Miracle-dz/UniFAR-FTUs huggingface dataset:
When Official Systems Overload: A Framework for Finding Social Media Calls for Help during Evacuations

During large-scale disasters it is not uncommon for Public Safety Answering Points (e.g., 9-1-1) to encounter service disruptions or become overloaded due to call volume. As observed in the two past United States hurricane seasons, citizens are increasingly turning to social media whether as a consequence of their inability to reach 9-1-1, or as a preferential means of communications. Relying on past research that has examined social media use in disasters, combined with the practical knowledge of the first-hand disaster response experiences, this paper develops a knowledge-driven framework containing parameters useful in identifying patterns of shared information on social media when citizens need help. This effort explores the feasibility of determining differences, similarities, common themes, and time-specific discoveries of social media calls for help associated with hurricane evacuations. At a future date, validation of this framework will be demonstrated using datasets from multiple disasters. The results will lead to recommendations on how the framework can be modified to make it applicable as a generic disaster-type characterization tool.
Survey of Methodologies, Approaches, and Challenges in Parallel Programming Using High-Performance Computing Systems

This paper provides a review of contemporary methodologies and APIs for parallel programming, with representative technologies selected in terms of target system type (shared memory, distributed, and hybrid), communication patterns (one-sided and two-sided), and programming abstraction level. We analyze representatives in terms of many aspects including programming model, languages, supported platforms, license, optimization goals, ease of programming, debugging, deployment, portability, level of parallelism, constructs enabling parallelism and synchronization, features introduced in recent versions indicating trends, support for hybridity in parallel execution, and disadvantages. Such detailed analysis has led us to the identification of trends in high-performance computing and of the challenges to be addressed in the near future. It can help to shape future versions of programming standards, select technologies best matching programmers’ needs, and avoid potential difficulties while using high-performance computing systems.
From Chance to Serendipity: Knowledge Workers' Experiences of Serendipitous Social Encounters

Serendipity refers to uncontrolled circumstances that lead to unexpected yet fortunate discoveries. The phenomenon has been studied extensively in relation to information retrieval. However, serendipity in the context of social encounters has been the subject of few empirical studies. In professional life, social serendipity might result in benefits such as fruitful collaboration, successful recruitment, discovery of novel information, and acquisition of crucial new perspectives from peers. Despite the potential significance of serendipity, particularly for knowledge work, there is a lack of empirical understanding of related subjective experiences and the role of technology within the process of encountering unsought findings. This qualitative study investigates knowledge workers’ detailed narratives of serendipitous social encounters and the related factors through an analysis of 37 responses to an international online survey. We provide a detailed account of the experiential characteristics and contextual qualities of the reported instances of social serendipity. Finally, we discuss the seemingly minor role of technology in social serendipity and research avenues to computationally enhance social serendipity.
Analysis and Design of Systematic Rateless Codes in FH/BFSK System with Interference

The asymptotic analysis of systematic rateless codes in frequency hopping (FH) systems with interference is first provided using discretized density evolution (DDE) and compared with the traditional fixed-rate scheme. A simplified analysis with Gaussian assumption of initial message is proposed in the worst case of interference, which has much lower complexity and provides a very close result to DDE. Based on this simplified analysis, the linear programming is employed to design rateless codes and the simulation results on partial-band interference channels show that the optimized codes have more powerful antijamming performance than the codes originally designed for conventional systems.
Semantic Smart World Framework

This paper presents a general Semantic Smart World framework (SSWF), to cover the Migratory birds’ paths. This framework combines semantic and big data technologies to support meaning for big data. In order to build the proposed smart world framework, technologies such as cloud computing, semantic technology, big data, data visualization, and the Internet of Things are hybrid. We demonstrate the proposed framework through a case study of automatic prediction of air quality index and different weather phenomena in the different locations in the world. We discover the association between air pollution and increasing weather conditions. The experimental results indicate that the framework performance is suitable for heterogeneous big data.
A Counselors-Based Intrusion Detection Architecture

—Intrusion Detection Systems (IDSs) are a fundamental component of defensive solutions. In particular, signature-based IDSs aim to detect malicious activities on computer systems and networks by relying on data classiﬁcation models built from a training dataset. However, classiﬁers performance can vary for each attack pattern. A common technique to overcome this issue is to use ensemble methods, where multiple classiﬁers are employed and a ﬁnal decision is taken combining their outputs. Despite the potential advantages of such an approach, its usefulness is limited in scenarios where (i) multiple expert classiﬁers present divergent results or (ii) representative data are missing to detect a speciﬁc attack class. In this work, we introduce the concept of counselor networks to deal with conﬂicts from different classiﬁers by exploiting the collaboration between IDSs that analyze multiple and heterogeneous data sources. Our empirical results demonstrate the feasibility of the proposed architecture in improving the accuracy of the intrusion detection process.
Nearest Ω-stable matrix via Riemannian optimization

We study the problem of finding the nearest $\Omega$-stable matrix to a certain matrix $A$, i.e., the nearest matrix with all its eigenvalues in a prescribed closed set $\Omega$. Distances are measured in the Frobenius norm. An important special case is finding the nearest Hurwitz or Schur stable matrix, which has applications in systems theory. We describe a reformulation of the task as an optimization problem on the Riemannian manifold of orthogonal (or unitary) matrices. The problem can then be solved using standard methods from the theory of Riemannian optimization. The resulting algorithm is remarkably fast on small-scale and medium-scale matrices, and returns directly a Schur factorization of the minimizer, sidestepping the numerical difficulties associated with eigenvalues with high multiplicity.
Requirements for Software to Support the use of Social Media in Emergency Management: A Delphi Study

Social Media contain a wealth of information that could improve the situational awareness of Emergency Managers during a crisis, but many barriers stand in the way. These include information overload, making it impossible to deal with the flood of raw posts, and lack of trust in unverified crowdsourced data. The purpose of this project is to build a communications bridge between emergency responders and technologists who can provide the advances needed to realize social media’s full potential. We are employing a Delphi study survey design, which is a technique for exploring and developing consensus among a group of experts around a particular topic. Participants include emergency managers and technologists with experience in software to support the use of social media in crisis response, from many countries. The topics of the study are described and preliminary, partial results presented for Round 1 of the study, based on 33 responses.
EMAssistant: A Learning Analytics System for Social and Web Data Filtering to Assist Trainees and Volunteers of Emergency Services

An increasing number of Machine Learning based systems are being designed to filter and visualize the relevant information from social media and web streams for disaster management. Given the dynamic disaster events, the notion of ​ relevant information evolves, and thus, the active learning techniques are often considered to keep updating the predictive models for the relevant information filtering. However, the active relevant feedback provided by the human annotators to update the models are not validated. As a result, they can introduce unconscious biases in the learning process of humans and can result in an inaccurate or inefficient predictive system. Therefore, this paper describes the design and implementation of an open-source technology-based learning analytics system ‘ ​ EMAssistant ​ ’ for the emergency volunteers or practitioners - referred as the trainee, to enhance their experiential learning cycle with the cause-effect reasoning on providing relevant feedback to the machine learning model. This continuous integration between the cause (providing feedback) and the effect (observing predictions from the updated model) in a visual form will likely to improve the understanding of the trainees to provide more accurate feedback. We propose to present the system design as well as provide hands-on exercises for the conference session.
Deep Learning on Computational-Resource-Limited Platforms: A Survey

Nowadays, Internet of Things (IoT) gives rise to a huge amount of data. IoT nodes equipped with smart sensors can immediately extract meaningful knowledge from the data through machine learning technologies. Deep learning (DL) is constantly contributing significant progress in smart sensing due to its dramatic superiorities over traditional machine learning. The promising prospect of wide-range applications puts forwards demands on the ubiquitous deployment of DL under various contexts. As a result, performing DL on mobile or embedded platforms is becoming a common requirement. Nevertheless, a typical DL application can easily exhaust an embedded or mobile device owing to a large amount of multiply and accumulate (MAC) operations and memory access operations. Consequently, it is a challenging task to bridge the gap between deep learning and resource-limited platforms. We summarize typical applications of resource-limited deep learning and point out that deep learning is an indispensable impetus of pervasive computing. Subsequently, we explore the underlying reasons for the high computational overhead of DL through reviewing the fundamental concepts including capacity, generalization, and backpropagation of a neural network. Guided by these concepts, we investigate on principles of representative research works, as well as three types of solutions: algorithmic design, computational optimization, and hardware revolution. In pursuant to these solutions, we identify challenges to be addressed.
A novel method detecting controversial interaction in the multiplex social comment network

With the rise of online social media, users from across the world can participate in opinion formation processes, and some discussions lead to controversial debates around this phenomenon. Controversy detection in social media can help explore public discourse spaces and understand topical issues. Previous controversial detection studies focus more on identifying the opinion or emotional orientation of a comment, while we focus on whether there is a controversial relationship between a comment and its replies. Here, we collect a dataset consisting of 511 news articles, 103,787 comments, and 71,579 users on the Chinese social media platform, Toutiao, and we study the controversial interactions on the subsets of this dataset. Our approach treats news, comments, and users as different types of nodes and constructs multiplex networks connected by user–comment links (i.e., publishing relationship), comment–news links (i.e., comment relationship), and comment–comment links (i.e., replying relationship). Furthermore, we propose a model based on deep learning to detect controversial interactions from these multiplex networks. Our supervised model achieves 83.24% accuracy, with an improvement compared to competitive models. Moreover, we illustrate the applicability of our approach using different ratios of training and testing sets. Our results demonstrate the usefulness of the multiplex networks model for controversial interaction detection and provide a new perspective on controversy detection problems.
Surgical Options in Treating Patients with Primary Hyperparathyroidism

Abstract Background Primary hyperparathyroidism is the third most common endocrine disorder for which surgical procedure called parathyroidectomy is the most effective treatment. Since the early 20th century, parathyroid surgery has improved extensively. With the advances in preoperative imaging and with understanding the causes of disease, new and minimally invasive surgical approaches overrode the standard bilateral exploratory operations. Directed parathyroidectomy is currently the standard technique for treatment of primary hyperparathyroidism worldwide. Conclusions Surgery is the only definitive treatment of primary hyperparathyroidism. The most appropriate type of surgical procedure depends on the number and localization of the hyperactive parathyroid glands, availability of modern imaging techniques, limitation of each type of procedure and expertise.
Virtues and Phronesis: Making Decisions in the Clinical Context

Bester, J. 2020. Beneficence, interests, and wellbeing in medicine: What it means to provide benefit to patients. The American Journal of Bioethics 20(3): 53–62. Brock, D. W. 1993. Quality of life measures in health care and medical ethics. In: The Quality of Life, ed. Martha Nussbaum and Amartya Sen, 95–132. New York: Oxford University Press. Crisp, R. 2017. Well-being. Stanford Encyclopedia of Philosophy (plato.stanford.edu/archives/fall2017/entries/ well-being/) (accessed December 4, 2019). DeGrazia, D. 1995. Value theory and the best interest standard. Bioethics 9(1): 50–61. DeGrazia, D. , and J. Millum. Value theory. In A Theory of Bioethics. (under review). Powers, M. , and R. Faden. 2006. Justice and Well-Being. In Social Justice: The Moral Foundations of Public Health and Health Policy, 15–49. New York: Oxford University Press.
Targeted axillary dissection of carbon‐tattooed metastatic lymph nodes in combination with post–neo‐adjuvant sentinel lymph node biopsy using 1% methylene blue in breast cancer patients

Staging the axilla post-NACT (neo-adjuvant chemotherapy) by sentinel lymph node biopsy (SLNB) may allow patients to avoid undergoing axillary lymph node dissection (ALND)1; however, its optimum usage is still questionable. Three prospective clinical trials (ACOSOG Z1071, SENTINA, and SN FNAC) evaluated the accuracy of SLNB post-NACT described identification rates (IR) ranging from 87.6%-92.9%. Overall, false-negative rates (FNRs) were higher than 10%, and all those trials did not achieve their primary end point. Reduction of the FNR (12.6%-9.8%) could be achieved in those patients with a normal axillary ultrasound (US) after NACT and three or more removed SLNs.2-4 Methylene blue (MB) 1% is used for SLNB in node-negative (–ve) early breast cancer with IR and FNR comparable to other blue dyes or radioactive isotopes at a very low cost5 The natural concept that the best LN to evaluate response to NACT is the pathologic proven LN before starting NACT has been previously emphasized. Marking +ve LNs pre-NACT and then removing them together with SLNB post-NACT was described as targeted axillary dissection (TAD). Applying a marker clip for +ve LNs under US guidance and then marking these LNs post-NACT with radioactive I125 seeds achieved IR of 96.7%, concordance among clipped LNs and SLNs reached 91%. 6 Caudle et al, 2016, used similar technique with IR of clipped nodes of 97.6%. The marked LN was recognized as SLN in 77% of cases.7 Donker et al, 2015, used only radioactive I125 seed for marking +ve ALNs pre-NACT with IR of 97%. 8 Tattooing of biopsied ALNs using sterile black carbon suspension (SpotTM) was tried as a technique for TAD. SpotTM is FDA-approved carbon-based marker for presurgical colonic tattooing and remains in the site for months. An equivalent to SpotTM is also available, BlackEye ink. Choy et al, 2015, used suspended carbon particles (SpotTM) to mark +ve ALNs in 28 cases. Overall, IR was 96.4%. 9 Park et al, in 2018, used CharcotraceTM for 20 cases pre-NACT, IR was 100% during surgery. Concordance among tattooed LNs and SLNs was described to be 75%. 10
The Amyloid, Tau, and Neurodegeneration (A/T/N) Classification Applied to a Clinical Research Cohort with Long-Term Follow-Up

Background: The unbiased amyloid, tau, and neurodegeneration (A/T/N) classification is designed to characterize individuals in the Alzheimer continuum and is currently little explored in clinical cohorts. Objective: A retrospective comparison of the A/T/N classification system with the results of a two-year clinical study, with extended follow-up up to 10 years after inclusion. Methods: Patients (n = 102) clinically diagnosed as Alzheimer’s disease (AD) with dementia or amnestic mild cognitive impairment (MCI), and 61 cognitively healthy control individuals were included. Baseline cerebrospinal fluid core biomarkers for AD (Aβ42, phosphorylated tau, and total tau) were applied to the A/T/N classification using the final clinical diagnosis at extended follow-up as the gold standard. Results: A + T + N+ was a strong predictor for AD dementia, even among cognitively healthy individuals. Amnestic MCI was heterogenous, considering both clinical outcome and distribution within A/T/N. Some individuals with amnestic MCI progressed to clinical AD dementia within all four major A/T/N groups. The highest proportion of progression was among triple positive cases, but progression was also common in individuals with suspected non-Alzheimer pathophysiology (A-T + N+), and those with triple negative status. A-T-N- individuals who were cognitively healthy overwhelmingly remained cognitively intact over time, but in amnestic MCI the clinical outcome was heterogenous, including AD dementia, other dementias, and recovery. Conclusion: The A/T/N framework accentuates biomarkers over clinical status. However, when selecting individuals for research, a combination of the two may be necessary since the prognostic value of the A/T/N framework depends on clinical status.
No-reflow phenomenon during percutaneous coronary intervention in a patient with polycythemia vera

Abstract Rationale: Acute myocardial infarction is the leading cause of mortality and morbidity in a patient with polycythemia vera (PV). However, the benefit of various percutaneous coronary intervention (PCI) technique on the patient with PV is relatively unexplored. Patient concern: A 46-year-old woman presented to the primary hospital complained about new-onset typical chest pain. Echocardiography examination showed inferior ST-elevation myocardial infarction (STEMIs) and increased cardiac markers. Complete blood count showed elevated hemoglobin, white blood cell, and platelet. Diagnosis: Coronary angiography revealed simultaneous total occlusion at proximal right coronary artery (RCA) and also at proximal left anterior descending (LAD) artery. Elevated hemoglobin and hematocrit with JAK2 mutation establish the diagnosis of PV. Interventions: We performed multi-vessel primary PCI by using direct stenting in RCA and aspiration thrombectomy in LAD after failed with balloon dilatation and direct stenting method. This procedure resulted in thrombolysis in myocardial infarction (TIMI)-3 flow in both coronary arteries. However, the no-reflow phenomenon occurred in the LAD, followed by ventricular fibrillation. After several attempts of resuscitation, thrombus aspiration, and low-dose intracoronary thrombolysis, the patient was returned to spontaneous circulation. The patient then received dual antiplatelet and cytoreductive therapy. Outcomes: The patient clinical condition and laboratory finding were improved, and the patient was discharged on the 7th day after PCI. Lessons: Cardiologist should be aware of the no-reflow phenomenon risk in the patient with PV and STEMI. Direct stenting, intracoronary thrombectomy, and thrombolysis are preferable instead of balloon dilatation for PCI technique in this patient.
Deciphering UV‐induced DNA Damage Responses to Prevent and Treat Skin Cancer

Ultraviolet (UV) radiation is among the most prevalent environmental factors that influence human health and disease. Even 1 h of UV irradiation extensively damages the genome. To cope with resulting deleterious DNA lesions, cells activate a multitude of DNA damage response pathways, including DNA repair. Strikingly, UV‐induced DNA damage formation and repair are affected by chromatin state. When cells enter S phase with these lesions, a distinct mutation signature is created via error‐prone translesion synthesis. Chronic UV exposure leads to high mutation burden in skin and consequently the development of skin cancer, the most common cancer in the United States. Intriguingly, UV‐induced oxidative stress has opposing effects on carcinogenesis. Elucidating the molecular mechanisms of UV‐induced DNA damage responses will be useful for preventing and treating skin cancer with greater precision. Excitingly, recent studies have uncovered substantial depth of novel findings regarding the molecular and cellular consequences of UV irradiation. In this review, we will discuss updated mechanisms of UV‐induced DNA damage responses including the ATR pathway, which maintains genome integrity following UV irradiation. We will also present current strategies for preventing and treating nonmelanoma skin cancer, including ATR pathway inhibition for prevention and photodynamic therapy for treatment.
Clinical profile and early therapeutic response to cabergoline of patients with hyperprolactinemia in a Cameroonian population

Hyperprolactinemia is responsible for 20 to 25% of consultations of secondary amenorrhea and 17% for female infertility. Dopamine agonists are the gold standard treatment of hyperprolactinemia. Although they are associated with various adverse effects, cabergoline is generally preferred due to better compliance, limited side effects and good therapeutic response. However, bromocriptine is widely and satisfactorily used in a context of limited availability of cabergoline. We sought to describe clinical manifestations of hyperprolactinemia and response to cabergoline in a sub Saharan Africa (SSA) setting. We describe the profile of all patients with a diagnosis of hyperprolactinaemia from 1st July 2012 to 15th May 2014 at the Endocrinology Department of Yaoundé Central Hospital. Patients with physiological hyperprolactinemia were not considered. All patients were routinely started on cabergoline at 0.5mg/week or at 1mg/week in case of macroprolactinoma or desire to become pregnant. The duration of follow up was 8-16 months. After three months of treatment, 8 of 10 patients with amenorrhea had menses and serum prolactin levels decreased significantly at month 2-3 (p = 0.025). In conclusion, our study suggests that cabergoline yields an excellent therapeutic response in a short period of time and may thus be cost saving in sub Saharan context despite its unit price.