Reviewerly/exHarmony huggingface dataset:
Spoofing E-Mail Detection Using Stacking Algorithm

In 2021, email spoofing and phishing increased by 220 percent [10]. With such a large number, cybercriminals are exploiting opportunities to spoof emails and phish for valuable information and credentials. Consequently, the average cost of cybercrime was $4.24 million in 2021.Cyber threats come in all forms and configurations [10], so why email? Because email is where 94 percent of all malware enters a computer [10]. As a necessary consequence, it is preferable to secure emails. We investigated various approaches before actually presenting our approach to spoofing email detection. In this paper, we have discussed about the architecture model of stacking algorithm. We have also proposed stacking algorithm is one of the best performer algorithm with respect to accuracy and speed of operations for detecting spoof emails. We have compared few algorithms characteristics of machine learning.
Decoupled pyramid correlation network for liver tumor segmentation from CT images

Automated liver tumor segmentation from computed tomography (CT) images is a necessary prerequisite in the interventions of hepatic abnormalities and surgery planning. However, accurate liver tumor segmentation remains challenging due to the large variability of tumor sizes and inhomogeneous texture. Recent advances based on fully convolutional network (FCN) for medical image segmentation drew on the success of learning discriminative pyramid features. In this paper, we propose a decoupled pyramid correlation network (DPC-Net) that exploits attention mechanisms to fully leverage both low- and high-level features embedded in FCN to segment liver tumor.We first design a powerful pyramid feature encoder (PFE) to extract multilevel features from input images. Then we decouple the characteristics of features concerning spatial dimension (i.e., height, width, depth) and semantic dimension (i.e., channel). On top of that, we present two types of attention modules, spatial correlation (SpaCor) and semantic correlation (SemCor) modules, to recursively measure the correlation of multilevel features. The former selectively emphasizes global semantic information in low-level features with the guidance of high-level ones. The latter adaptively enhance spatial details in high-level features with the guidance of low-level ones.We evaluate the DPC-Net on MICCAI 2017 LiTS Liver Tumor Segmentation (LiTS) challenge data set. Dice similarity coefficient (DSC) and average symmetric surface distance (ASSD) are employed for evaluation. The proposed method obtains a DSC of 76.4% and an ASSD of 0.838 mm for liver tumor segmentation, outperforming the state-of-the-art methods. It also achieves a competitive result with a DSC of 96.0% and an ASSD of 1.636 mm for liver segmentation.The experimental results show promising performance of DPC-Net for liver and tumor segmentation from CT images. Furthermore, the proposed SemCor and SpaCor can effectively model the multilevel correlation from both semantic and spatial dimensions. The proposed attention modules are lightweight and can be easily extended to other multilevel methods in an end-to-end manner.
A semantic approach to nonmonotonic reasoning: Inference operations and choice

Abstract This paper presents a uniform semantic treatment of nonmonotonic inference operations that allow for inferences from infinite sets of premisses. The semantics is formulated in terms of selection functions and is a generalisation of the preferential semantics of Shoham, Kraus et al., and Makinson. A selection function picks out from a given set of possible states (worlds, situations, models) a subset consisting of those states that are, in some sense, the most preferred ones. A proposition is a nonmonotonic consequence of a set of propositions iff holds in all the most preferred ‐states. In the literature on revealed preference theory, there are a number of well‐known theorems concerning the representability of selection functions, satisfying certain properties, in terms of underlying preference relations. Such theorems are utilised here to give corresponding representation theorems for nonmonotonic inference operations. At the end of the paper, the connection between nonmonotonic inference and belief revision, in the sense of Alchourrón, Gärdenfors, and Makinson, is explored. In this connection, infinitary belief revision operations, that allow for the revision of a theory with a possibly infinite set of propositions, are introduced and characterised axiomatically. Several semantic representation theorems are proved for operations of this kind.
The Use Of Virtual Reality In Teaching Students

The article discusses the issues related to the digitalization of higher education, the importance of which has rapidly increased due to the Covid-19 infection spread.The Russian Federation is developing and implementing promising high-tech end-to-end digital technologies in all spheres of the economy, including education supplying the labour market with employees who flexibly and quickly orient in the digital space.The possibilities of using digital technologies in education are presented based on the virtual reality technology when the teacher is replaced by interactive content, and students interact not with a living personality but with a virtual model.The possibilities of virtual reality are used as various kinds of simulators.Teachers also use them to demonstrate processes, phenomena, and objects that are impossible or extremely difficult to show in reality, for example, Google Earth VR and 3D Organ on VR Anatomy projects, 3D-Simodont Dental Trainer.The paper describes the advantages of using virtual reality technologies in teaching students and highlights the difficulties.They are connected with the high cost of equipment and software, together with the user's possible physical discomfort.The author notes that immersive virtual reality cannot replace the humanistic components of the education process, including personal contact between the teacher and the student.
Software Quality Assurance Plan: Setting Quality Assurance Checkpoints within the Project Life Cycle and System Development Life Cycle

Software Quality Assurance Plan (SQAP) is an effort driven activity which consists a set of documentation of procedures, workflows, techniques and tools relevant to the adopted software process improvement (SPI) methodology. It requires collective effort from the project team ensuring customer requirements are met, contract fulfilment and Service-Level-Agreement compliance. Most importantly, the SQAP will be used as the quality manual (QM) by the organisation to improve business’s operations, protect its reputation, accelerate change and meet customer needs. Any mis-conduct of SQAP will weaken customer relationship, damage in brand and reputation, major increased in both operational and financial costs; which further jeopardize the organization’s competitiveness and confidence level in the IT industry. As such, setting vital and critical checkpoints throughout different phases of project life cycle (PLC) and system development life cycle (SDLC) indeed is a crucial factor to successful implementation of SQAP. However, there are times a project manager and/or software quality assurance managers only place emphasis on the development scope of works but overlooked overall activities in the entire project life cycle. As a result, the setting of checkpoints in the SQAP has neglected to benchmark against the adopted standards resulting more than a set of SQAP within the organization. This has created unnecessary stress and burden to the project team when everyone is striving to implement all procedures and processes correctly within the constraint of allocated duration and timeline. This paper aims to outline a set of checkpoints throughout the PLC and SDLC to provide assistance and benchmarking for the IT industry implementing software project management as formal software process initiatives within the organization.
Secure Time Series Data Sharing with Fine-Grained Access Control in Cloud-Enabled IIoT

A growing number of Industrial Internet of Things (IIoT) devices and services collect massive time series data related to production, monitoring and maintenance. To provide ubiquitous access, scalability and sharing possibilities, the IIoT applications utilize the cloud to store collected data streams. However, secure storing of the massive and continuously generated data poses significant privacy risks, including data breaches for IIoT applications. Alongside, we need to protect the utility of the data streams by allowing benign services to access and run analytics securely and selectively.This paper introduces SmartCrypt, a data storing and sharing system that supports analytics over the encrypted time series data. SmartCrypt enables users to secure and fine-grain sharing of their encrypted data. Additionally, SmartCrypt guarantees data confidentiality in the presence of unauthorized parties by allowing end-to-end encryption using a novel symmetric homomorphic encryption scheme. We perform exhaustive experiments on a real-world dataset primarily to assess the feasibility of SmartCrypt for secure storing and sharing of IIoT data streams. The results show that SmartCrypt reduces more than 17% query time, 32% range query time and improves 9% throughput over the best performed scheme in the state-of-the-art.
Design Anthropological Filmmaking for Automated Futures

In this article I introduce the theory and practice of Design Anthropological Filmmaking as a mode of actively proposing alternative possibilities to the technological solutionist narratives and imaginaries that dominate industry, government and policy narratives about automated futures. I argue for documentary practice that is theoretically and critically aligned with a vision of the world as ongoingly emergent, and unfinished, and contests conventional innovation paradigms. To demonstrate this theory-practice dialogue I draw on my collaborative documentaries Laundry Lives and Smart Homes for Seniors which explore everyday life in the home with digital, emerging and automated technologies.
Probabilistic summarization via importance-driven sampling for large-scale patch-based scientific data visualization

Probabilistic summarization is the process of creating compact statistical representations of the original data. It is used for data reduction, and to facilitate efficient post-hoc visualization for large-scale patch-based data generated in parallel numerical simulation. To ensure high reconstruction accuracy, existing methods typically merge and repartition data patches stored across multiple processor cores, which introduces time-consuming processing. Therefore, this paper proposes a novel probabilistic summarization method for large-scale patch-based scientific data. It considers neighborhood statistical properties by importance-driven sampling guided by the information entropy, thus eliminating the requirement of patch merging and repartitioning. In addition, the reconstruction value of a given spatial location is estimated by coupling the statistical representations of each data patch and the sampling results, thereby maintaining high reconstruction accuracy. We demonstrate the effectiveness of our method using five datasets, with a maximum grid size of one billion. The experimental results show that the method presented in this paper reduced the amount of data by about one order of magnitude. Compared with the current state-of-the-art methods, our method had higher reconstruction accuracy and lower computational cost.
How Are Communication Channels on GitHub Presented to Their Intended Audience? – A Thematic Analysis

Communication is essential in software development, and even more in distributed settings. Communication activities need to be organized and coordinated to defend against the threat of productivity losses, increases in cognitive load, and stress among team members. With a plethora of communication channels that were identified by previous research in open-source projects, there is a need to explore organizational issues in how these communication channels are introduced, explained, and motivated for use among all project members. In this study, we wanted to understand which communication channels are used in GitHub projects and how they are presented to the GitHub project audience. We employed thematic analysis to analyze 151 artifacts in 90 GitHub projects. Our results revealed 32 unique communications channels that can be divided into nine different types. Projects mostly provide channels of different types, but for some types (e.g., chat) it is common to provide several channels. Maintainers are aware that channels have different properties and help the developers to decide which channel should be used in which case. However, this is not true for all projects, and often we have not found any explicit reasons why maintainers chose to provide one channel over another. Different channels can be used for different purposes and have different affordances, so maintainers have to decide wisely which channels they want to provide and make clear which channel should be used in which case. Otherwise, developers might feel overwhelmed of too many channels and information can get fragmented over multiple channels [31].
Remus: Efficient Live Migration for Distributed Databases with Snapshot Isolation

Shared-nothing, distributed databases scale transactional and analytical processing over a large data volume by spreading data across servers. However, static sharding of data across nodes makes such systems fail to timely adapt to changing workloads and struggle to obey the cloud pay-as-you-go model. Migrating shards between nodes online is a key technique to react to dynamic changes of workloads for cloud elasticity. Existing approaches introduce severely degraded performance and service interruption, resulting in SLA violation on the cloud; or they are tailor-made to deterministic databases. In this paper, we propose Remus, a new live migration approach for shared-nothing, distributed databases with snapshot isolation. Remus migrates shards between nodes with zero service interruption and minimal performance impact. This is achieved by an efficient unidirectional dual execution during migration. We implement Remus on a shared-nothing, distributed version of PolarDB-PG and evaluate it against state-of-the-art approaches using standard OLTP workloads TPC-C and YCSB, and hybrid workloads consisting of long-lived and short transactions. The results demonstrate Remus is the only effective approach to achieve the goal of zero transaction interruption, zero downtime and marginal performance impact, paving the way for applying the shared-nothing architecture to a cloud database which needs to provide elasticity while guaranteeing strict SLAs.
A Novel Corner Detection Algorithm Applied to Vision-Based Alignment Systems

In vision-based alignment systems, it is very important to detect the corner positions in images. It is based on these positions that the systems can calculate the offset between the workpieces and achieve their assembly alignment using robots. A simple and fast corner detection method is proposed, which can effectively overcome the data redundancy problem in traditional corner detection algorithms. First, the reduced image is figured out by scale transformation and down-sampling, and the corresponding edge binary image is obtained by gradient analysis. Then, all the edge coordinates are detected by the designed edge extraction operator, and an initial corner point is obtained by fitting analysis of these edge points. Due to the error arising from the above transformation and fitting, the point is not the target one. Finally, the Hough transform is used to detect the local linear features near the point, and the target point is determined by the linear intersection analysis. The experimental results show that the algorithm can effectively extract the target corner, which is basically consistent with the manually calibrated target point.
Mars-TRP: Classification of Mars imagery using dynamic polling between transferred features

Expeditions on Mars and interest in research orienting around these exploration missions have been accelerating more than ever, recently. Due to lack of active human interference in Mars missions, processing and accurate classification of images taken by the rovers is a very essential part of the system. Proper identification of landforms governs the accessibility of the mobile rovers on Mars' surface. Moreover, NASA has already collected over two million images from the planet, and more volumes are yet to arrive as these photographs serve as major documents for photogrammetry and studies based on remote sensing. Automatic labeling of incoming images and also making searching of the image database easier in the public interest requires highly accurate image classifiers. Deriving motivation from the above causes, this study intends to implement an efficient supervised multi-class image classifier for identifying Mars imagery. However, this objective is confronted by a major bottleneck. Most datasets that are accurately labeled, portray a highly skewed nature and insufficient data to train a deep model from scratch. The MSL surface imagery labeled dataset captured by the Curiosity rover, that has been considered for this study, is one such dataset with only 6691 images distributed unevenly into 25 classes. These obstacles are less signified in the existing literature and hence this paper addresses these challenges, outperforming the state-of-the-art metrics. Due to the absence of large data volume, a transfer learning based methodology was considered, using very deep convolutional networks pre-trained on ImageNet dataset. But images from Mars often involve a difference in hue, contrast and clarity when compared with images taken on Earth. Hence, the deep model was fine-tuned with our dataset and the extracted feature from the tuned neural network was used for the final classification. It was found that the results obtained from a single pre-trained model were not optimum and that ensemble approaches could unify many such results into a better result. Similar feature vectors were extracted from a few other pre-trained models. The whole setup converges into a dynamic routing module, a novel polling algorithm, which for each image, comes to an agreement about the best set of features while generating the output probability vector. The proposed approach is evaluated by several numeric metrics like accuracy, precision and recall, confusion matrices and roc curves, against the chosen individual pre-trained models and most prominent ensemble methods. Mars-TRP produces a test accuracy of around 88% in the standard test set of MSL surface dataset and an accuracy of 96% in the HiRise dataset, outperforming the individual pre-trained models, all the ensemble baselines and other existing approaches.
Secure search scheme for encrypted data in the VANET cloud with random query trapdoor

Integration of the Vehicular Adhoc Network (VANET) with cloud computing has played an important role in aiding the safety of vehicle drivers and passengers. Due to the dynamic nature of the vehicles and wireless communication, achieving security of outsourced data is still a major challenge in the vehicular cloud. The encryption of sensitive data prior to outsourcing is an elementary approach to achieve data confidentiality. However, it is difficult for users to search over encrypted data using customary search techniques. In this paper, we propose a secure search scheme by empowering the data users to create a random query trapdoor. We make use of the bloom filter and bilinear pairing operation to construct a secure index for keywords of each data file, which enables the vehicular cloud to carry out a search without deriving any helpful information about the query. We use the SIMITS2 simulator to implement the proposed scheme and test the performance in terms of key generation time, secure index construction time, trapdoor generation time and search time. The proposed scheme performs better than existing searchable encryption technologies based on hashing and attribute-based encryption.
A New Generation Communication System Based on Deep Learning Methods for the Process of Modulation and Demodulation from the Modulated Images

Demodulating the modulated signals used in digital communication on the receiver side is necessary in terms of communication. The currently used systems are systems with a variety of hardware. These systems are used separately for each type of communication signal. A single algorithm facilitates the classification and subsequent demodulation of signals without needing hardware instead of extra hardware cost and complex systems. This study, which aims to make modulation classification by using images of signals, provides this convenience. In this study, a classification and demodulation process is done by using images of digital modulation signals. Convolutional neural network (CNN), a deep learning algorithm, has been used for classification and recognition. Images of the signals of quadrate amplitude shift keying (QASK), quadrate frequency shift keying (QFSK), and quadrate phase shift keying (QPSK) digital modulation types at noise levels of 0 dB, 5 dB, 10 dB, and 15 dB were used. Thanks to this algorithm, which works without hardware, the success achieved is around 98%. Python programming language and libraries have been used in training and testing the algorithm. Demodulation processes of these signals have been performed for demodulation using the nonlinear autoregressive network with exogenous inputs (NARX) algorithm, an artificial neural network. As a result of using MATLAB, the NARX algorithm achieved approximately 94% success in obtaining the information signal. Thanks to the work done, it will be possible to classify and demodulate other communication signals without extra hardware.
Reinforced Tracker Based on Hierarchical Convolutional Features

In recent years, the development of visual tracking is getting better and better, but some methods cannot overcome the problem of low accuracy and success rate of tracking. Although there are some trackers will be more accurate, they will cost more time. In order to solve the problem, we propose a reinforced tracker based on Hierarchical Convolutional Features (HCF for short). HOG, color-naming and grayscale features are used with different weights to supplement the convolution features, which can enhance the tracking robustness. At the same time, we improved the model update strategy to save the time costs. This tracker is called RHCF and the code is published on https://github.com/z15846/RHCF. Experiments on the OTB2013 dataset show that our tracker can validly achieve the promotion of the accuracy and success rate.