Miracle-dz/UniFAR-FTUs huggingface dataset:
Capacity of Noisy Permutation Channels

We establish the capacity of a class of communication channels introduced by Makur. The <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>-letter input from a finite alphabet is passed through a discrete memoryless channel <inline-formula> <tex-math notation="LaTeX">$P_{Z|X}$ </tex-math></inline-formula> and then the output <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>-letter sequence is uniformly permuted. We show that the maximal communication rate (normalized by <inline-formula> <tex-math notation="LaTeX">$\log n$ </tex-math></inline-formula>) equals <inline-formula> <tex-math notation="LaTeX">${\frac{1}{ 2}} ( \textsf {rank}(P_{Z|X})-1)$ </tex-math></inline-formula> whenever <inline-formula> <tex-math notation="LaTeX">$P_{Z|X}$ </tex-math></inline-formula> is strictly positive. This is done by establishing a converse bound matching the achievability of Makur. The two main ingredients of our proof are: 1) a sharp bound on the Kullback-Leibler divergence of a uniformly sampled vector from a type class and observed through a DMC to an iid vector; and 2) the covering <inline-formula> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula>-net of a probability simplex with Kullback-Leibler divergence as a metric. In addition to strictly positive DMC we also find the noisy permutation capacity for <inline-formula> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula>-ary erasure channels, the Z-channel and others.
Deep Learning-Based Brain Tumour Detection Using Robust Active Shape Model Algorithm

Diagnosing cancer manual brain tumours from MRI images is difficult and time-consuming. Therefore, the accuracy and stability of the treatment planning section is an integral part of the treatment assessment. Diagnosing cancer manual brain tumours from MRI images is complicated and time-consuming. Therefore, the accuracy and precision of brain tumours, treatment planning units and consistency are essential for treatment evaluation. Therefore, accuracy and brain tumour, treatment regimen determination and stability are critical areas of treatment evaluation. Imaging plays a crucial role in diagnosing brain tumours. A brain tumour detection method based on digital image segmentation is proposed. Brain tumours are challenging to treat because they target only the body’s control organs. Our approach consists of two main processes, an automatic multistep (AMS) algorithm fitting method, which uses iteration to segment the brain contour roughly. An initial state of the Robust Active Shape Model (RASM) is found. Second, the initial segmentation results are fitted to the brain using an optimized surface detection method. Segmentation of the left and right brain separately for brain tumour detection using the RASM method improves limb patterns and segmentation of brain and brain tumours. This suggests that the flammability classification of the whole tumour, tumour core, and proliferative tumor for the model in the brain tumor dataset has a good generalization ability, with brain validation scores of 85.9%, 75.9%, and 70.3%, respectively.
Joint Design of OFDM Sequences and Mismatch Filter under Spectral Constraints

The OFDM sequences with low correlation sidelobe level (CSLL) is desired in many 5G wireless systems. The OFDM sequences and mismatch filter are jointly designed by maximizing the weighted merit factor (WMF) of the cross-correlation between the OFDM sequences and mismatch filter under the constraints of spectra, where MF refers to the ratio of the central lobe energy to the sum of all other lobes. To solve the nonconvex problem, we devise an efficient alternating optimization (AltOpt) algorithm. Numerical simulations are provided to demonstrate the effectiveness of the proposed algorithms.
Facial Emotion Classification in Emotional Intelligence using Deep Learning Techniques

Emotion recognition from facial expressions is relatively easy for humans, but challenging for a machine. Emotion identification from face features is a popular application of social signal processing, especially in the area of human-computer interaction. Many research has been conducted on the topic of automatic emotion identification, with most of them using some kind of machine learning. Nonetheless, in computer vision, even the identification of basic emotions such as anger, pleasure, contempt, fear, sorrow, and surprise remain a challenging problem. During the last several years, deep learning has received a lot of attention as a potential answer to many real-world problems, including emotion identification. Many preprocessing strategies were assessed to show how they influenced the CNN performance, and the convolutional neural methodology was refined to recognise 7 core emotions. Improvements in face characteristics and emotions based on emotion recognition are the subject of this study. Computers can make better projections about a person's state of mind and give more personalised replies if they can learn to detect or recognise facial expressions that evoke human emotions. Thus, we investigate the potential of a deep learning approach using a convolutional network to enhance feature-based emotion identification (CNN). Our dataset, consisting of about 32,298 images used for testing and training, features a wide variety of face expressions. The input picture may be cleaned up with the help of the preprocessing system, and the face can be more easily detected and its features extracted during the pretraining phase. Although the prior art study creates the categorization of different facial responses like the seven feelings of a facial acting coding system (FACS) without utilizing the optimization approach, our suggested article shows the same seven emotions of the FACS.
Analysis of Achievable Rate for SISO, MISO and MIMO-Orthogonal Frequency-Division Multiplexed (OFDM) Systems with Reconfigurable Intelligent Surface

The world has started using fifth generation mobile network, with over 1billion people now feeling the speed and power of 5G technology, testing and experiments for beyond 5G networks have started already. One of the most popular technical hardware that shows a potential to enhance this technology is Intelligent reflecting surfaces (IRSs). These have been in the picture for considerable amount of time, these are a low-cost passive element made up on PIN diodes. Intelligent reflecting surfaces (IRSs) also known as reconfigurable intelligent surfaces (RISs) have the capacity to direct the electromagnetic (EM) waves to a particular path. The 3D- Passive meta surface is digitally controlled and has low energy consumption and operates in full-duplex mode. The various Orthogonal Frequency-Division Multiplexed (OFDM) Systems when configured with IRS shows different trends, in the research article the behavior of Achievable rate for Single-Input Single Output (SISO), Multiple-Input Single-Output (MISO) and Multiple-Input Multiple-Output (MIMO) systems have been analyzed. The analyses have been done taking into consideration the mid-band 5G spectrum.
Application of Planned Behavior Theory for Analysis of Customer's Revisit Intention to a Small Business Coffee Shop in Malang City

This study examines how specific factors in the theory of planned behavior and additional perceived authenticity impact the revisit intention of SME-based coffee shops in Malang. Using purposive sampling, the sample was 239 respondents in Malang. Analyzed using Structural Equation Modeling (SEM) and tested empirically using Partial Least Square (PLS). This study added perceived authenticity as a novelty by focusing on the specific attributes that comply with the theory of planned behavior. This study will encourage SME-based coffee shop to gain consumer interest in making revisit intentions. Product quality, price fairness, and store atmosphere significantly affected attitudes. Service quality does not affect attitudes. Attitude and perceived authenticity had significant affect to revisit intention. Subjective norms and perceived behavioral control do not affect revisit intention.
Extended Vision Transformer (ExViT) for Land Use and Land Cover Classification: A Multimodal Deep Learning Framework

The recent success of attention mechanism-driven deep models, like vision transformer (ViT) as one of the most representatives, has intrigued a wave of advanced research to explore their adaptation to broader domains. However, current transformer-based approaches in the remote sensing (RS) community pay more attention to single-modality data, which might lose expandability in making full use of the ever-growing multimodal Earth observation data. To this end, we propose a novel multimodal deep learning framework by extending conventional ViT with minimal modifications, aiming at the task of land use and land cover (LULC) classification. Unlike common stems that adopt either linear patch projection or deep regional embedder, our approach processes multimodal RS image patches with parallel branches of position-shared ViTs extended with separable convolution modules, which offers an economical solution to leverage both spatial and modality-specific channel information. Furthermore, to promote information exchange across heterogeneous modalities, their tokenized embeddings are then fused through a cross-modality attention (CMA) module by exploiting pixel-level spatial correlation in RS scenes. Both of these modifications significantly improve the discriminative ability of classification tokens in each modality and thus further performance increase can be finally attained by a full token-based decision-level fusion module. We conduct extensive experiments on two multimodal RS benchmark datasets, i.e., the Houston2013 dataset containing hyperspectral (HS) and light detection and ranging (LiDAR) data, and Berlin dataset with HS and synthetic aperture radar (SAR) data, to demonstrate that our extended vision transformer (ExViT) outperforms concurrent competitors based on transformer or convolutional neural network (CNN) backbones, in addition to several competitive machine-learning-based models. The source codes and investigated datasets of this work will be made publicly available at https://github.com/jingyao16/ExViT.
Lookup Table Technique by using Irradiation Intensity and Duty Cycle for Faster MPPT Application

This research shows how Lookup Table technique can track MPP. This method uses calculation to find the exact value of duty cycle that make PV panel reach MPP. Therefore, its speed is faster than classical techniques especially P&O because knowing the exact value of duty cycle can make the controller control the switching device that makes PV reach MPP directly. Moreover, the swing at MPP is less P&O. However, the Lookup Table technique still be relied P&O to track MPP in the first period for automatically recording data into the Table for the Lookup Table technique. Hence, this method is going to be combined by both of P&O and Lookup Table. It is relied on the condition which technique is going to be operated. Lookup Table technique will be operated instead of P&O when the recorded data are enough and under defined the conditions that will be explain in this paper.
An Innovation Development of Routing for Smart Data Traffic Environment in Data Mining

The routing for smart data traffic environment in data mining is a complex process that requires advanced algorithms to allow for efficient data collection and analysis. The process starts with the initial data collection, which involves gathering data from various sources, such as sensors, web applications, and databases. Once the data is collected, it is processed and stored in a data warehouse. This data warehouse is then analyzed to identify patterns, correlations, and anomalies in the data. From there, algorithms are used to determine the optimal route for data traffic, based on factors such as distance, time, cost, and the likelihood of successful data transfer. The routing process involves both traditional and newer methods. Traditional methods include the use of routing tables, which allow for the efficient routing of data traffic to specific nodes in a network. Other methods include the use of route optimization algorithms, which take into account the traffic loads of different nodes in a network and prioritize the routing of data traffic in an optimal manner. Newer methods of routing data traffic include Software-Defined Networking (SDN), which is a software-based approach to routing data traffic. This approach allows for the use of automated routing algorithms that can quickly adjust the routing of data traffic based on the current traffic load.
Fast Forward-Backward Hankel Matrix Completion for Automotive Radar DOA Estimation Using Sparse Linear Arrays

Automotive multiple-input multiple-output (MIMO) radar with sparse linear arrays is a cost-effective solution to achieve large aperture size with low hardware cost and reduced mutual coupling. The challenges associated with automotive MIMO sparse linear arrays are the high sidelobes, which might result in angular detection errors. This paper presents a fast forward-backward Hankel matrix completion and matrix pencil method for joint array interpolation and super-resolution single-snapshot angle finding by exploiting the structure of Hankel matrix. The novelty of the proposed approach lies in two parts. It not only saves the computational cost of singular value decomposition (SVD) in each iteration of the matrix completion, but also increases the degrees of freedom to construct a low-rank matrix with larger dimensions using the same number antenna elements, as a result of which, more targets can be completed and estimated with better accuracy. Numerical results demonstrate the effectiveness and efficiency of the proposed method.
A bioinformatics and system biology technique to identify candidate biomarkers and functional pathways among stress and depression

Stress means a lot of mental or physical strain. It can result from anything that provokes negative emotions, such as an idea or an experience. The World Health Organization cites depression as the leading nonfatal cause of disability worldwide. Major depressive disorder is one of the most common mental health illnesses that results in long-term damage (MDD). A major obstacle to the correct identification of stress and depression is the lack of good diagnostic biomarkers. By analyzing gene networks, we hoped to identify potential biomarkers of this disease. Samples of stress and depression were used to create the microarray and RNA-seq datasets GSE183648 and GSE101521. Through the use of R, it is possible to search for frequently occurring DEGs and filter preprocessed datasets. A Venn diagram illustrating the overlap between different sets of DEGs rules. When a group of DEGs is established, the PPIs network is built from them, with hub nodes selected according to topological features. In the PPI network, we found that ITGB3, ANGPTL4, ITGB1, PPARA, and RXRA serve as important hub genes. This research includes KEGG pathways as well. The development of the Gene Ontology, studies of gene-miRNA interactions, TF-gene interactions, and module analysis networks are all seen as important next steps in this field of study. Last but not least, various therapeutic possibilities have been found with identical DEGs as their primary base.
An Efficient Machine Learning-Based Resource Allocation Scheme for SDN-Enabled Fog Computing Environment

Fog computing is an emerging technology which enables computing resources accessibility close to the end-users. It overcomes the drawbacks of available network bandwidth and delay in accessing the computing resources as observed in cloud computing environment. Resource allocation plays an important role in resource management in a fog computing environment. However, the existing traditional resource allocation techniques in fog computing do not guarantee less execution time, reduced energy consumption, and low latency requirements which is a pre-requisite for most of the modern fog computing-based applications. The complex fog computing environment requires a robust resource allocation technique to ensure the quality and optimal resource usage. Motivated from the aforementioned challenges and constraints, in this article, we propose a resource allocation technique for SDN-enabled fog computing with Collaborative Machine Learning (CML). The proposed CML model is integrated with the resource allocation technique for the SDN-enabled fog computing environment. The FogBus and iFogSim are deployed to test the results of the proposed technique using various performance evaluation metrics such as bandwidth usage, power consumption, latency, delay, and execution time. The results obtained are compared with other existing state-of-the-art techniques using the aforementioned performance evaluation metrics. The results obtained show that the proposed scheme reduces 19.35% processing time, 18.14% response time, and 25.29% time delay. Moreover, compared to the existing techniques, it reduces 21% execution time, 9% network usage, and 7% energy consumption.
Design and Implementation of Advanced CJDD Algorithm for MIMO Wireless Communication Systems

The paper proposes design and implementation of advancedCJDD algorithm for MIMO wireless communication systems and its implementation. The main objective of this novel Configurable Joint Detection and Decoding (CJDD) algorithm is to increase the quality of signal and reduce the complexity of the system. Theadvanced CJDD algorithm is based on the different characteristics which explores the tree search algorithms. This algorithm mainly provides impact on the system performance based on different aspects. So this paper presentadvancedCJDD algorithm and its implementationby using wireless communication systems. The results are compared with CJDD algorithm, advanced CJDD algorithm and advanced CJDD algorithm has its advantage in reducing the complexity, time, and cost and increases the accuracy in very effective way.
Let's Predict Who Will Move to a New Job

Any company's human resources department faces the challenge of predicting whether an applicant will search for a new job or stay with the company. In this paper, we discuss how machine learning (ML) is used to predict who will move to a new job. First, the data is pre-processed into a suitable format for ML models. To deal with categorical features, data encoding is applied and several MLA (ML Algorithms) are performed including Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), and eXtreme Gradient Boosting (XGBoost). To improve the performance of ML models, the synthetic minority oversampling technique (SMOTE) is used to retain them. Models are assessed using decision support metrics such as precision, recall, F1-Score, and accuracy.
Secure Cloud Data Encryption

Cloud database is an efficient way to manage data. Recently, various services are provided based on the cloud database. On the other hand, in addition to the efficiency, customers’ data privacy is one of the concerns. Particularly, for personal and sensitive data represented by financial data and medical data, the cloud privacy must be satisfied. This paper proposes a cloud database scheme in which the data is encrypted securely in users’ device. It is notable that the data is encrypted based on the current FIDO authentication scheme. The data is encrypted in a secure area in the user’s device and signed with an authentication key, and the information with its digital signature are sent to the service provider. This mechanism accomplishes the robust privacy in cloud data management. The security analysis confirms the advantage of the proposed scheme. In addition, the performance analysis confirms the feasibility of the scheme in terms of computation time.
Real-time vehicle shadow detection

In many traffic-related applications, such as traffic management and structural health monitoring for roads, an accurate estimation of a moving vehicle's size and shape is needed before proceeding further. However, due to the presence of cast shadows, these properties cannot be obtained accurately using common object detection systems. To deal with the problem of misclassifying shadows as foreground, various methods have been introduced. Most of these methods often fail to distinguish shadow points from the foreground object when the boundary between the umbra and the object is unclear due to camouflage. A novel method for detecting moving shadows of vehicles in real-time applications is presented. The method is based on two measurements, namely, the illumination direction and the intensity measurements in the neighbouring pixels in a scanned line. A major advantage of using image lines for classification is the ability to solve the problem associated with camouflages. Experimental results show that the proposed method is efficient in real-time performances and has achieved higher detection rate and discrimination rate when compared with two well-known methods.
Application of the differential Fabry–Perot interferometer in angle metrology

A differential Fabry–Perot interferometer (DFPI) was applied for the detection of ultra-small angles in the level of nanoradian (nrad) precision. During application, down to 1 nrad angular steps were generated using the available high precision small angle generator (HPSAG) and these steps were detected using the frequency stabilised lasers as an alternative and outperforming method to conventional angle interferometers. The use of the DFPI provided the displacement measurements (further angular displacements through optical configuration) with picometre sensitivity free from linearity errors. A coefficient of 1.436 964 × 10−9 arcsec Hz−1 (about 7 × 10−6 nrad Hz−1) for the DFPI was calculated using the generated–measured traceable angular values of the HPSAG and various angular steps were measured by the DFPI using this coefficient. The tests were carried out up to the range of 0.5 arcsec (2500 nrad) in angular steps down to 1 nrad. The work is in progress under the SIB58 Angles project and aims to reduce the noise level of the HPSAG with further statistical process and later to extend the measurement range of the DFPI readings with the same optical configuration, rather than using different beam spacing configuration in the DFPI.
Learning-Based Optimal Impedance Control for Space Manipulator Contact Tasks

This paper focuses on the contact control problem during operation tasks executed by space manipulator systems. First, a discounted value function is established to describe the interaction performance between the end-effector and the contact surface of object. Then, taking into account the effect of contact surface position, a model-free integral reinforcement learning (IRL) method with state feedback is applied to solve the optimal impedance parameters. Later, a state reconstruction technique with immunity to control noise is developed, whereby a novel model-free IRL algorithm is proposed which obviates the reliance on the velocity measurement and contact dynamics knowledge. Numerical simulations reveal the effectiveness of the proposed algorithm in space contact tasks.