Reviewerly/exHarmony huggingface dataset:
The Soundlapse Project: Exploring Spatiotemporal Features of Wetland Soundscapes

Abstract This article discusses an interdisciplinary project aimed at highlighting the acoustical heritage of wetlands by means of field recordings and a novel time-lapse montage method. The authors discuss a site-specific sound installation that was designed using original wetlands field recordings, live processing and spatial audio multichannel reproduction. The discussion focuses on spatial and temporal features of different types of recorded wetlands soundscapes. Future developments of this project will consider the implementation of a standalone spatiotemporal application, to be used in the context of virtual reality applications, game audio and interactive dance performance.
Review of Weapon Detection Techniques within the Scope of Street-Crimes

The ratio of street crimes in under developing is very high. Everyday violent incidents are reported, which happen mainly because a person having access to armory. In certain areas, armory is controlled by installing X-rays, or allocating police. In some cases, CCTV cameras are installed to monitor visual armory. But these methods of controlling armory are expensive and require a huge deal of manual efforts. However Deep Learning has revolutionized the weapons detection system using CNN’s architectures. In this review paper we have discussed various weapons detection paper within the scope of street crime.
Experiences from Exporting Major Proof Assistant Libraries

Abstract The interoperability of proof assistants and the integration of their libraries is a highly valued but elusive goal in the field of theorem proving. As a preparatory step, in previous work, we translated the libraries of multiple proof assistants, specifically the ones of Coq, HOL Light, IMPS, Isabelle, Mizar, and PVS into a universal format: OMDoc/MMT. Each translation presented great theoretical, technical, and social challenges, some universal and some system-specific, some solvable and some still open. In this paper, we survey these challenges and compare and evaluate the solutions we chose. We believe similar library translations will be an essential part of any future system interoperability solution, and our experiences will prove valuable to others undertaking such efforts.
Security Analysis of Quantum Noise Stream Cipher under Fast Correlation Attack

We conduct an experiment of security analysis on 300km QAM/QNSC transmission under fast correlation attack. Noise masking model and hypothesis testing theory are adopted. Experiment results provide several solutions to achieve secure transmission in QAM/QNSC.
Navigation system with SLAM-based trajectory topological map and reinforcement learning-based local planner

This paper presents a novel robotic navigation system integrating a visual simultaneous localization and mapping (V-SLAM) based global planner with a deep reinforcement learning (DRL) based local planner. On one hand, map of many modern popular V-SLAM systems is inhomogeneous point cloud, which contains many outliers and is too sparse for reliable global path planning. To address this problem, we propose a novel approach to generate a topological map with both trajectories and map points of V-SLAM. On the other hand, current state-of-the-art (SOTA) DRL-based local planners have shown great efficiency in obstacle avoidance. However, the SOTA DRL-based local planners are sometimes trapped by large obstacles and would fall into some local minimum during training. To address the problems, we propose a sub-target module and a mirror experience replay approach. Test results demonstrate that, our topological map generation is robust against outliers and sparsity of map points of V-SLAM, while our local planner achieves 9.61% success rate of obstacle avoidance higher than the SOTA DRL-based approach. Tests in real environment demonstrate the feasibility of our navigation system.
Multi-channel Chan-Vese model for unsupervised segmentation of nuclei from breast histopathological images

T he pathologist determines the malignancy of a breast tumor by studying the histopathological images. In particular, the characteristics and distribution of nuclei contribute greatly to the decision process. Hence, the segmentation of nuclei constitutes a crucial task in the classification of breast histopathological images. Manual analysis of these images is subjective, tedious and susceptible to human error. Consequently, the development of computer-aided diagnostic systems for analysing these images have become a vital factor in the domain of medical imaging. However, the usage of medical image processing techniques to segment nuclei is challenging due to the diverse structure of the cells, poor staining process, the occurrence of artifacts, etc. Although supervised computer-aided systems for nuclei segmentation is popular, it is dependent on the availability of standard annotated datasets. In this regard, this work presents an unsupervised method based on Chan-Vese model to segment nuclei from breast histopathological images. The proposed model utilizes multi-channel color information to efficiently segment the nuclei. Also, this study proposes a pre-processing step to select appropriate color channel such that it discriminates nuclei from the background region. An extensive evaluation of the proposed model on two challenging datasets demonstrates its validity and effectiveness.
Learning Multi-modal Attentional Consensus in Action Recognition for Elderly-Care Robots

This paper addresses a practical action recognition method for elderly-care robots. Multi-stream based models are one of the promising approaches for solving the complexity of real-world environments. While multi-modal action recognition have been actively studied, there is a lack of research on models that effectively combine features of different modalities. This paper proposes a new mid-level feature fusion method for two-stream based action recognition network. In multi-modal approaches, extracting complementary information between different modalities is an essential task. Our network model is designed to fuse features at an intermediate level of feature extraction, which leverages a whole feature map from each modality. Consensus feature map and consensus attention mechanism are proposed as effective ways to extract information from two different modalities: RGB data and motion features. We also introduce ETRI-Activity3D-LivingLab, a real-world RGB-D dataset for robots to recognize daily activities of the elderly. It is the first 3D action recognition dataset obtained in a variety of home environments where the elderly actually reside. We expect our new dataset to contribute to the practical study of action recognition with the previously released ETRI-Activity3D dataset. To prove the effectiveness of the method, extensive experiments are performed on NTU RGB+D, ETRI-Activity3D and, ETRI-Activity3D-LivingLab dataset. Our mid-level fusion method achieves competitive performance in various experimental settings, especially for domain-changing situations.
Predicting Particle Swarm Optimization Control Parameters From Fitness Landscape Characteristics

Selecting appropriate control parameters for the particle swarm optimization algorithm can be extremely time consuming and expensive, yet it is necessary in order to achieve optimal performance on a problem. Despite its significance, the issue of control parameter selection remains an open problem. This work leverages techniques from the field of fitness landscape analysis to characterize a large suite of benchmark problems. Extensive experimentation is performed to identify strong control parameters for each problem, and machine learning techniques are used to predict strong control parameters from the characterization of a problem. The results demonstrate that good generalization is possible with minimal training data. This suggests that the cost of parameter selection can be significantly reduced.
High-accuracy spectral interferometer with multi-Fabry–Perot Etalon for thickness measurement of the silicon wafer

We propose a high-accuracy spectral interferometer that uses multi-Fabry–Perot Etalon to measure the thickness of silicon wafer. Three Fabry–Perot etalons ( f 1 = 14 . 5 GHz, f 2 = 14 . 9 GHz and f 3 = 15 . 0 GHz) are used to measure the wafer thickness, and the measurement accuracy of the spectral interferometer is improved by taking the average value of the thickness measurements corresponding to the three Fabry–Perot etalons. An effective peak extraction algorithm is developed to extract the effective peak frequencies. The proposed algorithm uses the crest width threshold and average crest height to improve the measurement accuracy of the spectral interferometer. We design a calibration method for the optical system, which uses the free spectral range (FSR) characteristics of the Fabry–Perot Etalon to calculate the calibration distances to reduce the errors. We build an experimental setup and use a high-precision laser hologage for reference. The experimental results show that the errors between the measured values and the reference values are between 0.00037% and 0.00046%, and the standard deviation is 0.06 μ m. • A high-accuracy spectral interferometer for thickness measurement of the silicon wafer is proposed. • Multi-Fabry–Perot Etalon is developed to improve the measurement accuracy of the spectral interferometer. • An effective peak extraction algorithm is designed to extract peak frequencies by crest width threshold and average crest height. • A calibration method for the optical system is proposed to use the free spectral range characteristics of the Fabry–Perot Etalon. • The proposed scheme has high precision, which the measurement errors are within 0.1%.
Using Double Well Function as a Benchmark Function for Optimization Algorithm

The double well function (DWF) is an important theoretical model originating from quantum physics and has been used to describe the energy constraint problem in quantum mechanics and structural chemistry. Although its form may vary, the DWF has two different local minima in the one-dimensional case, and the number of local minima increases as its dimension grows. As a multi-stable function, the DWF is assumed to be a potential candidate for testing the performance of the heuristic optimization algorithm, which aims to seek the global minimum. To verify this idea, a typical DWF is employed in this paper, and a mathematical analysis is presented herein, and its properties as a benchmark function are discussed in different cases. In addition, we conducted a set of experiments utilizing a few optimization algorithms, such as the multi-scale quantum harmonic oscillator algorithm and covariance matrix adaptation evolution strategy; thus the analysis has been illustrated by the results of the numerical experiment. Moreover, to guide the design of an ideal DWF used as a benchmark function in experiment, different values of the decisive parameters were tested corresponding to our analysis, and some useful rules were given based on the discussion of the results.
Design and Evaluation of an Accessible Mobile AR Application for Learning About Geometry

Augmented reality is currently a very interesting technology due to the possibility of wide use on various affordable devices and at the same time high engagement and motivation potential for the user. These features are especially important in the educational field. As the number of developed prototypes in this area is rapidly increasing, only a limited part of them considers the implementation of accessibility, although accessibility is strongly required especially in educational solutions. The paper presents accessible mobile application for learning about geometry, specifically about geometry bodies, and this topic is often a challenge for children with learning difficulties. In addition to the accessibility option implemented, the actual design of the best-known universal design principles within the application is described, as accessibility relies heavily on them. The results of the application evaluation are presented, leading to the conclusion that this and future AR software solutions have great potential to be included in the curriculum as a tool for inclusive education, but only if the concept of accessibility is properly understood applied.
Kernel-Based k-Representatives Algorithm for Fuzzy Clustering of Categorical Data

Fuzzy cluster analysis plays an essential role in addressing unclear boundaries between clusters in data and aims to group objects into fuzzy clusters based on their similarities. In this paper, we propose a new method for fuzzy clustering of data with categorical attributes. Specifically, we first introduce a method for kernel-based representation of cluster centers in which the underlying distribution of categorical values within a cluster center is estimated as a weighted sum of the uniform distribution and their frequency distribution. We then extend the k-centers clustering method by applying this newly proposed method of cluster center presentation for fuzzy clustering of categorical data. The effectiveness and efficiency of the proposed method are demonstrated by conducting experiments on 16 realworld datasets and comparing the results with those of existing methods. In addition, our research can be regarded as the first attempt to apply a fuzzy silhouette scoring method that includes internal coherence and external separation of fuzzy clusters into clustering of categorical data.
Design and implementation of handwritten Chinese character recognition method based on CNN and TensorFlow

Chinese characters are an important medium for Chinese people to exchange information and perceive the world. With the advent of the information age, a large number of paper documents need to be electronically stored and shared. The recognition accuracy of paper printed Chinese characters and online handwritten Chinese characters has reached a high level. However, offline handwritten Chinese characters have a variety of styles and shapes. There is still a lot of room for improvement in the current recognition accuracy due to a large number of similar characters. This work proposes a "private customized" handwritten Chinese character recognition system based on convolutional neural networks. Users can train their neural network model according to their own writing style. The system includes four parts: character segmentation, Chinese character labeling, neural network training, and predictive recognition. The system can independently expand the data set during use, thereby continuously improving the recognition accuracy. The experimental results show that this method has a good recognition effect for Chinese characters, English letters, Arabic numerals, and punctuation marks, and the recognition accuracy rate can reach more than 98%.
PMNet: In-Network Data Persistence

To guarantee data persistence, storage workloads (such as key-value stores and databases) typically use a synchronous protocol that places the network and server stack latency on the critical path of request processing. The use of the fast and byte-addressable persistent memory (PM) has helped mitigate the storage overhead of the server stack; yet, networking is still a dominant factor in the end-to-end latency of request processing. Emerging programmable network devices can reduce network latency by moving parts of the applications’ compute into the network (e.g., caching results for read requests); however, for update requests, the client still has to stall on the server to commit the updates, persistently.In this work, we introduce in-network data persistence that extends the data-persistence domain from servers to the network, and present PMNet, a programmable data plane (e.g., switch or NIC) with PM for persisting data in the network. PMNet logs incoming update requests and acknowledges clients directly without having them wait on the server to commit the request. In case of a failure, the logged requests act as redo logs for the server to recover. We implement PMNet on an FPGA and evaluate its performance using common PM workloads, including key-value stores and PM-backed applications. Our evaluation shows that PMNet can improve the throughput of update requests by 4.31× on average, and the 99th-percentile tail latency by 3.23×.
Designing Multi-disciplinary Interactive Virtual Environments for Next-Generation Immersive Learning Experiences: Case Studies and Future Directions in Astrobiology, Anatomy and Cultural Heritage

This chapter describes the creation and deployment of the Immersive Learning Level Editor (iLLE), a new cross-platform IVE engineered at the University of New South Wales (UNSW) Sydney. It illustrates the design considerations, discipline-specific workflows and pedagogical strategies employed in beginning to formulate a new and coherent pedagogy for immersive learning that is transferable, distributable and scalable across disciplines and platforms. In this way, it provides a number of methodological templates for contemplating discipline-specific immersive learning needs.
Computing Specification-Compliant Reachable Sets for Motion Planning of Automated Vehicles

To safely and effectively participate in road traffic, automated vehicles should explicitly consider compliance with traffic rules and high-level specifications. We propose a method that can incorporate traffic and handcrafted rules expressed in time-labeled propositional logic into our reach ability analysis, which computes the over-approximative set of states reachable by vehicles. These reachable sets serve as low-level trajectory planning constraints to expedite the search for specification-compliant trajectories. Depending on the adopted specifications, related semantic labels are generated from predicates considering positions, velocities, accelerations, and general traffic situations. We exhibit the applicability of the proposed method with scenarios from the CommonRoad benchmark suite.
The Use and Effectiveness of an Online Diagnostic Support System for Blood Film Interpretation: Comparative Observational Study

The recognition and interpretation of abnormal blood cell morphology is often the first step in diagnosing underlying serious systemic illness or leukemia. Supporting the staff who interpret blood film morphology is therefore essential for a safe laboratory service. This paper describes an open-access, web-based decision support tool, developed by the authors to support morphological diagnosis, arising from earlier studies identifying mechanisms of error in blood film reporting. The effectiveness of this intervention was assessed using the unique resource offered by the online digital morphology Continuing Professional Development scheme (DM scheme) offered by the UK National External Quality Assessment Service for Haematology, with more than 3000 registered users. This allowed the effectiveness of decision support to be tested within a defined user group, each of whom viewed and interpreted the morphology of identical digital blood films.The primary objective of the study was to test the effectiveness of the decision support system in supporting users to identify and interpret abnormal morphological features. The secondary objective was to determine the pattern and frequency of use of the system for different case types, and to determine how users perceived the support in terms of their confidence in decision-making.This was a comparative study of identical blood films evaluated either with or without decision support. Selected earlier cases from the DM scheme were rereleased as new cases but with decision support made available; this allowed a comparison of data sets for identical cases with or without decision support. To address the primary objectives, the study used quantitative evaluation and statistical comparisons of the identification and interpretation of morphological features between the two different case releases. To address the secondary objective, the use of decision support was assessed using web analytical tools, while a questionnaire was used to assess user perceptions of the system.Cases evaluated with the aid of decision support had significantly improved accuracy of identification for relevant morphological features (mean improvement 9.8%) and the interpretation of those features (mean improvement 11%). The improvement was particularly significant for cases with higher complexity or for rarer diagnoses. Analysis of website usage demonstrated a high frequency of access for web pages relevant to each case (mean 9298 for each case, range 2661-24,276). Users reported that the decision support website increased their confidence for feature identification (4.8/5) and interpretation (4.3/5), both within the context of training (4.6/5) and also in their wider laboratory practice (4.4/5).The findings of this study demonstrate that directed online decision support for blood morphology evaluation improves accuracy and confidence in the context of educational evaluation of digital films, with effectiveness potentially extending to wider laboratory use.
Multi-class Tissue Classification in Colorectal Cancer with Handcrafted and Deep Features

Multi-class tissue classification from histological images is a complex challenge. The gold standard still relies on manual assessment by a trained pathologist, but it is a time-expensive task with issues about intra- and inter-operator variability. The rise of computational models in Digital Pathology has the potential to revolutionize the field. Historically, image classifiers relied on handcrafted feature extraction, combined with statistical classifiers, as Support Vector Machines (SVMs) or Artificial Neural Networks (ANNs). In recent years, there has been a tremendous growth in Deep Learning (DL), for all the image recognition tasks, including, of course, those concerning medical images. Thanks to DL, it is now possible to also learn the process of capturing the most relevant features from the image, easing the design of specialized classification algorithms and improving the performance. An important problem of DL is that it requires tons of training data, which is not easy to obtain in medical domain, since images have to be annotated by expert physicians. In this work, we extensively compared three classes of approaches for the multi-class tissue classification task: (1) extraction of handcrafted features with the adoption of a statistical classifier; (2) extraction of deep features using the transfer learning paradigm, then exploiting SVM or ANN classifiers; (3) fine-tuning of deep classifiers. After a cross-validation on a publicly available dataset, we validated our results on two independent test sets, obtaining an accuracy of 97% and of 77%, respectively. The second test set has been provided by the Pathology Department of IRCCS Istituto Tumori Giovanni Paolo II and has been made publicly available ( http://doi.org/10.5281/zenodo.4785131 ).
OpenPhi: an interface to access Philips iSyntax whole slide images for computational pathology

Digital pathology enables applying computational methods, such as deep learning, in pathology for improved diagnostics and prognostics, but lack of interoperability between whole slide image formats of different scanner vendors is a challenge for algorithm developers. We present OpenPhi-Open PatHology Interface, an Application Programming Interface for seamless access to the iSyntax format used by the Philips Ultra Fast Scanner, the first digital pathology scanner approved by the United States Food and Drug Administration. OpenPhi is extensible and easily interfaced with existing vendor-neutral applications.OpenPhi is implemented in Python and is available as open-source under the MIT license at: https://gitlab.com/BioimageInformaticsGroup/openphi. The Philips Software Development Kit is required and available at: https://www.openpathology.philips.com. OpenPhi version 1.1.1 is additionally provided as Supplementary Data.Supplementary data are available at Bioinformatics online.