area of natural language processing that focuses on identifying structured data from unstructured data. The community of Information Retrieval (IR) relies on accurate and high-performance IE to be able to retrieve high quality results from massive datasets. One example of IE is to identify named entities in a text, e.g., "Barack Obama served as the president of the USA". Here, Barack Obama and USA are named entities of types of PERSON and LOCATION, respectively. Another example is to identify sentiment expressed in a text, e.g., "This movie was awesome". Here, the sentiment expressed is positive. Finally, identifying various linguistic aspects of a text, e.g., part of speech tags, noun phrases, dependency parses, etc., which can serve as features for additional IE tasks. This tutorial introduces participants to a) the usage of Python based, open-source tools that support IE from social media data (mainly Twitter), and b) best practices for ensuring the reproducibility of research. Participants will learn and practice various semantic and syntactic IE techniques that are commonly used for analyzing tweets. Additionally, participants will be familiarized with the landscape of publicly available tweet data, and methods for collecting and preparing them for analysis. Finally, participants will be trained to use a suite of open source tools ( SAIL for active learning, TwitterNER for named entity recognition3, and SocialMediaIE for multi task learning), which utilize advanced machine learning techniques (e.g., deep learning, active learning with human-in-the-loop, multi-lingual, and multi-task learning) to perform IE on their own or existing datasets. Participants will also learn how social context can be integrated in Information Extraction systems to make them better. The tools introduced in the tutorial will focus on the three main stages of IE, namely, collection of data (including annotation), data processing and analytics, and visualization of the extracted information. More details can be found at: https://socialmediaie.github.io/tutorials/ .
Evaluating Simulated User Interaction and Search Behaviour

Simulating user sessions in a way that comes closer to the original user interactions is key to generating user data at any desired volume and variety such that A/B-testing in domain-specific search engines becomes scalable. In recent years, research on evaluating Information Retrieval (IR) systems has mainly focused on simulation as means to improve users models and evaluation metrics about the performance of search engines using test collections and user studies. However, test collections contain no user interaction data and user studies are expensive to conduct. Thus there is a need in developing a methodology for evaluating simulated user sessions. In this paper, we propose evaluation metrics to assess the realism of simulated sessions and describe a pilot study to assess the capability of generating simulated search sequences representing an approximation of real behaviour. Our findings highlight the importance of investigating and utilising classification-based metrics besides the distribution-based ones in the evaluation process.
GCRNet: Global Context Relation Network for Weakly-Supervised Temporal Action Localization

Weakly-Supervised Temporal Action Localization is a very challenging task of classifying and locating all actions in an untrimmed video because the frame-wise label is not given during the training stage while the only label is action class. Due to the complexity of video structure, previous methods do not take the advantage of the context information between long-term action-related frames. In this paper, we propose a Global Context Relation Network which introduces the self-attention mechanism. The first part uses the context relation module to encode features according to the relationship of the global context and merge them into the original features, which allows the network to better capture the long-term dependence on the video. Then the local feature encoding is performed by convolution to obtain a more accurate class activation sequence. Extensive experiments on two benchmark datasets THUMOS14 and ActivityNet1.3 and demonstrate our method outperforms existing state-of-the-art results on THUMOS14 and achieves very comparable performance on ActivityNet1.3.
Inversion method of ground dielectric constant in NEMP simulator working area based on unidirectional induction sensor

Based on the unidirectional induction characteristics of a unidirectional induction sensor, this paper separates the direct wave and reflected wave at a point directly below the working area of NEMP simulator; When the geometry of NEMP simulator is known, the transfer function between the field at the point to be investigated and its mirror point relative to the ground is established by full wave algorithm; Through the relationship between the field at the mirror point without ground and the field of scattered wave with ground, the ground dielectric constant can be solved conveniently.
Multiscale Feature Adaptive Integration for Crowd Counting in Highly Congested Scenes

Due to extreme scale variations in highly congested scenes, the accuracy of CNN-based crowd counting approaches still has considerable room for further improvements. In this paper, we propose a new multi-scale feature adaptive integrated network (MSFAINet) for crowd counting that adopts the multi-scale feature, hybrid attention, and dilated convolution. First, the proposed MSFAINet extracts feature maps from different levels by the improved VGG16 and focuses on more important information that represents features at different scales. Second, it adopts a hybrid attention mechanism to enhance the receptive field of an image while reducing the loss of feature information caused by channel competition and then passes these features into the dilated convolution combined with the traditional convolution. Finally, it generates the density estimation map by accelerating the convergence of the network. The proposed MSFAINet is used to conduct extensive studies to demonstrate the effectiveness of the approach on several mainstream datasets. From the experimental results, MSFAINet can extract and retain more detailed information and greatly reduce the influence of scale variations in crowd counting.
Benchmark Study for Flemish Twitter Sentiment Analysis

Microblogging websites such as Twitter have caused sentiment analysis research to increase in popularity over the last several decades. However, most studies focus on the English language, which leaves other languages underrepresented. Therefore, we conduct a benchmark study on several modeling techniques for sentiment analysis with a new dataset containing Flemish tweets. The key contribution of our paper lies in its innovative experimental design, where we compare different preprocessing techniques and vector representations to find the best-performing combination for a Flemish dataset. We compare models belonging to four different categories: lexicon-based methods, traditional machine learning models, neural networks, and attention-based models. We find that more preprocessing leads to better results, but the best-performing vector representation approach depends on the applied model. Moreover, an immense gap is observed between the performance of the lexicon-based approaches and that of the other models. The traditional machine learning approaches and the neural networks produce similar results, but the attention-based model is the best-performing technique. Nevertheless, a tradeoff should be made between computational expenses and performance gains.
The role of information technology in transportation

This chapter examines how generating and sharing data is transforming the transportation industry. While data is an input to production it will be considered in the context of a price-excludable public good. Basically, this requires the players in the transportation process to be treated like interdependent members of a club. Digitization will also be considered as a method for turning data into information. An avalanche of data coupled with relatively inexpensive and fast computing power has opened up opportunities to use predictive analytics to find patterns of behavior. Artificial intelligence and machine learning are also being used to place predictive systems on autopilot, so to speak. The benefits from these technologies are easy to discern but the process is not without challenges because human behavior is governed by psychology as well as the laws of physics. Another problem to consider is the relative ease of cyber theft of data compared to theft of tangible inputs. This can impede the desire to share data with different companies along the supply chain. There is some benefit to be found in using blockchain technology to secure information. However, the question arises as to how much information can and should be placed within a blockchain. Issues surrounding security, electronic documentation, contracting, and customer service will be evaluated.
Sociocultural Functional Literacy As a Planned Learning Outcome of a Primary Schoolchild

The article highlights the problem of the formation of socio-cultural literacy in a junior schoolchild, which is considered as the planned result&#x0D; of his education in primary school. The characteristic of the concept of “socio-cultural literacy” is given, the difficulties of the formation of its&#x0D; individual components are discussed: civic identity; legal and financial culture; tolerance, etc. The examples of methods and techniques aimed at the development of socio-cultural literacy are given, the expediency of using different forms of organization of education is revealed, which contributes to the formation of the ability of primary schoolchildren to constructively build relationships in children’s society and successfully interact.
Adapting a Requirements Engineering Process by Key Factors Estimation

Literature mainly focuses the adaptation of any requirements engineering process on the possible variations of elicitation techniques, mainly due to information sources characteristics. However, these particularities, usually called situational factors, are seldom considered in other activities of the requirements process. Most situational factors, when considered in software projects, have a high influence on the requirements process. Therefore, the different situations that may attempt against or may favor a successful requirements process should be identified at the beginning of the project. Additionally, some of such factors may evolve along software development life cycle; this should motivate a reengineering of the requirements process at some strategic milestones. In this chapter, a process for constructing and dynamically adapting a requirements process is proposed, focusing on the evolving factors. The process follows rules based on different combinations of situational factors at specific control points and manages a repository of process blocks to perform the tailoring.
Concept Description and Definition Extraction for the ANEMONE System

We present algorithms for computing definitions and concept descriptions that agents can use to restrict and adapt their knowledge with respect to signature shared with other agents. This ensures that knowledge shared is understood by the communication partners. We focus on agents that make use of description logic ontologies to represent their expertise. We have implemented and evaluated the performance of the algorithms in the form of a case study and on a freely accessible ontology. Our evaluation suggests that definition extraction can reduce the amount of messages exchanged by agents, thus optimising the communication time and effort of the agents.