Miracle-dz/UniFAR-FTUs huggingface dataset:
A Sentiment-Aware Contextual Model for Real-Time Disaster Prediction Using Twitter Data

The massive amount of data generated by social media present a unique opportunity for disaster analysis. As a leading social platform, Twitter generates over 500 million Tweets each day. Due to its real-time characteristic, more agencies employ Twitter to track disaster events to make a speedy rescue plan. However, it is challenging to build an accurate predictive model to identify disaster Tweets, which may lack sufficient context due to the length limit. In addition, disaster Tweets and regular ones can be hard to distinguish because of word ambiguity. In this paper, we propose a sentiment-aware contextual model named SentiBERT-BiLSTM-CNN for disaster detection using Tweets. The proposed learning pipeline consists of SentiBERT that can generate sentimental contextual embeddings from a Tweet, a Bidirectional long short-term memory (BiLSTM) layer with attention, and a 1D convolutional layer for local feature extraction. We conduct extensive experiments to validate certain design choices of the model and compare our model with its peers. Results show that the proposed SentiBERT-BiLSTM-CNN demonstrates superior performance in the F1 score, making it a competitive model in Tweets-based disaster prediction.
Creation of Corpus and Analysis in Code-Mixed Kannada-English Social Media Data for POS Tagging

Part-of-Speech (POS) is one of the essential tasks for many Natural Language Processing (NLP) applications. There has been a signiﬁ-cant amount of work done in POS tagging for resource-rich languages. POS tagging is an essential phase of text analysis in understanding the semantics and context of language. These tags are useful for higher-level tasks such as building parse trees, which can be used for Named Entity Recognition, Coreference resolution, Sentiment Analysis, and Question Answering. There has been work done on code-mixed social media corpus but not on POS tagging of Kannada-English code-mixed data. Here, we present Kannada-English code-mixed social media corpus annotated with corresponding POS tags. We also experimented with machine learning classiﬁcation models CRF, Bi-LSTM, and Bi-LSTM-CRF models on our corpus.
HealthyHeart: ML based Analysis and Prediction of Cardiovascular Diseases

Electrocardiogram or ECG waveform is one of the most common names that comes up in the healthcare sector. It is often the foremost step that is taken by the healthcare professionals when it comes to dealing with any cardiovascular issues. The signals from the ECG waveform are a reflection of how the heart functions. Any abnormalities encountered in the waveform are a reflection of underlying problems that a person may be suffering from. Using technology, especially Machine Learning and Artificial Intelligence in the healthcare industry can open up new doors and provide valuable assistance in the treatment of diseases. HealthyHeart is our proposed way that classifies CVD by using Random Forest Algorithm on the data obtained from the ECG signals. An accuracy of 97.7% was achieved by training the developed model over 979273 data entries. The accuracy rate indicates that HealthyHeart is a good fit for producing accurate results.
Boosting the Usage of Green Energy for EV Charging in Smart Buildings Managed by an Aggregator Through a Novel Renewable Usage Index

The growing trend of electric vehicles (EVs) and building integrated photovoltaics (BIPVs) is a promising means to reduce related climate change issues. EV loads can be managed via an aggregator to maximize the usage of green energy produced by photovoltaic units (PV) through smart charging strategies that exploit controllable EV demand connected to BIPV. Previous works have focused on the EV charging coordination in a smart BIPV, although without an optimization that encourages EV charging with the energy produced by the PV units. This paper proposes an aggregation strategy that maximizes a green energy index (GEI) for the smart charging coordination of EVs, which takes advantage of periods with high PV availability to charge the EV batteries; moreover, a post-processing stage for the GEI provides EV owners with information about the percentage of charged energy, period by period, that comes from PV generation. The results for a case study with 510 EVs integrated with 17 smart BIPVs show that the strategy effectively optimizes the usage of the energy produced by the PV units to charge the EVs, contributes to reduce non-renewable energy consumption of the building sector, and satisfies the EV owners’ energy requirements for transportation.
RDMO-SLAM: Real-Time Visual SLAM for Dynamic Environments Using Semantic Label Prediction With Optical Flow

Visual simultaneous localization and mapping (vSLAM) are considered a fundamental technology for augmented reality and intelligent mobile robots. However, rigid scene assumption is common in vSLAM, which limits the wide usage in populated real-world environments. Recently, with the widespread use of artificial neural networks, many solutions have tried to eliminate the influence of dynamic objects using semantic information provided by object detection or semantic segmentation. Mask R-CNN is popular in many applications, but is usually slow and limits the speed of vSLAM because it waits for the semantic results before camera ego-motion estimation. We had previously introduced a real-time vSLAM, RDS-SLAM, which isolates tracking and semantic segmentation by adding a semantic thread and moving probability estimation. However, Mask R-CNN only supplies a small amount of semantic information because only a few keyframes can be segmented within a short time. Therefore, in this study, we propose a novel vSLAM, RDMO-SLAM, which can leverage more semantic information while ensuring the real-time nature by adding semantic label prediction using dense optical flow. Besides, we also estimate the velocity of each landmark and use them as constraints to reduce the influence of dynamic objects in tracking. Demonstrations are presented, which compare the proposed method to comparable state-of-the-art approaches using dynamic sequences. We improved the real-time performance from 15 Hz (RDS-SLAM) to 30 Hz while keeping robust tracking in dynamic scenes.
Embraced Separation: Exploring methods of breath attunement in speculative infant swings

This paper explores a speculative design for an infant swing based on breath attunement. There are challenges for working parents to juggle, such as working from home and taking care of an infant, especially when infants want to be physically close to the parent. This design explores how a swing can be adaptable to subtle, dynamic changes in the parent that can soothe the infant while allowing the parent to be more hands-free. These dynamic changes include the use of breath sensors on the parent to change the rhythm of the swing, while also creating shifting amplifications of environmental sounds, as though they were being carried by the parent through the home. This work particularly explores qualities of movement and rhythm as key components of comfort for infants in their parent’s arms. Results of this paper include design sketches and a usability evaluation of a smartphone interface, as a speculative approach to designing embodied interactions between parent-child relationships.
Insight into traffic security: A correlation discovery of urban spatial features and traffic flow patterns

With the rapid development of urbanization, urban traffic security problems become increasingly prominent, and traffic accidents occur frequently. It is becoming increasingly critical to use data mining methods to solve actual problems in traffic security. But we don’t just rely on the analysis of urban traffic surface operation rules, it is to study the mechanism of traffic operation. Therefore, in this paper, we aim to discover the correlation between spatial features and traffic flow patterns in urban regions to improve traffic security. In order to obtain deeper spatial semantic meanings for every urban region, we propose a spatial feature method based on regional hierarchy, which fuses the hierarchical structure of region and POI (point of interest) information. For traffic flow patterns, we propose a self-representation learning optimization method to find the similarity of region traffic flow. Then, we match the spatial features and flow patterns to discover the correlation of them. Experiments show that our approaches are effective.
Lung Cancer Detection and Classification Based on Alexnet CNN

Lung cancer is among the most fatal disease in developed countries, and early diagnosis of the disease is difficult. Lung cancer diagnosis and treatment has been one of the most daunting challenges humans have encountered in recent decades. Early tumor diagnosis will continue to save a vast amount of lives around the world on a daily basis. This paper describes a method for classifying lung tumors as malignant or benign that combines a Convolutional Neural Network (CNN) with the AlexNet Network Model. AlexNet CNN is one of the transfer learning models. As compared to accuracy achieved by conventional neural network systems, the proposed CNN achieves a high degree of accuracy, which is more effective.
Integration of Multiband InSAR and Leveling Measurements for Analyzing the Surface Subsidence of Shield Tunneling at Beijing-Zhangzhou High-Speed Railway

In modern life, metro and high-speed rails have become indispensable transportation and have been playing an important role in many areas, especially in cities. The metro and high-speed rails have varying degrees of impact on the surface and surrounding buildings, which must be carefully understood to minimize the risk of hazards. This work is aimed at investigating the deformation of ground surface and the surrounding structures, caused by the excavation of underground tunnels. Because of the spatiotemporal characteristics of the land subsidence induced by underground tunnel construction, the measurements obtained from InSAR (Synthetic Aperture Radar Interferometry) and leveling techniques are integrated to study the subsidence phenomenon of the ground surface above the shield tunnels and the surrounding buildings. The subsidence-related parameters, including the advance angles, lag angles, and boundary angles, are derived from the deformation results. This study suggests that leveling and InSAR observations can provide technical support to study the subsidence of dense buildings on the surface of shield tunnel excavation and to protect the surrounding environment.
Effective Military Communication Using Multiband Wearable Antenna

Wearable antennas have become prime consideration for wearable devices as it appears compact, reliable, and robust for any solution. The traditional devices are handheld which increases the weight and systems are fragile. Thus, a wearable communication device can enhance the structure and application usages. Gadgets which is wearable on human and has contact to the body referred as Wireless Body Area Networks (WBAN). Communication applications are vastly used for commercial use and hence a lot of innovation happened on material design, fabrication techniques, material selection, etc. The military communication system is very essential for battlefield operations and so far, wearable devices are few adopted by the Military. This paper proposes a wearable multiband communication system that has been incorporated in the vest and that can be used during the battlefield. Further, the various challenges and issues in designing the wearable antennas are discussed. Multiband communication is faster and efficient hence SATCOM carried by a military person for terrestrial communication could be eliminated.
Design, Implementation and Performance Comparison of D- Latch Using Different Topologies

This article provides the comparison results of the D-latch circuit in three different topologies i.e. conventional CMOS (Complementary Metal Oxide Semiconductor), MCML (MOS Current Mode Logic), and BD-MCML (Bulk Driven-MOS Current Mode Logic). In the BD-MCML circuit, the bulk terminal is also used as one of the inputs. The voltage levels of input should not affect the operation of the MOSFET. In BD-MCML, the dynamic power is independent of frequency, So it can be used in high frequency applications with less power consumption. The comparison is made by considering the supply voltage for all the circuits to be 0.8 V. The performance of D- Latch with different topologies is evaluated and analyzed using Cadence Virtuoso tool in 45nm technology. In this paper, the performance analysis and comparison is done using three different parameters; power, delay, and PDP (Power Delay Product). This paper concludes that BD-MCML has better performance with respect to power and PDP as compared to the other topologies,
Head Gesture-Based on IMU Sensors: a Performance Comparison Between the Unimodal and Multimodal Approach

In this paper, we further develop systems used in head gesture recognition based on inertial sensors, previously developed and presented in other research papers. In the current research, we investigate the impact of increasing the number of inertial sensors as a condition to read head orientation with high accuracy. A set of eight predefined commands were evaluated with two systems that contain a single inertial sensor, respectively, three inertial sensors. The computational steps were done based on the Classical Machine Learning algorithms and Deep Learning algorithms. Each algorithm used in the study was trained based on raw data form of inertial signals. This study concluded that the increasing number of inertial sensors in the gestural recognition systems could improve the classification performance in the range of 10%-22%. Also, the average classification value calculated on ten predictive models is equal to 6.65%.
Pandemic as Game Mechanic: Simulation of Infection Spread for the Classroom.

In light of COVID-19, we created a novel simulation game, to explain exponential growth in disease spread. The simulation game is an open educational ressource (OER) for children to reflect on how test and isolation can be applied to stop contagious diseases. The game was reviewed in three classrooms (P3-P5) by a primary school teacher to pilot the applicability of the game in an educational setting. Based on qualitative feedback from pupils, we developed accompanying exercise sheets and website in close collaboration with the teacher.
Development of a Web-based Jahai – Malay Language Repository

The Digital Jahai – Malay Language Repository is a platform that was created to store and translate Jahai terms into two different languages which are English and Bahasa Melayu. The system was developed to assist the needs to translate a Jahai term using a text input. Previously, the only way to translate Jahai terms to English is by using a corpus in a paper published by Burenhalt. Until recently, no system was developed to translate the Jahai language to Bahasa Melayu. Since ethnic Jahai resides in Malaysia, it is more beneficial if we can develop our own language repository system that will translate the Jahai terms to Bahasa Melayu, and preserve the language using a sustainable format which is digitization. The proposed system is capable of identifying the Jahai terms using International Phonetic Alphabet (IPA) symbols by entering the special characters using a virtual keyboard. Several additional functions such as insert new Jahai terms and listening to Jahai term pronunciation are also provided. In the future, a mobile translation application will be developed to increase the usability of the existing system.
Automobile Drivers Distraction Avoiding System Using Galvanic Skin Responses

In this paper we are proposing a system which plays an important role to avoid distractions while driving. To achieve this goal, we are using galvanic skin responses also called as GSR along with EEG signals are used to calculate the cognitive abilities of the driver while driving during day and night times. Driving is an activity which needs a lot of attention. Also, it is continuous process of attentiveness if anything wrong goes then it may cause or lead to the death of the driver. Our main objective of the proposed system is to reduce the number of accidents caused due to distractions faced by the drivers while in driving. Although many methods or devices are available in the market to detect the distraction activities, they all are based on the techniques in which the camera is involved. The disadvantages of this techniques are that they predict or alerts the distractions lately or while the distraction is going on. So, these techniques are proven to be very dangerous to the drivers because of their latency. Since in this system distractions are detected using EEG signals which are very effective cause it gives us the signals from the brain which plays an important role in the task of driving. By using this signal, we can effectively reduce the distractions while driving and also, we can reduce number of accidents occurring during night times. Also it gives us the accurate distraction signals before the actual accident occur.
A Compact & Low Power Architecture of XXTEA192 Lightweight block cipher

RFID and IoT security threats have been increased due to the enormous usage of ubiquitous computing devices. Providing security to RFID tags is one of perplexing tasks as it has to meet rigid area and power requirements. This paper has proposed a compact serial architecture for XXTEA-192 block cipher which can be used in RFID applications. The RFID tags are constrained with limited area and power. XXTEA is the most secured version among the ciphers of TEA family. This cipher works on variable length message with a minimum message length of 62-bit and should be a multiple of 32-bit words. This feature enables the cipher to encrypt larger block sizes. Serial architecture of XXTEA-192 has been proposed that performs 32-bit operations like addition, XOR and shifting in each clock cycle. The proposed design has been implemented using verilog. Analysis of performance metrics like area, throughput, efficiency, power and energy has been performed on different families of FPGA devices like Spartan 6, Virtex 6 and Virtex 7. The proposed design has achieved 45 slices and a throughput of 498.44 Mbps on Spartan 6 making the architecture area and speed efficient for RFID applications.
Kinetic study of low-temperature CO2 plasmas under non-equilibrium conditions. I. Relaxation of vibrational energy

A kinetic model describing the time evolution of ∼70 individual CO2(X1Σ+) vibrational levels during the afterglow of a pulsed DC glow discharge is developed in order to contribute to the understanding of vibrational energy transfer in CO2 plasmas. The results of the simulations are compared against in situ Fourier transform infrared spectroscopy data obtained in a pulsed DC glow discharge and its afterglow at pressures of a few Torr and discharge currents of around 50 mA. The very good agreement between the model predictions and the experimental results validates the kinetic scheme considered here and the corresponding vibration–vibration and vibration–translation rate coefficients. In this sense, it establishes a reaction mechanism for the vibrational kinetics of these CO2 energy levels and offers a firm basis to understand the vibrational relaxation in CO2 plasmas. It is shown that first-order perturbation theories, namely, the Schwartz–Slawsky–Herzfeld and Sharma–Brau methods, provide a good description of CO2 vibrations under low excitation regimes.
Enhancing Fidelity of Description in Android Apps With Category-Based Common Permissions

Application description analysis is applied for various purposes in software engineering domains. Besides the inherent challenges from the ambiguity of natural language, sparse permission semantics raise the difficulties of predicting functionalities and permission usages from app descriptions. More specifically, the functionalities common to the app’s category are intentionally abbreviated by developers due to the limited number of characters, and the permissions are often over-claimed. These are the main reasons that cause false positives in predicting permissions from app descriptions. Such unmentioned permissions can only be detected as suspicious in previous studies where effective assistance for developers in refining app descriptions and preventing potential security risks is not provided. In this paper, we propose the FideDroid, a framework to identify category-based common permissions to offset those essential functionalities while assessing the fidelity of app descriptions. Our framework augments the labeled dataset of app descriptions to improve the prediction of permissions. FideDroid compares inferred permissions with used ones to reveal the suspicious and unnecessary permissions based on the prediction. It helps developers to refine app descriptions and maintain permission usages. In our experiments on large real-world apps, we analyzed and revealed that the category-based common permissions may cover more unmentioned functionalities without considering all possible permissions during app description analysis. In addition, we discovered three factors causing the inconsistency between descriptions and permission usages to be: 1) human interventions in writing description; 2) bad practices on permission usages; and 3) prolific developers. These findings will facilitate developers to refine app descriptions and optimize permission usages in the apps.
Design and Development of Virtual Banking Using Internet of Things

This paper is to present a cashless payment system that is mainly based on the fingerprint. The main motto of the project is to complete a transaction without using debit cards or cash. Because nowadays many unauthorized transactions are taking place due to this, anyone can drain the money in your account. Many banks are also trying to keep the security at a higher level. To accomplish a higher security, this research work uses a fingerprint technology, which can be different for every citizen. Finger print device is more flexible to use, and it is a low-cost device and reliable. By this device, a user can have a chance not to carry any cards or cash for any payments. Many are frustrated by remembering many passwords. The proposed research work can eradicate the problem with this device. Using this technology, the number of fraud transactions can be reduced. The user can get an alert whenever a transaction has been done whether it is successful or not. If any person enters the wrong password for more than three times, the image of the particular person who is performing the particular transaction will be captured and an email consisting of that captured photo will be sent to the registered mail. This device also consists of the speaker, which will be intimate on the successful completion of the transaction.
Pattern Recognition Classification for Myoelectric based Control of the Robotic Arm using Neural Network

Because of its relevance to society, human-robot interaction has long been a research subject for academics. The creation of a robust human-interactive robot that takes commands from electromyogram (EMG) signals is currently being investigated. This paper explores the movements of a system that enables signals to be recorded directly from a human body and then used to operate a small robotic arm. Pattern classification is done with neural network and used for gestures of the artificialarm.