Miracle-dz/UniFAR-FTUs huggingface dataset:
How Organizations Manage Crowds: Define, Broadcast, Attract, and Select

Crowdsourcing – a form of collaboration across organizational boundaries – provides access to knowledge beyond an organization’s local knowledge base. Integrating work on organization theory and innovation, the authors first develop a framework that characterizes crowdsourcing into a main sequential process, through which organizations (1) define the task they wish to have completed; (2) broadcast to a pool of potential contributors; (3) attract a crowd of contributors; and (4) select among the inputs they receive. For each of these phases, the authors identify the key decisions organizations make, provide a basic explanation for each decision, discuss the trade-offs organizations face when choosing among decision alternatives, and explore how organizations may resolve these trade-offs. Using this decision-centric approach, the authors continue by showing that there are fundamental interdependencies in the process that makes the coordination of crowdsourcing challenging.
Evolutionary Algorithms for QoE

Quality of Experience (QoE) is an important parameter in communication ways. The importance of QoE becomes more when we use Long Term Environment (LTE) to transmit data packets. However, there are some methods to measure QoE, the complexity of common methods is obstacle to use popularity. In this paper, we present an approach to measure QoE in LTE. Presented approach is usability as precise. We use three optimization algorithms in our approach: 1-intlligent automata 2-simulated annealing 3-monarchy butterfly optimization. Although the original release of used algorithms are single-objective, we improve them to multiobjective. We compare the performance of algorithms.
Summarizing Situational Tweets in Crisis Scenarios: An Extractive-Abstractive Approach

Microblogging platforms such as Twitter are widely used by eyewitnesses and affected people to post situational updates during mass convergence events such as natural and man-made disasters. These crisis-related messages disperse among multiple classes/categories such as infrastructure damage, shelter needs, information about missing, injured, and dead people. Moreover, we observe that sometimes people post information about their missing relatives and friends with personal details such as names and last seen location. The information requirements of different stakeholders (government, NGOs, and rescue workers) also vary a lot. This brings twofold challenges: 1) extracting important high-level situational updates from these messages, assigning them appropriate categories, and finally summarizing big trove of information in each category and 2) extracting small-scale time-critical sparse updates related to missing or trapped people. In this article, we propose a classification-summarization framework which first assigns tweets into different situational classes and then summarizes those tweets. In the summarization phase, we propose a two-step extractive-abstractive summarization framework. In the first step, it extracts a set of important tweets from the whole set of information, develops a bigram-based word-graph from those tweets, and generates paths by traversing the word-graph. Next, it uses an optimization technique based on integer linear programming (ILP) to select the most important tweets and paths based on different optimization parameters such as informativeness and coverage of content words. Apart from general classwise summarization, we also show the customization of our summarization model to address time-critical sparse information needs (e.g., missing relatives). Our proposed method is time- and memory-efficient and shows better performance than state-of-the-art methods in terms of both quantitative and qualitative judgment.
QoS Aware Transcoding for Live Streaming in Edge-Clouds Aided HetNets: An Enhanced Actor-Critic Approach

With the advances in hand-held devices (smartphones and tablets, etc.) and high speed wireless networks, users have an explosive growth demand for live streaming service. Due to the diversity of user equipments (UEs), the live streaming has to be transcoded into different versions. However, transcoding is a computationally expensive and time consuming process. Due to the dynamic characteristics of wireless network environment, providing high quality and low latency live videos for UEs is a big challenge. This study investigates user scheduling, transcoding decision, computational and wireless spectrum resources allocation problem in edge-clouds aided heterogeneous networks (HetNets). The research focuses on improving UEs’ quality of service (QoS) for live-streaming services, which includes quality of video and latency requirements. Different from existing literature, to approach the real wireless environment, the available computational and wireless spectrum resources are modeled as random processes in the work. Considering dynamic characteristics of wireless networks and available resources, the above problem is modeled as a Markov decision process (MDP). Since the action space of the MDP is multi-dimensional continuous variables mixed with discrete variables, it is difficult to solve this problem by traditional learning algorithms. Therefore, an enhanced actor-critic algorithm is proposed to resolve the problem, in which both the actor part and the critic part employ eligibility traces. Extensive simulation results with different system parameters show the effectiveness of the proposed algorithm.
Interrogating the Art of Developing Self-Learning Material for Open and Distance Learning (ODL) Students

Open and distance learning (ODL) thrives on students' utilisation of learning materials that designed in ways that allow them to learn on their own in the absence of a lecturer or tutor. This paper discusses what self-directed learning is and highlights the principles of writing self-learning materials for ODL learners. Various self-material materials utilised in open and distance learning are explored. In the paper, the writers further explore the nature, characteristics and importance of selflearning materials on ODL. The writers further discuss theories underpinning the utilisation of self-instructional materials. The paper further interrogates how learning material developers can engage in the effective development of self-learning materials in both print and electronic formats. Conclusions are drawn on the importance of professionally training material developers in developing high-quality self-learning materials for ODL learners.
Local Best Particle Swarm Optimization Using Crown Jewel Defense Strategy

Particle swarm optimization (PSO) is a swarm intelligence algorithm well known for its simplicity and high efficiency on various problems. Conventional PSO suffers from premature convergence due to the rapid convergence speed and lack of population diversity. It is easy to get trapped in local optima. For this reason, improvements are made to detect stagnation during the optimization and reactivate the swarm to search towards the global optimum. This chapter imposes the reflecting bound-handling scheme and von Neumann topology on PSO to increase the population diversity. A novel crown jewel defense (CJD) strategy is introduced to restart the swarm when it is trapped in a local optimum region. The resultant algorithm named LCJDPSO-rfl is tested on a group of unimodal and multimodal benchmark functions with rotation and shifting. Experimental results suggest that the LCJDPSO-rfl outperforms state-of-the-art PSO variants on most of the functions.
Machine Learning Applicationfor Active Exploration of Weld Sequence Scenarios

Distortion is a common problem in welded structures, and the process of finding an effective weld sequence to mitigate the distortion is a challenging task given a large number of possible combinations. Despite efficient simulation tools and powerful supercomputers, yet simulation tools have been limited by CPU time to optimize a welding sequence and therefore not mature for practical designs. To this end, we constructed and integrated machine learning (ML) algorithms with the simulation capability. These ML models were then trained to increase the fidelity by a wisely chosen training-set of simulation to construct a meta-model for active exploration of various weld sequence scenarios in real time. As opposed to existing ML algorithms that require an extensive data set to train, our algorithm picks relatively small training-set to construct a metamodel. We present an example of our algorithm implemented in a real welded structure project. Introduction Today’s structures are more complex and demanding much tighter fabrication tolerances than our routine practice. Welding plays a challenging role in meeting those tolerances in particular when it comes to distortion. Welding sequence and intermittent welding design, which determines the best welding pattern in multi-pass welds, are familiar techniques to control the distortion when dealing with multi-pass welded structures. Finding the best solution for such a design is usually intuitive and based on the similarity of previously welded structures because this is not feasible through shop trials. An alternative is welding simulation tools that model several sequences to select the one with minimal distortion. Excellent simulation software is now available to capture and couple thermal, microstructure and stress effects of welds based on 3D transient temperature and thermal stress-strain analysis [1]. Despite powerful supercomputers, yet welding simulation tools are limited by computational time, and therefore they are not mature for practical designs. For example, having “n” welds requires choosing from 2^n n! possible scenarios or combinations of the welds where n! counts for permutations and 2^n counts for change in the direction of welding, i.e., several million for typical weld consisting of 10 weld passes or more. More affordable approaches have been developed to generate a sufficient and reliable level of understanding of the behavior of structures in order to find an optimal sequence with a few numbers of simulation. One approach is to use a fast but low-fidelity model that captures the most dominant physics of the problem, for example, depositing each weld pass at once [2]. Although 1 Corresponding author and presenter; Mahyar.Asadi@applusrtd.com 1002 Contributed Papers from Materials Science and Technology 2019 (MS&T19) September 29–October 3, 2019, Oregon Convention Center, Portland, Oregon, USA DOI 10.7449/2019/MST_2019_1002_1009 Copyright © 2019 MS&T19®
Z*-Numbers, Data Structures, and Thinking in Machine-Mind Architecture

A generally intelligent cognitive machine is deliberative, reflective, adaptive, empathetic and rational; continually aiming for the best responses to complex real-world events. This article describes our efforts towards the realization of such a machine – an embodied machine-mind – endowed with abilities of multisensory processing, commonsense reasoning, reflection, consciousness, and empathy. A procedure for contemplation and comprehension by the machine-mind has been formalized. It uses: a) Z*-numbers for active-thought abstraction, b) multisensory data-structures to encapsulate objective and subjective real-time inputs, and transport these across framework-modules, and c) dynamic action-descriptor formulations for bespoke behavior, perception, and real-time attention modulation. The defined procedure acknowledges the machine-mind's interest in the subject being comprehended, and covers different levels of thinking (instinctive reactions to self-consciousness reasoning). This investigation contributes to the synthesis of ‘thinking machines’ for man-machine symbiosis.
Rear-End Crash Risk of CACC-Manual Driven Mixed Flow Considering the Degeneration of CACC Systems

The influence of (cooperative) adaptive cruise control (CACC/ACC) on rear-end crashes have been conducted by some previous studies. However, they usually did not consider the degeneration from stable CACC to unstable ACC when vehicle-to-vehicle (V2V) communication is not available. California PATH program validated the instability of ACC system by using real experimental data, which might have negative impacts on safety. Therefore, this paper evaluated the impacts of degeneration of CACC vehicles on rear-end crash risks. A micro-simulation based method was adopted to evaluate rear-end crash risks with the increase of the CACC market penetration rate (MPR). The results obtained were quite different from those in previous studies and showed that the rear-end crash risks of the full MDV flow increase before CACC MPR increases to a certain level. It was found that the instability of ACC vehicles might be the main factor for the negative results. The results of this paper provide helpful insight into the management of CACC vehicular flow in the V2V communication environment.
Parallel Processing Based on Fragmentation of English Text Pictures to Speed Up Recognition of Characters on a Multi-Core Computer

Script picture analyses use algorithms to turn images of text into readable computer data. The goal of script picture analyses is pattern recognition of script and graphics combinations from images for extraction of the data. This paper solves a long-existing problem in pattern recognition in script images regarding recognition of characters and words using a computer processor. The proposed system rely on main steps of execute, first step: is load English script of image file, second step: is partition image file to several image pages, third step: is divide the image into number of blocks, and final step: multi-block method can be used in each row of the script image with parallel processing using a multi-core computer (one processor P1, two processors P2, three processors P3, and four processors P4) with rapid processing for script images for pattern recognition . The outcome obtained with a multi-core computer is very fast for processing script images and is efficient and accurate. It can use the modified algorithm in multi-processor system that available now and not used all the capacity in processing in one processor as in old computers.
An Approach to Generalizing Taylor Series Integration for Low-Thrust Trajectories

The use of Taylor Series Integration (TSI) for the propagation of spacecraft trajectories and its combination with the relatively unknown Unified State Model (USM) coordinate system, resulting in the TSI-USM propagator, is known to allow fast propagation of interplanetary low-thrust trajectories. Unfortunately, the TSI-USM propagator is a problem-specific method, which is detrimental for its use in evolutionary algorithms. In this thesis, a method was developed to generalize the TSI-USM propagator. The TSI-USM was generalized to allow the propagation of a spacecraft trajectory in a central gravitational field subject to any pre-set thrust profile, without the need to adapt the internal working of the propagator. The three-dimensional thrust profile should be specified a priori, and with respect to time, by the user or by an evolutionary algorithm. The method used to generalize the TSI-USM requires the coefficients of the cubic spline interpolation of the pre-set thrust profile to compute the recurrence relations of the TSI-USM. Results show that, although the method indeed generalizes the TSI-USM, it is less accurate and requires more CPU time for a given accuracy than existing RK8(7)13M-based propagators. From this result, it can be expected that future attempts to generalize the TSI-USM propagator, without sacrificing its accuracy and computational speed, will be challenging.
A Microservice Architecture for the Design of Computer-Interpretable Guideline Processing Tools

Several tools exist that are designed to process computer interpretable guidelines (CIGs), each with a distinct purpose, such as detecting interactions or patient personalisation. While it is desirable to use these tools as part of larger decision support systems (DSSs) doing so is often not straightforward, as their design does not often support external interoperability or account for the fact that other CIG tools may be running in parallel, a situation that will become increasingly more prevalent with the increased adoption of CIGs in different parts of the health system. This results in an integration overhead, system redundancy and a lack of flexibility in how these tools can be combined. To address these issues, we define a blueprint architecture to be used in the design of guideline processing tools, based on the conceptualisation of key components as RESTful microservices. In addition, we define the types of data endpoints that each component should expose, for both the communication between internal components and communication with external components that exist as a part of a DSS. To demonstrate the utility of our architecture, we show how an example guideline processing tool can be restructured according to these principles, in order to enable it to be flexibly integrated into the DSS used in the CONSULT project.
Empirical single frequency network threshold for DVB-T2 based on laboratory experiments

DVB-T2 broadcasting with a single frequency network (SFN) allows an efficient management of frequency utilization and extends the coverage area, which will enable more people to view a broadcast. The SFN mode also increases the concentration of the signal in overlap areas. However, some difference of overlap areas in actual use of SFN networks may have some degradation of the received signal due to the effect of the SFN. In this research, we analyze SFN broadcasting in SISO mode. This paper represents the effects of delays on the SFN signal over different delay times within the guard interval (GI) by analyzing the minimum reception threshold. The analysis of received signal power, MER, bBER, LBER, and noise margin are studied using an actual digital television transmitter for the experiment. The results show that the minimum thresholds for a delay time at 0 microseconds will require higher receive signal power than other delay times within the GI. The results of this experiment are useful in designing the SFN network and make it possible to determine the appropriate C/N threshold for the design of the digital terrestrial television network.
Understanding Several Power Spectrum Density Estimation Algorithms Based on the Problems Solved

Starting from the estimation criterion and the essential problem of algorithmic computing, this paper introduces four kinds of spectral estimation methods commonly used in teaching: Periodogram to fit frequency vector under least square criterion ， AR model estimation converts spectrum estimation into parameter estimation, MVDR converts spectrum estimation into filter weight coefficient estimation, and MUSIC converts spectral estimation into subspace estimation. Then we analyze and solve the equation, derive the assumption of signal and the defects caused by this assumption.
Automated Deployment of an End-to-End Pipeline on Amazon Web Services for Real-Time Visual Inspection using Fast Streaming High-Definition Images

This thesis investigates various degrees of freedom and deployment challenges of building an end-to-end intelligent visual inspection system for use in automotive manufacturing. Current methods of fault detection in automotive assembly are highly manual and labor intensive, and thus prone to errors. An automated process can potentially be fast enough to operate within the real-time constraints of the assembly line, and can reduce errors. In automotive manufacturing, components of the end-to-end pipeline include capturing a large set of high definition images from a camera setup at the assembly location, transferring and storing the images as needed, executing object detection within a given time frame before the next car arrives in the assembly line, and notifying a human operator when a fault is detected. As inference of object detection models is typically very compute-intensive and memory-intensive, meeting the time, memory and resource constraints requires a careful consideration of the choice of object detection model and model parameters along with adequate hardware and environmental support. Some automotive manufacturing plants lack floor space to set up the entire pipeline on an edge platform. Thus, we develop a template for Amazon Web Services (AWS) in Python using the BOTO3 libraries that can deploy the entire end-to-end scalable infrastructure in any region in AWS. In this thesis, we design, develop, and experimentally evaluate the performance of system components, including the throughput and latency to upload high definition images to an AWS cloud server, the time required by AWS components in the pipeline, and the tradeoffs of inference time, memory and accuracy for twenty-four popular object detection models on four hardware platforms.
Improving Performance and Security in Anonymity Systems

Improving Performance and Security in Anonymity Systems Mohsen Imani, Ph.D. The University of Texas at Arlington, 2018 Supervising Professor: Gergely Zaruba Tor is an anonymity network that provides online privacy for the Internet users. Tor hides the user’s traffic among the others’ traffic. The more users Tor attracts, the stronger anonymity it provides. Unfortunately, users of the Tor anonymity system suffer from lessthan-ideal performance, in part because circuit building and selection processes are not tuned for speed. Moreover, there are some attacks like guard fingerprinting and website fingerprinting attacks that try to profile or de-anonymize the Tor users. In this dissertation, we propose methods to address both security and performance issues in Tor. We first examine the process of selecting among pre-built circuits and the process of selecting the path of relays for use in building new circuits to improve performance while maintaining anonymity. We also propose a method to improve the mechanism of picking guards in Tor. The guard selection mechanism in Tor suffers from security problems like guard fingerprinting and from performance issues. To address this problem, we propose a new method for forming guard sets based on Internet location. We construct a hierarchy that keeps clients and guards together more reliably and prevents guards from easily joining arbitrary guard sets. This approach also has the advantage of confining an attacker with access to
On Table Resource Virtualization and Network Slicing in Programmable Data Plane

Recently, the advances on programmable data plane (PDP) promote the studies on the network virtualization in a PDP-based substrate network (SNT). In this paper, we address the table resource virtualization and network slicing in PDP-based SNTs. We first leverage the idea of “Big-Switch” to design an effective table resource virtualization scheme in which the flow tables of a virtual switch (V-SW) can be installed in multiple adjacent substrate switches (S-SWs) according to their table sizes, while the feasible table size(s) on each S-SW are determined based on the global information of the SNT. By doing so, we can regulate the flow tables in the S-SWs in a more organized way to minimize memory fragmentation. Next, we address the network slicing based on the virtualization scheme, and come up with a three-layer VNE problem. To the best of our knowledge, such a VNE problem has not been studied before and the existing algorithms designed for traditional two-layer VNE problems can hardly solve it. We formulate an integer linear programming (ILP) model to solve the VNE problem exactly, and also design a time-efficient heuristic that can provide near-optimal solutions. Finally, we implement the heuristic in TPVX, which is a network hypervisor based on protocol-oblivious forwarding (POF), and also improve its performance by introducing source routing. The new TPVX is experimentally demonstrated in a real network testbed, and the results verify that our proposal maintains the additional latency caused by the three-layer VNE well and would not degrade the network services in virtual networks (VNTs).