Reviewerly/exHarmony huggingface dataset:
Study on OSINT-Based Security Control Monitoring Utilization Plan

Recently, as cybercrime and security threats that abuse the anonymity of the dark web and various social media continue to increase, the need for data analysis through OSINT has emerged. However, the reality is that public institutions and private companies are far short of response strategies compared to the increasing number of cases of infringement and public information that collect vast amounts of data. Today, with the development of the Internet, OSINT collects information from various public sources such as deep web and dark web as well as surface web such as social media, online community, and Internet news, so it must be able to monitor, collect and prevent public information using OSINT to effectively prevent leakage and infringement. In this study, the definition and concept of OSINT, and utilization plans in security monitor including characteristics are studied and presented, and OSINT information collection tools are identified and analyzed. Through this, OSINT-based security monitor utilization plans and considerations are presented.
Confidential Documents Sharing Model Based on Blockchain Environment

The blockchain platform has the advantage of providing shared information integrity assurance, data loss recovery, and configuration history tracing functions by applying cryptographic technology. This blockchain platform is used as a base technique for sharing information among users. However, when important information is shared through the blockchain platform, there is a disadvantage that other users can access the shared information. In the information service using the blockchain platform, it may be necessary to share defined users. Limited user's access to sharing information is allowed, but unauthorized user's access may have to be denied. To satisfy this requirement, this study proposes a model that can share confidential documents in a blockchain environment. The proposed confidential document sharing model operates on the blockchain platform and has the advantage of not using a key management server. If the proposed model is applied to the enterprise environment, it has the advantage of sharing confidential information while keeping all the advantages provided by the blockchain environment. However, since this study focused on the method on confidential file sharing, the sharing performance is inferior to other techniques, so additional research is needed.
A Denoisable Super Resolution Method: A Way to Improve Structure from Motion’s Performance against CMOS’s Noise

The quality of three-dimensional (3D) reconstruction algorithm Structure from Motion (SfM) is affected by the input image’s resolution and CMOS’s noise level. We propose a denoisable Super Resolution (SR) method to improve resolutions while denoising for SfM’s input images taken by a CMOS device, improving its performance on noisy images. The conventional deep learning SR algorithm does not consider denoising during the learning process. This results in the disability of simultaneously reducing noise and improving resolution. In our methods Add Noise before Downsampling (An-Ds) and Downsampling before Adding Noise (Ds-An), instead of expanding the training data, we extract the noise from a real-world noise dataset and selectively add it to low resolution (LR) images of the SR training set. Thus the SR algorithm can simultaneously improve resolution and reduce noise. Moreover, selectively adding noise also remain SR’s performance on clean images. We trained two representative SR algorithms (SRCNN and EDSR) using traditional and our designed methods to process both clean and noisy images. Without changing the SR network’s structure, improvements of 0.17 dB in Peak Signal Noise Ratio (PSNR) by Ds-An and 0.14 dB by An-Ds (approximately 20% of improvement in three years) were observed in noisy images’ experiments by EDSR. Meantime, there is only a little loss (less than 0.01 dB) on the clean images. The SfM’s results show a better reconstructed 3D model using our methods. Compared to non-preprocessing and conventional preprocessing, key metrics such as Mean Reprojection Error (MRE) reduced 51.9% and 12.6%, and 2D key-points matching rate improved 41.7% and 217%, respectively.
Binding efficiency and robustness for blockchains using reputation-based byzantine fault-tolerant consensus algorithms

Leader-based Byzantine fault-tolerant consensus algorithms suffer from vulnerabilities during routine view changes as they often follow a predefined schedule to passively rotate leadership among servers. However, the rotation becomes problematic under failures. In particular, when a scheduled server has crashed, the rotation still blindly assigns leadership to that server, causing performance degradation. This problem is exacerbated with further pronounced performance degradation when leadership rotation becomes more frequent. To address this problem, we propose a reputation-bounded active view-change protocol. Each server maintains a reputation state, calculated based on its behavior history, that reflects the likelihood of its correctness. During view changes, servers with a higher reputation are more likely to be elected, suppressing Byzantine faults while increasing the presence of correct leaders.
Steganalysis Techniques: A Systematic Review

World at present is full of data. Data plays a major role in all aspects in development in any field, future predictions, decision making etc. Securing data is very important not only to avoid breaches but also to shield the confidential messages. There comes the concept of Steganography. Steganography is the art of covering data up under an ordinary text or image or video. When steganography is misused by hiding malware in safe files, there comes the art of detecting steganography called Steganalysis. Some known methods in Steganalysis are Stego-only, Known cover, Known message, Chosen stego and lastly Chosen message attacks. This work presents a survey on recent trends in steganography and use of machine learning algorithms in steganalysis.
LINGUA E STILE DELLA TRATTATISTICA RINASCIMENTALE: LA TRANSILVANIA DI ANTONIO POSSEVINO (1583)

The paper aims to analyse the language and style of an Italian Renaissance treatise, Antonio Possevino’s Transylvania, which the author wrote in 1583 and revised for publication in the following years. Despite Possevino’s self-censorship, the publication of the book was stopped by the internal censorship of the Society of Jesus. The paper focuses on the direct tradition of the work, highlighting the language used by Possevino in the manuscripts studied and in the published editions, following the selection that the author makes at the morphological, lexical and syntactic levels. Its aim is to understand how and why Possevino adapts the form of his work to the informational content of the book.
I PRONOMI RELATIVI “DOPPI” O “INDIPENDENTI” DELL’ITALIANO E I LORO CORRISPONDENTI ROMENI

This article is a schematic presentation of the so-called “double” or “independent” relative pronouns in Italian and their Romanian equivalents. This linguistic aspect only seems difficult at first sight, as a different terminology is used for describing identical language facts.
MOTRv2: Bootstrapping End-to-End Multi-Object Tracking by Pretrained Object Detectors

In this paper, we propose MOTRv2, a simple yet effective pipeline to bootstrap end-to-end multi-object tracking with a pretrained object detector. Existing end-to-end methods, MOTR and TrackFormer are inferior to their tracking-by-detection counterparts mainly due to their poor detection performance. We aim to improve MOTR by elegantly incorporating an extra object detector. We first adopt the anchor formulation of queries and then use an extra object detector to generate proposals as anchors, providing detection prior to MOTR. The simple modification greatly eases the conflict between joint learning detection and association tasks in MOTR. MOTRv2 keeps the query propogation feature and scales well on large-scale benchmarks. MOTRv2 ranks the 1st place (73.4% HOTA on DanceTrack) in the 1st Multiple People Tracking in Group Dance Challenge. Moreover, MOTRv2 reaches state-of-the-art performance on the BDD100K dataset. We hope this simple and effective pipeline can provide some new insights to the end-to-end MOT community. Code is available at \url{https://github.com/megvii-research/MOTRv2}.
Optimized Haar wavelet-based blood cell image denoising with improved multiverse optimization algorithm

ABSTRACTABSTRACTBlood cells are complicated in the field of biomedicine. Several facilities now employ microscopic images to detect cells or parasites by technicians. But there is no medical device which can acquire the medical images without adding the noise. Hence, denoising the image is a vital process before predicting the illnesses for getting accurate results. In recent years, denoising the medical images by wavelet transform (WT) plays a major part in image processing. The major problem that occurs in denoising the medical images is to uphold the image quality of the denoised image. For that, a novel Optimised Haar WT integrated with Improved Multiverse Optimisation Algorithm based on Adaptive Thresholding function (IMVO-AT) is introduced to denoise the medical blood cell images. The proposed denoising method utilizes Haar WT (HWT), Improved Multiverse Optimisation Algorithm based on Adaptive Thresholding function (IMVO-AT), trilateral filter and an Inverse HWT (IHWT) to obtain the denoised blood cell image. Finally, the performance is evaluated in terms of PSNR, MSE, SMAPE, NC, SSIM, MAE, PC and RMSE for the proposed denoising approachand other denoising approaches. The experimental result depicts that the proposed denoising approach overcome all the other methods in terms of outcome and performance.KEYWORDS: Blood celloptimisationthresholdingwavelet transformimage processing Disclosure statementNo potential competing interest was reported by the author(s).Additional informationNotes on contributorsM. Mohana DhasMohana M. Dhas received M.C.A. degree from St. Joseph’s College, Cuddalore under the University of Madras in 1997 and M.Phil degree from Manonmaniam Sundaranar University Tirunelveli, India, in 2003. He Published 2 International Journal and 2 National Conference Paper. His specialisation is “Digital Image Processing”. His teaching experience is 24 years of service.N. Suresh SinghSuresh N. Singh received MCA, M.Phil degree in Computer Applications and Ph.D. in 2017 from Manonmaniam Sundaranar University, India. He is having more than 17 International and National Publications and 21 years of experience as a Professor. His research area of interest is Image Processing and Security. He is the Research Coordinator and Department head in Malankara Catholic College, Tamilnadu. He conducted Ph.D viva voice for a candidate in Bharathidasan University. He is a member in Examination committee of Kerala University, Manonmaniam Sundaranar University, Periyar University and Bharathidasan University and the Autonomous colleges in Tamil Nadu.
Editorial acknowledgement

British Journal of Mathematical and Statistical PsychologyVolume 76, Issue 1 p. 257-258 EDITORIAL ACKNOWLEDGEMENT Editorial acknowledgement First published: 18 November 2022 https://doi.org/10.1111/bmsp.12295Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL No abstract is available for this article. Volume76, Issue1February 2023Pages 257-258 RelatedInformation
Analysing Discourse Knowledge in Pre-Trained Language Models: From Constructed Data to Deep Neural Networks

Two case studies, discourse deixis and discourse entity representations, are presented in this chapter as examples of central themes in theories of discourse representation that can also be explored by means of deep neural network (DNN) models. 140We argue in favour of a methodology combining the controlled settings of psycholinguistic experimentation with the large coverage of current computational linguistics approaches using DNN models. In the case of discourse deixis, we first show experiments using standard psycholinguistic constructed examples and contrast them with experiments using corpus data. In both of these sets of experiments, the verbs participating in the causative-inchoative alternation have an effect on the choice between an event or entity antecedent for the pronouns it/this. Then, we propose a method to identify verbs presenting this property in an unsupervised way using pre-trained language models. In the case of discourse entity representations, we test the sensitivity of pre-trained language models to different referring expressions re-mentioning the same entity. The results of the experiments relying on DNNs turned out to be inconclusive, but they illustrate some of the challenges and advantages in scaling up the methods from psycholinguistics in order to test linguistic generalisations from theories of discourse representation.
Algebra and Language: Reasons for (Dis)content

This chapter offers reasons to be contented with algebraic methods in the study of language, and also reasons to be discontented. The reasons for discontent include the failure of algebraic methods to predict and understand deep learning models, and the fact that algebraic methods do not typically take learning into account. The reasons to be content include the fact that algebraic methods lead us to facts about linguistic structure that seem to be otherwise unavailable. In addition, algebraic methods generalise in elegant ways. We hold that the generalisations of algebraic methods offered by category theory and coalgebra might eventually be as important for the study of language as they are in contemporary theoretical computer science. We illustrate this by looking at coalgebraic formulations of Markov decision processes, bringing out their relation to automata. We discuss at length the interplay of algebraic and deep learning methods in natural language inference, showing that both sides make strong contributions.
Linguistically Guided Multilingual NLP: Current Approaches, Challenges, and Future Perspectives

The neural revolution has redefined – and many would argue, undermined – the place of traditional linguistics in natural language processing. The pace at which large unsupervised deep learning models conquer new territories of language understanding raises doubts as to the utility of rule-driven approaches and formalised linguistic knowledge in solving the challenges facing future language technology. Strikingly, the potential of leading neural architectures goes beyond solving well-defined problems and learning shortcuts to specific datasets: A growing body of evidence has revealed that they are capable of implicitly acquiring deeper linguistic knowledge directly from text. And yet, their 164success is only as certain as the availability of ever growing volumes of language data and resources, inevitable for the small elite of languages dominating the Web but far from guaranteed for thousands of others. It is clear that, to overcome data scarcity, neural models need to become smarter and more sample-efficient as language learners, strategically leveraging information available in one language to help perform a task in another. Could explicit, structured linguistic information help achieve this goal after all? From another perspective, could such human-tailored expert linguistic and symbolic knowledge provide adequate inductive biases to data-hungry neural NLP architectures, where such biases would be invaluable, especially in low-resource scenarios? In this chapter, we consider the empirical evidence for the ability of neural networks to autonomously acquire deeper linguistic understanding from data and discuss what they are still missing. To fill those gaps, we weigh the potential of typological information, intermediate dependency parsing training, linguistic knowledge transfer, and native speaker introspection to provide guidance for multilingual NLP in resource-lean scenarios.
Nanoelectronic Systems for Quantum Computing

Nanoelectronic systems have driven the technology advances for the past half century. But, as we move forward in the twenty-first century, new concepts for computing, notably quantum computing, have appeared that promise dramatic increases in computational efficiency. While these are new systems concepts, they likely will continue to be manufactured using the technology of nanoelectronics. Here, we discuss a variety of approaches which are currently being studied for applicability to quantum computing. But, issues such as scalability, which has facilitated the development of nanoelectronic systems so far, pose important questions for these new and novel approaches.
Threat assessments and higher education public safety

While many of us can link the historical discussions about behavioral threat and risk assessments on college campuses to the tragic events at Virginia Tech in 2007, recent events elsewhere have surfaced the issue yet again. In her October 13, 2022, article in Inside Higher ED , Colleen Flaherty discussed the history in light of the recent murder of a faculty member by a former student that took place at the University of Arizona on October 5, 2022 (read the article at https://bit.ly/3N7ULeP ).
A content-location-aware personalized POI recommendation model

Aiming at the data sparsity problem of user-POI matrix in point of interest (POI) recommendation, the more and more studies have explored the contextual factors such as geographical location, content information and social relations to deal with the above-mentioned problem. However, the current research lacks comprehensive analysis and utilization of the relations of these contextual factors. Therefore, we propose a content-location-aware topic model (CLATM) to simulate the user check-in behavior in the decision-making process from the dual-perspective of content and location by using the probability generation method. CLATM consists of two core modules: content topic modeling and location topic modeling. The user check-in content depends on the content topic and location topic. The content topic and location topic jointly determine the user check-in location to a certain extent. The geographic location depends on the location topic and obeys Gaussian distribution. The CLATM model not only properly integrates the important contextual factors such as content, location and geography, but also makes full use of the latent relations between these factors to alleviate the data sparsity effectively. The performance of CLATM model is evaluated on two real location-based social network (LBSN) datasets, Foursquare and Yelp. The experimental results show that the model is superior to the baselines in recall and normalize discounted cumulative gain (NDCG), with the maximum increase of about 141.09% and 94.44% in recall@20 and NDCG@20, respectively. It can be concluded that comprehensive use of the relations of contextual factors can effectively improve the POI recommendation performance.
SWOT analysis of sports industry in Junshan new city of Wuhan development zone under the concept of smart city

In recent years, many places in China are accelerating the modernization of urban governance driven by big data, cloud computing, in order to make the city better with “smart ”. General Secretary Xi Jinping has stressed the need to improve urban governance, promote innovation in governance means, governance models and governance concepts, accelerate the construction of smart cities, and take the lead in building a governance system that integrates and organically connects economic governance, social governance and urban governance. In May 2021, Wuhan was approved by the Ministry of Housing and Construction and the Ministry of Industry and Information Technology as one of the first pilot cities for the collaborative development of smart city infrastructure and intelligent networked vehicles. As the core of the future development of Wuhan Development Zone, Junshan New City is also the leading bird of regional innovation and transformation, with smart, intelligence and ecology being the keynote of the new city's development. With the national, provincial and municipal policy dividends, Wuhan Development Zone should take advantage of the momentum, not only to comprehensively plan for new energy and other emerging economic industries, but also to fully plan for sports industry in the new city, which is the “golden industry” of the 21st century. This paper analyzes the planning and development of sports industry in Junshan New City of Wuhan Development Zone from the perspective of smart city, and puts forward useful suggestions by comparing the advantages and disadvantages, in order to provide some theoretical references and ideas for the later stage of the study.
Comprehensive evaluation and decision-making model for university innovation base construction modes based on Matlab and AHP

By examining the development of colleges and universities, this paper analyzes the necessity of building innovation bases in colleges and universities and summarizes four construction modes that are currently used in colleges and universities. In order to provide the basis and decision-making method for colleges and universities to choose the mode of building innovation bases, this paper establishes a comprehensive evaluation and decision-making model for university innovation base construction modes based on the Matlab and AHP methods. The model establishes an evaluation index hierarchy model through AHP and uses the Matlab data analysis software to code, thereby realizing the calculation of the judgment matrix. In the meantime, consideration is given to the weights of the evaluation indexes and the evaluation subjects, and the arithmetic average method and the weighted average method are reasonably combined to obtain the final result. Through case analysis, the rationality and feasibility of the comprehensive evaluation and decision-making model established in this paper are verified.
Obstacle detection based on semantic segmentation for autonomous vehicles

A convolutional neural network segmentation method is proposed to meet the real-time requirements of automated driving systems and the problem of fuzzy target boundary segmentation and inaccurate segmentation of similar objects caused by pooling and convolution operations in current semantic segmentation methods of traffic scenes. A model with an attention mechanism has been proposed to detect obstacles in traffic scenes. The model constructs a feature extraction block and introduces various extended convolutions in the feature extraction process to extract more detailed image feature information. After the information is decoded, dual-channel and spatial attention modules are added, and functional information of different scales is combined during up-sampling, making the semantic information richer. The test results show that the proposed lightweight semantic segmentation model has a mean intersection ratio (MIoU) of 71.5% on the Cityscapes dataset, achieving good obstacle detection results, and meeting the real-time requirements of autonomous vehicles.