Reviewerly/exHarmony huggingface dataset:
Intelligent Part of Speech tagger for Hindi

English Part of Speech like noun, verb, adverb, adjective, pronoun, preposition, interjection, conjunction is somewhat similar in Hindi but not exactly the same. Hindi grammar has different Part of Speech (POS) based on its morphological features and the occurrence of a word/lexeme in a sentence. The existing techniques used in English language for POS tagging may not work properly for Indian language like Hindi. It is because the grammatical structure of the relatively free word order language like Hindi differs from English. Stochastic taggers may not give good performance as morphological information is not taken into account. The available Hindi word corpora usually have less frequency for individual tags. As a result, a larger size corpus having diversity in the type of sentences can provide better results. But, even after using smoothing techniques most these taggers fail to provide correct results in the presence of unknown words. Considering these aspects, this paper proposes an Intelligent POS tagger for Hindi language based on VITERBI and K-Nearest Neighbour, capable of providing more accurate results than VITERBI in the presence of unknown words.
Automatic Cropping of Handwritten Scanned Documents with Object Detection Algorithm

Automated cropping of handwritten letter fragments with the deep learning-based object detection algorithms is the current trend with an additional technique for the handling of handwritten text recognition with low cost and high speed. Handwriting is utilized to give access to people to do the extra analysis so it is required to be optically scanned as well as a machine familiar method. Owing to unhampered writing ways crosswise within linked and overlying characters, handwriting recognition endures an inspiring job. In this work, the technique is proposed for automatic cropping of handwritten letters from the image of the scanned document using the YOLOv3 algorithm for the detection in the supervision of XML files that contain the corner values of the images which are further learned by the CNN used to generate the bounding boxes. This method is an initial step in surveying the competency of diverse object detection methods in cropping handwritten letters from the scanned credentials. The Punjabi language handwritten dataset has 24 scanned documents used for training and 6 testing images are taken. The training is done on 267 letters and tested on 29 letters. The performance of the method shows the improvement in the accuracy of detection as well as facilitates the process of detection that leading to a reduction in time and costs. The 98.77% accuracy is achieved using the proposed technique which is better than state of art methods.
An efficacy analysis of data encryption architecture for cloud platform

In recent times, cloud computing is being utilized largely for storage and information sharing purposes in several established commercial segments, particularly those where online businesses are prevalent, such as Google, Amazon, etc. Cloud system presents several benefits to users in terms of easy operations, low implementation, and maintenance expenses. However, significant risks are encountered in the data security procedures of cloud systems. Although the area is frequently being analyzed and reformed, the concern of cloud data protection and user reliability remains under uncertainty due to growing cyber-attack schemes as well as cloud storage system errors. To deal with this risk and contribute to the endeavor of providing optimal data security solutions in cloud data storage and retrieval system, this paper proposes a Symmetric Searchable Encryption influenced Machine Learning based cloud data encryption and retrieval model. The proposed model enhances data security and employs an effective keyword ranking approach by using an Artificial Neural Network. The comparative assessment of the proposed model against multiclass SVM and Naïve Bayes has established the better operational potentiality of the model. The effectiveness of the proposed work is justified by the association between high TPR and low FPR. Further, a low CCR of 0.6973 adds up to the success of the proposed work.
A Survey on Web Service approaches targeting network traffic reduction

This paper provides a detailed literature survey about main web service approaches targeting network traffic reduction. Technically, web service has suffered from network congestions and bottlenecks as a result of their high redundant textual structures. In fact, twenty seven highly reputed publications are discussed in this literature survey paper focusing on significant approaches and models that aim to reduce network traffic. Furthermore, publications are categorized into: web service clustering techniques for traffic reduction, web service clustering techniques for service discovery, aggregation approaches, and compression approaches. Evaluation analysis has shown a potential performance and efficiency for web service clustering techniques for traffic reduction in comparison with other models. Evidently, web service clustering models have achieved a tremendous compression ratio up to 21 with an average of 11.9.
Changes in Korean Language Performance and Psychological Characteristics of Middle School Students in County-level City during the COVID-19 Period

코로나19로 인한 사회적 거리두기 정책은 학교 교육의 비정상성을 가져왔으며, 이는 학생들의 학력저하로 이어졌다. 대도시 중심의 데이터를 분석한 국내 연구들은 코로나19로 인해 수학보다는 국어수행이 저하되었다고 보고하고 있으나 해외 연구들은 반대로 수학수행이 가장 저하된 것으로 보고하고 있다. 본 연구는 강원도 군단위 중학생들의 학업적 수행이 코로나19시기를 거치면서 어떤 변화를 보였는지를 국어수행을 통해 알아보고 학습관련 심리적 특성들의 변화도 살펴보고자 하였다. 2021년도 초(81명)와 2022년 초(89명) 두 시점에 수집된 데이터가 분석에 사용되었으며 강원도 군단위 지역의 한 중학교에 재학중인 1학년 학생들이 연구에 참여하였다. 국어수행은 KBS한국어능력시험을 통해 평가되었으며 심리적 특성으로는 학습동기, 학습지연행동, 학업적자기조절효능감, 삶의 만족도, 그릿이 측정되었다. 연구결과, 국어수행에서는 두 연도간의 유의한 차이가 나타나지 않았다. 학습관련 심리적 변인들은 2021년도에 비해 2022년도에 더 유의하게 부정적으로 변화하였으며, 학습동기의 경우, 남학생이 여학생에 비해 2022년도에 더 크게 저하되는 경향이 관찰되었다. 그 외의 삶의 만족도와 그릿은 연도별 차이가 유의하지 않았다. 연구결과의 시사점, 향후 연구과제, 제한점 등이 논의되었다. The social distancing policy during the COVID-19 period brought about abnormalities in school education, which led to a decrease in students" academic ability. Domestic studies that analyzed data from large cities reported that Korean language performance had decreased compared to mathematics during the COVID-19 period, but overseas studies reported that mathematics performance had decreased the most. This study attempted to figure out how social distancing influenced the academic performance of middle school students in Gangwon-do through Korean language performance and to examine changes in learning-related psychological characteristics during the COVID-19 period. Data collected at two points, in early 2021 (81 people) and early 2022 (89 people), were used for analysis, and first-year middle school students in the county-level area of Gangwon-do participated in this study. Korean language performance was evaluated through the KBS Korean Language Test, and learning motivation, academic procrastination, academic self-regulation efficacy, life satisfaction, and grit were measured. The results showed that there was no significant difference between the two years in Korean language performance. Learning-related psychological variables changed more negatively in 2022 than in 2021. In learning motivation, the decline from 2021 to 2022 tended to be greater in male students than in female students. The differences in life satisfaction and grit of students by year were not significant. Implications and limitations are presented based on the results.
Group Discussion Based on Podcast and to Improve English Listening Skill to ESL Learners

Group Discussion Based on Podcast and to Improve English Listening Skill to ESLLearners. This study is focused on group discussion based on podcast to improveEnglish listening skill to ESL learners above fourteen (14) years old English learners inBethany Family Church, Bitung city. This study uses a quantitative method with a preexperimentaldesign by applying one pre-test and post-test design. The writer uses pretestand post-test as instruments. Data were analyzed statistically by using t-test. Theresults of this study indicate that podcasts and group discussion are effective inteaching listening comprehension to ESL English learners in Bethany Family Church,Bitung city. This can be seen from the results of the t-test, the significance and progressof the learners? scores on the post-test from the pre-test. The mean of pre-test scorewas 7,25 and the average post-test score was 8,625. This indicates a significant increasein the mean scores between the learners' pre-test and post-test. The results of thisstudy are expected to be useful for teachers, and learners to enrich their reference inteaching and learning English Listening.Keywords: Group Discussion Based on Podcast; ESL Learners
Similar Cases Retrieval for Illegal Electronic Data in Criminal Proceedings and Suggestions for Improving

In China's current system of rules for the exclusion of illegal evidence, the focus of adjustment is on the exclusion of illegal verbal evidence and illegal physical and documentary evidence.Based on the analysis of similar case search, this paper distills the following rules and legislative proposals: whether electronic data collected by illegal methods should be excluded, the evidence can be differentiated into defective evidence and illegal evidence according to the severity of the illegal methods, and the decision of whether to exclude it can be made on a case-by-case basis.Defective evidence can be corrected and adopted through legal procedures, but illegal evidence that cannot be examined to determine its authenticity, or the time, place and manner of its production or acquisition are in doubt and cannot provide the necessary proof or reasonable explanation should be excluded.
A Secure Order-Preserving Encryption Scheme Based on Encrypted Index

With the development of cloud computing, data owners usually choose to store data in the cloud. But cloud servers may not be fully trusted. Due to privacy concerns, data owners need to encrypt the data before storing it in the cloud. There is still a lack of solutions that satisfy both safety and efficiency. To perform secure queries on encrypted data, we propose a scheme. The scheme includes an encrypted index based on B $$^{+}$$ tree, which can hide the order information of sensitive data. It also includes a secure search algorithm based on the encrypted index, which is implemented through the interaction protocol between the client and the server. We analyze the scheme’s security and prove that the scheme can protect the privacy of data, queries, results, and access patterns. At last, we perform experiments on five datasets to verify that the scheme is effective.
FDP-LDA: Inherent Privacy Amplification of Collapsed Gibbs Sampling via Group Subsampling

Latent Dirichlet allocation (LDA) is a widely used fundamental tool for text analysis. Collapsed Gibbs sampling (CGS), as a widely adopted algorithm for learning the parameters of LDA, has the risk of privacy leakage. In this paper, we study the inherent privacy of CGS which is exploited to preserve the privacy for latent topic updates. We propose a method, called group subsampling, and a novel centralized privacy-preserving algorithm, called Fast-Differentially-Private LDA (FDP-LDA) to amplify the inherent privacy and improve the efficiency of traditional differentially private CGS. Theoretically, the general upper bound of the amplified inherent privacy loss in each iteration of FDP-LDA is verified mathematically. To our best knowledge, this is the first work that analyzes the inherent privacy amplification of differentially private CGS. Experimentally, results on real-world datasets validate the improved performances of FDP-LDA.
Non-Contractual Liability Applicable to Artificial Intelligence: Towards a Corrective Reading of the European Intervention

On 28 September 2022, the European Commission published proposals for revision of the legal rules on product liability and regulation of non-contractual liability for damage caused by the use of artificial intelligence. The two texts have convergent purposes, but are structured around different types of liability. The aim of this article is to demonstrate that the application of the principle of subsidiarity to European regulation of compensation for damage attributable to artificial intelligence requires more than adjustments to fault-based liability, with the necessary creation of compensation funds for injuries caused by high-risk artificial intelligence systems. The conclusion is supported by an analysis of the relationship between the innovation principle and the precautionary principle in the regulation of artificial intelligence and by the specific features of this emerging digital technology.
Image Encryption Using a Chaotic/Hyperchaotic Multidimensional Discrete System

With the evolution of the communication technology, fast and efficient tools for secure exchanged data are highly required in particular for image security. This chapter introduces a simplified and fast chaos-based scheme for multimedia data encryption and in particular for image security by a cipher application. The encryption algorithm is based on an extracted N-dimension (N-D) discrete time map. This considered chaotic/hyperchaotic discrete system includes several nonlinear terms and several parameters to generate a robust chaotic random key sequences satisfying image encryption requirements. The performance of the presented image encryption algorithm are analyzed by considering four important factors which are key space, correlation, complexity and running time. Results of the security analysis compared to similar proposals show that the described encryption scheme is more effective in terms of key stream cipher space, correlation, complexity and running time.
Evaluating Zero-Cost Active Learning for Object Detection

Object detection requires substantial labeling effort for learning robust models. Active learning can reduce this effort by intelligently selecting relevant examples to be annotated. However, selecting these examples properly without introducing a sampling bias with a negative impact on the generalization performance is not straightforward and most active learning techniques can not hold their promises on real-world benchmarks. In our evaluation paper, we focus on active learning techniques without a computational overhead besides inference, something we refer to as zero-cost active learning. In particular, we show that a key ingredient is not only the score on a bounding box level but also the technique used for aggregating the scores for ranking images. We outline our experimental setup and also discuss practical considerations when using active learning for object detection.
Attention-Empowered Residual Autoencoder for End-to-End Communication Systems

Channel autoencoders adopt neural networks to represent and optimize previous block-driven communication systems from an end-to-end perspective. The existing deep fully connected autoencoder needs retraining when the input length of bit sequences changes since it can only handle fixed-length data. Convolutional neural network (CNN)-based autoencoder can accept arbitrary lengths and is widely adopted. But it has limitations in 1) error floor in high signal-to-noise ratio (SNR) regions, and 2) poor performance against interference. Therefore, we first adopt a residual attention module to enhance the representation ability of the autoencoder, where channel and spatial attention focus on finding fine-grained clues between signals and noise. An adaptive data-flow merging scheme for demapper is further proposed to satisfy dynamic environments. Simulation results show that the proposed method can achieve great gains in coded and uncoded transmission scenarios. Our method is robust and generalizable against error floor and burst interference, as compared to a conventional system.
Privacy-Preserving Shortest Distance Query for Dynamic Large-Scale Road Network

The road network plays an important role in guiding people's daily travel. In the road network, the shortest distance query is one of the most basic query operations. With the scale of the road network continuously expanding, people choose to outsource the road network to the cloud server in the form of an encrypted graph. To the best of authors' knowledge, the existing privacy-preserving shortest distance query schemes over large-scale encrypted graphs are all designed for the static graph. Nonetheless, the graph of road network is often updated over time. These static privacy-preserving shortest distance query schemes cannot work well in this practical application. In order to address this problem, we explore how to implement the privacy-preserving shortest distance query in an incremental large-scale road network. We use the 2-hop cover label index as the basic secure index structure, which can realize the shortest distance fast query. In order to update the 2-hop cover label index safely and efficiently, we combine a special adjacency index as an auxiliary index structure with homomorphic encryption to implement a dynamic incremental update algorithm for the encrypted graph. We prove that the proposed scheme achieves adaptive semantic security and verify its practicality through experiments.
Online Hostility Towards Local Election Officials Surged in 2020

One of the biggest success stories of the 2020 presidential election was the ability of election officials to conduct a smooth and safe election despite the many challenges posed by the COVID-19 pandemic. This success story, however, brought a barrage of harsh rhetoric against election officials in online spaces, driven by false information about the legitimacy of elections. More election officials are now using social media to communicate with voters, but recent trends show that voters also use these channels to disrupt the provision of information by posting threats and negative rhetoric. Evidence from the 2020 election shows state election officials were subject to this behavior on Twitter. In this chapter, we investigate whether these trends exist at the local level by analyzing the content and sentiment of responses to local election official's tweets between 2012 and 2022. We collect and analyze tweets from all 258 Twitter accounts run by local election officials, analyzing their 264,905 tweets and the 133,199 replies to those tweets over the last decade. Considering the surges in resignations of local election officials since 2020, it is important to investigate how harsh language directed at them on social media adds to a hostile environment, making it difficult for election officials to administer elections.
The Precision and Repeatability of Media Quality Comparisons: Measurements and New Statistical Methods

This paper calculates confidence intervals for 89 datasets that use the 5-level Absolute Category Rating (ACR) method to evaluate the quality of speech, video, images, and video with audio. This data allows us to compute the subjective test confidence interval <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(\Delta $</tex-math> </inline-formula> SCI) for 5-level ACR tests. We use a confusion matrix to compare conclusions reached by 88 lab-to-lab comparisons, 22 method-to-method comparisons, and 12 comparisons between expert and naïve subjects. We estimate the differences in conclusions reached by ad hoc evaluations, compared to subjective tests. We recommend using the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">disagree</i> incidence rate to identify lab-to-lab differences (i.e., the likelihood that significantly different stimulus pairs receive opposing rank order from the two labs). <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Disagree</i> incidence rates above 0.31% are unusual enough to warrant investigation and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">disagree</i> incidence rates above 1.0% indicate differences in method, test environment, test implementation, or subject demographics. These incidence rates form the basis for a new statistical method that calculates the confidence interval of a metric ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Delta $</tex-math> </inline-formula> MCI). When <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Delta $</tex-math> </inline-formula> MCI is used to make decisions, the equivalence to a video-quality test (EVQT) method determines whether a metric acts similarly to a subjective test. When <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Delta $</tex-math> </inline-formula> MCI is not used, the metric is likened to a certain number of people in a video-quality test (PVQT). This information will help users make the better decisions when applying quality metrics. The algorithm code is made available for any purpose. Most of the ratings used in this paper come from open datasets.
The Implementation of a Java-Component Application Framework Based on the Pipes Architectural Pattern and Inversion of Control of Interfaces

This dissertation explores the benefits of the application of the pipes architectural pattern and inversion of control of interfaces to object oriented systems. The premise is that maximum reuse of software components is achieved when components are mutually unaware of each other's existence and therefore unaware of each other's interfaces - as has been commonplace in UNIX shell scripts. In UNIX shell scripts small programs are often assembled via pipes and the shell manages the connections between the components. The project discussed in this dissertation constructs the Control Root Framework, a component framework, which brings these concepts to Java. The Control Root Framework borrows heavily on the UNIX philosophy as authored by Turing award winner Ken Thompson who advocated the use of text streams and universal interfaces between components.This approach contrasts with conventional component frameworks which tend to expose the interface specifics of components and therefore create a tight coupling between components. This tight coupling requires client software to be designed specifically for the components it employs and causes incompatibilities as components and their interfaces evolve. This dissertation argues that at the architectural level where the majority of changes are likely to occur universal data orientation excels over the use of object orientated interfaces in achieving maximum software reuse.
Cooperative Path Planning Algorithm Based on Artificial Potential Field Method

Based on the original artificial potential field method for path planning, this paper proposes an improved artificial potential field method for collaborative path planning. Using the reverse planning method, firstly, the track node coordinates of each vehicle node entering the target area are calculated according to the mission requirements. In the process of calculating the target function, the range constraint is used instead of the time constraint as the consideration of time coordination, and the communication constraints between vehicle nodes are considered, Take the track point entering the target area as the planning starting point and the launch point as the target point for planning. On the premise of meeting the communication conditions and taking into account the mission requirements of simultaneous arrival, the final track is generated. Simulation results show that this method can plan the track that meets the mission requirements on the basis of considering both time cooperation and space cooperation.