Miracle-dz/UniFAR-FTUs huggingface dataset:
Adapting the Learning Mechanics and Game Mechanics Model Framework into a Serious Game for External Male Genitalia Education

The Serious Game is an emerging medium that is not only for entertainment purposes. It shows remarkable efficacy in education, training, and health. The effectiveness of the Educational Serious Game in health is positive behavior change. The health topic focused in this study is external male genitalia, specifically on male circumcision education. Adapting learning mechanics to sensitive issues like external male genitalia education applied to the game is challenging. The Learning Mechanics and Game Mechanics (LM-GM) model framework is proposed to design a Serious Game for male circumcision education. The LM-GM model can integrate the pedagogical and game design patterns into the game mechanics. Thus, the result is the game features or functionalities linked to the pedagogical aspects. From the LM-GM result analysis, the role-playing game is chosen as the primary genre of the game. Then, It also leads to main features such as an exciting story, dialog system, a turn-based battle interaction, item collection, and an in-game quiz evaluation. Usability testing using the System Usability Scale (SUS) is crucial before exposing a game to its target audience, ensuring that even sensitive content like male circumcision is suitable. In this case, ten young fathers, all circumcised, engaged in a two-day prototype gameplay session, emphasizing the importance of ease of use for prospective users such as adolescents. The SUS result shows a promising score of 80. Based on the SUS score, the LM-GM model has the potential to be used in the game for health education.
Proposed Signal Processing Method for Continuous Respiration Monitoring using UWB Radar

Respiration rate is one of the main vital signs that can measure a subject’s health condition. To measure respiration rate, the subject needs to attach some sensors to their skin. Due to its inconvenience, the contactless respiration monitoring method is studied today. The contactless respiration monitoring methods have a disadvantage in that the subject must stay static state during measurement. However, it is hard to stay static during measurement. To solve this problem, this paper proposes a method that can monitor the nonstatic subject’s respiration rate. As a result, our proposed method shows considerable accuracy with a nonstatic subject.
STRUCTURAL AND STRATIGRAPHIC EVOLUTION OF THE TARANAKI BASIN, OFFSHORE NORTH ISLAND, NEW ZEALAND

The Taranaki Basin contains the only commercial gas and condensate fields in New Zealand. Thirteen offshore wells have been drilled, three of which delineated the Maui Field while six deep tests have been drilled onshore, one of which discovered the Kapuni Field. The geology of the Taranaki Basin is synthesised into a transgressive stratigraphic framework which was modified by two tectonic phases, initial rifting and foundering, followed by wrench faulting. The basin consists of the Western Platform and Taranaki Graben Complex. The former was a relatively stable block throughout most of the Tertiary, only affected during Late Cretaceous to Eocene times by normal block faulting. The latter is bounded to the east by the Taranaki Fault Zone which was mainly active during the Miocene. To the west, the Graben Complex generally shallows across a series of en-echelon steep normal to reverse faults which often show drastic changes in throw over short distances. Upper Cretaceous coal measures were deposited in fault angle depressions. Marine sediments were deposited in western areas by Paleocene times. A regressive phase occurred during Eocene times when coal measures were deposited in southern and eastern areas. Quartzose sandstones of these coal measures are the reservoirs in the Kapuni and Maui Fields. In Late Eocene to Oligocene times, regional submergence recommenced and mainly calcareous sediments were deposited: pelagic -rich sediments in the west, neritic limestones, sandstones and mudstones in the south and east. With the development of the Taranaki Graben Complex from the Miocene onwards, sedimentary sequences consist of graben -fill mudstones and flysch, and prograding wedges comprising the continental shelf.
Socially-Aware Opportunistic Routing with Path Segment Selection in Quantum Networks

The conventional quantum teleportation schemes enable high-security network communications by establishing end-to-end entangled paths. However, those schemes focus on time synchronization and thus cause lots of idle time. Recent research suggests adopting an opportunistic scheme to forward data qubits as far as it can. However, this scheme lacks security since data qubits may be stored at malicious repeaters, which may peek at, destroy, or fake the data qubits. To this end, we design a new scheme called SOAR that considers trusted repeaters via social networks. Moreover, SOAR promotes the parallelism of swapping processes and thus leads to a less idle time of network resources than the other existing schemes. Furthermore, we design an algorithm called SAGE that can best fit SOAR by linking multiple subpaths via appropriate trusted repeaters to get an ideal path and augmenting least-hop paths to utilize the resources in quantum networks better. Simulation results manifest that SOAR outperforms the other schemes by 54%-89%; SAGE outperforms the other routing algorithms by 50% on average on SOAR.
Dynamically Aware Mask Position for Old Films Restoration

Films restoration is a challenging yet promising task that involves using deep learning techniques to restore both structured and unstructured defects in old films. Significant progress has been made in this task by leveraging Vision Transformer and other existing technologies. However, there is still room for improvement in accurately localizing structured mask positions. Therefore, this paper proposes a novel module called the Mask-Aware (MA) module. By incorporating this module, our proposed model can achieve more precise mask positions and obtain higher-quality restoration results. Specifically, the MA module utilizes dynamic convolutions that employ attention mechanisms and deformable convolutions to enhance the ability of the model to perceive spatiotemporal information, thereby boosting the ability of the model to localize masks. Extensive experimental results demonstrate that our proposed model can achieve better handling of structural defects in old films.
Energy-Efficient Acceleration of Deep Learning based Facial Recognition on RISC-V Processor

Feature extraction at battery-powered resource-constrained end nodes is always challenging. Direct hardware mapping of a deep neural network (DNN) on an embedded platform faces difficulties in terms of computational power and memory. Hence, this work targets to accelerate deep learning MobileNet-based face recognition (FR) on RISC-V to optimize energy consumption by reducing execution time. To implement this, the Raspberry Pi-based FR system is developed to analyze the effect on recognition time and power consumption for an embedded platform compared to the CPU, for the same DL model. Recognition time increases 5.76× on Raspberry Pi compared to CPU. Hence, DNN algorithms require higher computation capabilities to reduce delay on resource-constrained embedded platforms. We have utilized a Sipeed Maixduino RISC-V artificial intelligence (AI) and Internet of Things (IoT) board. The Maixduino board can accelerate deep learning operations with the knowledge processing unit (KPU) accelerator. It was analyzed that the MobileNet-v1 0.75 on the KPU-based Maixduino RISC-V board decreases the classification time and power consumption by 30 ×and 2.86 ×, respectively, compared to CPU. Power-delay product (PDP) a.k. a. energy and energy-delay product (EDP) is an important parameter for the battery-operated IoT end nodes. For MobileNet on a RISC-V-based accelerator (Maixduino KPU), we achieved 82.75× and 2582.77× less PDP and EDP, respectively, compared to the CPU. Maixduino-based FR system with MobileNet is high-speed and energy-efficient in contrast to the current state-of-the-art (SoA)'s lightest (MobileNet-v1 0.25) model on the accelerator.
Scalable Quantification of the Value of Information for Multi-Agent Communications and Control Co-design

Task-oriented communication design (TOCD) has gained significant attention from the research community due to its numerous promising applications in domains such as $\text{IoT}$ and industry 4.0. This paper introduces an innovative approach to designing scalable task-oriented quantization and communications in cooperative multi-agent systems (MAS). Our proposed approach leverages the TOCD framework and the concept of the value of information $(\text{VoI})$ to facilitate efficient communication of quantized observations among agents while maximizing the average return performance of the MAS-a metric that measures the task effectiveness of the MAS. Learning the VoI becomes a prohibitively large computational problem as the number of agents grows in the MAS. To address this challenge, we present a three-step framework. First, we employ reinforcement learning (RL) to learn the VoI for a two-agent, rather than for the original $N$ -agent system, reducing the computational costs associated with obtaining the value of information. Next, we design the quantization policy for a MAS with N agents, utilizing the learned VoI across a range of bit-budgets. The resulting quantization strategy for agents' observations, ensures that more valuable observations are communicated with greater precision. Finally, we apply RL to learn the agents' control policies, while adhering to the quantization policies designed in the previous step. Our analytical results showcase the effectiveness of the proposed framework across a wide range of problems. Numerical experiments demonstrate improvements in reducing the computational complexity required for obtaining VoI by five orders of magnitude in TOCD for MAS problems while compromising less than 1% on the average return performance of the MAS.
Spatio-Temporal Consistent Non-homogeneous Extreme Video Retargeting

Due to the availability of heterogeneous display devices and their aspect ratios, video retargeting has received considerable research attention among researchers. Non-consistent video retargeting can significantly affect a video’s spatial and temporal quality, particularly for extreme retargeting cases. Since no perfectly annotated datasets exist for video retargeting, deep learning-based techniques are rarely utilized. This paper proposes a method that learns to retarget videos by detecting the salient areas and shifting them to the appropriate location. First, we segment the salient objects using a unified Transformer model. Using convolutional layers and a shifting strategy, we shift and warp objects to the suitable size and location in the frame. We use 1D convolution for shifting the salient objects. We also use a frame interpolation technique to preserve temporal information. To train the network, we feed the retargeted frames to a variational auto-encoder network to map the retargeted frames back to the input frames. Besides, we design perceptual and wavelet-based loss functions to train our model. Thus, we train the network unsupervised. Extensive qualitative and quantitative experiments and ablation studies on the DAVIS dataset show the superiority of the proposed method over the existing state-of-the-art methods.
Eight-Element Low-Profile MIMO System for Thin 5G Smartphone Applications

This study introduces an eight element antenna array for ultrathin smartphones that works on LTE 42 band(3.4GHz3.6GHz). On the two side frames, which measure 140 $\times 4 \times 0.8$ mm3, the antenna elements are evenly distributed. The MIMO array is placed on the circuit board of dimension 150 $\times 75 \times 0.8$ mm3. There is enough space between antennas to achieve 13.5dB isolation. Efficiency is also better than 51%. The proposed design an excellent option for ultrathin smartphone applications as it provides high isolation and compact size.
Students’ Satisfaction in Online Class during COVID-19: An Empirical Study in Bangladesh Context

The COVID-19 pandemic caused higher education institutions to switch to online learning. This unique endeavor is being investigated to protect high-quality education. Due to its hasty and emergency implementation, students may have different opinions about this new education system. The primary objective of this study is to investigate the relationships between constructs such as students’ perceived effectiveness, institutional facility, teaching material, and overall satisfaction during the COVID-19 outbreak. Adopting a quantitative research approach, this study was conducted between November 8, 2020, and February 12, 2021, in private universities in Bangladesh. Data were utilized in structural equation modeling (SEM). The findings revealed that perceived effectiveness (0.68, p-value = 0.01), institutional facility (0.23, p-value = 0.01), and teaching material (0.14, p-value = 0.01) are all positively and significantly associated with satisfaction with an online class module. In addition, trajectory analysis establishes a link between satisfaction and effectiveness in e-learning through the SEM. The results of the study also indicate a strong and positive correlation between satisfaction with an online class module and perceived effectiveness, institutional setting, and teaching material. This study will serve as a catalyst for understanding students’ satisfaction in an online environment, as well as a benchmark for various stakeholders, particularly policymakers, practitioners, academicians, and institution owners, to take the necessary steps to ensure a sustainable online learning platform.
Analog/RF Performance of Oppositely Doped Core-Shell Junctionless Nanowire FET with Dual Material Gate and High-k Spacer

The impact of integrating Dual Material Gate (DMG) and high-k spacer on analog/RF characteristics of Oppositely Doped Core-Shell Junctionless Nanowire FET (ODCS-JLNWFET) is explored in this paper. The primary objective of this analysis is to assess the electrical as well as analog/RF performance of the proposed device (DMG-ODCS-JLNWFET with spacer) using various metrics such as subthreshold slope (SS), transconductance (gm), switching ratio (ION/IOFF), Transconductance Generation Factor (TGF), early voltage (VEA), output conductance (gd), total gate capacitance (CGG), and cut-off frequency (fT). The performance of DMG-ODCS-JLNWFET with spacer is compared with DMG-ODCS-JLNWFET without spacer, Single Material Gate (SMG) ODCS-JLNWFET with and without spacer. The results demonstrate enhanced electrical and analog/RF performance of the proposed device, offering valuable insights into its potential applications in the analog and digital domains at the nanoscale.
Thermal Effects of Lightning Currents in Metal Chains

This paper investigates the thermal effects of lightning currents on metal chains and answers whether wearing a metal chain necklace can save a person's life or potentially worsen their injuries. Equations are developed to model the final temperature of wires of several materials when subjected to lightning current. A wire-chain transformation factor is introduced to relate the thermal behavior of a wire to a chain, to extend the wire model to chains. The wire model has a maximum error of 7.8% when skin effects are considered. The experimental results found that chains exhibit arcing and exploding behavior, while wires melt. For the same material, thicker wires melt at a larger peak current than thin wires, while chains explode at a much lower current than wires melt, for the same material and gross diameter. This paper concludes that wearing a metal object might help channel a part of the current away from vital organs. Still, it does not guarantee complete safety and may potentially worsen burn-related injuries.
Single Image Super-Resolution using Information Augmentation

Spatial Super-Resolution (SR) of images is applied in a multitude of domains. These include enhancing the resolution of old images, microscopic images, telescopic images for remote sensing and astronomical observations. This paper focuses on single image spatial resolution problem using deep learning technique in remotely sensed images. In particular, edge augmentation technique is used as an augmentation technique to increase information content, following which, SR task is carried out using a modification of one of the standard deep learning models – Enhanced Deep Super Resolution (EDSR). The obtained results are compared to the original model to assess the improvement in performance.
On the Profitability of Selfish Mining Attack Under the Checkpoint Mechanism

Though designed with security in mind, blockchains are vulnerable to various kinds of attacks, especially when the network computational power is low. Selfish mining is one of the most rudimentary and notorious attacks, which maliciously renders blocks found by honest miners orphaned by strategically withholding and revealing the found blocks. In this paper, we analyze the profitability of selfish mining under the checkpoint mechanism—a mechanism that has been adopted as a finality gadget by many blockchains like Ethereum and Bitcoin Cash. We develop a rigorous analysis method and conduct quantitative evaluations in various scenarios to explore the mechanism's suppression effect on selfish mining. The results illustrate that the checkpoint mechanism can restrict the profit of selfish mining and increase the threshold of computational power that makes selfish mining profitable, suggesting that it is a practical defense mechanism against selfish mining.
A Comparative Analysis of Deep Learning Approaches in Bangla Document Categorization

The categorization of documents refers to the process of determining which category a given document falls within. In this paper, a new approach for categorizing bangla text documents is presented. This method utilizes Recurrent Neural Network and Recurrent Neural Network with attention layer models for classifying bangla text. This proposed method can be broken down into three distinct stages that occur in that order. In the beginning, the data that had been collected was processed into a bangla text document. Second, by building the architecture of the model, the training data was integrated into the model. At last, the performance of the model was assessed by computing its accuracy and F1 score on the testing dataset. The RNN Attention layer achieved an accuracy of 89.14% for 10 categories, and the RNN with BiLSTM achieved an accuracy of 70.39% for 12 categories. The percentage of accuracy improves when just certain types of articles are utilized, but since we have used numerous types of articles, the accuracy is low but this accuracy is more practical in our everyday lives.
Air-temperature control on diurnal variations in microseismicity at Laohugou Glacier No. 12, Qilian Mountains

ABSTRACT We conducted a 9-d seismic experiment in October 2015 at Laohugou Glacier No. 12. We identified microseismic signals using the short-term/long-term average trigger algorithm at four stations and classified them as long and short-duration events based on waveform, frequency, duration and magnitude characteristics. Both categories show systematical diurnal trends. The long-duration events are low-frequency tremor-like events that mainly occurred during the daytime with only several events per day. These events lasted tens of seconds to tens of minutes and are likely related to resonance of daytime meltwater. The dominant short-duration events mostly occurred during the night time with a peak occurrence frequency of ~360 h−1. Their short-duration (<0.2 s), high frequency (20–100 Hz) and dominance of Rayleigh waves are typical of events for near-surface crack opening. A strong negative correlation between the hourly event number and temperature change rate suggests that the occurrence of night-time events is controlled by the rate of night-time cooling. We estimated the near-surface tensile stress due to thermal contraction at night to be tens of kilopascals, which is enough to induce opening of surface cracks with pre-existing local stress concentrations, although we cannot exclude the effect of refreezing of meltwater produced during the day.
Unveiling Altered Concern in Women-Related Tweets: A Topic Modeling Analysis

In the dynamic realm of digital discourse, understanding the evolution of conversations is crucial. With the advent of social media, there has been increased attention to discussions on women’s issues and gender equality. A significant event in this domain is the 2017 protest against sexual harassment, which originated as a powerful social campaign. This paper analyzes the impact of this protest on the tweets surrounding women’s involvement and achievements, particularly in STEM (Science, Technology, Engineering, and Mathematics) disciplines. The primary challenge was data collection, given the constraints imposed by Twitter on this process. The research was initiated by deploying the BERTweet classification model, which effectively distinguished tweets related to women. The model’s performance was rigorously evaluated with a manually classified test set, and it has showcased impressive results of accuracy (99.65%), precision (99.50%), recall (99.79%), and F1 score (99.65%), respectively. Later the prominent topics were extracted by employing Structural Topic Modeling (STM). Statistical measures show that the topic concerning women had a noticeable change in proportion during the post-2017 period. To deepen insights, topic clustering and correlation analysis were exclusively conducted on women-related tweets. Overall, this paper provides a comprehensive view of the influence of a significant event on the evolving digital discourse.
Evaluation of IDS model by improving accuracy and reducing overfitting using stacking LSTM

Utilizing Machine Learning (ML) techniques to construct models for Intrusion Detection Systems (IDS) has emerged as a leading strategy to enhance security measures. Previously, we applied an ensemble of Support Vector Machines (SVM) for model training, achieving an accuracy detection rate of 88.6% on familiar datasets. However, it performs poorly on unknown datasets due to overfitting problems. This work aims to improve accuracy and tackle overfitting problems by implementing stacked Long Short-Term Memory (LSTM) networks. Furthermore, analysis of variance (ANOVA) with F-test is used to find the important feature. The synthetic minority oversampling technique (SMOTE) algorithm is applied to obtain balanced data. Several experiments were conducted to analyze the performance of the proposed approach, including investigating stacked LSTM with tuning hyperparameters. The proposed method achieved the highest prediction accuracy of 97.23% and an overfitting value of 0.24%.
Detection of Jupiter decametric emissions controlled by Europa and Ganymede with Voyager/PRA and Cassini/RPWS

The Jovian high‐latitude radio emissions produced by Jupiter's magnetosphere extend from a few kilohertz to 40 MHz. Part of the decametric (DAM) emissions are driven by the Galilean moon Io (Io‐DAM). As UV aurorae have been detected at the footprint of Europa and Ganymede, we expect that these moons drive Jovian radio emissions as well. To check this assumption, we used the ExPRES simulation code (Exoplanetary and Planetary Radio Emissions Simulator) to predict dynamic spectrum (time‐frequency spectograms) of the radio emissions controlled by the four Galilean moons. Then we compared the simulations to the Voyager/PRA and Cassini/RPWS radio observations of Jupiter (1979, and between 2000 and 2003, respectively). We present the first clear evidence for the existence of decametric emissions controlled by Europa and Ganymede. Their statistical analysis allows us to describe the average properties of the Europa‐DAM and Ganymede‐DAM emissions such as their spectrum, temporal variability, and occurrence as a function of moon phase and subobserver's longitude.