Miracle-dz/UniFAR-FTUs huggingface dataset:
Valuable First Forwarding (VFF) Transmission Strategy for Epidemic Routing in Urban Environments

-The message queue mode in sending buffer of mobile node can impact the performance of Delay Tolerant Network (DTN). In urban environment, mobile nodes have a certain community-based movement feature, and which is related to time of day. The queue management of Epidemic routing can be improved in this environment, because some messages of a node are difficult to be transmitted to another community group within short contact time and limited transmission bandwidth. Some packets have not got a transmission opportunity when the Time To Live (TTL) of packets are exhausted. In order to make the messages delivery as much, the queue management in sending buffer becomes an important issue in DTN. The valuable message should be forwarded first, but how to define the value of a message? Therefore, we focus on improving the performance of Epidemic routing in urban environments and Valuable First Forwarding (VFF) transmission strategy in sending buffer is proposed. The TTL and message hops are jointly considered in this strategy, and the simulation results suggest that the VFF outperforms the random, FIFO and optimized MinHop (MH) queue mode in end-to-end path latency, delivery ratio and network overhead ratio.
Tit for tat: burnout as a mediator between workplace incivility and instigated workplace incivility

Purpose – The purpose of this paper is to investigate the role of burnout as a mediator in the relationship between workplace incivility (WI) and instigated WI. Design/methodology/approach – A survey of 303 white collar employees from smallto medium-size industries in Australia was conducted. Self-reported measures were used to obtain data on WI, burnout, and instigated WI. Mediation analyses with bootstrap via PROCESS was used ascertain the proposed relationship. Findings – Results indicated that WI was positively linked to instigated WI. Importantly, results indicated that burnout fully mediated the relationship between WI and instigated WI. Research limitations/implications –The correlational and self-report nature of the study exclude inference about causality between variables and may be more prone to bias. However, despite these limitations, preand post-cautionary steps were taken to ensure that these biases were kept at bay as much as is possible. Practical implications – The study highlights that burnout may be an important underlying mechanism responsible for target’s and perpetrator’s uncivil relationships toward each other. Management should be cognizant of possible burnout among employees who experienced WI and to take appropriate training as preventive measures for WI. Originality/value – This study responded to the call for more empirical investigation of WI. This study also integrated conservation of resources and the spiral of incivility theories to develop a theoretical model which linked WI to instigated WI.
The LBT AO system on-sky results

The first LBT natural Guide Star Adaptive Optics system (FLAO#1) has been commissioned between May 2010 and June 2011. The system uses two key components namely the adaptive secondary mirror with 672 actuators and the pyramid sensor with up to 30x30 subapertures. During the on-sky commissioning the system reached very high performance for an 8m class telescope. Briefly, FWHM of 40mas and Strehl ratio higher than 90% have been measured in H band images together with contrast as high as 10 at 0.4 arcsec off axis. The paper describes the results achieved during the system commissioning in terms of SR vs. reference star magnitudes and achieved PSF contrast. Finally the paper briefly discusses the sky coverage achieved with FLAO#1 and compares the results with the sky coverage achieved by LGS AO systems.
Benchmarking Authoritative DNS Servers

In this paper, we examine the performance of four authoritative DNS server implementations (BIND, NSD, Knot DNS, and YADIFA). In our tests, we apply the measurement procedure defined in Section 9 of RFC 8219. Our aim is threefold: to provide DNS operators with ready to use measurement results to support their selection of the best fitting authoritative DNS server implementation for their needs, to assist researchers and DNS64 server developers in finding a suitable authoritative DNS server implementation for their DNS64 benchmarking measurements, and to advance the theory and practice of benchmarking DNS servers. We examine how the different conditions such as the number of active CPU cores, the size of the zone file, the applied timeout, and the type of the processor influence the performance of the tested authoritative DNS server implementations. The performance of all four tested DNS servers scales up more or less well with the number of CPU cores, except for YADIFA. The increase of the size of the zone file causes significant degradation only in the performance of BIND, which shows different anomalies described in the paper. The change of the timeout from 250ms (required by RFC 8219) to 100ms usually causes only a small performance degradation. We point out that NSD and Knot DNS can achieve an order of magnitude higher performance than BIND and YADIFA.
Topological Map Building Based on A Genetic Algorithm for Simultaneous Localization and Mapping

This paper proposes a method of self-localization and map building based on a steadystate genetic algorithm and a growing topological neural network for a mobile robot. The growing topological neural network sequentially adds a node to the map according to the measured distance by a laser range finder. When the difference between the measured distance and the map data is large, the proposed method corrects the self-location of the robot by using the steady-state genetic algorithm, and updates the map to be more accurate. Finally we show experimental results of the proposed method.
THE ROLE OF TRAIT EMOTIONAL INTELLIGENCE IN CAREER DECISION-MAKING DIFFICULTIES AND (CAREER DECISION) SELF-EFFICACY

The emotional intelligence and career decision-making analysis contributes to research-based knowledge and to career-counselling practice. The study analyzes trait emotional intelligence (further on EI) in the process of career decision-making, i.e. career decision-making difficulties, general self-efficacy and career-decision self-efficacy assessed in a sample of 322 Slovak high school students before second career-choice. The measures: Trait Emotional Intelligence Questionnaire-Short Form (TEIQue-SF; Petrides, 2009), Emotional and Personality Difficulties Scale (EPCD; Saka, Gati, & Kelly, 2008), General Self-Efficacy Scale (GSES; Schwarzer, & Jeruzalem, 1993), Career Decision Self-Efficacy Scale-Short Form (CDSE-SF; Betz, Klein, & Taylor, 1996). The statistical analysis: negative relations between global career decision-making difficulties and self-efficacy (general, career-decision), positive relations between self-efficacy (general, career-decision) and trait EI, and negative relations between trait EI and global career decision-making difficulties and their factors. The regression analyses: trait EI was a significant negative predictor of career decision-making difficulties, over and above career-decision self-efficacy. Trait EI is a predictor of more stable, pervasive emotional and personality-related aspects of career decision-making difficulties, above and over studied personality-related and career-related self-efficacy. Trait EI relevance and career decisions self-efficacy in the career-decision process are emphasized with practical implications for diagnostics and intervention within career-counseling.
Performance Analysis of Network Intrusion Detection System using Machine Learning

With the coming of the Internet and the increasing number of Internet users in recent years, the number of attacks has also increased. Protecting computers and networks is a hard task. An intrusion detection system is used to detect attacks and to protect computers and network systems from these attacks. This paper aimed to compare the performance of Random Forests, Decision Tree, Gaussian Naı̈ve Bayes, and Support Vector Machines in detecting network attacks. An up-to-date dataset was chosen to compare the performance of these classifiers. The results of the conducted experiments demonstrate that both Random Forests and Decision Tree performed effectively in detecting attacks. Keywords—Intrusion Detection System (IDS); classifiers; AI; machine learning; KDD99; CICIDS2017; DoS; U2R; R2L
Cloud-Edge Network Data Processing based on User Requirements using Modify MapReduce Algorithm and Machine Learning Techniques

Edge computing extends cloud computing to enhancing network performance in terms of latency and network traffic of many applications such as: The Internet of Things (IoT), Cyber-Physical Systems (CPS), Machine to Machine (M2M) technologies, Industrial Internet, and Smart Cities. This extension aims at reducing data communication and transmission through the network. However, data processing is the main challenge facing edge computing. In this paper, we proposed a data processing framework based on both edge computing and cloud computing, that is performed by partitioning (classification and restructuring) of data schema on the edge computing level based on feature selection. These features are detected using MapReduce algorithm and a proposed machine learning subsystem built on user requirements. Our approach mainly relies on the assumption that the data sent by edge devices can be used in two forms, as control data (i.e. real-time analytics) and as knowledge extraction data (i.e. historical analytics). We evaluated the proposed framework based on the amount of transmitted, stored data and data retrieval time, the results show that both the amount of sending data was optimized and data retrieval time was highly decreased. Our evaluation was applied experimentally and theoretically on a hypothetical system in a kidney disease center.
GROWTH AND DIFFERENTIATION OF MARROW STROMAL CELLS ON SCAFFOLDS CONTAINING POLYVINYL ALCOHOL IMPRINTED POLYCAPROLACTONE MICROSPHERES

Bone fracture is a medical condition in which a break in the continuity of bones occurs. It can be the result of high force impact or stress, or conditions that weaken the bone, such as osteoporosis, bone cancer, etc. Bone tissue has no sensors of the pain pathway. But, its break is extremely painful and poses a significant risk to the patient. A surgical procedure called bone grafting is commonly employed to repair severe bone fracture by replacing with a new bone. The bone used in bone grafting can come from the patient own body (autograft), a donor (allograft), or could be entirely manmade. There are problems associating autografts and allografts, such as donor site morbidity, disease transmission and/or host response reaction. Nevertheless, these dilemmas can be resolved or reduced by using an artificial bone graft. Generally, synthetic bone grafts are designed to perform as frameworks for which new living bone can grow. By taking physical, biochemical, and mechanical properties of the damaged bone into account, its structure and composition can be chosen to resemble as Polycaprolactone, Polyvinyl alcohol, Microsphere, Scaffold, Bone tissue engineering KEYW ORDS Published online: 20 December, 2016 *Corresponding Author: Assoc. Prof Dr. Jasadee Kaewsrichan Email: jasadee.k@psu.ac. th ABSTRACT ART ICLE INFO
Human Recognition from Video Sequences and Off-Angle Face Images Supported by Respiration Signatures

Human Recognition from Video Sequences and Off-Angle Face Images Supported by Respiration Signatures Xue Yang In this work, we study the problem of human identity recognition using human respiratory waveforms extracted from videos combined with component-based offangle human facial images. Our proposed system is composed of (i) a physiologybased human clustering module and (ii) an identification module based on facial features (nose, mouth, etc.) fetched from face videos. In our proposed methodology we, first, manage to passively extract an important vital sign (breath), cluster human subjects into nostril motion vs. nostril non-motion groups, and, then, localize a set of facial features, before we apply feature extraction and matching. Our novel human identity recognition system is very robust, since it is working well when dealing with breath signals and a combination of different facial components acquired in uncontrolled luminous conditions. This is achieved by using our proposed Motion Classification approach and Feature Clustering technique based on the breathing waveforms we produce. The contributions of this work are threefold. First, we collected a set of different datasets where we tested our proposed approach. Specifically, we considered six different types of facial components and their combination, to generate face-based video datasets, which present two diverse data collection conditions, i.e., videos acquired in head fully frontal position (baseline) and head looking up pose. Second, we propose a new way of passively measuring human breath from face videos and show comparatively identical output against baseline breathing waveforms produced by an ADInstruments device [1]. Third, we demonstrate good human recognition performance when using the proposed pre-processing procedure of Motion Classification and Feature Clustering, working on partial features of human faces. Our method achieves increased identification rates across all datasets used, and it manages to obtain a significantly high identification rate (ranging from 96%100% when using a single or a combination of facial features), yielding an average of 7% raise, when compared to the baseline scenario. To the best of our knowledge, this is the first time that a biometric system is composed of an important human vital sign (breath) that is fused with facial features in such an efficient manner.
Slide-induced impulse waves in mountainous regions

Impulse waves occur as a natural hazard particularly in mountainous regions with lakes surrounded by a steep shoreline. Landslides, rockslides, glacier calvings or avalanches then transfer its momentum to the water body thereby generating large tsunamigenic waves. At lakes, damages are expected for the shore vegetation or possible infrastructure. In presence of a reservoir, potential dam overtopping could lead to downstream damages due to (a) direct wave impact, (b) float impact, (c) deposited float and debris or even (d) complete dam failure. Using physical model tests conducted within various PhD theses at the Laboratory of Hydraulics, Hydrology and Glaciology (VAW) of ETH Zurich, the impulse wave generation and the wave-shore interaction were intensively investigated. Based on a computation guideline published in 2009, the possible hazard potential may be assessed. The present contribution provides an overview on impulse waves and an evaluation of underwater slide propagation and deposition features. These data are useful for the calibration of numerical models or the above mentioned assessment approach.
Embracing Localization Inaccuracy with a Single Beacon

This paper illustrates a new mechanism to determine the coordinates of the sensors using a beacon node and determines the definitive error associated with it. In UWSNs (underwater wireless sensor networks), actual and precise location of the deployed sensors which accumulate data is vital, because the accumulated data without the location information has less significance. Moreover it has limited value in the domain of location based services. In UWSN, trilateration or multilateration is exploited to assess the location of the deployed hosts; having three or more reference nodes to localize a deployed sensor is not pragmatic at all. On the other hand, non-linear equations are usually solved in conventional method where degree-of-freedom is uncertain to lead to an exclusive solution. In this paper, associated localization inaccuracies has been shown for a unique configuration where a single beacon is used to determine the coordinates of three deployed sensors simultaneously. Cayley-Menger determinant is used for the configuration and system of nonlinear distance equations have been linearized for better accuracy and convergence. Simulations with Euclidean distances validate the propounded model and reflect the acquired accuracies in sensors’ coordinates and bearings. Moreover, an experiment has been conducted with ultrasonic sensors in terrestrial environments to validate the proposed model; the associated inaccuracies were found to be generated from the distance measurement errors; on the other hand, considering Euclidean distances proves the model to be precise and accurate.
Multi-label Prediction in Time Series Data using Deep Neural Networks

This paper addresses a multi-label predictive fault classification problem for multidimensional time-series data. While fault (event) detection problems have been thoroughly studied in literature, most of the state-of-the-art techniques can't reliably predict faults (events) over a desired future horizon. In the most general setting of these types of problems, one or more samples of data across multiple time series can be assigned several concurrent fault labels from a finite, known set and the task is to predict the possibility of fault occurrence over a desired time horizon. This type of problem is usually accompanied by strong class imbalances where some classes are represented by only a few samples. Importantly, in many applications of the problem such as fault prediction and predictive maintenance, it is exactly these rare classes that are of most interest. To address the problem, this paper proposes a general approach that utilizes a multi-label recurrent neural network with a new cost function that accentuates learning in the imbalanced classes. The proposed algorithm is tested on two public benchmark datasets: an industrial plant dataset from the PHM Society Data Challenge, and a human activity recognition dataset. The results are compared with state-of-the-art techniques for time-series classification and evaluation is performed using the F1-score, precision and recall.
Acute Gastrointestinal Complications after Open Heart Surgery

Retrospective analysis revealed that 24 of 4401 adult patients (0.5%) developed severe gastrointestinal complications after open heart surgery during a 3-year period from January 1995. There were 4 women (17%) and 20 men (83%). Mean age was 61.7 ± 2.02 years. Gastrointestinal bleeding (33.3%), mesenteric ischemia (20.8%), pancreatitis (20.8%), hepatic dysfunction (16.7%), and cholecystitis (16.7%) were the most common complications. Mortality was 41.7% (10 patients). During the same period, mortality in the patients who did not develop gastrointestinal complications was 1.89% (p < 0.0001). Emergency basis, reoperation, combined operations, peripheral vascular disease, diabetes mellitus, chronic lung disease, and impaired left ventricle function were found to be risk factors for the development of postoperative gastrointestinal complications.
Tabea Cornel

Ingalhalikar et al.’s paper is the first laboratory study investigating sex differences in the human connectome with diffusion tensor imaging (DTI) based on a large number of participants. As the title suggests, the authors report significant sex differences in the structural connections within the brains of male and female participants respectively. In a nutshell, the brains of female subjects were found to have strong interhemispheric connections, whereas the intrahemispheric connections appeared stronger in males. These results have not been reported before and the authors argue that they are in line with earlier findings of structural brain sex differences. Furthermore, Ingalhalikar et al. propose that the measured differences provide an explanation for functional differences in the brains of females and males as well as for the developmental process of the constitution of these differences.
Running head: INTENITON TO UPLOAD VIDEO CONTENT ONLINE

This study examined the factors that are associated with people’s intention to upload video content online within the framework of the theory of planned behavior (TPB). Using data from a survey of college students (N = 241), structural equation modeling analyses revealed that individual descriptive norm is associated with the intention to upload video content online. In addition, ego-involvement played an essential role in accounting for both attitude toward the uploading behavior and the intention to upload. These findings suggest that integration of the constructs of the TPB, social norms, and ego-involvement can be a fruitful theoretical endeavor for understanding people’s intention to upload video content online. The theoretical implications and limitations were discussed.
Factors Influencing the Students’ Use of Learning Management Systems: A Case Study of King Abdulaziz University

The Technology Acceptance Model (TAM) has been one of the most popular models in explaining the acceptance of technologies and employed empirically in various studies. This study was conducted with the objective of investigating the acceptance and use of the learning management system (LMS) at King Abdulaziz University (KAU) from the perspectives of Saudi students. The study aims to understand the factors that lead to the proper utilization of LMS within the context of Saudi Arabia by examining the associations between the students’ actual use of LMS and external variables: prior experience of LMS, satisfaction, social influence, computer selfefficacy and the teacher role. The participants were male and female students at KAU during the 2016 Fall semester in different fields and education levels. This quantitative study used both online and paper-based questionnaires to ensure a sufficient sample size. Based on the non-probability convenience sampling technique, 150 responses were received, and 142 responses were used for data analysis. For estimating the reliability of the instruments, Cronbach’s alpha measure was employed. The 14 proposed hypotheses were examined using the regression analysis statistical technique. Findings demonstrate the original TAM hypotheses and show the factors that influence the adaption of LMS within the context of Saudi Arabia. The students’ use of LMS is positively influenced by prior experience of LMS, satisfaction, social influence, computer self-efficacy and the teacher role. As TAM has barely been used in understanding the students’ actual use of LMS in Saudi Arabia, the findings may provide a roadmap for future work to examine further variables that influence the use of LMS from the perspectives of Saudi students.
Integrated Urban Streets Design in Mid-size Chinese Cities: The case of Lishui

Streets can be described as “life between buildings” in the urban areas, they are regarded as the structure or skeleton of a city. They are a symbol of the identity of a city and the basic elements of its landscape. This research focus on urban street design in mid-size Chinese Cities: the case of Lishui. The following methods were used for the data collection during the field: typo-morphological data collection, field observation method and interviews. To investigated typology of streets and the ways in which we can accommodate different social cultural function of streets in modern Chinese society. And also, to investigate by linking theoretical concepts and street design practice as both the movement corridors and places that address the social role and cultural characteristics of the streets. As the results, this research proposes to develop theoretical and urban design principles of streets design, both as a global phenomenon and a locally responsive solution; in the context of Chinese, medium and small cities, to meet current and future needs in terms of quality of streets.