Reviewerly/exHarmony huggingface dataset:
Socio-Technical SIEM (ST-SIEM)

This article discusses the design and specifications of a Socio-Technical Security Information and Event Management System (ST-SIEM). This newly-developed artifact addresses an important limitation identified in today incident response practice—the lack of sufficient context in actionable security information disseminated to constituent organizations. ST-SIEM tackles this limitation by considering the socio-technical aspect of information systems security. This concept is achieved by correlating the technical metrics of security warnings (which are generic in nature, and the sources of which are sometimes unknown) with predefined social security metrics (used for modeling the security culture of constituent organizations). ST-SIEM, accordingly, adapts the risk factor of the triggered security warning based on each constituent organization security culture. Moreover, the artifact features several socio-technical taxonomies with an impact factor to support organizations in classifying, reporting, and escalating actionable security information. The overall project uses design science research as a framework to develop the artifact.
Artificial Intelligence in General

There are many kinds of uses for artificial intelligence (AI) in almost every field. AI is quite often used for control, computer aided design (CAD) and computer aided manufacturing (CAM), machine control, computer integrated manufacturing (CIM), production spot control, factory control, intelligent control, intelligent systems, deep learning, the cloud, knowledge bases, database, management, production systems, statistics, to assist sales forces, environment examination, agriculture, art, livings, daily life, etc. The present AI uses will be reexamined whether there is any matter to be considered further or not in AI research directions and their purposes behind the current status by looking at the history of AI development.
Assorted Sentiment Model for Publically Available Page of Facebook

ecommerce industries expose public page in the social network site (Facebook, twitter etc) for the intention of improving of business strategy. They extract public mood about the social network page in the forms of total likes, the total share of the page and sentiment of all comments to the social network page similar way celebrities expose public page in the social network sites for the intention of improving its fame. We have developed an assorted model for publicly available page of Facebook. This assorted model is the combination of data extractor model, language convertor and cleaned model, and sentiment analyzer model. Our data extractor model extract comments on all the posts of publicly expose Facebook page in the less span of time. Language convertor and cleaned model would work for conversion of text written in different Indian language to the English language and after that English written text would be cleaned through cleaned model. Language convertor is made after implementing CILTEL model. CILTEL model converts comments written in the Indian languages in the English language. Cleaning model will clean all the comments of all the posts on the Facebook page. Finally, sentiment extraction model will extract sentiments of all the comments of the Facebook page. We have implemented classification using three machine learning algorithm, namely naïve bayes algorithm, perceptron algorithm and rocchio algorithm for checking the performance of our sentiment analysis model. Our assorted sentiment analysis model is beneficial to users like marketing industry, election parties and celebrities.
Coarse and Fine Structure Combined Model For Full Reference Image Quality Assessment

The structure is proven to be an effective quality indicator for full-reference (FR) image quality assessment (IQA). Existing FR-IQA methods only extract fine structure for distortion detection. This leads to error detection when the distortion is invisible. To this end, we propose a coarse and fine structure combined FR-IQA method in this paper. In specific, we design a patch based structure detection method to detect the coarse structure in an image, and detect the fine structure using the gradient magnitude (GM). The proposed method is tested on TID2013, TID2018, CSIQ and LIVE databases. The validation results demonstrate that the proposed model have high prediction accuracy, yet have modest computational complexity.
Privacy Preserving for Sensitive Data using Data Masking Technique

In recent times, most the people are using internet where they are going to share sensitive information with other individual or with an organization like hospital, banking sector or business companies. So such huge amount of information will be stored on cloud. The attackers may try to hack the sensitive data and will try to misuse that data. So here the security for data comes first. There are numerous methods available to provide security for the data that is being shared among individuals or organizations. Most of the organizations take enough precautions to secure data that is shared with third party organizations. In recent times providing privacy for the sensitive data is high priority. The objective of this research is to discover the various data masking solutions for different applications for providing security to the data. Established data privacy method like AES or DES encryption technique proves to be proficient but time consuming. In order to avoid time consumption and to provide privacy for the data being shared, this paper proposes a information hiding method based on format-preserving encryption for sensitive data. This method will masquerade only sensitive data and make sure the encrypted data is still in the original format where it doesn’t consume much memory space. Organization like hospitals or banking sector or any business companies can use this format-preserving method to enhance the security of the data being shared. Tested the information on Spark illustrate that information hiding method based on format-preserving encryption can provide data privacy for sensitive data and preserve data format.
A Two-Branch Feature Modulated Network for Single Image Deraining

Rain removal from a single image has been attracting much research attention in computer vision. However, most exiting deraining methods explore the function maps from the input rain images to the clear deraining images, which ignore the information of rain streaks for modulating the feature maps of the deraining image. In this paper, we proposed a two-branch feature modulated network (TBFMNet) for exploring the clear deraining image and rain streak, respectively. During the network, the feature maps of the image branch are modulated by the attention feature maps from the rain-streak branch. Meanwhile, attention feature maps are extracted based on spatial attention unit (SAU) and channel attention unit (CAU), which can explore the essential spatial position and channel weight of rain streak. The extensive experiments demonstrate that the proposed method significantly boosts performance for single image deraining, including light rain scene and heavy rain scene.
Instructional Design Technology in Higher Education System

Higher educational Institutions all over the world are grappling with increased student population, several domains of learning and varied disciplines and instructors with varied experiences in using instructional design technologies. The chapter focuses on how it is possible to facilitate instructional design experiences for the stakeholders in higher education for creative learning. The chapter addresses the emergence of Instructional Design Technology (IDT). The role of IDT and its importance in higher educational institutions is studied with current practices in the field. The impact that this field had made in the evolution of instructional frameworks across the different layers of tertiary educational system is studied especially with regard to improving the teaching and learning experiences of educators and students respectively. The role of Creative Learning technologies' is discussed based on the success that these systems have enjoyed in improving instructional design.
Sparse Index Tracking Using Sequential Monte Carlo

We use a recently developed Sequential Monte Carlo (SMC) method by Duan(2019) to solve the Sparse Index Tracking problem. This is essentially a problem of selecting P stocks from a set of N correlated stocks in a linear regression framework, and we solve this optimization problem using a bayesian computation technique. We compare our approach with the widely-used LASSO and find that 1) SMC selects a more economically diverse basket in terms of the sector composition and 2) statistically outperforms LASSO most of the time in terms of out of sample tracking error.
DetNAS: Design Object Detection Network via One-Shot Neural Architecture Search

Previous NAS focus on image classification, so directly implementing traditional NAS, like the last research work DetNet, on object detection tasks are ineffective. In general, conventional NAS only searches the backbone network architecture while completely ignore the head network. Unlike image classification tasks, which can directly perform NAS on classification datasets such as ImageNet, object detection tasks require iterative training on classification and detection datasets multiple times. In addition, using traditional NAS methods on object detection tasks is computation-intensive (more than hundreds of GPU hours) because NAS typically has a two-stage workflow. The best sub-Network architecture derived from the super-Network must be retrained or fine-tuned. To resolve the above challenges, we propose DetNAS, a new NAS method targeting object detection tasks. First of all, DetNAS can accelerate the search process of neural network architecture to meet the various demands of object detection tasks. DetNet only searches the backbone network for object detection tasks, while DetNAS can simultaneously search for the backbone and head network during network architecture search. At the same time, inspired by the previous work, DetNAS replaced the single-path evolutionary algorithm in DetNet with progressive search, which further improved the search efficiency of the network structure. Secondly, previous research suggested a series of techniques to improve image classification-based network architecture search efficiency, but their effects on object detection tasks are still unknown. To thoroughly verify the validity of these conclusions, we conducted ablation experiments on multiple datasets and various experimental settings, which provided a valuable basis and reference for subsequent research work. As far as we know, DetNAS is the first One-shot NAS method that can search the backbone and head network simultaneously. We believe our work will open up a new direction to explore the architecture of object detection models.
Increasing Access, Social Inclusion, and Quality Through Mobile Learning

Mobile learning is part of a new learning landscape created by the availability of technologies and increasing digitization. As the use of mobile technology has increased worldwide, interest has grown in its potential for supporting flexible, accessible, and personalized education. As a result, higher education is facing a variety of challenges both now and in the coming decade (2020–2030) because of the continuing advances in technological development and digitization. Daily life, school, and work have become mobile. Moreover, the digitalized society fosters digital citizenship. Students entering higher education today have grown up using the internet and mobile devices. Universities need to offer a mix of face-to-face and online learning possibilities, such as open educational resources (OER) and massive open online courses (MOOC), which allow individuals to access education anywhere, anytime, and through any device. This article focuses on mobile learning (m-learning) in open learning educational contexts and quality enhancement in mobile learning in higher education. The article starts by defining mobile learning and the usefulness of m-learning in education, followed by some examples of mobile devices and a discussion of the principles of mobile learning. Examples of mobile learning design are then presented. Next, the advantages and uses of mobile learning in education are discussed. Because the issue of quality in e-learning and mobile learning is complex, this article focuses on course design, learning design, media design, and content. Additional dimensions of quality are security, accessibility, interactivity, flexibility, personalization, mobile devices, and their interfaces. Moreover, concepts such as personal learning and social innovation in relation to mobile learning are discussed. In the concluding section, future challenges are discussed.
Challenges in implementing video deduplication in cloud storage system

Cloud storage is one of the common services provided by the cloud application system. It helps the users to store their data at remote storage disks and provide them permission to access the data from anywhere through the internet. Any organisation or individual user who owns or shares the public cloud storage can store their digital data in cloud storage. When many users are sharing the cloud storage space, there is a possibility of duplicate data copies get stored in the cloud storage space. These duplicate copies of Video may lead to huge wastage of storage space, since video required huge memory. This can be solved with the help of 'Deduplication'. It is very difficult to handle Video eduplication, since the video storage involves many techniques. In this research work, we are going to identify and discuss the various challenges in implementing Video deduplication efficiently in the cloud storage space.
PAPER2VEC AND CITE2VEC METHODS FOR ANALYZING COLLECTIONS OF SCIENTIFIC PUBLICATIONS

Collections of scientific publications are growing rapidly. Scientists have access to portals containing a large number of documents. Such a large amount of data is difficult to investigate. Methods of document visualization are used to reduce labor costs, search for necessary and similar documents, evaluate the scientific contribution of certain publications and reveal hidden links between documents. The methods of document visualization can be based on various models of document representation. In recent years, word embedding methods for natural language processing have become extremely popular. Following them, methods for analyzing text collections began to appear to obtain vector representations of documents. Although there are many document analyzing systems, new methods can give new understandings of collections, have better performance for analyzing large collections of documents, or find new relationships between documents. This article discusses two methods Paper2vec and Cite2vec that get vector representations of documents using citation information. The text provides a brief description of the considered methods for analyzing collections of scientific publications, describes experiments with these methods, including the visualization of the results of the methods and a description of the problems that arise.
Algorithmic Techniques

Combinatorial problems on trees and graphs can be studied in the general framework of assigning values to nodes or vertices in such a way that some specified constraints are satisfied. Some of the basic algorithmic techniques for solving these problems—backtracking, branch-and-bound, divide-and-conquer, and dynamic programming—are presented in this chapter, and they are illustrated by the problem of computing the edit distance between two trees.
Ar Color Grab A Coloring Experience for Autistic Children

Augmented Reality (AR) brings together a hub of technologies together, thus allowing us to visualize computer generated data over reality. AR enhances the view by adding digital components to the view. Computer added elements are placed over a real environment indirectly by the use of AR, which increases the impact on the viewer. Various forms of elements like audio, video, animation or data may be added on the scene. The gap between the real environment and digital enhancements is bridged by Augmented Reality by creating an exciting situation. Therefore, a colouring book app, which leverages the use of Augmented Reality is presented, the app allows children to colour the characters of the colouring book, which can then be verified by a mobile device. The detection and tracking of the drawing are done using the application, and a 3-D view of the character is animated via a video stream. This video stream is developed according to the colouring done by a child. The visible and omitted regions of the 3D character are projected from the 2-D view using a process which is then presented in reality. A new outlier rejection algorithm is implemented to track a deformable surface for coloured drawings. Real time tracking and surface deformation recovery are provided. A pipeline that creates 2-D and 3- D content is effectively presented. The principal idea behind this project is to come up with an enhanced colouring experience for autistic children using AR, and also enable them to learn something out of it via a video option given by the application.
Towards a Mobile Augmented Reality System for Emergency Management

The impressive development of wearable computing and augmented/mixed reality technologies that has been occurring in recent years allows for devising ICT systems that can bring a disruptive innovation in how emergency medical operations take place. In this paper the authors describe first explorations in that direction, represented by a distributed collaborative system called SAFE (Smart Augmented Field for Emergency) for teams of rescuers and operators involved in a rescue mission. SAFE is based on the integration of wearable computing and augmented reality technologies with intelligent agents and multi-agent systems.
An open-ended computational construction grammar for Spanish verb conjugation

The Spanish verb phrase can take on many forms, depending on the temporal, aspectual and modal interpretation that a speaker wants to convey. At least half a dozen constructions work together to build or analyze even the simplest verb form such as hablo ‘I speak’. This paper documents how the complete Spanish verb conjugation system can be operationalized in a computational construction grammar formalism, namely Fluid Construction Grammar. Moreover, it shows how starting from a seed grammar that handles regular morphology and grammar one can create a productive grammar that captures systematicity in Spanish verb conjugation and can expand its construction inventory when new verbs are encountered.
Analysis and Measures of Column Sloshing Caused by Acceleration on Start-Stop for Stacker

research-article Analysis and Measures of Column Sloshing Caused by Acceleration on Start-Stop for Stacker Share on Authors: Youchun Cen China Tobacco Zhejiang Industrial Co. Ltd, China China Tobacco Zhejiang Industrial Co. Ltd, ChinaSearch about this author , Wei Lv China Tobacco Zhejiang Industrial Co. Ltd, China China Tobacco Zhejiang Industrial Co. Ltd, ChinaSearch about this author Authors Info & Claims AIAM2021: 2021 3rd International Conference on Artificial Intelligence and Advanced ManufactureOctober 2021 Pages 2944–2948https://doi.org/10.1145/3495018.3501211Online:14 March 2022Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Recognition of traffic lights based on YOLOv3-tiny

A traffic signal recognition algorithm based on yolov3-tiny is proposed in this paper. Based on yolov3-tiny network architecture, for network adjustments, some feature layers were removed, only two independent prediction branches were retained, with accuracy in exchange for speed and reduced hardware performance requirements. This adjustment allows the algorithm to be further optimized, can maintain high recognition, achieve the goal of real-time identification of traffic lights. In data set preparation, Extract the video into a picture file by timestamp using OpenCV-Python, And then use the Labellmg to mark the red, green and yellow traffic lights in the picture dataset, txt types of files that yolo models can call directly, Form a data set. In training models, Using a yolov3-tiny model downloaded from yolo website, Save the data set to the model file structure, And then modify the configuration file, Finally, configure the PyTorch virtual environment that the model runs, After running the program, you get a PT model, Call this model using Python, You can identify traffic lights in pictures and videos. Through actual testing, The accuracy of model recognition trained by this method is more than 94.97%.
WAYS OF COMMUNICATION OF WESTERN SIBERIA ON MAPS XVIII - BEGINNING XX CENTURIES

Resume: This article provides a brief overview of the maps of the routes of communication in Western Siberia of the pre-revolutionary period, stored both in federal archives and libraries of the country, and in local history museums in various cities and regions of the country. The paper proposes a typology of maps of communication routes of imperial Russia. The authors offer a selection of maps showing land roads, railways, waterways, postal routes, a brief description of each presented map is given.
Survey on Privacy Preserving Association Rule Data Mining

The progress in the development of data mining techniques achieved in the recent years is gigantic. The collative data mining techniques makes the privacy preserving an important issue. The ultimate aim of the privacy preserving data mining is to extract relevant information from large amount of data base while protecting the sensitive information. The togetherness in the information retrieval with privacy and data quality is crucial. A detailed survey of the present methodologies for the association rule data mining and a review of the state of art method for privacy preserving association rule mining is presented in this paper. An analysis is provided based on the association rule mining algorithm techniques, objective measures, performance metrics and results achieved. The metrics and the short comings of the various existing technologies are also analysed. Finally, the authors present various research issues which can be useful for the researchers to accomplish further research on the privacy preserving association rule data mining.
Sharing Large Datasets b etween Hadoop Clusters

The real information in other hand for large generous datasets either is direct data oriented stores or circulated license frameworks, in Hadoop being the prevailing open-source lifetime for 'Huge Data'. Real complete clamber stockpiling beginnings, be stroll as it may, bid urge for the competent allotment of expansive datasets over the Internet. Those frameworks that are generally utilized for the spread of extensive records, similar to Bit Torrent, should be adjusted to deal with difficulties, for example, organize joins with both high dormancy and high data transfer capacity, and versatile stockpiling backends wind are streamlined for gushing what's more, not unpredictable access. In this paper, we genuine Dela, a be awarded pounce on lead order supervision structural into the Hops Hadoop stage depart gives a start to finish answer for dataset sharing. Dela is intentional for colossal emerge amassing back ends and counsel trades that are both non-interfering to physical TCP change house and give predominant encipher throughput than TCP on high latency, high transmission capacity organize joins, for example, transoceanic system joins.