Reviewerly/exHarmony huggingface dataset:
Identifying the Main Service Elements for Customer-Oriented Live Guided Virtual Tours

Abstract Virtual tourism allows people to see and experience the world without physically travelling to the destination. The objective of this study is to identify service elements critical to the development of customer-oriented live guided virtual tours. A total of three focus group discussions were conducted, with 4–5 participants in each group. All the participants were Japanese who had previously participated in live guided virtual tours. The data was analysed with qualitative content analysis, where an inductive approach was used. The findings reveal that the main expectations of the participants are related to experiencing the local culture and way of life, feeling of actually being in the destination (sense of connection), social interaction and active participation. These factors make the virtual tour feel realistic and attractive and the tourism businesses need especially to focus on these when designing virtual tours for example by providing ample opportunities for the virtual tour participants to discuss with each other and learn from each other. This study brings valuable insights into the virtual tours research from the customer perspective, especially in a non-Western context.
A Simple Neural Network for Efficient Real-time Generation of Dynamically-Feasible Quadrotor Trajectories

In this work, we study the problem of efficiently generating dynamically-feasible trajectories for quadrotors in real-time. A supervised learning approach is used to train a simple neural network with two hidden layers. The training data is generated from a well-established trajectory generation method for quadrotors that minimizes jerk given a fixed time interval. More than a million dynamically-feasible trajectories between two random points in the three-dimensional (3D) space are generated and used as training data. The input of the neural network is a vector composed of initial and desired states, along with the final trajectory time. The output of the neural network generates the motion primitives of the trajectories, as well as the duration or final time of a segment. Simulation results show extremely fast generation of dynamically-feasible trajectories by the proposed learning algorithm, which makes it suitable for real-time implementation.
Non-Blind Image Deblurring Method Using Shear High Order Total Variation Norm

In this paper, we propose a shear high-order gradient (SHOG) operator by combining the shear operator and high-order gradient (HOG) operator. Compared with the HOG operator, the proposed SHOG operator can incorporate more directionality and detect more abundant edge information. Based on the SHOG operator, we extend the total variation (TV) norm to shear high-order total variation (SHOTV), and then propose a SHOTV deblurring model. We also study some properties of the SHOG operator, and show that the SHOG matrices are Block Circulant with Circulant Blocks (BCCB) when the shear angle is [see formula in PDF]. The proposed model is solved efficiently by the alternating direction method of multipliers (ADMM). Experimental results demonstrate that the proposed method outperforms some state-of-the-art non-blind deblurring methods in both objective and perceptual quality.
A web application for crowd counting by building parallel and direct connection-based CNN architectures

As deep learning techniques continue to iterate, the trend in the direction of convolutional neural network (CNN) applications is to be more relevant to life; in other words, the technology serves life. The research direction should be from the user’s point of view, and the designed and trained CNN model is combined with the web application to visually display how many people are in a crowd picture on a web page. In order to protect the user’s privacy needs, a registration module is developed, and only by entering the correct registration account and password can the relevant picture information, including the predicted number of people, the actual number of people, and other core information, be seen. In order to solve users’ technical doubts and understanding of the function, a question and answer module was developed in the web page, where users can ask questions and post them, and technicians or people with relevant knowledge background can post comments to solve the problem. The implementation of all webpage functions is based on the generalization capability of the CNN model, which needs to be adapted to as many crowd images as possible in different scenarios to achieve high accuracy in crowd recognition.
Impact of the Covid-19 Pandemic on the Use of Microsoft 365 and Learning Outcomes at the Technical University of Moldova

Faced with the restrictions imposed by the Covid-19 pandemic, universities, as providers of higher education, have been forced to provide their students exclusively through a digital interface. For this, the institutions were challenged to select resources and platforms to support online education. The World Health Organization (WHO) advocates for research focusing on the impact and prediction of post-Covid evolution on education, in order to obtain adequate explanations for these challenges. The Technical University of Moldova (UTM) is the only technical higher education institution in the Republic of Moldova. TUM invests continuously in information technology infrastructure, as well as in the digital education of both students and academics. The aim of the study is to evaluate the university potential, from the perspective of digital technologies, in providing now, but also in the future, e-Learning. The most dominant form of providing learning content to TUM, during the pandemic, was online lectures via real-time video conferencing, which is confirmed by the continuous increase in the number of active MTeams users. And e-Learning did not negatively influence students’ learning. The obtained results show that TUM has demonstrated its potential to ensure the continuity and good development of the teaching process during the pandemic, maintaining the good quality of studies.
Unsupervised Domain Adaptation with Self-selected Active Learning for Cross-domain OCT Image Segmentation

Segmentation of optical coherence tomography (OCT) images of retinal tissue has become an important task for the diagnosis and management of eye diseases. Deep convolutional neural networks have shown great success in retinal image segmentation. However, a well-trained deep learning model on OCT images from one device often fail when it is deployed on images from a different device since these images have different data distributions. Unsupervised domain adaptation (UDA) can solve the above problem by aligning the data distribution between labeled data (source domain) and unlabeled data (target domain). In this paper, we propose an UDA adversarial learning framework with self-selected active learning. The framework consists of two parts: domain adaptation module (DAM) and self-selected active learning module (SALM). The DAM learns domain-invariant features (i.e., common features) gradually to narrow the distribution discrepancy between two domains. The SALM introduces the target data into source domain through discrepancy method and similarity method, which promotes the DAM to learn unique features of target domain. Extensive experiments show the effectiveness of our method. Compared with the state-of-the-art UDA methods, our method has achieved better performance on two medical cross-domain datasets.
Noisy Graph Signal Vector Quantization with Codebook Allocation

We study the problem of vector quantization and codebook allocation for noisy graph signals. To solve the communication resource constraint, this paper considers vector quantization for noisy graph signals. Kernel regression over graphs is utilized to minimize distortion of the noisy graph signal vector quantization. For further distortion reduction and resource limitation settlement, we also propose a smoothness-based codebook allocation algorithm to allocate the codebook size to each vector quantizer. Simulation results show the effectiveness of the proposed vector quantization for noisy graph signal and the advantage of the proposed codebook allocation algorithm over the existing algorithms.
CCAD: A Collective Contextual Anomaly Detection Framework for KPI Data Stream

KPI (Key Performance Indicator) anomaly detection is critical for Internet companies to safeguard the availability and stability of services. Many online anomaly detection algorithms have been proposed in recent years. However, they are mainly designed to detect point anomalies, which fail to identify collective or contextual anomalies very well. Thus in this paper, we propose a framework of Collective Contextual Anomaly Detection (CCAD) for KPI data stream. Via Pearson correlation coefficient-based method to adapt to KPI stream, our framework addresses the limitation of time series discords algorithm and achieves a huge improvement in the accuracy for KPI stream. Moreover, instead of using a static threshold, we employ SPOT to generate an automatic threshold to determine anomalies. Extensive experimental results and analysis on multiple public KPIs show the competitive performance of CCAD and the significance of collective contextual anomalies.
Kalman Filtering for Learning with Evolving Data Streams

Processing data streams gained much importance in recent years. Standard machine learning algorithms do not cope well with non-stationary streaming data, where decision models evolve and generate so-called concept drift. Online adaptive algorithms emerged to solve these issues. They learn incrementally and generally require explicit forgetting mechanisms to adapt to concept drift. In this paper, we propose the application of Kalman filtering to handle evolving data streams. This novel approach addresses data stream mining and concept drift management challenges from a new perspective, directly modelling a representation suitable for the data streams. First, we study a Kalman filter based learning a pproach and investigate its integration into the Naïve Bayes algorithm, namely KalmanNB. Additionally, we propose the Hoeffding Kalman Tree, a combination of the Hoeffding Tree with KalmanNB. Empirical results demonstrate that the Kalman filter based approach inherently manages concept drifts, and it adapts to the emerging concept more rapidly than the state-of-the-art algorithms. Moreover, it is an accurate and robust approach and requires less storage while still being faster.
Image Segmentation Techniques with Machine Learning

Image segmentation is the fundamental step to analyze images and extract data from them. This paper concentrates on the idea behind the basic methods used. Image segmentation is the process of partitioning a digital image into multiple segments(i. e. sets of pixels). The goal of segmentation is to simplify or change the representation of an image into something that is more meaningful and easier to analyze. Segmentation is typically used to locate objects and boundaries(line, curve) in image. The Image segmentation is a set of segments that collectively cover the entire image. Each of pixels in a region are similar with respect to some characteristics colour, intensity or texture. The various segmentation techniques discussed in this paper.
Performance Analysis of Extended Lustgarten Index for Stability of Feature Selection

In machine learning, feature selection algorithms considerably reduce overfitting, improve accuracy, and speed up the training time by removing irrelevant and redundant features. To choose an appropriate feature selection algorithm, we need to consider its stability performance along with other performances such as classification accuracy and cardinality. Until now, several stability measures have been proposed, among which Kuncheva index is a popular measure. Subsequently, several modifications of the Kuncheva index were proposed, such as Wald's measure, Nogueira's measure, and Lustgarten's measure. In our previous work, we identified the limitations of Lustgarten's measure and proposed extension of the measure with an improved mathematical definition. This Extended Lustgarten's measure has been shown to have the ability to measure stability accurately. However, we did not empirically investigate the performance of the proposed measure, and therefore, this paper aims to measure the performance of the Extended Lustgarten Index. We first incorporated fifteen publicly available datasets and employed a filter subset based feature selection algorithm such as CFS (Correlation-based Feature Selection) on each dataset to find the feature subsets. After that, we assessed the stability of this feature selection algorithm for each dataset, considering Wald's measure, Nogueira's measure, Lustgarten's measure, and our Extended Lustgarten measure. Comparative results indicate that the proposed measure can adequately cover the stability properties and address the stability performance more correctly, unlike other measures.
Quantifying Dataset Quality in Radio Frequency Machine Learning

Given the significance of data within machine learning systems, quantifying how the quality of the available data affects the final performance is a vital component in development. Examining the relationship between a dataset's quantity and the trained system's performance by parametrically varying the available amount of data, new insights can be learned and used to answer questions more efficiently. Having a metric of quality will better enable the developer to ask questions about what one dataset is considering within it and how it improves or hurts the performance of the trained network, further allowing a deeper investigation and understanding of the unknowns that must be considered by the system. This work establishes the approach to regress the relationship between data quantity and system performance in a way that enables a quantitative comparison of quality for different datasets against a known good test set. Further, this approach allows for an impartial means of comparing the value of data, generated or otherwise acquired, toward the end system's final performance.
A Case Study of Interpretable Counterfactual Explanations for the Task of Predicting Student Academic Performance

One of the main topics of interest in the areas of Educational Data Mining and Learning Analytics is the prediction of students’ performance. To this end, a plethora of machine learning methods have been successfully applied to a range of educational data. Most of these methods, in order to increase the quality of their results, produce complex learning models, trading of their transparency of their inner mechanism. However, it is of utmost importance to develop understandable and explainable learning models. Comprehensive explanations linked with educational activities are valuable and give actionable insights to improve the learning outcome. In this context, the present study aims to examine the effectiveness of explainable and interpretable machine learning methods in the context of education. To demonstrate their practical usefulness, it presents a case study on interpretable counterfactual explanations. The results demonstrate the efficiency of the method and describe concrete steps that could be taken forwards in order to alter a model’s prediction.
Cross-Domain Feature Similarity Guided Blind Image Quality Assessment

This work proposes an end-to-end cross-domain feature similarity guided deep neural network for perceptual quality assessment. Our proposed blind image quality assessment approach is based on the observation that features similarity across different domains (e.g., Semantic Recognition and Quality Prediction) is well correlated with the subjective quality annotations. Such phenomenon is validated by thoroughly analyze the intrinsic interaction between an object recognition task and a quality prediction task in terms of characteristics of the human visual system. Based on the observation, we designed an explicable and self-contained cross-domain feature similarity guided BIQA framework. Experimental results on both authentical and synthetic image quality databases demonstrate the superiority of our approach, as compared to the state-of-the-art models.
ℓ<sub>2,1</sub>-norm minimization for Unsupervised Feature Selection from Incomplete Data

Unsupervised feature selection (UFS) has been widely used in many machine learning applications, since it can alleviate burdens on the curse of high dimensional data. However, existing works design the UFS methods with the assumption that the complete information of dataset can be observed. In this paper, we propose a UFS method from incomplete data by further introducing ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</inf> -norm minimization. In order to exploit feature relevance for UFS, we show that ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</inf> -norm minimization should be elaborately dealt with unobserved information, which can be finely described with the use of an indicator matrix. The proposed ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</inf> -norm minimization UFS from incomplete data (LUFS) can be directly solved by a convex optimization problem, and we design an alternative algorithm to optimize the convex problem, where the convergence of LUFS algorithm is also proved theoretically and experimentally. Empirical results demonstrate the effectiveness of LUFS with the state-of-the-art methods on real incomplete datasets.
Image dehazing with an untrained neural network

The haze floating in the atmosphere scatters and refracts atmospheric light, which leads to degradation of image quality. Recently, many learning-based dehazing methods have been proposed, which use hazy-clean image pairs to train network models. However, it is difficult to capture the hazy-clean image pairs. To overcome this difficulty, here we propose an unsupervised dehazing model using color attenuation prior (IDUNet), which avoids collecting hazy-clean image pairs, and need not use a lot of data to train parameters before dehazing. In brief, IDUNet only needs to input a haze image and can output a haze-free image. Then, IDUNet generates a handcrafted haze image using the haze-free image and an atmospheric scattering model (ASM). The haze-free image is optimized by iteratively minimizing a specific loss function scheme that forces the handcrafted haze image to imitate the input haze image. In-depth experiments show that IDUNet can dehaze without a training model and recovers satisfactory images.
Automatic Hypernym-Hyponym Relation Extraction With WordNet Projection

Semantic taxonomies such as WordNet are expensive to build manually. Based on the above ideas and problems, in this paper, we propose a new model for automatically extracting hypernym-hyponym relations from raw text. Our model is based on two scoring functions, one neural networks model for sentence including word pair and the other model used projection network, which learns semantic distance to noun pairs in semantic hierarchy of WordNet. At last, sum of two score is used to classify hypernym-hyponym relation from raw text. On the experimental tasks about hypernym-hyponym relation extraction, our methods get about the result of 0.82 F1-score.
Shedding Light in the Tunnel: Counting Flows in Encrypted Network Traffic

Network traffic analysis helps experts to under-stand the behavior of communication networks. By exploiting information from packet headers and payloads, ML has been also widely applied to extend the capabilities of traditional statistical approaches. However, modern traffic encryption hampers network traffic analysis, especially in the case of VPNs when multiple flows are encrypted and transported concurrently. An initial step toward extracting information from encrypted traffic is to discover the number of flows that coexist in an aggregated and encrypted data stream. In this paper we propose a technique for disclosing the number of flows aggregated and sent together via encrypted tunnels. We use LSTM cells to learn the relation between the number of flows and the different temporal combinations from available attributes regardless of encryption. Results indicate that predicting the number of flows in encrypted tunnels with a relatively small error is surprisingly possible, providing the basis for a wide range of future research.
Research on Optimization of Financial System of SMEs Based on Blockchain Technology

Blockchain, as a technology and tool for strengthening social trust and improving social relations, has the application prospect of changing the world. The application of blockchain in the field of accounting has also started, and it will bring great changes to the business of accounting and accountancy supervision. Small and medium-sized enterprises (hereinafter referred to as “SME”), as the pillar of social economy, should build decentralized, trusted, automated and modular financial systems based on blockchain technology which are according to their specific and actual needs.
Grouped Echo State Network with Late Fusion for Speech Emotion Recognition

Speech Emotion Recognition (SER) has become a popular research topic due to having a significant role in many practical applications and is considered a key effort in Human-Computer Interaction (HCI). Previous works in this field have mostly focused on global features or time series feature representation with deep learning models. However, the main focus of this work is to design a simple model for SER by adopting multivariate time series feature representation. This work also used the Echo State Network (ESN) including parallel reservoir layers as a special case of the Recurrent Neural Network (RNN) and applied Principal Component Analysis (PCA) to reduce the high dimension output from reservoir layers. The late grouped fusion has been applied to capture additional information independently of the two reservoirs. Additionally, hyperparameters have been optimized by using the Bayesian approach. The high performance of the proposed SER model is proved when adopting the speaker-independent experiments on the SAVEE dataset and FAU Aibo emotion Corpus. The experimental results show that the designed model is superior to the state-of-the-art results.
Explainability in Time Series Forecasting, Natural Language Processing, and Computer Vision

Various domains such as computer vision, natural language processing, and time series analysis have extensively applied machine learning algorithms in recent years. This chapter will discuss the research and applications of the interpretable and explainable algorithms in this domain. We will start with a time series algorithm survey, starting from traditional interpretable statistical models to modern deep learning algorithms. Next, we discuss NLP applications and the role of interpretability. Finally, we cover computer vision and how explainability has been a focus of considerable research. We will present a case study in each domain where the reader can get practical and real-world insights.