Reviewerly/exHarmony huggingface dataset:
S.U.S. You’re SUS!—Identifying influencer hackers on dark web social networks

Dark web is an obscured part of the Internet, specifically used for sharing exploits, data breaches, and other means of cybercrime. Dark web forums provide opportunities to share such data and exploits and assign user reputation and credibility through participation in discussions and sharing data, exploits, and hacks. Such activities can help develop metrics to enable identification of influential mal-actors facilitating efficient and effective defense against emerging cyber threats, particularly zero-day exploits. This paper proposes an AI-inspired framework to identify influencers on dark web social networks (INSPECT) through intelligent analysis of user-profiles, interactions, and other activities. INSPECT framework leverages Feature Engineering, Social Network Analysis, Semantic Analysis, and K-means clustering and calculates an influencer score representing the users’ significance within these forums. INSPECT has been evaluated using CrimeBB dataset comprising user profiles and activities within dark web forums to assess its effectiveness in identifying influential users on the dark web forums.
From Learner Profile Management to Learner Model Modeling

In the educational context, adaptive hypermedia can better guide the learner's learning by providing him with adaptive and personalized educational content, taking into account his learning profile. The idea presented in this chapter is to design and manage effective learner models for all adaptive hypermedia systems (adaptive, macro-adaptive e-learning, ITS, AHES, etc.). This chapter gives an overview of learner profiles and models by referring to the differences between models and learner profiles. The authors then address the learner model in adaptive systems by identifying different adaptive, macro-adaptive, ITS, and AHES e-learning systems. They then discuss learner modeling in terms of learner model content, specific information domains, independent information domains, and learner model components. Finally, they present the process of developing a learner model: steps and techniques, in particular the collection of data, the initialization of the learner model, and the update of the learner model.
Adapting to the Academic Environment

The present study is based on the desire to investigate the possible relationship between the level of adaptation of the students to the university environment and the registration of academic performance. The working hypothesis is, if the level of adaptation to the university environment is high then the level of performance increases. Operationally, the concept of adaptation integrates the following dimensions: motivation, application, performance, satisfaction, academic environment. For the present research, the authors consider only the first subscale of Student Adaptation to College Questionnaire, respectively the academic adaptation, reflected, in turn, in the following indicators: motivation, application, performance, academic environment. The correlation of the data—for the above-mentioned subscales—will generate a qualitative analysis of the degree of satisfaction of students and how it is reflected in academic achievement. The obtained results will allow building strategies to streamline the approach of academic integration with effects on increasing performance.
Length of the state trace: A method for partitioning model complexity

A novel and easy-to-compute measure is proposed that compares the relative contribution of each parameter of a mathematical model to the model’s mathematical flexibility or complexity, with respect to accounting for the results of some specific experiment. When the data space is a two-dimensional plot of the type used in standard state-trace analysis, then the model complexity contributed by a single parameter equals the length of the state trace (LOST) that results when that parameter is varied and all other parameters are held constant. For the normal, equal-variance, signal-detection model, the average LOST when the response-criterion parameter XC is varied is about four times greater than the average LOST when the sensitivity parameter d′ is varied. As a result, applying the signal-detection model to random data almost always leads to the conclusion that all the points share the same value of d′ but were generated under different values of XC. Parameters that have non-monotonic effects on performance, such as the attention-weight parameter that is used in popular exemplar and prototype models of categorization, tend to have large LOSTs, and therefore contribute to model flexibility more than parameters that have monotonic effects on performance. Comparing LOSTs for exemplar and prototype models also leads to some deep new insights into the structure of both models.
Aggregating discriminative embedding by triple-domain feature joint learning with bidirectional sampling for speaker verification

Each axis of the speech, including time-domain, frequency-domain, and spectral-domain data, that’s represents different physical meanings and different dimension information. Time-domain focus on physical signal versus time, the frequency-domain focus on the amount of signal in a given frequency band, and the spectral-domain focus on global power with speech, so only using the spectrogram to represent the whole information of speech to do speaker recognition will lose a lot of details information with the other dimensions. To tackle this limitation, we propose a triple-domain feature joint learning to enhance discriminative embedding from more dimensions for text-independent speaker verification. To further aggregate discriminative embedding, each domain uses a novel bidirectional sampling multi-scale feature aggregation network based on Fisher feature fusion to project spectrum features to more discriminative embeddings, termed TribiNet. Extensive experiments are conducted on a text-independent speaker verification dataset generated from the VoxCeleb corpus. The results demonstrate that the proposed method outperforms the state-of-the-art deep embedding architectures by at least 12%-58% in the test set. The ablation experiments further illustrate that our proposed approaches can achieve substantial improvement over prior methods.
A Solar Powered Irrigation System to Minimize Wastage of Water: A Bangladesh Perspective

This study proposes a design of a solar powered irrigation system for Bangladeshi farmers. The algorithm implemented in the final system is the Fuzzy logic system. Furthermore, the farmers may always water their fields in an automated way according to the needs of the system and preserve water for their family needs by using this whole system which can improve the irrigation scenarios for Bangladesh's rural areas. The study has also shown that the Fuzzy Logic algorithm has faster calibration rate of 66.23% and water saving accuracy of around 61% greater than than the Average Logic. The study also demonstrate the fact that, compared to the system running in diesel pump, the system running in solar pump has higher value and save a lot of money in the long run.
Study on Image Processing of Venipuncture Arm

With the development of science and technology, the medical service has been greatly improved. People almost have to undergo venipuncture when receiving treatment in hospitals. This process requires the experience of medical staff and manual operation. In order to accomplish venipuncture better, various vein imaging instruments and venipuncture robots are studied. In this study, through the self-built arm vein image acquisition device, the arm vein image is obtained, and the collected vein image is processed. The development of vein image processing is briefly introduced, and the process of image enhancement, filtering, and denoising, morphological processing, image thinning involved in vein image processing are analyzed in detail. Focused on the improvement of morphology processing, the problem of the vein skeleton gap in the process of vein image processing is solved to obtain a clear and connected vein skeleton. Preliminary preparations for the vein imaging equipment are proposed to provide the key data for the vein puncture robot.
Postprocessing System for Word-Filling and Quantifiers Based on Chinese Story Generation

In recent years, many fields have been related to introducing artificial intelligence to natural language generation. Although these natural language models have excellent results and generate smooth sentences, they are still not effective learning features such as character relationships, especially in the Chinese language. When a sentence is generated, it is necessary to pay attention to the following words to correctly predict and generate, such as quantifiers, which causes the generated words to be inappropriate and then affects the generation of subsequent words. Therefore, we developed a set of attention mechanism enhancement models, aiming at the generative language model that controls the ordering of generated speech parts, revising the different mechanisms of character fighting relationship and quantifier design, and adopting the traditional classification model one-against-all support vector machine training. The results show that the generated sentences are arranged in the same order as the original ones so the generation can be reliably controlled.
Research on Intelligent Matching Model Between Employees and Positions Based on Python Big Data Analysis

In order to improve the efficiency and accuracy of human resource management by big data technology, support vector machines are used to complete job matching. The element sample for the employee indicator is sparsely represented to obtain the matrix. The sample is binary classification by a support vector machine to judge the matching degree of employees to positions. Finally, the random transformation function is introduced to achieve dynamic recommendations in the big data environment. The experimental results show that the algorithm has high job matching accuracy, high dynamic recommendation efficiency, and batch recommendation.
Three-Dimensional Widgets for User Interfaces in Industrial Augmented Reality Systems

Recently, the application of augmented reality industrial monitoring systems in the Internet of Things (IoT) has gradually developed. This has opened up new opportunities for Industry 4.0. However, industrial augmented reality management and monitoring systems often develop specific APPs to perform specific tasks. If the user’s requirements change, it is often necessary to readjust the APP on the device to obtain the relevant data. Furthermore, user equipment with a limited capacity to process a large amount of monitoring data tends to be bulky. This often results in power-hungry and difficult-to-use hardware. Thus, we propose a workflow to pre-process the device readings provided by the IoT on a regional server and decouple the data aggregation and data presentation systems. The data are then transformed into web browser-compatible WebXR 3D widgets suitable for presentation in augmented reality. With redistributable widget generation code snippets, the efficiency of software development is improved, the bottleneck of the data aggregation is relieved, and customizable user interface options for different use cases are provided.
Human Action Recognition Based on Image Coding and CNN

In human action recognition, the way of collecting action data through video or photos is easily affected by factors such as perspective and light, and it is not easy to describe and extract features. To solve this problem, we researched human skeletal joint data and the use of the convolutional neural network (CNN). The joint data was converted into a PNG image by image coding. In addition, we proposed 3 descriptions of data arrangement order for grayscale image coding. Combined with 4 coding methods and RGB image coding, the coding scheme was expanded to 16 kinds, and used a CNN model with 9 layers structure to conduct comparative experiments on 16 kinds of coding schemes. Then, the influence of data arrangement order and coding methods was discussed based on action recognition results. The experimental results show that the “Zhi” font coding method under the data arrangement order Case 2 is easier to classify actions, and the accuracy of the test set is 96 %.
Design Mechanism of Oral English Intelligent Training Based on Virtual Reality

The development and application of virtual reality technology in language teaching have updated the traditional language teaching strategy. With the support of virtual reality technology, the change in oral English teaching is imperative. Through simulation and experiments, virtual reality enables learners to actively perceive and explore knowledge in specific learning situations and stimulates students’ enthusiasm for learning. This helps them improve their problem-solving ability and effectively accumulate knowledge. However, the design of oral English intelligent training using virtual reality technology remains to be further elucidated. In this context, we proposed an intelligent training framework for oral English using virtual reality. Firstly, we analyzed the design elements and proposed the overall architectural design scheme. The research results contribute to establishing a basic framework to apply virtual reality technology to English training.
Solar Power Photovoltaic Output Forecasting Using Multiple Methods Approach, Case Study: Cambodia

Solar energy is one of the most potential renewable energy sources of sunlight. Due to increase and satisfying demand for energy in developing countries like Cambodia, solar power energy is the main and significant energy to the procedure for supply local to reduce import power energy from neighboring’s countries. In this case, the ability to an accurate solar output forecasting is critical for planning to decide based on forecast conditions, while many forecasting methods have been improved for forecasted values. However, the specific research on solar power PV output forecasting in Cambodia is still lacking to secure better accuracy during the rapidly extending inquiry of energy. This study is conducted to investigate a trial of short-term forecasting of solar power photovoltaic output in Bavet city, Cambodia, using several methods for comparisons such as Neural Network (NN), Linear Regression (LR), and Autoregressive Moving Average (ARMA). This process is based on the daily reality historical data from $\mathrm{I}^{\mathrm{s}\mathrm{t}}$ January 2018 to 1$0^{\mathrm{t}\mathrm{h}}$ January 2019 which were recorded by Nation Control Center (NCC). Weather daily index data is obtained from the Renewable Energy Community of NASA Power Data Access Viewer Website Forecast of Global Energy Resources. The reliability of the forecasting of the three methods was assessed by using Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Square Error (RMSE). Based on the simulation result of these three models, the Neural Network model showed better accuracy and results that were promising and beneficial for solar forecasting in Cambodia.
Investigating Users’ Privacy Concerns of Internet of Things (IoT) Smart Devices

Although the number of smart Internet of Things (IoT) devices has grown in recent years, the public's perception of how effectively these devices secure IoT data has been questioned. Many IoT users do not have a good level of confidence in the security or privacy procedures implemented within IoT smart devices for protecting personal IoT data. Moreover, determining the level of confidence end users have in their smart devices is becoming a major challenge. In this paper, we present a study that focuses on identifying privacy concerns IoT end users have when using IoT smart devices. We investigated multiple smart devices and conducted a survey to identify users’ privacy concerns. Furthermore, we identify five IoT privacy-preserving (IoTPP) control policies that we define and employ in comparing the privacy measures implemented by various popular smart devices. Results from our study show that the over 86% of participants are very or extremely concerned about the security and privacy of their personal data when using smart IoT devices such as Google Nest Hub or Amazon Alexa. In addition, our study shows that a significant number of IoT users may not be aware that their personal data is collected, stored or shared by IoT devices.
Customer Service Chatbot Enhanced with Conversational Language Understanding and Knowledge Base

A customer service chatbot enhanced with conversational language understanding and knowledge base is developed. Here, we explore LUIS and QnA Maker which are unified as Azure cognitive service for language. LUIS is a cloudbased conversational AI service that responds a user intelligence. QnA maker is a knowledge base for custom question answering. This question-and-answer knowledge base is especially useful for customer dialogs. Hence, by combining the services, we provide a smart response and provide a knowledge base to understand and improve the service. We implement this chatbot on the LINE Bot platform. Users easily access it by simply adding the representative of this chatbot on LINE. In addition, we put marketing information through this chatbot. The experiments are conducted, and the results show the chatbot is feasible and has a high user acceptance.
Design of Video Acquisition System for Construction Transporter Self-driving on Deep Learning

When using deep learning technology to achieve foreground detection for Unmanned Ground Vehicles (UGV), its visual real-time processing tasks need to be completed with customized embedded platforms. A large amount of reliable visual data for deep learning is provided to a video acquisition system. We present a video acquisition system for deep learning in construction transporter self-driving application to effectively shield electromagnetic interference in various frequency bands, cope with complex scenes and different types of light pollution, simplify the processing of original image data by the visual controller and the transmission mode of installation wiring, and provide a solution with high stability, high bandwidth, high reliability, long distance and low delay for image data transmission.
Analysis of Impact of Synthetic Image Data with Multiple Randomization Strategies on Object Detection Performance

Real-time object detection is a smart tool in assisting in-field human workers. In order to achieve a better performance out of object detection deep learning models, tremendously larger quantities of data are needed to train the models’ network. However, obtaining data is expensive in terms of cost, extremely time-consuming, and prone to errors in the annotated part which reduces the detection performance. Hence, in recent years, many research has been focusing on developing alternatives to supplement real data with synthetic data. One of the most popular techniques to generate them is called domain randomization. In this research, an extensive analysis of the impact of various domain randomization strategies was conducted to discover the impact on object detection among the strategies to generate synthetic data from the example industrial objects. The strategies of object orientation and rendering randomization were comprehensively inspected. Insights on the usage of better synthetic data generation were given to improve object detection performance in selected aspects. The method is especially efficient when extremely limited real data were available, and the delicate employment of synthetic data has strengthened the object detection performance by up to 37% of improvement.
Design of Blockchain Technology-based Transcript and Analyzing Management Model for International Students

With globalization, asymmetric information among international students’ academic performance and qualifications, lack information transparency, bloated management bodies, high verification costs, and difficulties in fraud risk management are currently limiting the advancement of internationalization and standardization. These risks and problems also raise questions about the development of IT systems in the education administration and industry as well. Thus, by combining the study of international students’ study results and academic records, we design a blockchain-based study transcript and academic information system concept and architecture with the use of blockchain technology. The result helps to develop business models and authentication features in related fields, set up a rigorous environment, optimize existing processes and rules, and lay a technical and data foundation for the development of international students’ study transcripts and academic authentication.
Application of AI Data Mining in Legal Digital Resources in China Based on Big Data

Artificial intelligence technology has been widely used in libraries and information. Based on the related research on data mining in the CNKI database, we analyzed the growth law of the number of literature and the distribution of journals by using the bibliometrics method. Keywords in the literature were researched by using co-word analysis and SPSS software. Factor analysis, cluster analysis, and multidimensional scale analysis were conducted on the keyword matrix to reveal the hot spots and key points of artificial intelligence data mining in the field of legal digital resources.
Research on Chinese-English Hybrid Rhetorical Question Recognition Model and Corpus Construction of Intelligent Web Text

With the popularity of English in China, Chinese-English mixed rhetorical question has become a common expression in China. Mixed Chinese-English rhetorical questions have rich emotional overtones, and if they can be correctly identified, they improve the results of sentiment analysis and other tasks. Using semi-supervised learning and active learning methods, a semi-automatic collection of the rhetorical corpus is proposed to construct a Chinese-English rhetorical corpus of web text. Based on the corpus, the characteristics of Chinese-English mixed rhetorical questions are analyzed from the aspects of semantic features, positional features, and syntactic path features to carry out a rhetorical question recognition experiment. Experimental results show that the rhetorical question corpus constructed from online texts trains a rhetorical question recognition model with high performance, and the accuracy, recall, and F1 values of the model are higher than 90%. At the same time, the experimental results verify the effectiveness of syntactic path features and location features in identifying rhetorical questions.
A proposed model reference adaptive neural network controller based on Taylor development of nonlinear discrete system

This paper presents a developed model reference adaptive neural network control. It illustrates how the derivative function affects directly training speed. Indeed, the classical derivative of the sigmoid activation function decreases the learning speed because a large amount of error is not proportionally propagated back to the weights of the output layer. To overcome this difficulty, this paper proposes a Taylor development of the activation function which is capable of speeding up the learning process significantly and it can provide simultaneously stability of the learning process. The results of simulation show that using Taylor development has better effects both on response time and on tracking performance.