Reviewerly/exHarmony huggingface dataset:
An Effective Double-Layer Detection System Against Social Engineering Attacks

In recent years, social engineering attacks that use phishing emails as the medium and target specific groups of people have occurred frequently. Current enterprise systems are vulnerable to social engineering attacks. In addition, existing detection methods are relatively ineffective. Therefore, we propose a double-layer detection framework based on deep learning technology. First, a phishing email detection model based on Long Short-Term Memory (LSTM) and extreme gradient boosting tree (XGBoost) is designed from the perspective of individual security. Then, an insider threat detection model based on Bidirectional LSTM and Attention mechanism is designed from the perspective of group security. Finally, combined with the social engineering network attack simulation theory, a social engineering attack and defense simulation platform is established. In the double-layer frame-work, we use Bi-LSTM to obtain long-range dependent features of email body and user sequence information. Then XGBoost and Attention mechanism are used to further strengthen the network structure and improve the classification accuracy. Compared with traditional methods, our model does not require manual feature extraction, and can accurately identify phishing emails and insider threats. Finally, our proposed social engineering simulation platform verifies the effectiveness of the two-layer model. The experimental results show that our proposed framework has the characteristics of timely detection and after-the-fact investigation, which can effectively detect phishing attacks and insider threats faced by enterprise systems.
A Hybrid Learning Framework for Imbalanced Classification

Class imbalance is a well-known and challenging algorithmic research topic among the machine learning community as traditional classifiers generally perform poorly on imbalanced problems, where data to be learned have skewed distributions between their classes. This paper presents a hybrid framework named PRUSBoost for learning imbalanced classification. It combines a selective data under-sampling procedure and a powerful boosting strategy to effectively enhance classification performance on imbalanced problems. Different from the simple random under sampling algorithm, this framework constructs the training data of the majority or negative class by using a newly developed partition based under sampling approach. Experiments on several datasets from different application domains that carry skewed class distributions have shown that the proposed framework provides a very competitive, consistent, and effective solution to imbalanced classification problems.
A Systematic Literature Review of Virtual Reality in Engineering Education

Virtual Reality (VR) has proven to be a flexible tool used to simulate unusual scenarios, with the purpose of improving the training of future engineering professionals. Currently, a large number of articles referring to VR in education are published, so this review is necessary to give guidance to those researchers interested in the subject. In this review, 3 unknowns were resolved: 1) focusing areas of VR in engineering education 2) leading nations and 3) funding importance. 74 articles downloaded from the Web of Science database were reviewed using the PRISMA guide. It was found that VR is applied in a wide range of subjects focused on engineering in general. Taking a deeper look, Computer Science is the area that is receiving the most attention from VR. In addition, the United States is the country leading these investigations and the importance of financing is also seen in the present research. Finally, the lack of a common evaluative methodology was analyzed, as well as the problems of massification of VR and the impact of this technology on student´s motivation
Lightweight Privacy-Preserving Federated Incremental Decision Trees

Tree-based models are wildly adopted in various real-world scenarios. Recently, there is a growing interest in vertical federated tree-based model learning to build tree-based models by exploiting data from multiple organizations without violating data privacy regulations. However, most existing work focuses on batch learning settings where all training samples are prepared at once. They cannot be applied to scenarios where local samples come in a streaming manner. Additionally, the present federated learning algorithms suffer from inference attacks. In this paper, we present a novel solution that enables different organizations to jointly train a tree-based model in an incremental and privacy-preserving manner. Our solution is based on Very Fast Decision Tree (VFDT) for incrementally building a tree model. Since data statistics exchanged in the training process may implicitly disclose private information, we propose a protection mechanism based on order-preserving encryption. To further improve the efficiency of the solution, we compress the size of statistics by means of regional counting, which not only maintains model accuracy but also enhances privacy. We conduct extensive experiments on various real-world datasets and the results show the superiority of our solution in terms of both efficiency and privacy.
A Novel Set of Directives for Multi-device Programming with OpenMP

The latest versions of OpenMP have been offering support for offloading execution to the accelerator devices present in a variety of heterogeneous architectures via the target directives.However, these directives can only refer to one device at a time, which makes multi-device programming an explicit and tedious task.In this work, we present an extension of OpenMP in the form of a new set of directives (target spread directives) which offers direct support for multiple devices and allows the distribution of data and/or workload among them without explicit programming.This results in an additional level of parallelism between the host and the devices.The target spread directives were evaluated using the Somier micro-app in a PowerPC cluster node with up to four Nvidia Tesla V100 GPUs.The results showed a speedup of approximately 2X using four GPUs and the new directive set, in comparison with the baseline implementation which used one GPU and the existing target directive set.
Performance Analysis of Multi-Containerized MD Simulations for Low-Level Resource Allocation

This study discusses scheduling strategies to maximize ensemble throughput, which is the total throughput of multiple containers running simultaneously. Such a strategy is useful, for example, in ensemble runs of molecular dynamics (MD) simulations. To design the strategies, we need to tackle two major challenges: (1) how many containers and how many threads per container we should allocate, and (2) which low-level resources we should allocate to reflect workload characteristics. In particular, the latter challenge is important and inevitable for performance-sensitive applications because they effectively utilize low-level hardware such as simultaneous multi-threading (SMT) to maximize performance, while most container platforms do not handle the challenge. In this paper, as a preliminary experiment to implement scheduling strategies related to SMT, we examined whether ensemble throughput of MD simulations can be improved by deploying containers on separate logical cores even when they share the same physical cores. As a result, we obtained a 2.22-fold ensemble throughput compared with a one-container execution with 10 physical cores.
Applications oriented Technical Ecology for the Standardization of Blockchain in IEEE

Standards are an important key to the success of developing technologies, and formulating the right standards at the right time for technology development helps ensure the ease of use and interoperability of the technology, and blockchain technology is no exception. As blockchain technology has been officially incorporated into China’s new information infrastructure category, the application field of blockchain has expanded rapidly, gradually extending from the financial field and government affairs to other fields in the real economy. The real implementation of blockchain is inseparable from the open a complete standard system. The development of international standards aims to standardize the applications of blockchain, which can effectively break through the cognitive and technical barriers between different countries, industries and systems on a global scale, prevent application risks, and provide a standard basis for the development of the global blockchain industry. From the perspective of international standardization, this paper summarizes the current status of IEEE standards in the field of blockchain, comparatively analyzes the current status of major international standards organizations ISO, ITU-T and IEEE in the field of blockchain standards, focusing on the current status of IEEE blockchain standards analyzed, analyzed the characteristics of IEEE in blockchain technology standards and application standards. Through data analysis, carry out the planning and layout of future standards, assist the development of international standardization, promote the application and implementation of blockchain technology, and promote the joint application and development of blockchain technology in various industries.
Optimal temporal filtering for COW-QKD

We present a procedure to optimize the quantum bit error rate in coherent one-way quantum key distribution (COW-QKD) system. We built the testbed for COW-QKD, which supported a clock rate of 1 GHz. Temporal filtering was realized by varying gate delays applied to the single-photon detector and optimal selection of time window to receive logic bits. We observed that with adjustable temporal-filtering, we can improve on the quantum bit error rate, reducing it to 11.6%. The sifted key rate drops to less than 1 kbps, when we extend this QKD protocol over a distance of 150 km.
Increasing Transferability by Imposing Linearity and Perturbation in Intermediate Layer with Diverse Input Patterns

Despite high prediction accuracy, deep networks are vulnerable to adversarial attacks, designed by inducing human-indiscernible perturbations to clean images. Hence, adversarial samples can mislead already trained deep networks. The process of generating adversarial examples can assist us in investigating the robustness of different models. Many developed adversarial attacks often fail under challenging black-box settings. Hence, it is required to improve transferability of adversarial attacks to an unknown model. In this aspect, we propose to increase the rate of transferability by inducing linearity in a few intermediate layers of architecture. The proposed design does not disturb the original architecture much. The design focuses on significance of intermediate layers in generating feature maps suitable for a task. By analyzing the intermediate feature maps of architecture, a particular layer can be more perturbed to improve the transferability. The performance is further enhanced by considering diverse input patterns. Experimental results demonstrate the success in increasing the transferability of our proposition.
Multitarget Tracking Algorithm in Intelligent Analysis of Football Movement Training Stance

In recent years, with the continuous development of computer technology, deep learning has been widely applied to computer vision tasks and has achieved great success in areas such as visual detection and tracking. On this basis, making deep learning techniques truly accessible to people becomes the next objective. Target detection and tracking in football gesture training is a quite challenging task with great practical and commercial value. In traditional football training methods, target trajectories are often extracted by means of a recording chip carried by the player. However, the cost of this method is high and it is difficult to replicate in amateur stadiums. Some studies have also used only cameras to process targets in football videos. However, due to the similarity in appearance and frequent occlusion of targets in football videos, these methods often only segment targets such as players and balls in the image but do not allow them to be tracked. Target tracking techniques are of great importance in football training and are the basis for tasks such as player training analysis and match strategy development. In recent years, many excellent algorithms have emerged in the field of target tracking, mainly in the categories of correlation filtering and deep learning, but none of them are able to achieve high accuracy in player tracking for football training videos. After all, the problem of locating clips of interest to athletes from a full-length video is a pressing one. Traditional machine learning-based approaches to sports event detection have poor accuracy and are limited in the types of events they can detect. These traditional methods often rely on auxiliary information such as audio commentary and relevant text, which are less stable than video. In recent years, deep learning-based methods have made great progress in the detection of single-player video events and actions, but less so in the detection of sports video events. As a result, there are few sports video datasets that can be used for deep learning training. Based on research in computer vision and deep learning, this paper designs a multitarget tracking system for football training. To be specific, this algorithm uses multiple cameras for image acquisition in the stadium in order to accurately track multiple targets in the stadium over time. Furthermore, the framework for a single camera multitarget tracking approach has been designed based on deep learning-based visual detection methods and correlation filter-based tracking methods. This framework focuses on using data correlation algorithms to fuse the results of detectors and trackers so that multiple targets can be tracked accurately in a single camera. To sum up, this research allows for robust and real-time long-term accurate tracking of targets in football training videos through multitarget tracking algorithms and the intercorrection of multiple camera systems.
Secure framework for IoT technology based on RSA and DNA cryptography

Abstract Background The Internet of things (IoT) is the network of different objects or “things” containing sensors, software, and other technologies used to exchange data between devices and systems over the cloud. Such systems and networks should be provided with a proper cryptographic methodology to block unauthorized data transmission access. This issue is considered an essential challenge of resources that are shared on the data communication network, that is its security. So, in this study, a cryptosystem is proposed to maintain the security level of such systems with a new idea depending on DNA cryptography and DNA mixing. Results In this study, the proposed cryptosystem is based on the RSA algorithm and DNA cryptography concepts with a novel idea for mixing DNA strands obtained from encoding medical image and report to enhance the security level through the IoT networks. This system achieved a proper result in reconstructing images with high quality. The similarity between the original data and the restored one reached 92% through 18 s. Conclusions Such a proposed cryptosystem provided the feasibility of data security in network security, especially for E-health care through IoT system to help medical teamwork in handling medical data between hospitals safely. The result showed that RSA is a fast, efficient algorithm that can be utilized safely in cryptography schemes.
GHOSM: Graph-based Hybrid Outline and Skeleton Modelling for Shape Recognition

An efficient and accurate shape detection model plays a major role in many research areas. With the emergence of more complex shapes in real-life applications, shape recognition models need to capture the structure with more effective features to achieve high accuracy rates for shape recognition. This article presents a new method for 2D/3D shape recognition based on graph spectral domain handcrafted features, which are formulated by exploiting both an outline and a skeleton shape through the global outline and internal details. A fully connected graph is generated over the shape outline to capture the global outline representation while a hierarchically clustered graph with adaptive connectivity is formed on the skeleton to capture the structural descriptions of the shape. We demonstrate the ability of the Fiedler vector to provide the graph partitioning of the skeleton graph. The performance evaluation demonstrates the efficiency of the proposed method compared to state-of-the-art studies with increments of 4.09%, 2.2%, and 14.02% for 2D static hand gestures, 2D shapes, and 3D shapes, respectively.
On the Satisfiability of Context-free String Constraints with Subword-Ordering

We consider a variant of string constraints given by membership constraints in context-free languages and subword relation between variables. The satisfiability problem for this variant turns out to be undecidable. We consider a fragment in which the subword-order constraints do not impose any cyclic dependency between variables. We show that this fragment is NexpTime-complete. As an application of our result, we settle the complexity of control state reachability in acyclic lossy channel pushdown systems, an important distributed system model. The problem was shown to be decidable in [8]. However, no elementary upper bound was known. We show that this problem is NexpTime-complete.
Blockchain Information Sharing Mechanism Based on Embedded System in Project Management System

As a new force for online reconstruction, blockchain will bring major changes to resource-sharing activities and information service organizations. The research introduces blockchain and related technologies, provides two basic ways of sharing data sources, and conducts two extensive information source sharing based on blockchain. The article analyzes the various challenges that need to be overcome to implement a blockchain-based information sharing system and suggests that project information service organizations should actively deal with the challenges brought about by blockchain. Experimental research is being conducted on a blockchain-based control system, and a blockchain-based blockchain management system is being developed. On the basis of system analysis and design, the most important functional components of the software system are developed. They include functions such as recording customers, logging in, storing information, printing device information, invoking server-side smart contracts, and conducting audit management. It uses decentralized payments and statistical aggregation of data. The experimental results show that the efficiency of using the blockchain information sharing mechanism in the management system is 35% higher than that of the traditional method, which can more effectively deal with the problem of information delay during the project management process.
Quality improvement of undergraduate courses based on fuzzy analytic hierarchy process and entropy method

Since the curriculum is the core carrier to improve the level of talent cultivation in colleges and universities, strengthening the reform of course teaching and improving the quality of course teaching are fundamental to the survival and development of colleges and universities, and also an important part of higher education reform. In this study, a fuzzy analytic hierarchy process (AHP) and an entropy method were used to determine the weight of the core evaluation indicators of undergraduate course quality improvement, including four first-level indicators of the curriculum concept, curriculum resources, curriculum organization, and curriculum effectiveness, and 12 s-level evaluation indicators and weights. Then, based on a case study of the first-class undergraduate course “Management” of Anyang Normal University, the way to evaluate the course by the AHP and entropy method was explained. Finally, according to the evaluation results, the ideas of course construction were put forward, such as changing the course concept, enriching the course resources, paying attention to the course organization, and ensuring the course effectiveness, so as to improve the quality of undergraduate courses and also to improve the quality of undergraduate talent training with the improvement of course quality as the starting point.
Application-based Usability Evaluation Metrics

Testing is one of the vital stages in the software development life cycle (SDLC).Usability testing is a very important field that helps the applications be easily used by the end-users.Because of the importance of usability testing, a metrics has been developed to help in measuring the usability through converting the main qualitative usability attributes in ISO to quantitative steps that provide the developer a framework to follow in developing to achieve usability of their applications and helps the tester with a checklist and a tool to measure the usability percentage of their application.The framework provides a set of steps to achieve the usability attributes and answers the question of how you could measure this attribute with the defined steps.The framework results in a 95% average accuracy in the high-rate application and a 59% average accuracy in the low-rate application.Finally, the framework is programmed in a tool to measure the usability percentage of the application through a checklist and provides a scheme to help the developer achieve the best results in usability.
Fake Face Generator: Generating Fake Human Faces using GAN

As machine learning is growing rapidly, creating art and images by machine is one of the most trending topics in current time.It has enormous applications in our current day to day life.Various researchers have researched this topic and they try to implement various ideas and most of them are based on CNN or other tools.The aim of our work is to generate comparatively better real-life fake human faces with low computational power and without any external image classifier, rather than removing all the noise and maximizing the stabilization which was the main challenge of the previous related works.For that, in this paper, we tried to implement our generative adversarial network with two fully connected sequential models, one as a generator and another as a discriminator.Our generator is trainable which gets random data and tries to create fake human faces.On the other hand, our discriminator gets data from the CelebA dataset and tries to detect that the images generated by the generator are fake or real, and gives feedback to the generator.Based on the feedback the generator improves its model and tries to generate more realistic images.