Miracle-dz/UniFAR-FTUs huggingface dataset:
Point-of-Interest Recommender Systems Based on Location-Based Social Networks: A Survey from an Experimental Perspective

Point-of-Interest recommendation is an area of increasing research and development interest within the widely adopted technologies known as Recommender Systems. Among them, those that exploit information coming from Location-Based Social Networks are very popular nowadays and could work with different information sources, which pose several challenges and research questions to the community as a whole. We present a systematic review focused on the research done over the past 10 years about this topic. We discuss and categorize the algorithms and evaluation methodologies used in these works and point out the opportunities and challenges that remain open in the field. More specifically, we report on the leading recommendation techniques and information sources that have been exploited more often (such as the geographical signal and deep learning approaches) while we also examine the lack of reproducibility in the field that may hinder real performance improvements.
Twitch Users' Motivations and Practices During Community Mental Health Discussions

Live streaming is a form of media that allows streamers to directly interact with their audience. Previous research has explored mental health, Twitch.tv and live streaming platforms, and users' social motivations behind watching live streams separately. However, few have explored how these all intertwine in conversations involving intimate, self-disclosing topics, such as mental health. Live streams are unique in that they are largely masspersonal in nature; streamers broadcast themselves to mostly unknown viewers, but may choose to interact with them in a personal way. This study aims to understand users' motivations, preferences, and habits behind participating in mental health discussions on live streams. We interviewed 25 Twitch viewers about the streamers they watch, how they interact in mental health discussions, and how they believe streamers should discuss mental health on live streams. Our findings are contextualized in the dynamics in which these discussions occur. Overall, we found that the innate design of the Twitch platform promotes a user-hierarchy in the ecosystem of streamers and their communities, which may affect how mental health is discussed.
Finite Element and Cadaveric Simulations of theMuscular Dysfunction of Weightbearing Foot

The tibialis posterior is a strong invertor and a weak plantarflexor of the foot which supports the longitudinal arch of the foot and creates a rigid lever for the foot to facilitate propulsion. Inflammation and dysfunction of the posterior tibial tendon is a common pathological condition of foot which is painful and disabling. Untreated posterior tibial tendon insufficiency will lead to severe inflammation and dysfunction of the tendon and flattening of the foot arch. In this study, a three-dimensional finite element model of the human foot and ankle, incorporating geometrical and material nonlinearity was employed to investigate the load distribution and spatial motion of the ankle-foot structures during simulated midstance. Cadaveric foot simulations were done to validate the finite element predictions in terms of strains of the plantar fascia, and major joint movements. Unloading the posterior tibial tendon was found to increase the arch deformations and strains of the plantar ligaments especially the spring ligament. The lack of foot arch support with plantar fascia release and posterior tibial tendon dysfunction may lead to attenuation of surrounding soft tissue structures and elongation of foot arch, resulting in a progressive acquired flatfoot deformity.
"I Felt a Little Crazy Following a 'Doll'"

Virtual Influencers (VIs) are computer-generated characters, many of which are often visually indistinguishable from humans and interact with the world in the first-person perspective as social media influencers. They are gaining popularity by creating content in various areas, including fashion, music, art, sports, games, environmental sustainability, and mental health. Marketing firms and brands increasingly use them to capitalise on their millions of followers. Yet, little is known about what prompts people to engage with these digital beings. In this paper, we present our interview study with online users who followed different VIs on Instagram beyond the fashion application domain. Our findings show that the followers are attracted to VIs due to a unique mixture of visual appeal, sense of mystery, and creative storytelling that sets VI content apart from that of real human influencers. Specifically, VI content enables digital artists and content creators by removing the constraints of bodies and physical features. The followers not only perceived VIs' rising popularity in commercial industries, but also are supportive of VI involvement in non-commercial causes and campaigns. However, followers are reluctant to attribute trustworthiness to VIs in general though they display trust in limited domains, e.g., technology, music, games, and art. This research highlights VI's potential as innovative digital content, carrying influence and employing more varied creators, an appeal that could be harnessed by diverse industries and also by public interest organisations.
Dating Apps Are Used for More Than Dating

Use and design of dating apps has evolved in recent years to accommodate other interaction goals beyond dating, prompting some researchers to now refer to these apps as people-nearby applications (PNAs). With this expansion of use comes increased potential for misinterpretation of users' goals for meeting face-to-face, which can pose risks to user safety particularly when disparities in sexual expectations occur. We present a survey study (n=132) with users of several PNAs and with various motivations for PNA-use to understand how interaction goals are disclosed and detected. The study finds such practices to be far from consistent, with some appearing highly susceptible to misinterpretation such as purposely delaying self-presentation of interaction goals until meeting face-to-face, and implying sexual expectations through vague references to "fun. " We conclude by suggesting a modified version of the "swiping" feature in PNAs to facilitate consistent and overt self-presentation of interaction goals in ways that accommodate user tendencies discovered in the study.
The Computational Thematic Analysis Toolkit

As online communities have grown, Computational Social Science has rapidly developed new techniques to study them. However, these techniques require researchers to become experts in a wide variety of tools in addition to qualitative and computational research methods. Studying online communities also requires researchers to constantly navigate highly contextual ethical and transparency considerations when engaging with data, such as respecting their members' privacy when discussing sensitive or stigmatized topics. To overcome these challenges, we developed the Computational Thematic Analysis Toolkit, a modular software package that supports analysis of online communities by combining aspects of reflexive thematic analysis with computational techniques. Our toolkit demonstrates how common analysis tasks like data collection, cleaning and filtering, modelling and sampling, and coding can be implemented within a single visual interface, and how that interface can encourage researchers to manage ethical and transparency considerations throughout their research process.
Investigating the Use of Head Mounted Devices for Remote Cooperation and Guidance during the Treatment of Wounds

Professional caregivers often face complex situations in which they need the support of a colleague or a specialist. This is especially necessary for caregivers during their training or with less practice in certain tasks. Due to time and space restrictions, colleagues or specialists are not always available for local support. Remote support by streaming videos to a remote helper has been discussed in healthcare domains as a so-called teleconsultation. However, little is known about how to apply teleconsultation in care. We conducted a study with caregivers and remote helpers to compare head mounted devices (HMDs) for teleconsultation in care to two alternative solutions using smartphones. We found that despite lacking familiarity, HMDs have good potential for remote support in care, and that creating video streams with smartphones is not preferable for teleconsultations. We suggest that ideal support needs to balance freedom and guidance and suggest how such support needs to be further explored.
Joint Utility and Frequency for Pattern Classification

High-frequency itemset mining (HFIM) and high-utility itemset mining (HUIM) aim to discover itemsets with high occurrence and high utility, respectively, in a transaction database. A number of efficient algorithms have been developed to identify these high-utility itemsets (HUIs) or high-frequency itemsets (HFIs). Such algorithms play an increasingly important role in many occasions especially for analysis in commercial enterprises. In this paper, we propose a new model called joint utility and frequency for pattern classification, and two new algorithms, namely UFCgen and UFCfast. Both algorithms are designed to categorize each itemset into different type of patterns by setting the minimum thresholds of utility and frequency. We compare these algorithms on two datasets. The experimental results show that both algorithms can successfully collect three different types of itemsets from all candidate itemsets based on frequency and utility, and the list-based UFCfast algorithm outperforms the level-wise-based UFCgen algorithm in terms of execution time.
Improvement of ARC Considering Negative Locality of Reference in Virtualized Environment

In a virtualized environment using virtual computers, a two-level cache hierarchy for accessing storage devices is constructed, which consists of a host OS cache and a guest OS cache. In such an environment, if the upper cache (guest OS cache) uses LRU as a cache replacement algorithm, it is known that negative temporal locality of reference occurs in the lower cache (host OS cache).
Low standby leakage 12T SRAM cell characterisation

ABSTRACT In this work, a low power and variability-aware static random access memory (SRAM) architecture based on a twelve-transistor (12T) cell is proposed. This cell obtains low static power dissipation due to a parallel global latch (G-latch) and storage latch (S-latch), along with a global wordline (GWL), which offer a high cell ratio and pull-up ratio for reliable read and write operations and a low cell ratio and pull-up ratio during idle mode to reduce the standby power dissipation. In the idle state, only the S-latch stores bits, while the G-latch is isolated from the S-latch and the GWL is deactivated. The leakage power consumption of the proposed SRAM cell is thereby reduced by 38.7% compared to that of the conventional six-transistor (6T) SRAM cell. This paper evaluates the impact of the chip supply voltage and surrounding temperature variations on the standby leakage power and observes considerable improvement in the power dissipation. The read/write access delay, read static noise margin (SNM) and write SNM were evaluated, and the results were compared with those of the standard 6T SRAM cell. The proposed cell, when compared with the existing cell using the Monte Carlo method, shows an appreciable improvement in the standby power dissipation and layout area.
Science-Guided Machine Learning for Wall-Modeled Large Eddy Simulation

A deep learning algorithm is trained to predict wall-shear stress based on flow in the outer region of transitional and turbulent boundary layer flow. Flow variables sampled from the boundary layer region of eight high quality wall-resolved Large Eddy Simulations of transonic compressor cascades in the presence of shock-boundary layer interaction effects are used to train the model. About 1.1 TB of data stored in compressed text format generated from the LES calculations is used to perform the model training. The model is shown to be generic and able to predict complex boundary layer physics including laminar to turbulent transition caused by the shock-boundary layer interaction.
Bachelor Thesis in NanoScience

This report demonstrates the feasibility of easily and quickly generating mutants of a given protein, in silico. Using primarily MOPAC and PyMOL it is possible to produce mutations on a specific site, generate relevant rotamers of that mutant, evaluate and discard suboptimal rotamers, and prepare the new mutation for a reactivity evaluation using an interpolation of the two end states of the enzymatic reaction. This report also shows that when one generates these mutants from a heavily optimized enzyme structure, the CPU time needed for optimizing the mutant is drastically reduced compared to the initial optimization of the wild type.
Drug3D-DTI: Improved Drug-target Interaction Prediction by Incorporating Spatial Information of Small Molecules

A number of machine learning (ML) approaches for drug discovery have been available that rely only on sequential (1D) and planar (2D) information without effectively using the 3D information for generating features of drugs. However, 3D information of small molecules can reflect relative position of atoms more directly, which affects molecular properties. In this work, we present a new deep learning model called Drug3D-DTI for drug-target interaction prediction. Drug3D-DTI takes advantage of molecular spatial information, i.e., atom proximity in three-dimensional (3D) structures. We comprehensively evaluated the performance of Drug3D-DTI on two datasets with two tasks of regression and classification. In particular, we compared Drug3D-DTI with several existing methods including the two cutting-edge methods for compound-protein interaction prediction. From the experimental results, Drug3D-DTI clearly outperformed other methods under all settings. Further, this performance improvement was validated by ablation experiments and a case study. The implementation of Drug3D-DTI is available at (https://github.com/zhiruiliao/Drug3D-DTI).
DeepTrack: An ML-based Approach to Health Disparity Identification and Determinant Tracking for Improving Pandemic Health Care

The Coronavirus disease 2019 (COVID-19) pandemic has severely impacted countries around the world with unprecedented mortality and economic devastation and has disproportionately and negatively impacted different communities—especially racial and ethnic minorities who are at a particular disadvantage. Black Americans have a long-standing history of disadvantage (e.g., long-standing disparities in health outcomes) and are in a vulnerable position to experience the impact of this pandemic. Some studies indicate high-risk and vulnerability of the elderly and patients with underlying co-morbidities, however, little research paid attention to leveraging geographic information and machine learning (ML) to track the social and structural health determinants, which can provide a lower level of granularity. In this paper, we propose DeepTrack, a geospatial and ML-based approach to identify diverse determinants (including the structural, social, and constructural determinants) of health disparities in COVID-19 pandemic, which provides a lower level of granularity. We provide a thorough analysis of health disparities and diets based on multiple COVID-19 datasets and examine the structural, social, and constructural health determinants to assist in ascertaining why disparities (in racial and ethnic minorities who are particularly disadvantaged) occur in infection and death rates due to COVID-19 pandemic. We track determinants of nutrition and obesity through diet examination. Extensive experimental results show the effectiveness of our approach. The research provides new strategies for health disparity identification and determinant tracking with a goal to improve pandemic health care.
DiffBond: A Method for Predicting Intermolecular Bond Formation

Many tools that explore models of protein complexes are also able to analyze interactions between specific residues and atoms. A comprehensive exploration of these interactions can often uncover aspects of protein-protein recognition that are not obvious using other protein analysis techniques. This paper describes DiffBond, a novel method for searching for intermolecular interactions between protein complexes while differentiating between three different types of interaction: hydrogen bonds, ionic bonds, and salt bridges. DiffBond incorporates textbook definitions of these three interactions while contending with uncertainties that are inherent in computational models of interacting proteins. We used it to examine the barnase-barstar, Rapla-raf, and Smad2-Smad4 complexes, as well as a subset of protein complexes formed between three-finger toxins and nAChRs. Based on electrostatic interactions established by previous experimental studies, DiffBond was able to identify ionic and hydrogen bonds with high precision and recall, and identify salt bridges with high precision. In combination with other electrostatic analysis methods, DiffBond can be a useful tool in helping predict influential amino acids in protein-protein interactions and characterizing the type of interaction.
Extracting geographical characteristics about COVID-19 evolution worldwide using machine learning

Since the beginning of 2020, the whole world has been plagued by the coronavirus pandemic. During the last sixteen months, almost every country in the world has faced several epidemic waves. An intriguing question that arises is whether neighboring countries, similar in regard to their socioeconomic status and the restrictions employed to counter the spread of the virus, showcase similarities in their respective number of cases and deaths. To that end, in this paper we form three clusters of similar countries (European and USA, African-Asian and Latin American) and we use their cumulative data as training data for machine learning models (RNN family, TCN and Attention) that predict the respective cases and deaths of 4 fixed neighboring countries, namely Cyprus, Greece, Italy and Spain. The results of the experiments conducted show that these 4 countries accent bigger similarity with the European cluster, as expected. Thus, evidence is provided bolstering the claim that similar neighboring countries exhibit alike behavior regarding the repercussions of the COVID-19.
Edge-Gated Graph Neural Network for Predicting Protein-Ligand Binding Affinities

Predicting Protein-ligand binding affinities using Deep Learning can significantly shorten the drug development cycle. Recently, Graph neural network models have been developed, and are successfully used to accelerate the development of potential drugs. One major limitation of these GNN models is that they focus on node features (i.e., atom features), as these nodes carry the most important information of molecules. However, atoms are connected via different bonds in molecules, and we argue that such chemical bonds carry critical information for assessing how atomic features should be aggregated. To overcome the lack of bond-related information in earlier models, we here proposed a novel edge-gated graph neural network (egGNN) that predict the binding affinities between proteins and ligands. Specifically, our model treats chemical bonds as gates that control how information is extracted between atoms, this modification enables our model to extract more accurate information for different bonds. We tested our model using the CASF-2016 dataset, and found that the Pearson’s correlation coefficient (R) of egGNN is three percent higher than that of the best tested method (i.e., 0.86 vs 0.83), and the Root Mean Square Error (RMSE) is significantly lower than that of the best tested method (i.e., 1.12 vs 1.23) when compared to state-of-the-art-approaches. In ablation experiments, we demonstrate that our edge-gated feature extraction (EGFE) consoderably improves the performance of GNNs. These results indicate that egGNN represents a promising tool applicable for virtual screening, and should greatly assist in accelerating drug development.
Two-stage Clustering Method for Discovering People's Perceptions: A Case Study of the COVID-19 Vaccine from Twitter

Twitter is currently one of the most influential microblogging services on which users interact with messages. It is imperative to grasp the big picture of Twitter through analyzing its huge stream data. In this study, we develop a two-stage clustering method that automatically discovers coarse-grained topics from Twitter data. In the first stage, we use graph clustering to extract micro-clusters from the word co-occurrence graph. All the tweets in a micro-cluster share a fine-grained topic. We then obtain the time series of each micro-cluster by counting the number of tweets posted in a time window. In the second stage, we use time series clustering to identify the clusters corresponding to coarse-grained topics. We evaluate the computational efficacy of the proposed method and demonstrate its systematic improvement in scalability as the data volume increases. Next, we apply the proposed method to large-scale Twitter data (26 million tweets) about the COVID-19 Vaccination in Japan. The proposed method separately identifies the reactions to news and the reactions to tweets.
Using Model Class Reliance to Measure Group Effects on Non-Adherence to Asthma Medication

Asthma affects an estimated 300 million people across the world. Despite being a highly treatable condition using preventative inhalers, mortality rates remain unacceptably high, with lack of adherence to medications cited as a major cause. While various drivers for non-adherence have been considered in isolation, interactions between demographic, behavioural and situational factors have never been modelled in concert mostly due to the limited ability of traditional methods to group such a large variety of features. This was addressed in this paper through a non-linear modelling approach, leveraging a novel dataset obtained via online surveying of asthma patients. Application of traditional variable importance methods to examine explanatory factors, however, is not possible. This is due to the presence of high multicollinearity in the data, a highly common occurrence in big data, or any datasets which include a large number of input features. This results in insights being obfuscated by extensive shared information and non-linear interactions occurring across variables. To mitigate this, we introduce the first Grouped Feature approach to Model Class Reliance (Group-MCR), that is able to quantify the importance of specific variable sets in underpinning explanations. Cross-validated models achieve 71% accuracy, with Group-MCR revealing the importance of perceptual factors. Out of all the perceptual factors denial proves to be most predictive of non-adherence to asthma medication, indicating that public health interventions should not only target the physical aspects of asthma, but additionally focus on patients’ beliefs and perceptions as valuable parts of their treatment.