Miracle-dz/UniFAR-FTUs huggingface dataset:
Explainable machine learning-based cybersecurity detection using LIME and Secml

The field of Explainable Artificial Intelligence (XAI) has gained significant momentum in recent years. This discipline is focused on developing novel approaches to explain and interpret the functioning of machine learning algorithms. As machine learning techniques increasingly adopt “black box” methods, there is growing confusion about how these algorithms work and make decisions. This uncertainty has made it challenging to implement machine learning in sensitive and critical fields. To address this issue, research in machine learning interpretability has become crucial. One particular area that requires attention is the detection process and classification of malware. Handling and preparing data for malware detection poses significant difficulties for machine learning algorithms. Thus, explainability is a critical requirement in current research. Our research leverages XAI, a novel design of explainable artificial intelligence that uses cybersecurity data to gain knowledge about the composition of malware from the Microsoft large benchmark dataset-Microsoft Malware Classification Challenge (BIG 2015). We use the LIME explainability technique and the Secml python library to develop explainable prediction results about the class of malware. We achieved 94% accuracy using Decision Tree classifier.
DRL-Driven Joint Task Offloading and Resource Allocation for Energy-Efficient Content Delivery in Cloud-Edge Cooperation Networks

With the proliferation of mobile devices (e.g., vehicles and smartphones), rich media content services from massive users lead to high network resource consumption and energy usage. How to effectively allocate heterogeneous network resources and achieve green content delivery is a major challenge to be addressed in urgency, especially when vehicular users are involved and the spatiotemporal distribution of the content requests can change drastically. In this article, we propose a new deep reinforcement learning (DRL)-aided task offloading and resource allocation scheme, named TORA-DRL, to minimize power consumption in cloud-edge cooperation environments, where in-network caching and request aggregation are incorporated to reduce replicated transmissions of network contents. Based on the history of content requests and available network resources in the system, TORA-DRL jointly optimizes the decisions about task offloading, as well as computing, caching and communication resource allocation, adapting to the changes in network states and user requirements. Simulation results demonstrate that the proposed TORA-DRL solution converges fast and has much higher energy efficiency than the existing popular cloud-edge cooperation schemes under different network environments.
Learning Multitask Commonness and Uniqueness for Multimodal Sarcasm Detection and Sentiment Analysis in Conversation

Sarcasm is a form of figurative language device to express human inner feelings, where the author writes the positive sentence on surface form, while he/she actually expresses negative sentiment, vice versa. Sentiment, thus, comes into sight, and is closely related with sarcasm, leading to the recent popularity of multimodal sarcasm and sentiment joint detection in conversation (dialogue). The key challenges involve multimodal fusion and multitask interaction. Most of the existing studies have focused on building multimodal fused representation, while the commonness and uniqueness across related tasks has not received attention. To fill this gap, we propose a multimodal multitask interaction learning framework, termed MIL, for joint detection of sarcasm and sentiment. Specifically, a cross-modal target attention mechanism is proposed to automatically learn the alignment between texts and images/speeches. In addition, a multimodal interaction learning paradigm consisting of a dual-gating network, three separate fully connected layers that simultaneously capture the commonness and uniqueness. Comprehensive experiments on two benchmarking datasets (i.e., Memotion and MUStARD) show the effectiveness of the proposed model over state-of-the-art baselines with a significant improvement of 1.9%, 2.4% in terms of F1.
Motion deblurring based on local temporal compressive sensing for remote sensing image

Abstract. This paper presents a motion deblurring method which can obtain both the motion information and the recovered image based on local temporal compressive photography. In this method, video blocks are reconstructed at the corners of the image sensor during a single exposure period. The displacement vector, which will be used to build the prior point spread function (PSF) for image deblurring, is then estimated from the reconstructed videos. With the use of the prior PSF, better recovered images can be obtained with much less iteration. An experiment system is also presented to validate the effectiveness of the proposed method. The experimental results show that the proposed method could provide recovered images of high quality.
THE ROLE OF ISLAMIC SPIRITUALITY IN INDUSTRIAL TRAINING FOR AGRICULTURAL STUDENTS

The spiritual aspect is an essential element that needs to be emphasised among today's university students. It can shape their behaviour, actions, and decisions. Therefore, the main objective of this study is to identify the extent to which the programs conducted by the Faculty of Plantation and Agrotechnology can shape the spiritual values of students before they undergo industrial training outside the campus through placement in agricultural and plantation agencies throughout Malaysia. The Universiti Teknologi MARA (UiTM) Sabah Branch, Kota Kinabalu campus, was selected as the case study organisation for this qualitative study. 13 students participating in the program conducted by the Faculty of Plantation and Agrotechnology who were interviewed using a semi-structured format. Thematic data analysis was used to analyse the collected data. Overall, the findings of the qualitative study show that the program or activity carried out plays a role in increasing the spiritual value of students in terms of mujahadah al-nafs and fostering the character of al-ihsan.
On the online effects of subjectivity encoded in causal connectives

Causal relations between sentences differ in terms of subjectivity: they can be objective (based on facts) or subjective (based on reasoning). Subjective relations lead to longer reading times than objective relations. Causal connectives differ in the degree to which they encode this subjectivity. The Chinese connectives kejian ‘so’ and yin’er ‘so’ specify a high and low degree of subjectivity, respectively, whereas suoyi ‘so’ is underspecified for subjectivity. In an eye-tracking experiment we compare the effect of the specificity of these connectives in subjective and objective relations. In objective relations, the specificity of the connective has no effect on reading times. In subjective relations, reading times are shorter in sentences with the specified connective kejian than in sentences with the underspecified connective suoyi . These results suggest that readers prefer to interpret a relation as objective. Computing subjective relations requires extra processing time, which is diminished when the connective encodes the subjectivity.
Synergy of Social Capital of Local Residents and Transmigrates in Corn Farming in Garantung Village

A synergy of social capital of residents and transmigrates in corn farming in Garantung village. Social capital is an important factor in agricultural economic development. In this context, an analysis was carried out to find out the forms and contributions between the social capital of the local population and transmigration in the context of corn farming. The research paradigm used is the social definition paradigm with a qualitative research approach and a case study type of research. Subjects and informants in this study used purposive techniques, data obtained through interviews, observation, and documentation. Criteria for checking the validity of the data are carried out through the Credibility, Transferability, Dependability, and Conformability tests. Social capital in this study uses the theory of the concept of social capital developed by Robert Putnam, which consists of three elements: social networks, norms, and trust (Putnam, 1995). This social capital helps build relationships of mutual trust, collaboration, and togetherness in achieving common goals. The transmigration program significantly contributed to productivity and sustainability through corn farming in Garantung village by bringing social capital such as knowledge, skills, and economic resources. The synergy of social capital of residents and transmigration brings various benefits, including increased productivity of diversified agricultural businesses, exchange of knowledge and experience, and sustainability of agricultural businesses in Garantung village. Factors that influence synergy include cultural aspects, trust, common goals, effective communication, and good support and coordination from the government. The contribution of the synergy of the social capital of residents and transmigration in hybrid corn farming can lead to increased welfare, business opportunities, and increased productivity efficiently and effectively. The problems faced by farmers in general, such as patron-client networks, middlemen can be overcome through cooperation which in Indonesia is known as gotong royong; strengthening networks that can be formed in economic development, especially in Corn farming, is, of course, based on the strength of individual belief in the group.
Comparison of Central Aortic Pressure between Women with Preeclampsia and Normotensive Postpartum Women from an Urban Region of Western Mexico

Background and Objectives: Central aortic pressure (CAP) can be measured through noninvasive methods, and CAP wave analysis can provide information about arterial stiffness. The objective of this study was to compare CAP in women with preeclampsia and normotensive postpartum women from an urban region in western Mexico. Materials and Methods: We recruited 78 women in immediate puerperium, including 39 with preeclampsia and 39 with normotension, who received delivery care in our hospital between September 2017 and January 2018. Pulse wave analysis was used to assess central hemodynamics as well as arterial stiffness with an oscillometric device. For this purpose, the measurement of the wave of the left radial artery was obtained with a wrist applanation tonometer and the ascending aortic pressure wave was generated using the accompanying software (V 1.1, Omron, Japan). Additionally, the systolic CAP, diastolic pressure, pulse pressure, heart rate, and rise rate adjusted for a heart rate of 75 bpm were determined. The radial pulse wave was calibrated using the diastolic and mean arterial pressures obtained from the left brachial artery. For all the statistical analyses, we considered p < 0.05 to be significant. Results: The results were as follows: a systolic CAP of 125.40 (SD 15.46) vs. 112.10 (SD 10.12) with p < 0.0001 for women with and without preeclampsia, respectively. Systolic CAP was significantly elevated in women with preeclampsia and could indicate an elevated risk of cardiovascular disease. Conclusion: CAP is an important parameter that can be measured in this group of patients and is significantly elevated in women with postpartum preeclampsia, even when the brachial blood pressure is normal.
Optimal mixing in three-dimensional plane Poiseuille flow at high Péclet number

We consider a passive zero-mean scalar field organised into two layers of different concentrations in a three-dimensional plane channel flow subjected to a constant along-stream pressure gradient. We employ a nonlinear direct-adjoint-looping method to identify the optimal initial perturbation of the velocity field with given initial energy which yields ‘maximal’ mixing by a target time horizon, where maximal mixing is defined here as the minimisation of the spatially integrated variance of the concentration field. We verify in three-dimensional flows the conjecture by Foures et al. (J. Fluid Mech., vol. 748, 2014, pp. 241–277) that the initial perturbation which maximises the time-averaged energy gain of the flow leads to relatively weak mixing, and is qualitatively different from the optimal initial ‘mixing’ perturbation which exploits classical Taylor dispersion. We carry out the analysis for two different Reynolds numbers ( $Re=U_{m}h/\unicode[STIX]{x1D708}=500$ and $Re=3000$ , where $U_{m}$ is the maximum flow speed of the unperturbed flow, $h$ is the channel half-depth and $\unicode[STIX]{x1D708}$ is the kinematic viscosity of the fluid) demonstrating that this key finding is robust with respect to the transition to turbulence. We also identify the initial perturbations that minimise, at chosen target times, the ‘mix-norm’ of the concentration field, i.e. a Sobolev norm of negative index in the class introduced by Mathew et al. (Physica D, vol. 211, 2005, pp. 23–46). We show that the ‘true’ variance-based mixing strategy can be successfully and practicably approximated by the mix-norm minimisation since we find that the mix-norm-optimal initial perturbations are far less sensitive to changes in the target time horizon than their optimal variance-minimising counterparts.
A 10.8-14.5GHz 8-Phase 12.5%-Duty-Cycle Non-Overlapping LO Generator with Automatic Phase-and-Duty-Cycle Calibration for 60-GHz 8-Path-Filtering Sub-Sampling Receivers

A 10.8-14.5GHz 8-phase 12.5%-duty-cycle non-overlapping LO generator is proposed for 60-GHz 8-path-filtering sub-sampling receivers. A 4-stage ring oscillator is followed by reconfigurable injection-locked-oscillator NOR gates to generate 8-phase 12.5%-duty-cycle signals featuring automatic successive phase calibration and automatic frequency-domain duty-cycle calibration. The generator prototype measures minimum phase errors of ~0.10 and maximum 4th-harmonic output power of -5dBm with >30dB improvement while consuming 77.8mW from a 1V supply.
A Novel Approach to Simultaneous Phonocardiography and Electrocardiography During Auscultation

The combination of the phonocardiogram (PCG) and the electrocardiogram (ECG) allows the simultaneous evaluation of the heart’s mechanical and electrical conditions and could significantly improve the accuracy of an initial cardiovascular disease diagnosis. Both signals can be acquired using non-invasive and low-complexity devices. However, the currently available devices do not have a suitable form factor for the established medical routines, restricting their usability in clinical practice. They are also bound to proprietary tools that limit collaboration, which are constraints for both clinicians and researchers. To tackle these problems, we propose a novel electronic stethoscope that combines PCG and ECG sensors which use polymer-based dry electrodes with a previously validated 3D-printed acoustic stethoscope. We performed an experimental evaluation of the sensors, and the temporal relation between the two signals and heartbeat morphology were assessed. The effect of the rotation of the stethoscope head at different angles was also studied, further extending the state-of-the-art in the field. It was verified that the ECG lead voltages at different angles follow Kirchhoff’s law, similar to the Einthoven leads. By comparing the ECG waveforms between a reference and the experimental device, the best case reached a Pearson’s correlation coefficient of 0.916±0.008. The results demonstrate the validity of the proposed electronic stethoscope, establishing a foundation for further development. The accessibility, low cost, and simplicity of the device should significantly facilitate its integration into healthcare facilities and telemedicine interventions, as well as into research and education.
Results of numerical testing of algorithms for centering of focal spots in a Shack-Hartmann wave front sensor

The weighted centers of gravity algorithm calculate the centroids coordinates of the Shack-Hartmann patterns which are recorded by the Shack-Hartmann wave front sensor in the process of adaptive correction of turbulent distortions of laser radiation. It is believed that the algorithm of weighted centers of gravity gives an optimal result regardless of the signalto-noise ratio for point sources. In addition to the algorithm for weighted centers of gravity of focal spots, there are other algorithms for centering the image in the plane of the receiving device. Here, based on numerical experiments, we give an estimate of the weighting and quadrant algorithms for centering the focal spots of the registered Shack-Hartmann pattern.
Estimation of Left and Right Ventricular Ejection Fractions from cine-MRI Using 3D-CNN

Cardiac function indices must be calculated using tracing from short-axis images in cine-MRI. A 3D-CNN (convolutional neural network) that adds time series information to images can estimate cardiac function indices without tracing using images with known values and cardiac cycles as the input. Since the short-axis image depicts the left and right ventricles, it is unclear which motion feature is captured. This study aims to estimate the indices by learning the short-axis images and the known left and right ventricular ejection fractions and to confirm the accuracy and whether each index is captured as a feature. A total of 100 patients with publicly available short-axis cine images were used. The dataset was divided into training:test = 8:2, and a regression model was built by training with the 3D-ResNet50. Accuracy was assessed using a five-fold cross-validation. The correlation coefficient, MAE (mean absolute error), and RMSE (root mean squared error) were determined as indices of accuracy evaluation. The mean correlation coefficient of the left ventricular ejection fraction was 0.80, MAE was 9.41, and RMSE was 12.26. The mean correlation coefficient of the right ventricular ejection fraction was 0.56, MAE was 11.35, and RMSE was 14.95. The correlation coefficient was considerably higher for the left ventricular ejection fraction. Regression modeling using the 3D-CNN indicated that the left ventricular ejection fraction was estimated more accurately, and left ventricular systolic function was captured as a feature.
On the Network Coding-Based D2D Cooperative Recovery for Lossy Feedback-Free Broadcasting

This letter investigates the cooperation issue between two adjacent user nodes that can mutually recover the lost packets in a lossy wireless broadcast system by maintaining a device-to-device (D2D) communication pair. Unlike existing studies, we consider a feedback-free transmission model where the broadcast source node does not receive the reception status and channel conditions from its user nodes, and neither does it dynamically adjust its broadcast content. We model such best-effort content delivery scenario with a time-slotted system and propose a Network Coding based D2D Cooperative recovery scheme (NC2) to schedule the cooperation process between user nodes. NC2 maximizes the packet receiving reliability of the two user nodes by utilizing an adaptive coding window sliding strategy and proper transmission procedure. NC2 also considers the practical situation, such as the limited user device buffer size and the decoding latency of source packets. Theoretical bounds of the reliability and decoding delay of NC2 have been analyzed. Numerical results also validate the effectiveness and efficiency of the proposed scheme.
Sheffer and Brenke polynomials associated with generalized Bell numbers

In a recent paper, the first two authors have studied the integer number sequences generated by the higher order Bell polynomials. In this article we introduce new sets of Sheffer polynomial sequences based on the higher order Bell numbers, and we study their basic properties. Furthermore, we introduce new sequences of associated Sheffer and Brenke polynomial sequences. AMS 2010 Mathematics Subject Classification: 05A10, 11P81, 33C99.
ELC metamaterial based CPW‐fed printed dual‐band antenna

This letter presents metamaterial (MTM) based dual band antenna. Two resonances are obtained by making use of square ELC (Electric‐inductive‐capacitive) resonator and CR (Closed Ring) resonator. This research evolves an antenna having dimension only of 21 × 30 × 1.6 mm3 which operates at two bands from 3.28 GHz to 4.52 GHz and from 5.20 GHz to 5.87 GHz. This antenna can be useful for WiMAX and WLAN transceiver at 3.5 GHz and 5 GHz band respectively. It can also be used in RFID applications at 3.9 GHz band. This paper includes the parametric investigations as well as measurement details. Simulation and measurement data have shown good equivalence which emphasis the practicability of the proposed antenna. © 2016 Wiley Periodicals, Inc. Microwave Opt Technol Lett 59:304–307, 2017
Impact of Holmium and Nickel Substitution on Y-Type Hexagonal Ferrites Synthesized via Sol-gel Method

Sol-gel auto combustion route is employed to fabricate Y-type hexagonal ferrites Ba2-xHoxSr2-yNiyFe12O22(x = 0, 0.02, 0.04, 0.06, 0.08, 0.1 & y = 0, 0.1, 0.2, 0.3, 0.4, 0.5). All prepared samples are examined through XRD, SEM, and dielectric characterization.The prepared samples were then sintered at 950 °C for 6 hours. TGA analysis was carried out to find the estimated sintering temperature for the phase development. The XRD experiment was performed to determine the effect of substitution on structural parameters. XRD crystallite size was observed in the range of 5.5 – 19.79 nm. Lattice constant (a) was found in the range 6.08 – 6.17 Å, and that of c was found in the range 44.08 – 44.73 Å. The averagegrain size value wasnoted to be around ~ 2? m as calculated through SEM.Through Raman spectroscopy, six active modes (3E1g, 2A1g, and E2g) correspond to different vibrational modes of the prepared samples. LCR technique showed that the dielectric constant and the dissipation factor were decreased with increased frequency. A decrease in dielectric losses suggests these materials are for high-frequency applications.
Assessment of Different Deep Learning Models for Hardware Requirements and Pre-Processing Steps for Efficient Wind Power Forecasting

Wind power generating systems are increasing their presence in the whole energy fabric. However, intermittent energy production by wind power plants due to its environmental dependency poses a threat to the energy grid. To address this challenge of uncertainty, auxiliary energy storage units or accurate and reliable forecasting are two potential solutions. However, integration of energy storage units will increase the capital investment and in turn, will decrease the return on investment of wind power plants. So, an accurate forecasting mechanism is the most promising way to address the challenges of uncertainty. In recent years, lots of research works have proposed different Machine Learning (ML) or Deep Learning (DL) approaches for accurate wind power forecasting. In this proposed work different DL models are analyzed for wind power forecasting with different pre-processing methods. So, the major contribution of the work lies in the finding an optimal deep-learning model and pre-processing strategy for improving wind power prediction accuracy.
Oscillation Processes and Phase Estimation with Nonlinear State Space Models

A new model for time series with a specific oscillation pattern is proposed. The model consists of a hidden phase process controlling the speed of polling and a nonpara-metric curve characterizing the pattern, leading together to a generalized state space model. Identifiability of the model is proved and a method for statistical inference based on a particle smoother and a nonparametric EM algorithm is developed. In an extended version we also allow for a time-varying amplitude and baseline. For that situation a Rao-Blackwellized particle smoother that combines the Kalman smoother and an efficient sequential Monte Carlo smoother is suggested. The proposed algorithms are computationally efficient. The potential of the method for practical applications is demonstrated through simulations and an application to human electrocardiogram recordings.