Reviewerly/exHarmony huggingface dataset:
A Systematic Review of Digital, Cloud and IoT Forensics

Our world is proliferated with high-end devices that have multiple capabilities. These devices have an array of sensors embedded into them that collect an enormous amount of data. Because of rapid development in technology, these devices are capable of rendering state-of-the-art services that are based on the cloud. Assuredly, the proliferation of ultra-modern devices with high-end services, have modernized and eased our lives. But, some entities or adversaries of our society are exploiting the same technological innovation for their benefits. Such criminal activities have become even easier to be carried out with modern devices and other technologies. Fortunately, we can examine the confiscated digital devices such as android smartphones, laptops, smartwatches, etc., from a crime scene and gain insight into the criminal activities carried out by the adversaries. It is also possible for us to recover the data the adversaries have collected or transmitted. Since modern devices utilize the services provided by cloud computing, it naturally becomes essential for the analysis of the cloud in case of the investigation of a crime scene. IoT forensics is interdisciplinary, as the data to be investigated may be collected from sensors, smart devices, etc., connected to a crime scene and the cloud too. Digital Forensics is an easier task to be performed when compared to Cloud Forensics. The segregation and collection of evidence in a cloud forensic investigation is a mammoth task due to the distributed and multi-tenant nature of cloud computing. But there is no such problem associated with digital forensic investigation. In this chapter, we have reviewed the work on Digital, Cloud and IoT Forensics by the scientific community. We have compared their effort and the outcome of their work, and summarized the state-of-the-art in the field of Digital, Cloud and IoT Forensics. Our aim of this chapter is to create a thorough review that will help fellow researchers in thought association for getting a clearer picture of the current state of research on Digital, Cloud and IoT Forensics and find the research gaps.
Prediction of User’s Interest Based on Urdu Tweets

Recently the role of text analysis and classification in Data Mining has become significantly important. It is being used in Spam filtering, News classification, Noise reduction, and particularly Social Media Analysis. With the growing popularity and advancement in technology, sharing personal opinion and sentiments on social media is now the most convenient way deliver a message regarding a certain topic. Twitter is a very fast growing site like other social network sites because it provides tailored suggestions to its users where Promoted Accounts suggest accounts that people don’t currently follow and may find interesting. In this research work, we have filtered information on twitter present in Urdu text to improve the tailored suggestions based on the interests of a user. We have used supervised learning and natural language processing approach for Urdu short text. For this purpose, dataset consisting of 6000 tweets is used to compute the accuracy results over multinomial Naïve Bayes (NB), Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) classifier. Results shows that SVM performs better on all Unigram, Bigram and Trigram features as compared to NB and KNN.
Power efficient resource provisioning for cloud infrastructure using bio-inspired artificial neural network model

High-performance computing changes the way of computing. More than one-decade, the cloud computing paradigm has changed the way of computing, communication, and technology. The efficient resource provisioning or task scheduler policy improvement is a challenging issue in the service-oriented computing paradigm. This article work focuses on task scheduler policy improvement for better cloud application performance. The task scheduling algorithm aims to improve the performance of real-time applications in the cloud by reducing task waiting time, execution time, and power consumption. The proposed model is inspired by an Artificial Neural Network (ANN) based system with a training model using a genetic algorithm. Results exhibit that the proposed GA-ANN policy outperforms the (Big-Bang Big-Crunch cost-aware), Genetic cost-aware, and other existing approaches. The results show that the proposed GA-ANN model performs better than existing approaches taking power consumption, total completion time(ms), average start time (ms), and average completion time(ms) as performance metrics. The proposed GA-ANN model is validated using real-time user requests (standard workload file) from workload traces (San Diego Supercomputer Center (SDSC) Blue Horizon logs), and fabricated data sets. The proposed model improves power efficiency by 13 %, scheduling time by 77.14 % and total execution time by 36 %. Hence the proposed GA-ANN technique provides performance as compared to existing systems.
Human Eye Detector with Light-Weight and Efficient Convolutional Neural Network

The human eye detection plays an important role in computer vision. Along with face detection, it is widely applied in practical security, surveillance, and warning systems such as eye tracking system, eye disease detection, gaze detection, eye blink, and drowsiness detection system. There have been many studies to detect eyes from applying traditional methods to using modern methods based on machine learning and deep learning. This network is deployed with two main blocks, namely the feature extraction block and the detection block. The feature extraction block starts with the use of the convolution layers, C.ReLU (Concatenated Rectified Linear Unit) module, and max pooling layers alternately, followed by the last six inception modules and four convolution layers. The detection block is constructed by two sibling convolution layers using for classification and regression. The experiment was trained and tested on CEW (Closed Eyes In The Wild), BioID Face and GI4E (Gaze Interaction for Everybody) dataset with the results achieved 96.48%, 99.58%, and 75.52% of AP (Average Precision), respectively. The speed was tested in real-time by 37.65 fps (frames per second) on Intel Core I7-4770 CPU @ 3.40 GHz.
Who comes when the world goes Code Blue? A novel method of exploring job advertisements for COVID‐19 in health care

To explore the health workforce responses to COVID-19.Analysis of job advertisements.We collected advertisements for healthcare jobs which were caused by and in response to COVID-19 between 4 March-17 April 2020 for the United States, Canada, United Kingdom, Australia and New Zealand. We collected information on the date of the advertisement, position advertised and location. We categorized job positions into three categories: frontline, coordination and decision support.We found 952 job advertisements, 72% of which were from the United States. There was a lag period between reported COVID-19-confirmed cases and job advertisements by several weeks. Nurses were the most advertised position in every country. Frontline workers were substantially more demanded than coordination or decision-support roles. Job advertisements are a novel data source which leverages a readily available information about how workforces respond to a pandemic. The initial phases of the response emphasise the importance of frontline workers, especially nurses.
3D scenes semantic segmentation using deep learning based Survey

Abstract Semantic segmentation realization and understanding is a stringent task not just for computer vision but also in the researches of the sciences of earth, semantic segmentation decompose compound architectures in one elements, the most mutual object in a civil outside or inside senses must classified then reinforced with information meaning of all object, it’s a method for labeling and clustering point cloud automatically. Three dimensions natural scenes classification need a point cloud dataset to representation data format as input, many challenge appeared with working of 3d data like: little number, resolution and accurate of three Dimensional dataset . Deep learning now is the power and popular tool for data and image processing in computer vision, used for many applications like “image recognition”, “object detection”, “semantic segmentation”, In this research paper, provide survey a background for many techniques designed to 3 Dimensions point cloud semantic segmentation in different domains on many several available free datasets and also making a comparison between these methods.
Text Classification Using a Bidirectional Recurrent Neural Network with an Attention Mechanism

Text classification is an important semantic processing task in the field of natural language processing (NLP). Traditional text-classifying systems often rely on human-designed features, and most of existing methods fail to obtain the sentence information completely. In this paper, we design a recurrent structure to represent the input sentence, and apply attention mechanism to capture the most important semantic information in a sentence. We conduct experiments on the Internet Movie Database (IMDB). The experimental result shows that our method outperforms most of methods of text classification.
A four-part typology to assess organizational and individual security awareness

This article provides a four-part typology of security awareness. We argue that existing awareness typologies that distinguish problem awareness from solution awareness and that separate descriptive awareness from prescriptive awareness are on its own insufficient and need to be merged to have a complete picture of security awareness. Renaming and bridging both distinctions leads to four security awareness types: (1) Cognitive awareness of the threat; (2) Attitudinal awareness of the threat; (3) Cognitive awareness of the mitigation; and (4) Attitudinal awareness of the mitigation. Each type is subsequently explained in greater detail and illustrated by referring to the 2020 worldwide outbreak of COVID-19. Furthermore, it is demonstrated that the typology is applicable to study both organizational awareness and individual awareness.
Semantic Web Services Composition Model Using Ant Colony Optimization

The interest of this work is to discover the most frequent query parts that allows to resolve the optimization composition problem with the cooperation existing between the cooperating agent and the artificial ants, this is due to the exponential increase of the services available in the web that makes it very difficult to select and discover in a reasonable time. In this paper, we present a composition model using the ant colony algorithm and a cooperating agent to find quasi-optimal solutions. The experimental results obtained are encouraging and have been shown as efficient in terms of optimality.
Web Service Recommendation and QoS Prediction via Matrix Factorization

In web service the recommendation based on Quality of Service are gaining its popularity to favor the customers to identify the high-Quality Services on the internet. We are using the Collaborative Filtering (CF) based techniques to calculate the high level QoS values of web services which cannot be invoked by the user. The basic idea of Collaborative Filtering Techniques is to identify the users with similar QoS experiences and to predict the QoS requirements on web service. To calculate the values of QoS and user requirements will require the parameters of the user which contain some privacy information of the user. Some users are not willing to give such information to the third party. Our main challenge is to provide accurate web service recommendations to the users and preserving the privacy from the third party. To overcome this problem, we identified a new protocol for protecting the privacy and web service recommendation where an untrusted recommendation server is also able to provide the services to the user without knowing the private information of the individual user.
Smart Solution to Detect Images in Limited Visibility Conditions Based Convolutional Neural Networks

Decrease in visibility causes many difficulties in vision, tracking. Current classic object detection techniques do not give satisfying results in less visibility. It is essential to detect and recognize the objects under such conditions and devise a better object detection mechanism. The paper proposes a solution to this problem by using a multi step approach that uses Saliency techniques and modern object detection algorithms to obtain the desired results. The distorted image is enhanced via a deep neural network for visibility enhancement. The image frame of a better quality undergoes saliency techniques so that less visible objects are visible. Faster Region-based Convolutional Neural Network (R-CNN) then runs on the saliency output to yield bounding boxes for all the objects. The coordinates of the bounding boxes are then applied on the original image thus detecting all the objects in a distorted image with less visibility.
Understanding requirements prioritisation: literature survey and critical evaluation

Requirements prioritisation deals with the ranking or classification of user requirements based on their importance. This process is central to releasing a software product with features most favoured by users. While studies have explored the efforts that are dedicated to this cause, these tend to focus on a subset of the solutions that are available in the software engineering domain. Current techniques investigated in the software engineering domain do not consider the strengths inherent in requirements prioritisation techniques developed in other disciplines (e.g. product manufacturing), a gap that should be addressed. The authors thus conducted a comprehensive systematic mapping study and critical evaluation of studies that have provided implementations of requirements prioritisation techniques across multiple disciplines (including software engineering, product manufacturing, and engineering). Among their findings, they observed that while many solutions are targeted, quite often researchers have proposed solutions that were not evaluated. Most solutions were only validated as being operational, and the attributes studied had limited effects on performance outcomes. Their evidence suggests that new techniques may address the requirements prioritisation challenge if they are inspired by hybrid approaches developed across multiple disciplines. In addition, performance trade-offs are to be expected of such techniques, depending on their performance targets.
Testing Access Control List Policies in a Hadoop Environment

Hadoop has emerged over the past few years as the most popular framework to process large-scale datasets efficiently. The idea is to process large datasets using several programs on multi-nodes hardware. However, the execution of such model on large clusters can be more complex and could produce several failures due to bugs or hardware problems or misuse of sensitive data. In fact, data security and Map-Reduce calculations are key concerns when a Map-Reduce calculation is performed in public domain. In this paper, we propose an Access Control model based on XACML language to test the Access Control List policies on both the MapReduce framework and the Hadoop distributed file system. To this end, we transform both the Access Control List policies and the users' objectives using the XACML language. Then, we introduce a Multi-Agents system to test the objectives expressed by users against the ACL policies.
Deep Neural Networks With Distance Distributions for Gender Recognition of 3D Human Shapes

Automatic human gender recognition is an important and classical problem in artificial intelligence. Most of the previous gender recognition works are based on vision appearance and biometric characteristics. However, there are fewer gender recognition approaches for 3D human shapes. In this article, we propose a novel deep neural network learning method for gender recognition of 3D human shapes. Firstly, we introduce effective descriptors to distinguish male and female of 3D human shapes via probability distributions of biharmonic distances among points. Secondly, the above distances-based low-level descriptors are fed into a fully connected neural network for gender recognition. Furthermore, we construct a larger 3D human shape dataset for evaluation of the proposed gender recognition method by collecting and labeling human shape models. Compared with previous works, our method obtains higher recognition accuracy and has more advantages, such as posture invariant, robust to noises, and no need of landmarks or pre-alignment process.
Software Development Process Modeling with Patterns

Software developement process modeling with patterns allows to benefit from the advantages of these latter. Indeed, this modeling allows to benefit from the proved and reusable knowledge offered by patterns, which improves the quality of the models produced and reduces the modeling time and effort. In this article, we discuss the main modeling practices of software developement processes with patterns. We focuse on the advantages and difficulties of these practices.
Neuromorphic Spatiotemporal Information Processing Using Neuro-Photodetector Systems

Spatiotemporal information processing within the human brain is done by a joint task of neurons and synapses with direct optical inputs. Therefore, to mimic this neurofunction using photonic devices could be an essential step to design future artificial visual recognition and memory storage systems. Herein, we proposed and developed a proof-of-principle two-terminal device that exhibits key features of neuron (integration, leaky, and relaxation) and synapse (short- and long-term memory) together in response with direct optical input stimuli. Importantly, these devices with processing and memory features, are further effectively integrated to build an artificial neural network, which are enabled to do neuromorphic spatiotemporal image sensing. Our approach provides a simple but effective route to implement for an artificial visual recognition system, which also has applications in edge computing and the internet of things.
SCGA‐Net: Skip Connections Global Attention Network for Image Restoration

Abstract Deep convolutional neural networks (DCNN) have shown their advantages in the image restoration tasks. But most existing DCNN‐based methods still suffer from the residual corruptions and coarse textures. In this paper, we propose a general framework “Skip Connections Global Attention Network” to focus on the semantics delivery from shallow layers to deep layers for low‐level vision tasks including image dehazing, image denoising, and low‐light image enhancement. First of all, by applying dense dilated convolution and multi‐scale feature fusion mechanism, we establish a novel encoder‐decoder network framework to aggregate large‐scale spatial context and enhance feature reuse. Secondly, the solution we proposed for skipping connection uses attention mechanism to constraint information, thereby enhancing the high‐frequency details of feature maps and suppressing the output of corruptions. Finally, we also present a novel attention module dubbed global constraint attention, which could effectively captures the relationship between pixels on the entire feature maps, to obtain the subtle differences among pixels and produce an overall optimal 3D attention maps. Extensive experiments demonstrate that the proposed method achieves significant improvements over the state‐of‐the‐art methods in image dehazing, image denoising, and low‐light image enhancement.
Application layer classification of Internet traffic using ensemble learning models

Summary Accurate application layer classification of Internet traffic has been a necessary requirement for various regulatory, control, and operational purposes of Internet service provider (ISP). Due to the dynamic and ever evolving nature of Internet applications generating a diverse mixture of Internet traffic, it has been necessary to apply deep packet inspection (DPI) techniques for traffic classification. DPI methods offer accuracy but degrade overall network throughput and thus cause problems in ensuring quality of service (QoS) and maintaining service‐level agreements. Moreover, Internet traffic is mostly end to end encrypted. This in turn limits the applicability of DPI techniques and renders them useless, unless the encryption tunnel is broken by the service provider which would risk violating user privacy. To address these trade‐offs between classification accuracy, performance, and user privacy, we resort to machine learning (ML)‐based algorithms. In this article, we apply three ensemble ML algorithms and report their performance metrics in the application layer classification of Internet traffic.
Enhanced Medical Data Security and Perceptual Quality for Healthcare services

The rapid advancement of Multimedia Communication Technology offers a unified resource of distributing and remote access to patient data. Monitoring the patients at their home can extensively minimize the increasing traffic at medical centres and hospitals. However, when medical data is sent across internet, the question of retaining patient data confidentially and reliability is suspicious. This aspect serves as a motivation for this paper. The proposed framework exploits contourlet transform coefficients to choose contour regions of the medical image which combines steganography and encryption technique to protect patient confidential data. In this system, a unified iterative process is proposed to resolve such difficulty. Moreover, we have employed a renowned Encryption algorithm in order to secure patient related data. This framework allows medical image to conceal its resultant patient confidential information and other medical data to assure the combination between medical image and the rest. Experimental results illustrates that the proposed framework proves to offer minimum retrieval error rate and as well perform better for a variety of biomedical images including CT scan, MRI and X-rays than the existing schemes. The performance parameter namely Peak signal to noise ratio, Payload, SSIM and Mean square error values are computed for the reconstructed image. The outcome of the proposed framework is exceptionally gainful and serves as a solution for healthcare services.
DelayRepay: delayed execution for kernel fusion in Python

Python is a popular, dynamic language for data science and scientific computing. To ensure efficiency, significant numerical libraries are implemented in static native languages. However, performance suffers when switching between native and non-native code, especially if data has to be converted between native arrays and Python data structures. As GPU accelerators are increasingly used, this problem becomes particularly acute. Data and control has to be repeatedly transferred between the accelerator and the host.
Non Destructive Testing For Detection abnormal Object in The X-Ray Images

Abstract The clarification of weaknesses or defects detection in radiographs image is very important especially in medical science and non-destructive testing centers. The pressing need appears to develop the capabilities that serve image processing in several aspects to achieve the desired results and obtain useful information as possible from images. Therefore, it is very important to know the appropriate methods for image processing, especially with poor context radiographs images to improve their clarity and facilitate the interpretation process. In addition to providing important visual information that difficulty of obtaining from its sources noise-free. This research aims to detect non- normal objects that exist in the X-ray image efficiently through the adoption of digital image processing methods. By using the median filter addition to the morphological processes and obtaining satisfactory results. Where the accuracy of the proposed model reached 86.25 and the proposed model was applied to 80 casting images.