Reviewerly/exHarmony huggingface dataset:
Use RBF as a Sampling Method in Multistart Global Optimization Method

In this paper, a new sampling technique is proposed that can be used in the Multistart global optimization technique as well as techniques based on it. The new method takes a limited number of samples from the objective function and then uses them to train an Radial Basis Function (RBF) neural network. Subsequently, several samples were taken from the artificial neural network this time, and those with the smallest network value in them are used in the global optimization method. The proposed technique was applied to a wide range of objective functions from the relevant literature and the results were extremely promising.
Explainable AI for Gas Sensors

Releasing harmful pollutants like ozone and nitrogen dioxide gas into the atmosphere has been a serious concern in recent times. Such gases endanger the health of humans as well as other species and cause damage to the environment. As a result, it has become vital to monitor the air quality around us. With technological advancement, low-cost chemical gas sensors equipped with machine learning or deep learning algorithms can be employed to detect these gases and their concentrations. However, with the use of such algorithms, there comes a challenge to understanding why they made certain predictions in human terms. This paper aims to address this difficulty by adopting different methodologies of explainable artificial intelligence (XAI) for gas sensors. These methods in turn help understand the reasoning behind the predictions made by the models and at the same time facilitate the characterization of the sensor behavior.
ISM-Net: Mining incremental semantics for class incremental learning

Class incremental learning (CIL) aims to learn new classes from the data stream, where old class data is largely discarded due to data privacy or memory restrictions. A handful of exemplars cannot reflect the complete distribution of old classes, and the separation between old and new classes is hard to guarantee, which is an important cause of catastrophic forgetting. To overcome this problem, we first propose incremental semantics mining (ISM) to reduce the misclassification between old and new classes by excluding the semantics of old classes from the representation of new classes. Then, we propose a distillation-based representation expansion strategy to encode the incremental semantics into an additional representation space. Compared to the standard representation expansion strategy, our method features lower memory overhead and computational costs. In addition, an old model queue is proposed to facilitate the maintenance of earlier knowledge. Extensive experiments on CIFAR-100 and ImageNet datasets demonstrate the superiority of our method in both performance and parameter efficiency. Several state-of-the-art results are established under different incremental settings. Code: https://github.com/zihuanqiu/ISM-Net
Security of railway control systems: A survey, research issues and challenges

With the rapid development of railway transportation, higher requirements for capacity are increasing and amount of communication, computer and control technologies are applied in train control systems which is the popular method for train control. The application of 3 C brings the enhancement of railway transportation performance. However, the number of internal and external cyber security threats is rising, which could lead to some railway accidents. On the other hand, safety is the core of the design process for railway but security is rarely considered due to the closure of traditional railway systems. As cyber security is threatening the normal operation of industrial control systems and the critical infrastructure, security issues of train control systems should be paid more attention as railway transportation play an important role for society and economy. However, due to the safety-critical characteristics, safety assessment and analysis works have been performed for a long time, but cyber security related works are rarely considered. In the paper, we demonstrate the security situation of train control systems based on the inherent features and the experience of other industrial control systems, where the technical defects and potential threats are summarized. According to the practical engineering experience, the current security protection strategies are illustrated, which shows the popular security protection methods are limited and cannot realize the defense-in-depth. Finally, some research challenges of security issues of train control systems are identified.
Digital Authorship Attribution in Russian-Language Fanfiction and Classical Literature

This article is the third paper in a series aimed at the establishment of the authorship of Russian-language texts. This paper considers methods for determining the authorship of classical Russian literary texts, as well as fanfiction texts. The process of determining the author was first considered in the classical version of classification experiments using a closed set of authors, and experiments were also completed for a complicated modification of the problem using an open set of authors. The use of methods to identify the author of the text is justified by the conclusions about the effectiveness of the fastText and Support Vector Machine (SVM) methods with the selection of informative features discussed in our past studies. In the case of open attribution, the proposed methods are based on the author’s combination of fastText and One-Class SVM as well as statistical estimates of a vector’s similarity measures. The feature selection algorithm for a closed set of authors is chosen based on a comparison of five different selection methods, including the previously considered genetic algorithm as a baseline. The regularization-based algorithm (RbFS) was found to be the most efficient method, while methods based on a complete enumeration (FFS and SFS) are found to be ineffective for any set of authors. The accuracy of the RbFS and SVM methods in the case of classical literary texts averaged 83%, which outperforms other selection methods by 3 to 10% for an identical number of features, and the average accuracy of fastText was 84%. For the open attribution in cross-topic classification, the average accuracy of the method based on the combination of One-Class SVM with RbFS and fastText was 85%, and for in-group classification, it was 75 to 78%, depending on the group, which is the best result among the open attribution methods considered.
Feature Extraction and Naïve Bayes Algorithm for Defect Classification of Manalagi Apples

Abstract Apple is one of the trees that is widely cultivated and grows in subtropical areas. In Indonesia, there are many areas that cultivate apples, including Malang, Batu, Nongkojajar. One way to increase the economic value of apple farmers is by sorting them before sending them to the market. This is important to do in order to make it easier to determine the quality and selling price of manalagi apples. Most apple sellers will sort the apples manually which results in high costs, difficulty, and inconsistency in the sorting process. So far, the classification of defects in apples has been done using the naked eye. This also requires expertise or experts in distinguishing which apple defects are. However, experts have limitations, not all apple defects can be recognized or classified. In addition, each researcher only uses one image feature, namely the texture feature. In this study, using an image dataset of Manalagi apples totaling 337 images, where there are 184 images of good apples and 153 images of defective apples by extracting features on apples, it can be concluded that the nave Bayes method can be used to classify defects of manalagi apples based on texture.
Mixed Differential Privacy in Computer Vision

We introduce AdaMix, an adaptive differentially private algorithm for training deep neural network classifiers using both private and public image data. While pre-training language models on large public datasets has enabled strong differential privacy (DP) guarantees with minor loss of accuracy, a similar practice yields punishing trade-offs in vision tasks. A few-shot or even zero-shot learning baseline that ignores private data can outperform fine-tuning on a large private dataset. AdaMix incorporates few-shot training, or cross-modal zero-shot learning, on public data prior to private fine-tuning, to improve the trade-off. AdaMix reduces the error increase from the non-private upper bound from the 167–311% of the baseline, on average across 6 datasets, to 68-92% depending on the desired privacy level selected by the user. AdaMix tackles the trade-off arising in visual classification, whereby the most privacy sensitive data, corresponding to isolated points in representation space, are also critical for high classification accuracy. In addition, AdaMix comes with strong theoretical privacy guarantees and convergence analysis.
Small Object Detection using Multi-scale Feature Fusion and Attention

In deep learning, object detection has achieved a very large performance improvement. However, due to the few features available for small objects, network structure, sample imbalance and other reasons, the result is unsatisfactory in small object detection. In order to solve this problem, this paper proposes a method based on the combination of mutil-scale feature fusion and dilated convolution, which uses dilated convolution to expand the receptive field of feature maps at different scales and then extracts the high-level semantic information and low-level semantic information from the backbone network. The obtained feature maps of different receptive fields are fused to obtain the final feature map prediction information. In addition, we add a series of channel attention and spatial attention mechanisms to the network to better obtain the context information of the object in the image. Experiments show that this method can have higher accuracy than the traditional YOLOv3 network in the detection of small objects. In addition, the size of 640*640 images, we can achieve 31.5% accuracy in the detection of small objects in MS COCO2017. Compared with YOLOv5, there are 4 points of improvement.
Measurements and Modeling of a Monolithically Integrated Self-Spiking Two-Section Laser in InP

The self-spiking behavior of an integrated saturable absorber and gain section laser fabricated in an InP technology platform is analyzed. The gain, absorber and intensity dynamics are first inspected using the normalized Yamada model. This model shows excitable behavior as well as the relative refractory period, both of which are also present in biological neurons. Measurements of a two-section laser show irregular spike generation on the millisecond timescale, with a saturable absorber voltage controlled spike density. From our simulations, and from the quasi-random character and millisecond timescale at which these pulses occur, we conclude the laser is triggered by noise, an important characteristic in the operation of biological neurons. Simulations of the laser around the excitability threshold using a newly proposed model with an optical noise term show qualitatively similar self-spiking behavior as measured.
Early Detection of Parkinson's Disease using Spiral Test and Echo State Networks

Parkinson's disease is one of the most prevalent neurodegenerative diseases in the world, usually occurring after the age of 50, but in some cases, also affects younger people. It is a disease that affects movement, coordination, and muscle control, all of which cause a range of symptoms that affect patients' writing and drawing skills. Diagnosis is clinical, so it occurs mainly through the evaluation of the patient's movements, coordination, and muscle control. Therefore, the analysis of micrographic models can introduce a new methodology of investigation in the diagnosis and monitoring of Parkinson's disease. This study proposes an approach based on artificial intelligence in combination with the spiral test, which consists in asking the patient to draw a spiral, thanks to which it is possible to make the early diagnosis of Parkinson's disease. The classification is performed with a combination of an Echo State Network and an MLP layer. To validate the approach, several classification algorithms belonging to two macro groups (boosting decision trees based) were used as baseline. The results obtained are very satisfactory with the ESN-based classifier exhibiting an F-Score of 97.8%. The very encouraging results indicate that the proposed approach may be an effective contribution to improving Parkinson's diagnostics.
Split-U-Net: Preventing Data Leakage in Split Learning for Collaborative Multi-modal Brain Tumor Segmentation

Split learning (SL) has been proposed to train deep learning models in a decentralized manner. For decentralized healthcare applications with vertical data partitioning, SL can be beneficial as it allows institutes with complementary features or images for a shared set of patients to jointly develop more robust and generalizable models. In this work, we propose “Split-U-Net" and successfully apply SL for collaborative biomedical image segmentation. Nonetheless, SL requires the exchanging of intermediate activation maps and gradients to allow training models across different feature spaces, which might leak data and raise privacy concerns. Therefore, we also quantify the amount of data leakage in common SL scenarios for biomedical image segmentation and provide ways to counteract such leakage by applying appropriate defense strategies.
Vision-Language Matching for Text-to-Image Synthesis via Generative Adversarial Networks

Text-to-image synthesis is an attractive but challenging task that aims to generate a photo-realistic and semantic consistent image from a specific text description. The images synthesized by off-the-shelf models usually contain limited components compared with the corresponding image and text description, which decreases the image quality and the textual-visual consistency. To address this issue, we propose a novel Vision-Language Matching strategy for text-to-image synthesis, named VLMGAN*, which introduces a dual vision-language matching mechanism to strengthen the image quality and semantic consistency. The dual vision-language matching mechanism considers textual-visual matching between the generated image and the corresponding text description, and visual-visual consistent constraints between the synthesized image and the real image. Given a specific text description, VLMGAN* firstly encodes it into textual features and then feeds them to a dual vision-language matching-based generative model to synthesize a photo-realistic and textual semantic consistent image. Besides, the popular evaluation metrics for text-to-image synthesis are borrowed from simple image generation, which mainly evaluate the reality and diversity of the synthesized images. Therefore, we introduce a metric named Vision-Language Matching Score (VLMS) to evaluate the performance of text-to-image synthesis which can consider both the image quality and the semantic consistency between the synthesized image and the description. The proposed dual multi-level vision-language matching strategy can be applied to other text-to-image synthesis methods. We implement this strategy on two popular baselines, which are marked with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\text{VLMGAN}_{+\text{AttnGAN}}}$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\text{VLMGAN}_{+\text{DFGAN}}}$</tex-math></inline-formula> . The experimental results on two widely-used datasets show that the model achieves significant improvements over other state-of-the-art methods.
Digital rupee- A rival for cryptos?

The introduction of Central Bank Digital Currencies (CBDCs) as legal tender in various nations is something that several Central Banks around the world are planning in the near future.Similar intentions have been made public by the Reserve Bank of India (RBI), and an Indian CBDC can be expected soon.For any assessment of the impact, an evaluation of the potentials and difficulties brought on by the adoption of CBDCs is necessary.The current financial system is observing the entry of CBDC.The development of CBDC is accelerating the advancement of blockchain technology and virtual currencies with the support of Central Banks around the world.In this study, we examine the existing research on CBDC's in different countries.This research paper is analysing the differences between India's digital rupee and private Cryptocurrencies.Our current analysis can offer recommendations for the Introduction of Digital rupee in India.
Expanded Adaptive Scaling Normalization for End to End Image Compression

Recently, learning-based image compression methods that utilize convolutional neural layers have been developed rapidly. Rescaling modules such as batch normalization which are often used in convolutional neural networks do not operate adaptively for the various inputs. Therefore, Generalized Divisible Normalization (GDN) has been widely used in image compression to rescale the input features adaptively across both spatial and channel axes. However, the representation power or degree of freedom of GDN is severely limited. Additionally, GDN cannot consider the spatial correlation of an image. To handle the limitations of GDN, we construct an expanded form of the adaptive scaling module, named Expanded Adaptive Scaling Normalization (EASN). First, we exploit the swish function to increase the representation ability. Then, we increase the receptive field to make the adaptive rescaling module consider the spatial correlation. Furthermore, we introduce an input mapping function to give the module a higher degree of freedom. We demonstrate how our EASN works in an image compression network using the visualization results of the feature map, and we conduct extensive experiments to show that our EASN increases the rate-distortion performance remarkably, and even outperforms the VVC intra at a high bit rate.
An efficient coverage method for SEMWSNs based on adaptive chaotic Gaussian variant snake optimization algorithm

Soil element monitoring wireless sensor networks (SEMWSNs) are widely used in soil element monitoring agricultural activities. SEMWSNs monitor changes in soil elemental content during agriculture products growing through nodes. Based on the feedback from the nodes, farmers adjust irrigation and fertilization strategies on time, thus promoting the economic growth of crops. The critical issue in SEMWSNs coverage studies is to achieve maximum coverage of the entire monitoring field by adopting a smaller number of sensor nodes. In this study, a unique adaptive chaotic Gaussian variant snake optimization algorithm (ACGSOA) is proposed for solving the above problem, which also has the advantages of solid robustness, low algorithmic complexity, and fast convergence. A new chaotic operator is proposed in this paper to optimize the position parameters of individuals, enhancing the convergence speed of the algorithm. Moreover, an adaptive Gaussian variant operator is also designed in this paper to effectively avoid SEMWSNs from falling into local optima during the deployment process. Simulation experiments are designed to compare ACGSOA with other widely used metaheuristics, namely snake optimizer (SO), whale optimization algorithm (WOA), artificial bee colony algorithm (ABC), and fruit fly optimization algorithm (FOA). The simulation results show that the performance of ACGSOA has been dramatically improved. On the one hand, ACGSOA outperforms other methods in terms of convergence speed, and on the other hand, the coverage rate is improved by 7.20%, 7.32%, 7.96%, and 11.03% compared with SO, WOA, ABC, and FOA, respectively.
Deep Reinforcement Learning Task Assignment Based on Domain Knowledge

Deep Reinforcement Learning (DRL) methods are inefficient in the initial strategy exploration process in large-scale complex scenarios.This is becoming one of the bottlenecks in their application to large-scale game adversarial scenarios.This paper proposes a Safe reinforcement learning combined with Imitation learning for Task Assignment (SITA) method for a representative red-blue game confrontation scenario.Aiming at the problem of difficult sampling of Imitation Learning (IL), this paper combines human knowledge with adversarial rules to build a knowledge rule base; We propose the Imitation Learning with the Decoupled Network (ILDN) pre-training method to solve the problem of excessive initial invalid exploration; In order to reduce invalid exploration and improve the stability in the later stages of training, we incorporate Safe Reinforcement Learning (Safe RL) method after pre-training.Finally, we verified in the digital battlefield that the SITA method has higher training efficiency and strong generalization ability in large-scale complex scenarios.
Time Series Forecasting with Quantum Machine Learning Architectures

Time series forecasting has been a topic of special interest due to its applications in Finance, Physics, Environmental Sciences and many other fields. In this article, we propose two classical-quantum hybrid architectures for time series forecasting with a multilayered structure inspired by the multilayer perceptron (MLP): a Quantum Neural Network (QNN) and a Hybrid-Quantum Neural Network (HQNN). These architectures incorporate quantum variational circuits with specific encoding schemes and the optimization is carried out by a classical computer. The performance of the proposed hybrid models is evaluated in four forecasting problems: Mackey-Glass time series and USD-to-euro currency exchange rate forecasting (univariate time series) as well as Lorenz attractor and prediction of the Box-Jenkins (Gas Furnace) time series (multivariate time series). The experiments were conducted by using the built-in Pennylane simulator lighting.qubit and Pytorch. Finally, these architectures, compared to the MLP, CNN and LSTM show a competitive performance with a similar number of trainable parameters.
On Evaluating Pixel-Level Face Image Quality Assessment

A decisive factor for face recognition performance is face image quality (FIQ). It describes the utility of face images for automatic recognition. While this FIQ has conventionally been considered as a scalar for the whole image, emerging works suggest assessing pixel-level FIQs to provide higher explainability. However, the value of pixel-level qualities as a measure of utility (value for recognition) is not yet investigated. In this work, we address this by presenting two evaluation schemes, deletion evaluation curve (DEC) and insertion evaluation curve (IEC). The DEC investigates the change in recognition performance as pixels are deleted based on their quality. Complementary, the IEC reports the change in recognition performance as pixels are inserted based on their quality into a blurred image. Since pixel-level face image quality assessment (PLFIQA) methods assign high values to pixels that contain discriminant information, the recognition performance should decrease or increase when they are removed or added, respectively. We have successfully demonstrated the proposed evaluation scheme on two face recognition solutions by comparing a recently proposed PLFIQA method to a random baseline. With the growing interest in explainable face recognition, the proposed metrics will enable adequate comparison of future advances in pixel-level quality assessment.