Reviewerly/exHarmony huggingface dataset:
Efficient privacy preserving algorithms for hiding sensitive high utility itemsets

Utility-driven mining is a powerful data mining technique that aims to extract significant and valuable knowledge from different kinds of datasets. Nevertheless, analyzing datasets with sensitive or private information may raise security and privacy concerns. To balance utility maximization and privacy preservation, Privacy Preserving Utility Mining (PPUM) has been presented. The primary objective of PPUM algorithms is to conceal the sensitive knowledge that can be found via the application of utility mining algorithms to sensitive data. However, the current PPUM literature shows a paucity of privacy preserving algorithms scalable and efficient enough to handle large and dense datasets. Based on this perspective, this paper proposes three heuristic-based algorithms, namely Selecting the Most Real item sensitive utility First (SMRF), Selecting the Least Real item sensitive utility First (SLRF), and Selecting the most Desirable Item First (SDIF), to efficiently conceal all Sensitive High utility Itemsets (SHIs) while mitigating the projected detrimental impact on the non-sensitive information. The proposed algorithms rely on a novel concept, called Real Item Sensitive Utility (RISU), to effectively select a definite victim item for each SHI during the whole sanitization process. Furthermore, a new sensitive dataset sorting technique is proposed to reduce the time needed to find suitable transactions for sanitization. Through comprehensive experimental evaluations with state-of-the-arts, the viability of the proposed three algorithms was testified. The acquired findings clearly demonstrate the efficacy of the proposed algorithms in terms of reducing the sanitization time and side effects.
Linear-exponential loss incorporated deep learning for imbalanced classification

The class imbalance issue is a pretty common and enduring topic all the time. When encountering unbalanced data distribution, conventional methods are prone to classify minority samples as majority ones, which may cause severe consequences in reality. It is crucial yet challenging to cope with such problems. In this paper, inspired by our previous work, we borrow the linear-exponential (LINEX) loss function in statistics into deep learning for the first time and extend it into a multi-class form, denoted as DLINEX. Compared with existing loss functions in class imbalance learning (e.g., the weighted cross entropy-loss and the focal loss), DLINEX has an asymmetric geometry interpretation, which can adaptively focus more on the minority and hard-to-classify samples by solely adjusting one parameter. Besides, it simultaneously achieves between and within class diversities via caring about the inherent properties of each instance. As a result, DLINEX achieves 42.08% G-means on the CIFAR-10 dataset at the imbalance ratio of 200, 79.06% G-means on the HAM10000 dataset, 82.74% F1 on the DRIVE dataset, 83.93% F1 on the CHASEDB1 dataset and 79.55% F1 on the STARE dataset The quantitative and qualitative experiments convincingly demonstrate that DLINEX can work favorably in imbalanced classifications, either at the image-level or the pixel-level.
HRCTNet: a hybrid network with high-resolution representation for object detection in UAV image

Abstract Object detection in unmanned aerial vehicle (UAV) images has attracted the increasing attention of researchers in recent years. However, it is challenging for small object detection using conventional detection methods because less location and semantic information are extracted from the feature maps of UAV images. To remedy this problem, three new feature extraction modules are proposed in this paper to refine the feature maps for small objects in UAV images. Namely, S mall- K ernel- Block (SKBlock), L arge- K ernel- Block (LKBlock), and C onv- T rans- Block (CTBlock), respectively. Based on these three modules, a novel backbone called H igh- R esolution C onv- T rans N etwork (HRCTNet) is proposed. Additionally, an activation function Acon is deployed in our network to reduce the possibility of dying ReLU and remove redundant features. Based on the characteristics of extreme imbalanced labels in UAV image datasets, a loss function Ployloss is adopted to train HRCTNet. To verify the effectiveness of the proposed HRCTNet, corresponding experiments have been conducted on several datasets. On VisDrone dataset, HRCTNet achieves 49.5% on AP 50 and 29.1% on AP, respectively. As on COCO dataset, with limited FLOPs, HRCTNet achieves 37.9% on AP and 24.1% on AP S . The experimental results demonstrate that HRCTNet outperforms the existing methods for object detection in UAV images.
A Lightweight Network Deployed on ARM Devices for Hand Gesture Recognition

Hand gestures are a well-known and straightforward method of human-computer interaction.The majority of the study focused on hand gesture recognition.However, little work has been done to develop a complete set of gesture recognition applications.With the improvement of model feature extraction ability and the increase in the number of model parameters, it is becoming more challenging to achieve a small memory footprint on mobile devices based on an ARM architecture or CPU devices based on x86 architecture.However, these existing methods are heavy, requiring more memory and inference time.The execution of memory-efficient CNNs without compromising accuracy has been a challenge, especially when the inference has to be performed on an edge computing device in real time.Therefore, we propose a lightweight network for hand gesture recognition (LHGR-Net) and deploy it on a Raspberry Pi.LHGR-Net consists of three main parts: the base network structure, the multiscale structure (MSS), and the lightweight attention structure (LAS).We present pre-trained weights that are learned from other data to initialize the network structure.In addition, the LHGR-Net model was made to be deployed on a Raspberry Pi, and a deployed model can be used to control home appliances.Extensive experiments show that our method achieves almost as good as state-of-the-art performance in hand gesture recognition and running time.
Teaching Data Science by Visualizing Data Table Transformations: Pandas Tutor for Python, Tidy Data Tutor for R, and SQL Tutor

Data science instructors often find it hard to explain to students how a piece of code written in Python, R, or SQL executes in order to transform tabular data. They currently resort to hand-drawing diagrams or making presentation slides to illustrate the semantics of operations such as filtering, sorting, reshaping, pivoting, grouping, and joining. These diagrams are time-consuming to create and do not synchronize with real code or data that students are learning about. In this paper we show that a step-by-step visual representation of tabular data transforms can help instructors to explain these operations. To do so, we created a table visualization library that illustrates the row-, column-, and cell-wise relationships between an operation's input and output tables. On top of this library we built a trio of free web-based visualization tools - Pandas Tutor for Python, Tidy Data Tutor for R tidyverse, and SQL Tutor - that run users' code and automatically produce diagrams of how Python/R/SQL transforms data tables step-by-step from input to output. Since launching in Dec 2021, over 61,000 people from over 160 countries have visited our website to try out these tools.
Black-box attack against GAN-generated image detector with contrastive perturbation

The emergence of visually realistic GAN-generated facial images has raised concerns regarding potential misuse. In response, effective forensic algorithms have been developed to detect such synthetic images in recent years. However, the vulnerability of such forensic detectors to adversarial attacks remains an important issue that requires further investigation. In this paper, we propose a new black-box attack method against GAN-generated image detectors. It involves contrastive learning strategy to train an encoder–decoder anti-forensic network with a contrastive loss function. GAN-generated and corresponding simulated real images are constructed as positive and negative samples, respectively. By leveraging the trained attack model, we can apply imperceptible perturbation to input synthetic images for removing GAN fingerprint to some extent. GAN-generated image detectors may be deceived consequently. Extensive experimental results demonstrate that the proposed attack effectively reduces the accuracy of three state-of-the-art detectors on six popular GANs, while also achieving high visual quality of the attacked images. The source code will be available at https://github.com/ZXMMD/BAttGAND.
Perceived Use Cases, Barriers, and Requirements for a Smart Health-Tracking Toilet Seat: Qualitative Focus Group Study

Smart bathroom technology offers unrivaled opportunities for the automated measurement of a range of biomarkers and other data. Unfortunately, efforts in this area are mostly driven by a technology push rather than market pull approach, which decreases the chances of successful adoption. As yet, little is known about the use cases, barriers, and desires that potential users of smart bathrooms perceive.This study aimed to investigate how participants from the general population experience using a smart sensor-equipped toilet seat installed in their home. The study contributes to answering the following questions: What use cases do citizens see for this innovation? and What are the limitations and barriers to its everyday use that they see, including concerns regarding privacy, the lack of fit with everyday practices, and unmet expectations for user experience?Overall, 31 participants from 30 households participated in a study consisting of 3 (partially overlapping) stages: sensitizing, in which participants filled out questionnaires to trigger their thoughts about smart bathroom use and personal health; provotyping, in which participants received a gentle provocation in the form of a smart toilet seat, which they used for 2 weeks; and discussion, in which participants took part in a web-based focus group session to discuss their experiences.Participants mostly found the everyday use of the toilet, including installation and dismantling when necessary, to be relatively easy and free of complications. Where complications occurred, participants mentioned issues related to the design of the prototype, technology, or mismatches with normal practices in using toilets and hygiene. A broad range of use cases were mentioned, ranging from signaling potentially detrimental health conditions or exacerbations of existing conditions to documenting physical data to measuring biomarkers to inform a diagnosis and behavioral change. Participants differed greatly in whether they let others use, or even know about, the seat. Ownership and control over their own data were essential for most participants.This study showed that participants felt that a smart toilet seat could be acceptable and effective, as long as it fits everyday practices concerning toilet use and hygiene. The range of potential uses for a smart toilet seat is broad, as long as privacy and control over disclosure and data are warranted.
Interpretable federated learning for machine condition monitoring: Interpretable average global model as a fault feature library

Federated learning (FL) is an emerging technique used to prevent the two contradictory problems of data silos and data privacy. Different from centralized learning, FL makes it possible to learn a global model while private data are stored locally. Nevertheless, statistical heterogeneity is a major challenge that has not been well addressed in literature and the interpretability of the model is always ignored. In this paper, an interpretable FL framework is constructed for machine condition monitoring and fault diagnosis. Since fault characteristic frequencies (FCFs) and their harmonics are closely connected with specific machine fault types, an interpretable local client model is designed to identify the FCFs and their harmonics of different clients. Theoretical investigation on the additivity of local model parameters in this paper prove that learned local parameters from the frequency domain are actually FCFs and their harmonics and their additivity is capable of constructing a fault feature library, which is beneficial to providing different fault information and quickly diagnosing fault types. The effectiveness of the method is demonstrated by experiments with two independent bearing run-to-failure datasets.
Unveiling the potential of structural equation modelling in educational research: a comparative analysis of Ukrainian teachers' self-efficacy

This article delves into the application of structural equation modelling (SEM) methodology within the realm of educational research, enabling researchers to construct comprehensive multidimensional models that elucidate the intricacies of studied phenomena and processes. Based on well-established statistical techniques such as correlation, regression, factor analysis, variance analysis, and covariance analysis, SEM methodology utilizes deductive logic to preliminarily construct a structural model of variable relationships, subsequently tested for consistency with empirical data. This study presents a comprehensive overview of diverse SEM software employed in doctoral training programs across leading global universities, while showcasing a practical example of employing SEM methodology in educational research for training PhD students. An essential aspect of SEM training for specialists involves the careful selection or acquisition of representative and valid datasets. Furthermore, this research examines the Ukrainian teacher's self-efficacy model using SEM methodology and compares the obtained results with data from the internationally renowned Teaching and Learning International Survey (TALIS). The findings underscore the lower self-efficacy levels among Ukrainian teachers, particularly within the student engagement domain, thereby shedding light on crucial aspects of teacher effectiveness and potential areas for improvement.
Developing Novel Robust Loss Functions-Based Classification Layers for DLLSTM Neural Networks

In this paper, we suggest improving the performance of developed activation functionbased Deep Learning Long Short-Term Memory (DLLSTM) structures by employing robust loss functions like Mean Absolute Error (MAE) and Sum Squared Error (SSE) to create new classification layers.The classification layer is the last layer in any DLLSTM neural network structure where the loss function resides.The LSTM is an improved recurrent neural network that fixes the problem of the vanishing gradient that goes away and other issues.Fast convergence and optimum performance depend on the loss function.Three loss functions (default(Crossentropyex), (MAE) and (SSE)) that compute the error between the actual and desired output for two distinct applications were used to examine the effectiveness of the suggested DLLSTM classifier.The results show that one of the suggested classifiers' specific loss functions(SSE)) works better than other loss functions and does a great job.The suggested functions Softsign, Modified-Elliott, Root-sig, Bi-tanh1, Bi-tanh2, Sech and wave are more accurate than the tanh function. INDEX TERMS DNN, DLLSTM, loss
The Effect of Self-Assessment and Students’ Learning Autonomy towards Students’ Performance in Vocational Education

Creating an independent learning process realized through student-centred learning is quite challenging by many vocational educators particularly in selecting appropriate learning method. Therefore this study was purposed to analyze the effect of self-assessment and students’ learning autonomy towards students’ performance in vocational education. Quasi-Experimental with 2x2 factorial design was used as the research model of this study by involving 60 fourth semester students who took fashion design course. They were selected by using random-matching sampling technique. The data were collected thorough pre-test and post-test in which self-assessment and performance test rubrics were used as research instruments. The obtained data were analysed quantitatively particularly by conducting descriptive quantitative and inferential statistical analysis with the assistance of SPSS.25. The results showed that self-assessment along with the students’ learning autonomy affected students’ performance. Self-assessment improved students’ performance in which it was inseparable from learning autonomy as the influencing factor. In addition, the results drew that students were directly involved in the learning process and self-assessment was maximized by students’ learning autonomy in affecting their performance. The implication of this study strengthens the use of self-assessment and learning autonomy towards students’ performance in fashion design course.
Non-readily identifiable data collaboration analysis for multiple datasets including personal information

Multi-source data fusion, in which multiple data sources are jointly analyzed to obtain improved information, has attracted considerable research attention. Data confidentiality and cross-institutional communication are critical for the construction of a prediction model using datasets of multiple medical institutions. In such cases, data collaboration (DC) analysis by sharing dimensionality-reduced intermediate representations without iterative cross-institutional communications may be appropriate. Identifiability of the shared data is essential when analyzing data including personal information. In this study, the identifiability of the DC analysis is investigated. The results reveal that the shared intermediate representations are readily identifiable to the original data for supervised learning. This study then proposes a non-readily identifiable DC analysis only sharing non-readily identifiable data for multiple medical datasets including personal information. The proposed method solves identifiability concerns based on a random sample permutation, the concept of interpretable DC analysis, and usage of functions that cannot be reconstructed. In numerical experiments on medical datasets, the proposed method exhibits non-readily identifiability while maintaining a high recognition performance of the conventional DC analysis. The proposed method exhibits a nine percentage point improvement regarding the recognition performance over the local analysis that uses only local dataset for a hospital dataset.
Blockchain and Financial E-services

Blockchain is an append-only digital database with a decentralized peer-to-peer framework. Motivated by tremendous attention from both academic and industrial communities since the emergence of cryptocurrency, we introduce this novel technology and review its cut-of-edge research related to financial e-services in this chapter. This chapter first delineates the history and recent development of automation technology in the financial services industry. As one of the most controversial financial technologies in recent years, the concepts of blockchain and its first well-known application, cryptocurrency (e.g., Bitcoin), are comprehensively presented next, including data structure, user permission, consensus mechanism, etc. Additionally, integrated with other innovations, we also introduce how blockchain technology could impact financial e-services.
Rapid unpaired CBCT‐based synthetic CT for CBCT‐guided adaptive radiotherapy

In this work, we demonstrate a method for rapid synthesis of high-quality CT images from unpaired, low-quality CBCT images, permitting CBCT-based adaptive radiotherapy. We adapt contrastive unpaired translation (CUT) to be used with medical images and evaluate the results on an institutional pelvic CT dataset. We compare the method against cycleGAN using mean absolute error, structural similarity index, root mean squared error, and Frèchet Inception Distance and show that CUT significantly outperforms cycleGAN while requiring less time and fewer resources. The investigated method improves the feasibility of online adaptive radiotherapy over the present state-of-the-art.
Explainable AI via learning to optimize

Indecipherable black boxes are common in machine learning (ML), but applications increasingly require explainable artificial intelligence (XAI). The core of XAI is to establish transparent and interpretable data-driven algorithms. This work provides concrete tools for XAI in situations where prior knowledge must be encoded and untrustworthy inferences flagged. We use the "learn to optimize" (L2O) methodology wherein each inference solves a data-driven optimization problem. Our L2O models are straightforward to implement, directly encode prior knowledge, and yield theoretical guarantees (e.g. satisfaction of constraints). We also propose use of interpretable certificates to verify whether model inferences are trustworthy. Numerical examples are provided in the applications of dictionary-based signal recovery, CT imaging, and arbitrage trading of cryptoassets. Code and additional documentation can be found at https://xai-l2o.research.typal.academy .
The Feasibility of Live Streaming DW English As Reading Instructional Technology Media For ESP (Learner Perspective)

The use of technology in learning English-reading skills has grown rapidly. One such technology is the computer-aided learning media. The use of computer media in the field of education has many advantages as they are easier to access, disseminate and store. The aim of present study is to investigate the impact of computer assisted language learning (CALL) technology on Indonesian learners' reading skills. This study applied quantitative research. The results of study found that the use of information technology in learning is marked by the interaction between students and materials through internet technology, the existence of programmed teaching, and flexibility in the process for learning both in terms of time and place. It can be concluded that it is very possible for us to be easily literate through various media; with this we can improve the quality of reading.
Three-way decision-based co-detection for outliers

Outlier detection is an important research topic in data mining and machine learning. However, existing unsupervised outlier detection methods suffer from irrelevant and redundant attributes in high-dimensional data, and their performance is also limited by their outlier detection models that rely on only one view. In this study, we propose a three-way decision-based co-detection model for unsupervised outlier detection. Specifically, we first improve the local outlier factor (LOF) method by introducing the Gaussian kernel function to make the measure of local reachability density more accurate. Then, we introduce fuzzy rough sets to perform attribute reduction, which further reduces the negative effect of irrelevant and redundant attributes on the measure of sample similarity. Finally, we develop a co-detection model that is trained on the original view and the transformed view generated by principal component analysis and uses the strategy of the three-way decision to collaboratively detect outliers. The results of comparative experiments on the selected UCI datasets show that the proposed model outperforms state-of-the-art methods in terms of AUC-ROC index.