Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting Fake Job Postings Using Bidirectional LSTM

Fake job postings have become prevalent in the online job market, posing significant challenges to job seekers and employers. Despite the growing need to address this problem, there is limited research that leverages deep learning techniques for the detection of fraudulent job advertisements. This study aims to fill the gap by employing a Bidirectional Long Short-Term Memory (Bi-LSTM) model to identify fake job advertisements. Our approach considers both numeric and text features, effectively capturing the underlying patterns and relationships within the data. The proposed model demonstrates a superior performance, achieving a 0.91 ROC AUC score and a 98.71% accuracy rate, indicating its potential for practical applications in the online job market. The findings of this research contribute to the development of robust, automated tools that can help combat the proliferation of fake job postings and improve the overall integrity of the job search process. Moreover, we discuss challenges, future research directions, and ethical considerations related to our approach, aiming to inspire further exploration and development of practical solutions to combat online job fraud.
The Conceptualization and Implementation of a Patient Monitoring System Based on the Internet of Things for Use in Telemedicine

As a multifaceted approach to delivering healthcare remotely, telemedicine draws on expertise in areas as diverse as physiology, biomedical instruments, and communications. Telemedicine is the use of electronic networks and other forms of electronic communication to provide healthcare services to people who are geographically separated from one another. This is done so that people can receive accurate and up-to-date information about the causes, symptoms, and treatments of illness and injury, as well as receive ongoing training from healthcare professionals. It's an effective answer to problems including a lack of medical professionals, far-flung communities, and an overwhelming patient population. Testing, monitoring, and treating a patient by a doctor who is not present in the same room are all components of telemedicine. Because of this, getting from one place to another takes less time. Collecting data, putting it in a repository where it can be processed, and then sending it to another location are the pillars upon which telemedicine services are built. Despite its many benefits, telemedicine has yet to reach its full potential in practise. This is largely owing to the prohibitive cost of the specialised equipment required by telemedicine systems. Due to the inability of the doctor to assess the patient's vitals through video chat alone, this method of telemedicine falls short. To counter this, we need a system with precise sensors for monitoring vital signs and other health indicators.
An Integrated MPPT Control Strategy Using Circle Search-Firefly Algorithm (CSFA) for Photovoltaic System

Maintaining maximum power output and reducing energy loss for photovoltaic system demand the support of efficient and stable maximum power point tracking (MPPT) technology. The power-voltage (P-V) curve of photovoltaic system shows multi-peak on partially shaded conditions, which poses challenges to MPPT. The conventional approaches like P&O easily plunge into locally optimal value while the meta-heuristic intelligent optimization algorithms with powerful searching ability are suitable for the case of multi-peak. Consequently, an integrated MPPT control strategy using circle search-firefly algorithm (CSFA) is proposed, which fuses the efficient global searching capability of circle search algorithm (CSA) and the optimal population acquisition characteristic of firefly algorithm (FA). The tracking performance of the proposed CSFA-based MPPT strategy is compared with other technologies (CSA, FA, PSO, P&O) for static and dynamic conditions. The experimental results demonstrate the high efficiency, strong stability and fast convergence of the proposed CSFA-based strategy for MPPT of photovoltaic system.
Natural Inertia Response of Static Synchronous Compensator by PLL-Feedforward Control

With the increasing share of power electronics interfaced energy resources in the grid, the power system inertia has decreased gradually. Traditional inertia emulation technology is mostly based on measuring the rate of change of frequency (RoCoF) and cannot provide natural inertia response like synchronous generator (SG). Installed in many wind farms, the static synchronous compensator (STATCOM) is used for reactive power compensation. This paper proposed an additional inertial controller of STATCOM to provide natural inertia response without measuring RoCoF. The internal voltage motion equations of STATCOM with PLL-feedforward to DC voltage control loop are obtained. With the proposed controller, the STATCOM can provide both reactive power compensation and frequency regulation like synchronous condenser. Meanwhile, it does not change the basic control of STATCOM. A PSCAD/EMTDC model of IEEE two-area-four-machine system with D-PMSG windfarm and a STATCOM is presented to prove the effectiveness of the proposed inertia controller.
I-SPMS: IoT-Enabled Novel Smart Parking Management System with Load cell Deployment

The Internet of Things (IoT) is a large technical field that is actively being researched and implemented. Several IoT-based technologies offer extra benefits while lowering labour costs. As the world's population expands, so does the demand for transportation. As a result, the dependency on parking autos expands. People visiting public facilities in large cities, such as malls, parks, temples, and theatres, are finding it difficult to locate parking. Despite the existence of various established systems, the bulk of parking places are still controlled by hand. The only way to travel about in most smart cities is to use a well-organized indoor parking system. Regulations for automobile parking in the open air have yet to be created and implemented. Traditional parking methods are far too inefficient and inconvenient for metropolitan areas where it is difficult to find available spaces. This might result in a lot of traffic, small collisions, and public accidents. As a result, I-SPMS with weighbridgeload sensors is proposed. It may be built for the organised, well-timed, flexible, easy, and safe parking of automobiles in public spaces.
Delay-dependent passive control for linear systems with delay

Abstrcat The delay-dependent passive control for linear system with delay is discussed.First,a new integral inequality based on quadratic terms is established.Secondly,the inequality incorporating with the Lyapunov-Krasovskii functional method is used to derive the delay-dependent sufficient condition based on linear matrix inequality,which ensures that the system is passive.No model transformation is employed.Finally,some numerical examples are given to illustrate that the new results are less conservative than the existing ones.
Detect misinformation of COVID-19 using deep learning: A comparative study based on word embedding

Since its emergence in December 2019, there have been numerous news of COVID-19 pandemic shared on social media, which contain information from both reliable and unreliable medical sources. News and misleading information spread quickly on social media, which can lead to anxiety, unwanted exposure to medical remedies, etc. Rapid detection of fake news can reduce their spread. In this paper, we aim to create an intelligent system to detect misleading information about COVID-19 using deep learning techniques based on LSTM and BLSTM architectures. Data used to construct the DL models are text type and need to be transformed to numbers. We test, in this paper the efficiency of three vectorization techniques: Bag of words, Word2Vec and Bert. The experimental study showed that the best performance was given by LSTM model with BERT by achieving an accuracy of 91% of the test set.
Phylogeographic and demographic patterns reveal congruent histories in seven Amazonian White‐Sand ecosystems birds

The drivers of genetic diversity in Amazonia, the most species‐rich set of ecosystems on Earth, are still incompletely understood. Species from distinct Amazonian ecosystems have unique biogeographic histories that will reflect regional landscape and climatic drivers of genetic diversity. We studied bird species from patchy Amazonian white‐sand ecosystems (WSE) to evaluate the occurrence of shared biogeographic patterns to better understand the complex environmental and landscape history of Amazonia and its biodiversity.
Ambiguous Cylinders： a New Class of Impossible Objects

This paper presents a new class of surfaces that give two quite different appearances when they are seen from two special viewpoints. The inconsistent appearances can be perceived by simultaneously viewing them directly and in a mirror. This phenomenon is a new type of optical illusion, and we have named it the "ambiguous cylinder illusion", because it is typically generated by cylindrical surfaces. We consider why this illusion arises, and we present a mathematical method for designing ambiguous cylinders.
Periodic and Chaotic Exploding Dissipative Solitons

Abstract This article shows for the first time the existence of periodic exploding dissipative solitons. These non-chaotic explosions appear when higher-order non-linear and dispersive effects are added to the complex cubic–quintic Ginzburg–Landau equation modeling soliton transmission lines. This counter-intuitive phenomenon is the result of period-halving bifurcations leading to order (periodic explosions), followed by period-doubling bifurcations leading to chaos (chaotic explosions). This periodic behavior is persistent even when small amounts of noise are added to the system. Since for ultrashort optical pulses it is necessary to include these higher-order effects, it is conjectured that the predictions can be tested in mode-locked lasers.
Multimodality hemodynamic evaluation for optimizing management of anomalous origin of the right pulmonary artery from the aorta in an adolescent

The hemodynamics of anomalous origin of the pulmonary artery (PA) from the aorta is challenging. Different sources of blood supply to the lungs lead to a unique state of differential flow, pressure, and pulmonary vascular resistance in each lung. The decision for surgical reimplantation of the anomalous PA during infancy is easy. The assessment of operability beyond infancy, however, is perplexing. In this report, we describe stepwise multimodal hemodynamic evaluation and successful surgical management in a 15-year-old boy with an isolated anomalous origin of the right PA from the aorta. We also report 5-year hemodynamic data confirming sustained benefit over the long term, thus providing much-needed clinical validation of often cited Poiseuille's and Ohm's laws.
Understanding the Behaviour of Android Ransomware Attacks with Real Smartphones Dataset

Android Security has become a frequently targeted area by cyber attackers and is widely exploited for several benefits by unauthorized users. The open-source model and affordable access to Android platforms enable smartphone users with multiple free applications and on the other hand, the same platform can be misused by attackers to achieve their goals. Android Ransomware attack is one of the majorly used attacks in the android malware domain and can affect android users with huge information and financial losses. Smartphone users store their personal to professional all kind of information in their smartphones and data breaches from the same source can put them in harmful circumstances. This research article focuses on android ransomware network architecture for detailed analysis and forming a further roadmap for the detection of such attacks. With technological capabilities, the detection of ransomware attacks at an early stage can secure from at least some levels of losses. Threat intelligence can be created using artificial intelligence-based methods for android malware attacks, but that requires clear identification of attributes and features that are involved in a particular cyberattack. In this experimental study, we have worked on CIC-AndMal2017 dataset that is being created by generating android malware attacks on real smartphones. We targeted the android ransomware attacks from this dataset and performed detailed exploratory data analysis to conclude the behaviour of different android ransomware attacks. The results drawn from this experimental study help the researcher to build artificial intelligence-based ransomware detection methodologies for the android platform.
Heuristically Assisted Multiagent RL-Based Framework for Computation Offloading and Resource Allocation of Mobile-Edge Computing

Mobile-edge computing (MEC) as a promising technology enables it to satisfy ever-increasing demands for low-latency and ultrareliable services. However, due to the limitations of computing capability and the dynamic network environment, it is challenging to process massive data with low latency. In this article, we consider a dynamic MEC network with a high-performance edge server, multiple time-varying channels, and multiple mobile devices. We aim to find a policy that can maximize the processing success rate of computational tasks and the fairness index of the system while minimizing the process delays. To this end, we propose a heuristic-assisted multiagent reinforcement learning (RL)-based framework to realize the joint optimization of computation offloading and resource allocation. On the one hand, heuristic search is introduced in this framework to find a better resource allocation policy in edge servers and further assist the multiagent RL algorithm to determine offloading policy in mobile devices. On the other hand, a novel parameterized multiagent RL algorithm based on soft actor–critic (SAC) is also proposed to broaden the effectiveness and availability of the proposed framework. Simulation results of the average cumulative reward, success rate, processing delay, and fairness index fully verify the superiority of the proposed framework and algorithm for supporting this problem.
Infliximab Inhibits Colitis Associated Cancer in Model Mice by Downregulating Genes Associated with Mast Cells and Decreasing Their Accumulation

Inflammatory bowel diseases (IBDs), such as Crohn’s disease or ulcerative colitis, can be treated with anti TNF-alpha (TNF-α) antibodies (Abs), but they also put patients with IBDs at risk of cancer. We aimed to determine whether the anti TNF-α Ab induces colon cancer development in vitro and in vivo, and to identify the genes involved in colitis-associated cancer. We found that TNF-α (50 ng/mL) inhibited the proliferation, migration, and invasion of HCT8 and COLO205 colon cancer cell lines and that anti TNF-α Ab neutralized TNF-α inhibition in vitro. The effects of anti TNF-α Ab, infliximab (10 mg/kg) were investigated in mouse models of colitis-associated cancer induced by intraperitoneally injected azoxymethane (AOM: 10 mg/kg)/orally administered dextran sodium sulfate (DSS: 2.5%) (AOM/DSS) in vivo. Infliximab significantly attenuated the development of colon cancer in these mice. Microarray analyses and RT-qPCR revealed that mast cell protease 1, mast cell protease 2, and chymase 1 were up-regulated in cancer tissue of AOM/DSS mice; however, those mast cell related genes were downregulated in cancer tissue of AOM/DSS mice with infliximab. These results suggested that mast cells play a pivotal role in the development of cancer associated with colitis in AOM/DSS mice.
The TESS science processing operations center

The Transiting Exoplanet Survey Satellite (TESS) will conduct a search for Earth's closest cousins starting in early 2018 and is expected to discover ∼1,000 small planets with Rp < 4 R⊕ and measure the masses of at least 50 of these small worlds. The Science Processing Operations Center (SPOC) is being developed at NASA Ames Research Center based on the Kepler science pipeline and will generate calibrated pixels and light curves on the NASA Advanced Supercomputing Division's Pleiades supercomputer. The SPOC will also search for periodic transit events and generate validation products for the transit-like features in the light curves. All TESS SPOC data products will be archived to the Mikulski Archive for Space Telescopes (MAST).
AN EFFICIENT CALIBRATION PROCESS FOR THE PREDICTION OF ROCK STRENGTH THROUGH MACHINE LEARNING ALGORITHMS

Numerical models based on the discrete element method (DEM) have been widely used to predict the mechanical behaviors of rocks in rock engineering applications. Nevertheless, calibration of the model parameters is done by running numerous simulations and this time-consuming simulation process precludes the numerical platforms to be used as a practical tool in such applications. This study aims to accelerate the calibration process of the micro-parameters of three-dimensional (3D) numerical models built based on DEM and facilitate the generation of an efficient database by using machine learning algorithms in the prediction of rock strength. Namely, these algorithms are linear regression (LR), decision tree (DT) regression, and random forest (RF) regression. The appropriate methodology for predicting the uniaxial compressive strengths (UCS) of certain rock types was investigated using a dataset consisting of micro-parameters of 87 DEM-based rock models, generated through an open-source code, Yade. The performance of such methods was evaluated by using metrics including R-squared score (R2), mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE), and then their statistical discrepancies were analyzed. The most accurate prediction of UCS was obtained in the LR method and the lowest percentage of performance was derived from the RF algorithms. LR method provides the results efficiently during calibration of the micro-parameters of a DEM-based rock model.
Transcription of Indian Classical Music using Convolutional Recurrent Neural Network and CTC Loss

Swaras (notes) combined with appropriate Alankars (ornamentations) play a vital role in evoking the aesthetic attributes of a Raag in Indian classical music. It adds to the complexity of recognizing Swaras present in a given composition. However, recognizing the employed Swaras is critical for determining many qualities of a Raag, although the process takes time owing to improvisations or ornamentations. In this study, we have curated a sizable transcription dataset containing 25-second segments of music files and their corresponding transcription containing annotations for Swara, Alankar, and silence or noise. We used two features: STFT, CQT, and tonic normalized versions of STFT and CQT, along with melody contour from a music file as input to autoregressive convolution neural network models. All the variants of models are trained using CTC loss. The experiments show that combining convolution layers with recurrent layers using the tonic normalized CQT and melody contour achieves a good performance of 13 % WER.
Design & Development of IOT based Vertical Farming Monitoring System

The practice of vertical farming has gained popularity in peak development nations. Vertical farming is challenging to implement, though, because even little modifications to the environment can have a significant impact on the effectiveness and caliber of farming operations. As a result, the purpose of this paper is to offer a monitoring system for vertical farming that will assist in keeping track of the physical conditions of crops. The microcontroller will receive the data from the many sensors utilized in this system as either analogue input or digital input. After that, microcontroller will analyze the data and upload it to the Cloud. Additionally, the system will keep track of the location of the active equipment, making maintenance easier in the event that something breaks down. It is anticipated that the culture of vertical farming would develop, leading to a considerable rise in crop output and quality.
Perceptual Doping: A Hypothesis on How Early Audiovisual Speech Stimulation Enhances Subsequent Auditory Speech Processing

Face-to-face communication is one of the most common means of communication in daily life. We benefit from both auditory and visual speech signals that lead to better language understanding. People prefer face-to-face communication when access to auditory speech cues is limited because of background noise in the surrounding environment or in the case of hearing impairment. We demonstrated that an early, short period of exposure to audiovisual speech stimuli facilitates subsequent auditory processing of speech stimuli for correct identification, but early auditory exposure does not. We called this effect “perceptual doping” as an early audiovisual speech stimulation dopes or recalibrates auditory phonological and lexical maps in the mental lexicon in a way that results in better processing of auditory speech signals for correct identification. This short opinion paper provides an overview of perceptual doping and how it differs from similar auditory perceptual aftereffects following exposure to audiovisual speech materials, its underlying cognitive mechanism, and its potential usefulness in the aural rehabilitation of people with hearing difficulties.
Brain tumor detection and Classification using VGG 16

The rapid advancement of medical technology has brought about the impending arrival of the era of big data in the medical field. As a direct and immediate consequence of the analysis and mining of these data, there will be significant effects on the prediction, monitoring, diagnosis, and treatment of tumor-related issues. Due to the fact that it possesses a diverse collection of traits, a low survival rate, and an aggressive nature, brain tumours are widely regarded as the disease that is both the most lethal and the one that causes the greatest disability. It has been proposed that deep learning has the ability to solve the difficulties associated with diagnosing and treating brain cancers. Through the use of FL on brain tumor identification from MRI images, the purpose of this research is to find a solution to the problem of centralized data collecting. Using the Visual Geometry Group (VGG 16) as a tool for detecting brain cancers, implementing a convolutional neural network (CNN) model framework, and determining the parameters to train the model for this problem were the objectives of this research. The MR images might be analyzed with our method to spot brain cancers. The testing results showed that the algorithm worked better than the standard methods that are currently used for detecting brain tumors, with an excellent accuracy of 92%.
Comparing experts to novices: reduced satisfaction of search when searching with virtual breast tomosynthesis

Satisfaction of Search (SOS), an increased likelihood of missing a lesion/target when another lesion/target was already detected in the same image, is a known search problem in medical image perception and cognitive science. Currently, it is unknown the extent to which SOS affects lesion detection in breast imaging. Here we investigated SOS rates with novices (college undergraduates) and experts (residents, fellows, and certified radiologists) in virtual (computer-generated) tomograms. The virtual images were created with the OpenVCT framework simulating the breast anatomy of patients and could contain zero, one, or two masses. Participants were asked to search and click on up to two masses per image. When compared to novices, experts: 1) made fewer false alarms (experts 19.5%, novices 29.1%; marginally significant effect), 2) were better in correct rejections (experts 76.7%, novices 44.5%), and 3) made fewer SOS errors (experts 14.2%, novices 30.7%). These findings are the first to demonstrate the SOS effect in breast imaging and emphasize the importance of expertise in breast cancer detection.