Miracle-dz/UniFAR-FTUs huggingface dataset:
Decoding Gender and Musical Expertise From Brain Responses to Music: A Comparison of Functional Connectivity Measures

Individual differences, encompassing a wide array of physical, cognitive, and emotional characteristics such as age, gender, personality, musical expertise, and empathy, play a pivotal role in shaping our interactions with the world around us. These differences, stable over time and across situations, not only influence our end goals but also the processes through which we perceive and interact with them. Recent research in neuroscience has brought to light that each individual possesses unique and fundamentally stable functional brain connections. These connections remain consistent, irrespective of the task at hand, suggesting that functional brain networks could potentially be employed as a measure of stable individual traits. Such an approach could revolutionize personalized medicine, offering tailored therapeutic interventions based on an individual’s unique brain signature. In the context of music, distinct patterns in functional brain networks related to individual differences become crucial. Music, as a continuous task, offers a naturalistic paradigm to explore these patterns. This study aims to bridge the gap in the existing literature by examining how brain responses to music may vary based on individual characteristics, such as gender and musical expertise. Furthermore, previous studies have predominantly focused on Pearson correlation, which captures linear relationships but may not encapsulate the full complexity of functional brain networks. Therefore, our study explores various functional connectivity (FC) measures. By comparing these measures, we aim to understand which ones are most suitable for a given study, ensuring that the chosen measures capture the intricacies of FC effectively. The study utilized data from the ”Tunteet” project, which involved multiple fMRI scans and behavioral tests. A total of 36 participants underwent fMRI scanning while listening to three distinct 8-minute-long musical pieces representing different musical styles. Their responses were analyzed us-ing various temporal and spectral FC measures. The aim was to compare these FC measures to identify the one that captured the most variation associated with gender and musical expertise. Subsequently, a binary Support Vector Machine (SVM) was employed to classify distinct population groupings. Our results align with previous research, suggesting that musical preferences can indeed be considered as a distinct personality trait. This was particularly evident in the differences in liking ratings specific to gender and musical expertise. We also found that Coherence, a spectral-domain FC measure, captured the maximum variation for musical stimuli. However, when classifying
Proliferation of health misinformation on social media platforms: Proliferation of health misinformation on social media platforms: a systematic literature review a systematic literature review

This paper presents a systematic literature review to provide a conceptual framework and future research directions for the identification and detection of health misinformation on social networking sites. We have found the health misinformation problem is yet to evolve and requires more exploration towards the identification of reliable techniques. We have extracted the existing literature from five research databases. The focus of this review is to explore various concepts, techniques, and datasets adopted by the prior studies. After applying Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines we have got 27 final papers to include in this review. The findings show that despite adopting various techniques there was a lack of research done on concepts like fake news and healthcare fact-checking. Furthermore, this paper also identified significant feature and technique-based research gaps for future research directions.
Exploring the Linkages between Animal Abuse, Domestic Abuse, and Sexual Offending: A Scoping Review

Te abuse of animals is evident in diferent contexts. Tis article takes two discrete bodies of literature: threats and actual harm to animals in the context of domestic abuse and sex ofending in the context of animal abuse. Te reviews focus on the implications for risk assessment and risk management and explore the following questions: What is the international evidence base for threats or violence towards animals, with a specifc focus on risk assessment, in the context of domestic abuse? What is the international evidence base for threats or violence towards animals, with a specifc focus on risk assessment, in the context of sex ofending? What similarities and diferences are evident in the approaches in the two bodies of literature? A scoping review methodology was applied to the two bodies of animal abuse literature. Te domestic abuse literature elicited 27 studies for full inclusion, while the sex-ofending literature examined 15 papers. A broad analysis of the literature was taken to understand the implications for risk assessment and risk management. In the context of risk assessment, the literature indicates that threats or harm towards animals, including the sex abuse of animals, has the potential to be used in four ways: (1) animal abuse as an indicator of domestic abuse; (2) childhood animal abuse as a signal of other problems within a household; (3) animal abuse as an indicator of more severe domestic abuse; and (4) the inclusion of pet abuse as a unique form of domestic abuse. Te comparison of the literature on animal abuse and animal sex abuse highlighted not only diferent approaches theoretically and methodologically but also important similarities. Both literature highlighted gaps in risk assessment, risk management, and the lack of identifcation and training in this neglected area of the service system prevention and response.
BULLY NET: UNMASKING CYBER BULLIES ON SOCIAL NETWORKS

In the rapidly evolving landscape of online communication, the surge in cyberbullying has emerged as a critical challenge, necessitating innovative solutions for detection and prevention. Existing approaches often reply on simplistic keyword-based filters or rule-based methods, struggling to keep pace with the dynamic nature of cyberbullying scenarios. The intricate and varied nature of online harassment demands a more sophisticated system capable of discerning subtle nuances within social media interactions. Recognizing this gap, the proposed BullyNet system introduces a character-level convolutional neural network (CNN) approach to enhance the accuracy and adaptability of cyberbullying detection. By incorporating both word-based and character-based models, BullyNet aims to provide a holistic understanding of language expression and contextual cues, offering a nuanced solution to the complex challenges posed by cyberbullying. This system's multifaceted approach, encompassing preprocessing, training, and evaluation of CNN models, is designed to address the shortcomings of existing systems and contribute to the creation of a safer online environment. BullyNet
Forensic seismic evidence for precursory mobilization in Gaza leading to the October 7 terrorist attack

Seismic waves excited by human activity frequently mask signals due to tectonic processes, and 6 are therefore discarded as nuisance. Seismic noise-ﬁeld analysis is, however, a powerful tool for 7 characterizing anthropogenic activities. Here, I apply this analysis to examine seismic precursors 8 to the October 7 Hamas attack on Israel. The precursory activity in Gaza included massive 9 mobilization which took place in the hours leading to the attack, and was documented on multiple 10 media outlets. Favourable conditions, which arise due to a temporary lack of anthropogenic 11 activity in Israel, allow remote seismic stations to record signals due to Gaza vehicle traﬃc. 12 I use these seismograms in order to identify anomalous ground-motions, associate them with 13 pre-attack mobilization, and precisely determine their location. By applying array analysis to 14 three seismic stations located tens-of-kilometers from the Gaza strip, I was able to obtain valuable 15 information on the Hamas attack plans. This suggests that embedding seismic noise-ﬁeld analysis 16 into decision-making protocols could enhance preparedness, thus providing an opportunity to 17 blunt terrorist attacks and reduce the number of casualties. 18
The Relationship Between Embedding and Deductive Reasoning Ability on BERT

BERT is a pre-trained language model that has been shown to be effective for a variety of tasks, including natural language inference (NLI) and textual entailment. However, it is not clear how well BERT can identify logical correctness or inconsistency. In this paper, we investigate the ability of BERT to distinguish between logically correct sentences and those with logical contradictions. We perform this by comparing the similarity scores of these two types of sentences using a variety of BERT models. Our results were conducted to understand the relationship between BERT embeddings and ability of logical reasoning. To this end, we identified that BERT is able to capture some aspects of logical relationships between words or sentences. However, our results also show that the logical reasoning ability of BERT varies depending on the complexity of the logic involved. Our findings contribute to the understanding of how language models can be used for deductive reasoning tasks. In addition, we are the first to introduce a new zero-shot evaluation dataset for deductive reasoning. We believe that our works could be used to improve the performance of language models for these tasks.
Investigating Impact of 5G Mobility Management Over Handover

The advent of 5G and other technological advancements will provide enhanced mobile broadband, significantly reduced latency in communications, and the accommodation of larger mobile devices. This implies that, in the case of users with exceptionally fast speeds, it is imperative to ensure a seamless transition between cellular networks. The effective management of dense networks is crucial due to the significant presence of a multitude of mobile devices. In addition, the time-to-trigger(TTT) and hysteresis margins, which significantly impact the delay in handover and overall throughput, necessitate the need for dynamic adaptability. In this paper, we present a novel approach for addressing the issue of mobility management in the context of 5G. Hence, the objective of this research is to ascertain the optimal target cell for utilization as an advanced 5G Node B in order to address this issue. The selection of a target cell in this approach is determined by various parameters, including as the quality of the received signal, the power of the received signal, and the uplink signal-to-interference-plus-noise ratio (SINR). After the selection of a 5G node, user equipment (UE) within the designated cell will initiate a handover (HO) process to connect with the most optimal 5G node available. The simulation results demonstrate an improvement in the performance indicators of the anticipated handover scheme, namely in terms of handover failures, handover ping-pong, and packet loss.
Pothole Detection Using Transfer Learning Models: A Comparative Study

In this fast-paced modern world potholes are considered as some random holes on the surface of the roads and are considered as mere obstacles while traveling. But reality is much harsher than these considerations as these mere potholes are solely responsible for a significant amount of road accidents which involve hundreds of deaths and much higher property damages. This is a detailed comparative study of some popular deep-learning algorithms. The main objective of this comparative study is to find a better solution to tackle the pothole problem faced by the countries whose economy is based mostly on transport systems. The base model of these algorithms is tweaked to bring out their best results on the used dataset. Results are decided based on the output accuracy delivered by the respective algorithms. These algorithms include CNN, VGG19, VGG16, InceptionResNetV2, InceptionV3, MobileNetV2, and Xception. The MobileNetV2 with layer freezing has emerged as the best of all models used in this study with an accuracy of 96.37%. It has also taken the least computational time for each image.
Integrating Velox into TinkerPop for Graph Queries on Vectorized Engine

To enhance the query efficiency of relational databases and build a unified computing backend, Meta has developed Velox, a vectorized execution engine library based on columnar storage, Currently, there is no standardized specification for computation engine, and storage in graph databases, leading to failed to effectively utilize the vectorized processing capability of modern CPU. In this paper, we propose a middleware that primarily focuses on (1) non-invasively integrating Velox into the TinkerPop framework to provide unified vectorized engine acceleration for all graph databases supporting the TinkerPop specification; (2) conducting graph queries based on the relational data storage model, eliminating the overhead of transforming the storage model into a graph storage model; (3) validating the acceleration effect of the vectorized engine on interactive workload of graph queries under a single-node environment.
Handwriting Recognition System Using YOLO and CTC

In a multitude of applications, handwriting recognition holds significant importance, including tasks like transforming handwritten documents into digital format, automating form processing, and intelligent character recognition. This paper introduces a novel handwriting recognition system that integrates the You Only Look Once (YOLO) object detection algorithm with the Simple Handwritten Text Recognition (HTR) model. The YOLO algorithm is widely recognized for its ability to perform real-time object detection. By adapting it to the task of handwriting recognition, we can detect and localize handwritten text regions within images. The YOLO algorithm is trained on a large dataset of annotated images, enabling it to learn features specific to handwriting. Once the text regions are detected, the HTR model is employed for accurate recognition of the detected text. HTR is a deep learning-based approach that leverages recurrent neural networks (RNNs) to transcribe handwritten text into machine-readable format. The model is trained on a diverse set of labeled handwriting samples, enabling it to effectively recognize and convert handwritten characters into digital text. Our proposed system combines the strengths of YOLO's robust object detection capabilities with HTR's accurate text recognition. The integration of these two components allows for efficient and reliable handwriting recognition, even in challenging scenarios with varying handwriting styles and image qualities. To evaluate the performance of our system, we conducted experiments on benchmark datasets and compared the results with existing approaches. The experimental results demonstrate that our system achieves superior performance in terms of both detection accuracy and recognition accuracy, surpassing state-of-the-art methods. Overall, our proposed handwriting recognition system utilizing YOLO and HTR presents a robust and effective solution for converting handwritten text into digital format. This system can be applied in numerous real-world applications, contributing to the automation and digitization of handwritten documents, streamlining administrative processes, and facilitating efficient information retrieval.
Estimation of supplemental irrigation for Aman rice cultivation in Bogra and Dinajpur districts of Bangladesh

A study was carried out to analyze the rainfall and evapotranspiration for successful planning of two aman rice varieties i.e., BRRI dhan33 and BRRI dhan34 in Bogra and Dinajpur districts of Bangladesh. Reference crop evapotranspiration (ET 0 ) was determined by employing the FAO Penman-Monteith method. By multiplying ET 0 by crop coefficient (Kc) actual evapotranspiration of the two rice varieties at different growth stages was determined. Probability analysis was done by Weibull’s method and the expected rainfall and actual evapotranspiration at 75% probability level were estimated for different growth stages of BRRI dhan33 and BRRI dhan34 for 14 years. The results revealed that the ETc varied over the total growing season. After probability analysis, the supplemental irrigation was calculated for BRRI dhan33 during mid and late stages; but for BRRI dhan34, it was calculated during development, mid and late stages in Bogra and Dinajpur districts. The study was found quite effective to assess the water availability period for aman cultivation and to indicate when supplemental irrigation is necessary. These results may enable to optimize utilization of valuable water resources and will help to create an irrigation schedule for BRRI dhan33 and BRRI dhan34 for their successful cultivation. Progressive Agriculture 28 (1): 42-54, 2017
Reinforcement Learning-Based Task Offloading of MEC-Assisted UAVs in Precision Agriculture

Recently, mobile edge computing (MEC) assisted unmanned aerial vehicles (UAVs) have brought a revolution to the existing precision agriculture (PA). The target UAVs can execute various PA tasks with different heterogeneous resource requirements on the farm. However, due to the stringent service deadline of PA tasks and the battery limitation of UAVs, one of the promising solutions is to offload those computation tasks to MEC servers jointly. This paper explores the MEC-assisted task offloading problem with multi-UAVs under different deadline constraints in uncertain real-world environments. The diverse requirements of PA tasks, the heterogeneous network status, and the dynamic loading of MEC edge servers make the offloading decision an NP-hard problem. Therefore, we propose a reinforcement learning (RL)-based task offloading approach, BANDIT-SCH, to minimize total MEC system costs to achieve online task dispatching and scheduling in uncertain environments without further global information. The experiment results show that the performance of BANDIT-SCH is approximate to the upper bound strategy, which can foresee all edge servers' detailed status.
Position Interleaved Pulse Modulation for Terahertz Communications

Pulse-based communication systems are essential for terahertz (THz) communications, offering exceptional performance in diverse application scenarios, such as nanonetworks, THz integrated sensing and communications, and short-range ultra-high-speed communications. However, classical pulse-based modulations, such as time spread on-off keying (TS-OOK), pulse amplitude modulation (PAM), and pulse position modulation (PPM), suffer from limited capacity, mainly due to the absence of effective high-order modulation schemes and the long duration of consecutive transmission between adjacent pulses. Although these modulations have low energy consumption, they are difficult to balance the relationship between capacity and energy consumption. Recently, the generation of THz frequency and bandwidth continuously tunable (FBCT) pulses has enabled pulse-based M-ary QAM (PMQAM) to improve capacity, but it increases energy consumption compared with the classical modulations. To address these issues, an efficient high-order modulation method called position interleaved pulse modulation (PIPM) is proposed in this paper. More specifically, PIPM increases the dimension of modulation by using multiple pulse positions and interleaves the odd and even components of FBCT orthogonal pulses at these positions. In this design, PIPM utilizes the information from the odd component, even component, and pulse position to achieve high-order modulation. Furthermore, PIPM can balance the capacity and energy consumption by flexibly adjusting the relationship between the three-dimensional information. Detailed analysis of PIPM and its performance under multiple access scenarios is performed in this paper. Numerical results show that PIMP achieves a much better balance between energy efficiency and spectrum efficiency, as compared to existing schemes.
Agnostic Prediction of Multiple Types of Time-Varying Traffic in Optical Networks

Relentless competition among communications ser-vice providers, increasing expectations of users, and escalating variety of new applications and services trigger the need to develop advanced solutions that can support the optimization and management of communication networks. Prediction of network traffic is one of the possible solutions providing additional data analytics information. In this paper, we consider an application-aware optical network that transmits various types of time-varying traffic. We analyze traffic prediction under two scenarios. As a reference scenario, we assume that the system is aware of multiple traffic types, and various prediction models are developed and trained for each traffic type separately. The second scenario - agnostic prediction - assumes that a single prediction model agnostic of the traffic types is created and trained. We develop several models for each of the analyzed scenarios using various regression methods. Next, we run extensive numerical experiments on real and semi-synthetic datasets to verify the performance of the proposed regression methods and compare both analyzed scenarios. The obtained results demonstrate that the proposed prediction model agnostic to the forecasted type of traffic provides excellent results; in many cases, it outperforms the reference scenario with dedicated prediction models for each traffic type. Moreover, we evaluate the proposed model's adaptability to predict unseen-before traffic types, showing that the quality loss is negligible. Finally, we test the proposed framework in a multilayer network with time-varying traffic and show how using an aggregated model does not lead to bandwidth blocking increase compared to dedicated prediction models.
MoE-ESRGAN: A Super-Resolution Network for Multi-degraded Chinese Painting Images

The conventional super-resolution algorithms are unable to fully address the Chinese painting images that contain some artifacts and were taken many years ago. In this paper, we propose a novel SR model named MoE-ESRGAN for restoring multi-degraded Chinese painting images based on the structure of ESRGAN. To simulate the degradation of real-world Chinese painting images, this paper uses multi-degradation model, including bicubic downsampling, blur and noise, to obtain low-resolution images as our dataset. This paper further designs a new generator network by incorporates a set of Mixture-of-Experts (MoE) to enhance the network’s ability for mapping from low-resolution (LR) images to high-resolution (HR) images. Meanwhile, we assign the specific guidance to each expert in MoE to learn different image features. Based on the self-built Chinese painting image dataset, we compare the performance of our model with various models through experiments. Experimental results demonstrate that, compared with ESRGAN, our model has an average increase of 0.7 dB in peak signal-to-noise ratio (PSNR) and an average increase of 0.03 in structural similarity (SSIM). Meanwhile, our MoE-ESRGAN model exhibits high effectiveness in image denoising and deblurring. Furthermore, the reconstructed images of our model are subjectively more in line with human visual standards.