Reviewerly/exHarmony huggingface dataset:
Privacy-preserving spam filtering using homomorphic and functional encryption

Conventional spam classification requires the end-users to reveal the content of incoming emails to a classifier so that text analysis can be performed. On the other hand, new cryptographic primitives allow this classification task to be performed on encrypted emails without revealing the email contents, hence preserves user data privacy. In this paper, we construct a spam classification framework that enables the classification of encrypted emails. Our model is based on a neural network with a quadratic network component and a multi-layer perceptron network component. The quadratic network architecture is compatible with the operation of an existing quadratic functional encryption scheme. To protect email content privacy, we proposed two spam classification solutions based on homomorphic encryption (HE) and functional encryption (FE) that enables our classifiers to predict the label of encrypted emails. The evaluation results on real-world spam datasets indicate that our proposed spam classification solutions achieve accuracies over 95%. Our performance study and security analysis provide pros and cons of each proposed solution. For instance, the FE solution predicts a label of an encrypted email in less than 31 s whereas the HE solution takes up to 265 s to do so. Nonetheless, the HE solution is not prone to potential information leakage as the FE solution.
Asymmetry-aware bilinear pooling in multi-modal data for head pose estimation

The head pose on roll and yaw directions is decided by the asymmetric appearance in human faces, and the contextual information of asymmetric appearance is encoded in a head pose related neighborhood. However, CNNs used in existing head pose estimation methods often evenly performs on the features of full image. Thus it is hard to collect the contextual information of such asymmetric appearance by those methods. To address this issue, this paper proposes a novel head pose estimation method that could perceive the asymmetric appearance in human faces. Specifically, the awareness of such asymmetry is undertaken by the local pairwise feature interaction in head pose related neighborhood via bilinear pooling. Evaluations on two public datasets demonstrate that our method could achieve promising results.
Adapting Static Taint Analyzers to Software Marketplaces

Improper input validation is still one of the most severe problem classes in web application security, although there are concepts with a good problem-solution fit, such as static taint analysis. In practice, however, existing static taint analyzers suffer from both high false positive and false negative rates, making them impractical for effective detection of new vulnerabilities. In this work, we present an approach that aims to systematically specialize existing taint analyzers toward software marketplaces to improve both recall and precision of their analyses. To validate whether our approach is suitable for finding new vulnerabilities in web applications, we applied a specialized taint-analyzer to a random sample of 1,000 plugins from the WordPress plugin store. As a result, we were able to disclose ten CVE entries, including two vulnerabilities with a high or even critical CVSS score. Our preliminary results indicate the principle feasibility of our approach and show that it may be suitable for mass vulnerability detection in software marketplaces, providing a promising foundation for future works in this domain.
Local Feature Filtering Method for Dynamic Multiframe Video Sequence Images

To improve the quality of local feature filtering for dynamic multiframe video sequence images, this study is aimed at designing an improved nontexture class noise filtering algorithm based on noise construction denoising algorithm and gray histogram of pixel points, and then designs a texture noise denoising algorithm based on texture smoothing processing and circular gradient values. The two algorithms are combined to propose a comprehensive filtering and denoising algorithm for horizontal dynamic video images. The experimental test results show that the normalized correlation coefficient, mutual information quantity, peak signal-to-noise ratio, and information entropy of the integrated filter denoising algorithm are 0.950, 0.935, 0.816, and 0.933 after convergence of the training effect, which are significantly higher than those of the commonly used median denoising algorithm and Kalman denoising algorithm. However, the computational time consumption of the proposed integrated filtering and denoising algorithm is higher than that of the comparison algorithms. The experimental results show that the integrated filtering algorithm for dynamic video images designed in this study can achieve better filtering and image reconstruction results in application scenarios with lower requirements for the timeliness of processing results.
A smartphone authentication system based on touch gesture dynamics

Summary Different authentication techniques that we use today, are prone to shoulder surfing attacks and mimicry attacks. Thus, keystroke dynamics combined with time and motion‐based typing patterns have been studied for years. In this paper, we introduce and evaluate a touch gesture‐based application to authenticate a user based on their typing behavior in distinct contexts such as lying, sitting, standing, walking, stationary, climbing up and down the stairs, by leveraging different features extracted from multiple built‐in smartphone sensors. We use various attributes including time‐based features such as dwell time and flight time and motion‐based features such as accelerometer, gyroscope, and magnetometer readings. The proposed authentication model distinguishes the legitimate smartphone owner from impostors using hand gestures, touch and keystroke dynamics. We experimented with different design alternatives such as a combination of motion sensor features, time‐based features extracted from multiple devices. In addition, we evaluated the performance using various supervised machine learning algorithms to show how to achieve high authentication accuracy and least equal error rate. A thorough evaluation shows that the system achieves authentication with 99.8% accuracy with a high AUC of 0.99 and EER of 0.11%.
Artificial<scp>intelligence‐based</scp>remote monitoring system for automated anomaly detection in wireless sensor networks

Summary In recent decades, wireless sensor networks (WSNs) are employed in the remote monitoring system which experiences a huge delay due to the excessive time consumption by the image processing techniques. The existing video surveillance system is unable to detect the anomaly accurately and falls short in classifying the routine activity and the anomaly activities. Therefore, an efficient anomaly detection model is necessitated for identifying the anomaly behavior of malicious nodes in the WSN environment. This research work introduces a novel artificial intelligence (AI)‐based remote monitoring system for WSN. Primarily, the online video frames are retrieved from the wireless video sensors where the frames are generated by segmenting the entire stream of video as a constant‐sized structural frame. The sequence/stream of video segments is fed into the proposed AI framework to detect anomaly activities. It has multiple levels of hidden layers to compare the features of the input captured image with the preloaded reference image. Furthermore, a 5‐fold cross‐validation approach has been inspected to validate the efficacy of the suggested AI model. The performance results evident that the proposed AI system acquires a better prediction accuracy of 98.2% and precision of 97.3% than the existing monitoring systems.
Development and Information Security of Hybrid English Interactive APP based on Mobile Internet Communication Network

Development and information security testing of the hybrid English interactive APP based on mobile Internet communication network is studied in the paper. Multimodal teaching mainly emphasizes various interactions between learners' various senses and the external learning environment, that is, the use of multiple media and teaching methods in teaching to mobilize learners' multiple senses to work together. For achieving this goal, the technology should be integrated. Through the version control, the problem of the code submission and merging of different members is solved, and members are required to pull the latest code from the code base and merge with the local code every day, and then carry out development work. For the further optimization, the designed model is then integrated with the efficient APP structure.
Design of English Online Learning Platform Based on Deep Learning Technology

The rise of online education enables us to break through the limitation of space and time, and study with low cost and high efficiency, which has a great impact on traditional education. In the case that traditional education has not been completely replaced, it will form complementary advantages with traditional education, bringing a good situation for the education industry. This paper conducts research on the English online learning platform based on deep learning technology, introduces the deep learning model into the English online learning platform for personalized service, so as to improve students' English learning ability, and tests the designed platform through experiments. Through the test of the platform response time, it can be seen that the response time of the platform is controlled within 6s, which basically meets the design requirements of the platform. It can be seen from the evaluation of the use of the platform that the platform still has some deficiencies, mainly manifested in the complicated function of the platform design.
Opportunities and Challenges of Computer Network Management System in the New Era: from the Perspective of Information Flow

This paper analyzes the opportunities and challenges faced by the computer network management system under the background of the new era based on the information flow. Building a computer network management experimental platform based on the new media information flow is the key to achieving the teaching goals of the course. The construction of a real experimental platform requires a lot of investment Human and material resources also bring management problems. A comprehensive experimental platform for computer network management that integrates GNS3, VMware and SNMPc to build virtual simulations is proposed. On this experimental platform, students can implement experimental projects in the computer network management course, increasing efficiency by 6.7%.
Cloud Center Platform Integrating Industry-University Cooperation Mechanism of ACCA-Oriented Class-Running Data Analysis Algorithm

A new Hierarchical Reinforcement Learning (HRL) Option automatic generation algorithm is proposed, which takes the state space detected by the Agent at the initial stage of learning as the input, and uses the Improved Ant Colony Clustering Algorithm (ACCA) to cluster it. On the clustered state subsets, an internal strategy set is generated through experience playback learning, aiming at the standardization and practicability of school-enterprise cooperation information management methods. Based on the idea of stratification, this paper proposes a scheme for the design of a school-enterprise cooperation platform for higher vocational education based on service architecture. Combined with the cloud architecture design, based on the cloud service idea, the function of the platform in school-enterprise cooperation is analyzed, and the business of the multi-core service value chain is proposed. The process integration solution establishes the business process integration model of the multi-core service value chain from the three levels of data, service and process, and defines the rule set of the business process integration process.
Application of Data Guidance Site Generation Technology in the Cloud Platform Supporting the Construction of Subject Teams in Finance and Economics Applied Universities

On the basis of in-depth research on SOA architecture, Web services, software testing technology and data-guided site generation technology, this paper studies that the “Trinity” entrepreneurship project is an entrepreneurial practice teaching mode explored and practiced by finance and economics colleges and universities, project authenticity, knowledge integration, diversity and openness, teamwork and good attitude and other teaching characteristics. Build a practical teaching mode system with financial and economic characteristics; improve the working mechanism, strengthen the construction of teachers and practice bases, and build a practical teaching guarantee system for ideological and political courses.
3D Augmented Reality Cube Mobile Interface

Augmented Reality (AR) has picked up pace in recent times, but there is room for improvement, especially in terms of mobile applications. This paper examines work undertaken in creating a mobile application that enables participants to scan and capture online data onan AR Environment using a simulated cube model. The architecture of the prototype, including graphic elements, and a 3D interface, is described. The potential interactions between the cube and the prototype are explored. The interactive system was developed and runs on a typical mobile or handheld device. User testing was undertaken, resulting in good feedback as well as some ideas for further consideration for future work.
Research on Video Keyframe Extraction Method Based on Action Analysis

In the field of behavioral analysis, dividing keyframes based on video images to extract the motion information is one of the more common preprocessing methods, which can preserve as much semantic information as possible while compressing the data. In this paper, we propose a method for dividing video keyframes based on the analysis of human motion in video, which designs feature vectors based on human motion characteristics and gives suggestions for keyframe division by analyzing the change of quantitative values. It is experimentally confirmed that the method is less data-dependent, less time-consuming to engineer, more lightweight in the design of feature vectors and closer to the expected division results while ensuring relatively stable and reliable recognition results for downstream tasks, which provides more reliable antecedent support for the content analysis work of human motion videos.
Research and analysis of intelligent accounting algorithm under the background of big data

The research on big data intelligent accounting algorithm is the research on the algorithm of processing a large amount of data. It is observed that the amount of data being generated is growing exponentially and will continue to grow exponentially in the coming years. The main goal of this research work is to develop a computer program that can process such a huge amount of information without any human intervention or supervision. This will help reduce costs and improve efficiency by eliminating human and errors in the system. The main advantage of using big data intelligent accounting algorithm is that it helps reduce costs by automating tasks Taking big data as the background and from the perspective of big accounting, this paper believes that accounting is an artificial system, and the essence of accounting is to take the form of behavioral value of economic activities as the main object. Due to the influence of social environment changes, accounting is facing practical, theoretical and technical challenges.
A Comparison of the Effectiveness of Techniques for Predicting Binary Dependent Variables

The Invoice Disputes Team wants to identify further opportunities to reduce invoice disputes and the team would like to explore whether data analytics can drive further improvements. The purpose of this paper is to compare the effectiveness of the eight approaches to predict binary dependent variables according to the specified data. The techniques examined are Logistic Regression, Probit Regression, CHAID, CART, Neural Networks, Bagging, Random Forests and Boosting. This paper describes the data set, the effectiveness measures used and the approaches, and also shows the results for each of the eight approaches that are examined. The simulation results show that both Bagging and Random forests seem to do better than other approaches.
Greenhouse gas emissions mitigation potential from renewable energy development in Thailand’s industrial estates

This research assesses the potential of reducing greenhouse gas (GHG) emissions from the use of rooftop solar energy in factories located in Thai industrial estates. Most of the industrial plants located in industrial estate areas have large size with high production and energy intensive. The scope of the study covered all industrial factories located in the industrial estates of the Industrial Estate Authority of Thailand (IEAT). Renewable energy only considers solar rooftop installations, assessing on two levels based on the accuracy and source of the data, i.e. level 1, moderate data resolution and accuracy from database and online data of the IEAT and official website of the industrial estates, and level 2, high data resolution and accuracy from GIS data management program called QGIS program. Assessment according to level 1 is based on the proportional factor of the roof area of the potential community area. Assessment according to level 2 is based on the roof area data of general industrial and free zone obtained by using the QGIS survey through aerial imagery. The results showed that the calculation of the amount of GHG reduction potential based on the level 1 assessment, there is a potential roof area for installing solar rooftops of 12.05 million square meters, representing total installed capacity of 1,421.05 MW, equivalent to 0.95 MtCO 2e of GHG reduction potential annually. As for the GHG reduction potential from level 2 assessment from all aerial photographs, the study found that the potential roof area for installing solar rooftops is 6.55 million square meters, representing a total installed capacity of 773.02 MW, equivalent to 0.52 MtCO 2e of GHG reduction potential. The potential assessed above representing an estimated energy cost of 8,919-8,975 million baht for 1 MtCO 2e reduction of GHG emissions.
ONLINE BUG TRACKING SYSTEM

A small proportion of the world's most successful software developers use bug tracking.Many, including myself, didn't prioritise bug tracking.Instead, shared lists and email were used to monitor concerns.This process is prone to mistake, thus developers often dismiss problems they deem minor.The industry requires criteria for selecting the best systems tool from the available collection of system tools, which will aid in bug fix reporting and tracking.This system is used to keep track of all the products, bugs, and bug trackings.With this, you may keep track of all of the bugs you've encountered, including their causes and solutions.Collection of relevant data from the enormous and unorganised set of bug reports is still a difficult task, as there are numerous bug tracking systems that supply data via numerous means, such as web-based interfaces.
The Integrated Nested Laplace Approximation for Fitting Dirichlet Regression Models

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
Utilization of Virtual Reality Into Sailplane to Airplane Transition Training

This article deals with experimental assessment of whether SPL holders acquire the necessary competencies for the first solo flight in a powered aircraft using only virtual reality. The experiment was performed on a sample of 10 participants who were trained using a simulator based on virtual reality. All participants succeeded in the simulator training, although differences in the length of training required were observed. This training was followed by a flight in a real airplane that consisted of 3 traffic patterns. The participants also filled out questionnaires. This subjective self-assessment was then compared with an objective assessment (flight data, instructor's comments). The outcome was 5 successful and 5 unsuccessful participants of assessment flight in the real airplane. On a sample of 10 participants, areas that are suitable for training in a virtual reality environment were identified. Results indicate that virtual reality is a suitable complement to pilot training, but it is not suitable to use as the only means of flight training.
Deployment Strategy using DevOps Methodology: Cloud Container based Orchestration Frameworks

Software engineers spend half of their working hours going through the work of forerunners, but experts and great people of the community do emerge. Just as the working individuals have learnt the lessons about writing the code, the community has learned collective lessons to work with software development at scale. It is not easy to see these larger trends when they are busy doing small bug fixes. DevOps is one of these huge trends. It is a combination or say unification of two major traditionally separate worlds of software development and operations into one common cycle. It is not a recent act of invention, rather it is a result of years of iteration. Kubernetes, Docker Swarm, and Apache Mesos are a few of the different container orchestration frameworks that exist on the lookout. There is yet an absence of the most ideal solution for orchestration. The goal of this work is to close that gap and offer the relevant experimental results for the study.
Multi-sensor Data Collection and Centralized Analysis Platform based on Computer-Aided Nursing Sports Intelligent System

Multi-sensor data collection and the centralized analysis platform based on the computer-aided nursing sports intelligent system is designed in this paper. For the designed system, the core technologies contain the 2 aspects. (1) According to this feature, through the method of establishing and maintaining the task allocation table, all computing nodes read the task allocation table and determine the data, processing method, result message destination and other information that this node should process, so as to realize the dynamic load balancing of the real-time cluster system. (2) The sender maintains a timer and saves the most recently sent data packets. If the sender does not receive an acknowledgment packet from a member node in the cluster system within a specified time, the sender considers the node to be faulty. The disadvantage is that under normal communication conditions, a large number of the confirmation messages occupy the communication bandwidth. The system is applied to the computer-aided nursing sports intelligent platform and the results are tested.