Miracle-dz/UniFAR-FTUs huggingface dataset:
Depressive Disorder Remote Detection through Touchscreen Typing Behaviour

Depressive Disorder (DD) is a leading cause of disability worldwide. Passive tools for screening the symptoms of DD are essential in monitoring and limiting the spread of the disease. From an alternative perspective, individuals’ kinetic expression and activities, including smartphone interaction, reflect their mental status. Such widely available data in everyday life form a promising source of information on keystroke dynamics and their characteristics. This work explores how keystroke dynamics derived from touchscreen typing patterns have revealed the diagnosis of mental disorders, particularly depressive disorders. Different deep learning approaches were established to detect patients’ depressive tendencies denoted by the self-reported Patient Health Questionnaire-9 (PHQ-9) score based on keystroke digital biomarkers. In particular, Convolutional Neural Networks (CNN), Long-Short-Term-Memory (LSTM), and CNN-LSTM models were examined and compared. The keystroke sequences are captured unobtrusively during routine interaction with touchscreen smartphones in a non-clinical setting. This study used 23,264 typing sessions provided by 10 DD patients and 14 healthy controls (HC). The proposed approach was investigated under two keystroke feature combinations and validated utilizing a leave-one-subject-out (LOSO) cross-validation scheme. The best-performing LSTM-with-hold-time (LSTM-HT) model achieved an Area Under Curve (AUC) of 0.86 with the correlated probabilities for subjects’ status [95% confidence interval (CI):0.66–1.00, sensitivity/specificity (SE/SP) of 0.8/0.93]. Clinical relevance— The findings of this research have the potential to contribute to improving digital tools for objectively screening mental disorders in the wild. Moreover, they would potentially provide the users and their attending psychiatrists with information regarding the evolution of their mental health.
A Deep Learning Framework for Skull Stripping in Brain MRI

Skull-stripping, an important pre-processing step in neuroimage computing, involves the automated removal of non-brain anatomy (such as the skull, eyes, and ears) from brain images to facilitate brain segmentation and analysis. Manual segmentation is still practiced, but it is time-consuming and highly dependent on the expertise of clinicians or image analysts. Prior studies have developed various skull-stripping algorithms that perform well on brains with mild or no structural abnormalities. Nonetheless, they were not able to address the issue for brains with significant morphological changes, such as those caused by brain tumors, particularly when the tumors are located near the skull’s border. In such cases, a portion of the normal brain may be stripped, or the reverse may occur during skull stripping. To address this limitation, we propose to use a novel deep learning framework based on nnUNet for skull stripping in brain MRI. Two publicly available datasets were used to evaluate the proposed method, including a normal brain MRI dataset - The Neurofeedback Skull-stripped Repository (NFBS), and a brain tumor MRI dataset - The Cancer Genome Atlas (TCGA). The method proposed in the study performed better than six other current methods, namely BSE, ROBEX, UNet, SC-UNet, MV-UNet, and 3D U-Net. The proposed method achieved an average Dice coefficient of 0.9960, a sensitivity of 0.9999, and a specificity of 0.9996 on the NFBS dataset, and an average Dice coefficient of 0.9296, a sensitivity of 0.9288, a specificity of 0.9866 and an accuracy of 0.9762 on the TCGA brain tumor dataset.
A Reliable Antenna Array for the 28GHz mmWave Band in a 5G Massive MIMO Communication

The high communication performance specifications of the 5G wireless cellular communication standards require electronic components that can reliably support such standards. One such component is the antenna. The antenna is one of the most useful components in wireless communication, and hence, its performance heavily influences the performance of a wireless communication system. In this study, we propose a planar 10×10 microstrip patch antenna (MPA) array for a 5G massive multiple inputs multiple outputs (mMIMO) wireless communication. The antenna was designed and simulated using Pathwave Advanced Design System (ADS), and it achieves a gain of 21.97dBi, radiation efficiency of 0.93, bandwidth of 0.45GHz, and beamwidth of 0.76rad on the 28GHz millimeter wave (mmWave) band of 5G mMIMO communication.
Dynamic spectrum anti-jamming with distributed learning and transfer learning

Physical-layer security issues in wireless systems have attracted great attention. In this paper, we investigate the spectrum anti-jamming (AJ) problem for data transmissions between devices. Considering fast-changing physical-layer jamming attacks in the time/frequency domain, frequency resources have to be configured for devices in advance with unknown jamming patterns (i.e. the time-frequency distribution of the jamming signals) to avoid jamming signals emitted by malicious devices. This process can be formulated as a Markov decision process and solved by reinforcement learning (RL). Unfortunately, state-of-the-art RL methods may put pressure on the system which has limited computing resources. As a result, we propose a novel RL, by integrating the asynchronous advantage actor-critic (A3C) approach with the kernel method to learn a flexible frequency pre-configuration policy. Moreover, in the presence of time-varying jamming patterns, the traditional AJ strategy can not adapt to the dynamic interference strategy. To handle this issue, we design a kernel-based feature transfer learning method to adjust the structure of the policy function online. Simulation results reveal that our proposed approach can significantly outperform various baselines, in terms of the average normalized throughput and the convergence speed of policy learning.
University of Birmingham GMM-VRD

—Concept drift is a change in the joint probability distribution of the problem. This term can be subdivided into two types: real drifts that affect the conditional probabilities p ( y | x ) or virtual drifts that affect the unconditional probability distribution p ( x ) . Most existing work focuses on dealing with real concept drifts. However, virtual drifts can also cause degradation in predictive performance, requiring mechanisms to be tackled. Moreover, as virtual drifts frequently mean that part of the old knowledge remains useful, they require different strategies from real drifts to be effectively tackled. Motivated on this, we propose an approach called Gaussian Mixture Model for Dealing With Virtual and Real Concept Drifts (GMM-VRD), which updates and creates Gaussians to tackle virtual drifts and resets the system to deal with real drifts. The main results show that the proposed approach obtained the best results, in terms of average accuracy, in relation to the literature methods, which propose to solve that same problem. In terms of accuracy over time, the proposed approach showed lower degradation on concept drifts, which indicates that the proposed approach was efﬁcient.
Leveraging Knowledge Diversity in Hierarchically Differentiated Teams

Past research has been equivocal about the information sharing and performance effects of knowledge diversity in teams. In an attempt to resolve this equivocality, scholars have suggested that status hierarchy may play a role, proposing that status differences can constrict team-level information sharing in diverse teams by privileging the knowledge of higher-status members and discounting the knowledge of lower-status members. Although there is some evidence to support this notion, there is also evidence to suggest that status differences may amplify information sharing in diverse teams by offering status enhancement incentives to members who share unique knowledge. In this paper, we reconcile these different predictions by suggesting that the effects of status hierarchy on the relationship between knowledge diversity and team information sharing will depend on the stability of the hierarchy. Using a diverse sample of 156 teams across 110 organizations, we found that status differences constricted information sharing in knowledge-diverse teams when hierarchy stability was high and amplified information sharing in knowledge-diverse teams when hierarchy stability was low. These information sharing effects, in turn, affected team performance. Our study highlights how hierarchy stability is critical for understanding whether status differences constrict or amplify information sharing in knowledge-diverse teams.
Emotionally Specific Backchanneling in Social Human-Robot Interaction and Human-Human Interaction

Backchanneling models, designed to enhance the interactive capabilities of robots, have primarily been trained on human-human interaction data. However, applying such data directly to social robots raises concerns due to dissimilarities in the way humans and robots exhibit verbal and nonverbal behaviors, particularly in the domain of emotional backchannels. This research aims to address this gap by conducting an exploratory study on the differences in human backchanneling behaviors during interactions with humans and social robots in various emotional contexts (e.g., happy and sad). Our findings reveal significant variations in emotionally specific backchannels between human-human and human-robot interactions under different emotional contexts. These results highlight the importance of designing backchanneling models that are tailored for human-robot interactions.
SDN-Enabled Context-Aware IoT Framework for Energy and Resource Management in Smart Buildings

Smart buildings have been getting a lot of interest because of their potential to improve energy efficiency and maximize resource usage. Incorporating software-defined networks (SDN), machine learning methods, and air quality monitoring, this research aims to create a system for managing energy and resources in smart buildings. The proposed solution controls the HVAC (Heating, Ventilating, and Air Conditioning) systems to respond to changes in the air quality, temperature, and humidity in real-time. The study kicks off with the deployment of a software-defined network (SDN) architecture, with the SDN controller acting as the hub for all network management and control. As a result, the building’s actuators, sensors, and HVAC system communicate and coordinate with one another more effectively. NFV (Network Function Virtualization) within the SDN architecture is used to set up a Fog node, which performs data processing locally and reduces latency.
Implants talk to each-other: RF heating changes when two DBS leads are present simultaneously during MRI

Deep brain stimulation (DBS) has proven to be an effective treatment for Parkinson’s disease and other brain disorders. The procedure often involves implanting two elongated leads aimed at specific brain nuclei in both the left and right hemispheres. However, evaluating the safety of MRI in patients with such implants has only been done on an individual lead basis, ignoring the possibility of crosstalk between the leads. This study evaluates the impact of crosstalk on power deposition at the lead tip through numerical simulation and phantom experiments. We used CT images to obtain patient-specific lead trajectories and compared the power deposition at the lead tip in cases with bilateral and unilateral DBS implants. Our results indicate that the RF power deposition at the lead tip can vary by up to 6-fold when two DBS leads are present together compared to when only one lead is present. Experimental measurements in a simplified case of two lead-only DBS systems confirmed the existence of crosstalk.Clinical Relevance—Our results indicate that RF heating of implanted leads during MRI can be affected by the presence of another lead in the body, which may increase or decrease the power deposition in the tissue depending on the position and configuration of the leads.
Primary color decoding using deep learning on source reconstructed EEG signal responses

The brain’s response to visual stimuli of different colors might be used in a brain-computer interface (BCI) paradigm, for letting a user control their surroundings by looking at specific colors. Allowing the user to control certain elements in its environment, such as lighting and doors, by looking at corresponding signs of different colors could serve as an intuitive interface. This paper presents work on the development of an intra-subject classifier for red, green, and blue (RGB) visual evoked potentials (VEPs) in recordings performed with an electroencephalogram (EEG). Three deep neural networks (DNNs), proposed in earlier papers, were employed and tested for data in source- and electrode space. All the tests performed in electrode space yielded better results than those in source space. The best classifier yielded an accuracy of 77% averaged over all subjects, with the best subject having an accuracy of 96%. Clinical relevance— This paper demonstrates that deep learning can be used to classify between red, green and blue visual evoked potentials in EEG recordings with an average accuracy of 77%.
Real-time iris image quality evaluation implemented in Ultrascale MPSoC

Iris recognition consists of measuring the unique patterns of the coloured and textured part of the eye (the iris), and comparing the obtained "signature" with those of a dataset to verify and authenticate identity. The uniqueness of the iris pattern makes the technique more robust and accurate for verification compared to other biometric systems. One of the main challenges of iris recognition is the ability to work with people on the move and at an increasing distance (over 1 metre). While the first implementations of iris recognition require the eyes to be placed at a close distance to the sensor and to maintain a static position during eye detection and capture, nowadays the iris recognition technique is progressing to be less intrusive. In this scenario, the problem of detecting the iris with an adequate quality (contrast, resolution) to be useful to identify the person, can force the framework to be able to process a large amount of frames per second (fps). This article describes the implementation in an MPSoC (Multiprocessor System-on-Chip) of a core that allows an eye detection framework to quickly evaluate the quality of the captured iris images. Thus, it is possible to remove the poor quality images from the subsequent processing steps. This implementation is intended to discard invalid images for person recognition therefore reducing the load on other parts of the system. It has been successfully integrated into an eye detection framework capable of processing over 50 fps working with a 16 Mpixel sensor.
Immune Activation Modulation via Magnetically Localized Bacteria Based Micro/Bio Robot (BBMBR)

Understanding tumor’s microenvironment is one of the key factors in the cancer therapy. Especially, from the perspective of immunotherapy, immune desert or cold tumor is referred as significantly downregulated T cell in-filtration due to lack of immune surveillance in the tumor microenvironment. There are many studies are dedicated to convert cold tumor to hot tumor for enhancing the efficacy of immunotherapy. In this study, we suggested selective immune activation system through the spatiotemporal control of the bacteria as an immune boosting agent. To this end, we have developed bacteria-based micro/bio robot system (BBMBR) by attaching bacteria with magnetic nanoparticles (MNP) so that the localization can be controlled through the magnetic field. The biomanufacturing results showed that BBMBR includes 6.6 ± 1.54 MNP attached and the presence ratio of bacteria-MNP out of total bacteria population reached 75.2 ± 3.37%. Spatial controllability experiments have shown that rotational and translation localization has been controlled as intended. The function of the immune modulation system through BBMBR was confirmed through experiments that magnetically driven BBMBR localization induced localized immune activation. M1-phenotype differentiation of macrophage cells were quantified CD80 staining, and overall immune response level was evaluated through IL-6 measurements. As the distance from the activation point increased, the population of M1 differentiated macrophages decreased, and when the movement of BBMBR was magnetically restricted, overall immune activation was found to be regulated downward. Proposed BBMBR and immune modulation framework could introduce a powerful new paradigm in cancer treatment by improving the localization controllability of immune-boosting agent and the spatial immune activation strategies.
Design for Hip Abduction Assistive Device Based on Relationship Between Hip Joint Motion and Torque During Running

Numerous attempts have been made to reduce metabolic energy while running with the help of assistive devices. A majority of studies on the assistive devices have focused on the assisting torque in the sagittal plane. In the case of running, however, the abduction torque in the frontal plane at the hip joint is greater than the flexion/extension torque in the sagittal plane. During running, as does an elastic body, the abduction torque and the motion of the hip joint have a linear relationship, but are opposite in direction. It is expected that the hip abduction torque can be assisted with a simple passive method by using an elastic body that reflects the movement characteristics of the hip joint. In this study, therefore, a system to assist hip abduction torque using a leaf spring was proposed with a prototype testing. While running with the assist system proposed, the leaf spring aids the abduction torque on the stance phase, and the torque is not generated due to the passive revolute joint on the swing phase. The joint angle is changed with respective to the rotation in the flexion/extension direction to prevent discomfort torque during swing phase and to increase the duration of the torque action during stance phase. A preliminary test was conducted on one subject using the prototype of the hip joint abduction torque assistive device. The participant with the assistive device reduced metabolic energy by 5% compared to the case without abduction torque assist while running at 2.5m/s. In order to increase the amount of metabolic reduction, the device shall be supplemented by system mass reduction and hip joint position optimization.
Universal Lesion Detection Utilising Cascading R-CNNs and a Novel Video Pretraining Method

According to the WHO, approximately one in six individuals worldwide will develop some form of cancer in their lifetime. Therefore, accurate and early detection of lesions is crucial for improving the probability of successful treatment, reducing the need for more invasive treatments, and leading to higher rates of survival. In this work, we propose a novel R-CNN approach with pretraining and data augmentation for universal lesion detection. In particular, we incorporate an asymmetric 3D context fusion (A3D) for feature extraction from 2D CT images with Hybrid Task Cascade. By doing so, we supply the network with further spatial context, refining the mask prediction over several stages and making it easier to distinguish hard foregrounds from cluttered backgrounds. Moreover, we introduce a new video pretraining method for medical imaging by using consecutive frames from the YouTube VOS video segmentation dataset which improves our model’s sensitivity by 0.8 percentage points at a false positive rate of one false positive per image. Finally, we apply data augmentation techniques and analyse their impact on the overall performance of our models at various false positive rates. Using our introduced approach, it is possible to increase the A3D baseline’s sensitivity by 1.04 percentage points in mFROC.
A Phase-based EEG Epoch Selection Method for Decoding Bi-directional Hand Movement Imagination in Stroke Patients

Electroencephalogram (EEG) based non-invasive Brain Computer Interface (BCI) system is gaining significant attention as a promising solution for stroke rehabilitation. Accurate selection of informative EEG time segment, that accommodates the specific neural activity patterns associated with the underlying mental task can help to improve the efficacy of the BCI system. In this work, we propose a phase-based EEG epoch selection algorithm to extract the discriminative EEG time segment corresponding to bi-directional hand motor imagery. The imagined center-out hand movement in two directions is decoded using the selected epoch of the EEG, recorded from 16 stroke patients with hemiparesis and specifically hand weakness. Phase Lock Value (PLV) EEG features extracted from the selected EEG epoch is used as discriminative feature for binary classification of imagined hand movement direction using Linear Discriminant Analysis. The use of selected EEG epoch yielded an improvement of 11.5% and 11.7% in the average direction classification accuracy of calibration and feedback session data respectively, compared to the baseline method employing the whole EEG signal. In addition to improvement in decoding accuracy, the epoch selection also yielded an average Information Transfer Rate (ITR) of 39.8±24.6 bits per minute, which is 86% improvement compared to the baseline method.Clinical Relevance— The proposed Motor Imagery (MI)-BCI system may be of clinical relevance as an active rehabilitation tool for stroke-affected patients, to enhance neural plasticity and recovery of centre-out activities of affected hand and forms a strong platform for MI-BCI coupled with exoskeletons or prosthesis rehabilitation.
Carbon Storage in Mountain Land Use Systems in Northern Thailand

Conversion of forested land for agriculture has obvious detrimental effects on its ecological functions, but these effects are not uniform. Mountain land use systems are diverse, encompassing managed forests and cultivated land. This study examined land use systems in 3 mountain villages in northern Thailand with different patterns of cultivation and evaluated the amount of carbon they have accumulated. Land use and management by individual farmers and communities were determined by interviews, field verification, and mapping. Biomass carbon in trees was determined nondestructively, and carbon in ground cover, litter, and soil organic matter was determined by chemical analysis of replicated samples. The 3 villages, with access to land ranging from 1.3 to 6.3 ha per capita, managed largely pristine headwater forests for security of water supply and made a living from crop production supplemented by harvests of timber, firewood, and other forest products from managed community forests. Cultivated land varied in composition and management among the villages, from shifting cultivation with fallow periods of different lengths to permanent cultivation of food and commercial crops. Per capita carbon storage in the villages well exceeded average per capita carbon dioxide emissions in Thailand, with most of the carbon stored in the forests. This has important implications for programs that offer incentives to mountain villages to maintain or enhance their carbon storage, such as the United Nations’ REDD (Reducing Emissions from Deforestation and Forest Degradation) program.