Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving the quality of the audio sources using Gaussianity reduction technique

This research has developed a novel technique that is based on the fundamental property of background and foreground signals. Background signals are a result of the inferential summation of large number of sources, while the foreground signals are a result of limited number of sources. This makes the statistical properties of the signal very different. Using negative entropy, this article demonstrates that it is possible to obtain the foreground signals from the mixture of foreground and background signals. The technique is based on mixing the noisy recording with a similar known signal and separating the signals using negative entropy based independent component analysis (ICA). The results indicate that the technique is successful in significantly improving the quality of the audio signals.
Detecting the Presence of Fake News Content on Social Networking Platforms using ML & NLP

When it comes to our existence, news is extremely important. Topical and breaking news are very useful in daily life for enhancing and enriching our understanding of the global trends. Social platforms i.e., social media are emerging to be several of the world's primary digital communication sources for millions and millions of people. This method of news communication stands out due to their low budget, rapid reach and easily accessible sources. However, this is yet to tackle with wavering trustworthiness and significantly speculative exposure to ‘Fake news’, which are mostly intentionally written to attract views and to mislead the viewers. This rapid spread of fake news has the potential on a large scale to negatively impact on a particular individual and the society as well. Hence, the topic fake news and the rapid spread of the same has become emerging research in recent times which tends to attract stupendous attention. Majority of the commonly existing fake news on social media are shared across many other platforms. Taking deep learning in hand, here we propose a technique which works on the basis of deep learning and cuts down the spread of bogus news on social networking sites.
Rapid Defect Detection in Cigarette Capsules Based on 1D Residual Network with Multi-Feature Fusion

Defect detection has a widespread application in industrial production. However, most of the current deep learning-based methods are unable to simultaneously meet the accuracy and efficiency requirements of detection. To address this issue, this work proposes a one-dimensional residual network with multi-feature fusion (1D ResNet_mix) for defect detection in cigarette capsules, which is combined with a novel, simple, yet effective feature descriptor called histogram of edge direction (HED). Specifically, multiple handcrafted features such as shape, texture, and color are fused before the input layer of the neural network, enabling the model to not rely on large amounts of data. The global histogram statistics applied to the binary edge direction graph can reduce the time consumption generated by the overlapping windows in the histogram of oriented gradient (HOG). Experimental results show the superiority of our new feature descriptor compared with HOG, and the 1D ResNet_mix significantly outperforms several excellent machine learning methods, especially in terms of model convergence speed, complexity, and number of parameters than the standard ResNet. It achieves an average test accuracy of 99.58% with a detection speed of 96.89 fps. The proposed approach with high performance can train the classifier quickly under a limited training dataset and a smaller number of iterations, making it well suited to meet the requirements of real-time defect detection systems.
The Influence-Maximizing Node Selection Algorithm Based on Local Isolated Centrality in Large-scale networks Implemented by Distributed Graph Computing

Identifying influence-maximizing (IM) nodes is an effective strategy for designing efficient communication networks or contain the spreading of information for rumors and epidemics with limited budgets. With the growing of the sizes of the social networks, finding solutions to the problem of intfuence − maximizing vertices becomes more and more challenging. Tra-ditionally, the number of users finally affected was the focus. However, the information diffusion speed was becoming a bottle-neck of IM problems. In this paper, we proposed a locality-aware vertex selection strategy based on local isolation centrality (LIC), while the information diffusion experiments used the Independent Cascade (IC) model. The computing scheme was implemented by a distributed graph computing framework. The superiority of the LIC algorithm was confirmed by evaluated on three real-world datasets. It was indistinguishable from algorithms such as K-shell, H-index, degree centrality (DC), local centrality (LC), and VoteRank at the diffusion cascade scale. But it showed better superiority in diffusion speed. More importantly, by comparing LIC with the VoteRank algorithm, which was the closest algorithm on diffusion speed to that of LIC, the huge advantage of the LIC algorithm in terms of time cost was found. We believe that the locality-aware LIC algorithm was more suitable for the influence-maximization problem in very large-scale social networks.
Effect of Grasping Speed During Wearable Robotic Glove-Based Motor Imagery Training

Wearable robotic gloves have recently gained attention in the field of rehabilitation for their ability to support the movement of affected hands. One of the widely used rehabilitation methods for grasping is through action observation, where participants continuously monitor specific actions while thinking as if they were performing the bodily movements themselves. While previous studies claim that such a method, based on the mirror neuron system, may effectively induce brain activity related to corresponding movements, it is unclear how the speed of grasping movements may affect neural activity performance. In this paper, we conducted a preliminary study with healthy participants to investigate whether the speed of grasping from a wearable robotic glove may influence the motor imagery performance of users during action observation. Our results show that using a wearable robotic glove to demonstrate grasping movements improves motor imagery performance compared to when no movements are provided. Additionally, our experiment conveys that the grasping speed of the wearable glove has an impact on motor imagery performance. Our study highlights that detailed experimental designs, such as manipulating grasping speed for action observation, play an important role in rehabilitation.
Enhancing Cross-lingual Medical Concept Alignment by Leveraging Synonyms and Translations of the Unified Medical Language System

Well-developed medical terminology systems like the Unified Medical Language System (UMLS) improve the ability of language models to handle medical entity linking tasks. However, such magnificent terminology systems are only available for few languages, such as English. For Chinese, both simplified and traditional, the lack of well-developed terminology systems remains a big challenge to unify Chinese medical terminologies by linking medical entities as concepts. In this study, we purpose a translation enhanced contrastive learning scheme which leverages translations and synonyms of UMLS to infuse knowledge into the language model, and present a cross-lingual pre-trained language model called TeaBERT that aligns cross-lingual Chinese and English medical synonyms well at semantic level. Comparing with former cross-lingual language models, TeaBERT significantly outperforms on evaluation datasets, with 93.21%, 89.89% and 76.45% of Top 5 accuracy on ICDI0-CN, CHPO and RealWorld dataset respectively, and achieves new state-of-theart performance without task specific fine-tuning. Our contrastive learning scheme can not only be used for enhancing Chinese-English medical concepts alignment, but also be applied to other languages facing the same challenges.
Human Occlusion in Ultra-wideband Ranging: What Can the Radio Do for You?

Applications of ultra-wideband (UWB) for distance estimation (ranging) and localization often involve users wearing tags. Unfortunately, the human body causes significant signal attenuation, reducing ranging accuracy. This specific case of non-line-of-sight (NLOS) condition has received little attention in the literature. Further, state-of-the-art techniques tackling generic NLOS are often based on machine learning, limiting their exploitation on embedded devices. We pursue an alternative approach and show that the features offered by the UWB transceiver, largely neglected by the literature, can be directly exploited to reliably detect human occlusions and optimize ranging accordingly. We base our findings on an extensive exper-imental campaign exploring many radio, system, and deployment dimensions in two environments, resulting in practical guidelines immediately available to the designers of UWB-based systems.
Contrastive Self-Supervised Learning for Predicting Disease-RNA Associations

Predicting disease-RNA associations is important for disease diagnosis and treatment. The traditional biological experiment method has the disadvantage of being time-consuming and laborious. Therefore, a growing number of studies have focused on predicting disease-RNA associations using deep learning methods. Aiming at the characteristics of sparse disease-RNA association data and few labels, we propose a novel prediction method named SCLDA based on contrastive self-supervised learning. This is a new attempt in the field. We also propose a data augmentation method based on RNA similarity. We tested SCLDA and other advanced methods on lncRNA and miRNA datasets, respectively, and the results show that SCLDA achieves the best performance.
Implementation Of Fake Review Detection Using Passive Aggressive Classifier

In the recent year we have been experiencing a huge surge on internet, due to many people have started using internet. Nowadays there are many paid and fake reviews flooding the e-commerce websites like Amazon, Flipkart and many other e-commerce websites. In which many customers make decisions based on these fake reviews or comments provided by others who had similar experiences. In today's competitive environment, anyone may write anything, which has resulted in an increase in the number of bogus reviews. So, to overcome this problem of fake reviews we will propose a machine learning model. The model will be trained with 40000 datasets to predict the fake reviews. Machine learning is the one of the trending techniques to predicts the output based on the experience. Furthermore, we can test the model using testing data which is also a part of the data set. We are using Passive Aggressive Classifier and we have achieved an accuracy of 83.53%.
SGD-DABiLSTM based MRI Segmentation for Alzheimer’s disease Detection

Alzheimer's disease (AD) is a neurodegenerative ailment that causes memory and cognitive skills to deteriorate over time. It is a terrible neurological ailment that causes memory and cognition impairments, as well as behavioural issues, neuropsychiatric disorders and impairment in everyday tasks. AD is diagnosed as a cause of death in elderly people. It is also one of the most difficult diseases to diagnose, especially in the early stages, using standard manual approaches. Magnetic resonance imaging (MRI) is a popular tool for detecting neurodegenerative disorders because of its high spatial resolution. In this work a deep attention bidirectional long short-term memory (DABiLSTM) with stochastic gradient optimisation (SGDO) is utilised for the detection of AD. For pre-processing brain MRI images, the gaussian bilateral filter is used. The anomaly section of the obtained image is segmented using a BiLSTM based on deep attention. The system is optimised using stochastic gradient descent optimization (SGDO), which minimises the neutral network's error rate. This work is implemented using the MATLAB tool and the Alzheimer's Disease Neuroimaging Initiative 2 (ADNI2) dataset. When compared with current approaches, the proposed method obtained an accuracy of 94.63 % in the detection of AD.
timeTree: How to Represent Time Sequence in a Threat Tree

Threat tree can be used as a tool to represent the threat conditions for certain risks. The conditions are represented as children, and the resulting risk is represented as a father. The relations among conditions could be “and” or “or”, and the resulting risk could be further as a condition for another risk that is the father of the immediate upper layer. Those iteratively representations can finally form a threat tree to tackle all possible risks. However, current threat tree can only describe static risks, and cannot show the dynamics for a threat route, which is a serial of conditions with time sequence for a risk. Thus, in this paper, we propose a tree model with time sequence semantics to extend the representation capabilities of a threat tree. Our model is incrementally enhancement of original threat tree, so that legacy threat tree can remain unamended compatibly. The data structure and algorithms are also proposed to show the usability of the proposed model.
Application of machine learning algorithm for prediction of heart disease

Healthcare is the one the domain in which every organization is working on it globally. It need to make it better and better via different researches and the innovations in this sector. Machine learning is the most used algorithm in every sector and organization. It not merely transforming the field of healthcare but also able to emulate the various human intelligence from the surrounding in order to solve various problems. Machine learning in health care sector has shown its various application in it. Disease prediction is one of the applications of it. This study helps to predict heart diseases with the help of its algorithm to determine the risk and treat the patient in the healthcare system. In this paper we have taken out the data from the kaggle and implemented it using the Jupiter notebook tool. The three algorithm we have implemented (naïve bayes, decision tree and random forest) out of which random forest is giving. Hence, with the help of the following algorithms it is possible for us to predict the heart disease with more accuracy. This research will able to predict various disease over its various symptoms more easily and accurately.
Numerical Simulation of Effect of Tripping Rod on Increase in Lift Force Acting on a Cylinder Using Lattice Boltzmann Method

The lattice Boltzmann method is used to examine the effect of tripping rods on the lift force acting on a circular cylinder subjected to flows with a Reynolds number ranging from 100 to 500. The ratios of the diameter of the tripping rod to that of the cylinder are 0.08, 0.10, and 0.11. The angular position of the tripping rod is varied from 20◦ to 120◦. Numerical results show that the lift force is smaller than that without the tripping rod if the angular position ranges from 20◦ to 40◦. Further, the numerical simulation shows that, if the angular position is greater than 50◦, the lift force surpasses that without the tripping rod and is maximized if the angular position is around 80◦ for a case with a Reynolds number of 100 and 60◦ for a case with a Reynolds number of 500.
Evolving Interest for Information Diffusion Prediction on Social Network

Even though conventional methods have contributed a lot to predicting information diffusion utilizing an end-to-end framework, they omit to consider the reason why each participant is involved in the cascade. Inspired by the ubiquitous pattern that users are inclined to take part in the discussion with attractive content and like-mind participants, we propose a temporal evolving interest-driven cascade prediction framework, named TEIC. The proposed TEIC is capable of automatically capturing the interest-driven forwarding behavior of individuals from the micro perspective and assembling them for macro-level cascade size prediction. Taking historical social actions as the input, the temporal evolving interest encoder is designed to characterize individual social preferences that change dynamically over time. Furthermore, we adopt a cascade aggregator to integrate microscopic interest-driven social actions into macroscopic cascade representations for predicting the incremental diffusion scale. We compare the TEIC with the multiple baselines, including hand-crafting feature regression, generative methods and deep learning-based models. Extensive experiments on two real-world datasets demonstrate that the proposed model significantly outperforms state-of-the-art approaches.
Detection of Pneumonia Based on ResNet Improved by Attention Mechanism

In recent years, Covid-19 is one of the major health challenges facing the human population. Due to the highly infectious nature of Covid-19 and the difficulty of detecting symptoms in the early stages, it is definitely necessary to combine X-ray for the diagnosis of pneumonia. Using traditional neural networks such as VGG, ResNet, and DenseNet to diagnose pneumonia based on X-ray images faces a number of difficulties. These models have insufficient spatial information extraction capability and are prone to overfitting on the training set. The attention mechanism is a means to improve model performance by helping the model better extract channel and spatial features from the feature maps. To identify pneumonia more accurately, we combined the ResNet network and CBAM attention mechanism to design the ResNet101-cbam model with a series of data augmentation methods as well as training strategies. We used the same approach to add attention mechanisms to ResNet50, ResNet101 and ResNet152 and tested their performance. The results show that ResNet101-cbam is the best performing model overall. It achieved a recall of 0.8205, a precision of 0.822, and an accuracy of 0.8285 on the test set, while the original pretrained ResNet101 had a precision of 0.7280 and an accuracy of 0.7644. Its performance were better than the more complex model: ResNet152-cbam, a little bit, but the training speed is improved by more than 25%. More importantly, the model with the added attention mechanism effectively overcomes the effects of positive and negative sample imbalance. The ResNet101-cbam model can be used as a medical aid, which can improve diagnostic efficiency and help us better deal with large-scale pneumonia epidemics.
Fractional Order Controller Design for Self-Balancing Two-Wheeled Robot and Performance Comparison Using Different Optimization Techniques

There has been ongoing experiments on a two-wheeled self-balancing robot for several years, and it is almost similar to the model of an inverted pendulum on a cart. The presented paper describes the model, design, working and simulation of a two-wheeled self-balancing robot. Properties of the system under study are extremely non-linear. Moreover, there is a great deal of instability in the controller of proposed system. To improve the stability of mobile robots, this paper introduces fractional order PID (FOPID) or PIλDμ controller for a non-linear and high-order system and compares it with the conventional PID. FOPID controller is designed specifically to adjust the parameters of the system more accurately according to desired specifications. The factorrs of the considered controller are optimized using both Nelder-Mead (N-M) technique and Particle Swarm Optimization (PSO) technique. The results obtained by N-M technique are compared with those of PSO technique. N-M tuned FOPID controller significantly improves the quality of controller in contrast to PSO technique. Simulation results also indicate that Nelder-Mead tuned FOPID is a better and more stable controller as compared to classical PID or PSO tuned FOPID, for a non-linear system like a two-wheeled self-balancing robot.
Research on pedestrian collision test method based on depth learning algorithm

The research on automatic driving test scenarios and collision warning methods is of great significance to the training and test verification of vehicle active safety and accelerated automatic driving algorithm. This paper deeply studies the in-depth learning algorithm, incorporates the common active safety technology in the autonomous driving scene, uses the principle of back propagation to carry out pedestrian collision detection, and analyzes different types of vehicle and pedestrian head collision test results, and verifies the effectiveness of the proposed pedestrian collision simulation algorithm, provides technical reference for the real-time collision warning model and collision avoidance strategy of intelligent connected vehicles.
UAV Swarm Trajectory and Cooperative Beamforming Design in Double-IRS Assisted Wireless Communications

Non-terrestrial communications have emerged as a technological enabler for seamless connectivity and ubiquitous computation services in the upcoming beyond fifth generation (B5G) and sixth generation (6G) networks. However, there exist numerous practical technical limitations, such as high deployment cost, massive energy consumption, high probability of information transmission blockage and dynamic propagation environments and so forth. Thanks to the rapid developments of meta-materials, the cost-effective and energy-efficiency intelligent reconfigurable surface (IRS) has been globally recognized as a revolutionized technology to construct smart radio environments. In this paper, a novel double-IRS assisted unmanned aerial vehicles (UAV)-swarm-enabled communication network architecture is proposed, where two UAV swarms are integrated with the main IRS reflector and subreflector, respectively. The energy minimization problem of UAV swarm carried main IRS is formulated, subject to a list of quality of service (QoS) constraints. To tackle the formulated challenging problem, we first decouple the original problem into two subproblems. Then, a heuristic algorithm is proposed, where the enhanced differential evolution (DE) algorithm is proposed to optimize the UAV swarm trajectory and the alternate optimization algorithm is utilized to optimize the cooperative reflect beamforming vector. Numerical results validate that the proposed algorithm outperforms several selected advanced algorithms regarding UAV swarm energy consumption. Moreover, the network performance under the different number of IRS elements is investigated.
Towards Planning Package Delivery Paths by Exploiting the Crowd of Private Cars

Crowdsourced delivery can provide cost-effective deliveries and it is becoming increasingly important in practice. This paper considers a package delivery problem which exploits relays of private cars to transport packages via their commuter routes. Specifically, we propose a method of planning package delivery paths based on the commuter routes of private cars. Using the commuter routes and time constraints as the reference, it can find optimal delivery paths and assign drivers of private cars to transport packages. The simulation experiment results show the validity and effectiveness of such method.