Reviewerly/exHarmony huggingface dataset:
Synthetic LiDAR-Labeled Data Set Generation to Train Deep Neural Networks for Object Classification in IoT at the Edge

Light detection and ranging (LiDAR) sensors are increasing in popularity due to the advantages they provide over 2-D sensors in IoT object detection and classification applications, because of their ability to provide very precise distances to objects. Deep learning algorithms need a huge amount of data during training to obtain high accuracy results. When using 2-D images, a vast quantity of data sets are publicly available, but this is not the case for LiDAR point clouds. Each LiDAR model generates a point cloud with unique properties, which causes the data sets not to be compatible between different LiDAR models. As a result, when using deep learning with LiDARs, it is necessary to generate the data sets manually. For this purpose, the data must be captured and then labeled one by one, which is a very time and cost-consuming process. To overcome this issue and to reduce the development time when using LiDAR sensors with deep learning algorithms, a methodology is proposed in this article to automatically generate point cloud data sets using a 3-D simulator for autonomous cars. In this regard, a data set can be generated for any LiDAR model by adding the specific LiDAR parameters to the simulator. Besides, custom scenarios can be designed and generated, based on the final deployment location, to provide a simulated solution very close to the final implementation. With the proposed methodology, a simulation can be performed to select the LiDAR that best fits certain application requirements, in contrast to the traditional approach where the LiDAR must first be purchased.
COSM2IC: Optimizing Real-Time Multi-Modal Instruction Comprehension

Supporting real-time, on-device execution of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multi-modal referring instruction comprehension</i> models is an important challenge to be tackled in embodied Human-Robot Interaction. However, state-of-the-art deep learning models are resource-intensive and unsuitable for real-time execution on embedded devices. While model compression can achieve a reduction in computational resources up to a certain point, further optimizations result in a severe drop in accuracy. To minimize this loss in accuracy, we propose the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">COSM2IC</i> framework, with a lightweight Task Complexity Predictor, that uses multiple sensor inputs to assess the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">instructional complexity</i> and thereby dynamically switch between a set of models of varying computational intensity such that computationally less demanding models are invoked whenever possible. To demonstrate the benefits of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">COSM2IC</i> , we utilize a representative human-robot collaborative “table-top target acquisition” task, to curate a new multi-modal instruction dataset where a human issues instructions in a natural manner using a combination of visual, verbal, and gestural (pointing) cues. We show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">COSM2IC</i> achieves a 3-fold reduction in comprehension latency when compared to a baseline DNN model while suffering an accuracy loss of only <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 5%. When compared to state-of-the-art model compression methods, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">COSM2IC</i> is able to achieve a further 30% reduction in latency and energy consumption for a comparable performance.
Constructing few-weight linear codes and strongly regular graphs

Linear codes with few weights have applications in data storage systems, secret sharing schemes, graph theory and so on. In this paper, we construct a class of few-weight linear codes by choosing defining sets from cyclotomic classes and we also establish few-weight linear codes by employing weakly regular bent functions. Notably, we get some codes that are minimal and we also obtain a class of two-weight optimal punctured codes with respect to the Griesmer bound. Finally, we get a class of strongly regular graphs with new parameters by using the obtained two-weight linear codes.
Self-explaining neural network with concept-based explanations for ICU mortality prediction

Complex deep learning models show high prediction tasks in various clinical prediction tasks but their inherent complexity makes it more challenging to explain model predictions for clinicians and healthcare providers. Existing research on explainability of deep learning models in healthcare have two major limitations: using post-hoc explanations and using raw clinical variables as units of explanation, both of which are often difficult for human interpretation. In this work, we designed a self-explaining deep learning framework using the expert-knowledge driven clinical concepts or intermediate features as units of explanation. The self-explaining nature of our proposed model comes from generating both explanations and predictions within the same architectural framework via joint training. We tested our proposed approach on a publicly available Electronic Health Records (EHR) dataset for predicting patient mortality in the ICU. In order to analyze the performance-interpretability trade-off, we compared our proposed model with a baseline having the same set-up but without the explanation components. Experimental results suggest that adding explainability components to a deep learning framework does not impact prediction performance and the explanations generated by the model can provide insights to the clinicians to understand the possible reasons behind patient mortality.
On the Informativeness of Information System Ontologies

Abstract The current (still limited) use of the notion of informativeness in the domain of information system ontologies seems to indicate that such ontologies are informative if and only if they are understandable for their final recipients. This paper aims at discussing some theoretical issues emerging from that use which, as we will see, connects the informativeness of information system ontologies to their representational primitives, domains of knowledge, and final recipients. Firstly, we maintain that informativeness interacts not only with the actual representational primitives, but also with their variability over time. Secondly, we discuss the correspondence between representational primitives and domains of knowledge of those ontologies. Finally, we explore the possibility of an epistemological discrepancy between human beings and software systems on the understanding of ontological contents.
Fourth-Order Partial Differential Equation Framelet Fusion-Based Colour Correction and Contrast Enhancement for Underwater Images

A framelet augmented fourth-order forward-reverse partial differential equation (PDE)-fusion-based algorithm is proposed for underwater image enhancement. The algorithm combines framelet domain transform-based fusion of modified base, detail and amplified detail layers in a PDE-based formulation. The extracted layers via framelet decomposition with adaptive threshold computation comprise the detail and approximation components of the images, which are amplified, attenuated and aggregated. Additions include a modified global contrast enhancement/color correction function and a suitable color space transformation to enhance difficult underwater images with flat non-overlapping color channel histograms. Also, gradient domain fusion of several color corrected image layers and fuzzy rule-based enhancement is combined in the proposed PDE-based fusion framework. Furthermore, variational illumination correction was also employed for better enhancement of dark underwater images. Experimental comparisons indicate that the proposed approaches yield better overall visual and numerical results in most cases when compared with state-of-the-art methods.
A FINGERPRINT REMOVAL METHOD BASED ON FRACTAL–CRIMINISI TECHNOLOGY

Against the circumstance of redundant fingerprint information remaining in photographed images due to lens contamination, a fingerprint removal method is proposed by combining fractal technology and the Criminisi method. Fingerprints on an image are usually distributed in a dispersive pattern, and show statistical fractal characteristic. Therefore, based on the Criminisi method, the textural complexity of image pixel blocks expressed by box-counting fractal dimension is introduced into this method as a parameter of pixel block size and subitem of the data item with priority. On the one hand, this way of operation can select the size of the pixel blocks to be removed adaptive to the box fractal dimension; on the other hand, the box fractal dimension, as the subitem of the data item with priority, can avoid the effect of particular value and improve the reliability and stability of priority. The experimental results show that the proposed method can satisfactorily perform the fingerprint removal task and fill the information.
Surface code for low-density qubit array

Surface code is a promising candidate for the quantum error corrections needed for fault-tolerant quantum computations because it can operate on a two-dimensional grid of qubits. However, the gates and control lines become dense as more and more qubits are integrated, making their design and control difficult. This problem can be alleviated if the surface code can operate on sparse qubit arrays. Here, we give an solution for an array in which qubits are placed on edges as well as on nodes of a two-dimensional grid. The qubits on the edges are divided into two groups: those in one group act as the deputies of data qubits; the others act as deputies of the syndrome qubits. Syndrome outputs are obtained by multiplying the measured values of the syndrome and edge qubits. The procedure for the quantum part is the same as that of the ordinary surface code, making the surface code applicable to sparse qubit arrays.
Experimental mutual coherence from separable coherent qubits

Quantum coherence is a fundamental resource in modern quantum physics with important applications in quantum technologies. In composite quantum systems, various forms of coherence emerge and play an important role, such as the global coherence, coherence of local subsystems, and the recently introduced mutual coherence. We investigate states that maximize the mutual coherence in various subspaces of the overall two-qubit Hilbert space and discover a nontrivial asymmetric optimal state in the three-dimensional subspace. We experimentally generate this optimal state from two factorized photonic qubits by a strictly incoherent probabilistic quantum operation that projects the input state onto the desired three-dimensional subspace. For comparison, we also experimentally test the preparation of states with maximal mutual coherence by unitary transformations of input product states. These proof-of-principle tests demonstrate the initial steps of control of mutual quantum coherence in qubit systems.
Classroom Behaviour of Talent Cultivation in Colleges and Universities Supported by Deep Learning Technology

Among the current significant issues in talent cultivation is changing colleges’ unique talent cultivation strategies and regulating the spontaneous connection of the talent cultivation, industrial, and entrepreneurial chains. In other terms, the growing disparity between college and university incorporation and industry demand for talent has made the growth of creative applied skills targeted to the industry want an acute problem. That is why colleges and universities must cultivate practical talent in a way that is effective, strong, low-cost, and creative. As a result, this research uses deep learning to develop an ensemble convolutional neural network with an attention mechanism to increase the performance and reliability of talent cultivation in universities and colleges and assess the talents properly. For this research, the datasets are gathered with the primary student and classroom information. Followed by standardising the data, the normalization technique is utilised. Then, the proposed method is employed for the data analysis. ANOVA, Chi-square test, and Student’s <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>t</mi> </math> -test are applied for the statistical analysis. Finally, the performance of the proposed technique is examined and compared with existing methods to prove the students’ highest performance in talent cultivation.
Military application of blockchain technology for future battlefield operations

In order to further explore the application scenarios of blockchain in the military field, starting from the basis of blockchain technology, this paper analyzes the development requirements of military blockchain, and focuses on the application scenarios of blockchain technology in the military field from the four directions of battle command and control system, equipment management, logistics supply chain management and human resource management. On this basis, the challenges and Countermeasures of blockchain in military application are given, which will provide ideas and reference for the implementation of military blockchain application.
Factors Affecting Employees' Susceptibility to Cyber-Attacks

ABSTRACT We examine factors associated with employees' susceptibility to phishing attacks in a professional services firm and a financial services firm (bank). We measure three dimensions of suspicion (skepticism, suspicion of hostility, and interpersonal trust), and three cognitive traits (risk-taking propensity, cognitive [inhibitory] control, and social cognition), while controlling for demographic and work context factors. We find that these traits interact in complex ways in determining individuals' susceptibility to phishing attacks. Bank employees are more susceptible to being phished than professional services firm employees, but within the bank, the employees with professional certificates are less susceptible to phishing attacks than other bank employees. Also, employees with self-reported responsibility for cybersecurity are less likely to be phished. These findings could be used to create a screening tool for identifying which employees are particularly susceptible to phishing attacks, to tailor training, or redesign jobs to counter those susceptibilities and reduce security risk.
Geological and structural position of the Svetlinsky gold deposit (Southern Urals)

The paper presents the geological and structural position of the large Svetlinsky gold deposit in the Kochkar anticline (Southern Urals), localized in the zone of the Late Paleozoic (D3) deep thrust of the western dip. The study confirms and clarifies the notion of its multiphase and polychronism. The thrust caused bending moments in its wings, subsidence of the lying crust, emergence of a shallow marine basin with rapid accumulation of terrigenous carbonate sediments (C1v), and formation of numerous landslide structures. The heating of rocks in the anticline core was accompanied by granitization and dome formation. A small Svetlinsky dome formed in the immediate vicinity of the thrust, creating a thermobaric gradient field (С2). The zone of dome dynamic influence also includes the adjoining thrust area, complicated by a series of sub-vertical thrusts of sub meridional strike and numerous steeply dipping subparallel cracks of the latitudinal strike, synchronously filled with vein quartz and accompanied by hydrothermal metasomatic rock transformations. The formation of the gold deposit occurred during the post-collisional relaxation stage (from P1 to, probably, the Early Jurassic). The association of gold mineralization with the Svetlinsky dome is indicated by the presence of native gold in Neogene ravine placers in the dome area and marbles of the Svetlinsky deposit, in association with fluorite, F-phlogopite, Cr-muscovite, pink topaz, pure quartz, and native sulphur. The presence of native gold in Neogene ravine placers in the dome area and marbles of the Svetlinsky deposit, in association with fluorite, F-phlogopite, Cr-muscovite, pink topaz, pure quartz, and native sulphur, indicates the association of gold mineralization with the Svetlinsky dome.
Incorporating history and future into non-autoregressive machine translation

In non-autoregressive machine translation, the target tokens are generated by the decoder in one shot. Although this decoding process can significantly reduce the decoding latency, non-autoregressive machine translation still suffers from the sacrifice of translation accuracy. We argue that the reason for such decrease is the lack of the target dependencies, history and future information, between target tokens. So, in this work, we propose a novel method to address this problem. We suppose the hidden representation of a target token from the decoder should consist of three parts: history, present, and future information. And we dynamically aggregate such parts-to-whole information with capsule network for the decoder to improve the performance of non-autoregressive machine translation. In addition, to ensure the capsules learn the information as we expect, we introduce an autoregressive decoder. Several experiments on benchmark tasks demonstrate that the explicit modeling of history and future information can significantly improve performance of NAT model. Extensive analyses show that our model is able to learn history and future information as we expect.
Mathematical model based on fractional trace operator for COVID-19 image enhancement

The medical image enhancement is major class in the image processing which aims for improving the medical diagnosis results. The improving of the quality of the captured medical images is considered as a challenging task in medical image. In this study, a trace operator in fractional calculus linked with the derivative of fractional Rényi entropy is proposed to enhance the low contrast COVID-19 images. The pixel probability values of the input image are obtained first in the proposed image enhancement model. Then the covariance matrix between the input image and the probability of a pixel intensity of the input image to be calculated. Finally, the image enhancement is performed by using the convolution of covariance matrix result with the input image. The proposed enhanced image algorithm is tested against three medical image datasets with different qualities. The experimental results show that the proposed medical image enhancement algorithm achieves the good image quality assessments using both the BRISQUE, and PIQE quality measures. Moreover, the experimental results indicated that the final enhancement of medical images using the proposed algorithm has outperformed other methods. Overall, the proposed algorithm has significantly improved the image which can be useful for medical diagnosis process.
Visually guided computer-mouse clicking interferes with multiple-object tracking (MOT)

Many everyday tasks require coordinated action towards specific items moving among others (e.g. touching, pointing). Pylyshyn (2001) proposed that multiple-object tracking (MOT), the ability to monitor positions of specific target items as they move among others, is an integral part of coordinated action towards targets. In support of this, Terry & Trick (2021) found that when participants were required to touch any moving item that changed colour (visually guided touch) it not only interfered with MOT – but it interfered differentially, with less interference for touching items that changed colour if they were also targets in MOT. However, direct touch may represent a special case. In this study, we attempted to replicate the finding using a different coordinated action: computer-mouse clicks. Participants tracked 2-3 targets in MOT while clicking any item that changed colour. Mouse-clicking also interfered differentially with MOT, which suggests a common mechanism may underlie these processes.
Effective and Robust Boundary-Based Outlier Detection Using Generative Adversarial Networks

Outlier detection aims to identify samples that do not match the expected patterns or major distribution of the dataset. It has played an important role in many domains such as credit card fraud identification, network intrusion detection, medical image processing and so on. The inherent class imbalance in datasets is one of the major reasons why this problem is difficult to solve. The small number of outliers are not adequate to characterize their own overall distribution, which makes it difficult for classifiers to effectively learn the demarcation (boundary) between normal samples and outliers. To address this problem, we introduce an effective and robust Boundary-based Outlier Detection method using Generative Adversarial Networks (BOD-GAN). Here, we extract the border data containing normal samples and outliers, expand them to form the initial reference boundary outliers. With the min-max game between a generator and two discriminators in GAN, the boundary outliers are further augmented by BOD-GAN, which, together with the boundary normal data, provides the valuable demarcation information for classifier. However, the increase of the data dimension may bring some gaps in the initial boundary, which are difficult to effectively fill by the augmentation method alone. To address this, we innovatively add density-loss to the loss function of the generator to explore these boundary gaps, making our model rather robust even with the high dimensional data. The extensive experimental evaluation demonstrates that our proposed method has achieved significant improvements compared with existing classic and emerging (i.e., GAN-based) outlier detection methods.
The Chronicles of Kunene: The Lion, the Omuhimba and the Drone

Many technology projects are driven by (hidden) agendas which prioritize the one or the other stakeholder or the technology itself, thereby creating tensions and compromising important perspectives. In the field of digital conservation and wildlife management, the focus has often been on safeguarding wildlife (the lion), not incorporating socio-economic factors of communities (the Omuhimba), by exploiting technologies concerned with wildlife data collection only (the drone). Concerned with reconciling heterogeneous perspectives the authors present the development and conceptualisation of an integrated wildlife monitoring system in Southern Africa. The authors postulate that a community-based co-design approach, grounded in the Ubuntu philosophy, leads to novel and innovative technology designs embracing ecocentrism.
What’s Next? Predicting Hamiltonian Dynamics from Discrete Observations of a Vector Field

We present several methods for predicting the dynamics of Hamiltonian systems from discrete observation of their vector field. Each method is either informed or uninformed of the Hamiltonian property. We empirically and comparatively evaluate the methods and observe that information that the system is Hamiltonian can be effectively informed, and that different methods strike different trade-offs between efficiency and effectiveness for different dynamical systems.