Reviewerly/exHarmony huggingface dataset:
An Indirect Inversion Scheme for Retrieving Toxic Metal Concentrations Using Ground-Based Spectral Data in a Reclamation Coal Mine, China

A reclamation coal mine in Baishui County of Shaanxi Province, China, was selected as the study area to develop a fast survey method for estimating soil heavy metal concentrations using spectral data. A portable object spectrometer manufactured by Analytical Spectral Devices (ASD) was used to measure soil spectral reflectance, and an X-ray fluorescence device was utilized to obtain the content of heavy metals. The Savitzky-Golay filter, first derivative reflectance (FDR), second derivative reflectance (SDR), continuum removal (CR), and continuous wavelet transform (CWT) were used to transform the original reflectance (OR) spectra for enhancing the spectral characteristics, respectively. Furthermore, correlation analysis was introduced to determine the characteristic bands and the correlations of heavy metals. Partial least squares regression (PLSR), extremely learning machine (ELM), random forest (RF), and support vector machine (SVM) were implemented for quantitatively determining relations between heavy metal contents and spectral reflectance. The outcomes demonstrated that the spectral transformation methods could effectively capture the characteristic bands and increase the relations between heavy metal contents and spectral reflectance. The relation between Fe and Ni was close with a relatively high correlation coefficient (r = 0.741). RF combined with CWT at the decomposition scales of 9 demonstrated the best performance with the highest Rv2 (0.71) and the lowest RMSEv (1019.1 mg/kg) for inferring Fe content. Ni content was inferred based on the close relationship between Fe and Ni. The result of RF was better than other methods with the highest Rv2 (0.69) and the lowest RMSEv (1.94 mg/kg) for estimating Ni concentration. Therefore, the RF model was chosen for mapping Fe and Ni contents in the study area. The present study revealed that the indirect inversion methods using spectral data can be effectively used to predict heavy metal concentrations. The outcomes supply a new perspective for retrieving heavy metal content based on hyperspectral remotely sensed technology.
On the role of artificial intelligence in psychiatry

Recently, there has been growing interest in artificial intelligence (AI) to improve efficiency and personalisation of mental health services. So far, the progress has been slow, however, advancements in deep learning may change this. This paper discusses the role for AI in psychiatry, in particular (a) diagnosis tools, (b) monitoring of symptoms, and (c) delivering personalised treatment recommendations. Finally, I discuss ethical concerns and technological limitations.
Possibilities of using virtual reality technologies in education

This article will discuss innovative technologies for effective organization of training; the possibilities and advantages of augmented and virtual reality technologies. The concept of virtual reality will discuss in detail. The opinions of experts in the field of VR technologies are summarized, the main devices and technical aspects that connect users with the virtual environment are highlighted. It outlines the key concepts, technologies and perspectives, issues and challenges that are applicable to the application of these technologies in the educational process.
MonoIndoor++: Towards Better Practice of Self-Supervised Monocular Depth Estimation for Indoor Environments

Self-supervised monocular depth estimation has seen significant progress in recent years, especially in outdoor environments, i.e., autonomous driving scenes. However, depth prediction results are not satisfying in indoor scenes where most of the existing data are captured with hand-held devices. As compared to outdoor environments, estimating depth of monocular videos for indoor environments, using self-supervised methods, results in two additional challenges: (i) the depth range of indoor video sequences varies a lot across different frames, making it difficult for the depth network to induce consistent depth cues for training, whereas the maximum distance in outdoor scenes mostly stays the same as the camera usually sees the sky; (ii) the indoor sequences recorded with handheld devices often contain much more rotational motions, which cause difficulties for the pose network to predict accurate relative camera poses, while the motions of outdoor sequences are pre-dominantly translational, especially for street-scene driving datasets such as KITTI. In this work, we propose a novel framework-MonoIndoor++ by giving special considerations to those challenges and consolidating a set of good practices for improving the performance of self-supervised monocular depth estimation for indoor environments. First, a depth factorization module with transformer-based scale regression network is proposed to estimate a global depth scale factor explicitly, and the predicted scale factor can indicate the maximum depth values. Second, rather than using a single-stage pose estimation strategy as in previous methods, we propose to utilize a residual pose estimation module to estimate relative camera poses across consecutive frames iteratively. Third, to incorporate extensive coordinates guidance for our residual pose estimation module, we propose to perform coordinate convolutional encoding directly over the inputs to pose networks. The proposed method is validated on a variety of benchmark indoor datasets, i.e., EuRoC MAV, NYUv2, ScanNet and 7-Scenes, demonstrating the state-of-the-art performance. In addition, the effectiveness of each module is shown through a carefully conducted ablation study and the good generalization and universality of our trained model is also demonstrated, specifically on ScanNet and 7-Scenes datasets.
The value of cross-data set analysis for automobile insurance fraud detection

This study focuses on personal automobile policies underwritten. Its aim is to provide decision support and to apply new models with good predictive performance and high operational efficiency. We propose a new approach by constructing a score that evolves over the life of a claim. It consists of creating a score at the opening of a claim and another derived from the information of the first adjuster’s report. Natural language processing is also used on a textual variable relating to the description of the claim provided by the agency. The fraud score is estimated by using a gradient boosting machine (GBM) and a neural network. The results are interpreted using the local interpretable model-agnostic explanations (LIME). They show that fraud detection is improved when all the information and the textual variable are included. Furthermore, we observe that the GBM method overperforms the neural network approach. • A new score framework based on a dataset that distinguishes the information available at the opening of the claim from the first appraisal. • Our aim is to build a score that changes over the life of a claim. The information of the adjuster is complementary to detect fraud. • Natural language processing is also used upon a text variable related to the description of the claim • The fraud score in automobile insurance is estimated by using GBM and neural networks. • The results are interpreted using the local interpretable model-agnostic explanations (LIME)
Detection of genuine entanglement for multipartite quantum states

We study genuine multipartite entanglement of arbitrary n-partite quantum states by representing the density matrices in terms of generalized Pauli operators. While the usual Bloch representation of a density matrix uses three types of generators in the special unitary Lie algebra $$\mathfrak {su}(d)$$ , the Weyl representation with generalized Pauli operators has one uniformed type of generators that simplifies computation. In this paper, we take the advantage of this simplicity to derive useful and operational criteria to detect genuine tripartite entanglement. We also generalize the results to obtain a sufficient criterion to detect genuine entanglement for multipartite quantum states in arbitrary dimensions. The new method can detect more genuine entangled states than previous methods as backed by detailed examples.
Collaborative training of heterogeneous reinforcement learning agents in environments with sparse rewards: what and when to share?

In the early stages of human life, babies develop their skills by exploring different scenarios motivated by their inherent satisfaction rather than by extrinsic rewards from the environment. This behavior, referred to as intrinsic motivation, has emerged as one solution to address the exploration challenge derived from reinforcement learning environments with sparse rewards. Diverse exploration approaches have been proposed to accelerate the learning process over single- and multi-agent problems with homogeneous agents. However, scarce studies have elaborated on collaborative learning frameworks between heterogeneous agents deployed into the same environment, but interacting with different instances of the latter without any prior knowledge. Beyond the heterogeneity, each agent’s characteristics grant access only to a subset of the full state space, which may hide different exploration strategies and optimal solutions. In this work we combine ideas from intrinsic motivation and transfer learning. Specifically, we focus on sharing parameters in actor-critic model architectures and on combining information obtained through intrinsic motivation with the aim of having a more efficient exploration and faster learning. We test our strategies through experiments performed over a modified ViZDooM’s My Way Home scenario, which is more challenging than its original version and allows evaluating the heterogeneity between agents. Our results reveal different ways in which a collaborative framework with little additional computational cost can outperform an independent learning process without knowledge sharing. Additionally, we depict the need for modulating correctly the importance between the extrinsic and intrinsic rewards to avoid undesired agent behaviors.
Extended kernel density estimation for anomaly detection in streaming data

Machine Learning algorithms based on kernel density estimation (KDE) are said to be well suited for anomaly detection. However, existing approaches mainly cover point anomaly detection. Many industrial applications also require detecting drifts, contextual and collective anomalies. Due to the demand for small latencies, edge computing gains significance and hybrid models are no adequate solution. The main contribution is the EEM-KDE algorithm, which includes extensions of the KDE for detecting all aforementioned types of anomalies in streaming data in the edge. Finally, the proposed algorithm is evaluated showing the capability to detect different types of anomalies using an industrial control unit.
POSTER: ODABE: Outsourced Decentralized CP-ABE in Internet of Things

The ‘things’ layer in Internet of Things (IoT) consists of a massive number of devices, many of which are power and resource constrained. Decentralized Attribute-based Encryption (DABE) provides a one-to-many scheme that fits the distributed nature of IoT, however requires extensive computation power which makes its adoption difficult. In this work, we proposed an encryption model in DABE utilizing a single node, without revealing the secrets to the outsider node. The results showed that our model significantly improved the DABE.
Verifiable Timed Linkable Ring Signatures for Scalable Payments for Monero

Decentralized cryptocurrencies still suffer from three interrelated weaknesses: Low transaction rates, high transaction fees, and long confirmation times. Payment Channels promise to be a solution to these issues, and many constructions for cryptocurrencies, such as Bitcoin and Ethereuem, are known. Somewhat surprisingly, no solution is known for Monero, the largest privacy-preserving cryptocurrency, without requiring system-wide changes like a hard-fork of its blockchain like prior solutions. In this work, we close this gap for Monero by presenting the first provably secure payment channel protocol that is fully compatible with Monero’s transaction scheme. Notably, the payment channel related transactions are identical to standard transactions in Monero, therefore not hampering the coins’ fungibility. With standard techniques, our payment channels can be extended to support atomic swap of tokens in Monero with tokens of several other major currencies like Bitcoin, Ethereum, Ripple, etc., in a fungible and privacy-preserving manner. Our main technical contribution is a new cryptographic tool called verifiable timed linkable ring signatures (VTLRS), where linkable ring signatures can be hidden for a pre-determined amount of time in a verifiable way. We present a practically efficient construction of VTLRS which is fully compatible with the transaction scheme of Monero, and allows for users to make timed payments to the future which might be of independent interest to develop other applications on Monero. Our implementation results show that even with high network latency and with a single CPU core, two regular users can perform up to 93500 payments over 2 min (the block production rate of Monero). This is approximately five orders of magnitude improvement over the current payment rate of Monero.
PUASIoT: Password-Based User Authentication Scheme for IoT Services

In today’s scenario where there is an exponential increase in the need for IoT services, user authentication is an essential feature for device security. IoT services allow many devices to be accessed and connected over the Internet anytime and anywhere and thus pose a risk over its privacy and security. A single-factor authentication scheme can be used to provide robust security, privacy, and secure access over devices. To deal with a range of IoT devices with varying storage, we have propounded a lightweight novel password-based user authentication scheme with low computation costs. This scheme uses lightweight XOR and hash operations. AVISPA tool has been used to prove security against various attacks.
An Efficient CNN Accelerator Using Inter-Frame Data Reuse of Videos on FPGAs

Convolutional neural networks (CNNs) have had great success when applied to computer vision technology, and many application-specific integrated circuit (ASIC) and field-programmable gate array (FPGA) CNN accelerators have been proposed. These accelerators primarily focus on the acceleration of a single input, and they are not particularly optimized for video applications. In this article, we focus on the similarities between continuous inputs in video, and we propose a YOLOv3-tiny CNN FPGA accelerator using incremental operation. The accelerator can skip the convolution operation of similar data between continuous inputs. We also use the Winograd algorithm to optimize the conv <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3\times 3$ </tex-math></inline-formula> operator in the YOLOv3-tiny network to further improve the accelerator’s efficiency. Experimental results show that our accelerator achieved 74.2 frames/s on ImageNet ILSVRC2015. Compared to the original network without Winograd algorithm and incremental operation, our design provides a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.10\times $ </tex-math></inline-formula> speedup. When compared with other YOLO network FPGA accelerators applied to video applications, our design provided a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.13\times $ </tex-math></inline-formula> – <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$18.34\times $ </tex-math></inline-formula> normalized digital signal processor (DSP) efficiency and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.10\times $ </tex-math></inline-formula> – <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$14.2\times $ </tex-math></inline-formula> energy efficiency.
On Module Unique-SVP and NTRU

The NTRU problem can be viewed as an instance of finding a short non-zero vector in a lattice, under the promise that it contains an exceptionally short vector. Further, the lattice under scope has the structure of a rank-2 module over the ring of integers of a number field. Let us refer to this problem as the module unique Shortest Vector Problem, or mod-uSVP for short. We exhibit two reductions that together provide evidence the NTRU problem is not just a particular case of mod-uSVP, but representative of it from a computational perspective. First, we reduce worst-case mod-uSVP to worst-case NTRU. For this, we rely on an oracle for id-SVP, the problem of finding short non-zero vectors in ideal lattices. Using the worst-case id-SVP to worst-case NTRU reduction from Pellet-Mary and Stehlé [ASIACRYPT’21], this shows that worst-case NTRU is equivalent to worst-case mod-uSVP. Second, we give a random self-reduction for mod-uSVP. We put forward a distribution $$D^{\textrm{uSVP}}$$ over mod-uSVP instances such that solving mod-uSVP with a non-negligible probability for samples from $$D^{\textrm{uSVP}}$$ allows to solve mod-uSVP in the worst-case. With the first result, this gives a reduction from worst-case mod-uSVP to an average-case version of NTRU where the NTRU instance distribution is inherited from $$D^{\textrm{uSVP}}$$ . This worst-case to average-case reduction requires an oracle for id-SVP.
Deep Audio Waveform Prior

Convolutional neural networks contain strong priors for generating natural looking images [1]. These priors enable image denoising, super resolution, and inpainting in an unsupervised manner. Previous attempts to demonstrate similar ideas in audio, namely deep audio priors, (i) use hand picked architectures such as harmonic convolutions, (ii) only work with spectrogram input, and (iii) have been used mostly for eliminating Gaussian noise [2]. In this work we show that existing SOTA architectures for audio source separation contain deep priors even when working with the raw waveform. Deep priors can be discovered by training a neural network to generate a single corrupted signal when given white noise as input. A network with relevant deep priors is likely to generate a cleaner version of the signal before converging on the corrupted signal. We demonstrate this restoration effect with several corruptions: background noise, reverberations, and a gap in the signal (audio inpainting).
Coordinated Optimization Scheduling of Data Center and Electricity Retailer Based on Cooperative Game Theory

As the number of internet data centers (IDC) increases, the energy consumption of modern data centers is growing rapidly, which takes an increasing part of the global electricity consumption. In implementation, to cover the electricity consumption and reduce the electricity bill, data centers are often deployed with renewable energy, energy storage system (ESS) and conventional generators, which has brought the characteristics of both flexible electricity load and micro grid to the data centers. Therefore, the data center micro grids can adjust its operations according to the real time electricity price and cut down the operational cost. However, the rising of electricity will force the data center to start up the conventional generators, which leads to the incensement of operational cost and carbon tax. In view of this, we propose a coordinated operation scheduling method of data centers and electricity retailers based on cooperative game theory. In this method, a cooperative game union comprised of the data center and the electricity retailer is established to minimize the total cost. And the extra income of the alliance is distributed using the Shapley value method. A simulation is launched on Gurobi software, and the results have illustrated that the proposed method can reduce the data center operational cost by 5%.
A Systematic Method for Outlier Detection in Human Gait Data

When it comes to observing and measuring human gait data for further analysis, determining whether the observed behavior is within the normal range of variability, or should be considered abnormal, is very challenging. Moreover, usually gait data are multivariate including motion capture, electromyography, force measurements, etc., each source having its own unique causes of irregularities and anomalies. This paper introduces a unique algorithm for outlier detection in periodic gait data using multiple sources and multiple procedures to improve the overall accuracy. The proposed algorithm’s performance is evaluated using realistic synthetic gait data to gauge its accuracy to a truly objective known solution. It is shown that the proposed method is able to detect 91.2% of the true outliers in an extensive synthetic dataset, while only producing false positives at a rate of 0.1%, outperforming other procedures usually utilized in gait data outlier detection. The proposed method is a systematic way of removing outliers from gait data, with direct applications to human biomechanics, rehabilitation and robotics, and can be applied to other scientific fields dealing with periodic data.
Squamous Cell Carcinoma of Skin Cancer Margin Classification From Digital Histopathology Images Using Deep Learning

Objectives Now a days, squamous cell carcinoma (SCC) margin assessment is done by examining histopathology images and inspection of whole slide images (WSI) using a conventional microscope. This is time-consuming, tedious, and depends on experts’ experience which may lead to misdiagnosis and mistreatment plans. This study aims to develop a system for the automatic diagnosis of skin cancer margin for squamous cell carcinoma from histopathology microscopic images by applying deep learning techniques. Methods The system was trained, validated, and tested using histopathology images of SCC cancer locally acquired from Jimma Medical Center Pathology Department from seven different skin sites using an Olympus digital microscope. All images were preprocessed and trained with transfer learning pre-trained models by fine-tuning the hyper-parameter of the selected models. Results The overall best training accuracy of the models become 95.3%, 97.1%, 89.8%, and 89.9% on EffecientNetB0, MobileNetv2, ResNet50, VGG16 respectively. In addition to this, the best validation accuracy of the models was 94.7%, 91.8%, 87.8%, and 86.7% respectively. The best testing accuracy of the models at the same epoch was 95.2%, 91.5%, 87%, and 85.5% respectively. From these models, EfficientNetB0 showed the best average training and testing accuracy than the other models. Conclusions The system assists the pathologist during the margin assessment of SCC by decreasing the diagnosis time from an average of 25 minutes to less than a minute.