Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimizing the noisy MIMO Interference Channel at High SNR

Centralized algorithms for weighted sum rate (WSR) maximization for the K-user frequency-flat MIMO Interference Channel (MIMO IFC) with full channel state information (CSI) are considered. Maximization of WSR is desirable since it allows the system to cover all the rate tuples on the rate region boundary for a given MIMO IFC. First, we propose an iterative algorithm to design optimal linear transmitters and receivers. The transmitters and receivers are optimized to maximize the WSR of the MIMO IFC. Subsequently, we study the problem of WSR maximization in the High SNR regime. Starting from the High SNR approximation of the WSR we observe that the optimization problem in High SNR becomes, in a first instance, an exploration of the (discrete) pre-log region. Once the optimal pre-log distribution if found, for a given set of weights, the WSR optimization becomes the maximization of the High SNR Rate offset. To avoid the many local optima indicated by this analysis, the use of Deterministic Annealing in 1/SNR is suggested.
Mantle Source and Magmatic Evolution of the Dying Spreading Ridge in the South China Sea

The genesis of seamount chains along fossil spreading ridges reflects the mantle melting dynamics of dying spreading center. The South China Sea developed a seamount chain along its fossil ridge shortly after cessation of spreading. International Ocean Discovery Program Expedition 349 has recovered three volcanic layers, a shallow volcanic breccia layer and two deeper (upper and lower sections) basalt layers, at Site U1431 near the fossil ridge of the South China Sea. Despite the mid‐ocean ridge basalt (MORB)‐like geochemistry of the lower and upper section basalts, they show distinct geochemical differences that indicate a change of mantle source and a transition from spreading to intraplate volcanism. While the lower section basalts could reflect the last stage volcanism before the cessation of ridge spreading, the upper section basalts with whole‐rock MgO up to ~20 wt. % were formed by intraplate volcanism with significant olivine accumulation once the spreading center had become inactive. The MORB type basalts at Site U1431 have Hawaii‐like high olivine Ni and Fe/Mn and low olivine Ca and Mn and bulk‐rock low CaO that are distinctly different from normal global MORBs, suggesting an anomalous mantle source in lithology. We suggest that an eclogite‐/pyroxenite‐rich component, possibly sourced from the Hainan hot spot, played a fundamental role in the transition from spreading to intraplate volcanism of the dying spreading ridge of the South China Sea.
Interannual Agulhas Leakage Variability and Its Regional Climate Imprints

We investigate the interannual variability of Agulhas leakage in an ocean-eddy-resolving coupled simulation and characterize its influence on regional climate. Many observational leakage estimates are based on the study of Agulhas rings, whereas recent model studies suggest that rings and eddies carry less than half of leakage transport. While leakage variability is dominated by eddies at seasonal time scales, the noneddy leakage transport is likely to be constrained by large-scale forcing at longer time scales. To investigate this, leakage transport is quantified using an offline Lagrangian particle tracking approach. We decompose the velocity field into eddying and large-scale fields and then recreate a number of total velocity fields by modifying the eddying component to assess the dependence of leakage variability on the eddies. We find that the resulting leakage time series show strong coherence at periods longer than 1000 days and that 50% of the variance at interannual time scales is linked to the smoothed, large-scale field. As shown previously in ocean models, we find Agulhas leakage variability to be related to a meridional shift and/or strengthening of the westerlies. High leakage periods are associated with east–west contrasting patterns of sea surface temperature, surface heat fluxes, and convective rainfall, with positive anomalies over the retroflection region and negative anomalies within the Indian Ocean to the east. High leakage periods are also related to reduced inland convective rainfall over southeastern Africa in austral summer.
Low-Complexity Chase Decoding of Hermitian Codes with Re-encoding Transform and Fast Factorization

This paper proposes the low-complexity Chase (LCC) decoding for Hermitian codes, which is facilitated by the re-encoding transform (ReT) and fast factorization (FF). By identifying $\eta$ unreliable received symbols, $2^{\eta}$ test-vectors are formulated. Kotter's interpolation is performed for decoding the test-vectors, in which the common element interpolation is performed once and the uncommon element interpolation is further performed in a binary tree growing fashion. The ReT is introduced to reduce its complexity and latency. Moreover, the FF is introduced to obtain the estimated codewords directly from the interpolation outcome. It eliminates the redundant encoding that identifies the most likely decoding output. Our simulation results show that the decoding complexity and latency are effectively reduced over the prototype LCC decoding of Hermitian codes.
Nanoporous Carbon Nanofibers Decorated with Platinum Nanoparticles for Non-Enzymatic Electrochemical Sensing of H2O2

We describe the preparation of nanoporous carbon nanofibers (CNFs) decorated with platinum nanoparticles (PtNPs) in this work by electrospining polyacrylonitrile (PAN) nanofibers and subsequent carbonization and binding of PtNPs. The fabricated nanoporous CNF-PtNP hybrids were further utilized to modify glass carbon electrodes and used for the non-enzymatic amperometric biosensor for the highly sensitive detection of hydrogen peroxide (H2O2). The morphologies of the fabricated nanoporous CNF-PtNP hybrids were observed by scanning electron microscopy, transmission electron microscopy, and their structure was further investigated with Brunauer–Emmett–Teller (BET) surface area analysis, X-ray photoelectron spectroscopy, X-ray diffraction, and Raman spectrum. The cyclic voltammetry experiments indicate that CNF-PtNP modified electrodes have high electrocatalytic activity toward H2O2 and the chronoamperometry measurements illustrate that the fabricated biosensor has a high sensitivity for detecting H2O2. We anticipate that the strategies utilized in this work will not only guide the further design and fabrication of functional nanofiber-based biomaterials and nanodevices, but also extend the potential applications in energy storage, cytology, and tissue engineering.
Improved AC Coefficients Compression Using Alternate Adaptive Scanning and Multi-Feature Determined Context Arithmetic Coding

For nearly three decades, JPEG has been the dominant method for compressing still images. Despite its simplicity, its significance remains undiminished. In this study, we compressed JPEG AC coefficients with different methods. The conventional zig-zag scan order was substituted with an alternative adaptive scanning order and determined by the likelihood of its neighboring coefficients being non-zero and the gradients calculated from the DC coefficients. While run/length was coded in JPEG, we encoded them separately to extract more information to reduce redundancies. Then, three types of contexts were determined by the position of diagonal entries, the mean value of the neighboring corresponding entries, and the run value. Additionally, we incorporated frequency table initialization, range adjustments, and adaptive bounding according to the concept of Context-Based Adaptive Variable-Length Code (CAVLC). The proposed method outperformed the JPEG by an 8.92% reduction in bpp on average.
DDoS Attack Detection System for IoT Enabled Smart City Applications with Correlation Analysis

The topic of cybersecurity remains a significant concern for industries involved in digital operations, as seen by the recurring increase in security incidents. The increasing use of Internet of Things (IoT) devices in various settings such as homes, offices, transportation, and healthcare has resulted in a corresponding rise in the frequency of malicious assaults. Machine Learning (ML) is frequently utilized for the purpose of detecting attacks on IoT devices. This study presents a novel approach to cybersecurity-focused network traffic correlation analysis and the detection of Distributed Denial of Service (DDoS) attacks using artificial intelligence techniques in the context of an IoT-enabled smart city, with the aim of promoting sustainability. A traffic analysis was conducted by utilizing a wireshark network analyser tool, which effectively improves data transmission. The experimental investigation was conducted to evaluate various performance metrics, including accuracy, precision, recall, attack detection rate and time. The proposed technique achieved anaccuracy rate of 96%, precision and recall rates are 93.6% and 91% respectively.
Evaluating Category Membership for Information Architecture

We examine two approaches for evaluating category membership within an information taxonomy: card sorting and a simple statistical approach based on the word frequencies of content items. To assess these two approaches, we developed a category structure for a web site based on card sorting. Metrics from the card sorting were obtained to predict problematic tasks. Potentially problematic tasks were also identified using word frequencies of the content items under each category. We then conducted a web navigation study to compare actual user performance of tasks identified by the two approaches. Despite its simplicity, the word-based statistical approach produced marginally better predictions. In particular, it predicted problematic tasks that took nearly twice as much time on average to complete than the remaining tasks in the web navigation study. We review these results and discuss the general usefulness of card sorting and word-based statistics for designing information taxonomies such as those used in web sites and menu systems. Author
Two-Dimensional Resource Pattern Optimization for Interference Avoidance in Heterogeneous Networks

Interference management for heterogeneous networks (HetNets) has been a research highlight in recent years; one effective method is interference avoidance by resource allocation. Typical resource-allocation methods are arranging orthogonal resources between a macrocell and femtocells, such as in the frequency domain in intercell interference coordination (ICIC) or in the time domain in enhanced ICIC (eICIC). However, wireless channels experience time–frequency fading appealing for a 2-D resource-allocation scheme. By optimizing the resource pattern based on variable channel conditions and by introducing a group lasso term, our scheme exploits channel variations in both the time and frequency domains and, in addition, avoids the interference. By grouping the resources in our optimization model, our scheme is flexible and robust against heavy bursty traffic. It is also lightweight in terms of coordination overhead and is completely distributed, making it suitable for a standard Long-Term Evolution (LTE) procedure. Extensive simulations investigate the effect of the coordination period and the group number on different performance metrics and the effectiveness of our proposed method compared with sole frequency-domain and time-domain resource-allocation approaches.
Establishment of an Animal Model of Vascular Restenosis with Bilateral Carotid Artery Grafting

Background Vascular restenosis occurring after CABG is a major clinical problem that needs to be addressed. Vein grafts are associated with a higher degree of stenosis than artery grafts. However, the mechanism responsible for this effect has not been elucidated. We aimed to establish a rabbit model of vascular restenosis after bilateral carotid artery grafting, and to investigate the associated spatiotemporal changes of intimal hyperplasia in carotid artery and jugular vein grafts after surgery. Material/Methods Twenty adult New Zealand white rabbits (10 males; 10 females), weighing 2.0–2.5 kg, were obtained from the Experimental Animal Center of Southern Medical University, Guangzhou, China (License No.: scxk-Guangdong-2006-0015). We quantitatively analyzed intimal thickness, area, and degree of stenosis in carotid artery and jugular vein bridges. Results After 8 weeks of a high-fat diet, rabbit carotid arteries showed early atherosclerotic lesions. With increasing time after surgery, carotid artery and jugular vein grafts showed histopathological and morphological changes, including smooth muscle cell migration, lipid deposition, intimal hyperplasia, and vascular stenosis. The degree of vascular stenosis was significantly higher in vein grafts than in artery grafts at all time points – 35.1±6.7% vs. 16.1±2.6% at Week 12, 56.2±8.5% vs. 23.4±3.4% at Week 16, and 71.2±1.3% vs. 25.2±5.3% at Week 20. Conclusions Rabbit bilateral carotid arteries were grafted with carotid artery and jugular vein bridges to simulate pathophysiological processes that occur in people after CABG surgery.
Flexible Nonlinear Blind Signal Separation in the Complex Domain

This paper introduces an Independent Component Analysis (ICA) approach to the separation of nonlinear mixtures in the complex domain. Source separation is performed by a complex INFOMAX approach. The neural network which realizes the separation employs the so called "Mirror Model" and is based on adaptive activation functions, whose shape is properly modified during learning. Nonlinear functions involved in the processing of complex signals are realized by pairs of spline neurons called "splitting functions", working on the real and the imaginary part of the signal respectively. Theoretical proof of existence and uniqueness of the solution under proper assumptions is also provided. In particular a simple adaptation algorithm is derived and some experimental results that demonstrate the effectiveness of the proposed solution are shown.
Intelligent Reflecting Surface for Assisted Satellite-to-Vehicle Communications

The satellite-to-vehicle communication link quality is suffering from a signal degradation inside tunnels and undergrounds which is one of the challenges of automatic driving at present and the future vehicle-to-everything (V2X) technology. In this paper, passive intelligent reflecting surface (IRS) assisted satellite-to-vehicle communications inside tunnels and undergrounds is presented to ensure system reliability and passenger safety. The presented IRS is passive and requires no power to redirect the incoming satellite signal in the direction of the tunnel to maintain the satellite-to-vehicle connectivity. The simulation results show that the presented IRS can steer the incident beam by 30° towards the desired tunnel direction over the 26-28 GHz frequency band with enhanced signal strength.
HECNNet: Hybrid Ensemble Convolutional Neural Network Model with Multi-Backbone Feature Extractors for Soybean Disease Classification

The purpose of this study is to classify soybean plant diseases using a new method called Hybrid Ensemble Convolutional Neural Networks (HECNNet). To effectively extract unique characteristics and categorize pictures, the proposed approach makes use of convolutional neural networks. The suggested approach is organized around three main phases: data collection, preprocessing, and classification. The novel component of this study is the hybrid ensemble model, which consists of ResNeXt, SqueezeNet, and SegNet, three strong backbone networks. With convolutional layers, dropouts, and max-pooling layers carefully adjusted to match the needs of the dataset, these backbone networks function as feature extractors and classifiers. HECNNet is the product of the interplay between these three extractors. Notably, the prediction scores from each sub-network are combined using a weighted sum operation in HECNNet, which significantly improves the final recognition rate compared to a single network method. The proposed algorithm tackles the complexity of soybean disease categorization and obtains an amazing accuracy rate of 91.7%. This study is a major advance in agricultural disease diagnosis since it provides a reliable and effective method for diagnosing ailments affecting soybean plants.
Impact of Attention on Visual Sentiment Analysis

With the increasing use of social media among people, the amount of data is increasing, making its analysis more and more challenging. Many existing works focus on textual sentimental analysis. However, with the escalating use of images and videos, the need to develop a robust visual sentiment analysis model has been pressuring more than ever. This work compares the impact of several visual attention models proposed recently: shuffle, triple, and coordinate attention for visual sentimental analysis. Different models focus on different regions and have different generalization techniques used. The models were tested by integrating the attention mechanism with the baseline deep learning model, resnet50. The new models were trained and tested for the popular MVSA-Single dataset. The models were compared on the basis of computational power requirements and the improvement in the performance to find the best possible trade-off. According to experimental results, Coordinate attention has the best accuracy of 89.73%. However, Triple attention has the best trade-off: an accuracy of 0.8932 with 1200 additional trainable parameters in comparison to 530280 in coordinate attention.
Blur-Shift Attack: Deceivng Visual Tracker With Adversarial Blurred Template

Adversarial attack in Visual Object Tracking (VOT) aims at deceiving trackers by adding imperceptible perturbations to video frames. Despite there have been numerous adversarial attack methods developed for VOT, achieving a balance among the criteria of efficiency, real-time performance, and stealthiness remains a persistent challenge. To address this issue, Blur-Shift attack is proposed to effectively fool the Siamese tracker by solely perturbing the tracking template while ensuring remarkable real-time performance and stealthiness of the attack. Concretely, a highly efficient perturbation generator is trained with the blur loss and the channel-shift loss, which can shift the predicted box to the edge of the video frame with a natural adversarial blurred pattern added to the template. Substantial experimental results on OTB100, LaSOT, and VOT2018 illustrate that our attack method superbly satisfies the three criteria mentioned above when targeting SiamRPN++. Furthermore, our method has great transferability and is able to deceive other state-of-the-art trackers such as DaSiamRPN and SiamMask without retraining.
Safer Navigation: The AI-Powered Smart Cane for the Visually Impaired

This work proposes a flexible, foldable, lightweight, robust, and smart cane to aid visually impaired individuals and patients in navigating unfamiliar environments safely. The developed smart cane is equipped with an ultrasonic sensor, RFID reader, and an AI machine vision sensor for object detection and identification in the range of 30 cm to 9 m. A mobile application with a text-to-voice feature is developed that connects with the cane using Bluetooth to provide voice warnings to the user based on processed sensor output. The developed cane is also equipped with a vibration sensor activated when an obstacle is closer than 30 cm, serving as a backup of voice warnings. Object detection and classification using multiple sources (AI vision sensor, ultrasonic sensor, and RFID readers) ensures robust and uninterrupted operation in case of failure of one sensor. In emergencies, users can send text messages with their location to a pre-selected caretaker’s phone number using the installed SOS button. The rechargeable battery offers more than 24 hours of operation and warns users when the battery is low. The prototype has been successfully executed and tested in various environments and aims to improve the safety and quality of life for visually impaired individuals and hospital patients.
Two Stage Video Classification Approach Using Convolution Neural Network

This research study focuses on video categorization, which is a crucial area of computer vision with uses in entertainment, education, and surveillance. Convolutional Neural Networks (CNNs) are used in a two-stage approach in the suggested methodology. The video is first broken down into frames, which are subsequently supplied into a CNN model. Another CNN model that predicts the video class uses the output from this model as input. A publicly accessible dataset is used to assess the efficacy of this approach, demonstrating its competitive accuracy performance in comparison to the most advanced techniques. The outcomes indicate that the technique can effectively classify videos and has the potential to be used in a variety of real-world scenarios.
Analysis and Implementation of Social Robots to Increase Language Learning Capability

Learning a language might seem like a challenging task. Fortunately, social robots are becoming more popular in certain parts of the world, especially in education. Social robots are believed to accelerate language learning, ultimately increasing language learning abilities. Thus, the purpose of this research is to prove whether the implementation of social robots can potentially have a positive influence on the language learning process. Several factors will be tested to support the purpose of this research; this includes the perceived value of social robots, intention to use social robots, perceived usefulness, robots' service assurance, empathy of social robots, information sharing process, perceived ease of use, robots' tangibles, robots' engagement, disadvantages of using robots, and advantages of using robots. The sampling method used in this research is non-probability purposive sampling. One hundred fifty-five participants from the JABODETABEK area and nine outside of the area participated in this study. Study participants must fill in an online Google Form questionnaire, and the data will be analyzed further using SMART PLS. The study results indicate that only six out of eleven hypotheses have a significant impact.
Analysis of Spectrum Needs for Network-Wide Ultra-Reliable Communication with Network Sharing

Previous works suggest that the magnitude of spectrum needed to support ultra-reliable communication services can be far beyond today's norm (e.g., tens of gigahertz of bandwidth). Multi-operator network sharing, in which mobiles can connect to operators' base stations in the network sharing arrangement, is an alternative way to offset the high demand for spectrum. This paper assesses the spectrum needs for ultra-reliable communication services in multi-operator network sharing. Using meta distributions, we obtain new closed-form expressions for the spectrum needs in two sharing scenarios. Our expressions reveal the relationship between spectrum needs and reliability targets and indicate that multi-operator network sharing, particularly when coupled with multi-connectivity, can reduce the demand for spectrum by orders of magnitude.