Reviewerly/exHarmony huggingface dataset:
Non-linear shrinking of linear model errors

Artificial neural networks (ANNs) can be a powerful tool for spectroscopic data analysis. Their ability to detect and model complex relations in the data may lead to outstanding predictive capabilities, but the predictions themselves are difficult to interpret due to the lack of understanding of the black box ANN models. ANNs and linear methods can be combined by first fitting a linear model to the data followed by a non-linear fitting of the linear model residuals using an ANN. This paper explores the use of residual modelling in high-dimensional data using modern neural network architectures. By combining linear- and ANN modelling, we demonstrate that it is possible to achieve both good model performance while retaining interpretations from the linear part of the model. The proposed residual modelling approach is evaluated on four high-dimensional datasets, representing two regression and two classification problems. Additionally, a demonstration of possible interpretation techniques are included for all datasets. The study concludes that if the modelling problem contains sufficiently complex data (i.e., non-linearities), the residual modelling can in fact improve the performance of a linear model and achieve similar performance as pure ANN models while retaining valuable interpretations for a large proportion of the variance accounted for. The paper presents a residual modelling scheme using modern neural network architectures. Furthermore, two novel extensions of residual modelling for classification tasks are proposed. The study is seen as a step towards explainable AI, with the aim of making data modelling using artificial neural networks more transparent.
An automotive case study on the limits of approximation for object detection

The accuracy of camera-based object detection (CBOD) built upon deep learning is often evaluated against the real objects in frames only. However, such simplistic evaluation ignores the fact that many unimportant objects are small, distant, or background, and hence, their misdetections have less impact than those for closer, larger, and foreground objects in domains such as autonomous driving. Moreover, sporadic misdetections are irrelevant since confidence on detections is typically averaged across consecutive frames, and detection devices (e.g. cameras, LiDARs) are often redundant, thus providing fault tolerance. This paper exploits such intrinsic fault tolerance of the CBOD process, and assesses in an automotive case study to what extent CBOD can tolerate approximation coming from multiple sources such as lower precision arithmetic, approximate arithmetic units, and even random faults due to, for instance, low voltage operation. We show that the accuracy impact of those sources of approximation is within 1% of the baseline even when considering the three approximate domains simultaneously, and hence, multiple sources of approximation can be exploited to build highly efficient accelerators for CBOD in cars.
Optimal maneuver strategy for an autonomous underwater vehicle with bearing-only measurements

Track-before-detect (TBD) algorithms have been proven to circumvent the challenges of measurement-to-track association and have excellent robustness under harsh conditions. These benefits are aptly relevant for developing unmanned passive sonar tracking systems deployed on an autonomous underwater vehicle (AUV). This work considers the optimal maneuver problem exclusively for passive sonar TBD bearing-only localization algorithms, which is essential for an AUV to enhance observability autonomously. To solve this problem, we derive the Fisher information matrix (FIM) of the TBD algorithms, whose determinant reflects the observability. The determinant of the FIM is utilized as a cost function to design an optimal maneuver strategy (OMS). Although the cost function is nonconvex, we demonstrate that the optimal global solution maximizing the cost function can be analytically established. In addition, the designed OMS is extended to the condition with a course constraint to consider physical limitations in practical applications. Finally, simulated and real experiments are performed to verify the effectiveness of the designed OMS.
Extension of the Dip-test Repertoire - Efficient and Differentiable p-value Calculation for Clustering

Over the last decade, the Dip-test of unimodality has gained increasing interest in the data mining community as it is a parameter-free statistical test that reliably rates the modality in one-dimensional samples. It returns a so called Dip-value and a corresponding probability for the sample's unimodality (Dip-p-value). These two values share a sigmoidal relationship. However, the specific transformation is dependent on the sample size. Many Dip-based clustering algorithms use bootstrapped look-up tables translating Dip- to Dip-p-values for a certain limited amount of sample sizes. We propose a specifically designed sigmoid function as a substitute for these state-of-the-art look-up tables. This accelerates computation and provides an approximation of the Dip- to Dip-p-value transformation for every single sample size. Further, it is differentiable and can therefore easily be integrated in learning schemes using gradient descent. We showcase this by exploiting our function in a novel subspace clustering algorithm called Dip'n'Sub. We highlight in extensive experiments the various benefits of our proposal.
Boosting StarGANs for Voice Conversion with Contrastive Discriminator

Nonparallel multi-domain voice conversion methods such as the StarGAN-VCs have been widely applied in many scenarios. However, the training of these models usually poses a challenge due to their complicated adversarial network architectures. To address this, in this work we leverage the state-of-the-art contrastive learning techniques and incorporate an efficient Siamese network structure into the StarGAN discriminator. Our method is called SimSiam-StarGAN-VC and it boosts the training stability and effectively prevents the discriminator overfitting issue in the training process. We conduct experiments on the Voice Conversion Challenge (VCC 2018) dataset, plus a user study to validate the performance of our framework. Our experimental results show that SimSiam-StarGAN-VC significantly outperforms existing StarGAN-VC methods in terms of both the objective and subjective metrics.
Heterogeneous Graph Contrastive Multi-view Learning

Inspired by the success of Contrastive Learning (CL) in computer vision and natural language processing, Graph Contrastive Learning (GCL) has been developed to learn discriminative node representations on graph datasets. However, the development of GCL on Heterogeneous Information Networks (HINs) is still in the infant stage. For example, it is unclear how to augment the HINs without substantially altering the underlying semantics, and how to design the contrastive objective to fully capture the rich semantics. Moreover, early investigations demonstrate that CL suffers from sampling bias, whereas conventional debias- ing techniques are empirically shown to be inadequate for GCL. How to mitigate the sampling bias for heterogeneous GCL is another important problem. To address the aforementioned challenges, we propose a novel Heterogeneous Graph Contrastive Multi-view Learning (HGCML) model. In particular, we use metapaths as the augmentation to generate multiple subgraphs as multi-views, and propose a contrastive objective to maximize the mutual information between any pairs of metapath-induced views. To alleviate the sampling bias, we further propose a positive sampling strategy to explicitly select positives for each node via jointly considering semantic and structural information preserved on each metapath view. Extensive experiments demonstrate HGCML consistently outperforms state-of-the-art baselines on five real-world benchmark datasets. To enhance the repro- ducibility of our work, we make all the code publicly available at https://github.com/Zehong-Wang/HGCML.KeywordsGraph contrastive learningheterogeneous information networkself-supervised learninggraph neural networkmulti-view learning
Abnormal Behavior Detection Based on Dynamic Pedestrian Centroid Model: Case Study on U-Turn and Fall-Down

With the increasing number of video surveillance cameras in public buildings, it has become challenging, yet significant to detect abnormal pedestrian behaviors in crowd management, to prevent crowd accidents. Although current advancements in human action recognition based on computer vision can help detect abnormal behaviors after their incidence, majority of them lack the ability to detect potential characteristics prior to the occurrence of real abnormal behaviors. Hence, in this study, we addressed this issue by proposing a novel dynamic centroid model (DCM) of a human body, and rebuilding pedestrian joint sub-segments from human skeleton key nodes obtained in camera images. We built a weighted centroid-combined force model based on Newton’s second law, considering acceleration, mass inertial of human body sub-segments, and internal constraints. Thereafter, pedestrian kinematic and dynamic parameters were analyzed, such as speed, trajectory, force. Furthermore, abnormal behavior detection criteria were constructed for typical abnormal-behavior cases: U-turn and fall-down. Comparative experiments between the proposed DCM and the state-of-the-art methods were conducted. The experimental results showed that the model was capable of detecting abnormal behaviors, with mean values of lead time of 277 ms in U-turn behavior, and 562 ms in fall-down behavior, prior to the captured occurrence of these two abnormal behaviors. Finally, a de-occlusion algorithm was designed and jointly used with DCM, validated by a fall-down detecting experiment including partial occlusion. Therefore, this study holds significant value for the prevention of abnormal pedestrian behaviors in public places.
Automatic Piecewise Extreme Learning Machine-Based Model for S-Parameters of RF Power Amplifier

This paper presents an automatic piecewise (Auto-PW) extreme learning machine (ELM) method for S-parameters modeling radio-frequency (RF) power amplifiers (PAs). A strategy based on splitting regions at the changing points of concave-convex characteristics is proposed, where each region adopts a piecewise ELM model. The verification is carried out with S-parameters measured on a 2.2–6.5 GHz complementary metal oxide semiconductor (CMOS) PA. Compared to the long-short term memory (LSTM), support vector regression (SVR), and conventional ELM modeling methods, the proposed method performs excellently. For example, the modeling speed is two orders of magnitude faster than SVR and LSTM, and the modeling accuracy is more than one order of magnitude higher than ELM.
Visual aids in teaching ukrainian as a foreign language

The significantly accelerating pace of life and the increasing amount of information along with the emergence of new cognitive features of modern students have triggered the necessity to select the appropriate ways of presenting educational information. One of the ways to solve the problem is to involve various aids for visualizing knowledge, by providing language and communicative samples during the language training process of foreign students at Ukrainian higher education institutions. This article reveals the essence of the visualization concept, substantiates its use in teaching the Ukrainian language to foreign students, and covers the principles of visual aids selection such as goal orientation, functionality, complexity, and system quantization. The article provides the experimentally tested types of educational materials created with the help of visualization, such as infographics, word clouds, mental maps, memes, and wimmelbuchs. It is proved that the implementation of visual aids in the educational process also has a positive effect on the formation of cognitive interests, and improves the development of such qualities as initiative, independence, and tolerance in intercultural communication. The obtained results show that the selected types of visual aids foster the conscious acquisition of the Ukrainian language, the formation of aspect language skills, and the development of language skills as the basis of communicative competence of foreign students. The authors outline the role of Ukrainian teachers: to create such visual aids which not only complement the language materials but also reflect the socio-cultural context, i.e., form part of the Ukrainian-speaking environment. The use of the proposed set of visual aids for language training of foreign students provides ample opportunities for the formation of phonetic, lexical-grammatical, and communication skills, creates the effect of immersing a foreigner in the linguistic and cultural context visualizing each fragment of communicative interaction in brief form, and at the same time fulfills the function of a visual stimulus for communication.
Biological Image Processing Algorithm Based on Attention Mechanism and Convolutional Neural Network

This paper proposes a model algorithm based on convolutional neural network combined with attention mechanism to realize fast and accurate identification of biological image. Firstly, deformable convolution is used to extract features in the horizontal and vertical directions, respectively. Secondly, attention modules are used to capture remote dependencies in one spatial direction, while accurate position information is retained in another spatial direction, so that information in both vertical and horizontal directions can be retained; after a series of transformations, the attention vector is obtained and multiplied back to the original feature vector as a weight factor. The experimental results show that the proposed algorithm can effectively improve the image quality, improve the image clarity, avoid color distortion, and achieve good results in both synthetic and real low-illumination images, and the subjective and objective evaluation indicators are better than the contrast algorithm.
Implementation of the EPIC Earthquake Early Warning System in the Bursa Province (Türkiye) and Its Surroundings

Earthquake early warning systems aim to reduce the potential danger by providing a warning in the seconds before strong ground shaking occurs. In this study, we implemented EPIC, an early warning algorithm for Bursa province and its surroundings, which is a seismically active region. We replayed 104 earthquakes of M ≥ 3.5, which occurred in and around the study area between 2012 and 2021. We derived period and amplitude-based magnitude-scaling relationships using peak displacement amplitude (Pd) and predominant period (Tpmax) parameters of the first 4 s of P-wave arrivals. We investigated the performance of magnitude-scaling relationships through testing with real-time data. We observed an improvement when comparing the magnitude estimates made with the newly developed equations with the default equations used for California. We have also found that magnitude estimation with Pd gives better results than Tpmax for estimating the accurate final magnitude. We aim to adapt the EPIC early warning system, implemented for Bursa province and its surroundings, specifically for each region of Türkiye where the earthquake risk is high.
An Exploratory Study on the Implementation of Student Loans in Alleviating Poverty and Its Contribution to Human Capital Development in Sierra Leone

The investment in education creates present costs but brings benefits in the future such as increased earnings, improved health, economic growth, long life, and better service delivery in the place of work.The student expenses include but are not limited to tuition fees, books, and lodging costs which can How to cite this paper:
Following the Master’s Hands: Capturing Piano Performances for Mixed Reality Piano Learning Applications

Piano learning applications in Mixed Reality (MR) are a promising substitute for physical instruction when a piano teacher is absent. Existing piano learning applications that use visual indicators to highlight the notes to be played on the keyboard or employ video projections of a pianist provide minimal guidance on how the learner should execute hand movements to develop their technique in performance and prevent injuries. To address this gap, we developed an immersive first-person piano learning experience that uses a library of targeted visualizations of the teacher's hands and 3D traces of hand movements in MR. Seeing the piano teacher's hands while hearing the music is central to developing the novice's musical intuition. We introduced an end-to-end workflow to accurately capture the pianist's technical gestures and align them with the musical score. We recorded pianists playing technical exercises and music pieces. We developed a multimodal performance dataset (MPD) comprising virtual hand models, keyboard (MIDI) recordings and the corresponding music scores, and different visualizations of hand traces capturing movement. Finally, we developed Pianoverse, an MR application to assist piano learning, and performed exploratory user testing with novice piano players to understand the impact of multimodal representations of movement on skill learning. Our initial observations suggest that apprehending the movement traces of a recorded performance over a physical keyboard increases the learner's ability to position their body and hands correctly and to replicate hand gestures while playing from written music. Further research will focus on automating performance data collection and a comprehensive evaluation of the use of leading movement traces in piano learning.
Environment monitoring of mining area with comprehensive mining ecological index (CMEI): a case study in Xilinhot of Inner Mongolia, China

The monitoring and evaluation of environmental and ecological status in mining area is critical for effective mineral management guidance, and remote sensing is a cost-effective solution for covering a wide spatial area with high temporal frequency. However, the diverse landscape in mining areas presents a challenge for finding a suitable monitoring method. To address this challenge, this study proposes a remote sensing-based comprehensive mining ecological index (CMEI), which integrates vegetation greenness, soil wetness, urban heat, air quality and water quality indicators obtained from Landsat images and Moderate Resolution Imaging Spectroradiometer (MODIS) products. The integration is achieved through a principal component analysis (PCA) to encapsulate various aspects of the environment in opencast mining areas. The proposed CMEI was then applied to assess the performance of an ecological restoration project carried out in the Xilinhot coalfield in Inner Mongolia, China, over the past two decades. Our findings show that the overall ecological environment in the dumping sites and backfilling sites of Xilinhot coalfield has improved from a score of 0.15 in 2005 to 0.33 in 2020, according to the CMEI. Nevertheless, our study also highlights that some newly established dumping sites require further strengthening of management and maintenance measures. The CMEI presents a novel and effective approach for monitoring and evaluating the ecological environment in mining areas, and it can potentially be applied to assess the ecological environment of opencast mining areas globally.
Integration of Square Fiducial Markers in Patient-Specific Instrumentation and Their Applicability in Knee Surgery

Computer technologies play a crucial role in orthopaedic surgery and are essential in personalising different treatments. Recent advances allow the usage of augmented reality (AR) for many orthopaedic procedures, which include different types of knee surgery. AR assigns the interaction between virtual environments and the physical world, allowing both to intermingle (AR superimposes information on real objects in real-time) through an optical device and allows personalising different processes for each patient. This article aims to describe the integration of fiducial markers in planning knee surgeries and to perform a narrative description of the latest publications on AR applications in knee surgery. Augmented reality-assisted knee surgery is an emerging set of techniques that can increase accuracy, efficiency, and safety and decrease the radiation exposure (in some surgical procedures, such as osteotomies) of other conventional methods. Initial clinical experience with AR projection based on ArUco-type artificial marker sensors has shown promising results and received positive operator feedback. Once initial clinical safety and efficacy have been demonstrated, the continued experience should be studied to validate this technology and generate further innovation in this rapidly evolving field.
Rank Flow Embedding for Unsupervised and Semi-Supervised Manifold Learning

Impressive advances in acquisition and sharing technologies have made the growth of multimedia collections and their applications almost unlimited. However, the opposite is true for the availability of labeled data, which is needed for supervised training, since such data is often expensive and time-consuming to obtain. While there is a pressing need for the development of effective retrieval and classification methods, the difficulties faced by supervised approaches highlight the relevance of methods capable of operating with few or no labeled data. In this work, we propose a novel manifold learning algorithm named Rank Flow Embedding (RFE) for unsupervised and semi-supervised scenarios. The proposed method is based on ideas recently exploited by manifold learning approaches, which include hypergraphs, Cartesian products, and connected components. The algorithm computes context-sensitive embeddings, which are refined following a rank-based processing flow, while complementary contextual information is incorporated. The generated embeddings can be exploited for more effective unsupervised retrieval or semi-supervised classification based on Graph Convolutional Networks. Experimental results were conducted on 10 different collections. Various features were considered, including the ones obtained with recent Convolutional Neural Networks (CNN) and Vision Transformer (ViT) models. High effective results demonstrate the effectiveness of the proposed method on different tasks: unsupervised image retrieval, semi-supervised classification, and person Re-ID. The results demonstrate that RFE is competitive or superior to the state-of-the-art in diverse evaluated scenarios.
Spatial Modeling of Flood-Vulnerability as Basic Data for Flood Mitigation

Identifying risks in flood-prone areas is necessary to support risk management decisions. This research was conducted to establish a vulnerability model of flood hazards in the city of Pontianak. The model was based on the scoring and weighting of biophysical factors. The AHP method and logical formulations were used to establish the model. The result showed that the accuracy of the model used by AHP to determine the vulnerability of floods was 80% in Pontianak City. The accuracy of the model using logical formulations to determine the vulnerability level of a flood was 84%. The Kappa accuracy value in model 1 is 76.7%. The model of flood vulnerability explains that most of Pontianak City has a very high level of flood vulnerability, which is 31,440,568.8 m2 or 29.11% of the total research area of 108,003,319.8 m2. The vulnerable area is 29,945,485.7 m2 or 27.73%, and the less safe area is 22,126,936.3 m2 or 20.49%, with the safe area being 24,490,328.7 m2or 22.67% of the total area. This research contributes to the government to establish policies regarding flood management and urban development in the future, and as an effort to mitigate against flooding. Doi: 10.28991/CEJ-2023-09-04-02 Full Text: PDF