Reviewerly/exHarmony huggingface dataset:
Calibrating mirror-assisted multi-view digital image correlation system using a speckled planar object

Abstract Recently, a novel low-cost and easy-to-implement mirror-assisted multi-view digital image correlation (MV-DIC) technique that uses only a vertically arranged binocular stereo-digital image correlation (stereo-DIC) system and two planar mirrors was proposed for panoramic/dual-surface kinematic field measurements. The mirror-assisted MV-DIC, which is equivalent to a regular MV-DIC system comprising three/two identical stereo-DIC systems, can simultaneously measure three/two different regions of a test sample. To perform panoramic/dual-surface kinematic field measurements with this technique, one of the most important procedures is accurately calibrating the reflection transformation of each mirror to transform all measured regions to a common world coordinate system. Previous works calibrate the reflection transformation by decorating speckle patterns on the mirrors or adjusting the mirrors to various poses, which either occupies precious spatial resolution of the sensor or is complicated in implementation. In this work, an easy-to-implement and practical method that employs only a speckled planar object is proposed to calibrate the reflection transformation of the planar mirrors. The effectiveness and accuracy of the proposed method are well evaluated and validated. Compared with existing methods, the proposed method holds the advantage of being efficient in sensor utilization and simple in implementation and is therefore highly recommended for the practical use of the mirror-assisted MV-DIC technique.
Verifying Equivalence Properties of Neural Networks with ReLU Activation Functions

Neural networks have become popular methods for tackling various machine learning tasks and are increasingly applied in safety-critical systems. This necessitates verified statements about their behavior and properties. One of these properties is the equivalence of two neural networks, which is important, e.g., when neural networks shall be reduced to smaller ones that fit space and memory constraints of embedded or mobile systems. In this paper, we present the encoding of feed-forward neural networks with ReLU activation functions and define novel and relaxed equivalence properties that extend previously proposed notions of equivalence. We define $$\epsilon $$ - and top-k-equivalence and employ it in conjunction with restricting the input space by hierarchical clustering. Networks and properties are encoded as mixed integer linear programs (MILP). We evaluate our approach using two existing reduction methods on a neural network for handwritten digit recognition.
Over-the-Air Computing for 6G - Turning Air into a Computer.

Wireless data aggregation (WDA), referring to aggregating data distributed at devices, is a common operation in 5G-and-beyond networks for supporting applications related to distributed sensing, learning, and consensus. Conventional WDA techniques that are designed based on a separated-communication-and-computation principle encounter difficulty in meeting the stringent throughput/latency requirements imposed by emerging applications (e.g., auto-driving). To address this issue, over-the-air computation (AirComp) is being developed as a new WDA solution by integrating computation and communication. By exploiting the waveform superposition property of a multiple-access channel, AirComp turns the air into a computer for computing and communicating functions of distributed data at many devices, thereby dramatically accelerating WDA. In view of growing interests on AirComp, this article provides a timely overview of the technology by discussing basic principles, advanced techniques, applications, answering frequently asked questions, and identifying promising research opportunities.
Pyramid Inter-Attention for High Dynamic Range Imaging

This paper proposes a novel approach to high-dynamic-range (HDR) imaging of dynamic scenes to eliminate ghosting artifacts in HDR images when in the presence of severe misalignment (large object or camera motion) in input low-dynamic-range (LDR) images. Recent non-flow-based methods suffer from ghosting artifacts in the presence of large object motion. Flow-based methods face the same issue since their optical flow algorithms yield huge alignment errors. To eliminate ghosting artifacts, we propose a simple yet effective alignment network for solving the misalignment. The proposed pyramid inter-attention module (PIAM) performs alignment of LDR features by leveraging inter-attention maps. Additionally, to boost the representation of aligned features in the merging process, we propose a dual excitation block (DEB) that recalibrates each feature both spatially and channel-wise. Exhaustive experimental results demonstrate the effectiveness of the proposed PIAM and DEB, achieving state-of-the-art performance in terms of producing ghost-free HDR images.
Image Segmentation and Adaptive Contrast Enhancement for Haze Removal

With a view to restoring image details of heavily hazy images, we propose an adaptive contrast enhancement algorithm specifically for haze removal. It is composed of 3 parts. The first part is to segment the input image into flat background of air space and foreground which is the rest of the image. A specific gradient matrix is defined to generate a gradient feature value to identify the pixels of very weak signals with the presence of noise of similar amplitude. In the second part, a CLAHE-based method is developed and applied to the foreground to provide a stronger enhancement to weaker signal variations while the background is protected from noise enhancement. A specifically designed filter is then applied to remove noise caused by the discontinuity between the foreground and background areas, while preserving the enhanced image details. The proposed algorithm has been tested and its effectiveness has been proven by the test results.
MYNursingHome: A fully-labelled image dataset for indoor object classification.

A fully labelled image dataset serves as a valuable tool for reproducible research inquiries and data processing in various computational areas, such as machine learning, computer vision, artificial intelligence and deep learning. Today's research on ageing is intended to increase awareness on research results and their applications to assist public and private sectors in selecting the right equipments for the elderlies. Many researches related to development of support devices and care equipment had been done to improve the elderly's quality of life. Indoor object detection and classification for autonomous systems require large annotated indoor images for training and testing of smart computer vision applications. This dataset entitled MYNursingHome is an image dataset for commonly used objects surrounding the elderlies in their home cares. Researchers may use this data to build up a recognition aid for the elderlies. This dataset was collected from several nursing homes in Malaysia comprises 37,500 digital images from 25 different indoor object categories including basket bin, bed, bench, cabinet and others.
Compact Design of On-chip Elman Optical Recurrent Neural Network

We propose an on-chip optical Elman recurrent neuron network (RNN) architecture for high-speed sequence processing using Mach-Zehnder interferometers and looped waveguides. The proposed design paves way for future integrated-photonics-based artificial intelligence hardware design.
Modeling Epistemic and Aleatoric Uncertainty with Bayesian Neural Networks and Latent Variables

In this thesis we develop a novel probabilistic model, the Bayesian neural network with latent variables (BNN+LV). This model class can describe complex stochastic patterns in the data via latent input variables, while, at the same time, account for epistemic uncertainty via a distribution over the network parameters. We investigate the applicability of BNN+LV for decision making, including regression, active learning and reinforcement learning.
An AI based research on optimization of university sports information service

The focus of this article is to explore the application of artificial intelligence in university sports information services based on the development trend of artificial intelligence technology. Research and analyze the characteristics and functions of new intelligent information service tools, and explore the effects of artificial intelligence in optimizing university sports information services from the three aspects of intelligent evolution information service, intelligent push information, and intelligent retrieval information, and the connotation of intelligent environment Analyze characteristics and technical support to promote the optimization and upgrading of university sports information services, research on the transformation of evaluation methods from manual evaluation to intelligent evaluation, and from standardized evaluation to differential evaluation, and specifically analyze the connotation of intelligent evaluation and differential evaluation, Features and key technologies, analyze the general process of intelligent evaluation, and summarize the implementation suggestions for intelligent evaluation. And discuss the application of artificial intelligence in university sports information services from scientific decision-making and automated management.
Deep Learning for Classification of Radiology Reports with a Hierarchical Schema

Radiological reports are a valuable source of textual information, which can be exploited to improve clinical care and to support research. Such information can be extracted and put into a structured form using machine learning techniques. Some of them rely not only on the classification labels but also on the manual annotation of relevant snippets, which is a time consuming job and requires domain experts. In this paper, we apply deep learning techniques and in particular Long Short Term Memory (LSTM) networks to perform such a task relying only on the classification labels. We focus on the classification of chest computed tomography reports in Italian according to a classification schema proposed for this task by the radiologists of Spedali Civili di Brescia. Each report is classified according to such schema using a combination of neural network classifiers. The resulting system is a novel classification system, which we compare to a previous system based on standard machine learning techniques which used annotations of relevant snippets.
Secure optical communication using a quantum alarm

Optical fibre networks are advancing rapidly to meet growing traffic demands. Security issues, including attack management, have become increasingly important for optical communication networks because of the vulnerabilities associated with tapping light from optical fibre links. Physical layer security often requires restricting access to channels and periodic inspections of link performance. In this paper, we report how quantum communication techniques can be utilized to detect a physical layer attack. We present an efficient method for monitoring the physical layer security of a high-data-rate classical optical communication network using a modulated continuous-variable quantum signal. We describe the theoretical and experimental underpinnings of this monitoring system and the monitoring accuracy for different monitored parameters. We analyse its performance for both unamplified and amplified optical links. The technique represents a novel approach for applying quantum signal processing to practical optical communication networks and compares well with classical monitoring methods. We conclude by discussing the challenges facing its practical application, its differences with respect to existing quantum key distribution methods, and its usage in future secure optical transport network planning.
Non Subsampled Shearlet Transform Based Fusion of Multiple Exposure Images

Fusion of multiple exposure images has attracted attention over the past decade and several algorithms have been developed, so as to capture the entire dynamic range of the scene in a single image. Capturing images with changes in exposure settings leads to a set of multiple exposure images with different areas of the scene highlighted in different image. Weak edges and fine textures of the image are lost during an under or over exposure. Also for objective evaluation we need to measure and both the structural and textural information in the images simultaneously. To address this issue an algorithm based on the Non-subsampled shearlet transform (NSST) for fusing multiple exposure images is proposed so as to depict clearly the dimly lit, brightly lit and well lit regions in a single fused image. In the proposed algorithm NSST decomposition is first performed on the images to obtain the multi-scale and multi-direction representations. The high frequency bands are fused by retaining the pixels with the highest value coefficients at each sub band at each level. Whereas the low frequency bands are fused by averaging operation. Proposed method leads to better results in visual quality.
ACM SIGSOFT Empirical Standards.

Empirical Standards are brief public document that communicate expectations for a specific kind of study (e.g. a questionnaire survey). The ACM SIGSOFT Paper and Peer Review Quality Initiative generated empirical standards for common research methods in software engineering. These living documents, which should be continuously revised to reflect evolving consensus around research best practices, can be used to make peer review more transparent, structured, harmonious and fair.
Variations in Spectral Signals of Heavy Metal Contamination in Mine Soils Controlled by Mineral Assemblages

This paper illustrates a spectroscopic analysis of heavy metal concentration in mine soils with the consideration of mineral assemblages originated by weathering and mineralization processes. The mine soils were classified into two groups based on the mineral composition: silicate clay mineral group (Group A) and silicate–carbonate–skarn–clay mineral group (Group B). Both soil groups are contaminated with Cu, Zn, As, and Pb, while the contamination level was higher for Group A. The two groups exhibit different geochemical behaviors with different heavy metal contamination. The spectral variation associated with heavy metal was highly correlated with absorption features of clay and iron oxide minerals for Group A, and the absorption features of skarn minerals, iron oxides, and clay minerals for Group B. It indicates that the geochemical adsorption of heavy metal elements mainly occurs with clay minerals and iron oxides from weathering, and of skarn minerals, iron oxides, and clay minerals from mineralization. Therefore, soils from different secondary mineral production processes should be analyzed with different spectral models. We constructed spectral models for predicting Cu, Zn, As, and Pb in soil group A and Zn and Pb in soil group B using corresponding absorptions. Both models were statistically significant with sufficient accuracy.
Inductive Reasoning with Equality Predicates, Contextual Rewriting and Variant-Based Simplification

We present an inductive inference system for proving validity of formulas in the initial algebra $$T_{\mathcal {E}}$$ of an order-sorted equational theory $$\mathcal {E}$$ with 17 inference rules, where only 6 of them require user interaction, while the remaining 11 can be automated as simplification rules and can be combined together as a limited, yet practical, automated inductive theorem prover. The 11 simplification rules are based on powerful equational reasoning techniques, including: equationally defined equality predicates, constructor variant unification, variant satisfiability, order-sorted congruence closure, contextual rewriting and recursive path orderings. For $$\mathcal {E} = (\varSigma , E \uplus B)$$ , these techniques work modulo B, with B a combination of associativity and/or commutativity and/or identity axioms.
An Interior Point Method Solving Motion Planning Problems with Narrow Passages

Algorithmic solutions for the motion planning problem have been investigated for five decades. Since the development of A* in 1969 many approaches have been investigated, traditionally classified as either grid decomposition, potential fields or sampling-based. In this work, we focus on using numerical optimization, which is understudied for solving motion planning problems. This lack of interest in the favor of sampling-based methods is largely due to the non-convexity introduced by narrow passages. We address this shortcoming by grounding the solution in differential geometry. We demonstrate through a series of experiments on 3 Dofs and 6 Dofs narrow passage problems, how modeling explicitly the underlying Riemannian manifold leads to an efficient interior point non-linear programming solution. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
How AI can Change/Improve/Influence Music Composition, Performance and Education: Three Case Studies

The use of artificial intelligence in science is happening more and more frequently, and often artificial intelligence can be seen in different approaches to creating music and art. In this paper, I will present some of the research that has been carried out, which involve the use of artificial intelligence in the field of composition, performance, and music education. The main focus in the field of composition will be on AIVA – the first virtual composer created with artificial intelligence, which is registered with an author’s rights society. In the field of performance, we’ll mostly talk about Yamaha's experiment where the world-renowned dancer Kaiji Moriyama controls a piano with his body movements, and in the context of education, this paper reviews some of the possibilities in a variety of artificial intelligence approaches to music education. Lastly, I will conclude the paper by presenting the direction of and possible future for the use of artificial intelligence in music.
The Runner's Journey: Identifying Design Opportunities for Running Motivation Technology

Running is a popular recreational sport, yet for many amateur runners it remains challenging to turn intentions into sustainable running behavior. Although the market offers a myriad of running-related devices that aim to motivate runners, these often focus on the training itself and not on overcoming the barriers experienced prior to the run. A better understanding of these barriers to running is essential to identify design opportunities for technologies supporting amateur runners. We conducted two complementary studies among participants of a women-only running event. Combining an online survey (N = 114) and a journey mapping activity (N = 13), we investigated the influence of motivational barriers and enablers in runners' rituals. Based on our findings, we created the Runner's Journey, a visual narrative highlighting actionable design opportunities for running motivation technology. We propose five design recommendations to overcome barriers among amateur runners.
Backlogs are a dynamic system, not a warehousing problem

Addressing casework backlogs would seem to represent “low hanging fruit” for increasing offender apprehension and improving justice. Yet, after years of grant funding for backlog reduction and capacity building, backlogged cases, especially DNA cases, continue to increase in U.S. forensic laboratories. Why? This paper suggests a shift from linear, mechanical thinking to a systems thinking approach may help to see ways to leverage laboratories from dysfunctional operational states burdened by history to new ways of seeing themselves as part of a system of systems. The A3 method is offered as a practical approach to initiating a systems approach.
Speaker Representation Learning Using Global Context Guided Channel and Time-Frequency Transformations

In this study, we propose the global context guided channel and time-frequency transformations to model the long-range, non-local time-frequency dependencies and channel variances in speaker representations. We use the global context information to enhance important channels and recalibrate salient time-frequency locations by computing the similarity between the global context and local features. The proposed modules, together with a popular ResNet based model, are evaluated on the VoxCeleb1 dataset, which is a large scale speaker verification corpus collected in the wild. This lightweight block can be easily incorporated into a CNN model with little additional computational costs and effectively improves the speaker verification performance compared to the baseline ResNet-LDE model and the Squeeze&Excitation block by a large margin. Detailed ablation studies are also performed to analyze various factors that may impact the performance of the proposed modules. We find that by employing the proposed L2-tf-GTFC transformation block, the Equal Error Rate decreases from 4.56% to 3.07%, a relative 32.68% reduction, and a relative 27.28% improvement in terms of the DCF score. The results indicate that our proposed global context guided transformation modules can efficiently improve the learned speaker representations by achieving time-frequency and channel-wise feature recalibration.
An Efficient Robust Blind Watermarking Method Based on Convolution Neural Networks in Wavelet Transform Domain

Digital watermarking is one of the most widely used techniques for the protection of ownership rights of digital audio, images, and videos.One of the desirable properties of a digital watermarking scheme is its robustness against attacks aiming at removing or destroying the watermark from the host data.Different from the common watermarking techniques based on the spatial domain or transform domain, in this paper, a novel scheme of digital image blind watermarking based on the combination of the discrete wavelet transform (DWT) and the convolutional neural network (CNN) is proposed.Firstly, the host images are decomposed by the DWT with 4 levels and, then, the low frequency sub-bands of the first level and the high frequency sub-bands of the fourth level are used as the input data and the output target data to train the CNN model for embedding and extracting the watermark.Experimental results show that the proposed scheme has superior performance against common attacks of JPEG compression, mean and median filtering, salt and pepper noise, Gaussian noise, speckle noise, brightness modification, scaling, cropping, rotation, and shearing operations.
Survey on Hyperparameter Optimization Using Nature-Inspired Algorithm of Deep Convolution Neural Network

Hyperparameters play an important role in Convolutional Neural Network (CNN). It is a tedious job for researchers to select proper hyperparameters manually for better performance on CNN. Lots of techniques are available to optimize the hyperparameter of CNN; however, some of the techniques give good result and others not. Nature-inspired algorithms as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) have put a profound effect on hyperparameter optimization in several domains. This paper presents a detailed review of the hyperparameter optimization of CNN using nature-inspired algorithms. It also includes a brief description of parameters of different CNN models used for image classification, segmentation, and styling. The review provides the researcher with the proper idea about the CNN model, its hyperparameter, and techniques used for its optimization.