Reviewerly/exHarmony huggingface dataset:
Research on a Random Route-Planning Method Based on the Fusion of the A* Algorithm and Dynamic Window Method

Path planning is a hot topic at present. Considering the global and local path planning of mobile robot is one of the challenging research topics. The objective of this paper is to create a rasterized environment that optimizes the planning of multiple paths and solves barrier avoidance issues. Combining the A* algorithm with the dynamic window method, a robo-assisted random barrier avoidance method is used to resolve the issues caused by collisions and path failures. Improving the A* algorithm requires analyzing and optimizing its evaluation function to increase search efficiency. The redundant point removal strategy is then presented. The dynamic window method is utilized for local planning between each pair of adjacent nodes. This method guarantees that random obstacles are avoided in real-time based on the globally optimal path. The experiment demonstrates that the enhanced A* algorithm reduces the average path length and computation time when compared to the traditional A* algorithm. After fusing the dynamic window method, the local path is corrected using the global path, and the resolution for random barrier avoidance is visualized.
Comparison of ontology with non-ontology tools for educational research

Providing complex digital support for scientific research is an urgent problem that requires the creation of useful tools. Cognitive IT-platform Polyhedron has used to collect both existing informational ontology-based tools, and specially designed to complement a full-stack of instruments for digital support for scientific research. Ontological tools have generated using the Polyhedron converter using data from Google sheets. Tools “Search systems”, “Hypothesis test system”, “Centre for collective use”, “The selection of methods”, “The selection of research equipment”, “Sources recommended by Ministry of Education and Science of Ukraine”, “Scopus sources”, “The promising developments of The National Academy of Sciences of Ukraine” were created and structured in the centralized ontology. A comparison of each tool to existing classic web-based analogue provided and described.
Autoregressive Predictive Coding: A Comprehensive Study

We review autoregressive predictive coding (APC), an approach to learn speech representation by predicting a future frame given the past frames. We present three different views of interpreting APC, and provide a historical account to the approach. To study the speech representation learned by APC, we use common speech tasks, such as automatic speech recognition and speaker verification, to demonstrate the utility of the learned representation. In addition, we design a suite of fine-grained tasks, including frame classification, segment classification, fundamental frequency tracking, and duration prediction, to probe the phonetic and prosodic content of the representation. The three views of the APC objective welcome various generalizations and algorithms to learn speech representations. Probing on the suite of fine-grained tasks suggests that APC makes a wide range of high-level speech information accessible in its learned representation.
Visual Comparison of Networks in VR

Networks are an important means for the representation and analysis of data in a variety of research and application areas. While there are many efficient methods to create layouts for networks to support their visual analysis, approaches for the comparison of networks are still underexplored. Especially when it comes to the comparison of weighted networks, which is an important task in several areas, such as biology and biomedicine, there is a lack of efficient visualization approaches. With the availability of affordable high-quality virtual reality (VR) devices, such as head-mounted displays (HMDs), the research field of immersive analytics emerged and showed great potential for using the new technology for visual data exploration. However, the use of immersive technology for the comparison of networks is still underexplored. With this work, we explore how weighted networks can be visually compared in an immersive VR environment and investigate how visual representations can benefit from the extended 3D design space. For this purpose, we develop different encodings for 3D node-link diagrams supporting the visualization of two networks within a single representation and evaluate them in a pilot user study. We incorporate the results into a more extensive user study comparing node-link representations with matrix representations encoding two networks simultaneously. The data and tasks designed for our experiments are similar to those occurring in real-world scenarios. Our evaluation shows significantly better results for the node-link representations, which is contrary to comparable 2D experiments and indicates a high potential for using VR for the visual comparison of networks.
A strong construction of S-box using Mandelbrot set an image encryption scheme

The substitution box (S-box) plays a vital role in creating confusion during the encryption process of digital data. The quality of encryption schemes depends upon the S-box. There have been several attempts to enhance the quality of the S-box by using fractal chaotic mechanisms. However, there is still weakness in the robustness against cryptanalysis of fractal-based S-boxes. Due to their chaotic behavior, fractals are frequently employed to achieve randomness by confusion and diffusion process. A complex number-based S-box and a chaotic map diffusion are proposed to achieve high nonlinearity and low correlation. This study proposed a Mandelbrot set S-box construction based on the complex number and Chen chaotic map for resisting cryptanalytic attacks by creating diffusion in our proposed algorithm. The cryptosystem was built on the idea of substitution permutation networks (SPN). The complex nature of the proposed S-box makes it more random than other chaotic maps. The robustness of the proposed system was analyzed by different analysis properties of the S-box, such as nonlinearity, strict avalanche criterion, Bit independent criterion, and differential and linear probability. Moreover, to check the strength of the proposed S-box against differential and brute force attacks, we performed image encryption with the proposed S-box. The security analysis was performed, including statistical attack analysis and NIST analysis. The analysis results show that the proposed system achieves high-security standards than existing schemes.
A bilayer color digital image correlation method for the measurement of the topography of a liquid interface

One of the most essential characteristics of the liquid interface is the topography dominated by hydrodynamics or capillary effects. Although there have developed 3D imaging techniques for the measurement of such topography, their setups, operations and reconstructions are relatively complex and suffer from low efficiency. Here we report a bilayer color digital image correlation (BC-DIC) method for the measurement of the topography of a liquid interface. The basic principle of the new method is the refraction of beneath bilayer color speckle patterns at the liquid interface, which makes it possible to perform 3D reconstruction through 2D measurement of the virtual displacement field of the speckle patterns. The equations for BC-DIC in different situations are deduced and discussed in detailed. Validation experiments are carried out to reconstruct the topography of triangular prism, planoconvex, complex transparent surface and sloshing water surface. The results show that BC-DIC is feasible and accurate for measuring the topography of transparent objects, including liquid interface. This new method paves the way for investigating surface and interface phenomena, such as capillarity and wetting issues, hydrodynamics on liquid interface, etc.
Sports activity recognition with UWB and inertial sensors using deep learning approach

Nowadays the sensor-based human activity recognition is a key issue in the field of the physical exercises’ recognition. The purpose of this paper is to recognise exercise sports, such as squats, pull-ups and dips, using a dataset based on three ultra-wideband (UWB) sensors with additional inertial data and a deep learning approach in order to achieve an appropriate rate of the recognition of sports exercises with reduced computational effort. Firstly, a dataset, containing 273 samples of dips, 215 samples of pull-ups and 956 samples of squats, plus an additional 2024 samples of the input signals acquired during breaks, was created. Next, an optimal set of hyperparameters of Convolutional Neural Network (CNN) architecture in order to balance the accuracy rate and computational effort was achieved. Finally, it was discovered that acceleration signals have the highest energy and therefore, a comparison of the accuracy rate between CNN, using all signals and CNN, using acceleration only, was carried out (97.5% vs. 97.7%). Moreover, much less computational effort, expressed in number of multiplications, (1.8e4 vs. 1.2e5) was achieved. The practical usefulness is presented, by facilitating the implementation of the presented UWB sensor-based system for recognising such physical exercises as squats, pull-ups and dips on smartphones and commonly available wearable sensor devices.
A Single-Stage Arbitrary-Oriented Detector Based on Multiscale Feature Fusion and Calibration for SAR Ship Detection

Ship detection is a challenging task in the synthetic aperture radar (SAR) automatic target recognition due to the large aspect ratio, arbitrary orientation, and dense arrangement of ships and severe background interference in the inshore scenes. Although considerable progress has been made in recent research, there have still been certain challenges in achieving fast and efficient detection of arbitrary-oriented ships in SAR images. To address these challenges, this paper proposes a single-stage detection method based on multi-scale feature fusion and calibration. The proposed detection method can detect arbitrary-oriented ships in SAR images with high accuracy and speed. Specifically, a head network with the stepwise regression from the coarse- to fine-grained detection is designed to detect arbitrary-oriented ships accurately. In addition, a feature enhancement module is constructed to fuse and refine shallow texture features and deep semantic features, aiming to obtain multi-scale fusion features containing sufficient contextual information. Finally, an attention module is used to calibrate multi-scale fusion features to highlight the ship information while suppressing interference from the surrounding background. The effectiveness of the proposed method is verified by experiments on two public SAR ship datasets and a panoramic SAR image. The experimental results show that compared with the other rotation detectors, the proposed detection method has competitive detection results and can achieve state-of-the-art detection performance.
A trusted peer-to-peer market of joint energy and reserve based on blockchain

With the increasing penetration of distributed energy resources, the traditional producer-centric electricity market is moving to a prosumer-centric market, where prosumers can trade with each other in an autonomous pattern. However, there remain research gaps on the pricing and allocation of joint energy and reserves in an autonomous prosumer-centric market. This paper firstly designs a joint energy-reserve peer-to-peer (P2P) trading mechanism. The mechanism not only enables P2P energy trading but also quantifies the reserve cost and the value of flexibility. Then, a blockchain-based trading algorithm is proposed to implement a trustworthy prosumer-centric market. A pipelined delegated Byzantine fault tolerance (PDBFT) consensus algorithm is proposed to ensure robustness and improve the efficiency of the autonomous trading process. Numerical results show the effectiveness of the trading mechanism and the performance of the blockchain-based trading algorithm. Compared with only considering energy trading, the proposed mechanism reduces the total cost of the market by 16.03%. Compared with using the traditional practical Byzantine fault tolerance (PBFT) consensus algorithm, the computational time of market clearing on blockchain is decreased by 45.90%.
Human Joint Skeleton Tracking Using Multiple Kinect Azure

Development of human skeleton tracking requires high accuracy with affordable installation to enlarge their applicability. This paper proposes human skeleton tracking using multiple cameras to minimize occlusion. We combined multiple human tracking modules from Azure based on ONNX Runtime. Each module uses passive infrared as the human segmentation and recognition process and depth information to transform to the 3D position. Each module’s output skeleton data is separated into six parts: right-upper limb, left-upper limb, right-lower limb, left-lower limb, head, and torso. Then those part is compared and combined based on the confidence evaluation based on the occlusion probabilistic. Furthermore, 35 joint skeleton angles are also generated based on the anatomy of the human musculoskeletal model. The effectiveness of the proposed model has been validated from the comparison with related work and some applications of human muscle activity monitoring.
Extension of DBSCAN in Online Clustering: An Approach Based on Three-Layer Granular Models

In big data analysis, conventional clustering algorithms have limitations to deal with nonlinear spatial datasets, e.g., low accuracy and high computation cost. Aiming at these problems, this paper proposed a new DBSCAN extension algorithm for online clustering, which consists of three layers, considering DBSCAN, granular computing (GrC), and fuzzy rule-based modeling. Firstly, making use of DBSCAN algorithms’ advantages at extracting structural information, spatial data are clustered via DBSCAN into structural clusters, which are subsequently described by structural information granules (IG) via GrC. Secondly, based on the structural IGs, a series of granular models are constructed in the medium space, and utilized to form fuzzy rules to guide clustering on spatial data. Finally, with the help of structural IGs and granular rules, a rule-based modeling method is constructed in the output space for online clustering. Experiments on a synthetic toy dataset and a typical spatial dataset are implemented in this paper. Numerical results validate the feasibility to the proposed method in online spatial data clustering. Moreover, comparative studies with conventional methods and existing DBSCAN variants demonstrate the superiorities of the proposed method, as well as accuracy improvement and computation overhead reduction.
Declarative AI design in Unity using Answer Set Programming

Declarative methods such as Answer Set Programming show potential in cutting down development costs in commercial videogames and real-time applications in general. Many shortcomings, however, prevent their adoption, such as performance and integration gaps. In this work we illustrate our ThinkEngine, a framework in which a tight integration of declarative formalisms within the typical game development workflow is made possible in the context of the Unity game engine. ThinkEngine allows to wire declarative AI modules to the game logic and to move the computational load of reasoning tasks outside the main game loop using an hybrid deliberative/reactive architecture. In this paper, we illustrate the architecture of the ThinkEngine and its role both at design and run-time. Then we show how to program declarative modules in a proof-of-concept game, and report about performance and related work.
BIoU: An Improved Bounding Box Regression for Object Detection

Object detection is a predominant challenge in computer vision and image processing to detect instances of objects of various classes within an image or video. Recently, a new domain of vehicular platforms, e-scooters, has been widely used across domestic and urban environments. The driving behavior of e-scooter users significantly differs from other vehicles on the road, and their interactions with pedestrians are also increasing. To ensure pedestrian safety and develop an efficient traffic monitoring system, a reliable object detection system for e-scooters is required. However, existing object detectors based on IoU loss functions suffer various drawbacks when dealing with densely packed objects or inaccurate predictions. To address this problem, a new loss function, balanced-IoU (BIoU), is proposed in this article. This loss function considers the parameterized distance between the centers and the minimum and maximum edges of the bounding boxes to address the localization problem. With the help of synthetic data, a simulation experiment was carried out to analyze the bounding box regression of various losses. Extensive experiments have been carried out on a two-stage object detector, MASK_RCNN, and single-stage object detectors such as YOLOv5n6, YOLOv5x on Microsoft Common Objects in Context, SKU110k, and our custom e-scooter dataset. The proposed loss function demonstrated an increment of 3.70% at APS on the COCO dataset, 6.20% at AP55 on SKU110k, and 9.03% at AP80 of the custom e-scooter dataset.
Discovering hidden geothermal signatures using non-negative matrix factorization with customized k-means clustering

Discovery of hidden geothermal resources is challenging. It requires the mining of large datasets with diverse data attributes representing subsurface hydrogeological and geothermal conditions. The commonly used play fairway analysis approach typically incorporates subject-matter expertise to analyze regional data to estimate geothermal characteristics and favorability. We demonstrate an alternative approach based on machine learning (ML) to process a geothermal dataset from southwest New Mexico (SWNM). The study region includes low- and medium-temperature hydrothermal systems. Several of these systems are not well characterized because of insufficient existing data and limited past explorative work. This study discovers hidden patterns and relations in the SWNM geothermal dataset to improve our understanding of the regional hydrothermal conditions and energy-production favorability. This understanding is obtained by applying an unsupervised ML algorithm based on non-negative matrix factorization coupled with customized k-means clustering (NMFk). NMFk can automatically identify (1) hidden signatures characterizing analyzed datasets, (2) the optimal number of these signatures, (3) the dominant data attributes associated with each signature, and (4) the spatial distribution of the extracted signatures. Here, NMFk is applied to analyze 18 geological, geophysical, hydrogeological, and geothermal attributes at 44 locations in SWNM. Using NMFk, we find data patterns and identify the spatial associations of hydrothermal signatures within two physiographic provinces (Colorado Plateau and Basin and Range) and two sub-regions of these provinces (the Mogollon-Datil volcanic field and the Rio Grande rift) in SWNM. The ML algorithm extracted five hydrothermal signatures in the SWNM datasets that differentiate between low (<90°C) and medium (90-150°C)-temperature hydrothermal systems. The algorithm also suggests that the Rio Grande rift and northern Mogollon-Datil volcanic field are the most favorable regions for future geothermal resource discovery. NMFk also identified critical attributes to identify medium-temperature hydrothermal systems in the study area. The resulting NMFk model can be applied to predict geothermal conditions and their uncertainties at new SWNM locations based on limited data from unexplored regions. The code to execute the performed analyses as well as the corresponding data can be found at https://github.com/SmartTensors/GeoThermalCloud.jl.
Probing speech emotion recognition transformers for linguistic knowledge

Large, pre-trained neural networks consisting of self-attention layers (transformers) have recently achieved state-of-the-art results on several speech emotion recognition (SER) datasets.These models are typically pre-trained in self-supervised manner with the goal to improve automatic speech recognition performance -and thus, to understand linguistic information.In this work, we investigate the extent in which this information is exploited during SER fine-tuning.Using a reproducible methodology based on open-source tools, we synthesise prosodically neutral speech utterances while varying the sentiment of the text.Valence predictions of the transformer model are very reactive to positive and negative sentiment content, as well as negations, but not to intensifiers or reducers, while none of those linguistic features impact arousal or dominance.These findings show that transformers can successfully leverage linguistic information to improve their valence predictions, and that linguistic analysis should be included in their testing.
A Survey on Temporal Sentence Grounding in Videos

Temporal sentence grounding in videos (TSGV), which aims at localizing one target segment from an untrimmed video with respect to a given sentence query, has drawn increasing attentions in the research community over the past few years. Different from the task of temporal action localization, TSGV is more flexible since it can locate complicated activities via natural languages, without restrictions from predefined action categories. Meanwhile, TSGV is more challenging since it requires both textual and visual understanding for semantic alignment between two modalities (i.e., text and video). In this survey, we give a comprehensive overview for TSGV, which (i) summarizes the taxonomy of existing methods, (ii) provides a detailed description of the evaluation protocols (i.e., datasets and metrics) to be used in TSGV, and (iii) in-depth discusses potential problems of current benchmarking designs and research directions for further investigations. To the best of our knowledge, this is the first systematic survey on temporal sentence grounding. More specifically, we first discuss existing TSGV approaches by grouping them into four categories, i.e., two-stage methods, single-stage methods, reinforcement learning-based methods, and weakly supervised methods. Then we present the benchmark datasets and evaluation metrics to assess current research progress. Finally, we discuss some limitations in TSGV through pointing out potential problems improperly resolved in the current evaluation protocols, which may push forwards more cutting-edge research in TSGV. Besides, we also share our insights on several promising directions, including four typical tasks with new and practical settings based on TSGV.
Human Activity Recognition Using Self-Powered Sensors Based on Multilayer Bidirectional Long Short-Term Memory Networks

Sensor-based human activity recognition (HAR) requires the acquisition of channel state information (CSI) data with time series based on sensors to predict human behavior. Many existing approaches are based on wearable sensors and cameras, which increases the burden and privacy issues for patients. Self-powered sensors are capable of noncontact collection of time series data generated by human activity while ensuring their own stable operation. In this article, we propose a deep-learning-based framework for contactless real-time activity detection of humans using self-powered sensors, which is called multilayer bidirectional long short-term memory (MBLSTM). The collected Wi-Fi CSI data are fed into our proposed network model, which is then used to learn representative features of both sides from the original continuous CSI measurements. The attention model is used to assign different weights to the learned features, and finally, activity recognition is performed. Experimental results show that our proposed method achieves an accuracy of more than 96% for the recognition of six activities in multiple rounds of testing, outperforming other benchmark methods used for comparison.