Miracle-dz/UniFAR-FTUs huggingface dataset:
Auto-labeling of sensor data using social media messages: a case study for a smart city

Recently, the deployment of various Internet of Things (IoT) sensors has encouraged smart cities to accumulate a large volume of data. When machine learning models utilize such accumulated raw data to predict events and situations, various systems of a smart city, such as traffic accident management systems, can be further developed by utilizing the predicted events and situations. However, although there has been a large volume of various IoT raw data on smart cities, such data do not have labels that are related to events or situations. Data with meaningful labels are required for the training of the models. Because these sensor data do not have meaningful labels, the data cannot be utilized into the models. There are several existing methods for labeling, but they have different drawbacks. In this study, we investigate the feasibility of utilizing social media messages to extract meaningful labels for machine learning to predict events and situations in smart city environments. As a case study, we compared the extracted labels from social media messages with the events and situations found in announced traffic news, and other articles. The results show that it is feasible to utilize social media messages as a source for meaningful labels of events and situations. We also propose an improved clustering algorithm using an outlier detection technique to extract meaningful labels in a more robust way. Furthermore, for other researchers who want to utilize the IoT raw data, we analyze and release the refined sensor data on which there were unknown noise.
A wifi-slicing MANO solution to enable elastic wifi-sharing communication service end-to-end provision

This work sets out by examining the principle that WiFi-sharing home-premises hotspots can significantly expand the wireless broadband communication capacities of 5th Generation (5G) networks, which is critical for both Ultra-Dense Networking (UDN) and Ultra-Reliable Low Latency (URLL) use cases. However, current WiFi-sharing deployments (e.g., the FON pioneer carrier) push to the hotspot's owner the task of manually configuring his physical Consumer Premises Equipment (pCPEs) to fix a slowdown in WiFi connectivity or even connection breaks. Since most of them lack minimum networking expertise, this stands as a critical weakness. To tackle this issue, we introduce the WiFi-Slicing Management and Orchestration (WS-MANO) solution to automatically provision, in a carrier-grade manner, elastic WiFi-sharing communication services atop WiFi-Slice-defined domains. The WS-MANO harnesses Software-Defined Wireless Networking (SDWN) and Multi-Access Edge Computing (MEC) technologies to autoscale WiFi-Slices to keep Quality of Experience (QoE). A real testbed-premised proof of concept provided evidence around the feasibility and effectiveness of WS-MANO in provisioning elastic WiFi-Slices while keeping users to perceive affordable QoE at the same time.
A flexible and lightweight interactive data mining tool to visualize and analyze digital citizen participation content

Addressing information overload in current e-participation platforms, we present a lightweight web application consisting of a simple HTML-based data panel that, through the use of date, location and category based filters, and several interactive graphs, allows visualizing, exploring and analyzing data obtained from public deliberation platforms in an easy and clear way. The tool, which implements natural language processing, text similarity, and graph clustering techniques to group citizen proposals, may serve as a decision support system for the municipal government, and may contribute to increase the citizens' participation and engagement.
A3Graph: adversarial attributed autoencoder for graph representation learning

Recent years have witnessed a proliferation of graph representation techniques in social network analysis. Graph representation aims to map nodes in the graph into low-dimensional vector space while preserving as much information as possible. However, most existing methods ignore the robustness of learned latent vectors, which leads to inferior representation results due to sparse and noisy data in graphs. In this paper, we propose a novel framework, named A3Graph, which aims to improve the robustness and stability of graph representations. Specifically, we first construct an aggregation matrix by the combining positive point-wise mutual information matrix with the attribute matrix. Then, we enforce the autoencoder to reconstruct the aggregation matrix instead of the input attribute matrix. The enhancement autoencoder can incorporate structural and attributed information in a joint learning way to improve the noise-resilient during the learning process. Furthermore, an adversarial learning component is leveraged in our framework to impose a prior distribution on learned representations has been demonstrated as an effective mechanism in improving the robustness and stability in representation learning. Experimental studies on real-world datasets have demonstrated the effectiveness of the proposed A3Graph.
'A Library of People'

Social media platforms provide access to informational and emotional resources that can enable low-income populations to further their socioeconomic mobility and cope with unexpected life demands. However, lack of both interpersonal trust and a sense of shared identity often prevent low-income individuals from eliciting resources from the diverse networks embedded in these platforms. Building on past research, we investigated factors that facilitated and deterred low-income members of the community-based non-profit organization Family Independence Initiative (FII) from seeking informational and emotional support from other members on the organization's social media platform, UpTogether. We found that despite participants' perceived shared identity, members primarily requested resources from other UpTogether members through offline interactions due to lack of interpersonal trust. We extend existing research on the limitations of shared identity and the role of interpersonal trust and social norms in facilitating resource-seeking interactions among strangers in low-income contexts. We suggest that social media platforms incorporate pseudonymous posting to facilitate relationship development and allow users to disclose their needs without revealing identifying information.
An energy saving approach: understanding microservices as multidimensional entities in p2p networks

With the use of microservices, many software solutions have been improved in terms of scalability, response efficiency, ease of load balancing among others. However, it is still a challenge to dynamically deploy them according to devices' heterogeneity and energy consumption concerns, while maintaining a defined QoS. Centralized and decentralized approaches that manage microservices deployment have the traditional pros and cons long discussed over time. While the former offer greater control over distributed application components, the latter offers frugal network negotiations, no system-wide crashes, privacy, among others. This work focuses on identifying "ideal" host candidates for microservices' execution in a decentralized network, applying run-time scheduling operations (migration or duplication) to reduce energy consumption. To do this, we created a scheduling algorithm using MAAN (a P2P approach) to interpret a decentralized network as a multidimensional resource (capacity-demand) space, which supports range queries in a logarithmic quantity of hops. In this way, a node that runs a set of microservices is able to 1) map them in terms of their execution requirements (i.e. CPU frequency, RAM capacity, Network rate and disk speed) 2) Select an ideal microservice to be moved or duplicated, 3) find ideal node(s) that meet all those requirements in an optimal computational complexity and 4) negotiate the movement or duplication of the selected microservice, by analyzing energy consumption and QoS criteria.
Main Memory Database Recovery

Many of today’s applications need massive real-time data processing. In-memory database systems have become a good alternative for these requirements. These systems maintain the primary copy of the database in the main memory to achieve high throughput rates and low latency. However, a database in RAM is more vulnerable to failures than in traditional disk-oriented databases because of the memory volatility. DBMSs implement recovery activities (logging, checkpoint, and restart) for recovery proposes. Although the recovery component looks similar in disk- and memory-oriented systems, these systems differ dramatically in the way they implement their architectural components, such as data storage, indexing, concurrency control, query processing, durability, and recovery. This survey aims to provide a thorough review of in-memory database recovery techniques. To achieve this goal, we reviewed the main concepts of database recovery and architectural choices to implement an in-memory database system. Only then, we present the techniques to recover in-memory databases and discuss the recovery strategies of a representative sample of modern in-memory databases.
Adaptive flash sorting for memory-constrained embedded devices

Databases running on embedded devices require efficient sorting algorithms for aggregation, joins, and output ordering. Previous research has produced algorithms optimized for small-memory, flash embedded devices that either use multiple read passes to avoid writes or optimize the external merge sort algorithm. Depending on the data input distribution and memory characteristics, neither approach always outperforms the other. This work produces an adaptive flash sorting algorithm that dynamically determines the best sorting approach at run-time. Experimental results demonstrate that the adaptive sorting algorithm combines the best features of both approaches and allows overall superior performance.
smCompactor: a workload-aware fine-grained resource management framework for GPGPUs

Recently, graphic processing unit (GPU) multitasking has become important in many platforms since an efficient GPU multitasking mechanism can enable more GPU-enabled tasks running on limited physical GPUs. However, current GPU multitasking technologies, such as NVIDIA Multi-Process Service (MPS) and Hyper-Q may not fully utilize GPU resources since they do not consider the efficient use of intra-GPU resources. In this paper, we present smCompactor, which is a fine-grained GPU multitasking framework to fully exploit intra-GPU resources for different workloads. smCompactor dispatches any particular thread blocks (TBs) of different GPU kernels to appropriate stream multiprocessors (SMs) based on our profiled results of workloads. With smCompactor, GPU resource utilization can be improved as we can run more workloads on a single GPU while their performance is maintained. The evaluation results show that smCompactor improves resource utilization in terms of the number of active SMs by up to 33% and it reduces the kernel execution time by up to 26% compared with NVIDIA MPS.
Myofibroblast-Derived Exosome Induce Cardiac Endothelial Cell Dysfunction

Background: Endothelial cells (ECs) play a critical role in the maintenance of vascular homeostasis and in heart function. It was shown that activated fibroblast-derived exosomes impair cardiomyocyte function in hypertrophic heart, but their effect on ECs is not yet clear. Thus, we hypothesized that activated cardiac fibroblast-derived exosomes (FB-Exo) mediate EC dysfunction, and therefore modulation of FB-exosomal contents may improve endothelial function. Methods and Results: Exosomes were isolated from cardiac fibroblast (FB)-conditioned media and characterized by nanoparticle tracking analysis and electron microscopy. ECs were isolated from mouse heart. ECs were treated with exosomes isolated from FB-conditioned media, following FB culture with TGF-β1 (TGF-β1-FB-Exo) or PBS (control) treatment. TGF-β1 significantly activated fibroblasts as shown by increase in collagen type1 α1 (COL1α1), periostin (POSTN), and fibronectin (FN1) gene expression and increase in Smad2/3 and p38 phosphorylation. Impaired endothelial cell function (as characterized by a decrease in tube formation and cell migration along with reduced VEGF-A, Hif1α, CD31, and angiopoietin1 gene expression) was observed in TGF-β1-FB-Exo treated cells. Furthermore, TGF-β1-FB-Exo treated ECs showed reduced cell proliferation and increased apoptosis as compared to control cells. TGF-β1-FB-Exo cargo analysis revealed an alteration in fibrosis-associated miRNAs, including a significant increase in miR-200a-3p level. Interestingly, miR-200a-3p inhibition in activated FBs, alleviated TGF-β1-FB-Exo-mediated endothelial dysfunction. Conclusions: Taken together, this study demonstrates an important role of miR-200a-3p enriched within activated fibroblast-derived exosomes on endothelial cell biology and function.
Botulinum Neurotoxin Therapy for Depression: Therapeutic Mechanisms and Future Perspective

Depression is one of the most common mental disorders, which causes global burden. Antidepressants and psychotherapies are the mainstay of treatment for depression, which have limited efficacy. Thus, alternative approaches for preventing and treating depression are urgently required. Recent clinical trials and preclinical researches have clarified that peripheral facial injection of botulinum neurotoxin type A (BoNT/A) is a rapid, effective and relative safe therapy for improving some symptoms of depression. Despite its safety and efficacy, the underlying therapeutic mechanisms of BoNT/A for depression remains largely unclear. In the present review, we updated and summarized the clinical and preclinical evidence supporting BoNT/A therapy for the treatment of depression. We further discussed the potential mechanisms underlying therapeutic effects of BoNT/A on depression. Notably, we recently identified that the anti-depressant effects of BoNT/A associated with up-regulation of 5-HT levels and brain-derived neurotrophic factor (BDNF) expression in the hippocampus in a preclinical mouse model. In summary, these studies suggest that BoNT/A therapy is a potential effective and safe intervention for the management of depression. However, fundamental questions remain regarding the future prospects of BoNT/A therapy, including safety, efficacy, dose-response relationships, identification of potential predictors of response, and the precise mechanisms underlying BoNT/A therapy.
mHealth: Potentials and Risks for Addressing Mental Health and Well-Being Issues Among Nepali Adolescents

Adolescents are slowly being recognized as a generation, worldwide, that may require different policy approaches to improve staggering statistics on their failing well-being, including mental health. By providing the support to allow the next generation to achieve better mental health outcomes, they are going to be more economically successful and the future economic growth of nations can be better assured. Adoption of mobile-based health interventions (e.g., mHealth) has garnered a lot of attention toward this end. While mHealth interventions are growing in popularity, many researchers/policy-makers appear to have neglected assessing potential (indirect) costs/negative consequences from their use. Evidence from the developed world shows strong associations between extensive cell phone use and negative mental health outcomes, but similar research is minimal in developing world contexts. Additionally, the bulk of work on the outcomes of mobile phone use is studied using a unidirectional approach with blinders to front-end motivations. Using primary data from a large-scale, school-based survey of older adolescents in southwestern Nepal (N = 539), this work investigates such a tension between mobile/smartphone usage as a true mobile health (mHealth) opportunity in Nepal or as a potential problem, introducing additional deleterious well-being effects from over-use. Founded in Basic Psychological Needs Theory (BPNT), robust results of analyses using full structural modeling approaches (and traditional regression-based sensitivity analyses) indicate support for the BPNT framework in explaining statistically significant positive associations between bullying and anxiety, as well as, negative associations between bullying and grit, including evidence to support the mediating role of problematic mobile phone use in these relationships. More than 56% of the sample showed indicators of mild to moderate anxiety and over 10% claim experiences of bullying, coupled with over 75% of the sample scoring above the midline of a problematic mobile phone use scale, all of which motivates the relevance of our findings. Potential policy implications of these findings, and mention of other intriguing avenues for future work are further discussed.
Efficacy and Safety of Panax Notoginseng Saponins (Xueshuantong) in Patients With Acute Ischemic Stroke (EXPECT) Trial: Rationale and Design

Background: Although revascularization treatment is recommended as the first-line therapy for patients with non-minor acute ischemic stroke (AIS), it only benefits a minority of patients. Previous studies have reported the positive effects of Panax notoginseng saponins (PNS) (Xueshuantong lyophilized powder) on AIS, however, there have been no rigorous trials. This study aims to assess the efficacy and safety of PNS therapy for patients with AIS. Methods: The Evaluation of Xueshuantong in Patients with acutE ischemiC sTroke (EXPECT) trial is a multicenter, randomized, placebo-controlled, double-blind study aiming to enroll 480 patients in China. Eligible patients with AIS within 72 h of symptom onset will randomly receive either PNS or PNS placebo for 10 days and subsequently be followed up to 90 days. The primary outcome will be a change in the National Institute of Health Stroke Scale (NIHSS) score from baseline to 10 post-randomization days. The secondary outcomes include early neurological improvement (proportion of patients with NIHSS score 0–1), and Patient-Reported Outcomes Scale for Stroke score at 10 post-randomization days, the proportion of patients with life independence (modified Rankin Scale score of 0–1), the proportion of patients with a favorable outcome (Barthel Index ≥90), and Stroke-Specific Quality of Life score at 90 days. Adverse events or clinically significant changes in vital signs and laboratory parameters, regardless of the severity, will be recorded during the trial to assess the safety of PNS. Conclusions: To our knowledge, this study is the first double-blind trial to assess the efficacy and safety of PNS in patients with AIS. Findings of the EXPECT trial will be valuable in improving evidence regarding the clinical application of PNS therapy in patients with AIS ineligible for revascularization treatment in the reperfusion era.
The Prenatal Origin of Childhood Leukemia: Potential Applications for Epidemiology and Newborn Screening

Childhood leukemias are heterogeneous diseases with widely differing incident rates worldwide. As circulating tumors, childhood acute leukemias are uniquely accessible, and their natural history has been described in greater detail than for solid tumors. For several decades, it has been apparent that most cases of childhood acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) initiate in utero. Circumstantial evidence in support of this contention includes the young age of onset and high rate of concordance among identical twins. “Backtracking” of leukemic somatic mutations, particularly gene translocations, to cord blood and dried blood spots collected during the perinatal period has provided molecular proof of prenatal leukemogenesis. Detection of a patient's leukemia translocation in easily accessible birth samples, such as dried blood spots, is straightforward with the knowledge of their idiosyncratic breakpoints. However, to translate these findings into population-based screening and leukemia prevention requires novel methods able to detect translocations at all possible breakpoints when present in a low frequency of cells. Several studies have attempted to screen for leukemic translocations, mainly the common ETV6-RUNX1 translocation, in cord blood samples from healthy children. Most studies have reported finding translocations in healthy children, but estimates of prevalence have varied widely and greatly exceed the incidence of leukemia, leading to concerns that technical artifact or contamination produced an artificially inflated estimate of translocation prevalence at birth. New generation techniques that capture the presence of these translocations at birth have the potential to vastly increase our understanding of the epidemiology of acute leukemias. For instance, if leukemic translocations are present at birth in a far higher proportion of children than eventually develop acute leukemia, what are the exposures and somatic molecular events that lead to disease? And could children with translocations present at birth be targeted for prevention of disease? These questions must be answered before large-scale newborn screening for leukemia can occur as a public health initiative. Here, we review the literature regarding backtracking of acute leukemias and the prevalence of leukemic translocations at birth. We further suggest an agenda for epidemiologic research using new tools for population screening of leukemic translocations.
Recovery of Depleted miR-146a in ALS Cortical Astrocytes Reverts Cell Aberrancies and Prevents Paracrine Pathogenicity on Microglia and Motor Neurons

Reactive astrocytes in Amyotrophic Lateral Sclerosis (ALS) change their molecular expression pattern and release toxic factors that contribute to neurodegeneration and microglial activation. We and others identified a dysregulated inflammatory miRNA profile in ALS patients and in mice models suggesting that they represent potential targets for therapeutic intervention. Such cellular miRNAs are known to be released into the secretome and to be carried by small extracellular vesicles (sEVs), which may be harmful to recipient cells. Thus, ALS astrocyte secretome may disrupt cell homeostasis and impact on ALS pathogenesis. Previously, we identified a specific aberrant signature in the cortical brain of symptomatic SOD1-G93A (mSOD1) mice, as well as in astrocytes isolated from the same region of 7-day-old mSOD1 mice, with upregulated S100B/HMGB1/Cx43/vimentin and downregulated GFAP. The presence of downregulated miR-146a on both cases suggests that it can be a promising target for modulation in ALS. Here, we upregulated miR-146a with pre-miR-146a, and tested glycoursodeoxycholic acid (GUDCA) and dipeptidyl vinyl sulfone (VS) for their immunoregulatory properties. VS was more effective in restoring astrocytic miR-146a, GFAP, S100B, HMGB1, Cx43, and vimentin levels than GUDCA, which only recovered Cx43 and vimentin mRNA. The miR-146a inhibitor generated typical ALS aberrancies in wild type astrocytes that were abolished by VS. Similarly, pre-miR-146a transfection into the mSOD1 astrocytes abrogated aberrant markers and intracellular Ca2+ overload. Such treatment counteracted miR-146a depletion in sEVs and led to secretome-mediated miR-146a enhancement in NSC-34-motor neurons (MNs) and N9-microglia. Secretome from mSOD1 astrocytes increased early/late apoptosis and FGFR3 mRNA in MNs and microglia, but not when derived from pre-miR-146a or VS-treated cells. These last strategies prevented the impairment of axonal transport and synaptic dynamics by the pathological secretome, while also averted microglia activation through either secretome, or their isolated sEVs. Proteomic analysis of the target cells indicated that pre-miR-146a regulates mitochondria and inflammation via paracrine signaling. We demonstrate that replenishment of miR-146a in mSOD1 cortical astrocytes with pre-miR-146a or by VS abrogates their phenotypic aberrancies and paracrine deleterious consequences to MNs and microglia. These results propose miR-146a as a new causal and emerging therapeutic target for astrocyte pathogenic processes in ALS.