Miracle-dz/UniFAR-FTUs huggingface dataset:
MSIC: Malware Spectrogram Image Classification

The heavy reliance on digital technology, by individuals and organizations, has reshaped the traditional economy into a digital economy. In response, cybercriminals’ attention has shifted dramatically from showing off skills and conducting individual attacks into high sophisticated attacks with financial gain as the goal. This, inevitably, poses a challenge to the cybersecurity community as they strive to find solutions to preserve the confidentiality, availability and integrity of the individual users’ and corporates’ private data and services. Cybercriminals mainly deploy malware to achieve their goals, which could be in the form of ransomware, botnets, etc. The use of encryption, packing and polymorphism techniques makes it harder to detect the malware files, especially when these are created in great numbers every day. In this paper, a novel framework, named Malware Spectrogram Image Classification (MSIC), is proposed. It employs spectrogram images in conjunction with the convolution neural network to classify a malware file to its corresponding family and to differentiate it from a benign file. Further, this research shares with the research community two privately collected labeled malicious and benign datasets. The evaluation of MSIC showed its effectiveness to be 91.6% F-measure and 92.8% accuracy in classifying malware files to their corresponding families, in comparison to, respectively, 90.6% and 92.3% results produced by the grayscale image classification approach. Likewise, in classifying files as malicious or benign, MSIC scored 96% F-measure and accuracy results compared to 95.5% with the grayscale solution. Also, MSIC required less computational time in converting and resizing the files than the grayscale framework.
Road Traffic Condition Monitoring using Deep Learning

The traffic surveillance system is accumulated with an enormous amount of data regarding road traffic each and every second. Monitoring these data with the human eye is a tedious process and it also requires manpower for monitoring. Deep learning approach (Convolutional Neural Network) can be utilized for traffic monitoring and control. The traffic surveillance data are pre-processed to construct the training dataset. The Traffic net is constructed by transferring the network to traffic applications and retraining it with self-established data set. This Traffic net can be used for regional detection in large scale applications.Further, it can be implemented across-the-board. The efficiency is admirably verified through speedy discovery in the high accuracy in the case study. The tentative assessment could pull out to its successful application to a traffic surveillance system and has potential enrichment for the intelligent transport system in future.
Open Circuit Switch Fault Detection in Multilevel Inverter Topology using Machine Learning Techniques

Multilevel inverter is mostly used in high voltage and high power applications in industry. The possibility of faults raises with an increment in the number of switches in multilevel inverter. In power industries, the reliability of multilevel inverters is one of the main concerns. Hence methods for detecting switch faults are required to improve in the reliability. This paper is mainly focused on open circuit switch fault detection for multilevel inverter. The proposed scheme identifies failed switches by monitoring capacitor current and switches current data. The diagnosis techniques are Artificial Neural Network (ANN), k-Nearest Neighbors (KNN), Support Vector Machines (SVM) and Decision Tree (DT). These methods are only capable for diagnosing failed switches. On identification of the faulty switch, switching sequence has to be reconfigured such that the output voltage is restored to its healthy operating conditions.
Robust Controllers for a Single-Stage Boost DC-AC Inverter

The main feature of a Boost DC-AC inverter is to step-up the input DC voltage and convert this boosted dc voltage into desired AC voltage in a single stage without the use of output filter requirement. The loads fed can be self-governing or solar based home gadgets. The conversion efficiency is less due to more number of stages in a two stage converter. This work aims to simulate a boost DC-AC inverter feeding both linear and non-linear loads in a single stage. Here two boost converters are differentially connected across the load, in order to get a pure sinusoidal waveform as an output. It requires four switches only. Also it is low in cost and in compact size. Two controllers are presented in this work. One is a dual loop controller and other one is a PWM based sliding mode controller. Both the controllers are operated at a switching frequency of 10 kHz. A 220V, 50Hz pure sinusoidal waveform is obtained for different loads (single phase load of 1.5kW, 0.8pf lag) with less steady state error, low THD’ and convergence. Even when a non-linear load is connected both controllers work in a robust manner. It is observed that the PWM based SMC is more accurate in tracking the voltage with less steady state error and settling time. Chattering of SMC is observed through phase portrait. The simulation results are performed using power system computer aided design PSCAD/EMTDC 4.6 tool.
An Overview of High Speed Streaming in 5G

The fifth generation of mobile communications (5G) provides advanced features which were not witnessed ever before. Its data rates, end to end latency, and several other performance parameters are absolutely exemplary. Extremely high data rates and low latency are very much suitable for applications related to multimedia communication. In fact, multimedia streaming is a priority area for 5G. The main requirements of multimedia streaming are fulfilled by 5G technologies. In this article, we analyze 5G features for high-speed streaming. Real-time streaming was not a popular application over mobile platforms in the previous mobile generations. However, these streaming related issues will be resolved in 5G. We go through the modern day streaming requirements and their infrastructure. The high bandwidths required in the core network of the 5G will be provided by the fast networking facilities of optical networks. In the access part MIMO and large constellation for modulation will serve the high speed streaming requirements. In this article, we logically correlate the advanced 5G features with high-speed streaming requirements.
The Problem of Leaf Morphology and Evolution in the Monocotyledons

A review of the previous literature on the morphology and evolution of monocotyledonous leaves reveals a number of interpretive controversies and contradictions. The phyllode theory, which has been the prevailing interpretation of the origin and evolution of monocotyledonous leaves, is evaluated and is shown to have been biased by a preoccupation with temperate monocotyledons and the exclusive study of mature leaf morphology and vascular anatomy. Recent investigations of comparative development have provided a broader basis for the evaluation of structural relationships of leaves in the monocotyledons and dicotyledons and have served to eliminate some of the interpretive inconsistencies that were based on the study of vascular anatomy only. The most controversial leaf type in the monocotyledons has been the unifacial (radially symmetrical) leaf found in a number of divergent families. Contrary to the original Phyllode theory of the Candolle (1827) and Arber (1918), developmental comparison of unifacial appendages of monocotyledons with rachis leaves of Umbelliferae suggests that the terete leaf axis of the former is not simply an elaborated petiole but is positionally equivalent to the laminar region of a dicotyledonous leaf. Instead of being expanded in surface by marginal growth, the unifacial moncotyledonous leaf is thickened developmentally and hence has become nearly radial in summetry. Thus the original phyllode concept of the loss of the blade retion and the expansion and assumption of its function by the petiole no longer seems valid for monocotyledonous leaves. In addition, comparative developmental studies, as well as the analysis of serial changes in leaf morphology, have confirmed that where dorsiventral, laminar leaves are developed in the monocotyledons, they are developmental elaborations of the basal, meristem-encircling part of the leaf primordium, and the distal upper leaf zone remains rudimentary. Therefore, blade regions of such dorsiventral appendages are not homologous with those of dicotyledons because they have a different developmental origin; laminae of dicotyledons are derived from the upper leaf zone. It is suggested that the basic construction of monocotyledonous leaves consists of two regions: (1) a distal unifacial upper sector; and (2) a proximal bifacial sheath; variations in leaf morphology in a range of monocotyledonous taxa are therefore due to reciprocal elaboration, or suppression of these two sectors, or both. It is emphasized, however, that before any model of this sort can be extended to the monocotyledons as a whole, it must be evaluated rigorously in the broadest range of representatives from each major family.
Human Postural Control Under High Levels of Extremely Low Frequency Magnetic Fields

Background: International agencies such as the International Commission on Non-Ionizing Radiation Protection (ICNIRP) and the International Committee on Electromagnetic Safety (ICES) of the Institute of Electrical and Electronics Engineers (IEEE) need further data to set international guidelines to protect workers and the public from potential adverse effects to Extremely Low-Frequency Magnetic Fields (ELF-MF). Interestingly, electromagnetic induction has been hypothesized to impact human vestibular function (i.e. through induced electric fields). To date, a theoretical 4 T/s vestibular threshold was proposed to modulate postural control, but data is lacking above this limit. Objectives: This research aimed to investigate the impact of full head homogeneous ELF-MF stimulations above the 4 T/s threshold on human postural control. Methods: Postural control of twenty healthy participants was analyzed while full head homogeneous ELF-MF stimulations (20 Hz, 60 Hz, and 90 Hz) up to 40 T/s were applied. Velocity, main direction and spatial dispersion of sway were used to investigate postural modulations. Results: Despite a conclusive positive control effect, no significant effects of ELF-MF exposures on velocity, spatial dispersion, and direction of the postural sway were found for our 3 frequency conditions. Conclusions: The homogeneous full head MF stimulations oriented vertically and delivered at high frequencies induced E-fields having a weaker impact than anticipated, possibly because they impacted only a small portion of the vestibular system. This resulted in an absence of effect on postural control outcomes.
Implementation of Viola-Jones Algorithm for Object Detection Using FPGA

This research presents the design and implementation of hardware for face detection based on the system of the Viola-Jones Algorithm in the Field Programmable Gate Array (FPGA) using VHSIC Hardware Description Language (VHDL); VHSIC (Very High Speed Integrated Circuit). The data used in this study is based on real conditions and uses sample images where in this research we use the images sample with dimensions 450×375 pixels which is like the resolution of webcam that using for the Xilinx Zybo (Zynq Board). In this research, the proposed architecture is using VHDL and implemented in Xilinx Zybo Zynq-7000 ARM/FPGA SoC Trainer Board. To measure the performance of the FPGA-based face detection system from the Viola-Jones Algorithm, the number of frames processed per second (FPS) must be measured, where the processes must be designed which are divided into two types of partitioning, there are software and hardware designs. This is accomplished by measuring the time elapsed from capturing an image to displaying the detection results. More specifically this is the time elapsed from the start of a frame capture to the end of drawing face bounding boxes on to the captured image. An experiment was conducted to gather the minimum, maximum and average FPS of FPGA face detection system of the Viola-Jones Algorithm. From the process of this research we’ve been got hardware latency just for the 3rd modules implemented is 125,207 millisecond, whereas software implemented (run on ARM Cortex 9 / Hardcore Zybo Processor) is 3642,993 millisecond, so speedup by implementing these 3 modules as a hardware accelerator is 29,095 rounds.
High Voltage Gain DC-DC Non-Isolated Converter with Generalized Stages

In this paper, a non-isolated dc-dc converter is proposed to provide a high voltage gain with a low inductance requirement. The circuit can be generalized for multiple stages to realize the desired DC output voltage from a low DC voltage source. In the proposed topology, the capacitors and inductors are charged/magnetized in parallel and discharged/demagnetized in series during switched on and off period respectively to obtain a high step-up voltage gain. The proposed power circuit is suitable for many applications based on renewable energy and energy storage where the desired DC-link voltage requirement can be as high as 560V (three-phase inverter input), and input voltage can be as low as 48V (a string of four batteries or fuel cell). A detailed analysis of the steady-state operation of the power converter is provided in this paper to obtain the voltage gain of the converter. The circuit design is simulated in MATLAB Simulink to verify the results of the analysis of this converter. Further, the design guidelines are provided for the generalized form of the converter to achieve desired steady-state input-output gain.
Few-shot Learning for eNodeB Performance Metric Analysis for Service Level Assurance in LTE Networks

With the increasing network topology complexity and continuous evolution of the new wireless technology, it is increasingly challenging to address the network service outage with traditional methods. In order to improve the network operation efficiency, a suitable machine learning technique is used to learn and classify individual base station into different issue based on multiple performance metrics during a specific time window. However an issue classification with supervised learning requires a large amount of labeled dataset, which takes costly human-labor and time to annotate data. To mitigate the cost and time issues, we propose a method based on few-shot learning that uses Prototypical Networks algorithm to complement the eNodeB states analysis. Using dataset from a live LTE network consists of thousand of eNodeB, our experiment results show that the proposed technique provides high performance while using a low number of labeled data.
Localization Based on Channel Impulse Response Estimates

Location-awareness using wireless signals is a key enabler for numerous emerging applications. Inspired by the recently proposed soft information (SI)-based localization, this paper develops a localization algorithm based on estimates of the channel impulse response (CIR), which inherently contains position information. We propose a delay-origin uncertainty model for describing the conditional distribution of the delays in CIR given node positions. A scalable localization algorithm is designed using belief propagation (BP) on a factor graph that incorporates the uncertainty model. The performance of the developed algorithm is quantified for mmWave signals using QuaDriGa channel simulator, showing decimeter-level localization accuracy in typical indoor environments.
Automated optimal firewall orchestration and configuration in virtualized networks

Emerging technologies such as Software-Defined Networking and Network Functions Virtualization are making the definition and configuration of network services more dynamic, thus making automatic approaches that can replace manual and error-prone tasks more feasible. In view of these considerations, this paper proposes a novel methodology to automatically compute the optimal allocation scheme and configuration of virtual firewalls within a user-defined network service graph subject to a corresponding set of security requirements. The presented framework adopts a formal approach based on the solution of a weighted partial MaxSMT problem, which also provides good confidence about the solution correctness. A prototype implementation of the proposed approach based on the z3 solver has been used for validation, showing the feasibility of the approach for problem instances requiring tens of virtual firewalls and similar numbers of security requirements.
An Improved Squirrel Search Algorithm With Reproductive Behavior

The squirrel search algorithm (SSA) is a recently proposed nature-inspired algorithm based on the dynamic foraging and gliding behavior of squirrels. Because of its simplicity and stability, the squirrel algorithm has attracted increasing research interest. However, the lack of exploration ability of the SSA may lead to premature convergence to the local optimum. To overcome this disadvantage, an improved SSA with reproductive behavior (RSSA) is proposed to solve the numerical optimization problem. First, the reproductive behavior of the invasive weed algorithm (IWO) is introduced to the conventional SSA to generate offspring individuals, and these offspring individuals are scattered into the search space by Gaussian distribution to complete the location update. This method makes it possible for individuals with poor fitness to enter the next generation search, improving the exploration ability of the SSA. Second, an adaptive step strategy is proposed to adaptively adjust the search step of squirrels according to the distance between each squirrel and other family members. This strategy effectively balances the exploration and exploitation of the algorithm. Finally, the performance of the proposed RSSA algorithm is evaluated using Wilcoxon’s test on unimodal, multimodal, fixed-dimensional multimodal and CEC 2014 benchmark functions. Experimental results and statistical tests show that RSSA has better performance in terms of convergence, accuracy, and search capability compared with other state-of-the-art algorithms.
CDED-Net: Joint Segmentation of Optic Disc and Optic Cup for Glaucoma Screening

Glaucoma is an eye disease that can cause loss of vision by damaging the optic nerve. It is the world’s second leading cause of blindness after cataracts. Early diagnosis of glaucoma is a key to prevent permanent blindness as it has no noticeable symptoms in its early stages. Color fundus photography is used for examining the optic disc (OD) which is an important step in the diagnoses of glaucoma. This is done by estimating the cup-to-disc ratio (CDR). In this paper, we proposed a Cup Disc Encoder Decoder Network (CDED-Net) for the joint segmentation of optic disc (OD) and optic cup (OC). We have eradicated the pre-processing and post-processing steps to reduce the computational cost of the overall system. Segmentation of (OD) and OC is modeled as a semantic pixel-wise labeling problem. The model was trained on the DRISHTI-GS, RIM-ONE and REFUGE datasets. Experiments show that our CDED-Net system achieves state-of-the-art OD and OC segmentation results on these datasets.
A simplified method for identifying Fusarium spp. occurring on wheat

The 18 Fusarium species or varieties (F. acuminatum, F. anthophilum, F. avenaceum, var. avenaceum, F. culmorum, F. dimerum var. dimerum, F. equiseti var. equiseti, F. graminearum, F. merismoides var. merismoides, F. nivale var. nivale and var. majus, F. oxysporum var. oxysporum, F. poae, F. sacchari var. subglutinans, F. sambucinum var. sambucinum, F. solani var. solani, F. sporotrichoides var. sporotrichoides, F. tricinctum, and F. verticillioides) most frequently isolated from wheat are described and illustrated. The experimental conditions used in this study, such as medium, near ultraviolet irridiation at a wave-length of λ = 300–400 nm (black light), and temperature, cause some alteration of the morphology of some of the taxa to which plant pathologists are accustomed if the strains are grown on richer media and (or) without black light. This does not matter, however, because the method described here allows each species or variety to develop a unique and stable set of morphological features, which d.
Spoofing Control Strategy for Precise Position Offset Based on INS/GNSS Tightly Coupled Navigation

To deceive Inertial Navigation System(INS)/Global Navigation Satellite System(GNSS) integrated navigation terminals, the entry point is still to inject GNSS spoofing signals to the target system. However, the spoofing control strategy in integrated navigation mode for particular tasks such as fixed-point capture or directional drive needs further research. Whether applying GNSS spoofing attacks or not, the Kalman filter correction gain matrix element in the tightly coupled navigation system always maintains its stability, which gives us valuable clues to develop an effective spoofing attack. On this basis, this article focuses on the feasibility and operability of spoofing attacks under tightly coupled navigation conditions and innovatively proposes a spoofing control strategy that can achieve accurate position offsets: Specific spoofing signals are implanted to the target tightly coupled navigation terminal at the beginning of the spoofing attack. The amounts of pseudo-range spoofing signals corresponding to different satellites are respectively the projections of the position increments to be deceived on the sight vector of the integrated navigation terminal to each satellite. Simulation experiments and tightly coupled navigation terminal experiments verify the correctness and effectiveness of the spoofing control strategy.
Niches and Coexistence of Ant Communities in Puerto Rico: Repeated Patterns

I studied ant coexistence in adjacent areas of upland tropical forest, grassland, and agricultural land in San Lorenzo, Puerto Rico. Data on food utilization, daily activity, nesting sites, microhabitat utilization and interspecific aggression were collected. Ants' tolerance to 45? C was determined in the laboratory. Agricultural and grassland ants eat grain, liquid food or insects, or grow fungus; only liquid drinking and insectivorous ants occurred in the forest. Some species within food groups in the agricultural and grassland ants differed in size of food consumed or daily activity. Some species from agricultural land were restricted to different crops, but spatial microhabitat utilization contributed little to species separation. Forest species differed in their use of litter depths, daily activity and microhabitat. Results of interspecific aggression were influenced by priority effects and microclimate. I suggest that some species coexist by nonequilibrium processes or by stochastic events.