Reviewerly/exHarmony huggingface dataset:
USEFUL EXPERIENCE OF ONLINE LEARNING

It is hardly imagine our world without Internet: modern technologies have penetrated into different spheres from common life to industrial enterprises. The other place, where applications of IT are widely used, seems to be education. The online learning combined with the involvement of a wide variety of methods in training different subjects (including English) has become a leading dominant in modern education during the pandemic COVID-19. This useful experience will allow to implement it now in different situations, that can immerge unexpectedly or sometimes just for convenience. Being skillful and equipped with corresponding platforms (such as ZOOM, Moodle, Teams, Kahoots and many others) and methods suitable in such situations, teacher can make up an atmosphere of communication based on diverse activities online. The natural environment of speaking the language being studied will be the most effective type of interaction of students within the framework of online lesson. Basic knowledge is the basis for obtaining new competences through discussion of certain topical issues using the tasks set by teacher as well as through illustrative, audio and video materials which are more available in this case. Besides the teacher plays an important role as a mentor of students' communicative activity and creating a favorable atmosphere for the active involvement of all participants into the process of language acquisition in specific conditions online. The integrative approach will allow to highlight the most significant details in both advantages and disadvantages of online training and to analyze them respectively. Taking into consideration the obtained experience there is a certain possibility to summarize it in the frames of this article.
Influential Observations in Bayesian Regression Tree Models

AbstractAbstractBayesian Classification and Regression Trees (BCART) and Bayesian Additive Regression Trees (BART) are popular Bayesian regression models widely applicable in modern regression problems. Their popularity is intimately tied to the ability to flexibly model complex responses depending on high-dimensional inputs while simultaneously being able to quantify uncertainties. This ability to quantify uncertainties is key, as it allows researchers to perform appropriate inferential analyses in settings that have generally been too difficult to handle using the Bayesian approach. However, surprisingly little work has been done to evaluate the sensitivity of these modern regression models to violations of modeling assumptions. In particular, we will consider influential observations, which one reasonably would imagine to be common—or at least a concern—in the big-data setting. In this article, we consider both the problem of detecting influential observations and adjusting predictions to not be unduly affected by such potentially problematic data. We consider three detection diagnostics for Bayesian tree models, one an analogue of Cook’s distance and the others taking the form of a divergence measure and a conditional predictive density metric, and then propose an importance sampling algorithm to re-weight previously sampled posterior draws so as to remove the effects of influential data in a computationally efficient manner. Finally, our methods are demonstrated on real-world data where blind application of the models can lead to poor predictions and inference. Supplementary materials for this article are available online.KEYWORDS: Applied statistical inferenceBig dataNonparametric regressionUncertainty quantification Supplementary MaterialsThe online supplementary materials include proofs of equations and propositions (supplement.pdf) and example code to reproduce figures in the paper (bartinfluence.zip).Additional informationFundingThe work of MTP was supported in part by the National Science Foundation (NSF) under Agreement DMS-1916231 and in part by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. OSR-2018-CRG7-3800.3. The work of EIG was supported by NSF DMS-1916245. The work of REM was supported by NSF DMS-1916233.
Extended neighborhood-based road and median filter for impulse noise removal from depth map

In recent years there has been growing interest in the study of depth map's impulse noise detection and removal. The Rank-ordered Absolute Differences (ROAD) based on neighborhood statistics can effectively detect impulse noise for RGB images. However, since the statistical object of the ROAD is each pixel value in the neighborhood, and the neighborhood may contain other impulse noise, invalid depth zero-pixel points and other singular points, especially for large areas with depth missing, it is difficult for the algorithm to ensure the correctness and accuracy of the detection results. Therefore, the ROAD algorithm is not fully applicable to depth maps. Similarly, the median filter, as an effective impulse noise filtering algorithm, also has the same deficiency, so it is not suitable for areas in depth maps with no depth value. To address these issues, we present here an Extended Neighborhood-based ROAD and Median Filter algorithm to remove impulse noise from depth map. This approach improves the functionality of the ROAD algorithm and the Median Filter by extending the neighborhood of the current pixel adaptively, shielding zero-valued pixels and filtering out detected impulse noises in real time during detection, therefore effectively improving the accuracy of impulse noise filtering. It consequently reduces the miss rate and the error detection rate, and ultimately achieves a better edge-preserving and denoising effect. Our empirical evidence shows that the proposed algorithm outperforms existing denoising algorithms on both quantitative measures and visual perception qualities.
DCAM: Disturbed class activation maps for weakly supervised semantic segmentation

In the field of weakly supervised semantic segmentation (WSSS), Class Activation Maps (CAM) are typically adopted to generate pseudo masks. Yet, we find that the crux of the unsatisfactory pseudo masks is the incomplete CAM. Specifically, as convolutional neural networks tend to be dominated by the specific regions in the high-confidence channels of feature maps during prediction, the extracted CAM contains only parts of the object. To address this issue, we propose the Disturbed CAM (DCAM), a simple yet effective method for WSSS. Following CAM, we adopt a binary cross-entropy (BCE) loss to train a multi-label classification model. Then, we disturb the feature map with retraining to enhance the high-confidence channels. In addition, a softmax cross-entropy (SCE) loss branch is employed to increase the model attention to the target classes. Once converged, we extract DCAM in the same way as in CAM. The evaluation on both PASCAL VOC and MS COCO shows that DCAM not only generates high-quality masks (6.2% and 1.4% higher than the benchmark models), but also enables more accurate activation in object regions. The code is available at https://github.com/gyyang23/DCAM.
Behaviour driven development: A systematic mapping study

Behaviour Driven Development (BDD) uses scenarios written in semi-structured natural language to express software requirements in a way that can be understood by all stakeholders. The resulting natural language specifications can also be executed to reveal correct and problematic parts of a software. Although BDD was introduced about two decades ago, there is a lack of secondary studies in peer-reviewed scientific literature, making it difficult to understand the state of BDD research and existing gaps. To understand the current state of BDD research by conducting a systematic mapping study that covers studies published from 2006 (when BDD was introduced) to 2021. By following the guidelines for conducting systematic mapping studies in software engineering, we sought to answer research questions on types of venues in which BDD papers have been published, research types, contribution types, studied topics and their evolution, as well as evaluation methods used in published BDD research. The study identified 166 papers which were mapped. Key results include the following: the dominance of conference papers; scarcity of research with insights from the industry; shortage of philosophical papers on BDD; acute shortage of metrics for measuring various aspects of BDD specifications and the processes for producing BDD specifications; the dominance of studies on using BDD for facilitating various software development endeavours, improving the BDD process and associated artefacts, and applying BDD in different contexts; scarcity of studies on using BDD alongside other software techniques and technologies; increase in diversity of studied BDD topics; and notable use of case studies and experiments to study different BDD aspects. The paper improves our understanding of the state of the art of BDD, and highlights important areas of focus for future BDD research.
AdaSTopk: Adaptive federated shuffle model based on differential privacy

The shuffle model is a promising federated learning method, which has the advantages of the high accuracy of central federated learning and the high security of local federated learning. Although the shuffle model can well address the privacy security and model accuracy issues in federated learning, the existing research on shuffle models mainly has the following problems: First, the layers in machine learning models have different ranges of weights, and only some weights are important. Perturbing all weights equally will discard the importance of some weights and affect the accuracy of the model. Second, due to the perturbation of the weights of all clients, the dimension of model aggregation will increase, and the privacy budget will surge. In this paper, an adaptive top-k differential privacy federated shuffle model is proposed to address the above-mentioned issues, which dynamically adjusts the size of top-k iteratively, enabling the client and the shuffler to control the number of weight parameters that add perturbations, ensuring the importance of weights and reducing the privacy budget under the high-dimensional model. It is found when top-k comes to extremes, the security of the model decreases. To overcome this challenge, this paper proposes a double perturbation mechanism of top-k and non-topk with subsampling, which improves the security of the model and further reduces the privacy budget. Under the three data distributions of independent and identical distribution, non-independent and identical distribution, and non-independent and different distribution, experiments are conducted on three datasets, including MNIST, Fashion-MNIST, and CIFAR-10. The experimental results show that the model proposed in this paper achieves good performance and efficiency for privacy protection.
How Trust, Brand Awareness and Digital Marketing Enhance Higher Education Marketing

The purpose of this academic paper is to examine the roles of trust, brand awareness, and digitalization in marketing higher education institutions. In today's digital age, universities and colleges are increasingly relying on digital marketing strategies to attract potential students. The paper highlights the importance of building trust and creating brand awareness among students in the higher education sector, and how digitalization plays a critical role in achieving these objectives. The literature review indicates that trust and brand awareness are crucial factors in the decision-making process of students when choosing a higher education institution. Establishing trust is important to attract new students and retain current ones. Institutions with a strong reputation and brand awareness are more likely to attract students who are looking for a high-quality education. Additionally, the paper discusses how digitalization has changed the way higher education institutions market themselves. With the rise of social media and online platforms, universities and colleges are now able to reach a wider audience and engage with students on a more personal level. The paper also provides empirical evidence from various studies conducted on the role of trust, brand awareness, and digitalization in higher education marketing. Findings show that students who perceive high levels of trust in an institution are more likely to enroll and recommend it to others. In addition, students who have high levels of brand awareness of an institution are more likely to have a positive perception of it and consider enrolling. Furthermore, digital marketing activities have been found to be effective in attracting potential students, as they provide an accessible and convenient way for students to learn about institutions and their offerings. Overall, this paper emphasizes the importance of trust and brand awareness in higher education marketing and the role that digitalization plays in achieving these objectives. The findings of this paper can be used to guide institutions in developing effective marketing strategies that focus on building trust, creating brand awareness, and leveraging digitalization to reach a wider audience of potential students.
Analisis Sentimen Opini Masyarakat Terhadap Kuliner DKI Jakarta dengan Metode Naïve Baiyes dan Support Vector Machine

Culinary matter is an integral part of people’s everyday lives, especially for those who like to taste a variety of flavors and uniqueness of different food. It is easier to find information about the culinary you want to try through sites and social media. With the information, visitors can get an idea of the taste, price, and location of culinary treats. In the DKI Jakarta area, social media play an important role in providing data on people's assessments of culinary treats. Considering the use of social media platforms such as Instagram, Facebook, and Twitter, the researchers apply the methods of Naive Bayes and Support Vector Machine that later produce a result that can be used as a basis for assessing culinary treats in Jakarta. This research takes data from three sources, namely Twitter, Zomato and Quora. The data collection period for Twitter is from March 01, 2022-June 30, 2022, while for Zomato and Quora from September 2021 to June 2022, during which 2520 data are collected. The data will be then processed to draw a conclusion. The accuracy test on three sources show the accuracy score of Naïve Bayes is higher than that of the Support Vector Machine, with the accuracy of Naïve Bayes of 76.00% and that of the support vector machine of 74.00%, resulting in more positive responses than negative responses and indicating that culinary treats in Jakarta is considered good by the community.
Evaluation of the Effect of Adopting Genetically Modified Cotton and its Impact on Tax Transformation and Employment in the Cotton Value Chain (A Case Study of the Kingdom of Eswatini)

Cotton plays a major role in the economy of Eswatini, employing over 10% of the country's population and contributing 2.1% of the country's Gross Domestic Product (GDP).It is an important cash crop and source of livelihood for Swazis living in drought-prone areas.Farmers grow cotton under rain-fed conditions resulting in a serious yield decline due to climate.Production of cotton under rain-fed conditions has negatively impacted cotton yield.Yield losses adversely affected the cotton supply chain, yet the industry is a major contributor to the country's employment.The industry faces challenges due to the current predicament, culminating in the country's largest cotton ginnery operating at 10 per cent, and spinners are currently non-functional, reducing employment opportunities.Finally, the textile industry imports cotton yarn from China, India, and Taiwan.The Kingdom of Eswatini recently adopted genetically modified cotton under irrigation to increase the cotton supply in the cotton value chain.This strategy is a viable alternative for Eswatini to sustain the country's economy through cotton production.The study evaluates the effect of adopting genetically modified cotton and its effects on tax transformation and employment in the cotton value chain, with the view to creating the needed employment and creating the tax base for the country.The study design was exploratory to investigate the potential to create employment and transformation of tax.Senior managers in the cotton value chain were deemed key informants due to their knowledge and understanding of the current dynamics in Eswatini's cotton industry.The study revealed that adopting genetically modified cotton would increase cotton yield due to a guarantee of moisture availability during production.The study indicates an increased supply of cotton in the value chain, influencing employment numbers in cotton ginning, spinning, weaving and textile.The finding further reveals an increase in tax contribution from corporate tax and employment tax throughout the cotton value chain.Finally, the study recommends the development of strong policies to encourage cotton processing with the kingdom to eliminate the export of raw cotton.
Lossless Reversible Data Hiding in Encrypted Image for Multiple Data Hiders Based on Pixel Value Order and Secret Sharing

Reversible data hiding in encrypted images (RDH-EI) is instrumental in image privacy protection and data embedding. However, conventional RDH-EI models, involving image providers, data hiders, and receivers, limit the number of data hiders to one, which restricts its applicability in scenarios requiring multiple data embedders. Therefore, the need for an RDH-EI accommodating multiple data hiders, especially for copyright protection, has become crucial. Addressing this, we introduce the application of Pixel Value Order (PVO) technology to encrypted reversible data hiding, combined with the secret image sharing (SIS) scheme. This creates a novel scheme, PVO, Chaotic System, Secret Sharing-based Reversible Data Hiding in Encrypted Image (PCSRDH-EI), which satisfies the (k,n) threshold property. An image is partitioned into N shadow images, and reconstruction is feasible when at least k shadow images are available. This method enables separate data extraction and image decryption. Our scheme combines stream encryption, based on chaotic systems, with secret sharing, underpinned by the Chinese remainder theorem (CRT), ensuring secure secret sharing. Empirical tests show that PCSRDH-EI can reach a maximum embedding rate of 5.706 bpp, outperforming the state-of-the-art and demonstrating superior encryption effects.
Lightweight Implicit Blur Kernel Estimation Network for Blind Image Super-Resolution

Blind image super-resolution (Blind-SR) is the process of leveraging a low-resolution (LR) image, with unknown degradation, to generate its high-resolution (HR) version. Most of the existing blind SR techniques use a degradation estimator network to explicitly estimate the blur kernel to guide the SR network with the supervision of ground truth (GT) kernels. To solve this issue, it is necessary to design an implicit estimator network that can extract discriminative blur kernel representation without relying on the supervision of ground-truth blur kernels. We design a lightweight approach for blind super-resolution (Blind-SR) that estimates the blur kernel and restores the HR image based on a deep convolutional neural network (CNN) and a deep super-resolution residual convolutional generative adversarial network. Since the blur kernel for blind image SR is unknown, following the image formation model of blind super-resolution problem, we firstly introduce a neural network-based model to estimate the blur kernel. This is achieved by (i) a Super Resolver that, from a low-resolution input, generates the corresponding SR image; and (ii) an Estimator Network generating the blur kernel from the input datum. The output of both models is used in a novel loss formulation. The proposed network is end-to-end trainable. The methodology proposed is substantiated by both quantitative and qualitative experiments. Results on benchmarks demonstrate that our computationally efficient approach (12x fewer parameters than the state-of-the-art models) performs favorably with respect to existing approaches and can be used on devices with limited computational capabilities.
Efficacy of Deep Neural Embeddings-Based Semantic Similarity in Automatic Essay Evaluation

Semantic similarity is used extensively for understanding the context and meaning of the text data. In this paper, use of the semantic similarity in an automatic essay evaluation system is proposed. Different text embedding methods are used to compute the semantic similarity. Recent neural embedding methods including Google sentence encoder (GSE), embeddings for language models (ELMo), and global vectors (GloVe) are employed for computing the semantic similarity. Traditional methods of textual data representation such as TF-IDF and Jaccard index are also used in finding the semantic similarity. Experimental analysis of an intra-class and inter-class semantic similarity score distributions shows that the GSE outperforms other methods by accurately distinguishing essays from the same or different set/topic. Semantic similarity calculated using the GSE method is further used for finding the correlation with human rated essay scores, which shows high correlation with the human-rated scores on various essay traits.
Mitigating IoT Privacy-Revealing Features by Time Series Data Transformation

As the Internet of Things (IoT) continues to expand, billions of IoT devices are now connected to the internet, producing vast quantities of data. Collecting and sharing this data has become crucial to improving IoT technologies and developing new applications. However, the publication of privacy-preserving IoT traffic data is exceedingly challenging due to the various privacy concerns surrounding users, IoT networks, and devices. In this paper, we propose a data transformation method aimed at safeguarding the privacy of IoT devices by transforming time series datasets. Based on our measurements, we have found that the transformed datasets retain the intrinsic value of the original IoT data and maintains data utility. This approach will enable non-expert data owners to better understand and evaluate the potential device-level privacy risks associated with their IoT data while simultaneously offering a reliable solution to mitigate their concerns about privacy violations.
AatMatch: Adaptive Adversarial Training in Semi-Supervised Learning Based on Data-Driven Decision-Making Models

Data-driven decision-making is the process of using data to inform your decision-making process and validate a course of action before committing to it. The quality of unlabeled data in real-world scenarios presents challenges for semi-supervised learning. Effectively leveraging unlabeled data for learning is challenging due to the need for labeled information, while the scarcity of labeled data requires efficient and flexible data augmentation methods. To address these challenges, this paper proposes the AatMatch algorithm, which uses a momentum model, coarse learning, and adversarial training to generate adversarial examples for different classes. The algorithm sets the threshold for generating pseudo-labels and reinforces the results with adversarial perturbations based on evaluation results. In addition, a more refined learning strategy for unlabeled data is adjusted by setting adaptive weights based on the confidence of each unlabeled data point, thereby mitigating the adverse effects of low-confidence unlabeled data on the model. Experimental evaluations on several datasets, including CIFAR-10, CIFAR-100, and SVHN, demonstrate the effectiveness of the proposed AatMatch algorithm in semi-supervised learning. Specifically, the algorithm achieves the lowest error rates for multiple scenarios on these datasets.