Reviewerly/exHarmony huggingface dataset:
IoT Device Identification Using Device Fingerprint and Deep Learning

The foundation of security in IoT devices lies in their identity. However, traditional identification parameters, such as MAC address, IP address, and IMEI, are vulnerable to sniffing and spoofing attacks. To address this issue, this paper proposes a novel approach using device fingerprinting and deep learning for device identification. Device fingerprinting is generated by analyzing inter-arrival time (IAT), round trip time (RTT), or IAT/RTT outliers of packets used for communication in networks. We trained deep learning models, namely convolutional neural network (CNN) and CNN + LSTM (long short-term memory), using device fingerprints generated from TCP, UDP, ICMP packet types, ICMP packet type, and their outliers. Our results show that the CNN model performs better than the CNN + LSTM model. Specifically, the CNN model achieves an accuracy of 0.97 using the IAT device fingerprint of ICMP packet type, and 0.9648 using the IAT outlier device fingerprint of ICMP packet type on a publicly available dataset from the crawdad repository.
Fault-tolerant quaternary belief propagation decoding based on a neural network

The article discusses the challenge of finding an efficient decoder for quantum error correction codes for fault-tolerant experiments in quantum computing. The study aims to develop a better decoding scheme based on the flag-bridge fault tolerance experiment. The research compares two decoding algorithms, a deep neural network decoding scheme and a simple decoder, and a recurrent neural network decoding scheme based on the belief propagation algorithm variant MBP 4 algorithm. The study improved the syndrome extraction circuit based on the flag-bridge method to meet the requirements of fault-tolerant experiments better. Two decoding schemes were studied, a combination of a deep neural network and a simple decoder and a recurrent neural network structure based on the MBP 4 algorithm. The first scheme used neural networks to assist simple decoders in determining whether additional logical corrections need to be added. The second scheme used a recurrent neural network structure designed through the variant MBP 4 algorithm, along with a post-processing method to pinpoint the error qubit position for decoding. Experimental results showed that the decoding scheme developed in the study improved the pseudo-threshold by 39.52% compared to the minimum-weight perfect matching decoder. The two decoders had thresholds of approximately 15.8% and 16.4%, respectively. The study’s findings suggest that the proposed decoding schemes could improve quantum error correction and fault-tolerant experiments in quantum computing.
A feature selection using improved dragonfly algorithm with support vector machine for breast cancer prediction

Breast cancer is the most common cancer-related death in women, accounting for 16% of all cancer-related fatalities globally. Breast cancer is fatal in just half of all cases. Radiologists may misread worrisome lesions due to imaging quality concerns and diverse breast densities, which raises the false-(positive and negative) ratio, as the primary explanation for the problem. Early intervention is critical in building a current prognosis process that can successfully limit disease consequences and increase recovery. Patients are being referred back for biopsies to dispel suspicions when the inconsistent feature-extraction approach is used for manual screening of breast abnormalities in traditional schemes. With the Multi-kernel Support Vector Machine (MKSVM), we develop a new modality for the prediction of breast cancer and train its properties using the supervised machine learning approaches. Furthermore, the IDA-MKSVM technique achieved maximum average accuracy is 95.75%, average sensitivity is 94.29%, average specificity is 95.16% and average F-score is 95.39% for different training datasets. For selecting ideal features, the Improved Dragonfly Algorithm (IDA) is also used. A 10-fold cross validation procedure is used in the system under consideration to ensure accuracy. The UCI machine learning repository holds the breast cancer diagnosis data referred to as the Wisconsin breast cancer diagnosis data set.
Issues of modernization of academic activities in higher education in Russia

Subject. This article examines the issues of reform of the education system of Russia. Objectives. The article aims to propose measures to transform higher education, taking into account the goals of ensuring the technological sovereignty of Russia. Methods. For the study, we used general scientific research methods. Results. The article identifies trends in the development of the Russian higher education system and offers recommendations for the modernization of scientific and educational activities in Russia. Conclusions. Modern Russian higher education needs crisis management. It is possible to develop the concept of crisis management in the field of education only on the basis of the concept of socio-economic development of the country.
Big data simulation of science and technology finance supporting Shaanxi urban–rural integration development based on BP neural network algorithm

In the context of the current era, if China wants to implement the strategic goal of rural revitalization, it must promote the integrated development of urban and rural areas. This paper takes Shaanxi Province as the main research goal, which is at the strategic intersection of the "the Belt and Road", has an important position, can affect the process of regional coordination and urban and rural integration throughout the country, and is a major province of characteristic agriculture and a national transportation hub. In this context, this paper uses BP neural network algorithm to simulate and analyze the coordinated development of urban and rural areas based on the actual development of science and technology finance in Shaanxi Province. After obtaining the economic status data of the province, this paper builds a regional characteristic platform that can provide targeted science and technology finance services, which can achieve diversified processing, complete multi entity fund supply and maintain the transparency of information between the supply and demand sides. Based on the field survey and literature review and analysis, it can be seen that the ten prefecture level cities included in the study area and province have different levels of coordinated development of urban and rural economy, and there is a Matthew effect characteristic of uneven strength, so the regional differences show a weakening feature, and they need to be adjusted gradually to make them develop in an orderly manner. Based on the research conclusion, this paper carries out the construction of urban–rural economic coordinated development and scientific and technological financial model support, and analyzes the financial support function and actual effect of the province in detail. In this paper, the BP neural network algorithm is applied to the integration of urban and rural development in Shaanxi Province to conduct big data simulation for science and technology finance.
Recommending tasks based on search queries and missions

Abstract Web search is an experience that naturally lends itself to recommendations, including query suggestions and related entities. In this article, we propose to recommend specific tasks to users, based on their search queries, such as planning a holiday trip or organizing a party. Specifically, we introduce the problem of query-based task recommendation and develop methods that combine well-established term-based ranking techniques with continuous semantic representations, including sentence representations from several transformer-based models. Using a purpose-built test collection, we find that our method is able to significantly outperform a strong text-based baseline. Further, we extend our approach to using a set of queries that all share the same underlying task, referred to as search mission, as input. The study is rounded off with a detailed feature and query analysis.
Enhancement of Asymmetrically Color-Cast Sandstorm Image Using Saturation-Based Color Correction and Hybrid Transmission Network

The images discussed in this manuscript show atmospheric conditions of smog, sandstorm, and dust. Moreover, the images were taken in various environments and have features such as dimness or color cast. The smoggy image has a greenish or bluish color veil, and the sandstorm image has a yellowish or reddish color veil because of the various sand particles. Various methods have been used to enhance images containing dust. However, if the color-cast ingredients are not considered during image enhancement, then the enhanced image will have a new, artificial color veil that did not appear in the input image, as the color-veiled image does not have a uniform color channel. Certain channels are attenuated by sand particles. Therefore, this paper proposes a color-balancing method based on saturation to enhance asymmetrically cast colors due to the attenuation of the color channel by sand particles. Moreover, because the balanced image contains dust and the distribution of hazy ingredients is asymmetrical, a dehazing procedure is needed to enhance the image. This work used the original image and a reversed image to train the hybrid transmission network and generate the image’s transmission map. Moreover, an objective and subjective assessment procedure was used to compare the performance of the proposed method with that of other methods. Through the assessment, the performance of the proposed method was shown to be superior to other methods’ performance.
Improving Few-Shot Multi-Speaker Text-to-Speech Adaptive-Based with Extracting Mel-Vector (EMV) for Vietnamese

Training a multi-speaker Text-to-Speech (TTS) model requires multiple speakers’ voices to generate an average speech model. However, the average speech synthesis model will be distorted or averaged, resulting in low quality if the new speaker’s voice has too little data to train. The existing methods require fine-tuning the model; otherwise, the model will achieve low adaptive quality. However, for synthesis voice to achieve high adaptive quality, at least thousands of fine-tuning steps are required. To solve these issues, in this paper, we propose a Vietnamese multi-speaker TTS adaptive-based technique that synthesizes high-quality speech and effectively adapts to new speakers, with two main improvements: (1) propose an Extracting Mel-Vector (EMV) architecture with three components, the Encoder–Decoder–Embedding Features, which enables complete learning of speaker features with Mel-spectrograms as input for few-shot training and (2) a continuous-learning technique called “data-distributing” preserves the new speaker’s characteristics after many training epochs. Our proposed model outperformed the baseline multi-speaker synthesis model and achieved a MOS score of 3.8/4.6 and SIM of 2.6/4 with only 1 min of the target speaker’s voice.
A Weighted Mean Field Reinforcement Learning Algorithm for Large-Scale Multi-Agent Collaboration

Reinforcement learning has been proven to be an effective approach for solving multi-agent coordination problems in a dynamic open environment. For dealing with multi-agent cooperation issues, the mean field multi-agent reinforcement learning method can better overcome the problems of slow learning speed, unstable convergent performance, and poor learning effect. However, the original mean field algorithm cannot extract features well when agents cooperate. In order to solve the large-scale multi-agent coordination problem, in this paper, the mean field multi-agent reinforcement learning algorithm is improved and optimized by combining the multi-head attention mechanism, and the attention-based mean field (MFA) structure is designed. The employment of a multi-head attention mechanism can optimize the interaction among agents, extract more effective cluster features and enable agents to learn more efficient strategies. This paper first introduces the framework structure of MFA and then expounds on the relevant theoretical basis based on the Q-Learning and Actor-Critic algorithms, and finally conducts large-scale multi-agent cooperative experiments on the MAgent platform. The experimental results show that compared with the baseline algorithm, the attention-based mean field Q-learning (MFQA) and attention-based Actor-Critic (MFACA) algorithms can make large-scale multi-agent clusters converge to higher rewards, and perform better than the original mean field multi-agent algorithm.
Spatially distributed lane planning for navigation in 3D environments

Abstract This article introduces the new problem of planning spatially distributed lanes for supporting multi‐agent navigation applications in 3D environments. Our proposed approach computes the max‐flow of a 3D medial axis representation of the environment in order to globally compute collision‐free lanes exploring the entire free space of the volumetric scene. Our method addresses agent clearance and path dispersion in order to provide a comprehensive solution to globally compute lanes to be used by multiple agents in 3D environments. By selecting the desired lane dispersion our approach offers an intuitive and powerful way to explore variations in the computed collections of lanes. Dispersion is addressed with a combination of new techniques based on max flow computation, clearance‐based path separation, and adaptive shortcut‐based smoothing.
Extraction and normalization of IR indexing terms and phrases in a highly inflectional language

Abstract Term-based indexing of documents is conventionally implemented by stemmers or their corpus-based improvements, both of which encode implicit linguistic information. Terms are directly derived from document content such that a unique indexing approach is available at indexing run-time. For highly inflectional languages where term variation is high, such techniques are more error-prone. The main focus of the current study is the extraction and normalization of single terms and phrases and the proposal of authenticated control of indexing. The proposed approach relies on the use of explicit linguistic knowledge, appropriately encoded in large language resources. Such control guarantees the highest possible expansion factor for indexing terms as well as indexing consistency. Moreover, it offers a framework where different and eventually contradicting indexing criteria can be practiced, conventional and Natural Language Processing ( NLP )-based Information Retrieval ( IR ) applications can be served, while adaptations can be made for tuning to a specific domain or corpus.
An advanced fuzzy C-Means algorithm for the tissue segmentation from brain magnetic resonance images in the presence of noise and intensity inhomogeneity

Segmentation of brain Magnetic Resonance Images (MRIs) into various brain tissues such as white matter (WM), grey matter (GM), and cerebrospinal fluid (CSF) is very important to detect and diagnose different brain-related disorders at the primitive level. Accurate segmentation of brain MRIs is very difficult because of the intricate anatomical structure of the tissues, the existence of Intensity Inhomogeneity (IIH), noise, and Partial Volume Effects (PVE). Clustering-based methods are generally used to segment brain images. This work proposes a Chaotic based Enhanced Firefly Algorithm Integrated with Fuzzy C-Means (CEFAFCM) for the segmentation of brain tissues WM, GM, and CSF from brain MRIs. The proposed method can handle IIH, PVE, and noise. CEFAFCM is a spatially modified FCM algorithm combined with the Firefly Algorithm (FA) along with a chaotic map for the initialization of the population of fireflies. The algorithm is tested with brain MRIs acquired from the BrainWeb database. The experimental results demonstrate that the proposed technique is producing better results in comparison with some existing brain MRI segmentation methods such as FCM, BCFCM, FAFCM, and En-FAFCM.
Video compression using improved diamond search hybrid teaching and learning-based optimization model

Video compression is necessary to recreate a video without sacrificing quality. Nowadays, researchers are focusing on global optimization approaches to determine the optical flow of the neighboring pixels in video processing. In this work, a novel improved diamond search-hybrid teaching-learning based optimization (IDS-HTLBO) methodology has been proposed to compress the videos and increase the video quality. This method uses a diamond search pattern with a secure number of search points for per frame of the video. The hybridization of DS algorithm and TLBO algorithm are applied in this methodology to reduce computational complexity. Moreover, this method reduces the computational unpredictability of block matching. The quality of the image was validated with 3D reconstruction by the structured light approaches. The experimental result shows that the proposed IDS-HTLBO algorithm achieves a maximum average value of 53.17 dB, 0.44 and 11.57 in terms of peak-to-signal-noise ratio, mean squared error, and compression ratio respectively.
The Best RX is Better UX: Redesigning, Migrating, and Usability Testing a Hospital Library Website

The John S. Marietta Library team provides onsite services as part of a collaborative agreement between the UNT Health Science Center and JPS Health Network. The team was tasked with migrating the hospital library website to a new content management system (CMS) in Summer 2021. The migration project provided an opportunity to reimagine the look and feel of the website and conduct usability testing to improve user experience (UX). Conducting thorough usability testing with a diverse group of clinicians and staff provided valuable insights regarding the search behavior and needs of this unique patron population.
Quantitative phase-amplitude surface determination in reflection differential interference contrast profilometry

An approach for measuring the surface profile of the samples with reflection variations using the Nomarski differential interference method is presented. The system is analyzed with Jones's matrices tool, and polarization effects of a non-polarizing beam splitter are taken into account. Equations are also developed to allow the determination of the surface profile from interference intensity when the sample reflectively is not uniform. The validity and accuracy of the measurement are verified by measuring the adhered silicon cantilever on a substrate with high reflectivity. This method is expected to be a valuable tool to inspect elements of micromechanics and microsystems.
Distributing quantum states with finite lifetime

This paper considers a quantum node tasked with the teleportation of multiple information-carrying qubit (ICQ) streams, each to a different receiver, by means of entanglements, local operations, and classical communication. Our vision is that the node establishes entangled qubit pairs (EQPs) with the receivers before the arrival of ICQs, rather than waiting for their arrival. In this vein, the paper focuses on the class of protocols referred to as class $\mathcal{I}$ that instantaneously teleport arriving ICQs using preestablished EQPs, preventing arriving ICQs from decohering. The excess ratio ${\ensuremath{\varepsilon}}_{\mathrm{r}}$ is introduced as a quantifier of the system resources per arriving ICQ, and ${\ensuremath{\varepsilon}}_{\mathrm{r}}=1$ is shown to be a critical threshold. With ${\ensuremath{\varepsilon}}_{\mathrm{r}}&gt;1$: for arrival streams characterized by interarrivals stochastically larger than exponential random variables, any member of class $\mathcal{I}$ teleports all arriving ICQs after a finite transient. With ${\ensuremath{\varepsilon}}_{\mathrm{r}}&lt;1$: for stationary ergodic arrival streams, there exists no protocol that teleports all arriving ICQs after a finite transient. This work thus establishes the ultimate limit for distributing quantum states with finite lifetime. Within class $\mathcal{I}$, a protocol referred to as fresh information delivery (FID) is introduced and its optimality is proven. The operational characteristic of FID is provided in terms of the tradeoff between the waiting time of the EQP before it is utilized for teleportation and the excess ratio. Numerical experiments, comparing the proposed FID protocol with alternatives, corroborate the theoretical results. The results in this paper can be used for designing quantum nodes, paving the way for the implementation of the future quantum internet.
Artificial Intelligence Approaches for Studying the <i>pp</i> Interactions at High Energy Using Adaptive Neuro-Fuzzy Interface System

Adaptive Neuro-Fuzzy Inference System (ANFIS), a popular machine learning model, is introduced in this chapter. ANFIS has a long development history and good agreement on scientific accomplishments. The value of ANFIS has grown dramatically along with the great interest in deep learning. We will examine how machine learning and ANFIS are related. Different methods can be used to implement machine learning models. ANFIS is a Fuzzy Inference System (FIS) that works within the context of adaptive networks. It merges the ideas of Artificial Neural Networks (ANNs) and Fuzzy Logic (FL) into a single framework. This framework can learn to estimate nonlinear functions and operates as a universal estimator. This chapter aimed to investigate the behavior of D mesons ratios production cross section (D+/D0,D∗+/D0,Ds+/D0,andDs+/D+), differential production cross section of prompt (D0,D+, D∗+andDs+ mesons) as a function of PT in pp collisions at (s = 5.02 and 7 TeV) and predict the behavior for others. The ANFIS model was created through a series of trial-and-error experiments. The ANFIS-based model simulation results perfectly fit the experimental data. When tested with non-training data points, the ANFIS prediction capabilities performed well. The ANFIS offers extensive procedures for high-energy physics modeling.
Adaptive graph regularized transferable regression for facial expression recognition

Facial expression recognition (FER) has tremendous potential in affective computing and human-computer interaction fields. Traditional FER algorithms usually perform the training and testing of models on a single domain. However, face images in real environments usually come from different domains, which greatly limits the performance of traditional algorithms. To handle this cross-domain recognition problem, we put forward a novel transfer learning model, called adaptive graph regularized transferable regression (AGTR), which can learn a discriminative projection matrix by embedding relaxed label regression, class sparsity structure, and adaptive graph structure into a unified framework. To be specific, in our method, we develop a relaxed label regression to learn a projection matrix. Then, we exploit a class sparsity structure in each class of samples separately to obtain a consistent subspace. Further, we design a novel adaptive graph structure, which can adaptively discover the geometric relationship between samples. Finally, we verify the advancement of our approach on four public facial expression databases.
Earthquake Detection with tinyML

Abstract Earthquake detection is the critical first step in earthquake early warning (EEW) systems. For robust EEW systems, detection accuracy, detection latency, and sensor density are critical to providing real-time earthquake alerts. Traditional EEW systems use fixed sensor networks or, more recently, networks of mobile phones equipped with microelectromechanical systems (MEMS) accelerometers. Internet of things edge devices, with built-in tiny machine learning (tinyML) capable microcontrollers, and always-on, internet-connected, stationary MEMS accelerometers provide the opportunity to deploy ML-based earthquake detection and warning using a single-station approach at a global scale. Here, I test and evaluate tinyML deep learning algorithms for earthquake detection on a microcontroller. I show that the tinyML earthquake detection models can generalize to earthquakes outside the training set.