Miracle-dz/UniFAR-FTUs huggingface dataset:
RHN: A Residual Holistic Neural Network for Edge Detection

Edge detection plays a very important role in many image processing and computer vision applications. Use of deep convolutional neural networks (DCNNs) has significantly advanced the performance of image edge detection techniques. Existing DCNN techniques, which make use of residual learning, exhibit a good edge detection performance at the expense of an extremely high computational complexity. There are a few VGG16-based DCNN techniques for edge detection that have been proposed with relatively much lower complexity. In this paper, by using the mechanism of residual learning, a new VGG16-based DCNN technique for edge detection is proposed with a view to provide a performance superior to that provided by other such networks while still preserving their low complexity. The proposed network is experimented on different datasets and is shown to outperform all the other VGG16-based techniques designed to solve the problem of edge detection.
On the Practical Limitations of Scalable Electronic 2D Beamsteering Using Metamaterials at Micro and Millimetre-wave Frequencies

In this paper, the practical limitations of scalable electronic two-dimensional (2D) beamsteering using metamaterials at Micro- and Millimetre-wave frequencies, are addressed. In particular, the practical effects of using varactor diodes on a metamaterial-based beamsteering transmitarray, are studied and characterised, at four different frequencies of interest: 5, 14, 28 and 60 GHz. The parasitic effects intrinsic to such components, namely the internal resistance and internal inductance, are being studied. A thorough parametric analysis in CST Microwave Studio is conducted, in order to ascertain how the varactor's parasitics affect the frequency response, insertion loss, bandwidth and maximum achieved phase shift and consequent beamsteering resolution, at the proposed frequencies. The parametric study targets the parasitic resistance and inductance values of varactor diodes commercially available, making this study useful for any RF transmitarray implementation.
Discovering Types of Smartphone Usage Sessions from User-App Interactions

Understanding how and why people use their smartphones has enabled use cases ranging from correlating behaviour with psychological states through to on-device tasks such as app recommendations. However, being able to effectively and pervasively capture usage behaviour is challenging due to the wide range of functions, apps and interactions that are possible. In this paper, we examine how embedding physical user-app activity (e.g., taps and scrolls) can provide a rich basis for summarising device usage. Using a large dataset of 82,758,449 interaction events from 86 users over an 8-week period we combine feature embedding and unsupervised learning to extract prominent interactions within clusters of smartphone usage sessions. We find that high-level features such as session length, unlock state, and app switches are not representative of these clusters and can give a false sense of similarity or dissimilarity between sessions. The results motivate further exploration of the utility of using user-app interaction behaviour as the basis for the aforementioned use cases.
IoTBox: Sandbox Mining to Prevent Interaction Threats in IoT Systems

Internet of Things (IoT) apps provide great convenience but exposes us to new safety threats. Unlike traditional software systems, threats may emerge from the joint behavior of multiple apps. While prior studies use handcrafted safety and security policies to detect these threats, these policies may not anticipate all usages of the devices and apps in a smart home, causing false alarms. In this study, we propose to use the technique of mining sandboxes for securing an IoT environment. After a set of behaviors are analyzed from a bundle of apps and devices, a sandbox is deployed, which enforces that previously unseen behaviors are disallowed. Hence, the execution of malicious behavior, introduced from software updates or obscured through methods to hinder program analysis, is blocked.While sandbox mining techniques have been proposed for Android apps, we show and discuss why they are insufficient for detecting malicious behavior in a more complex IoT system. We prototype IoTBox to address these limitations. IoTBox explores behavior through a formal model of a smart home. In our empirical evaluation to detect malicious code changes, we find that IoTBox achieves substantially higher precision and recall compared to existing techniques for mining sandboxes.
3-way Parallel Fusion of Spatial (sMRI/dMRI) and Spatio-temporal (fMRI) Data with Application to Schizophrenia

Advances in brain imaging acquisition techniques allow data from multiple modalities to be collected from each subject offering different but limited views of the structural, functional or temporal properties of human brain. Multimodal fusion can provide a way to leverage different perspectives from multiple complementary modalities. However, most current fMRI-related multimodal fusion approaches are restricted to the second-level 3D features, rather than the original 4D fMRI data. Here we are motivated to propose a novel 3-way fusion approach that can incorporate temporal information from fMRI by parallelizing “group ICA” and “parallel ICA” under a global optimization. Simulations show that GICA+pICA provides more accurate inter-modality linkage detection under both strong and weak correlations. In real data application, one linked fMRI-sMRI-dMRI component was identified showing differences between schizophrenia and controls in all modalities, demonstrating the stability of GICA+pICA to identify inter-modality linkage among three modalities.
Timber Log Based Barrier for Electromagnetic Site Shielding

This paper presents the development of a periodic array consisting of timber logs for electromagnetic site shielding. With this study, it is possible to understand the effects caused by wooden barriers in order to achieve a better shielding effectiveness (SE) or electromagnetic transparency at selected frequencies to control the exposure of electromagnetic radiation on the population and to reduce co-channel interference in telecommunications. The spacing between wooden poles' centre is parametrised within a simulation environment in the frequency range of 1.5 to 6 GHz and the results provided by the simulation software are assessed against measurement results of a real site barrier prototype. Simulation and measurement results presents a good agreement, where the pole's spacing affects SE significantly. As a replacement to current barriers, mainly on motorways, a low industrial processing and low-cost wooden barrier with specified electromagnetic characteristics may be used.
Exploring future large-scale ROADM architectures

Most of today's optical networks are based on reconfigurable optical add/drop multiplexers (ROADMs) nodes. However, current ROADM architectures have poor scalability due to limitations on the wavelength selective switches dimension. Hence, due to the constant increase in data traffic and the demand for more dynamic and flexible networks, current architectures might become a bottleneck in the foreseen large-scale ROADMs. In this work, several architectures for large-scale ROADMs proposed to overcome this bottleneck are studied in terms of hardware cost and in-band crosstalk generation and compared with large-scale ROADMs built with conventional architectures. We show that ROADMs based on a sub-system modular architecture, also known as interconnected architecture, exhibit a significant hardware cost reduction in relation to conventional architectures and are also advantageous regarding the in-band crosstalk generation. Moreover, in this work, an analysis of optical filtering effects, amplified spontaneous emission noise and in-band crosstalk impact in the performance of an optical network, with nodes based on the interconnected architecture, is performed through Monte-Carlo simulation.
Blind Source Separation In Dynamic Cell Imaging Using Non-Negative Matrix Factorization Applied To Breast Cancer Biopsies

We propose a method to fully exploit the dynamic signal produced by a recently developed non-invasive imaging modality: Dynamic Cell Imaging based on Full Field Optical Coherence Tomography, towards fast extemporaneous tissue assessment. The non-negative matrix factorisation method is used in an interpretable and quantifiable fashion to extract the signals coming from different structures of breast tissue in order to characterize cancerous tissue.
Influence Of Moringa Oleifera Seeds Oil In Liver Protection And Blood Lipid Reduction In Diabetic Rats

The present study has been conducted to estimate the influence of Moringa oleifera seeds oil in protection the liver and reduction of blood lipids in diabetic rats. Forty male rats were divided into four groups. Non-diabetic rats fed standard diet only (G1), non-diabetic rats fed standard diet + Moringa seeds oil (1.8 mg/kg) (G2), diabetic rats fed standard diet (G3) and diabetic rats fed standard diet + Moringa seeds oil (1.8 mg/kg) (G4). The study conducted for four weeks. The results showed that liver weight and relative liver weight were significantly increased in diabetic rats (G3) compared with (G1) and significantly decreased in diabetic rats treated with Moringa seeds oil (G4) compared with (G3). Treatment diabetic rats with Moringa seeds oil (G4) showed significantly reduction in serum glucose level comparing with nontreated diabetic rats (G3). The diabetic rats (G3) showed significantly elevation in levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), low-density lipoprotein (LDL), total cholesterol(CHOL) and significant decreased in high-density lipoprotein (HDL) compared with (G1). Diabetic rats treated with Moringa seeds oil (G4) showed a significant reduction in liver enzyme markers and improved in lipid profile parameters compared to non-treated diabetic rats (G3). The study concluded that Moringa seeds oil can be protect the liver and reduce blood lipids in diabetes. The potential mechanism of Moringa seeds oil in protection the liver possibly resulting from ability of free radical scavenging by antioxidants in the oil. Journal of Home Economics, Volume 28, December (4), 2018 911
Theoretical Consideration of Mismatch Compensation for MEMS Resonator Having Unaligned Principle Axes

In this paper, a theoretical aspect of frequency and damping mismatch compensation in a MEMS rate integrating gyroscope (RIG) controlled by CW and CCW rotational modes is reported. The mismatches of a resonator could be compensated by the amplitudes and phases of the driving signals, even if the principle axes of stiffness and damping are not aligned to the X-Y coordinate. The proposed theoretical formula well consistent with the previously reported experimental results, as well as the numerical simulation.
Prediction of Depression among Women Using Random Oversampling and Random Forest

Mental Health is one of the significant issues affecting lives, especially during the COVID-19 pandemic. One of the many mental health problems is Depression. According to the latest Malaysian National Health and Morbidity Survey, around 30% of adults were affected by mental health sometimes in their lives. Machine Learning plays an essential role in uncovering knowledge hidden in medical data; hence, medical prediction can classify and predict illness. However, sometimes medical data can be imbalanced, thus affecting the accuracy of classifiers. Therefore, sampling can correct the balancing before classification. In this research, we employed SMOTE as a random oversampling method and Random Forest as a classification to accurately predict Depression among women. The generalized prediction model can be used to screen depression among women in Malaysia effectively.
GAZEL: Runtime Gaze Tracking for Smartphones

Although work has been conducted on smartphone gaze tracking, the existing techniques are not pervasively used because of their heavy weight and low accuracy. Our preliminary analysis shows that these techniques would work better if their models were trained with data from tablets which have large screens. In this paper, we propose GAZEL, a runtime smartphone gaze-tracking scheme that achieves high accuracy on real devices. The key idea of GAZEL, a tablet-to-smartphone transfer learning, is to train a CNN model with data collected from tablets and then transplant the model to a smartphone. To achieve the goal, we designed a new CNN-based model architecture that is head pose resilient and light enough to operate at runtime. We also exploit implicit calibration to alleviate errors caused by differences in users’ visual and device characteristics. The experiment results with commercial smartphones show that GAZEL achieves 27.5% better accuracy on smartphones compared to the state-of-the-art techniques and provides gaze tracking at up to 18 fps which is practically usable at runtime.
Identification of a small-scale pig farm infected with Brucella suis linked to a clinical case of human brucellosis in Buenos Aires province, Argentina

Here we report a case of human brucellosis due to Brucella suis in a person who worked in a small-scale pig farm. The farm had no history of clinical brucellosis, and signs of the disease were not observed upon clinical examination of the animals. Serum from all the 3 boars, 16/22 sows and 9/25 gilts was obtained for serological examination by Buffered Plate Agglutination Test (BPAT), Rose Bengal Test (RBT) and Fluorescent Polarization Assay (FPA). Bacteriological culture and Direct Fluorescence Antibody Test (DFAT) were performed in tissue samples from a seropositive boar and a sow. Specific antibodies were detected in 53 % (10/19) adult pigs, while all sampled gilts were seronegative. B. suis biovar 1 was isolated from one boar. In contrast, while the bacterium was not isolated from any tissue from a seropositive sow, it was detected by DFAT. From the bacteriological and serological evidence of B. suis endemic infection in the pig farm and the lack of preventive measures and biosecurity practices, it is concluded that the person most likely acquired the disease from the infected animals or by contact with contaminated environment in the farm.
An Attention-Based Hybrid Deep Learning Framework Integrating Temporal Coherence And Dynamics For Discriminating Schizophrenia

The heterogeneity of schizophrenia makes it difficult to discover reliable imaging biomarkers, and most existing fMRI-based classification methods fail to combine temporal coherence between brain regions and temporal dynamics of brain activity. Therefore, we proposed a unified Hybrid Deep Learning Framework that effectively integrates temporal Coherence and Dynamics (HDLFCD) to classify psychiatric disorders by combining C-RNN, DNN and SVM. An attention module was also introduced into the C-RNN model to improve classification accuracy and interpretability without increasing the computation complexity. An accuracy of 85% was achieved in a large multi-site WRI dataset with 542 healthy controls and 558 schizophrenia patients, in which striatum, dorsolateral prefrontal cortex and cerebellum were identified as the most group-discriminative brain regions by the attention module. Note that the proposed framework is an end-to-end general module, which not only shows high superiority in combining multiple sources of information, but also can be easily applied to integrate other multimodal data.
An Overview of Machine Learning-Based Techniques for Solving Optimization Problems in Communications and Signal Processing

Despite the growing interest in the interplay of machine learning and optimization, existing contributions remain scattered across the research board, and a comprehensive overview on such reciprocity still lacks at this stage. In this context, this paper visits one particular direction of interplay between learning-driven solutions and optimization, and further explicates the subject matter with a clear background and summarized theory. For instance, machine learning and its offsprings are trending because of their enhanced capabilities in automating analytical modeling. In this realm, learning-based techniques (supervised, unsupervised, and reinforcement) have grown to complement many of the optimization problems in testing and training. This paper overviews how machine learning-based techniques, namely deep neural networks, echo-state networks, reinforcement learning, and federated learning, can be used to solve complex and analytically intractable optimization problems, for which specific cases are examined in this paper. The paper particularly overviews when learning-based algorithms are useful at solving particular optimizing problems, especially those of random, dynamic, and mathematically complex nature. The paper then illustrates such applications by presenting particular use-cases in communications and signal processing including wireless scheduling, wireless offloading and resource management, power control, aerial base station placement, virtual reality, and vehicular networks. Lastly, the paper sheds light on some future research directions, where the dynamicity and randomness of the underlying optimization problems make deep learning-driven techniques a necessity, namely in sensing at the terahertz (THz) bands, cellular vehicle-to-everything, 6G communication networks, underwater optical networks, distributed optimization, and applications of emerging learning-based techniques.
A Dynamic Robust DL-Based Model for Android Malware Detection

The dramatic increase in Android-based smart devices has brought technological revolution to improve the overall quality of life and thus making it worth a billion-dollar market. Despite the huge hype surrounding Android market, the prevalent and potentially sophisticated malicious mobile malware has become a serious threat to the popular Android platform and an ideal target for varied cyber adversaries. Conversely, multivector malware efficient and timely detection is extremely challenging because it usually hides itself under legitimate third party software’s and having the capability to be easily crafted on any executable file extension. To better streamline this complex issue of paramount concern, the authors propose a highly proficient hybrid deep learning (DL)-enabled intelligent multi-vector malware detection mechanism. The devised approach leverages Convolutional Neural Networks and Bidirectional Long Short-Term Memory (BiLSTM) to efficiently identify persistent malware. The proposed technique has been thoroughly evaluated with publicly available datasets, standard performance metrics, and state-of-the-art hybrid DL-driven architectures and benchmark DL algorithms. Besides, the proposed framework has been cross-validated and shows out performance both in terms of time efficiency and detection accuracy.
Spectroscopic Confirmation of Uranium(VI)−Carbonato Adsorption Complexes on Hematite

Evaluating societal risks posed by uranium contamination from waste management facilities, mining sites, and heavy industry requires knowledge about uranium transport in groundwater, often the most significant pathway of exposure to humans. It has been proposed that uranium mobility in aquifers may be controlled by adsorption of U(VI)−carbonato complexes on oxide minerals. The existence of such complexes has not been demonstrated, and little is known about their compositions and reaction stoichiometries. We have used attenuated total reflectance Fourier transform infrared (ATR-FTIR) and extended X-ray absorption fine structure (EXAFS) spectroscopies to probe the existence, structures, and compositions of ⋮FeOsurface−U(VI)−carbonato complexes on hematite throughout the pH range of uranyl uptake under conditions relevant to aquifers. U(VI)−carbonato complexes were found to be the predominant adsorbed U(VI) species at all pH values examined, a much wider pH range than previously postulated based on analogy t.
Analysis of Electromagnetic Vibration of Permanent Magnet Synchronous Motor under Static and Dynamic Eccentricity Fault

In order to study the influence of different eccentricity on the electromagnetic vibration of permanent magnet synchronous motor (PMSM), this paper takes a 48-slot 8-pole interior PMSM for electric vehicle as an example, establishes an analytical analysis model of radial electromagnetic force, and analyzes the variation laws of spatial order and time frequency of electromagnetic force under static and dynamic eccentric respectively. The differences between the effects of static and dynamic eccentricity on the electromagnetic vibration of the motor under different eccentricities are compared. The results show that with the increase of eccentricity, both static and dynamic eccentricity will aggravate the electromagnetic vibration of the motor. Static eccentricity will aggravate the electromagnetic vibration corresponding to the original vibration frequencies, but will not produce the new vibration frequencies. While dynamic eccentricity has little effect on the electromagnetic vibration at the original vibration frequencies, but introduces some new vibration frequencies, and the new frequencies are more likely to cause motor resonance.
NaviMine: A tool for mining trajectories through behavior prediction

Due to the recent progress in sensing technologies, we can now observe trajectories of various moving objects such as humans, automobiles, birds, and fish. In this study, we developed a tool that can mine trajectories from moving objects, especially, a tool that helps discover clues related to determining the destination of the moving objects. For example, chum salmon (Oncorhynchus keta) search and run up to their natal rivers after they migrate from the open ocean, i.e., salmon run. What clues do salmon use to find their destinations? In the study, we focus on initial short trajectory segments from moving objects and use these segments to predict the final destinations of the moving objects. In the case of salmon, we use initial segments after they arrived at the coastal area to predict which river the salmon will enter. If the prediction accuracy is high, it can be considered that there are clues related to setting their destinations in the initial segments. The proposed method finds the time when a clue might exist by computing prediction accuracies varying the duration of initial short segments. For example, when the prediction accuracy suddenly increases when using initial $t\ \mathrm{h}$ segments compared to that when using initial $t- 1$ h segments, we can speculate that clues are hidden in segments between $t-1$ and $t$. We use movement data of salmon (Oncorhynchus keta) returning to Otsuchi Bay, Iwate, Japan to validate the effectiveness of our proposed method.
Obstacles and Reasons for Non-adoption of Activity-Based Costing/Management at Saudi Universities

Despite the global trend of upgrading costing systems in higher education institutions (HEIs), Saudi universities have not yet planned to adopt activity-based costing/management (ABC/M) to provide reliable information that would support decisions. Hence, it will be interesting to research the obstacles and reasons for Saudi universities not adopting the ABC/M. In this research, Majmaah University (MU) was used as a case study and data were collected by mailing questionnaires to employees working in the financial affairs unit. The study found various obstacles to adopting ABC/M at MU, among which the technical issues were the most dominant. The authorities' lack of interest in updating current costing systems and the lack of awareness of ABC/M by academic units were the unique reasons for MU not adopting ABC/M. Therefore, the Kingdom of Saudi Arabia (KSA) authorities should pay much attention to improving already existing costing systems in universities. In addition, this study discusses the limitations and future prospects.