Miracle-dz/UniFAR-FTUs huggingface dataset:
Variant Gated Recurrent Units With Encoders to Preprocess Packets for Payload-Aware Intrusion Detection

This paper investigates variant-gated recurrent units with encoders to preprocess packets for payload-aware intrusion detection. The variant-gated recurrent units include an encoded gated recurrent unit (E-GRU) and an encoded binarized gated recurrent unit (E-BinGRU). First, the originally collected traffic is split into packets that are segmented into fixed length. Next, the temporal features of the segmented packets with payloads and headers are extracted by the encoders of variant-gated recurrent units. Then, the performance of the intrusion detection system (IDS) is evaluated in terms of accuracy, detection rate, and false alarm rate. It is worth noting that we use the encoder to automatically preprocess network packets to obtain the features that accurately represent the network packets. The variant-gated recurrent units automatically learn network packet payload and header features to effectively improve the detection rate of the IDS. In addition, the E-BinGRU drastically reduces the memory size required and replaces most arithmetic operations with the bit-wise operations. E-GRU and E-BinGRU have never been used before in the network intrusion detection. The experimental results based on ISCX2012 show that the intrusion detection based on the investigated variant-gated recurrent units achieves higher accuracy and detection rates than three of the state-of-the-art methods. The accuracy rates of E-GRU and E-BinGRU are up to 99.9% and 99.7%, respectively, and the detection rates of E-GRU and E-BinGRU are up to 99.9% and 99.8%, respectively. The memory usage of E-GRU is approximately 32 times that of GRU.
Multi-scale Spatiotemporal Information Fusion Network for Video Action Recognition

Two-stream convolutional networks have shown excellent performance in video action recognition in recent years. However, it remains unclear how to model the correlation between the temporal and spatial streams more effectively. First, the spatial stream and temporal stream pay attention to different aspects, which can lead to different recognition results. Second, the variety in the length of optical flow fields tends to have a great impact on the classification results. In this paper, we propose a novel multi-scale spatiotemporal information fusion network to fuse the spatial and temporal features. Specifically, our network takes advantage of multi-scale temporal information to better utilize the motion cues. Considering the complementary relationship between the spatial and temporal features, we take the hierarchical fusion strategies and asynchronous fusion method to fuse the two-stream features. Experimental results on two benchmark datasets (UCF101 and HMDB51) show that the proposed network achieves competitive performance.
Exploring Surface Acoustic Wave Devices for Sound-Based Reservoir Computing

In this work, we present an advancement in integrated MEMs reservoir computing (RC) systems, using surface acoustic wave devices. In the past, we have demonstrated that non-linear processing is feasible using piezoelectric SAW resonators, by using a time multiplexed resonator system. In this work, we demonstrate that feature masking, and time-delayed response coupling can be achieved using SAW devices. These elements form three of four essential building blocks for an integrated mechanical reservoir computer.
Analysis of Local Secure Connectivity of Legitimate User in Stochastic Wireless Networks

In this paper, we investigate the local secure connectivity in terms of the probability of existing a secure wireless connection between two legitimate users and the isolated security probability of a legitimate user in stochastic wireless networks. Specifically, the closed-form expressions of the probability that there is a secure wireless communication between two legitimate users are derived first. Then, based on these equations, the corresponding isolated secure probability are given. The characteristics of local secure connectivity are examined in four scenarios combined from two wireless channel conditions (deterministic/Rayleigh fading) and two eavesdropper configurations (non-colluding/colluding). All the derived mathematical equations are validated by the Monte-Carlo simulation. The obtained numerical results in this paper reveal some interesting features of the impact of eavesdropper collusion, wireless channel fading, and density ratio on the secure connection probability and the isolated security probability of legitimate user in stochastic networks.
Trending Topics of Malaysia Through Social Media Analytics

Trending topics can be a good way of gaining a bird's eye view on what people are interested in. It can be in the form of discussions, keywords or even hashtags. The aim of this project is to obtain an overview on what are the trending topics currently being discussed on social media in a country, in this case, Malaysia. With an idea of what is being discussed by most of the people on social media, it is possible to seize this opportunity to analyze those topics and include them in various media including television, radio, feeds and websites. Broadcasting companies also are interested to see if they are displaying topics which are being discussed by their target audience. This can be a good strategy to get more people to indulge in the services the company has to offer and gives a good opportunity for this company to engage with its audience. This project will guide the broadcasting company in gaining more insights to what is being discussed on social media and major keyword used by audience. Through the use of social media data analytics, this project successfully shows that there is 57% alignment of the various channels of the largest broadcasting corporation in Malaysia with the trending topics in social media.
Improving Generative Adversarial Networks with Adaptive Control Learning

Generative adversarial networks (GANs) are well known both for being unstable to train and for the problem of mode collapse, particularly when trained on data collections containing a diverse set of visual objects. This study introduces an adaptive hyper-parameter learning procedure for GANs as an alternative to the existing static approach. The proposed procedure is designed to mitigate the impact of instability and saturation in the original by dynamically adjusting the ratio of the training steps of both the generator and discriminator. To accomplish this, we track and analyze stable training curves of relatively narrow datasets and use them as the target fitting lines when training more diverse data collections. Experimental results show that the proposed model improves the stability and generates more realistic images.
Deep Transcranial Magnetic Stimulation for the Treatment of Negative Symptoms in Schizophrenia: Beyond an Antidepressant Effect.

BACKGROUND Negative symptoms of schizophrenia show limited response to both typical and atypical antipsychotics. Repetitive transcranial magnetic stimulation applied over the prefrontal cortex (PFC) has been proposed as an adjuvant to pharmacological treatment of negative symptoms in schizophrenia, but whether the improvements obtained are specific to negative symptoms or attributable to antidepressant effects is still unclear. OBJECTIVE The aim of the present study is to determine to which extent the improvements in negative symptoms of schizophrenia obtained after high-frequency stimulation of the bilateral PFC using deep TMS (dTMS) are attributable to antidepressant effects. METHODS Repetitive dTMS was administered to the PFC in a cohort of 16 patients with schizophrenia under successful pharmacological control of positive symptoms and predominant negative symptoms. Patients were treated using high-frequency (18 Hz) bilateral stimulation applied over the lateral PFC bilaterally using Brainsway H-2 coil. The effects of dTMS on negative symptoms were measured using the Scale for the Assessment of Negative Symptoms and the Positive and Negative Syndrome Scales. We then compared the improvements in negative symptoms obtained in patients showing depressive symptoms (≥7 points) with those found in patients without depression (>7 points), as determined by the Calgary Scale for Depression. RESULTS Repetitive dTMS treatment induced significant improvements in negative symptoms as assessed using both Scale for the Assessment of Negative Symptoms and Positive and Negative Syndrome Scales. Comparison of the improvements obtained in patients with or without depression at the beginning of treatment revealed similar improvements in negative symptoms, irrespective of subjacent depression. CONCLUSIONS Our data suggest that the beneficial effects of high frequency dTMS of the PFC cannot be attributed solely to its antidepressant effects.
A Single-Switch High Step-Up DC-DC Converter Based on Integrating Coupled Inductor and Voltage Multiplier Cell for Renewable Energy Applications

In this paper, a high step-up, single switch, non-isolated, coupled inductor based DC-DC converter is proposed for sustainable energy applications. A high voltage gain is achieved by applying a coupled inductor and two voltage multiplier cells in the structure of proposed converter. The passive clamp that is connected to the main switch recovers the coupled inductor leakage inductance energy and limits the voltage spike on the power switch. Thus, low on-state resistance of the power switch can be adapted to decrease the conduction loss. The features such as coupled inductor with a low turn ratio, high voltage conversion ratio in suitable duty cycles, , high efficiency, recycled energy of leakage inductor and low voltage stress on the power switches and diodes make the presented converter appropriate for sustainable energy applications. The steady state analysis and voltage stress of the power switch and diodes of the converter are expressed in detail. Finally, simulation results in PSIM software have been presented to confirm the performance of the suggested structure.
Textual Analysis of Privacy Policies to Understand the Effect of GDPR

The General Data Protection Regulation (GDPR) has far-reaching implications that extend beyond the European Union (EU) borders. Its visionary concepts have worked as a catalyst, inspiring states and regions worldwide to rethink their data privacy policies. The GDPR is the foundation of an international privacy framework that crosses political and cultural boundaries, encouraging a collaborative effort to protect personal data. This study aims to explore the GDPR's multifaceted effects on data privacy, including organisational, legal, and technological aspects. To provide comprehensive insights, data was collected from the privacy policies of the top 50 websites for both the pre-GDPR and postGDPR eras. Textual analysis of these policies allowed for a comparison using keyword frequency, personal information disclosure, GDPR-related terms, and the enumeration of user rights being evaluated. To examine the structural changes in privacy rules, we use the average frequency with which a specific group of words. The finding reveals that the average word count of privacy rules has increased significantly, rising from 2355.74 to 5882.82. This shift indicates a positive trend towards increasing transparency and clarity in communicating privacy policies. Privacy policies are now written in more precise, accessible language, emphasising the essence of GDPR's objective and improving user comprehension. In the post-GDPR, key terms such as “Consent,” “Data Breach,” “Data Subject,” “Data Controller,” and “Data Protection” feature prominently, indicating more awareness and more substantial compliance obligations. Implementing GDPR user rights under amended rules demonstrates a noteworthy dedication to user data protection.
A Novel Indoor Human Occlusion Identification Method With BLE Signals

The continuous development of the Internet of Things (IoT) and the Industrial Internet has given rise to many new application scenarios and demands, which put forward new demands and challenges for wireless short-range communication technologies. Among them, Bluetooth Low Energy (BLE) stands out in indoor single-station ranging by virtue of its low cost and low power. However, in the indoor environment, BLE signal propagation is easily occluded by the human body to form none-line-of-sight (NLOS) propagation which affects the range accuracy. In this work, we explore the difference in the amplitude and phase information of the channel response in the presence or absence of human occlusion, using frequency hopping and multi-packet transmission, and then we propose an identification method based on the coefficient of variation (COV), which can effectively identify the presence of human occlusion. Experimental results in indoor scenes show that our new features have higher identification accuracy than previous features.
Bifurcation Generated True Random Numbers In Nonlinear Micromechanical Resonators

This work demonstrates a nonlinear micromechanical resonator for true random number generation through constructive utilization of internal resonance (IR). Specifically, the IR system manifests distinct pure sinusoidal and amplitude modulations (AM), delineated by the saddle-node on an invariant circle (SNIC) bifurcation. Operating in proximity to this bifurcation point with a fixed frequency actuation setup, slight background thermal fluctuations induce stochastic shifts in the bifurcation boundary. Therefore, the nonlinear resonator exhibits stochastic transitions between pure sinusoidal and AM response branches, causing the IR induced equidistant frequency spikes, i.e., frequency combs, to present and vanish randomly, thereby serving as the foundation for realizing bit "0/1". In this work, the generated 40960-bit sequence passes the NIST SP800-22 statistical test [1] without post-processing. Moreover, the NIST SP800-90B independent and identically distributed (IID) test of [2] indicates a minimum entropy of 0.730292.
Twitter Analysis for Disaster Management

Disaster always leads to response from the people and in this new technology generation, people give their feedback and raise concerns through their views on social media platform. Twitter is used for such response and information exchange. This paper reports in depth twitter analysis of Nepal Earthquake and identification of various factors in the disaster module. Based on Twitter data collected between last week of April and first week of May 2015. 40,236 raw messages apparently related to the Nepal earthquake were retrieved, preprocessed and analysis were done on the same. This paper shows measures and analysis of the situation using geolocation feature for identifying danger zones and visual analytics of the dataset. With the help of the keyword generation algorithm different parameters were introduced. The results show that our disaster module can work on different hashtags and no need for prior defining of parameters by humans provided the data set is available for that unique problem
An Improved Image Up-Scaling Technique using Optimize Filter and Iterative Gradient Method

In numerous realtime applications, image upscaling often relies on several polynomial techniques to reduce computational complexity. However, in high-resolution (HR) images, such polynomial interpolation can lead to blurring artifacts due to edge degradation. Similarly, various edge-directed and learning-based systems can cause similar blurring effects in high-frequency images. To mitigate these issues, directional filtering is employed post corner averaging interpolation, involving two passes to complete the corner average process. The initial step in low-resolution (LR) picture interpolation involves corner pixel refinement after averaging interpolation. A directional filter is then applied to preserve the edges of the interpolated image. This process yields two distinct outputs: the base image and the detail image. Furthermore, an additional cuckoo-optimized filter is implemented on the base image, focusing on texture features and boundary edges to recover neighboring boundary edges. Additionally, a Laplacian filter is utilized to enhance intra-region information within the detailed image. To minimize reconstruction errors, an iterative gradient approach combines the optimally filtered image with the sharpened detail image, generating an enhanced HR image. Empirical data supports the effectiveness of the proposed algorithm, indicating superior performance compared to state-of-the-art methods in terms of both visual appeal and measured parameters. The proposed method's superiority is demonstrated experimentally across multiple image datasets, with higher PSNR, SSIM, and FSIM values indicating better image degradation reduction, improved edge preservation, and superior restoration capabilities, particularly when upscaling High-Frequency regions of images.
Machine Learning in the Quantum Era

Machine Learning (ML) has been extensively utilized in various scientific and engineering domains. But the inherent constraints and computational complexity that arise in classical machine learning are particularly evident when dealing with large-scale, high-dimensional datasets or when attempting to solve intricate optimisation problems. Quantum systems exhibit unconventional patterns that are typically not effectively generated by traditional systems and can also tackle high dimensional and large size datasets efficiently. Therefore, it is believed that quantum computing systems may outperform traditional computing devices in machine learning tasks. Quantum Machine Learning (QML) has emerged as a rapidly developing field that combines the concepts of quantum mechanics and machine learning to solve complex problems across various disciplines. The domain of quantum machine learning investigates the development and execution of quantum software with the potential to facilitate machine learning at a much superior pace compared to traditional computers. However, the major challenges encountered are hardware limitations, noise, and development of quantum algorithms. The objective of this paper is to provide an insight on the foundational principles, essential algorithms, various associated platforms, potential applications and challenges being faced by QML. As data continues to grow in size and complexity, together with the emergence of nonlinear and high-dimensional real-world situations, the computing benefits offered by QML have the potential to facilitate significant advancements in the fields of healthcare, finance, robotics, logistics, communication, and cyber security. This paper presents the basis for future research work related to development of quantum algorithms in the domain of machine learning.
A Dual-band Broadband Conical Beam Antenna Based on TM₀₁ and TM₀₂ mode

A dual-band broadband conical beam antenna operating at Ku-/ Ka-band is presented in this paper. The proposed antenna adopts a novel across-layer folded structure which consists of two substrates. A circular patch with coupling slot is printed on the lower substrate as a monopole at Ku-band, and a circular patch and a loop patch are printed on the upper substrate to achieve operating at Ka-band. A wideband performance is obtained by introducing shorting vias to excite TM01 and TM02 mode in both bands. The simulation results show that the proposed antenna operates at 11.74 GHz-14.47 GHz in Ku-band and 32.20 GHz-35.63 GHz in Ka-band with 10 dB impedance bandwidth of 20.8% and 10.1%. And conical beam radiation patterns are achieved within the two bands, the peak gains in the lower and upper bands are 5.4 dBi and 9.9 dBi respectively.
Automated Detection of Pneumonia Using Pre-Trained Convolutional Neural Networks in X-Ray Images

Pneumonia stands as a potentially lethal ailment, predominantly impacting the elderly population and occasionally leading to life-threatening outcomes. The timely identification of Pneumonia holds immense significance in preserving numerous lives. This research paper focuses on early detection and categorization of individuals afflicted by Pneumonia using their chest X-rays. The diagnosis is performed using a convolutional neural network from the foundational level up, yielding incredibly accurate results. Deep Learning models automate the procedure, guaranteeing swift, skillful, and proficient results when presented with patients' X-ray images. This study aims to categorize and assess the chest X-ray images into two groups. Convolutional neural networks show better classification effectiveness in this case when compared to feature-based picture classification techniques. In this work, three pre-trained models—ResNet-50, VGG-19, and DenseNet-121 are compared and evaluated for their performance in binary categorization of chest X-ray images. The results of the evaluation demonstrate that the DenseNet-121 model surpasses the performance of all three models, achieving an accuracy of 99.4%. This indicates the exceptional ability of the DenseNet-121 model in accurately classifying individuals afflicted by Pneumonia based on their chest X-ray images. The high accuracy underscores the potential of deep learning models, particularly CNNs, in automating the diagnostic procedure with swift, skillful, and proficient results. Comparing the sensitivity, specificity, accuracy, and F1-score of each unique model allows for the evaluation of the pre-trained model's efficacy.
Nano-Net MoS2-Based NO2 Sensor at Room Temperature Using Facile Fabrication with Electrospun Fiber Mask

Nano-net MoS2-based NO2 sensors were fabricated using a simple electrospinning method, creating edge sites and S-vacancies on the MoS2 surface. The sensor showed a 300% improvement in response to NO2 at room temperature, with a notable 57% response to 10 ppm of NO2. The enhanced performance is attributed to increased active sites and defects, including sulfur vacancies, created through nano-patterning of MoS2. These modifications result in a higher defect concentration and improved adsorption-desorption dynamics at room temperature. The sensor's ability to detect lower concentrations of NO2 and its improved recovery time represents a substantial advancement in gas sensing technology, offering promising applications in environmental monitoring and industrial safety.
3D-Microprinted PDMS-Based Microfluidic Vessels for Organ-on-a-Chip Applications

Microphysiological systems—also known as "organ-on-a-chip (OOC)" systems—hold considerable promise for applications including drug screening, disease modeling, and personalized medicine. A critical barrier to OOC efficacy, however, stems from manufacturing challenges that hinder the accurate recreation of 3D architectures and material properties of in vivo organ systems. To provide a new pathway to address these issues, here we leverage "Two-Photon Direct Laser Writing (DLW)" to 3D print physiologically relevant polydimethylsiloxane (PDMS) microvessels directly atop 3D microfluidic chips—fabricated via the "Vat Photopolymerization (VPP)" technique, "Liquid-Crystal Display (LCD)" 3D printing. Fabrication results revealed effective production of both interweaving and independent 3D microfluidic vessels with inner diameters (IDs) and wall thicknesses ranging from 80–100 μm and 5–10 μm, respectively, as well as pre-designed (i.e., as-printed) micropores with 5 μm diameters. Preliminary experimental results for MDA-MB-231 cells seeded within the porous microvessels revealed that the 3D PDMS system supported cell viability. In addition, pressure-vacuum experiments revealed that the permeation effects could be tuned to regulate the microenvironmental conditions internal and external to the porous microvessels. In combination, this work serves as a fundamental proof of principle for establishing entirely new classes of 3D microphysiological systems for diverse OOC applications.