Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimizing Real-Time NIR Image Segmentation: Enhancing Accuracy Through Bilateral Fusion for False Negative Mitigation

False negative (FN) and false positive (FP) are one of the main causes of poor performance of tasks such as image segmentation and detection. To reduce the frequency of occurrence of FN and FP, this paper proposes bilateral fusion to strengthen information exchange within a deep learning model with a multi-branch structure. The proposed bilateral fusion improved the performance of the detail-context branch structure model, which is currently widely used in real-time semantic segmentation tasks, and can be easily applied to other models with similar structures.
Facial Expression Recognition in the Wild using Artificial Rabbits Optimizer based Residual Neural Network

In affective computing, emotion acknowledgement in the wild is a much-studied area. Although there have been advancements, the difficulty of emotion acknowledgement in the wild due to head movement, face deformation, illumination fluctuation, etc. remains an open subject. To improve the model's simplification ability and, by extension, its performance across a variety of learning tasks, it is crucial that a wide variety of features be extracted by the deep neural network. Interest in facial expression detection in natural settings has grown in recent years despite the difficulty of obtaining discriminative and informative characteristics from partially obscured photos. In the first stage of this paper's pre-processing, the noisy pictures are enhanced by Contrast Limited Adaptive Histogram Equalization (CLAHE). After that, Residual Neural Network (ResNet) features extraction is used, and the same method serves as an emotion classifier. An iterative technique called stochastic gradient descent (SGD) is utilized to refine the ResNet model's objective function. Finally, the Artificial Rabbits Optimization Algorithm (ARO) is used to choose the best value for, thereby enhancing the reliability of the categorization. The suggested method is effective, as evidenced by experimental findings on three popular facial expression recognition in-the-wild datasets: AffectNet, AFEW Dataset, and RAF-DB, where it accomplishes state-of-the-art presentation with 96% accuracy and improves upon existing models by roughly 5% to 8%.
Transmittance Hyperspectral Capture System and Methodology Assessment for Blood-Liquid Serum Samples Analysis

Hyperspectral imaging analyzed by machine learning algorithms is a powerful tool to classify materials, tissues, molecules and pathogens. By analyzing the electromagnetic spectrum of liquid serum samples, it has been demonstrated that it is possible to predict which patients with possible head trauma injury will have a possible result on computer tomography. This process is being carried out with very complex, slow and expensive spectrometric techniques. To tackle this problem, this study presents a simple hyperspectral imaging system that allows the capture of multiple serum samples with one single scan, without light artifacts as it works in transmittance and without a high data redundancy rate. Throughout this paper, the main characteristics of this system, the preprocessing chain necessary to extract the information from these captures, the working methodology, and the analysis performed are presented. Hyperspectral images of plasma from 405 patients were captured and the signatures obtained from this system were compared with the signatures captured by a spectrometer, which served as a reference system. With a mean correlation of 97.3% and a standard deviation of 3.6%, the presented system not only captures correctly liquid samples, but also provides spatial information and can capture many more samples in a single scan. In addition, a statistical study is presented on which spectrum bands present a higher concentration of information, which will be very beneficial for future analysis.
Ciclosporin use in multi-drug therapy for meningoencephalomyelitis of unknown aetiology in dogs.

OBJECTIVE To evaluate the efficacy and safety of ciclosporin therapy alone or in combination with corticosteroids and/or ketoconazole in dogs with diagnosis of meningoencephalomyelitis of unknown aetiology. METHODS Medical records of 10 dogs diagnosed with meningoencephalomyelitis of unknown aetiology and treated with ciclosporin therapy alone or in combination with corticosteroids and/or ketoconazole were reviewed at the Veterinary Medical Teaching Hospital, University of Wisconsin-Madison. Laboratory abnormalities, side effects, clinical and cerebrospinal fluid responses to treatment and association between blood ciclosporin level and response to treatment were evaluated. Histopathological diagnosis was available in three patients. RESULTS No significant abnormalities were detected on serial complete blood count and serum chemistry panel in any of the dogs. Side effects of ciclosporin therapy included excessive shedding, gingival hyperplasia and hypertrichosis. Overall median survival time for all dogs in the study was 930 days (range, 60 to more than 1290 days). In all dogs, serial cerebrospinal fluid analysis showed a marked improvement in the inflammation. CLINICAL SIGNIFICANCE Results suggest that ciclosporin either alone or in combination with ketoconazole may be a safe and effective treatment for meningoencephalomyelitis of unknown aetiology in dogs.
Synthesis of Photoplethysmograph Signals from Noises with Desired Heart and Respiration Rates

Measurement of various kind of physiological parameters from the photoplethysmograph (PPG) signal has become an emerging area of research in recent years. Hence different state of the art deep learning algorithms are proposed in this domain. But the main problem of using such algorithms is scarcity of biomedical data of the target domain. Thus to alleviate such problems in this paper a novel method of synthesizing PPG data with required variance and desired heart rate (HR) and respiration rate (RR) is proposed. A deep CNN-LSTM autoencoder has been proposed which attempts to reduce an error having three components, targeting to match the morphology with desired variance with the template PPG, HR and RR with the required values. Over 600 samples were generated from the accelerometer noises and yielded a very good result in terms of HR, RR tracking, measured in terms of Mean absolute error (MAE) and KL Divergence, to find out the variance.
Multimodal Speaker Recognition: Combining FFT, CNN, Speech-to-Text, BERT-Based Punctuation Restoration and Sentence Correction

In this paper, we look at technological solutions to problems with speech recognition, text-to-speech conversion, sentence correction, and punctuation restoration. Convolutional neural networks (CNNs) for model development, the Fast Fourier transform (FFT) for feature extraction from data, and the potent Bidirectional Encoder Representations from Transformers (BERT) model for context-aware sentence correction are all included into our methodology. Our study intends to enhance the precision, context sensitivity, and usability of language processing systems by merging CNNs, FFT, and BERT. By providing excellent transcription and precise sentence correction, this holistic approach pushes the limits of audio-based applications.
Applying Dynamic Change Impact Analysis in Component-based Architecture Design

Change impact analysis plays an important role in maintenance and evolution of component-based software architecture. Viewing component replacement as a change to composition-based software architecture, this paper proposes a component interaction trace based approach to support dynamic change impact analysis at software architecture level. Given an architectural change, our approach determines the architecture elements causing the change and impacted by the change. Firstly, component-based software architecture and component interaction trace are defined. An algorithm for generating component interaction trace from static structure model of software architecture and UML sequence diagram is provided. Secondly, the taxonomy of changes on composition-based software architecture is presented, according to which a set of impact rules are suggested to determine the transfer of the changes in component and among components. Thirdly, by performing slicing on component interaction traces according to impact rules, the impact analysis results are obtained. Finally, the architecture design of SOCIAT, a tool supporting our approach, is developed and explained
Key Vertex Pairs Extraction for Graph Classification Based on Probability Distribution Learning

Graph classification is a hot topic of machine learning for graph-structured data, and it is also a very potential and valuable research. However, the difficulty of graph classification is challenging and special, which is quite different from the normal classification problems. One of the most difficult points of graph classification is that the numbers of vertex neighbors in graphs are usually variable, which makes the number of weights uncertain and ambiguous. Recent work such like the graph attention network apply the transformer on the graph neural network. However, the learned attentions cannot strictly reveal the importance of each part of graph, which makes the model less explainable. Moreover, for small datasets, it performs less effectively because of the excessive parameters. In order to overcome these difficulties, we propose a lightweight model with an edge weighting function based on the probability distributions of node pair features learned by the Gaussian mixture model. Although the proposed framework is simple, the experimental results shows its effectiveness on small datasets.
Adaptive Antenna Activation for UAV Communication with Jitter

Unmanned aerial vehicle (UAV) communication system suffers from rapid changes in communication channels, i.e., UAV jitter, due to unintended fluctuations of UAV body. UAV jitter have a great negative impact on establishing robust transmission link in the UAV millimeter-wave (mmWave) communication system. We proposed a robust analog beamforming scheme to maintain stable link connections in the presence of UAV jitter. The method involves minimizing outage probability through beam widening and activated antenna element selection based on Angle of Arrival (AoA) information. Numerical experiments confirm its superiority over a narrow beam approach and highlight the impact of UAV jitter on outage performance.
Joint Antenna Selection and Spatial Switching for Energy Efficient MIMO SWIPT System

In this paper, we investigate joint antenna selection and spatial switching for quality-of-service-constrained energy efficiency (EE) optimization in a multiple-input multiple-output simultaneous wireless information and power transfer system. A practical linear power model taking into account the entire transmit–receive chain is accordingly utilized. The corresponding fractional-combinatorial and non-convex EE problem, involving joint optimization of eigenchannel assignment, power allocation, and active receive antenna set selection, subject to satisfying minimum sum-rate and power transfer constraints, is extremely difficult to solve directly. In order to tackle this, we separate the eigenchannel assignment and power allocation procedure with the antenna selection functionality. In particular, we first tackle the EE maximization problem under fixed receive antenna set using Dinkelbach-based convex programming, iterative joint eigenchannel assignment and power allocation, and low-complexity multi-objective optimization-based approach. On the other hand, the number of active receive antennas induces a tradeoff in the achievable sum-rate and power transfer versus the transmit-independent power consumption. We provide a fundamental study of the achievable EE with antenna selection and accordingly develop dynamic optimal exhaustive search and Frobenius-norm-based schemes. Simulation results confirm the theoretical findings and demonstrate that the proposed resource allocation algorithms can efficiently approach the optimal EE.
ROC Based Performance Evaluation of Machine Learning Classifiers for Multiclass Imbalanced Intrusion Detection Dataset

Distinguishing between hazardous and benign traffic is a crucial issue in network security. Strategies like firewalls that restrict unauthorized network access using established rules and Artificial Intelligence are already in use. This study employs various supervised machine learning methods to classify malicious network traffic. Furthermore, this study has determined that accuracy and False Acceptance Rate are not particularly informative metrics in this context because of the highly imbalanced nature of the dataset utilized in this research. More appropriate metrics, such as the F1 score and ROC score, are recommended. ROC analysis revealed that for complete coverage, accurate hostile traffic classification necessitates allowing a 30% False Positive Rate. Among all the algorithms tested in this work, the Random Forest algorithm exhibited the highest performance with an F1 score of 0.81, while XGBoost achieved the highest ROC score of 0.9695. With thorough research, Machine Learning techniques can be used to mitigate cyberattacks successfully.
IoT-MGSec: Mitigating Man-in-the-Middle Attacks in IoT Networks Using Graph-Based Learning

The Internet of Things presents a transformative era of device connectivity while creating a new paradigm shift in the process. This, however, has been met with some major pitfalls, such as an increase in device insecurity, characterized by Main-in- The Middle (MiTM) attacks. In this paper, we propose IoT-MGSec, a novel solution to mitigating MiTM attacks using graph-based learning. Our approach employs graph modeling and embedding techniques to learn node and edge features such that we can generate a robust classifier to detect MiTM attacks with high accuracy. We validate the effectiveness of our approach by comparing its performance to baseline models, and the results indicate that our approach outperforms the baseline models. The findings suggest that this approach offers a more robust solution to detecting and mitigating Man-in-the-middle attacks, and it holds potential for integration into real-time intrusion detection systems, further enhancing its capacity to secure devices within the loT landscape.
3D CD-LinkNet with Cascaded Dilated Convolution for Liver Vessel Segmentation

Automatic segmentation of liver blood vessels from CT images is very helpful for clinical surgery. Due to the small volume, variable shape and low contrast of CT images, it is difficult to segment blood vessels in abdominal CT images. To address these issues, this paper attempted to segment liver blood vessels in three steps. The first step is pre-processing to improve the contrast of the CT images. The second step is to segment liver blood vessels through a newly proposed 3D segmentation network with dilated convolutions. The third step is to perform post-processing on the segmentation results. Segmenting liver blood vessels in 3D views may result in better segmentation results due to the increased volume of blood vessels. Dilated convolutions are cascaded to extract features from a wider field of view, thereby solving the issue of small volume and variable shape. This method has been tested on a public dataset 3Dircadb-01 and compared with several existing methods. Experimental results show, the proposed method achieved the highest segmentation accuracy, with Dice score 0.6596. The method proposed in this paper is limited to blood vessel segmentation in the liver region and can be used in conjunction with appropriate liver segmentation methods.
One contact for every twelve residues allows robust and accurate topology‐level protein structure modeling

A number of methods have been described for identifying pairs of contacting residues in protein three‐dimensional structures, but it is unclear how many contacts are required for accurate structure modeling. The CASP10 assisted contact experiment provided a blind test of contact guided protein structure modeling. We describe the models generated for these contact guided prediction challenges using the Rosetta structure modeling methodology. For nearly all cases, the submitted models had the correct overall topology, and in some cases, they had near atomic‐level accuracy; for example the model of the 384 residue homo‐oligomeric tetramer (Tc680o) had only 2.9 Å root‐mean‐square deviation (RMSD) from the crystal structure. Our results suggest that experimental and bioinformatic methods for obtaining contact information may need to generate only one correct contact for every 12 residues in the protein to allow accurate topology level modeling. Proteins 2014; 82(Suppl 2):208–218. © 2013 Wiley Periodicals, Inc.
1-φ Dual Boost ANPC Type Inverter Topology With High Voltage Gain

This article proposes a novel seven-level boost active neutral point clamped topology with reduced component counts and a 3vin/(1-D) voltage gain. On the front side, a dual-input boost converter is used to reduce the stress on the dc-dc converter, and all the capacitors are inherently self-balanced. The front side dc-dc converter has two inductors, which minimize the high impulse source current that occurs during the charging of capacitors to capacitors. Further, the SiC-devices-based simulation for the proposed topology is presented to validate the performance under a higher switching frequency. A detailed comparison is presented with other recent boost-type topologies to show the superiority of the proposed topology. The proposed 7L-ANPC inverter topology is implemented and tested in the laboratory for various operating conditions, and the corresponding results are presented.
Research on vehicle routing problem based on improved dragonfly algorithm

Aiming at the low carrying rate and high fuel consumption cost of vehicle routing optimization, a vehicle routing model with time window is constructed. An improved dragonfly algorithm integrated with random learning strategy is designed and applied to solve the problem. The real-value calculation form of the five behaviors of the original dragonfly algorithm is changed into random learning target position pairs and applied to vehicle routing problem with time windows. By comparing the results with those of other three algorithms, the effectiveness and feasibility of improved effective dragon algorithm are further verified, which can reasonably allocate logistics capacity and human resources, and realize the cost reduction and efficiency increase of enterprise business.
Insulated, Passivated & Adhesively-Promoted Bonding Wire using Al2O3 Nano Coating

Bonding wires are composed of conductive metals of Au, Ag & Cu with excellent electrical conductivities for transmitting power and signals to wafer chips. Wire metals do not provide electrical insulation, adhesion promoter and corrosion passivation. Adhesion between metal wires is extremely weak, which is responsible for wire cut failures during thermal cycling. Organic coating for electrical insulation does not satisfy bondability and manufacturability, and it is complex to apply very thin organic coating on metal wires. Automotive packages require enhanced reliability of packages under harsh conditions. LED and power packages are susceptible to wire cut failures. Contrary to conventional OCB behaviors, forming gas was not required for free air ball formation for both Ag and Pd-coated Cu wires with Al2O3 passivation.
Enhancing Web Application Penetration Testing with a Static Application Security Testing (SAST) Tool

Static Application Security Testing (SAST) is a crucial methodology employed to assess the security posture of an organization's applications by scrutinizing source code for vulnerabilities susceptible to exploitation. Typically, SAST tools adopt rule-based techniques to meticulously scan source code for security shortcomings, ultimately enhancing the overall quality of applications. Notably, Java and PHP stand as two widely used languages in the development of open-source web applications, each bearing distinct security reputations. In the contemporary landscape, the sheer volume of code makes it increasingly challenging to conduct exhaustive line-by-line vulnerability assessments. This research introduces a novel SAST methodology implemented in the Python programming language. This approach is tailored to pinpoint vulnerabilities within the application layer, with a particular focus on identifying SQL injection and Cross-Site Scripting (XSS) vulnerabilities within both Java and PHP codebases. Thus, this research seeks to validate the system's efficacy in detecting these critical vulnerabilities in PHP and Java programming languages. To evaluate the system's performance, penetration testing techniques are employed to establish proof of concept. These techniques entail conducting sample tests on vulnerable web applications, such as DVWA (PHP) and WebGoat (Java). Subsequently, an in-depth analysis of the testing outcomes is conducted using confusion matrices, recall, and precision metrics. The proposed SAST methodology presents a valuable resource for software developers, facilitating the analysis of source code or compiled code versions to unearth security flaws. The testing results indicate an accuracy rate of 20.7% for the SAST, underscoring its potential to contribute significantly to the enhancement of application security. Cross-site scripting (XSS), SQL Injection, Web Vulnerability, Web Security, Static Application Security Testing (SAST)
Citation Analysis of Most Prolific Authors in the Field of Library and Information Science in Nigeria

The aim of the study is to carry out a citation analysis of ten most prolific academic scholars in the field of Library and Information Science (LIS) in Nigeria and also determine their authorship pattern. Google scholar database was used in searching for articles published from 2000 to May, 2018. Ten academic researchers in the field of LIS in Nigeria were studied. Results from the analysis show that, Tella, Adeyinka with 72 articles had a total of 1,740 citations and is the most cited author among the ten most prolific authors in the field of Library and Information Science in Nigeria, followed by Adomi, E. Esharenana with 904 citations from 47 articles, and Nwagwu, E. Williams with 684 citations from 70 articles. The authorship patterns of the publications revealed that majority of the articles were co-authored papers. Further analysis showed that the degree of collaboration among the researchers was high. The correlation of the most cited articles with most cited journals revealed the Library Philosophy & Practice as the most cited journal. The study calls for authorities in institutions and editors of local journals to find solutions to the inclusion of local journals in international indexing and abstraction databases to present a realistic picture of scholarly publications.
A Practical Evaluation in Openstack Live Migration of VMs Using 10Gb/s Interfaces

Live Migration (LM) of Virtual Machine (VM) is a process of transferring a working VM from on host to another host of a different physical machine without interfering the VM. In datacentre networks, LM enables flexibility in resource optimisation, fault tolerance and load balancing. However, in real time, the resource consumption and latency of live VM migration reduce these benefits to much less than their potential. In this paper, we present the results of an experimental study that evaluates LM in our unique high speed optical fibre network connecting Northern Ireland, Dublin and Halifax (Canada). We observe that using Pre-Copy LM extra large amounts of stressed memory leads to non convergence over high latency paths. However, using Post-copy LM the total migration time as well as downtime is dominated by specific memory utilisation patterns inside the virtualised guest. We experience variation in total migration time and downtime using Post-Copy LM considering Quality of Service (QoS) parameters, which can have significant impact in the cloud applications performance.