Miracle-dz/UniFAR-FTUs huggingface dataset:
Join t Beamforming and Scheduling Design in Hierarchical Cognitive Radio Networks

Cognitive radio (CR) has been recently regarded as an important technique to improve spectrum utilization. In the thesis, we present a joint design approach to integrate power allocation, antenna beamforming and user scheduling to maximize the system sum rate of multiple users in the hierarchical CR networks with multicarrier transmissions. Concurrent transmissions between CR users and licensed users are considered to improve the spectrum efficiency. The challenge of hierarchical CR networks is to manage mutual interference between the CR and licensed systems. We first apply the semi-definite relaxation (SDR) technique to transfer the original mixed integer non-linear problem (MINLP) to a convex problem. Then, a sum rate optimization algorithm is proposed to determine the optimal power allocation, antenna beamforming and user scheduling under the limitation of the interference to the licensed networks. The simulation results show that the proposed algorithm can improve the sum rate at least 20% compared to an exhaustive search user scheduling plus zero-forcing beamforming, while overcoming the primary system with sacrifice of 10% sum rate below the maximum sum rate. The proposed methodology provides many important insights into the system design and deployment principles for future hierarchical CR networks.
Language Constructs for Context-oriented Programming

ContextL is an extension to the Common Lisp Object System that allows for Context-oriented Programming. It provides means to associate partial class and method definitions with layers and to activate and deactivate such layers in the control flow of a running program. When a layer is activated, the partial definitions become part of the program until this layer is deactivated. This has the eect that the behavior of a program can be modified according to the context of its use without the need to mention such context dependencies in the aected base program. We illustrate these ideas by providing dierent UI views on the same object while, at the same time, keeping the conceptual simplicity of objectoriented programming that objects know by themselves how to behave, in our case how to display themselves. These seemingly contradictory goals can be achieved by separating class definitions into distinct layers instead of factoring out the display code into dierent classes.
A Survey of State-Action Representations for Autonomous Driving

In this survey, we list di(cid:27)erent representations for the state and action spaces of a driving policy. We focus on the literature dedicated to decision making rather than perception, and on the context of autonomous driving only. To formulate the autonomous driving problem as a Markovian Decision Process, one must (cid:28)rst describe the state of the system and its encoding in a space S . We list the main state representations found in the literature in sections 1 and 2. Then, one must de(cid:28)ne a set of actions A that gives the agent control over the state dynamics, which we explore in section 3.
Modeling Physical and Chemical Nonequilibrium Transport of Herbicide in Soils From Different Tillage Systems.

The physical and chemical nonequilibrium transport o f alachlor were studied in a surface Gigger soil from different tillages through tracer studies, and batch and miscible displacement experiments. Batch experiments indicated initially fast reaction followed by slow adsorption. Adsorption and desorption results indicated time dependent hysteretic behavior and was best described by a multireaction model incorporating nonlinear equilibrium reaction, a reversible kinetic mechanism, and a consecutive irreversible mechanism. The model predicted alachlor hysteresis and adsorption-desorption kinetics satisfactorily based on parameters obtained from adsorption experiments. Tracer (Eosin Y and Blue FCF dyes) studies showed non-uniformly stained areas in undisturbed soil cores (6.4 cm i.d, 15 cm length) and indicated more pronounced preferential flow and physical nonequilibrium solute transport in no-till than in conventional tillage. Tritium breakthrough curves (BTCs) indicated earlier breakthrough associated with bimodal peaks in short pulses for no-till. The shape o f BTCs were also dependent on flow direction. The superimposed experimental data from short pulses well predicted the data of long pulses. The classical convective-dispersive equation was inadequate and there was no improvement in describing tritium BTCs using physical nonequilibrium models (mobile-immobile and stochastic models) for soils from no-till. Miscible displacement results indicated that alachlor BTCs in soils of no-till were more asymmetrical, with earlier breakthrough and longer tailing than soils from conventional tillage. A multireaction transport model (MRTM) was not satisfactory for
Designing effective MOOCs

Massive open online courses (MOOCs) have expanded significantly in recent years and are challenging traditional educational fee-paying offerings. The advantages of MOOCs are cited as the fact that they are free, that they enable participants to be part of a global community of peers and to have the experience of learning through social media and that they offer the potential for opening up educational and facilitating social inclusion. Nonetheless there are challenges associated with MOOCs. Firstly, most have very high dropout rates. Secondly, there are challenges with approaches to recognition of learning and issues with learner authentication and cheating. Thirdly, there are issues with providing support at scale. This paper argues that effective design of MOOCs is key. It focuses on the description of a 12-dimensional classification schema for describing and designing MOOCs. It then uses the schema to describe five MOOCs, which are respectively based on associative, cognitive, constructivist, situative and connectivist pedagogies. It then describes the 7Cs of Learning Design framework and discusses how it can be used to design MOOCs.
Human Posture Recognition Based on Images Captured by the Kinect Sensor

In this paper we combine several image processing techniques with the depth images captured by a Kinect sensor to successfully recognize the five distinct human postures of sitting, standing, stooping, kneeling, and lying. The proposed recognition procedure first uses background subtraction on the depth image to extract a silhouette contour of a human. Then, a horizontal projection of the silhouette contour is employed to ascertain whether or not the human is kneeling. If the figure is not kneeling, the star skeleton technique is applied to the silhouette contour to obtain its feature points. We can then use the feature points together with the centre of gravity to calculate the feature vectors and depth values of the body. Next, we input the feature vectors and the depth values into a pre-trained LVQ (learning vector quantization) neural network; the outputs of this will determine the postures of sitting (or standing), stooping, and lying. Lastly, if an output indicates sitting or standing, one further, similar feature identification technique is needed to confirm this output. Based on the results of many experiments, using the proposed method, the rate of successful recognition is higher than 97% in the test data, even though the subjects of the experiments may not have been facing the Kinect sensor and may have had different statures. The proposed method can be called a “hybrid recognition method”, as many techniques are combined in order to achieve a very high recognition rate paired with a very short processing time.
The Role of Context for Recommendations in Digital Libraries

948 DOI: 10.7763/IJSSH.2015. V5.585   Abstract—This paper discusses the role of context in making recommender systems for digital libraries. It first introduces different types of recommender systems then discusses the role of context in making recommendations for users searching in digital libraries. This study highlights the important role of context and the need for more investigation on context-aware recommender system since exploiting contextual information in recommender systems is an effective approach to create more accurate and relevant recommendations. The article stresses the need for qualitative research on the subject in order to find out the elements that constitute the context from the users’ viewpoint and the way a context-aware recommender system could be effectively designed. Past studies seem to have failed to survey on enough attention to the role of context in designing recommender systems for digital libraries.
LUNAR SURFACE SAMPLING FEASIBILITY EVALUATION METHOD FOR CHANG’E-5 MISSION

Abstract. As China’s first unmanned spacecraft to collect lunar surface samples and return them to Earth, the Chang’E-5 detector is a crucial probe that will complete lunar surface sampling in China’s lunar exploration project. This lunar sampling will be the first successful lunar surface sampling return mission in China. Sampling decisions needs to be made based on topographical analysis results and characteristics of the area to be explored. Due to the unknown extraterrestrial terrain and uncertainty of sampled objects, we propose a sampling feasibility estimation for safely implementing lunar surface sampling.Our strategy took into account the influence of factors that may interfere with the sampling process, and provided quantitative assessment of the sampling feasibility for the area to be explored. We combined the three-dimensional topography of the lunar surface with five parameters of the sampling area, flatness, slope, slope aspect, accessibility of the mechanical arm distal end, and safety of sampling conditions. The first three values were calculated based on a digital elevation model (DEM) of the landing area generated using stereo images. The other values were computed based on the mechanical properties of the arm and kinematic analysis of its articulated joints. Based on the above-mentioned quantitative parameters, they were weighed to obtain an evaluation value for the sampling feasibility of each DEM pixel. Meanwhile, a multichannel sampling area analysis graph was generated that combined all the above indicators as well as the sampling feasibility values, which provides a visualization for determining detection targets in the Chang’E-5 sampling mission.
Finding a path in the hierarchical road networks

In a hierarchical road network, all roads can be classified into two groups according to their attributes, such as their speed limit and the number of lanes. By splitting the whole network into high-level and low-level subnetworks, the size of the road network to be searched can be reduced, and more human-oriented paths can be obtained. In this paper, we define a convenient path in the hierarchical road network and propose an algorithm for finding that path. The proposed algorithm produces a convenient and approximately shortest path with little computational effort by reducing the number of nodes to be considered. The difference between the length of the paths produced by the proposed algorithm and by existing algorithms and that of the shortest path of the whole network is discussed. Some experimental results are given to show the efficiency of the proposed algorithm.
Nonprofit Crossover Leadership: A Phenomenological Study of Nonprofit Executives from For-Profit Backgrounds

This qualitative study explored the motivations and experiences of for-profit leaders who crossed sectors to become nonprofit executives. Eight participants described their career pathways and initial experiences in a for-profit setting. Pathway stories to nonprofit leadership began with educational background, followed successive for-profit roles, and concluded with one or more nonprofit executive leadership experiences. The study revealed nonprofit crossover leaders followed a motivation for missional alignment, whether consciously known early in their career progression or discovered later, and shared a system of beliefs, values, and ethics ultimately pointing toward nonprofit executive positions. Nonprofit crossover leaders demonstrated varying postures of preparedness for their initial experiences in the nonprofit sector and implemented organizational changes early in their tenures following a similar set of strategic priorities. Authentic leadership theory explained the missional motivation and values-based system of decision-making guiding their career pathways into nonprofit roles. However, nonprofit crossover leaders favored the more mechanistic elements of the structural frame over the human resource, political, and symbolic frames (Bolman & Deal, 2017) in the organizational changes they implemented. The study contributes to a largely under-researched trend in the nonprofit sector of hiring executives with for-profit backgrounds (Su & Bozeman, 2009) and expands understanding of nonprofit crossover leadership with implications for leaders and nonprofit organizations, as well as for institutions offering nonprofit leadership programs.
A review on resilience assessment of energy systems

ABSTRACT Energy systems are regularly subject to major disruptions affecting economic activities, operation of infrastructure and the society as a whole. Resilience assessment comprises the pre-event oriented classical risk assessment as a central element, but it goes beyond that because it also includes and evaluates post-event strategies to improve the functioning of the system during its future operation. First, an overview of resilience definitions used across various scientific disciplines is presented, followed by an in-depth analysis of resilience assessment and quantification for energy systems. The relevant literature is classified by approach and according to four key functions of resilience: resist, restabilize, rebuild, and reconfigure. Findings show that irrespective of the research field, a resilient system always operates with an aim to minimize the potential consequences resulting from a disruptive event and to efficiently recover from a potential system performance loss.
Une application à la consommation d'informations et à la quasi-consommation de services marchands

Using an original panel data and factorial analysis, this article captures a part of the learning process of home Net surfers, from a cognitive and experiential point of view. It shows the existence of several phases of learning: the browsing of the Web, through relatively long but not very frequent sessions, followed, after a relative intensification of uses, by utilitarian practices of the Web, through shorter and more frequent sessions. It also appears that the development of Internet surfing and of e-commerce are relatively independent. A few indications suggest that a new dynamic is possible with the new access technologies.
How metaphorical framings build and undermine resilience during change: A longitudinal study of metaphors in team-driven planned organizational change

ABSTRACT Change is a constant feature of organizing and one that requires resilience, or the ability to effectively face challenges. Although research demonstrates important findings about resilience during chaotic change like crises, less is known about resilience in mundane situations like planned change. This study explores team-driven planned organizational change, offering insights about how team members metaphorically frame change, analyzing how their framing fluctuates over time relative to perceptions of team success. Our three theoretical contributions extend theory about metaphors and organizational change, showing how negative framings of change are endemic to teams, regardless of perceived success; generate knowledge about resilience in organizing by showing how metaphors both build and undermine resilience; and extend applied theory about stakeholder participation in bureaucratic organizations.
A Sampling Method for Volume Entropy Analysis

Entropy-based approaches for anomaly detection are appealing since they provide more finegrained insights than traditional traffic volume analysis. However, there has been little effort to make it more efficient on the large scale and high speed network, especially for the volume entropy analysis. The sampling methods of volume entropy analysis are studied in this paper. First of all, the sampling constraint of volume entropy analysis is studied and the heavy-tailed distribution of Netflow records is released. Then a special stratified sampling named record-size based regression sampling is proposed. The experiments show it observably decreases the processing time and storage space without losing much fluctuation accuracy.
Localization in Wireless Sensor Networks

Localization of nodes in wireless sensor networks (WSNs) is important to many applications. Data are generally meaningless without location information; knowing the location of a node is a common goal in WSN applications. In this paper we study the possibilities of localization in WSNs, based on RSS measurements. We first studied the properties and methods of localization in general, followed by properties and algorithms specific to WSNs. We restricted our research to algorithms which use radio frequency received signal strength as a ranging method. Our study finds that no single best algorithm for localization exists to date. Each algorithm has a different purpose and properties. Learning from these algorithms and techniques, a more advanced localization system is
Optimum Power Allocation for Relay Network Based on Artificial Noise

In this work, a relay network incorporating artificial noise processing is analyzed and the mathematical expression of SOP (Secrecy Outage Probability) is obtained where four-node configuration is considered. The relay node is assumed to be unauthenticated. Moreover, under the constraint of the sum power of the source node and the jammer node, an Optimum Power Allocation (i.e., OPA) scheme is proposed by simultaneously minimizing SOP and maximizing average secrecy rate. The scheme is compared with the approach of Equal Power Allocation (i.e., EPA) in terms of SOP and average secrecy rate. The analysis and simulation results show that the OPA scheme can improve performance of the relay network significantly.
Natural Language Document and Event Association Using Stochastic Petri Net Modeling

The purpose of this research is to design and implement a new methodology that captures thenatural language understanding of events from English natural language text and model it using StochasticPetri Nets. To establish a baseline of recent natural language processing (NLP) and understanding (NLU)research, two surveys are presented. One is a general survey in NLP and NLU methodologies forprocessing multi-documents. It summarizes and presents methodologies in terms of their features,capabilities, and maturity. The second survey focuses on graph-based methods for NL text processing andunderstanding and analyzes them in terms of their functional descriptions, capabilities and maturities. Inrecent years, NLP/NLU researchers have narrowed their domain to graph methodologies due to improvedefficiency over older methods. Thus, to accomplish our goal, we firstly implemented a NL text to graphconversion method. This method extracts events in terms of their agents, actions, and patients from subjectnouns, verbs, and object nouns within each phrase and sentence of a text and produces a graph consisting ofnodes representing nouns and verbs and edges representing their relations. A significant effort went intohandling complex sentences consisting of multiple phrases, active and passive sentences, and multipleagents, actions, and patients. The graph provides a baseline implementation, which we could relate to othergraph methodologies and provide a structured approach to NLP and NLU from text. Next, we embedded anew NL text-graphs to Stochastic Petri Net (SPN) graph conversion methodology into our model torepresent events associated with NL text. SPN graphs provide not only a structured representation thatgraphs provide, but also other capabilities, such as representing and adjusting timing using its transitioncomponents, constraining flow with its inhibiting places, stochastic behavior of its markings, and colormarkings [89, 90]. We use these added capabilities from SPN modeling to capture new NLU capabilitiesof events from NL text. We demonstrated sentence disambiguation of events.
A Three Phase Conjugated Heat Transfer Solver Applied To Additive Manufacturing

Additive manufacturing (AM) is growing rapidly in the industry and is dependent on a number of process parameters and complex multiphysics. In the process, metal powder is melted by a laser beam forming a metal structure. Optimization of the process is very hard and is currently dependent on simple melt pool models. Therefore, there is a lot of trial and error involved in finding a robust AM process. To increase the understanding of the process we propose a physical and CFD-based melt pool model. The model includes a three phase conjugated heat transfer transfer solver, where the gas and melted solid (fluid) are treated with the volume-of-fluid method. To accurately capture the flow of the fluid a temperature dependent rheology model is employed. The phase transition occurs over a small temperature span, in which affected cells are transferred to the other phase solver. Finally, a ray trace based heat source model is used to simulate how an array of powder particles on a substrate are partially melted by a laser beam and the final solidified structure is formed.
Robust MIMO model following with application to trajectory motion control of robot arms

A model reference control (MRC) is studied for linear multi-input, multi-output systems with unknown dynamics. The control generates an estimate of an equivalent disturbance acting on the system, by the mechanism of time delay, and uses the estimate as a compensation signal. The analysis is presented in discrete time using the Euler operator. Experimental tests on the trajectory motion control of a two-degree of freedom SCARA robot are provided to corroborate the theoretical results. <<ETX>>
Tracking of splitting targets in clutter using an interacting multiple model joint probabilistic data association filter

The authors present a recursive algorithm that allows tracking of a target that splits into two targets in a cluttered environment using measurements obtained from a sensor with limited resolution. The approach consists of the interacting multiple model algorithm combined with a coupled version of the joint probabilistic data association filter. Simulation results show the effectiveness of this approach. <<ETX>>
Incorporating Diegetic Elements to Increase Engagement in Games for Engineering Education

One of the difficulties in developing educational games is maintaining player engagement. This engagement is critical for games to provide effective learning experiences. One way to increase engagement in games is to limit interruptions during game play. In educational games, this can be accomplished by incorporating learning or problem-solving elements diegetically. Diegetic elements are those that are part of the game scene. With this in mind, a series of games for Science, Technology, Engineering, and Mathematics (STEM) education has been developed focusing on content in courses that are prerequisites to the engineering curriculum. These games cover topics in pre-calculus, calculus, physics, and chemistry and incorporate learning elements diegetically. This paper will focus on techniques for implementing learning components as diegetic elements in games to increase player engagement. Additionally, concrete examples will be provided to further elucidate these techniques.