Miracle-dz/UniFAR-FTUs huggingface dataset:
Secure Sensitive Information on IoT using Machine Learning

In a number of industries, including banking, cybersecurity, healthcare, and others, risk assessment is an essential procedure. By automating data analysis, seeing trends, and offering insights that might help organisations make better decisions, artificial intelligence (AI) tools can greatly improve risk assessment. Here are some typical AI methods and strategies for risk assessment. Machine learning is the first step, followed by Natural Language Processing (NLP), Reinforcement Learning, Predictive Analytics, and so on. While AI may significantly improve risk assessment, it ought to be used in combination with human judgement and domain knowledge. Additionally, when using AI for risk assessment, it's imperative to address issues with the accuracy of data, bias, and ethics. To guarantee the accuracy and continued relevance of risk evaluations, regular verification of models and updating is also necessary. The importance of the attributes in the UNSW-NB15 collections is therefore thoroughly analysed in this work. This research analyses a UNSW-NB15 collection of data generation to address the problems associated with the lack of any network reference data sets. This data collection combines network traffic assault actions that are now synthesised with real-world modern norms. The UNSWNB15 information set's characteristics are produced using both established and cutting-edge techniques. Many Machine Learning algorithms are used to analyse the intrusion by measuring the accuracy and other features. Here the data set is classified as Binary classification and Multi Class Classification where Machine learning are applied to find out the intrusion. Random Forest Classifier (RFC) performance good for binary classification and Linear Support Vector Machine(LSVm)performance is good for Multiclass Classification.
Social Media as a Negative Source of Political News in a Polarized Society? Indonesian and Filipino Students' Perception

The polarized society due to political elections in Indonesia and the Philippines is inseparable from many aspects. This study aims to elaborate the perception of Indonesian and Filipino students of social media as a political news source in a polarized society during the Covid-19 pandemic. This study uses a qualitative method. The result of this study shows that Indonesian and Filipino students used social media as a reference news source. For Indonesian students, Instagram became the most influential social media, whilst for the Filipino students, Facebook was the most influential social media. Indonesian students negatively perceived social media as a political news source due to the power of social media to increase or decrease polarization, whereas Filipino students have a reversed point of view. The positive perception of media is related to the ability of social media to give the knowledge about the political actors. According to Indonesian and Filipino students, political polarization in society occurs and continues because it is caused by fanaticism, group influence and the media. This research provides new insights to the public about how social media can affect social conditions concerning political contestation, and provides insight to political contestants about the social media usage as a source of political news among the younger generation.
Comparative Rumen Metagenome and CAZyme Profiles in Cattle and Buffaloes: Implications for Methane Yield and Rumen Fermentation on a Common Diet

A study was undertaken to compare the rumen microbial community composition, methane yield, rumen fermentation, and CAZyme profiles between cattle and buffaloes. The primary aim of this study was to ascertain the impact of the host species on the above when diet and environmental factors are fixed. A total of 43 phyla, 200 orders, 458 families, and 1722 microbial genera were identified in the study. Bacteroidetes was the most prominent bacterial phylum and constituted >1/3rd of the ruminal microbiota; however, their abundances were comparable between cattle and buffaloes. Firmicutes were the second most abundant bacteria, found to be negatively correlated with the Bacteroidetes. The abundances of Firmicutes as well as the F/B ratio were not different between the two host species. In this study, archaea affiliated with the nine phyla were identified, with Euryarchaeota being the most prominent. Like bacterial phyla, the abundances of Euryarchaeota methanogens were also similar between the cattle and buffaloes. At the order level, Methanobacteriales dominated the archaea. Methanogens from the Methanosarcinales, Methanococcales, Methanomicrobiales, and Methanomassiliicoccales groups were also identified, but at a lower frequency. Methanobrevibacter was the most prevalent genus of methanogens, accounting for approximately three percent of the rumen metagenome. However, their distribution was not different between the two host species. CAZymes affiliated with five classes, namely CBM, CE, GH, GT, and PL, were identified in the metagenome, where the GH class was the most abundant and constituted ~70% of the total CAZymes. The protozoal numbers, including Entodiniomorphs and Holotrichs, were also comparable between the cattle and buffaloes. Results from the study did not reveal any significant difference in feed intake, nutrient digestibility, and rumen fermentation between cattle and buffaloes fed on the same diet. As methane yield due to the similar diet composition, feed ingredients, rumen fermentation, and microbiota composition did not vary, these results indicate that the microbiota community structure and methane emissions are under the direct influence of the diet and environment, and the host species may play only a minor role until the productivity does not vary. More studies are warranted to investigate the effect of different diets and environments on microbiota composition and methane yield. Further, the impact of variable productivity on both the cattle and buffaloes when the diet and environmental factors are fixed needs to be ascertained.
A Machine Learning Based Employee Mental Health Analysis Model

Mental health has become a major concern in the workplace due to various factors such as workload, workplace culture, and personal issues, resulting in employees experiencing stress, anxiety, and depression. The role of machine learning techniques (MLT) is to analyze employee mental health and has gained interest to identify potential problems and provide support. Therefore, it is crucial to understand employee mental health and identify potential risk factors to maintain a healthy workforce and promote a positive work environment. aims to analyze the mental health of employees in the workplace using machine learning algorithms applied to a dataset to identify the most accurate model for predicting mental health outcomes in employees. Overall, this study emphasizes the significance of utilizing machine learning algorithms to analyze mental health outcomes in the workplace, enabling organizations to take appropriate steps to improve the mental health of their employees and create a healthier work environment.
Towards design and development of new joint modules for humanoid ergoCub 2.0*

The cases of musculoskeletal diseases in workplaces like industrial warehouses and healthcare units is rapidly increasing. The ergoCub project aims to introduce a new humanoid robot platform for ergonomic human-robot collaborations in such workplaces in order to mitigate the risks on the health of the workers. This article presents the mechanical design and development of a new compact and powerful joint module for the forthcoming version of ergoCub 2.0. The proposed joint achieves continuous torque of 40.5 N m, repeatable peak torque of 77.5 N m, thus increasing the torque density by 163% and the power density by 61% as compared to the current joints. Additionally, the digital twin of the joint is presented and the simulations for sensorless torque estimation are demonstrated.
Human echolocation: localizing reflections of self-generated oral sounds in laboratory

The active sensing and perception of the environment by auditory means is typically known as echolocation. Through the emission of oral sounds and the interpretation of the reflections in relation to the direct sound, blind people can acquire spatial knowledge about their surroundings and improve their mobility in unknown spaces. While this technique is becoming more common in Orientation & Mobility training, it has not yet become a mainstream practice. This paper aims, on one hand, at presenting this modality of perception and its underlying sensory mechanisms and, on the other hand, at showing the results of a laboratory experience at the Laboratory of Acoustics at KULeuven, in which we investigate the ability of echolocation-naïve sighted subjects to use echolocation for aligning themselves toward virtual silent targets generated through an acoustic virtual reality system. It is shown that all subjects were able to complete the tasks, although detection of targets at closer distances entailed more difficulty than at further distances. Significant individual training effects were observed and should be accounted for in future similar tests.
Silicon Nitride High Confinement Thermally- and E/O Tuned Photonic Integrated Platfrom

In this work we present developed silicon nitride high confinement thermally- and E/O tuned platform, that shows propagation losses lower than 0.05 dB/cm for 1550 nm wavelength and lower than 0.30 dB/cm for 935 nm wavelength. This results are achieved by material properties optimization and scattering on roughness and stitching errors minimization. We also show here nanophotonic devices fabricated basing on developed technology, automatic fiber coupling and wire bonding.
Superiority of Neural Networks for Trading Volume Forecasts of Stocks and Cryptocurrencies

Trading volume is an important variable to successfully capture market risks along with asset price/returns. Recently, there has been a growing interest in deep learning methods to forecast the trading volume of stocks using historical volatility as a feature. Unlike the existing work, a novel datadriven log volatility forecast is proposed in this paper as an extra feature to improve trading volume forecasts. Recently, neural networks for volatility and neural nets for electricity demand forecasting, constructed with nnetar function, have shown to be superior. The novelty of this paper is to demonstrate the neural network based on the nnetar function from the forecast package in R for trading volume forecast shows superiority over the other neural network.
Deep Learning Based Cyberbullying Detection in Bangla Language

The Internet is currently the largest platform for global communication including expressions of opinions, reviews, contents, images, videos and so forth. Moreover, social media has now become a very broad and highly engaging platform due to its immense popularity and swift adoption trend. Increased social networking, however, also has detrimental impacts on the society leading to a range of unwanted phenomena, such as online assault, intimidation, digital bullying, criminality and trolling. Hence, cyberbullying has become a pervasive and worrying problem that poses considerable psychological and emotional harm to the people, particularly amongst the teens and the young adults. In order to lessen its negative effects and provide victims with prompt support, a great deal of research to identify cyberbullying instances at various online platforms is emerging. In comparison to other languages, Bangla (also known as Bengali) has fewer research studies in this domain. This study demonstrates a deep learning strategy for identifying cyberbullying in Bengali, using a dataset of 12282 versatile comments from multiple social media sites. In this study, a two-layer bidirectional long short-term memory (Bi-LSTM) model has been built to identify cyberbullying, using a variety of optimisers as well as 5-fold cross validation. To evaluate the functionality and efficacy of the proposed system, rigorous assessment and validation procedures have been employed throughout the project. The results of this study reveals that the proposed model’s accuracy, using momentum-based stochastic gradient descent (SGD) optimiser, is 94.46%. It also reflects a higher accuracy of 95.08% and a F1 score of 95.23% using Adam optimiser as well as a better accuracy of 94.31% in 5-fold cross validation.
Deciphering Faces: Enhancing Emotion Detection with Machine Learning Techniques

Facial emotion detection holds significant relevance across various domains, from psychology and marketing to education and security. Despite its importance, prevalent techniques often grapple with issues like low precision, susceptibility to lighting changes, obstructions, and distinct facial characteristics. Addressing these challenges, our research embarked on devising a robust and precise facial emotion detector harnessing the potential of machine learning, focusing on convolutional neural networks (CNN). Comprehensive testing revealed that our model surpasses existing state-of-the-art techniques, showcasing superior performance on benchmark datasets. The salience of our research is underscored by its profound implications for myriad real-world applications hinging on accurate facial emotion recognition. We present an enhanced model, distinguished not just by its accuracy but also its robustness, making it apt for diverse scenarios from insightful marketing initiatives and nuanced medical diagnoses to enriched educational experiences. Through this endeavor, we have accentuated the transformative capacity of machine learning in refining and redefining facial emotion detection methodologies.
Transfer-Printing of III - V Photodetector for High-Bandwidth Si -Photonic Integrated Coherent Receiver

Coherent optical communication is being treated as a promising candidate to tackle the increasing demand for faster data rate and band capacity in both data- and telecom domain. With receiving of downstream coherent data in high data rate, novel approaches of high bandwidth coherent receiver design and fabrication will be very necessary. In this work, we demonstrate the integration process of III-V photodetectors on silicon photonic integrated circuits through transfer-printing technique by evanescent coupling, on 8-inch wafer scale. An 8-channel silicon photonic integrated coherent receiver of 42 GHz operation bandwidth for quadrature-phase shift-keying (QPSK) optical communication in C-band application is demonstrated, showing a polarization-independent responsivity of 0.65 -1 A/W with dark current within 90 pA. The integrated photodetectors $(5.3 \mathrm{x}20\mu \mathrm{m}^{2}$ mesa) have a 3 dB bandwidth of 42 GHz at -2 V reverse bias.
Menaklukkan Tantangan Digital: Resiliensi UMKM Polewali Mandar dalam Era Pasar Digital

Penelitian ini bertujuan untuk menguraikan tantangan dan solusi yang dihadapi oleh Usaha Mikro, Kecil, dan Menengah (UMKM) di Polewali Mandar dalam mengadopsi bisnis digital. Dalam penelitian ini, metode yang digunakan adalah studi literaur. Tujuan penelitian ini adalah mengidentifikasi tantangan utama yang dihadapi UMKM dalam mengadopsi bisnis digital dan menyajikan solusi yang dapat membantu mereka mengatasi tantangan tersebut. Hasil penelitian menunjukkan bahwa terdapat upaya resiliensi yang telah dilakukan oleh UMKM di Polewali Mandar dalam menghadapi tantangan tersebut. Solusinya adalah dukungan dan kolaborasi pendampingan yang dilakukan secara berkelanjutan para pemangku kepentingan untuk memperkuat literasi digital, akses teknologi, pengembangan strategi bisnis yang adaptif, serta membangun budaya yang mendukung inovasi dan transformasi digital. Sehingga UMKM Polewali Mandar mampu mengatasi tantangan dan dapat berkontribusi, menaklukkan tantangan digital dan mencapai kesuksesan dalam era pasar digital yang semakin kompleks.
Transcription Factors and Their Regulatory Roles in the Male Gametophyte Development of Flowering Plants

Male gametophyte development in plants relies on the functions of numerous genes, whose expression is regulated by transcription factors (TFs), non-coding RNAs, hormones, and diverse environmental stresses. Several excellent reviews are available that address the genes and enzymes associated with male gametophyte development, especially pollen wall formation. Growing evidence from genetic studies, transcriptome analysis, and gene-by-gene studies suggests that TFs coordinate with epigenetic machinery to regulate the expression of these genes and enzymes for the sequential male gametophyte development. However, very little summarization has been performed to comprehensively review their intricate regulatory roles and discuss their downstream targets and upstream regulators in this unique process. In the present review, we highlight the research progress on the regulatory roles of TF families in the male gametophyte development of flowering plants. The transcriptional regulation, epigenetic control, and other regulators of TFs involved in male gametophyte development are also addressed.
A Framework to Design and Build a Height Controllable Eversion Robot

Eversion robots are used in many different environments for a variety of applications. However, the vast majority of these environments are land based and minimal work has been done on environments that are 3-dimensional (3D) such as in air or underwater. To be able to successfully operate in such environments, eversion robots need to be capable of operating in 3D, including adjusting their configuration along the vertical axis. This vertical motion for eversion robots has not been developed before. This paper proposes a design for an eversion robot that, when fully extended, is able to float in air, using varying amounts of helium to change its buoyancy and hence, the amount of lift force in the vertical direction. The robot in this paper consists of a fixed base, one main continuous everting chamber with a constriction at half its length to form two links and two actuation pouches, which are externally attached. Furthermore, the calculations for determining the quantity of helium required to generate a specific force to lift the robot a specified distance, for position changes is shown. Finally, a kinematic model of the robot shows how the robot's link tip positions affect the shape and position of the robot.
Deep Learning Method to Analyze the Bi-LSTM Model for Energy Consumption Forecasting in Smart Cities

Smart cities and IoT solutions are improving urban efficiency, resource optimization, and public safety by using modern technologies. Deep residual Bi-LSTM (Long Short-Term Memory) models can analyze and forecast complicated and time-varying data. This study examines how the deep residual Bi-LS TM model might improve smart city and IoT solutions. The model has broad use since it captures long-term interdependence and extracts meaningful representations from sequential data. Traffic prediction, energy consumption forecasting, environmental monitoring, predictive maintenance, public safety, and emergency response are discussed. The deep residual Bi-LSTM model provides realtime insights, accurate forecasts, and quick data processing to improve smart city systems and IoT solutions, making cities more sustainable, efficient, and secure.
Analysis of the Research Progress of Electromagnetic Railgun Based on CiteSpace

An electromagnetic railgun is a new type of weapon with ultrahigh speed based on electromagnetic thrust. It is used in important military domains, such as long-range strikes, strategic air defense, and ballistic interception. It is also used in aerospace fields, such as space debris cleanup, microsatellite launch, and space station relay launch, so its future prospects are excellent. We analyzed 1324 publications on electromagnetic railgun using CiteSpace and identified information including authors, institutions, journals, and popular research topics and trends. Our analysis found that the University of Texas is the research institution that has published the most documents on electromagnetic railgun, and French researcher Markus Schneider has published the most documents. The journals of IEEE Transactions on Magnetics and IEEE Transactions on Plasma Science have played an important role in promoting the development of electromagnetic railgun. The research direction of electromagnetic railgun has a distinct multidisciplinary characteristic because it touches on physics, engineering science, mathematics, materials science, energy and fuel, communication technology, instrumentation, and computer science. The development process of electromagnetic railgun can be grouped into three stages: basic theoretical research, engineering research, and system optimization research. The major research topics include electromagnetic force, armature design, rail materials, pulsed power supply, armature–rail contact surface characteristics, and coupling analysis of multiple physical fields. Future electromagnetic railgun will be scalable in terms of armature–rail contact characteristics and life span, energy storage density of power supply, current-carrying capability and thermal management of launcher materials, optimization design of armature structure, and complex data processing of control systems.
A Reliable Spectrum Sensing Method Based on Deep Learning for Primary User Emulation Attack Detection in Cognitive Radio Network

Cognitive radio network (CRN) is highly vulnerable to malicious attacks, which can significantly impact the effectiveness and reliability of spectrum sensing. Existing deep learning-based works have proven ineffective in accurately recognizing an unknown attacker due to the prior information about the attacker for training purposes is not easily available in advance. To address the challenge of reliably identifying the known primary user (PU) and the unknown attacker, we combine deep learning with extreme value theory (EVT) to propose a reliable spectrum sensing method for primary user emulation attack (PUEA) detection. Importantly, our method does not rely on acquiring the prior information about the attacker during the training phase. The proposed deep learning-based method not only enables the secondary user (SU) to accurately confirm the presence of PU but also effectively identifies unknown attackers. In addition, the network structure of proposed method is redesigned to extract diverse multi-domain features. The simulation results indicate that the proposed method exhibits remarkable performance under the presence of unknown attacker.
Inner speech classification using inter-trial coherence framework for feature extraction

EEG signal analysis for inner speech recognition has garnered interest in recent years. Participants in these studies are prompted to perform a mental task involving inner speech within a specific time frame. This study utilizes a dataset of inner speech recordings for four words. While the dataset includes recordings from multiple subjects and sessions, this work focuses on the first session of one subject. A novel feature extraction method based on the inter-trial coherence framework is proposed for inner speech classification. Two classifiers, k-nearest neighbors and support vector machines, are trained and evaluated using the extracted features. Results indicate that the proposed method achieves comparable or superior performance compared to previous works. Future work aims to extend the methodology to include the remaining subjects and sessions in the dataset. By doing so, the generalizability and robustness of the method can be further assessed.