Reviewerly/exHarmony huggingface dataset:
A Framework for Securing Data Warehouse using Image shield and User behavior pattern

The outsourced data inside the data dispersion middle server are calm and unsecure when compared with the current methods and security measures. Lost in Client get to benefits control tends to unsecure data sharing inside the stockroom. Existing Login affirmation is executed by utilizing extraordinary username and mystery word as substance organize. But this system faces colossal challenges from software engineers; organize interlopers or irregular works out where people can get the user&#x0027;s mystery word easily by a number of hacking techniques. In this way, this paper proposes the system for multilevel secured login confirmation system utilizing OTP, picture hotspot security and capture methodologies. The building for picture hot spot is utilized to avoid the unauthorized client looking over the system and it as well avoid from hacking the watchword and unusual works out inside the stockroom So that we propose a Methodology based on guidelines such as Multilevel secured confirmation system to secure from harmful clients Secured Client control benefits for data scrutinized and sort in and Taking after the client conduct plan based on development log and Within the occasion that any unordinary activity is done by the individuals who are getting to data stockroom, the admin will be educated and this irregular development will be captured by keeping up a log record of all the clients. Cutting edge shows up has been proposed utilizing four level security techniques by checking the Picture Hotspot Security. AES Calculation is utilized to scramble and translate the login inconspicuous components in database for more information security to administer information proprietorship and security. For blended information capacity in information stockroom framework utilizing progressed record security and Information advantage Official.
Session-based DASH streaming

The MPEG DASH standard is widely deployed in over-the-top streaming services. The standard defines two key components: a manifest format to describe the presentation and a set of segment formats to describe the media segments. While the DASH's manifest format, Media Presentation Document (MPD) provides a set of extensive tools to describe the presentation timeline, this document is usually created for a large set of DASH clients and therefore it can be cached in CDNs for a large population. If the MPD needs to be customized per client, the cache efficiency of storing a single MPD for all clients would be lost. Recently the MPEG Systems Working Group (ISO/IEC/SC29/WG3) developed a new standard that allows an MPD to be customized at each client using an external document and a set of processing rules. The first version of the Session-Based DASH standard (ISO/IEC 23009-8) was recently finalized and will be published in the upcoming months.
Research on the Current Situation and Countermeasures of English Listening Teaching Based on Multimedia Intelligent-Embedded Processor

In order to improve the effect of English listening teaching, this paper combines the interactive needs of English listening teaching to analyze the current problems in English listening teaching. Moreover, according to the actual needs of the intelligent English teaching system, this paper conducts research on the high-order cumulant signal-to-noise ratio estimation method and combines data analysis to improve the algorithm to obtain an intelligent algorithm suitable for English listening teaching. In addition, this paper combines the algorithm to construct an English listening teaching system based on a multimedia intelligent-embedded processor and applies the embedded processor to intelligent English listening teaching. Finally, this paper builds an intelligent system to solve the current problems in English listening teaching and improve the effect of English teaching.
Distributed Evolutionary Feature Selection for Big Data Processing

Feature selection has become a powerful dimensional reduction strategy and an effective tool in handling high-dimensional data. Feature selection aims to reduce the dimension of the feature space, to speed up and reduce the cost of the learning model and that by selecting the most relevant feature subset to data mining and machine learning tasks. The selection of optimal feature subset is an optimization problem that proved to be NP-hard. Metaheuristics are traditionally used to deal with NP-hard problems since they are well known for solving complex and real-world problems in reasonable period of time. Genetic algorithm (GA) is one of the most popular metaheuristics algorithms, which proved to be effective for an accurate feature selection task. However, in the last few decades, data have become progressively larger in both numbers of instances and features. This paradigm is being popularly termed as Big Data. With the tremendous growth of dataset sizes, most current feature selection algorithms and exceptionally GA become unscalable. To improve the scalability of a feature selection algorithm on big data, the distributed computing strategy is always adopted such as MapReduce model and Hadoop system. In this paper, we first present a review for the most recent works which handle the use of Parallel Genetic algorithm in large datasets. Then, we will propose a new Parallel Genetic algorithm based on the Coarse-grained parallelization model (island model). The parallelization of the process and the distribution of the partitioning of data will be performed using Hadoop system with an Amazon cluster. The performance and the scalability of the proposed method were theoretically and empirically compared to the existing feature selection methods when handling large-scale datasets and results confirm the effectiveness of our proposed method.
Building the Predicting Model of a Road accident using data mining technique

This research applies data mining technique to assess the probability of diabetic paproposed factors and model affecting performance in road accident prediction by using the classification techniques. The sample data is road accidents in Thailand in the year 2020, totaling 11,128 rows. Results show road accident composed of 7 factors including road type factor, cause of accident factor, type of accident factor, climate during an accident factor, number of car involved in accident factor, number of injered and number of deaths factor. Comparison of classification models showed that Naïve Bays technique was the most suitable technique in prediction of road accident with 100% of accuracy, K-Nearest Neighbors 98.99% and decision Tree model 91.69%.
CI-Block: A Blockchain System for Information Management of Collaborative Innovation

Blockchain technology ensures the security of cross-organizational data sharing in the process of collaborative innovation.It drives the development of collaborative innovation in discrete manufacturing to intelligent innovation.However, collaborative innovation is a multi-role, networked, and open resource-sharing process.Therefore, it is easy to form information barriers and increase the risk of cooperation between organizations.In this paper, we firstly analyze the blockchain-based information management models in the traditional discrete manufacturing collaborative innovation process.Then, we found that in the process of industry-university-research (IUR) collaborative innovation, consensus servers maintain too many connections due to the high latency between them, which leads to lower consensus performance and efficiency.To solve this problem, we proposed the dependency analysis (DA) model, which separates and assembles transactional data and non-transactional data into different blocks by analyzing read_set and write_set of transactions.Besides, we propose the out-of-order Raft (OORaft) model to allow non-transactional blocks to be copied in parallel, which can also prioritize transactional blocks.Finally, we implement the blockchain model in the discrete manufacturing scenario based on Hyperledger Fabric.The experimental results show that our model improves the transactions per second (TPS) performance of 1.9X-3.7Xand reduces transactional data committing latency by more than 40% in the target scenario.
Pedestrian Trajectory Prediction in Heterogeneous Traffic using Facial Keypoints-based Convolutional Encoder-decoder Network

Future pedestrian trajectory prediction offers great prospects for many practical applications such as unmanned vehicles, building evacuation design and robotic path planning. Most existing methods focus on social interaction among pedestrians but ignore the fact that heterogeneous traffic objects (cars, dogs, bicycles, motorcycles, etc.) have significant influence on the future trajectory of a subject pedestrian. Also, the walking direction intention of a pedestrian may be referred by his/her facial keypoints. Considering this, this work proposes to predict a pedestrian's future trajectory by jointly using neighboring heterogeneous traffic information and his/her facial keypoints. To fulfill this, an end-to-end facial keypoints-based convolutional encoder-decoder network (FK-CEN) is designed, in which the heterogeneous traffic and facial keypoints are input. After training, FK-CEN is evaluated on 5 crowded video sequences collected from the public datasets MOT-16 and MOT-17. Experimental results demonstrate that it outperforms state-of-the-art approaches, in terms of prediction errors.
A Crowdsourcing Methodology for Improving the Malayalam Wordnet

Lexical or lexico-semantic resources play a crucial role in representing the languages on the Internet. The meanings help us to understand the knowledge without any boundary. The resources are developed using machine translation or with the help of linguistic experts. However, due to various limitations like funding or lack of expert support, the development of language resources may not be progressive. This paper discusses the importance of crowdsourcing to build improved and up-to-date language resources and proposes a crowdsourcing methodology to construct a lexico-semantic resource. We designed our methodology for native speakers to identify lexical gaps in the target language, define the concepts based on a source language, provide words for a specific concept based on the source language, and define a concept based on the word in the target language. We tested our methodology for Malayalam wordnet and evaluated the responses with an expert. The proposed approach showed 92 percent accuracy in agreement with the expert.
A recognition technology for the output pattern of likely metamorphic relation of neutron diffusion program

The metamorphic test is a method to alleviate the unexpected value of the Oracle problem. The key point is the identification of the metamorphic relations. The identification of the metamorphic relations of scientific calculation programs is also a complex problem, and the likely of the metamorphic relation of the program can provide enlightening information for the identification of the metamorphic relations. The likely metamorphic relation can be regarded as the implicit expression of the input pattern and output pattern. This paper proposes an output pattern recognition technology based on the likely metamorphic relations of GEP. The technology is mainly aimed at the core neutron diffusion calculation program. The input pattern of the program, and then generate input data and run the program. Finally, in the corresponding output data results, through GEP data mining technology, the output pattern expressed in a variety of functional forms is obtained, which is further compared with analytical solutions and verified to be reliable likely metamorphic relations.
A Robust Multiframe Image Super-Resolution Method in Variational Bayesian Framework

Multiframe image super-resolution (MISR) combines complementary information of a set of low-resolution (LR) images to reconstruct a high-resolution (HR) one. In this study, we propose a robust and fully data-driven MISR method in the variational Bayesian framework. Different from the existing variational super-resolution (SR) methods, we use the l1 norm-based observation model, which takes the acquisition noise, outliers, and impulse noise into account. Furthermore, we have evaluated three typical image prior models, and the most appropriate one is chosen for our proposed method. The proposed method has the following advantages: (1) the HR image and all parameters are automatically estimated in an optimal stochastic sense; (2) the algorithm is robust to impulse noise and outliers. Extensive experiments with synthetic and real images demonstrate the advantages of the proposed method.
Remote Desktop Software as a forensic resource

Remote Desktop Software (RDS) enables the controlling of a computer system without the need for physical access. Operations are sent to the remote machine and executed as if performed by a local user. With an unprecedented shift to remote working due to the COVID-19 Pandemic, more people are working on home devices without enterprise IT support and therefore reliant upon this software to collaborate and keep their systems available and secure. RDS complicates a Forensic Investigation as any person with remote access privileges or knowledge of bypassing them could be responsible for an action. Despite its importance and prevalence, forensic research into RDS is minimal. As a market-leading solution for Windows, TeamViewer is an impactful starting point to demonstrate that such software is forensically-valuable to explore. This paper shows that with suitable evidence, an Investigator can identify which machines have performed remote control or been controlled, transferred files and have been remotely rebooted, among other events. We also highlight a potential privacy concern due to inadequate uninstallation processes. To illustrate the value of our findings we publish a Python module for Autopsy that automatically locates, processes and visualises key TeamViewer artefacts for an Investigator.
Porting BQM perceptual video quality measure to hardware

With the explosive growth in video, fast and reliable perceptual video quality assessment using objective quality measures is needed, as recognized by Netflix [1, 2], SSIMWAVE [3], Beamr [4, 5] and Facebook [6, 7], to name a few. The Beamr Quality Measure (BQM) is a full reference image and video quality measure that can reliably assess the perceived quality of reconstructed image or video content, which was compressed using block-based methodologies. Due to demand for metrics which can be applied in real-time or on large amounts of video, an efficient and low-cost implementation is needed, making a hardware, or on chip solution, desirable. This paper details the process of porting BQM to hardware. Adaptations required for the hardware implementation are described, the optimizations applied are reviewed and some results are presented.
A Fast Clustering-based Recommender System for Big Data

For years, recommender systems (RS) have emerged as a powerful tool to enable users to find appropriate information according to their needs. Different recommendation methods have been proposed and can be categorized as collaborative filter, content-based, and Hybrid/Ensemble approach. However, the exponential growth of digital information in the recent decades often referred to Big Data, poses new challenges for the current RS. Following this spirit, our work proposes a novel fast clustering-based Recommendation method (denoted as FCR) designed on top of Apache Spark. Comprehensive experiments on a real-world dataset have verified the advantages of our proposed method. It is effective in alleviating the problem of data sparsity and item cold-start. The training and inference time is quick while the slight increase of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) is acceptable.
Long‐time target tracking algorithm based on re‐detection multi‐feature fusion

This paper considers the problem of long-term target tracking in complex scenes when tracking failures are unavoidable due to illumination change, target deformation, scale change, motion blur, and other factors. More specifically, a target tracking algorithm, called re-detection multi-feature fusion, is proposed based on the fusion of scale-adaptive kernel correlation filtering and re-detection. The target tracking algorithm trains three kernel correlation filters based on the histogram of oriented gradients, colour name, and local binary pattern features and then obtains the fusion weight of response graphs corresponding to different features based on average peak correlation energy criterion and uses weighted average to complete the position estimation of the tracked target. In order to deal with the problem that the target is occluded and disappears in the tracking process, a random fern classifier is trained to perform re-detection when the target is occluded. After comparing the OTB-50 target tracking dataset, the experimental results show that the proposed tracker can track the target well in the occlusion attribute video sequence in the OTB-100 test dataset and has a certain improvement in tracking accuracy and success rate compared with the traditional correlation filter tracker.
Agile Transformation Challenges and Solutions in Bureaucratic Government: A Systematic Literature Review

Information technology (IT) provides ease, convenience, and efficiency to run some errands. IT is not only facilitating services to customers, but also as the main backbone to drive business and management in an organization. A lot of IT projects were established to solve problems or improve business problems with IT. The urgency to quickly execute IT projects within a short time made some organizations have to consider changing their development methods from predictive (waterfall) to adaptive (agile). However, transforming to agile in public sector, especially a convoluted hierarchical organization like government, was not as easy as less bureaucratic ones. A layered bureaucracy could hinder the application of agile methods. This study was conducted to summarize challenges of agile transformation in a bureaucratic government and its solutions. A Systematic Literature Review (SLR) was applied as the main method of this research. There were 14 relevant papers from the past 5 years identified from six databases resulting in 20 challenges grouped into 4 categories using TOEI (technology-organization-environment-individual) framework. Several solutions to each challenge were mapped as a recommendation for other bureaucratic governments before transforming to agile. The findings can also be used for future research exploration of agile transformation challenges in other organization types.
Rethinking graph data placement for graph neural network training on multiple GPUs

The existing Graph Neural Network (GNN) systems adopt graph partitioning to divide the graph data for multi-GPU training. Although they support large graphs, we find that the existing techniques lead to large data loading overhead. In this work, we for the first time model the data movement overhead among CPU and GPUs in GNN training. Based on the performance model, we provide an efficient algorithm to divide and distribute the graph data onto multiple GPUs so that the data loading time is minimized. The experiments show that our technique achieves smaller data loading time compared with the existing graph partitioning methods.
Res2Net based Text Independent Speaker recognition system

In recent years, speaker recognition has become a hot research topic, especially after the DNN based embedding has started showing impressive results even for short duration audio and that too independent of the spoken text. Speaker recognition is basically used for identity recognition and with the improved results, it is now widely used in criminal investigation, voice biometric, and other interest-based customer services. In scenarios where recognition is to be done irrespective of what has been spoken, the ResNet-based architecture are able to efficiently extracts speaker embedding by implementing the residual connections to the convolutional network and standardization of the residual blocks. But their performance degrades when dealing with complex input feature space. To deal with these problems, other variants of ResNet have been experimented with. Out of these is the ResNext architecture that has been initially used for Image processing tasks and has been reported to outclass all previous Deep Neural architectures. Res2Net is known for introducing scale as another dimension in place of width and depth that improved the representation capacity of the model. This paper explores and implements Res2Net model for Speaker-recognition and verification tasks as well, on a Hindi Speakers dataset taken from Farmer&#x2019;s Agri-query system and some in-house data collected from the volunteers for this task. We evaluate and compare the proposed Res2Net based systems with the ResNet model and EER has been reported which indicates the superior performance of the proposed system over the ResNet conventional model.
Using Operational Earthquake Forecasting Tool for Decision Making: A Synthetic Case Study&amp;#160;

&amp;lt;p&amp;gt;Operational earthquake forecasting (OEF) provides timely information about the time-dependent earthquake probabilities, which facilitates resilience-oriented decision-making. This study utilized the tools developed within the TURNkey (Towards more Earthquake-Resilient Urban Societies through a Multi-Sensor-Based Information System enabling Earthquake Forecasting, Early Warning and Rapid Response Actions) project funded by the European Union&amp;amp;#8217;s Horizon 2020 research and innovation programme to demonstrate the benefits of OEF to the decision support system.&amp;amp;#160; The considered tools are developed based on the state-of-the-art knowledge about seismology and earthquake engineering, involving the Bayesian spatiotemporal epidemic-type aftershock sequence (ETAS) forecasting model, the time-dependent probabilistic seismic hazard assessment, the SELENA (SEimic Loss EstimatioN using a logic tree Approach) risk analysis, cost-benefit analysis and the multi-criteria decision-making methodology. Moreover, the tools are connected to the dense seismograph network developed also within the TURNkey project and, thus, it is capable of real-time updating the forecasting based on the latest earthquake information and observations (e.g., earthquake catalogue). Through a case study in a synthetic city, this study first shows that the changes in the earthquake probabilities can be used as an indicator to inform the authorities or property owners about the heightened seismicity, based on which the decision-maker can, for example, issue a warning of the potential seismic hazard. Moreover, this study illustrates that OEF together with the risk and loss analysis provides the decision-maker with a better picture of the potential seismic impact on the physical vulnerabilities (e.g., damage, economic loss, functionality) and social vulnerabilities (e.g., casualty and required shelters). Finally, given the decision-maker&amp;amp;#8217;s preference, this study shows how the hazard and risk results are used to help the decision-maker to identify the optimal action based on cost-beneficial class and the optimality value computed based on the multi-criteria decision-making methodology.&amp;lt;/p&amp;gt;