Miracle-dz/UniFAR-FTUs huggingface dataset:
Temporal Reliability of Welfare Estimates from Revealed Preferences

Revealed preference methods for estimating welfare changes have been the subject of few reliability tests. In this paper, we utilize a unique panel data set of Iowa households to test the stability of revealed preference recreational demand models. We find that welfare estimates of changes in water quality are not temporally stable over the full range of years in our data, while welfare estimates associated with a site closure are often, but not always, more reliable. Our results highlight important factors for policy makers and practitioners to consider when transferring welfare estimates of recreational demand studies over time. These findings emphasize the need for extensive temporal reliability tests of other revealed preference approaches.
Fast, sound, and effectively complete dynamic race prediction

Writing concurrent programs is highly error-prone due to the nondeterminism in interprocess communication. The most reliable indicators of errors in concurrency are data races, which are accesses to a shared resource that can be executed concurrently. We study the problem of predicting data races in lock-based concurrent programs. The input consists of a concurrent trace t, and the task is to determine all pairs of events of t that constitute a data race. The problem lies at the heart of concurrent verification and has been extensively studied for over three decades. However, existing polynomial-time sound techniques are highly incomplete and can miss simple races. In this work we develop M2: a new polynomial-time algorithm for this problem, which has no false positives. In addition, our algorithm is complete for input traces that consist of two processes, i.e., it provably detects all races in the trace. We also develop sufficient criteria for detecting completeness dynamically in cases of more than two processes. We make an experimental evaluation of our algorithm on a challenging set of benchmarks taken from recent literature on the topic. Our algorithm soundly reports hundreds of real races, many of which are missed by existing methods. In addition, using our dynamic completeness criteria, M2 concludes that it has detected all races in the benchmark set, hence the reports are both sound and complete. Finally, its running times are comparable, and often smaller than the theoretically fastest, yet highly incomplete, existing methods. To our knowledge, M2 is the first sound algorithm that achieves such a level of performance on both running time and completeness of the reported races.
Bis(3-hydroxypyridine-κN)bis(3-nitrobenzoato-κO)zinc(II)

The title complex, [Zn(C7H4NO4)2(C5H5NO)2], has site symmetry 2. The ZnII ion is located on a crystallographic twofold rotation axis and assumes a distorted tetrahedral ZnN2O2 coordination geometry. Molecules are linked by an intermolecular O—H⋯O hydrogen bond and π–π stacking interactions between pyridine rings [centroid–centroid speparation 3.594 (1) Å].
The Factors Influencing Safety Behavior of Medical Staffs in Emergency Room of a Medical Center in Taiwan

The outburst of Severe Acute Respiratory Syndrome (SARS) during 2003 spring-summer has caused popular attention on safety of medical staffs. Some medical staffs died in the infectious disease. But most of the unsafe events can be prevented as long as safety behavior is being practiced. This study explored the factors affecting workers’ safety behavior and the advice is governed to administrators in emergency room. The study has surveyed 199 medical staffs regarding basic demographics, measurements of safety climate, and safety behavior, from July to October in 2005. The main findings are as follows: 1. Four dimensions were divided by factor analysis of the questionnaire, which were classified into four constructs such as ”manager commitment”, ”fatalistic attitude/cognition”, ”communication and reward”, and ”safety behavior”. 2. ”Management commitment” presented the consistent and significant effect on safety behavior among physicians, nurses, and assistants. 3. In the population of nurse, the score of safety behavior increased by age. 4. The study has demonstrated that management commitment is positively associated with safety behavior. It prompts that regulation of safety is required to protect medical staffs from hazards. The findings of this research have landed on the importance of safety climate and management commitment for safety of medical staffs. The responsibility and effectiveness of a manager's role and policy are equally important for securing institutional environment
FACTORS ASSOCIATED WITH PREVENTIVE BEHAVIORS OF HEPATITIS B AMONG HIGH SCHOOL GIRLS USING THE HEALTH BELIEF MODEL

:ناگدنسیون ينادسی ناوضر 1 ، هجیولع ينامز هتشرف * 2 ، تیريخ ارتيم 3 ، ريما يعيفش 4 ناگدازدنمجرا دمحم ، 5 1 ٌؽکطواؼ ،يـبتسفپ ؽضـا يسبىضـبک يًدطواؼ يکوضكپ ًولػ ٌبگطواؼ ،ييبمبم ي يـبتسفپ قاًَا ـًپبض يؽىخ نافيا ، 2 ٌيفگ ـبيؼبتسا تضاؽُث يمًمػ ، ٌؽکطواؼ ،تملاس فث فثؤم يػبمتخا لماًػ تبقیقطت كکفم ،تضاؽُث قاًَا ـًپبض يؽىخ يکضكپ ًلػ ٌبگطواؼ نافيا ، 3 تضاؽُث شقًمآ ؽضـا يسبىضـبک يًدطواؼ ، وطواؼ ،يمًمػ تضاؽُث ٌيفگ ،توضاؽُث ٌؽک قاًَا ـًپبض يؽىخ يکضكپ ًلػ ٌبگطواؼ نافيا ،
MARKET ORIENTATION AND THE SYNERGISTIC EFFECT OF MEDIATING AND MODERATING FACTORS ON PERFORMANCE : THE CASE OF THE FASHION CLUSTER 1

University of Minho, School of Management and Economics. jcpinho@eeg.uminho. pt It is commonly accepted that a market-oriented behaviour has a positive impact on organizational performance. This study provides additional support for the classical relationship between market orientation and organizational performance and goes further by not only including in the model the mediating role of both the organizational learning and organizational innovation, but also examining the potential moderating impact of the environmental conditions in the previous relationship. The analysis is based on data collected from a sample of 130 Portuguese SME operating in the fashion cluster. Implications and future research questions based on the findings are offered at the conclusion of the paper.
Industrial Internet of Things Based Ransomware Detection using Stacked Variational Neural Network

To protect the Industrial Internet of Things (IIoT) systems against ransomware attacks, their host machines systems activities need to be efficiently monitored by an efficient detection model that is able to accurately detect ransomware behavior and trigger an alarm before its impact extends to the critical control systems. However, the detection models for these hosts' machines encounter significant challenges in dealing with a high dimension data, few numbers of trained observations, and the dynamic behavior of ransomware. Therefore, there is a need for an efficient detection model that can address these challenges. In this paper, we propose a detection model based on the stacked Variational Auto-Encoder (VAE) with a fully connected neural network that is able to learn the latent structure of system activities and reveal the ransomware behavior. Further, we also come up with a data augmentation method based on VAE for generating new data that can be used in training a fully connected network in order to improve the generalized capabilities of the proposed detection model. The results showed that our proposed model achieved considerable performance in detecting ransomware activities.
Applications of Distributed Ledger Technologies to the Internet of Things

Distributed Ledger Technologies (DLTs) and blockchain systems have received enormous academic, government, and commercial interest in recent years. This article surveys the integration of DLTs within another life-changing technology, the Internet of Things (IoT). IoT-based applications, such as smart home, smart transport, supply chain, smart healthcare, and smart energy, promise to boost the efficiency of existing infrastructures and change every facet of our daily life. This article looks into the challenges faced by such applications and reviews a comprehensive selection of existing DLT solutions to those challenges. We also identify issues for future research, including DLT security and scalability, multi-DLT applications, and survival of DLT in the post-quantum world.
HinHRob: A Performance Robot for Glove Puppetry

China’s intangible cultural heritage glove puppetry is a thousand-year form of performance. However, this ancient cultural and artistic treasures are faced with difficulties of protection and inheritance. Our approach is to integrate robotics with the glove puppetry, design and develop the glove puppetry robot HinHRob. It simulates the performances of puppeteers and interacts with the audience in real-time. The robot not only can perform with professional puppeteers but also attract interest from audiences of different cultural backgrounds and ages. The creative approach of combining technology and the intangible cultural heritage may open a new mode for the ancient human legacy, the glove puppetry.
Reliable Communication and Latency Bound Generation in Wireless Cyber-Physical Systems

Low-power wireless communication has been widely used in cyber-physical systems that require time-critical data delivery. Achieving this goal is challenging because of link burstiness and interference. Based on significant empirical evidence of 21 days and over 3.6 M packet transmissions per link, we propose both routing and scheduling algorithms that produce latency bounds of the real-time periodic streams and accounts for both link bursts and interference. The solution is achieved through the definition of a new metric Bmax that characterizes links by their maximum burst length, and by choosing a novel least-burst-route that minimizes the sum of worst-case burst lengths over all links in the route. With extensive data-driven analysis, we show that our algorithms outperform existing solutions by achieving accurate latency bound with much less energy consumption. In addition, a testbed evaluation consisting of 48 nodes spread across a floor of a building shows that we obtain 100% reliable packet delivery within derived latency bounds. We also demonstrate how performance deteriorates and discuss its implications for wireless networks with insufficient high-quality links.
The Influence of Spatial Awareness on VR: Investigating the influence of the familiarity and awareness of content of the real space to the VR

A common practice of VR experiments is to create a 3D model replica of the experience room to ease the transition of the real world to the virtual environment. However, the biggest issue with this method is that different spaces would need lots of preparation of 3D models. We propose to use a semi-unified covered space so that the user won't be able to compare the real space and the virtual environment, and some methods to make users believe that the VR space is real. It helps the user to believe in the existence of virtual objects that are not in the real space, and increases the possibility of the existence of the 3D object in real space.
Fundus imaging using DCRA toward large eyebox

We propose a novel fundus imaging system using a dihedral corner reflector array (DCRA) that is an optical component to work as a lens but does not have a focal length or an optical axis. A DCRA has a feature that transfers a light source into a plane symmetric point. Conventionally, using this feature, a DCRA has been used to many display applications, such as virtual retinal display and three-dimensional display, in the field of computer graphics. On the other hand, as a sensing application, we use a DCRA for setting a virtual camera in/on an eyeball to capture a fundus. The proposed system has three features; (1) robust to eye movement, (2) wavelength-independent, (3) a simple optical system. In the experiments, the proposed system achieves 8 mm of large eyebox. The proposed system has a possibility to be applied to preventive medicine for households that can be used in daily life.
DataBlinder: A distributed data protection middleware supporting search and computation on encrypted data

Business application owners want to outsource data storage, including sensitive data, to the public cloud for economical reasons. This is often challenging since these businesses are and remain responsible for regulatory compliance and data protection, even though cloud providers may do their best to offer (data) protection. Meanwhile, data protection techniques evolve and get better because of continuous research and improvement of advanced encryption. Numerous cryptographic tactics have been proposed, e.g., searchable symmetric encryption (SSE) and homomorphic encryption (HE), that support search and aggregation functions on encrypted data. Each of these tactics has a trade-off between security, performance and functionality, but there is no one-size-fits-all solution. For the application developer, the underpinning concepts of these tactics are complex to comprehend, complex to integrate in a distributed application, and prone to implementation mistakes. In this paper we present DataBlinder, a distributed data access middleware that provides crypto agility by means of configurable fine-grained data protection at the application level. DataBlinder supports adaptive runtime selection of data protection tactics, and offers a plugin architecture for such tactics based on a key abstraction model for protection level, performance and supported query functionality. We have developed this middleware in close collaboration with businesses that face these challenges and offer cloud-based applications in e-finance, and e-health, by implementing and integrating state-of-the-art cryptographic schemes to DataBlinder. This paper illustrates the case of medical data protection with FHIR-compliant [30] medical data.
Robust Vision-Based Daytime Vehicle Brake Light Detection Using Two-Stage Deep Learning Model

Today's ADAS functions can be divided into active control, early warning and other auxiliary three major categories, including Adaptive Cruise Control (ACC), Autonomous Emergency Braking (AEB), Forward Collision Warning (FCW), which uses radar and some sensors to measure the distance between itself and the front, and uses this as a parameter for analysis. However, in addition to the distance, if it is possible to know the information of the vehicle in front of the vehicle in a timely manner and transmit the real-time vehicle information of the vehicle in front through the Internet of Vehicle, it will be possible to accurately determine the driving conditions of the surrounding vehicles. Therefore, we proposed a daytime vehicle brake light detection system that uses a single image as the input without object tracking. The experimental results show that our proposed system can achieve very high resolution under various weather conditions.
Grey Literature

With "grey literature" we identify materials and research produced outside of the traditional academic publishing and distribution channels. Currently available grey literature spans from industrial whitepapers and technical reports to blog post and videos published on the web. It hence varies considerably in type, quality and publication means, even if the common point is that no independent peer reviewing occurs before a content is made available to the public.
Design, Implementation and Performance Measurement of Raspberry Gate in the IoT Field

The Internet of Things (IoT) is the third wave of the world's information industry after computers, the Internet, and mobile communication networks. In the era of the rapid development of IoT applications, there needs to be more focus on security. To solve security problems in IoT, we propose the Raspberry Gate, a small security gateway built using Raspberry Pi, and the Raspberry Guardian, a human community to update information, manage and control IoT devices. We discuss how the Raspberry Gate can be built and maintained. Furthermore, we also provide a comparison of Raspberry Gates made with different types of Raspberry Pi models.
FaaS Orchestration of Parallel Workloads

Function as a Service (FaaS) is based on a reactive programming model where functions are activated by triggers in response to cloud events (e.g., objects added to an object store). The inherent elasticity and the pay-per-use model of serverless functions make them very appropriate for embarrassingly parallel tasks like data preprocessing, or even the execution of MapReduce jobs in the cloud. But current Serverless orchestration systems are not designed for managing parallel fork-join workflows in a scalable and efficient way. We demonstrate in this paper that existing services like AWS Step Functions or Azure Durable Functions incur in considerable overheads, and only Composer at IBM Cloud provides suitable performance. Successively, we analyze the architecture of OpenWhisk as an open-source FaaS systems and its orchestration features (Composer). We outline its architecture problems and propose guidelines for orchestrating massively parallel workloads using serverless functions.
Experience: Managing Misinformation in Social Media - Insights for Policymakers from Twitter Analytics

Governance of misinformation is a serious concern in social media platforms. Based on experiences gathered from different case studies, we offer insights for the policymakers on managing misinformation in social media. These platforms are widely used for not just communication but also content consumption. Managing misinformation is thus a challenge for policymakers and the platforms. This article explores the factors of rapid propagation of misinformation based on our experiences in the domain. An average of about 1.5 million tweets were analysed in each of the three different cases surrounding misinformation. The findings indicate that the tweet emotion and polarity plays a significant role in determining whether the shared content is authentic or not. A deeper exploration highlights that a higher element of surprise combined with other emotions is present in such tweets. Further, the tweets that show case-neutral content often lack the possibilities of virality when it comes to misinformation. The second case explores whether the misinformation is being propagated intentionally by means of the identified fake profiles or it is done by authentic users, which can also be either intentional, for gaining attention, or unintentional, under the assumption that the information is correct. Last, network attributes, including topological analysis, community, and centrality analysis, also catalyze the propagation of misinformation. Policymakers can utilize these findings in this experience study for the governance of misinformation. Tracking and disruption in any one of the identified drivers could act as a control mechanism to manage misinformation propagation.
Nanoscapes: Authentic Scales and Densities in Real-Time 3D Cinematic Visualizations of Cellular Landscapes

3D computer-animated representations of complex biological systems and environments are often vastly oversimplified. There are a number of key reasons: to highlight a distinct biological mechanism of interest; technical limitations of hardware and software computer graphics (CG) capabilities; and a lack of data regarding cellular environments. This oversimplification perpetuates a naive understanding of fundamental cellular dynamics and topologies. This work attempts to address these challenges through the development of a first-person interactive virtual environment that more authentically depicts molecular scales, densities and interactions in real-time. Driven by a collaboration between scientists, CG developers and 3D computer artists, Nanoscapes utilizes the latest CG advances in real-time pipelines to construct a cinematic 3D environment that better communicates the complexity associated with the cellular surface and nanomedicine delivery to the cell.
The impact of work distribution on in situ visualization: a case study

Large-scale computer simulations generate data at rates that necessitate visual analysis tools to run in situ. The distribution of work on and across nodes of a supercomputer is crucial to utilize compute resources as efficiently as possible. In this paper, we study two work distribution problems in the context of in situ visualization and jointly assess the performance impact of different variants. First, especially for simulations involving heterogeneous loads across their domain, dynamic load balancing can significantly reduce simulation run times. However, the adjustment of the domain partitioning associated with this also has a direct impact on visualization performance. The exact impact of this side effect is largely unclear a priori as generally different criteria are used for balancing simulation and visualization load. Second, on node level, the adequate allocation of threads to simulation or visualization tasks minimizes the performance drain of the simulation while also enabling timely visualization results. In our case study, we jointly study both work distribution aspects with the visualization framework MegaMol coupled in situ on node level to the molecular dynamics simulation ls1 Mardyn on Stampede2 at TACC.
Computing 3D Clipped Voronoi Diagrams on GPU

Computing clipped Voronoi diagrams in 3D volume is a challenging problem. In this poster, we propose an efficient GPU implementation to tackle this problem. By discretizing the 3D volume into a tetrahedral mesh, the main idea of our approach is that we use the four planes of each tetrahedron (tet for short in the following) to clip the Voronoi cells, instead of using the bisecting planes of Voronoi cells to clip tets like previous approaches. This strategy reduces computational complexity drastically. Our approach outperforms the state-of-the-art CPU method up to one order of magnitude.
Condition Monitoring of Refrigeration Compressor Using Sound Signal Based on Convolutional Neural Networks

In this paper, the condition monitoring of refrigeration compressor used in ice making factory at Chonburi, Thailand is investigated. The streaming sound data collected from designed circuit with IoT technology is proposed. The 6-channel FFT spectrum training dataset with target dataset is introduced and used as input features for machine learning approaches such as ANN and CNN. In preliminary study, the target conditions are classified into 3 classes namely run open valve, run close valve and stop. The classification result demonstrates that the accuracy provided by ANN and CNN methods have similar values at approximately 99% in this study with comparison and evaluation.
Building Own Blockchain

Own is a decentralized fundraising, digital trading and security token issuance platform that runs on a native, custom-built dual-blockchain infrastructure. This paper describes the implementation and our experiences with the public, permissionless part of Own dual blockchain. We describe Own public blockchain consensus, which is based on the Tendermint BFT consensus algorithm, and which we modified towards simplicity and robustness, and open-source implemented from scratch in F#. We further detail how Own Delegated Proof of Stake (DPoS) mechanism works and the staking cryptoeconomics around its native utility token (CHX). Finally, we report on Own public blockchain performance both in experimental (testnet) deployments and extracting performance numbers from history of the live blockchain network (mainnet), which consisted of 83 active validators in August 2019.