Miracle-dz/UniFAR-FTUs huggingface dataset:
Evolving Games and the Social Contract

Evolutionary game theory provides a set of tools that can be used to model the evolution of social contracts. These models traditionally take the game individuals play as fixed and consider how behavior within this game may change over time. However, one of the most important aspects of the formation of social contracts is the evolution of the game itself. This chapter presents a method for modeling evolving games that fits within the standard methods of evolutionary game theory and applies this approach to examples relevant to the evolution of the social contract. The scope and limits of this method are discussed and this way of representing evolving games is contrasted with other approaches that have been considered.
User-Friendly Resource Design: Providing Accessible Ground Squirrel Extension for the Web

Author(s): Dimson, Monica; Baldwin, Roger A. ; Quinn, Niamh | Abstract: In terms of internet access, California farmers are some of the most well-connected in the country. According to the USDA, 70% of farms in the U.S. have access to the web as of 2015. In California, 93% of counties exceed that national average. This prompts us to take advantage of the opportunity to employ passive extension approaches, such as websites, which can save both time and resources for Extension audiences and personnel alike. Because digital media can be distributed without the expenses associated with static print resources, this allows us to update our information platforms with greater ease and frequency. Our goal is not to replace print publications, but to adapt Extension resources for a web context and thus connect with a large and geographically extensive audience, especially those who may not customarily seek or have access to more traditional Extension services. A website allows homeowners, landowners, and pest-control professionals to consult this resource freely, at their leisure, and to adopt best management practices more quickly. However, the movement toward cloud-based resources comes with greater web familiarity and thus greater expectations for the websites people use. We are currently developing a ground squirrel best management practices website. In order to provide a user-oriented experience comparable to that of other digital media resources, we need to consider the specific needs and behaviors of a web-based audience. We aim to achieve this by synthesizing new and existing resources into consumable, approachable content on a website that focuses on usability, clarity, visual impact, and site-wide cohesion. We will discuss these goals and how we achieved them.
Multiple Target Tracking Via Dynamic Point Clustering on a UAV Platform

This research focuses on solving the problem of tracking multiple ground targets from a single or multiple UAV’s utilizing a dynamic clustering algorithm. Each UAV is assumed to have only one camera that is mounted on a two axis gimbal. The location of each of the targets can be ascertained by using a geolocation method with the camera as the primary sensor. The targets will not be able to transmit any of their location data to the UAV’s. An extended Kalman filter is used to estimate the location and velocity of the targets with the afore mentioned geolocation used as the update step. The algorithm uses the estimated target locations and the distance from the UAV to different point locations to find the maximum number of targets that the camera can view at once. These clusters of points are then subject to a cost function where the goal is to minimize the overall uncertainty of all of the system. While also staying below a maximum allowable uncertainty for individual targets. The approach taken by this method is scalable with the number of UAV’s and ground targets that it can track to allow for it to be used in a wide variety of circumstances. The targets future locations can also be evaluated by the clustering algorithm to get the locations that the camera should point in order to minimize the uncertainty.
Social Engineering: How U.S. Businesses Strengthen the Weakest Link against Cybersecurity Threats

The purpose of this transcendental phenomenological qualitative study was to investigate how IS professionals working in U.S. businesses make sense of their lives and experiences as they address and prevent vulnerabilities to social engineering attacks. This larger problem was explored through an in-depth study of social engineering and its effect on IS professionals working in U.S. businesses operating within healthcare, financial services, and educational industries across the central and northwest regions of Louisiana. Through its use of a phenomenological research design, the study bridged a gap in the social engineering literature, which was primarily comprised of studies that utilized a quantitative methodology. The use of a qualitative approach allowed participants to give voice to their beliefs, thoughts, and motivations about the work they do. The findings, consisting of ten themes and two subthemes, present the essence of experience of six IS professionals addressing and preventing social engineering vulnerabilities in their workplace. The findings revealed that the lived experience of protecting an organization from social engineering attacks involves the unification of people across the enterprise to develop a strong security-minded culture. Additionally, participants shared two primary beliefs, (1) that social engineering attacks would never be eradicated and (2) that IS professionals depend on everyone in the organization to protect the organization from social engineering attacks. The study offers recommendations to IS professionals, business leadership, HR professionals, educators, consultants, vendors, and researchers.
Compound Fault Diagnosis of Centrifugal Pumps Using Vibration Analysis Techniques

Centrifugal pumps are widely used in many different industrial processes, such as power generation stations, chemical processing plants, and petroleum industries. The problem of failures in centrifugal pumps is a large concern due to its significant influence on such critical industries. Particularly, as the core, parts of a pump, bearings and the impellers are subject to different corrosions and their faults can cause major degradation of pump performances and lead to the breakdown of production. Therefore, an early detection of these types of faults would provide information to take timely preventive actions. This research investigates more effective techniques for diagnosing common faults of impellers and bearings with advanced signal analysis of surface vibration. As overall vibration responses contain a high level of broadband noises due to fluid cavities and turbulences, noise reduction is critical to developing reliable and accurate features. However, considering the modulation effect between the rotating shaft, vane passing components and any structural resonances, a modulation signal bispectrum (MSB) method is mainly used to extract these deterministic characteristics of modulations, which differs from previous researches in that the broadband vibration is often characterised with statistical methods, high frequency demodulation along spectrum analysis. Both theoretical analysis and experimental evaluation show that the diagnostic features developed by MSB allow impellers with inlet vane damages and exit vane faults to be identified under different operating conditions. It starts with an in-depth examination of the vibration excitation mechanisms associated with each type of common pump faults including impeller leakages, impeller blockages, bearing inner race defects and bearing outrace defects. Subsequently, fault diagnosis was carried out using popular spectrum and envelope analysis, and more advanced kurtogram and MSB analysis. These methods all can successfully provide correct detection and diagnosis of the faults, which are induced manually to the test pump. Envelope analysis in a bands optimised with Kurtogram produces outstanding detection results for bearing faults but not the impeller faults in a frequency range as high as several thousand hertz (about 7.5kHz). In addition, it cannot provide satisfactory diagnostic results in separating the faults across different flow rates, especially when the compound faults were evaluated. This deficiency is because they do not have the capability of suppressing the random noises. Meanwhile, it has found that the MSB analysis allows both impeller and bearing faults to be detected and diagnosed. Especially, when the pump operated with compound faults both the fault types and severity can be attained by the analysis with acceptable accuracy for different flow rates. This high performance of diagnosis is due to that MSB has the unique capability of noise reduction and nonlinearity demodulation. Moreover, MSB diagnosis can be a frequency…
A Scalable Feature Based Clustering Algorithm for Sequences with Many Distinct Items

Various sequence data have grown explosively in recent years. As more and more of such data become available, clustering is needed to understand the structure of sequence data. However, the existing clustering algorithms for sequence data are computationally demanding. To avoid such a problem, a feature-based clustering algorithm has been proposed. Notwithstanding that, the algorithm uses only a subset of all possible frequent sequential patterns as features, which may result in the distortion of similarities between sequences in practice, especially when dealing with sequence data with a large number of distinct items such as customer transaction data. Developed in this article is a feature-based clustering algorithm using a complete set of frequent sequential patterns as features for sequences of sets of items as well as sequences of single items which consist of many distinct items. The proposed algorithm projects sequence data into feature space whose dimension consists of a complete set of frequent sequential patterns, and then, employs K -means clustering algorithm. Experimental results show that the proposed algorithm generates more meaningful clusters than the compared algorithms regardless of the dataset and parameters such as the minimum support value of frequent sequential patterns and the number of clusters considered. Moreover, the proposed algorithm can be applied to a large sequence database since it is linearly scalable to the number of sequence data.
SRA: Secure Reverse Auction for Task Assignment in Spatial Crowdsourcing

In this paper, we study a new type of spatial crowdsourcing, namely competitive detour tasking, where workers can make detours from their original travel paths to perform multiple tasks, and each worker is allowed to compete for preferred tasks by strategically claiming his/her detour costs. The objective is to make suitable task assignment by maximizing the social welfare of crowdsourcing systems and protecting workers’ private sensitive information. We first model the task assignment problem as a reverse auction process. We formalize the winning bid selection of reverse auction as an <inline-formula><tex-math notation="LaTeX">$n$</tex-math><alternatives><mml:math><mml:mi>n</mml:mi></mml:math><inline-graphic xlink:href="liu-ieq1-2893240.gif"/></alternatives></inline-formula>-to-one weighted bipartite graph matching problem with multiple 0-1 knapsack constraints. Since this problem is NP-hard, we design an approximation algorithm to select winning bids and determine corresponding payments. Based on this, a Secure Reverse Auction (SRA) protocol is proposed for this novel spatial crowdsourcing. We analyze the approximation performance of the proposed protocol and prove that it has some desired properties, including truthfulness, individual rationality, computational efficiency, and security. To the best of our knowledge, this is the first theoretically provable secure auction protocol for spatial crowdsourcing systems. In addition, we also conduct extensive simulations on a real trace to verify the performance of the proposed protocol.
Customer-to-Customer (C2C) Connectedness in Services – Unlocking a New Source of Value Co-Creation

Contemporary service organizations create value through multiple inter-organizational relationships. The relationships between the service organization and its customer have been the dominating focus in literature on value co-creation in large service organizations. This paper introduces the novel phenomenon of value co-creation emerging around interpersonal and digital customer-to-customer (C2C) connections. With the advent of the Internet, social media, “digital native” customers, and the availability of big data and new analytical tools, service organizations are afforded to tap into existing social relationships among their customer base or even to ‘engineer’ novel connections. We present a theory-led model to explain how firms can create new sources of service value through “C2C connected value co-creation”. By extending Teece’s concept of complementary asset in the service context, this model subsumes two major building blocks: a firm’s potential for C2C connections and complementary service assets, namely the network capital, mirroring service resources, and advancing services resources of the connected customer. We present three strategies for C2C connected service value co-creation describing combinations of these building blocks: (1) embedding, (2) substituting, and (3) expanding. Multiple case examples illustrate our propositions. Our novel form of theorizing has implications for future research on management of information systems in service organizations.
DIAT: Data Integrity Attestation for Resilient Collaboration of Autonomous Systems

Networks of autonomous collaborative embedded systems are emerging in many application domains such as vehicular ad-hoc networks, robotic factory workers, search/rescue robots, delivery and search drones. To perform their collaborative tasks the involved devices exchange various types of information such as sensor data, status information, and commands. For the correct operation of these complex systems each device must be able to verify that the data coming from other devices is correct and has not been maliciously altered. In this paper, we present DIAT – a novel approach that allows to verify the correctness of data by attesting the correct generation as well as processing of data using control-flow attestation. DIAT enables devices in autonomous collaborative networks to securely and efficiently interact, relying on a minimal TCB. It ensures that the data sent from one device to another device is not maliciously changed, neither during transport nor during generation or processing on the originating device. Data exchanged between devices in the network is therefore authenticated along with a proof of integrity of all software involved in its generation and processing. To enable this, the embedded devices’ software is decomposed into simple interacting modules reducing the amount and complexity of software that needs to be attested, i.e., only those modules that process the data are relevant. As a proof of concept we implemented and evaluated our scheme DIAT on a state-of-the-art flight controller for drones. Furthermore, we evaluated our scheme in a simulation environment to demonstrate its scalability for large-scale systems.
Local Government Debt and Regional Competition in China

The GDP tournament among local governments plays a crucial role in supporting China's growth miracle. Investment-driven economic growth has led to a surge in local government debt in the past decade. To understand the relationship between local government debt and regional competition in GDP growth, we build a simple decision model of local governors based on prospect theory. Using a comprehensive data set of prefectural-level government debt, our empirical results confirm the theoretical prediction that regional competition in GDP growth does affect the dynamics of local government debt. The effect presents an asymmetric pattern, in the sense that a local government with GDP growth falling behind its competitors tends to issue debt more aggressively, while the effect is insignificant for local governments with GDP growth ahead of their competitors'. Furthermore, we find that the regulatory policy implemented at the end of 2015 that places a ceiling on local government debt effectively dampens the impact of regional competition on local government debt. However, local government investment remains strongly responsive to the regional GDP growth gap, probably because of alternative financial channels such as land financing. The above finding indicates that the debt regulatory policy cannot largely prevent local governments from implementing a pro-growth strategy induced by regional competition.
Don't Make Me Look Bad: How the Audit Market Penalizes Auditors for Doing Their Job

We examine the reputational impact of an audit office issuing adverse internal control opinions (adverse ICOs). While prior research has examined how clients that receive an adverse ICO respond, we focus on clients that did not receive an adverse ICO. We find that audit offices that issue more adverse ICOs experience lower growth and that this effect is stronger when the adverse ICO is associated with a more visible client or refers to an entity-level control weakness. Finally, we find that clients are less likely to select auditors with a history of issuing adverse ICOs and that auditors are able to recoup some of their lost growth when they issue fewer adverse ICOs. Our results indicate that the market for audit services penalizes auditors for disclosing information critical of management, which undermines the value of direct-to-investor auditor communications and provides insight into potential longer-term implications of the expanded auditor's report.
Creating a desire to purchase, looking at consumer behavior and manipulation tactics

Companies are investing more and more into cookies and other data retrieving methods. This data is used to target consumers specifically to their interests. With as a main goal: creating a desire to purchase their good/service. How do companies create this desire? How can they maximize such a desire? Looking at consumer behavior and the usage of manipulation tactics might give the solutions.
TRAINING PARENTS OF HYPERACTIVE CHILDREN IN BEHAVIOR MANAGEMENT

Over the past two decades there has been increasing recognition of a disorder among children commonly referred to as hyperactivity, hyperkinesis, and/or minimal brain dysfunction. Children so identified are generally characterized by a short attention span, overactivity, low frustration tolerance, aggressiveness, defiance, and learning difficulties. Moderate estimates place the prevalence of hyperactivity somewhere between 5% and 10% of the school-age population. The rate of referral of hyperactive children to children's mental health services is certainly even higher. In professional journals there has been a proliferation of articles dealing with hyperactivity, its biological and behavioral characteristics, etiology, treatment, and long-term prognosis. Controversial issues have arisen, most notably concerning medicating children for this behavioral disorder, but even concerning the existence of the disorder (Schrag & Divoký, 1975). The clinical treatment of hyperactive children has received considerable attention, particularly because the "treatment of choice" for the symptoms has generally been central nervous system stimulant medication. In looking through the literature concerning the treatment of hyperactive children, one finds that the overwhelming percentage of articles deal with evaluations of the efficacy of a particular drug or the relative efficacy of several medications. The number of school-age children receiving such stimulants as dextroamphetamine sulphate and methylphenidate hydrochloride is rising
A User-Oriented Language for Specifying Interconnections Between Heterogeneous Objects in the Internet of Things

We propose a user-oriented language to enable users to specify interconnections between heterogeneous objects in the Internet of Things (IoT). Based on the idea of the use case specification technique in software engineering, our language provides users with a natural language like syntax to allow them to specify when or under what conditions they want which objects to be connected. To support this language, we have also developed a transformation mechanism that automatically translates users’ specification into the source code. We have evaluated this language through an experiment and a survey. The main contributions of this paper are: 1) a simple natural language that enables the users to specify which objects to connect and when and 2) a transformation mechanism that automatically translates users’ specifications into source code and dynamically attaches the code to relevant applications. This paper represents a first step in bringing the IoT closer to their users.
ON THE USE OF DEEP LEARNING METHODS ON MEDICAL IMAGES

Deep Learning algorithms have recently been reported to be successful in the analysis of images and voice. These algorithms, specifically Convolutional Neural Network (CNN), have also proven themselves to be highly promising on images produced by medical imaging technologies, as well. By use of deep learning algorithms, researchers have accomplished several tasks in this field including image classification, object and lesion detection and segmentation of different tissues in a medical image. Researchers mostly focused on medical images of neurons, retina, lungs, digital pathology, breast, heart, abdomen and skeleton system to take advantage of the Deep Learning approach. This study reviews literature studies of recent years that utilized Deep Learning algorithms on medical images in order to present a general picture of the relevant literature.
Cognitive social zones for improving the pedestrian collision avoidance with mobile robots

Social behaviors are crucial to improve the acceptance of a robot in human-shared environments. One of themost important social cues is undoubtedly the social space. This human mechanism acts like a repulsive field to guaranteecomfortable interactions. Its modeling has been widely studied in social robotics, but its experimental inference has beenweakly mentioned. Thereby, this paper proposes a novel algorithm to infer the dimensions of an elliptical social zone froma points-cloud around the robot. The approach consists of identifying how the humans avoid a robot during navigationin shared scenarios, and later use this experience to represent humans obstacles like elliptical potential fields with thepreviously identified dimensions. Thus, the algorithm starts with a first-learning stage where the robot navigates withoutavoiding humans, i.e. the humans are in charge of avoiding the robots while developing their tasks. During this period,the robot generates a points-cloud with 2D laser measures from its own framework to define the human-presence zonesaround itself but prioritizing its closest surroundings. Later, the inferred social zone is incorporated to a null-space-based(NSB) control for a non-holonomic mobile robot, which consists of both trajectory tracking and pedestrian collisionavoidance. Finally, the performance of the learning algorithm and the motion control is verified through experimentation.
External Search Effort for Wine

Over the last two decades the United Kingdom has become a nation of wine‐drinkers. However, due to climatic conditions, very little is actually produced in the UK, Almost all the £6 billion market is made up of imported wine from all corners of the world. The traditional suppliers from France, Germany and Italy have, over recent times, been confronted with the wines from the New World. Supermarkets have led the way in stocking wines from both the traditional and New World countries. Furthermore, their attention to merchandising has been the catalyst for the surge in wine experimentation.