Reviewerly/exHarmony huggingface dataset:
Interactive Context-Aware Recommendation Over Online Sites

Context-aware recommendation has emerged as the leading edge of systems over online sites due to its smart context exploitation in improving recommendation quality. However, sometimes the system may fail to capture a userâ€™s instant contexts from her activity history when her current needs do not follow her regular user behaviour patterns, which might be caused by external situations relevant to her, such as a time critical event or a special arrangement. To solve this problem, in this work, we propose an Interactive Context-Aware Recommender System (I-CARS), which allows users to interact and represent their needs, so the system can personalize and refine user preferences. I-CARS iteratively asks a question to a user to trigger feedback in term of her recent contexts and incorporates the response to recommend items most likely satisfying her recent interests. Specifically, we first propose a Personalized Weighted Context-Aware Matrix Factorization (PW-CAMF) that enables the personalization of important contexts for each user. Then we propose a new question selection strategy that exploit user preferences through feedback. We have conducted comprehensive experiments over seven real datasets. The experimental results prove the high performance of our I-CARS system in terms of recommendation quality and response time.
Strain Fields Measurement Using Frequency Domain Savitzky-Golay Filters in Digital Image Correlation

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The Recovery Scheme of Computer-Generated Holography Encryption-Hiding Images Based on Deep Learning

In order to improve the security of ciphertext images in transmission，a scheme of using neural network is proposed to restore the encryption-hiding images based on chaotic iris phase mask and computer-generated holography (CGH), which solve the great difficulty of illegal attacks in the symmetric-asymmetric hybrid encryption system. Firstly, the ciphertext image based on CGH and chaotic iris phase mask is generated, and the ciphertext image is hidden into a carrier image. A large number of hidden image and plaintext image pairs are produced as a dataset. Next, the neural network is built by continuous training and testing. Finally, the established neural network can fit the mapping relationship between the hidden image and the plaintext image. It is not necessary to extract the ciphertext image from the hidden image before decrypting. This scheme can directly realize the decryption of the hidden image. The scheme (encryption-decryption-hiding) are elaborated in detail. The simulation experiments show that the scheme is feasible and has good robustness.
A Novel Hybrid Sampling Framework for Imbalanced Learning

Class imbalance is a frequently occurring scenario in classification tasks. Learning from imbalanced data poses a major challenge, which has instigated a lot of research in this area. Data preprocessing using sampling techniques is a standard approach to deal with the imbalance present in the data. Since standard classification algorithms do not perform well on imbalanced data, the dataset needs to be adequately balanced before training. This can be accomplished by oversampling the minority class or undersampling the majority class. In this study, a novel hybrid sampling algorithm has been proposed. To overcome the limitations of the sampling techniques while ensuring the quality of the retained sampled dataset, a sophisticated framework has been developed to properly combine three different sampling techniques. Neighborhood Cleaning rule is first applied to reduce the imbalance. Random undersampling is then strategically coupled with the SMOTE algorithm to obtain an optimal balance in the dataset. This proposed hybrid methodology, termed "SMOTE-RUS-NC", has been compared with other state-of-the-art sampling techniques. The strategy is further incorporated into the ensemble learning framework to obtain a more robust classification algorithm, termed "SRN-BRF". Rigorous experimentation has been conducted on 26 imbalanced datasets with varying degrees of imbalance. In virtually all datasets, the proposed two algorithms outperformed existing sampling strategies, in many cases by a substantial margin. Especially in highly imbalanced datasets where popular sampling techniques failed utterly, they achieved unparalleled performance. The superior results obtained demonstrate the efficacy of the proposed models and their potential to be powerful sampling algorithms in imbalanced domain.
State Sensing of Bubble Jet Flow Based on Acoustic Recognition and Deep Learning

This study covers the accidental generation of bubble jet flow caused by steam generator (SG) tubes damaging in sodiumcooled fast reactors (SFRs). The main objective of this study is to develop a new state sensing method of bubble jet flow based on acoustic recognition and deep learning. Prior to the application of this method to actual SFRs, we utilize air and water as simulant fluids in order to perform the proof of concept. This study is divided into three phases. The first phase is the acquisition of pipe flow sound and bubble jet flow sound, each of which simulates the normal and anomaly sound from SG tubes in SFRs. The second phase is the preprocessing of acoustic signals and feature extraction. The third phase is the building of deep learning models and performance evaluation. As a result, every of our proposed models could distinguish between pipe flow sound and bubble jet sound with an accuracy of almost 100.00%, and the best model could classify pipe flow sound and three types of bubble jet flow sound with an accuracy of 99.76%. This result suggests that the acoustic recognition with deep learning has great potential to sense the state of bubble jet flow in actual SFRs.
Assessment of Problems in Requirement Engineering Affecting Software Quality of a Software Development Firm in Ghana

Requirement engineering, or requirement elicitation, is generally the first step in any software development project. The requirements are the primary determinants of how software should be designed and implemented. As a result, in the field of software development, failing to capture the requirements unambiguously has become a challenge. This article examines the software quality issues connected with requirement engineering, as well as the challenges that come with them, in the context of a Ghana-based software firm. A histogram analysis was conducted using data from the firmâ€™s online application used to track all defects and issues for each project, which are logged under a unique project ID. The Capability Maturity Model Integration, Prototyping, ISO 9001, Walkthroughs and Formal Inspections were the compared models that could be used to solve software quality problems in requirement engineering.
Reversing Our Ways from x86 VM Configurations onto ARM-Based Raspberry Pis

Cybersecurity ranges allow students to explore challenges in realistic system conditions and often play a central role in related courses. The pre-built scenarios also serve well in "capture the flag'' competitions. Existing ranges are typically built using server equipment that is difficult to transport or are hosted as virtual machines. We have created a mobile cyber range by porting existing exercises to Raspberry Pis. Apart from the mobility advantage, our range requires hardware that is significantly more affordable for institutions. Since existing range images depend on specific x86 binaries and architectural features, we present the methodology through which we overcame such challenges as well as preliminary results.
Pedagogical design of the educational environment of the University in the conditions of realizing the possibilities of the Technopark

This article defines the possibilities of the Technopark in the development of pedagogical design of the educational environment of the University. The specifics of the future teacher’s professional training in the conditions of digitalization of education are highlighted. The typology of educating situations is presented, which are the basis for creating a universal fund of evaluative means of educating future teachers and their readiness for educational activities in professional activity (applicable for all training profiles). The nature, content and the results of the use of educational situations in the professional training of future teachers are determined. The methods of constructing pedagogical reality are described. The concept of “pedagogical design” and its possibilities in the training of future teachers are considered.
Review of "Composition and Big Data edited by Amanda Licastro and Benjamin Miller," Licastro, A. &amp; Miller, B. (Eds.). (2021). <i>Composition and big data</i> . University of Pittsburg Press.

The evolution of digital tools and platforms has ushered in new possibilities for researchers, scholars, and practitioners of rhetoric and composition and adjacent fields like technical communication. These technologies change the ways we can gather, store, and use larger datasets, prompting new discussions on what big data methods look like in the field. The chapters housed in Amanda Licastro and Benjamin Miller's edited collection Composition and Big Data investigate the promises, concerns, and areas for further conversation regarding the applications of big data methods in composition-focused research.
Convolutional neural network for real-time main transformer detection

Abstract For substation constructions, the main transformer is the dominant electrical equipment, and its arrival and operation affect the progress of project directly. In the context of smart grid construction, in order to improve the efficiency of real-time main transformer detection, this paper proposes an identification and detection method based on the SSD algorithm. The SSD algorithm is able to extract the target device (such as main transformer) accurately and the Lenet algorithm module can analyse the features contained in the image. To improve the accuracy of the detection method, the image migration algorithm of VGG-Net is used to expand the negative samples of main transformers to improve the generalisation of the algorithm. Finally, the image set collected in the real substation projects is used for validation, and result shows that the method identifies main transformers more accurately, with high effectiveness and feasibility.
XDesign

We introduce XDesign, a web-based interactive platform that guides learners through a multi-stage design process for creating user-centered explanations of AI models. Results from a course deployment show that students were able to identify concrete user needs in interacting with explanations, highlight user tasks to support the needs, and design a user interface that aids the tasks.
Removing the Veil: Shining Light on the Lack of Inclusivity in Cybersecurity Education for Students with Disabilities

There are currently over one billion people living with some form of disability worldwide. The continuous increase in new technologies in today's society comes with an increased risk in security. A fundamental knowledge of cybersecurity should be a basic right available to all users of technology. A review of literature in the fields of cybersecurity, STEM, and computer science (CS) has revealed existent gaps regarding educational methods for teaching cybersecurity to students with disabilities (SWD's). To date, SWD's are largely left without equitable access to cybersecurity education. Our goal is to identify current educational methods being used to teach SWD's concepts of cybersecurity, evaluate these methods, and classify the observed trends.
A Tool to Teach Expressions with Feedback About Broken Laws

We developed a web-based tool for learning the order of evaluating expressions in C++ and Python languages. The variety of operator precedence and associativity among programming languages and the lack of direct visualization make understanding expression evaluation difficult for some students. The key feature of the new system is a detailed explanation of errors, containing fault reasons---the subject domain laws that the student violated. We evaluated the tool with 14 first-year Computer Science students and received positive feedback. This tool can be used for learning new concepts during homework without requiring more class time because it provides enough feedback for students to learn on their own.
Complex Network in Modeling Urban Anti-terrorism Actions

Aiming at the hot issues in urban anti-terrorism operations, this paper analyzes the current research status of the application of complex network theory to urban anti-terrorism operations modeling, combs the effectiveness analysis methods of network models, and points out the limitations of using complex network theory to model combat operations. And put forward research directions worth exploring in the future.
Global Dynamic Path Planning Algorithm Based on Harmony Search Algorithm and Artificial Potential Field Method

For public places with complex environment and many dynamic obstacles, a dynamic path planning method combining global and local planning is proposed in this paper. Firstly, the mathematical model is established, and solved by harmony search algorithm. Secondly, optimization is performed by opposition-based learning and optimal learning, and the extraction rate of the harmony memory is dynamically adjusted. Finally, by combining the artificial potential field method and adding local target points, a global dynamic path planning algorithm considering the global path optimization and real-time obstacle avoidance performance is constructed. Experiments show that the algorithm has more advantages in path length and smoothness, and can avoid dynamic obstacles.
Weight Separation for Memory-Efficient and Accurate Deep Multitask Learning

We propose a new concept called Weight Separation of deep neural networks (DNNs), which enables memory-efficient and accurate deep multitask learning on a memory-constrained embedded system. The goal of weight separation is to achieve extreme packing of multiple heterogeneous DNNs into the limited memory of the system while ensuring the prediction accuracy of the constituent DNNs at the same time. The proposed approach separates the DNN weights into two types of weight-pages consisting of a subset of weight parameters, i.e., shared and exclusive weight-pages. It optimally distributes the weight-pages into two levels of the system memory hierarchy and stores them separately, i.e., the shared weight-pages in primary (level-1) memory (e.g., RAM) and the exclusive weight-pages in secondary (level-2) memory (e.g., flask disk or SSD). First, to reduce the memory usage of multiple DNNs, less critical weight parameters are identified and overlapped onto the shared weight-pages that are deployed in the limited space of the primary (main) memory. Next, to retain the prediction accuracy of multiple DNNs, the essential weight parameters that play a critical role in preserving prediction accuracy are stored intact in the plentiful space of secondary memory storage in the form of exclusive weight-pages without overlapping. We implement two real systems applying the proposed weight separation: 1) a microcontroller-based multitask IoT system that performs multitask learning of 10 scaled-down DNNs by separating the weight parameters into FRAM and flash disk, and 2) an embedded GPU system that performs multitask learning of 10 state-of-the-art DNNs, separating the weight parameters into GPU RAM and eMMC. Our evaluation shows that memory efficiency, prediction accuracy, and execution time of deep multitask learning improve up to 5.9x, 2.0%, and 13.1x, respectively, without any modification of DNN models.
Pedestrian Detection Algorithm Combining Attention Mechanism and Nonmaximum Suppression Method

With the involution of pedestrian detection technology, higher requirements are put forward for the detection accuracy under the conditions of insufficient light, target occlusion, and too small scale. Without information and multiscale pedestrian target, visible light single-mode pedestrian detection algorithm has poor performance. To solve the above problems, a pedestrian detection algorithm combining attention mechanism and nonmaximum suppression method is proposed in this study, aiming to improve the accuracy of pedestrian detection. In addition, residual network ResNet-50 and IoU (intersection over union) loss function are also adopted to improve pedestrian detection accuracy. Attention mechanism was used to optimize and highlight pedestrian area features, and meanwhile, the nonmaximum suppression method was applied to improve the robustness of the algorithm. Experimental results show that the detection accuracy of the proposed algorithm is significantly higher than that of the traditional convolutional neural network algorithm.
Financial risk analysis system and supervision based on big data and blockchain technology

Abstract Financial risk is closely related to the risk of finance. For both institutions and financial industry, once financial risk occurs, it will bring great harm. It is very necessary to forewarn financial risks before they occur. Through the analysis and supervision of financial risk can effectively control the occurrence of financial crisis. In the current Internet era, blockchain technology has been widely used in various aspects. In finance, in particular, blockchain has brought great convenience to the analysis and supervision of its risks. This paper aims to study financial risk analysis and related regulatory research through the relevant technologies of big data and blockchain. This paper puts forward the way of combining theory and model to study financial risk. The risk value is obtained through the study of risk‐related attributes. Experimental results show that the risk rate of financial risks under big data and blockchain technology has been reduced by 10%–15%. This degree of risk reduction is of great application in actual financial risk control. Financial security is closely related to financial risks and financial crises. Both risks and crises can be used to explain and measure the degree of security, and security can also be used to explain and measure risks and crises. The higher the degree of security, the lower the risk; conversely, the greater the risk, the lower the degree of security; a crisis is the result of a large‐scale accumulation of risks, and a crisis is serious insecurity, which is an extreme of financial security.
A comparison of proximity-based methods for detecting temporal anomalies in business processes

Outlier detection in process mining refers to aspects such as infrequent behavior in relation to the underlying business process models or to anomalous latencies of task execution, termed as temporal anomalies. In this work, we focus on the latter form of anomalies and we aim at investigating in depth the behavior of several proximity-based variants, which are shown to outperform simple statistical ones. We investigate multiple distance functions and approaches to establishing the outlierness of traces or individual tasks, and we explain the superiority of our proposals over existing proximity and probability distribution fitting-based techniques yielding up to 2.05X higher F1 score. We also provide guidelines as to which variant to be chosen based on the type of anomalies targeted and the dataset characteristics.
A virtual multi-label approach to imbalanced data classification

One of the most challenging issues in machine learning is imbalanced data analysis. Usually, in this type of research, correctly predicting minority labels is more critical than correctly predicting majority labels. However, traditional machine learning techniques easily lead to learning bias. Traditional classifiers tend to place all subjects in the majority group, resulting in biased predictions. Machine learning studies are typically conducted from one of two perspectives: a data-based perspective or a model-based perspective. Oversampling and undersampling are examples of data-based approaches, while the addition of costs, penalties, or weights to optimize the algorithm is typical of a model-based approach. Some ensemble methods have been studied recently. These methods cause various problems, such as overfitting, the omission of some information, and long computation times. In addition, these methods do not apply to all kinds of datasets. Based on this problem, the virtual labels (ViLa) approach for the majority label is proposed to solve the imbalanced problem. A new multiclass classification approach with the equal K-means clustering method is demonstrated in the study. The proposed method is compared with commonly used imbalance problem methods, such as sampling methods (oversampling, undersampling, and SMOTE) and classifier methods (SVM and one-class SVM). The results show that the proposed method performs better when the degree of data imbalance increases and will gradually outperform other methods.
A Modified LZW Algorithm Based on a Character String Parallel Search in Cluster-Based Telemetry Data Compression

The volume of telemetry data is gradually increasing, both because of the increasingly larger number of parameters involved, and the use of higher sampling frequencies. Efficient data compression schemes are therefore needed in space telemetry systems to improve transmission efficiency and reduce the burden of required spacecraft resources, in particular regarding their transmitter power. In our primary study, a D-CLU algorithm was proposed to perform lossless compression for telemetry data, and achieve better performance. However, a limitation of this algorithm is that the compression time may become longer when the clustering head (CH) and outlier (which are compressed by LZW algorithm) numbers increase. To reduce compression delay, this paper proposed a modified character string (MCS) parallel search strategy for LZW algorithm (denoted by MCS-based LZW). The proposed MCS-based LZW algorithm designs coding principle, dictionary update rule and search strategy according to the character string matching results. Example verification and simulation results show that the proposed algorithm can effectively decrease the dictionary search times, and thus reduce the compression time.
Data Mining Integration with PostgreSQL Extension by K-Means, ID3 and 1R Method

Data mining is a tool that allows users to quickly access large amounts of data. The purpose of this study was to analyze the integration of data mining technique algorithms into the PostgreSQL database management system. The method used in this research is K-Means, ID3 and 1R, the tools used to implement data mining using RapidMiner and PostgreSQL tools. In this study, the number of rows to be analyzed is 100,000 records, 500,000 records, and 1,000,000 records. The results obtained are the algorithm implemented to validate the data by using an experimental design that serves to observe the time that the analysis of the algorithm that has been integrated into the DBMS is smaller than the results from Rapidminer. As the number of records increases, data analysis becomes difficult using RapidMiner. Data mining techniques, Database management system, Partition, Response time