Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic networks differentiate the language ability of children with cochlear implants

Background Cochlear implantation (CI) in prelingually deafened children has been shown to be an effective intervention for developing language and reading skill. However, there is a substantial proportion of the children receiving CI who struggle with language and reading. The current study–one of the first to implement electrical source imaging in CI population was designed to identify the neural underpinnings in two groups of CI children with good and poor language and reading skill. Methods Data using high density electroencephalography (EEG) under a resting state condition was obtained from 75 children, 50 with CIs having good (HL) or poor language skills (LL) and 25 normal hearing (NH) children. We identified coherent sources using dynamic imaging of coherent sources (DICS) and their effective connectivity computing time-frequency causality estimation based on temporal partial directed coherence (TPDC) in the two CI groups compared to a cohort of age and gender matched NH children. Findings Sources with higher coherence amplitude were observed in three frequency bands (alpha, beta and gamma) for the CI groups when compared to normal hearing children. The two groups of CI children with good (HL) and poor (LL) language ability exhibited not only different cortical and subcortical source profiles but also distinct effective connectivity between them. Additionally, a support vector machine (SVM) algorithm using these sources and their connectivity patterns for each CI group across the three frequency bands was able to predict the language and reading scores with high accuracy. Interpretation Increased coherence in the CI groups suggest overall that the oscillatory activity in some brain areas become more strongly coupled compared to the NH group. Moreover, the different sources and their connectivity patterns and their association to language and reading skill in both groups, suggest a compensatory adaptation that either facilitated or impeded language and reading development. The neural differences in the two groups of CI children may reflect potential biomarkers for predicting outcome success in CI children.
Accurately predicting hit songs using neurophysiology and machine learning

Identifying hit songs is notoriously difficult. Traditionally, song elements have been measured from large databases to identify the lyrical aspects of hits. We took a different methodological approach, measuring neurophysiologic responses to a set of songs provided by a streaming music service that identified hits and flops. We compared several statistical approaches to examine the predictive accuracy of each technique. A linear statistical model using two neural measures identified hits with 69% accuracy. Then, we created a synthetic set data and applied ensemble machine learning to capture inherent non-linearities in neural data. This model classified hit songs with 97% accuracy. Applying machine learning to the neural response to 1st min of songs accurately classified hits 82% of the time showing that the brain rapidly identifies hit music. Our results demonstrate that applying machine learning to neural data can substantially increase classification accuracy for difficult to predict market outcomes.
Higher-Order Attitudes Toward Risk: Behavioral Characterizations of Differentiability

Higher-order differentiability is one of the common properties of von Neumann Morgenstern (vNM) utility functions used in economic applications. In particular, it is indispensable to analyze higher-order attitudes toward risk. We shall study three classes of finite-order differentiable vNM utility functions. Then we shall discuss simple and behaviorally interpretable conditions which are necessary and sufficient for a vNM utility function to belong to one of those three classes.
Association of physical activity and sitting with metabolic syndrome and hyperglycemic clamp parameters in adolescents – BRAMS pediatric study

Introduction Obesity and metabolic syndrome (MetS) have immediate and long-term consequences on adolescent health and well-being. Among the available treatments for MetS in adolescents, behavioral interventions such as increasing physical activity (PA) are preferred. This study aimed to investigate the association of PA and sitting time with MetS and a complete set of metabolic health parameters. Methods Data from the Pediatric Brazilian Metabolic Syndrome Study (BRAMS-P), a cross-sectional multicenter study conducted using a convenience sample of 448 Brazilian adolescents (10y–19y), were used. Sociodemographic and lifestyle information were collected using a standardized questionnaire. Daily PA and sitting time were estimated from the International PA Questionnaire. Anthropometric parameters, body composition, and blood pressure were measured by trained researchers. Blood lipids, uric acid, hepatic enzymes, creatinine, glycated hemoglobin, glucose, and insulin were measured in fasting blood samples, and the Homeostasis Model Assessment for Insulin Resistance was calculated. A subsample of 57 adolescents underwent the hyperglycemic clamp protocol. Results The odds for metabolic syndrome were higher among adolescents who spent >8h sitting (OR (95%CI)=2.11 (1.02 – 4.38)), but not in those classified as active (OR (95%CI)=0.98 (0.42 – 2.26)). Adolescents who spent more time sitting had higher BMI, waist circumference, sagittal abdominal diameter, neck circumference, percentage of body fat, and worse blood lipid profile. The insulin sensitivity index was moderately and positively correlated with moderate-to-high PA in minutes per day (rho=0.29; p=0.047). Conclusion Time spent sitting was associated with worse metabolic parameters and must be restricted in favor of adolescent health. Regular PA is associated with improved insulin sensitivity and may be encouraged not only in adolescents with obesity or metabolic disorders but also to prevent adverse metabolic outcomes in normal-weight adolescents.
The influence of pterygium on corneal densitometry evaluated using the Oculus Pentacam system

Purpose To assess the effect of pterygium on corneal densitometry (CD) values. Methods One hundred and nine patients (155 eyes) with primary pterygium were divided into a severe pterygium group (79 eyes) and a mild-to-moderate pterygium group (76 eyes) according to pterygium severity. Among them, 63 patients had monocular pterygium; and 25 patients (38 eyes) underwent pterygium excision combined with conjunctival autograft follow-up. A Pentacam anterior segment analyzer was used to obtain the CD values and corneal morphological parameters, including central corneal thickness (CCT), flat-axis keratometry (K1), steep-axis keratometry (K2), corneal astigmatism, irregular astigmatism, and spherical aberration. CD was subdivided into four concentric radial regions based on corneal diameter and three layers according to depth. Results CD values at 0–12 mm of the anterior 120 μm layer, 0–10 mm of the center layer and full thickness, and 2–6 mm of the posterior 60 μm layer were significantly higher in eyes affected by pterygium than in the contralateral unaffected eyes (all P < 0.05). CD values were significantly higher in the severe pterygium group than in the mild to moderate pterygium group (all P < 0.05). Corneal astigmatism, irregular astigmatism, K1, K2, CCT, and spherical aberration correlated with CD values in eyes with pterygium (all P < 0.05). CD values at 6–10, 0–12 mm in the anterior 120 μm layer and full thickness, 10–12 and 0–12 mm in the center layer were significantly decreased 1 month after pterygium surgery compared with those before surgery (all P < 0.05). Conclusion Patients with pterygium had increased CD values, particularly in the anterior and central layers. CD values were correlated with pterygium severity grading and corneal parameters. Pterygium surgery partially reduced the CD values.
Age-related decline of online visuomotor adaptation: a combined effect of deteriorations of motor anticipation and execution

The literature has established that the capability of visuomotor adaptation decreases with aging. However, the underlying mechanisms of this decline are yet to be fully understood. The current study addressed this issue by examining how aging affected visuomotor adaptation in a continuous manual tracking task with delayed visual feedback. To distinguish separate contributions of the declined capability of motor anticipation and deterioration of motor execution to this age-related decline, we recorded and analyzed participants' manual tracking performances and their eye movements during tracking. Twenty-nine older people and twenty-three young adults (control group) participated in this experiment. The results showed that the age-related decline of visuomotor adaptation was strongly linked to degraded performance in predictive pursuit eye movement, indicating that declined capability motor anticipation with aging had critical influences on the age-related decline of visuomotor adaptation. Additionally, deterioration of motor execution, measured by random error after controlling for the lag between target and cursor, was found to have an independent contribution to the decline of visuomotor adaptation. Taking these findings together, we see a picture that the age-related decline of visuomotor adaptation is a joint effect of the declined capability of motor anticipation and the deterioration of motor execution with aging.
Computerized music-based intervention module for auditory processing and working memory in older adults

Introduction The contribution of technology to the field of health is vast, both in diagnosis and management. More so, the use of computer-based intervention has become increasingly widespread over the past decade. Human beings experience a decline in auditory processing and cognitive skills as they age, consistent with deterioration of other bodily functions. In addition, speech perception abilities in both quiet and in the presence of noise are impacted by auditory processing abilities and cognitive skills such as working memory. This pilot study explored the use of music as an intervention for improving these skills and employed a computerized delivery of the intervention module. Method A battery of tests was carried out to assess the baseline auditory processing and working memory skills in eight older adults between the ages of 56 and 79 years, all of whom had normal hearing. Following the assessment, a short-term computerized music-based intervention was administered. The style of music chosen was Carnatic classical music, a genre widely practiced in Southern India. The intervention module involved note and tempo discrimination and was carried out for a maximum of 10 half-hour sessions. The multi-level intervention module was constructed and administered using Apex software. Following the intervention, the auditory processing and cognitive skills of the participants were reassessed to study any changes in their auditory processing and working memory skills. Results and discussion There were positive changes observed in all the auditory processing and some of the working memory abilities. This paper discusses in detail the systematic structuring of the computerized music-based intervention module and its effects on the auditory processing and cognitive skills in older adults.
Improving dental epithelial junction on dental implants with bioengineered peptides

Introduction: The functionalization of titanium (Ti) and titanium alloys (Ti6Al4V) implant surfaces via material-specific peptides influence host/biomaterial interaction. The impact of using peptides as molecular linkers between cells and implant material to improve keratinocyte adhesion is reported. Results: The metal binding peptides (MBP-1, MBP-2) SVSVGMKPSPRP and WDPPTLKRPVSP were selected via phage display and combined with laminin-5 or E-cadherin epithelial cell specific peptides (CSP-1, CSP-2) to engineer four metal-cell specific peptides (MCSPs). Single-cell force spectroscopy and cell adhesion experiments were performed to select the most promising candidate. In vivo tests using the dental implant for rats showed that the selected bi functional peptide not only enabled stable cell adhesion on the trans-gingival part of the dental implant but also arrested the unwanted apical migration of epithelial cells. Conclusion: The results demonstrated the outstanding performance of the bioengineered peptide in improving epithelial adhesion to Ti based implants and pointed towards promising new opportunities for applications in clinical practice.
AI-On-Skin: Towards Enabling Fast and Scalable On-body AI Inference for Wearable On-Skin Interfaces

Existing artificial skin interfaces lack on-skin AI compute that can provide fast neural network inference for time-critical applications. In this paper, we propose AI-on-skin - a wearable artificial skin interface integrated with a neural network hardware accelerator that can be reconfigured to run diverse neural network models and applications. AI-on-skin is designed to scale to the entire body, comprising tiny, low-power, accelerators distributed across the body. We built 7 AI-on-Skin application prototypes and our user trials show AI-On-Skin achieving 20X and 50X speedup over off-body inference via Bluetooth and on-body centralized microprocessor based inference approach respectively. We also project the power performance of AI-on-skin with our accelerator fabricated as silicon chips instead of emulated on FPGAs and show 10X further power savings. To the best of our knowledge, AI-on-Skin is the first ever wearable prototype to demonstrate skin interfaces with on-body AI inference.
EEG-Based Brain-Computer Interactions in Immersive Virtual and Augmented Reality: A Systematic Review

Brain-computer interactions allow humans to passively or actively control computer systems using their brain activity. For more than a decade now, these interactions have been implemented and evaluated in immersive virtual environments where they prompt novel means of human interaction with systems. In this paper, we present a systematic review of 76 studies published over the last 10 years that develop and evaluate immersive virtual reality or augmented reality systems with electroencephalography-based interactions. The aim of the review is to summarize and highlight trends in technology design, research methods, current practices, techniques used in systems of this kind, challenges and opportunities that present direction for future research in this area. Our analysis uncovers useful insights, limitations, and highlights of the trends, innovations, and usability and technical challenges at the intersection of brain-computer interfaces and immersive technologies, as well as recommendations for future research.
Temporal-Weighted Bipartite Graph Model for Sparse Expert Recommendation in Community Question Answering

Community Question Answering (CQA) websites are valuable knowledge repositories where individuals exchange information by asking and answering questions. With an ever-increasing number of questions and high in-flow and out-flow of users in these communities, a key challenge is to design effective strategies for recommending experts for new questions. This requires robust approaches that facilitate modeling users’ expertise given their changing interests and sparse historical data, at the same time being computationally less expensive for periodic updates. In this paper, we propose a simple graph diffusion-based expert recommendation model for CQA, that can outperform state-of-the-art convolutional neural network and transformers-based deep learning representatives and collaborative models. Our proposed method learns users’ expertise in the context of both semantic and temporal information to capture their changing interests and activity levels with time. Experiments on six real-world datasets from the Stack Exchange network demonstrate that our approach outperforms competitive baseline methods. Further, experiments on cold-start users (users with a limited historical record) show our model achieves an average of 50% performance gain compared to the best baseline method.
A Framework for User Interface Adaptation to Emotions and their Temporal Aspects

Designing and adapting user interfaces (UIs) based on context and individuals' dynamic characteristics is essential to improve user experience and performance. Emotions are a significant aspect of users' characteristics since they lead to certain behaviors. Consequently, adopting emotions in UI adaptation is a crucial task for developers. However, it is difficult considering the various characteristics of emotions, particularly their variability and temporal aspects. This paper presents a framework for UI adaptation covering specific dimensions of emotions and their temporal aspects. More specifically, the mechanism permits UI adaptation to i)different types of emotions categorizations (basic emotions, facial action units, and arousal/valence) and ii) timing (duration) based on different emotions consolidation. Examples from a serious game prototype illustrate the proposed adaptation mechanisms.
Verification of ns-3 Wi-Fi Rate Adaptation Models on AWGN Channels

The performance of Wi-Fi networks depends on the ability of devices to adapt their transmissions to dynamic channel/network conditions. Hence, “Rate Adaptation Algorithms (RAAs)” have been devised to allow nodes to select appropriate modulation and coding schemes (and other parameters) in response to varying channel/network conditions. These algorithms are neither standardized nor typically divulged by vendors, and devising a ‘performance-optimal’ RAA for specific scenario remains an active topic that necessitates a complex, multi-parameter cross-layer (PHY/MAC) approach. The ns-3 network simulator offers detailed models of the Wi-Fi medium access control (MAC) layer, including three reference RAA implementations; however testing and validation of these RAA models has been very limited to date. This paper reports on initial test and validation for ns-3 RAA models via 802.11n/ac/ax simulations. After describing the RAA scope and implementations, we explore and summarize insights from test results as to a) whether the ns-3 RAAs are able to achieve the correct rates as configuration is varied and b) how they respond to step changes in the received signal-to-noise ratio (SNR) as a means for exploring their convergence properties.
Fairness and Sustainability in Multistakeholder Tourism Recommender Systems

In the travel industry, Tourism Recommender Systems (TRS) are gaining popularity as they simplify trip planning for travelers by offering personalized recommendations for accommodations, activities, destinations, and more. Ensuring fairness in TRS involves considering the needs and viewpoints of different stakeholders, including consumers, item providers, the platform, and society. Although previous research has focused on fairness in TRS from a multistakeholder perspective, little attention has been given to generating sustainable recommendations. This doctoral thesis introduces the concept of Societal Fairness (S-Fairness) to consider the impact of tourism on non-participating stakeholders (society) such as residents, who may be affected by tourism issues such as increased housing prices, environmental pollution, and traffic congestion. The objective of this research is to contribute to the field of TRS by (1) modeling sustainability for societal fairness, (2) developing a fair multistakeholder TRS that balances sustainability concerns with other stakeholders while minimizing trade-offs, and (3) evaluating the approach through user studies and offline dataset evaluation to ensure user acceptance of recommendations.
Adaptive Context-Aware Planning Support for Students with Autism

Managing university life is challenging for any student, including fast-paced self-reliant learning, first experiences with independent daily living, and the social demands of student life. These struggles are even more pronounced in students with Autism Spectrum Disorder (ASD), who might face additional difficulties, including interpersonal deficits, organisational challenges, lacking self-advocacy skills and sensory overload. This thesis uses a participatory design process to research how an adaptive, context-aware system could support students with autism in planning their student life, including managing time, tasks, stress and sensory stimulation. The first interviews resulted in a focus on intelligent planning systems with a low-effort interaction design. The design process will incrementally build towards intelligent support for personalised interactive scheduling as well as in-the-moment next-action recommendations that are context-aware and adaptive to data on the user’s stress level and sensory stimulation exposure.
Application of visible light-infrared image fusion technology in power system fault detection

Infrared thermal imaging is widely used in industrial inspection due to its advantages such as passive identification, non-contact detection, long detection distance and strong environmental adaptability. In power systems, infrared thermal imaging can be used to carry out live detection of power equipment to prevent or examine potential risk and threats. This paper provides a fault detection method for power equipment through the visible light-infrared image fusion technology. The information of infrared image is collected through infrared thermal imager, and the infrared image is preprocessed. The scale invariant feature transform (SIFT) feature point detection algorithm is used to extract the difference between visible light image and infrared image. The feature points are screened and registered by random sample consensus (RANSAC) algorithm to realize the fusion of the visible light image and the infrared image of the power equipment, so as to detect the working status of the power equipment and accurately locate the fault source when a fault occurs.
Using RFID in the Engineering of Interactive Software Systems: A Systematic Mapping

IoT, Industry 4.0, and ubiquitous applications demand an intense exchange of information between actors (humans, animals, plants, or other objects) and things. Different interaction technologies, techniques, and methods can support it, including RFID, a well-known technology capable of identifying and locating actors. However, the variety of application domains and interaction possibilities jeopardize the use of RFID without a clear understanding of its usage issues. Therefore, we have performed a systematic mapping to characterize the adoption of RFID technology in this kind of interactive software system by intercepting human-computer interaction and software engineering issues. Our goal is to reveal (i) which application domains address RFID; (ii) why RFID is used instead of other technologies; (iii) who interacts through RFID and how the interaction is; (iv) what are the requirements and limitations associated with it; and (v) what are the common quality characteristics and measures which should be considered for their evaluation. The results showed that (i) RFID is specially used for implementing implicit interactions in healthcare, embedded systems, and manufacturing applications. (ii) Passive UHF RFID is usually adopted due to being contactless, low-cost, and well-performing indoors. (iii) It interacts with humans and things in the same proportion, most frequently to collect locations of sensor data constantly. (iv) RFID presents limitations, such as causing electromagnetic interference and not working well near metallic walls. However, tags work under high temperatures and can be well hidden in cement or clothes. (v) Interactive system designers usually create applications with invisible infrastructures, which tend to keep users' focus on the real world.