Reviewerly/exHarmony huggingface dataset:
Estonian Speech Recognition and Transcription Editing Service

This paper describes the latest iteration of our Estonian speech recognition system and the publicly available transcription editing service.The system is now based on an end-to-end wav2vec2.0model.It achieves a word error rate of 6.9% on a test set of broadcast conversations.Besides recognition it performs speaker diarization, speaker identification, Estonian language detection, and punctuation restoration.The service consists of a speech processing pipeline, web server and a web-based user interface for end-users, offering transcript editing and speaker annotation functionality.The core components of the service have been made open-source and deployed internally by multiple public and private institutions.
A node selection algorithm to graph-based multi-waypoint optimization navigation and mapping

Autonomous robot multi-waypoint navigation and mapping have been demanded in many real-world applications found in search and rescue (SAR), environmental exploration, and disaster response. Many solutions to this issue have been discovered via graph-based methods in need of switching the robotos trajectory between the nodes and edges within the graph to create a trajectory for waypoint-to-waypoint navigation. However, studies of how waypoints are locally bridged to nodes or edges on the graphs have not been adequately undertaken. In this paper, an adjacent node selection (ANS) algorithm is developed to implement such a protocol to build up regional path from waypoints to nearest nodes or edges on the graph. We propose this node selection algorithm along with the generalized Voronoi diagram (GVD) and Improved Particle Swarm Optimization (IPSO) algorithm as well as a local navigator to solve the safety-aware concurrent graph-based multi-waypoint navigation and mapping problem. Firstly, GVD is used to form a Voronoi diagram in an obstacle populated environment to construct safety-aware routes. Secondly, the sequence of multiple waypoints is created by the IPSO algorithm to minimize the total travelling cost. Thirdly, while the robot attempts to visit multiple waypoints, it traverses along the edges of the GVD to plan a collision-free trajectory. The regional path from waypoints to the nearest nodes or edges needs to be created to join the trajectory by the proposed ANS algorithm. Finally, a sensor-based histogram local reactive navigator is adopted for moving obstacle avoidance while local maps are constructed as the robot moves. An improved -spline curve-based smooth scheme is adopted that further refines the trajectory and enables the robot to be navigated smoothly. Simulation and comparison studies validate the effectiveness and robustness of the proposed model.
TS-GCN: Aspect-level sentiment classification model for consumer reviews

The goal of aspect-level sentiment classification (ASC) task is to obtain the sentiment polarity of aspect words in the text. Most existing methods ignore the implicit aspects, resulting in low classification accuracy. To improve the accuracy, this paper proposes a classification model for consumer reviews, abbreviated as TS-GCN (Truncated history attention and Selective transformation network-Graph Convolutional Networks). TS-GCN can classify sentiment from both explicit and implicit aspects. Firstly, we process the text by the BERT model and the BiLSTM model to obtain the text features. Secondly, the GCN model completes explicit sentiment classification by training text features. Due to the lack of implicit words, the GCN model cannot classify implicit sentiments. Finally, we predict implicit words based on the TS model, which makes up for the deficiency of the GCN model and completes the sentiment classification of implicit words. TS-GCN is proved on several datasets in the consumer reviews field. The results of experiments show that the TS-GCN can improve the accuracy and F1 of ASC.
Detection and classification of white blood cells with an improved deep learning-based approach

The analysis of white blood cells, which defend the body against deadly infections and disease-causing substances, is an important issue in the medical world.The concentrations of these cells in the blood, examined in 5 classes, i.e. monocytes, eosinophils, basophils, lymphocytes, and neutrophils, vary according to the types of diseases in the body.The peripheral blood smear is widely used to analyze blood cells.Manual evaluation of this method is laborious and time-consuming.At the same time, many environmental and humanistic parameters affect the method's performance.Therefore, in the presented study, a real-time detection process is realized.Firstly, YOLOv5s, YOLOv5x, and Detectron 2 R50-FPN pretrained models in the object recognition framework are used.Next, two original contributions are made to the study to improve the model's performance.The first contribution includes optimizing the activation function, an essential criterion in training the model, and an arrangement provided in the architecture.With this proposed approach, an improvement of 0.006 is achieved in the recognition rates of all classes.The second contribution is the combined use of the YOLO and Detectron2 frameworks, which have two different object evaluation processes.The success rate achieved with this hybrid structure provided an improvement between 3.44% and 14.7% compared to the outputs obtained from the YOLO and Detectron2 pretrained models.In addition, the maximum accuracy rate of this hybrid structure on the test dataset for detection and classification of white blood cells is obtained as 98%.
Social crisis detection using Twitter based text mining-a machine learning approach

Social-media and blogs are increasingly used for social-communication, an idea and thought publishing platform. Public intentions, wisdom, problems, solutions, mental states are shared in social media. Text is being the best and the most common way to communicate over social networks. All kinds of data shared in social sites like Facebook, Twitter, and Microblogs. People from different pursuance uses these media to publish thoughts and convey messages through text. Consequently, occurrences in social life are rapidly discussed in social blogs in daily manner. This work aims at discovering ongoing social crisis from the Twitter data. Text mining technique and sentiment analysis were applied to detect the current social crisis from the social sites. Twitter data were collected to identify the recent social crisis. Furthermore, the identified crisis was compared to reputed newspapers. A hybrid method used to detect recent social issues resulted nicely. However, our proposed analysis shows identifying rate 89%, 95%, 83%, 53%, and 98% for the top 5 identified crisis accordingly in the date between 27 February and 11 March 2020. The strategy used in this study for the detection of recent social crisis will contribute to the social life and findings of crisis will be eliminated easily.
Multi-spectral template matching based object detection in a few-shot learning manner

Multi-spectral template matching (MSTM) based object detection approaches can be widely used in robotics and aerospace systems for fine-grained object discovery. However, the performance of existing nearest neighbor search based nonparametric paradigms (e.g., correlation coefficient and lp-norm) turns out to be unsatisfactory. These paradigms tend to suffer from two defects: 1) they fail to select from the raw features the discriminative ones that can help distinguish between the target and background; 2) the domain shift between the template and search spectra has not been well addressed within the feature space. In this work, we propose a data-driven MSTM method to address these two issues. First, Exemplar-SVM (E-SVM) is applied to execute feature selection and target/background categorization jointly, which is facilitated by its max-margin mechanism. To enable the learning process where the template is regarded as a single positive sample, knowledge transfer is executed to attain negative samples from other domains, e.g., large-scale public datasets. Then, the hard negative samples are mined to help train a discriminative classifier. Concerning practical applications, we also augment the template with different image degradations and extend E-SVM from the original one-shot learning approach to its few-shot version. Second, a multi-domain adaptation approach via unsupervised multi-domain subspace alignment is proposed to tackle multi-domain shift problem. Here the multiple domains relate to template, search, and negative ones considering both offline learning and online matching. The wide-range experimental results on two multi-spectral datasets demonstrate the effectiveness of our method. The tailored dataset and code will be released publicly.
Bi-Directional Feature Fixation-Based Particle Swarm Optimization for Large-Scale Feature Selection

Feature selection, which aims to improve the classification accuracy and reduce the size of the selected feature subset, is an important but challenging optimization problem in data mining. Particle swarm optimization (PSO) has shown promising performance in tackling feature selection problems, but still faces challenges in dealing with large-scale feature selection in Big Data environment because of the large search space. Hence, this paper proposes a bi-directional feature fixation (BDFF) framework for PSO and provides a novel idea to reduce the search space in large-scale feature selection. BDFF uses two opposite search directions to guide particles to adequately search for feature subsets with different sizes. Based on the two different search directions, BDFF can fix the selection states of some features and then focus on the others when updating particles, thus narrowing the large search space. Besides, a self-adaptive strategy is designed to help the swarm concentrate on a more promising direction for search in different stages of evolution and achieve a balance between exploration and exploitation. Experimental results on 12 widely-used public datasets show that BDFF can improve the performance of PSO on large-scale feature selection and obtain smaller feature subsets with higher classification accuracy.
Automatically weighted binary multi-view clustering via deep initialization (AW-BMVC)

Clustering is inherently a process of exploratory data analysis. It has attracted more attention recently because much real-world data consists of multiple representations or views. However, it becomes increasingly problematic when dealing with large and heterogeneous data. It is worth noting that several approaches have been developed to increase computational efficiency, although most of them have some drawbacks: 1) Most existing techniques consider equal or static weights to quantify importance across different views and samples, so common and complementary features cannot be used. 2) The clustering task is performed by arbitrary initialization without caring about the rich structure of the joint discrete representation, and thus poorly executed. In this paper, we propose a novel approach called ”Auto-Weighted Binary Multi-View Clustering Via Deep Initialization” for large-scale multi-view clustering based on two main scenarios. First, we consider the distinction between different views based on the importance of samples, and therefore apply a dynamic learning strategy for the automatic weighting of views and samples. Second, in the context of initializing binary clustering, we develop a new CNN feature and use a low-dimensional binary embedding by exploiting the efficient capabilities of Fourier mapping. Moreover, our approach simultaneously learns a joint discrete representation and performs direct clustering using a constrained binary matrix factorization; the optimization problem is perfectly solved in a unified learning model. Experimental results conducted on several challenging datasets demonstrate the effectiveness and superiority of the proposed approach over state-of-the-art methods in terms of accuracy, normalized mutual information, and purity.
Different Path Planning Techniques for an Indoor Omni-Wheeled Mobile Robot: Experimental Implementation, Comparison and Optimization

Omni-wheeled mobile robots (Omni WMRs) are commonly used in indoor navigation applications like surveillance, search and rescue, and autonomous transportation. They are always characterized by their versatility, mobility and high payload. This paper presents the mechatronic design, low-level control and high-level control of an indoor 4 Omni-Wheeled Mobile Robot (4OWMR). Since autonomy and path planning are research necessities for WMRs, four heuristic and probabilistic path-planning techniques are chosen for experimental implementation. The selected techniques are PRM (Probabilistic Roadmaps), RRT (Rapidly exploring Random Tree), RRTSTAR (RRT*), and ASTAR (A*) algorithms. The proposed environments are static, expressed by maps with unknown nodes and obstacles. Local path planning is implemented with simultaneous localization and mapping (SLAM). Path planning techniques are programmed, and the obtained paths are optimized by a multi-objective genetic algorithm technique to ensure the shortest path and its smoothness. The optimized paths are deployed to the 4OWMR. The obtained results are compared in terms of travel time, travel distance, average velocity and convergence error. A ranking technique is utilized to rank the obtained results and show the most preferred technique in terms of energy consumption and convergence accuracy in addition to the overall ranking. Experimental results showed that the Hybrid A* algorithm produced the best-generated paths with respect to other techniques.
Underwater polarization imaging for visibility enhancement of moving targets in turbid environments

Polarization imaging techniques have more prominent advantages for imaging in strongly scattered media. Previous de-scattering methods of polarization imaging usually require the priori information of the background region, and rarely consider the effect of non-uniformity of the optical field on image recovery, which not only reduces the processing speed of imaging but also introduces errors in image recovery, especially for moving targets in complex scattering environments. In this paper, we propose a turbid underwater moving image recovery method based on the global estimation of the intensity and the degree of polarization (DOP) of the backscattered light, combined with polarization-relation histogram processing techniques. The full spatial distribution of the intensity and the DOP of the backscattered light are obtained by using frequency domain analysis and filtering. Besides, a threshold factor is set in the frequency domain low-pass filter, which is used to adjust the execution region of the filter, which effectively reduces the error in image recovery caused by estimating the DOP of the backscattered light as a constant in traditional methods with non-uniform illumination. Meanwhile, our method requires no human-computer interaction, which effectively solves the drawbacks that the moving target is difficult to be recovered by traditional methods. Experimental studies were conducted on static and moving targets under turbid water, and satisfactory image recovery quality is achieved.
Optical and SAR Image Dense Registration Using a Robust Deep Optical Flow Framework

The coregistration of optical and synthetic aperture radar (SAR) imageries is the bottleneck in exploring the complementary information from the two multimodal datasets. The difficulties lie in not only the complex radiometric relationship between them, but also the distinct geometrical models of the optical and SAR imaging systems, which cause it nontrivial to explicitly depict the spatial relationship between the corresponding image regions when elevation fluctuations exist. This article aims to investigate the optical flow technique for the pixelwise dense registration of the high-resolution optical and SAR images, so as to get rid of the outlier removal and geometric mapping procedures, which have to be conducted in the classical image registration approaches that are based on sparse feature point matching. Herein, a deep optical flow framework is designed. First, a dilated feature concatenation method is proposed to enhance the discriminability of the pixelwise features for similarity measurement. An effective network training strategy is used, based on a smoothed flow loss, and also a training dataset that contains simulated elevation fluctuations. Second, we propose a self-supervised optical flow fine-tuning strategy. It incorporates the strength of the blockwise matching approach, which produces better matching precision, into the proposed pixelwise matching procedure. In this way, the accuracy of the optical-SAR dense registration is substantially improved. Extensive experiments conducted on the 1-m resolution optical-SAR image pairs of different land-cover types and distinct topographic conditions indicate that the proposed optical-SAR optical flow network -Ft framework is quite robust, and has the potential to perform the optical-SAR image dense registration in practical applications. The Python code of the proposed deep optical flow network will be made available.
Remapping cybersecurity competences in a small nation state

The impact of cybersecurity (CS) on public well-being is increasing due to the continued digitisation process of all industry sectors. The protection of information systems rests upon a sufficient number of CS specialists and their competences. A cyber-competence map describing the capacity and trends of the CS workforce is an essential element of the workforce development strategy. Large enterprises tend to have narrowly specialised employees with clearly identifiable roles. Still, most enterprises in small countries are SMEs. Therefore, the tasks and responsibilities of many CS-related specialists overlap the functions of several roles. This paper aims to develop a small-state cybersecurity competence map consistent with the standards of professional organisations. The work applies a combined qualitative and quantitative methodological approach to collect data using questionnaires and expert interviews in the CS field organisations. The study includes a representative public survey, a large-scale survey of company executives, an exploratory CS expert survey, and a comprehensive job posting analysis. Finally, a national CS competence map is presented and verified using two qualitative semi-structured interviews with field professionals. Even though the map reflects a status of a small nation state, it is activity-based and might be applicable in any country. As a future research direction, we will investigate the impact of early and late exposure to cybersecurity competences in education and framework applicability.
The rise of website fingerprinting on Tor: Analysis on techniques and assumptions

Tor is one of the most popular anonymity networks that allows Internet users to hide their browsing activity. Hiding browsing activity is essential for Internet users to increase their privacy. Only Tor users should know the website they are browsing. However, an attacker can utilise the Website Fingerprinting (WF) attack to identify webpages browsed by Tor users. WF is a significant threat to Internet users' privacy as Tor should conceal the browsed webpages' information. Existing WF studies focused on the investigation to improve the identification capabilities, overlooking the systematic discussion and assessment of existing techniques. In addition, existing surveys and analyses reviewed insufficient variation of WF on Tor techniques. Therefore, this survey paper aims to provide a systematic and thorough review of various WF on Tor techniques. First, we discuss WF on Tor techniques in five primary aspects: threat model, victim target, website realm, traffic feature, and traffic classifier. We analyse and classify the reviewed studies on each WF aspect. The classification facilitates in-depth understanding and comparison between WF on Tor techniques. Furthermore, this paper investigates nine assumptions exercised in WF on Tor: closed-world, sequential browsing, isolated traffic, replicability, traffic parsing, passive webpage, disabled cache, static content, and single webpage. These assumptions limit the WF on Tor's practicality in real-world scenarios. Our analysis and classification indicate that most WF on Tor studies apply these assumptions despite being only suitable in controlled environments or laboratory experiments. In addition, most reviewed studies often lack transparency on the assumptions exercised in their studies, risking misunderstanding the WF on Tor techniques' actual practicality. At the end of this survey, we present WF on Tor taxonomy and highlight 21 WF on Tor research's limitations and gaps with plausible recommendations. We also discuss the WF on Tor studies' contribution category and development phase.
Multiview Jointly Sparse Discriminant Common Subspace Learning

Multiview data leads to the demand for classifying samples from various views, and the large gap between different views makes the classification task challenging. Recently, researchers have extended linear discriminant analysis (LDA) to multi-view scenarios. However, the extended methods are generally associated with the small-class problem, that is, the projection size is limited by the number of classes. In addition, they are sensitive to variations in images or outliers. To solve these problems, this study proposes a generalized robust multiview discriminant analysis (GRMDA) to obtain a linear transform for each view and for learning multiview jointly sparse discriminant common subspace. GRMDA aims to achieve both maximal between-class and minimal within-class variation for data from multiple views in a common space. Instead of formulating the ratio trace problem, we reformulate GRMDA inspired by maximum margin criterion (MMC) to address the small-class problem. Moreover, the proposed method achieves stronger robustness by reconstructing the within-class and between-class scatter terms from the definition of L2,1 norm. Furthermore, GRMDA ensures joint sparsity using the L2,1 norm-based regularization term. Additionally, we present an iterative algorithm, convergence proof, and complexity analysis. Experiments on six popular databases, that is, COIL100, USPS/MNIST, Extended Yale Face B, AR, BBCSport, and multiple feature datasets, were conducted to evaluate the performance of GRMDA against the state-of-the-art multiview methods. The experimental results demonstrate that the proposed method can achieve a significant performance with strong robustness and fast convergence.