Miracle-dz/UniFAR-FTUs huggingface dataset:
Existing Data Sources for Clinical Epidemiology: Database of the National Hospital Organization in Japan

Abstract This review introduces the National Hospital Organization (NHO) database in Japan. The NHO has maintained two databases through a system of data collection from 140 hospitals in the NHO. National Hospital Organization Clinical Data Archives (NCDA) is collecting clinical information in real time from the electronic medical records since January 2016, and Medical Information Analysis (MIA) databank is collecting daily insurance claims data since April 2010. The NHO database covers more than 8 million patients in 140 hospitals throughout Japan. The database consists of the information of patient profiles, hospital admission and discharge, diagnosis with ICD-10 codes, text data from medical chart, daily health insurance claims such as medical procedures, medications or surgeries, vital signs and laboratory data, and so on. The NHO database includes a wide variety of diseases and settings, including acute, chronic and intractable diseases, emergency medical services, disaster medicine, response to emerging infectious disease outbreaks, medical care according to health policies such as psychiatry, tuberculosis, or muscular dystrophy, and health systems in sparsely populated non-urban areas. Among several common diseases, the database has representativeness in terms of age distribution compared with the Patient Survey 2017 by the Ministry of Health, Labour and Welfare. Interested researchers can contact (700-dbproject@mail.hosp.go.jp) the NHO database division to obtain more information about the NHO database for utilization.
Unwrapping Software Projects Success in Asia: Assessing the Role Of Authentic Leadership, Psychological Empowerment, and Job Engagement in Project Success Using a Serial-Mediation Approach

This research examines the impact of authentic leadership style on project success through mediation effects of psychological empowerment and job engagement in the software industry of Pakistan. Data were collected from 343 self-reports of employees working on different software projects in Pakistan. As per results, authentic leadership enhanced project success directly and indirectly through the serial-mediation effects of psychological empowerment and job engagement. The current findings contribute to prior literature in many ways: (i) social exchange theory—by identifying new antecedents and outcomes of job engagement; (ii) leadership literature—by offering an integrated framework with psychological empowerment and job engagement as two explanatory mechanisms (mediators) linking the under researched authentic leadership to project success; (iii) project management literature—by unwrapping new antecedents (i.e., authentic leadership, psychological empowerment, and job engagement) to project success; offers an empirical case for the potential applications of the undermined leadership style, authentic leadership, in the software sector.
Understanding summer wind systems over the eastern Mediterranean in a high‐resolution climate simulation

Regional and local wind systems are often complex, particularly near coastal areas with a highly variable orography. Thus, the realistic representation of regional wind systems in weather and climate models is of strong relevance. Here, we evaluate the ability of a 13‐year convection‐permitting climate simulation in reproducing the interaction of several regional summer wind systems over the complex orography in the eastern Mediterranean region. The COSMO‐CLM simulations are driven by hourly ERA‐5 reanalysis and have a spatial resolution of 2.8 and 7.0 km. The simulated near‐surface wind fields are compared with unique very high‐resolution wind observations collected within the “Dead Sea Research Venue” project (DESERVE) and data from the Israel Meteorological Service synop network. The high‐resolution COSMO‐CLM simulations largely reproduce the main characteristics of the regional wind systems (Mediterranean and Dead Sea breeze, slope winds in the Judean Mountains and winds along the Jordan Rift valley), whereas ERA‐5 is only able to represent the Mediterranean Sea breeze. The high‐resolution simulations substantially improve the representation of regional winds, particularly over complex orography. Indeed, the 2.8 km simulation outperforms the 7.0 km run, on 88% of the days. Two mid‐July 2015 case studies show that only the 2.8 simulation can realistically simulate the penetration of the Mediterranean Sea Breeze into the Jordan Rift valley and complex interactions with other wind systems like the Dead Sea breeze. Our results may have profound implications for regional weather and climate prediction since very high‐resolution information seems to be necessary to reproduce the main summertime climatic features in this region. We envisage that such simulations may also be required at other regions with complex orography.
AI-DRIVEN MAIZE YIELD FORECASTING USING UNMANNED AERIAL VEHICLE-BASED HYPERSPECTRAL AND LIDAR DATA FUSION

Abstract. The increased availability of remote sensing data combined with the wide-ranging applicability of artificial intelligence has enabled agriculture stakeholders to monitor changes in crops and their environment frequently and accurately. Applying cutting-edge technology in precision agriculture also enabled the prediction of pre-harvest yield from standing crop signals. Forecasting grain yield from standing crops benefits high-throughput plant phenotyping and agriculture policymaking with information on where crop production is likely to decline. Advanced developments in the Unmanned Aerial Vehicle (UAV) platform and sensor technologies aided high-resolution spatial, spectral, and structural data collection processes at a relatively lower cost and shorter time. In this study, UAV-based LiDAR and hyperspectral images were collected during the growing season of 2020 over a cornfield near Urbana Champaign, Illinois, USA. Hyperspectral imagery-based canopy spectral & texture features and LiDAR point cloud-based canopy structure features were extracted and, along with their combination, were used as inputs for maize yield prediction under the H2O Automated Machine Learning framework (H2O-AutoML). The research results are (1) UAV Hyperspectral imagery can successfully predict maize yield with relatively decent accuracies; additionally, LiDAR point cloud-based canopy structure features are found to be significant indicators for maize yield prediction, which produced slightly poorer, yet comparable results to hyperspectral data; (2) regardless of machine learning methods, integration of hyperspectral imagery-based canopy spectral and texture information with LiDAR-based canopy structure features outperformed the predictions when using a single sensor alone; (3)the H2O-AutoML framework presented to be an efficient strategy for machine learning-based data-driven model building.
Bug cemetery: a case study of terrestrial isopod distribution on a brick wall in the Czech Republic

﻿Abstract Although terrestrial isopods (Oniscidea) are primarily soil- and surface-dwelling invertebrates, they can also be found on tree trunks and walls. This study evaluated distribution patterns of terrestrial isopods on a brick wall during the first hours of night in autumn. Four species of terrestrial isopods were recorded with Armadillidiumversicolor being the dominant one. Terrestrial isopods were distributed from ground level up to a height of 2 m, but preferred a 70–80 cm height band. The highest number of active individuals was observed 3 h after astronomical dusk. Potential predators of terrestrial isopods were abundant during the same time and at the same height.
Stakeholder perspectives on ‘Swiss quality’ healthcare in the context of inbound medical tourism to Switzerland: An exploratory qualitative study

Background Global literature on medical travel focuses on patients from high-income countries traveling to middle-income countries reputed for providing high quality care at significantly lower costs. However, little is known about the movement of wealthy foreign patients to high-income countries such as the USA, the UK or Switzerland. Objective In this paper, we focus on Swiss medical tourism stakeholders and their strategies to attract a niche of self-paying foreign patients to undergo medical treatment in Switzerland. We describes how they harness the label of ‘Swiss quality’ healthcare to promote Switzerland in global medical travel industry. Methods We conducted semi-structured, in-depth interviews with 30 medical tourism stakeholders including 15 healthcare professionals (HCPs) treating self-paying foreign patients in publicly funded and privately owned hospitals in French- and German-speaking regions of Switzerland. The data were inductively coded using thematic analysis. Results We present their views on ‘Swiss quality’ healthcare under three themes: health-related human resources, Swiss medical infrastructure, and foreign patients’ perceived trust in the ‘Swiss quality’ label. Participants equated Switzerland's global image and ‘Swiss quality’ as a ‘brand’ and believed that foreign patients’ trust in ‘Swiss quality’ is a unique selling point of Swiss healthcare system. Conclusion By problematizing the use of ‘Swiss quality’ label to attract wealthy patients to Switzerland, we highlight a few ethical questions that deserve further scrutiny.
A Review on Productivity Improvement in Manufacturing Processes

Productivity is a matter of some debate since it is directly related to the GDP growth of a specific company or manufacturing firm. Productivity is the lifeblood of any industrial company's profitability. The product that is not produced in sufficient quantities results in lower-income because there is simply less to sell. Prices may need to be raised if production levels are low in order to earn the necessary profitability for survival. However, if buyers can find a lower price elsewhere, this move is likely to drive them away. Productivity is one of the most important markers of success in the manufacturing industry. In truth, most factories' primary goal is to produce enough items to grow and meet customer satisfaction. In this review, we have discussed various factors which influence the productivity of the workplace. Furthermore, methodologies adopted by several authors, previously in the sector of enhancing the productivity of a particular machine or a workplace, have been discussed. This review is focused on the assessment of different projects executed in order to improve the productivity of a particular machine like VMC or CNC or a workplace for a manufacturing company. Key Words- Productivity, VMC, CNC, Production, Customer Satisfaction.
Minimal peace in Northeast Asia: a realist-liberal explanation

Abstract Northeast Asia is usually associated with conflict and war. Challenging this prevailing view, this article shows that the sub-region has achieved minimal peace since its peaceful transition from the Cold War to the post-Cold War period. The questions posed are: (a) what factors are responsible for Northeast Asia’s minimal peace? ; and (b) how will these factors respond to the worsening US-China competition since 2010? This article’s argument is two-fold. First, Northeast Asia’s minimal peace is explained by three realist-liberal factors: America’s hegemony; strong economic interdependence among the Northeast Asian states; and a stable institutional structure in East Asia, including Northeast Asia. These factors kept a stable balance of power, ensured development and prosperity, and mitigated the political and strategic tensions between the states. Second, Northeast Asia’s minimal peace would be durable to counter the negative effects of the Sino-US competition in the coming decades. While the economic interdependence and institutional building factors have shown resilience, the US hegemony faces a robust challenge from China. Nevertheless, the US hegemony is durable because of America’s enduring relative strategic and economic advantages over China, the expanded role of America’s regional allies to preserve US preponderance and China’s problems in building an alternative regional order.
One Fuzzing Strategy to Rule Them All

Coverage-guided fuzzing has become mainstream in fuzzing to automatically expose program vulnerabilities. Recently, a group of fuzzers are proposed to adopt a random search mechanism namely Havoc, explicitly or implicitly, to augment their edge exploration. However, they only tend to adopt the default setup of Havoc as an implementation option while none of them attempts to explore its power under diverse setups or inspect its rationale for potential improvement. In this paper, to address such issues, we conduct the first empirical study on Havoc to enhance the understanding of its characteristics. Specifically, we first find that applying the default setup of Havoc to fuzzers can significantly improve their edge coverage performance. Interestingly, we further observe that even simply executing Havoc itself without appending it to any fuzzer can lead to strong edge coverage performance and outper-form most of our studied fuzzers. Moreover, we also extend the execution time of Havoc and find that most fuzzers can not only achieve significantly higher edge coverage, but also tend to perform similarly (i.e., their performance gaps get largely bridged). Inspired by the findings, we further propose HavocMAB, which models the Havoc mutation strategy as a multi-armed bandit problem to be solved by dynamically adjusting the mutation strategy. The evaluation result presents that HavocMAB can significantly increase the edge coverage by 11.1% on average for all the benchmark projects compared with Havoc and even slightly outperform state-of-the-art QSYM which augments its computing resource by adopting three parallel threads. We further execute HavocMAB with three parallel threads and result in 9% higher average edge coverage over QSYM upon all the benchmark projects.
Poster: Improved Website Fingerprinting Attacks based on Tor Network Protocols

—Tor network protocols enhance Tor network’s privacy, security, and resilience to deanonymization attacks by exchanging information on many steps and data. The information, on the other hand, is commonly used in Tor network protocols. Network packets required for Tor protocols are exchanged at the beginning to support both onion services and general websites via Tor browsers. These packets are common in each category. To analyze how common Tor protocol packets affect the website ﬁngerprinting attacks, we create the feature using network packets removed of the ﬁrst several packets. Experimental results show that classiﬁcation performance improves as the number of the beginning packets removed increases. This pilot study indicates that Tor network protocols designed to improve the security may be vulnerable to website ﬁngerprinting attacks.
LiM-HDL: HDL-Based Synthesis for In-Memory Computing

HDLs are widely used in EDA for abstract specification and synthesis of logic circuits. Despite the popularity and the many benefits of HDL-based synthesis, it has not yet been performed for in-memory computing. Hence, there is a need to design a particular HDL which supplies efficient and compatible descriptions. In this paper, we enable HDL-based synthesis for the Programmable Logic-in-Memory (PLiM) computer architecture. We present LiM-HDL - a Verilog-based HDL - which allows for the detailed description of programs for in-memory computation. Having the description given in LiM-HDL, we propose a synthesis scheme which translates the description into PLiM programs, i.e. a sequence of resistive majority operations. This includes lexical and syntax analysis as well as preprocessing, custom levelization and a compiler. In our experiments, we show the benefits of LiM-HDL compared to classical Verilog-based synthesis. We show in a case-study that LiM-HDL can be used to implement programs with respect to constraints of specific applications such as edge computing in IoT, for which the PLiM computer is of particular interest and where low area is a key requirement. In our case-study, we show that we can reduce the number of ReRAM devices needed for the computation of an encryption module by 69%.
Virtualization-support cases in engineering education

— The paper presents cases of applying hardware virtualization techniques as support for education activities in two different courses and a master thesis within the degree International MSc on Telecommunication Engineering at the Technical University of Denmark (DTU). The triggering problem is presented in each of the cases, together with the benefits and drawbacks of using virtualization to cope with it.
Identiﬁcation of Political Leaning in German News Articles

Media coverage plays an important role in shaping personal and public opinion, as it is often a primary source of information. Especially in today’s world where ideological division with subsequent polarization of the political discourse is constantly increasing across social networks, the responsibility of media professional to ensure an unbiased reporting is growing. However, the coverage often exhibits a speciﬁc political leaning that is reﬂected in the articles and is commonly referred to as media bias . Many works already exist in computational social science that explore this phenomenon using English-language newspaper articles. This thesis aims to investigate the phenomenon in the German media landscape. This work uses a new methodology to attribute newspaper articles to a political leaning based on the party afﬁliation of the politician who shared the article on social media. We used AbgeordnetenWatch — a platform containing short proﬁles of politicians — to generate a list of German MPs including their Twitter proﬁles. We extract their tweets, using the Twitter API, and select all tweets that reference an article in the German media landscape. We extract all articles, including the title, the summary, the actual text and several meta information. In the end, we obtain a dataset with a total of over 40 thousand articles from the most pop-ular German media houses and the respective politician who shared the article. We examine the resulting dataset with respect to three research questions: (1) How effective are standard classiﬁcation approaches with frequency-based features and the standard neural approach in detecting bias from the right-wing political fringe, when using the articles shared by all other parties as negative samples? (2) How effective are the same standard approaches in detecting bias from right-wing political fringe, when training against the articles shared by other parties separately? (3) How do the same approaches perform when applying to the detection of the left-wing party DIE LINKE , i.e.: a) when using the articles shared by all other parties as negative samples, analogous to (1); b) when training articles of DIE LINKE separately against articles of every other party, analogous to (2). The results of the experiments regarding the second and the third research questions are promising: the state-of-the-art model,
VIBE: A Design Space for VIsual Belief Elicitation in Data Journalism

The process of forming, expressing, and updating beliefs from data plays a critical role in data‐driven decision making. Effectively eliciting those beliefs has potential for high impact across a broad set of applications, including increased engagement with data and visualizations, personalizing visualizations, and understanding users' visual reasoning processes, which can inform improved data analysis and decision making strategies (e.g., via bias mitigation). Recently, belief‐driven visualizations have been used to elicit and visualize readers' beliefs in a visualization alongside data in narrative media and data journalism platforms such as the New York Times and FiveThirtyEight. However, there is little research on different aspects that constitute designing an effective belief‐driven visualization. In this paper, we synthesize a design space for belief‐driven visualizations based on formative and summative interviews with designers and visualization experts. The design space includes 7 main design considerations, beginning with an assumed data set, then structured according to: from who, why, when, what, and how the belief is elicited, and the possible feedback about the belief that may be provided to the visualization viewer. The design space covers considerations such as the type of data parameter with optional uncertainty being elicited, interaction techniques, and visual feedback, among others. Finally, we describe how more than 24 existing belief‐driven visualizations from popular news media outlets span the design space and discuss trends and opportunities within this space.
A MULTI-STAKEHOLDER PERSPECTIVE ON FACTORS AFFECTING TECHNOLOGY TRANSFER FROM ACADEMIA TO INDUSTRY IN THE BIOECONOMY

In the bioeconomy, technology transfer from academia to industry may exploit its strong innovation potential. However, this process is challenged by a wide variety of factors. This paper uses the mixed-method approach of concept mapping to investigate the factors as perceived by multiple stakeholders that influence technology transfer in the bioeconomy. Our findings suggest that the interconnectedness and the perceived individual factors vary across the different stakeholders involved. Based on our findings, we discuss practical implications both for the involved stakeholders and particularly for policy makers on how to achieve effective technology transfer in the bioeconomy.
Poster: L4 (Long Long Long Long) Pointer to Prevent Buffer Overflow*

—This paper introduces a new method to ensure spatial memory safety using vector operations supported by an existing architecture. The proposed scheme with ”L4 Pointers” uses special 128-bit pointers to support the safety of pointer arithmetic operations. It uses vector operations in the commonly used architecture to encode the metadata of each memory chunk. We also introduce a prototype of the L4 pointer based on the LLVM. Experimental results show that our L4 pointer method shows better performance than existing spatial memory safety supports, while maintaining the atomicity of the pointer arithmetic operations.
Serum b -Trace Protein and Risk of Mortality in Incident Hemodialysis Patients

Background and objectives Residual kidney function in dialysis patients is associated with better survival, but there are no simple methods for its assessment. b -Trace protein is a novel endogenous ﬁ ltration marker of kidney function that is not removed during hemodialysis and may serve as a marker for residual kidney function similar to serum creatinine in patients not on dialysis. The objective of this study was to determine the association of serum b -trace protein with mortality in incident hemodialysis patients. Design, setting, participants, & measurements Serum b -trace protein was measured in baseline samples from 503 participants of a national prospective cohort study of incident dialysis patients with enrollment during 1995 – 1998 and follow-up until 2004. Outcomes were all-cause and cardiovascular disease mortality analyzed using Cox regression adjusted for demographic, clinical, and treatment factors. Results Serum b -trace protein levels were higher in individuals with no urine output compared with individuals with urine output (9.0 6 3.5 versus 7.6 6 3.1 mg/L; P , 0.001). There were 321 deaths (159 deaths from cardiovascular disease) during follow-up (median=3.3 years). Higher b -trace protein levels were associated with higher risk of mortality. The adjusted hazard ratio and 95% con ﬁ dence interval for all-cause mortality per doubling of serum b -trace protein was 1.36 (1.09 – 1.69). The adjusted hazard ratios (95% con ﬁ dence intervals) for all-cause mortality in the middle and highest tertiles compared with the lowest tertile were 0.95 (0.69 – 1.32) and 1.72 (1.25 – 2.37). Similar results were noted for cardiovascular disease mortality. The serum level of b -trace protein is an independent predictor of death and cardiovascular disease mortality in incident