Miracle-dz/UniFAR-FTUs huggingface dataset:
Vertex nomination

Vertex nomination is a subclass of recommender systems operating on attributed graphs. Attributed graphs are an attractive way to represent data from a diverse set of natural and manmade phenomena. Frequently, these data have latent attributes or class memberships that are of interest and uncovering them is of some intrinsic value. So‐called recommender systems address the ‘more like this’ problem: given a subset of the data labeled as ‘interesting’, find unlabeled examples that are ‘similarly interesting’, often with the assumption that they are from the same latent class or possess similar latent attributes. Unsurprisingly, recommender systems operating on attributed graphs have generated an interesting body of research, detailing both theoretical and practical advances for a range of applications. This advanced review examines the relevant literature, particularly focused on the importance and inclusion of edge‐ and vertex attributes, used in conjunction with the graph structure. We include example applications from human language technology, biology, and neuroscience for concreteness, although the algorithms discussed are widely applicable. WIREs Comput Stat 2014, 6:144–153. doi: 10.1002/wics.1294
Logical structure and relevance

This thesis sets out to investigate relevance-theoretic pragmatics and its contribution to the study of linguistic meaning from a mentalist outlook. Adopting an internalist perspective with respect to the semantics of language seems to create serious problems for traditional accounts, which customarily seek to separate some common core of meaning from contextualisation. Against this background, it is argued that the ways in which an individual's mentally represented linguistic meanings are pervasively affected by his system of beliefs can be realistically addressed from a cognitive point of view through the implementation of the proposals of Relevance Theory regarding the inferential processes involved in the interpretation of utterances. In this setting, linguistic meaning is always provided through inference against the context of utterance and the need for a stable semantic content that is identical across individuals largely evaporates. In pursuit of this argument, the existing account of context within the relevance-theoretic framework is initially reviewed and extended. Then, on the basis of current research on the human cognitive capacity for metarepresentation and joint attention, it is suggested that it is an innate predisposition which enables us to efficiently align our contexts in instances of communication. In addition, it is argued that these highly developed mind-reading abilities are partially responsible for our natural tendency to develop an understanding of the world which closely resembles that of our peers; a point further elucidated by reference to Searle's notion of the Background. Finally, the semantics/pragmatics distinction is readdressed from a cognitive perspective and a case is made for the substitution of the externalist theory of semantic content with a more psychologically plausible contextualist counterpart within Relevance Theory itself.
Extension of Refinement Algorithm for Manually Built Bayesian Networks Created by Domain Experts

Generally, Bayesian networks are constructed either from the available information or starting from a naïve Bayes. In the medical domain, some systems refine Bayesian network manually created by domain experts. However, existing techniques verify the relation of a node with every other node in the network. In our previous work, we define a Refinement algorithm that verifies the relation of a node only with the set of its independent nodes using Markov Assumption. In this work, we did propose Extension of Refinement Algorithm that uses both Markov Blanket and Markov Assumption to find the list of independent nodes and adhere to the property of considering minimal updates to the original network and proves that less number of comparisons is needed to find the best network structure.
FastRule: Efficient Flow Entry Updates for TCAM-Based OpenFlow Switches

With an increasing demand for flexible management in software-defined networks (SDNs), it becomes critical to minimize the network policy update time. Although major SDN controllers are now optimized for rapid network update at the control plane, there is still room for data plane optimization in terms of update time, when using TCAM-based physical SDN commodity-off-the-shelf switches. A slow update directly affects network performance and creates bottlenecks. To minimize the flow entry update time, a dependency graph, a kind of directed acyclic graph (DAG), can be used for the access management of flow entries at the switch. Thanks to the DAG, unnecessary entry movements, which are the main factor slowing down flow entry updates, can be avoided. However, existing algorithms show limitations when updates become very frequent. We propose a new flow entry update algorithm, called FastRule, that exploits a greedy strategy with an efficient data structure to accelerate flow entry update with a DAG approach. Moreover, we also adjust our algorithm for other flow table layouts to make it scalable. We elaborate on the correctness of FastRule and test our algorithm using a hardware switch. Compared with existing algorithms, the evaluation shows that our algorithm is about $100 \times $ faster than state-of-the-art solutions with a flow table of $1k$ size.
Software Development Effort Estimation Using Soft Computing

Software development effort estimation is a daunting task that is being carried out by software developers as not much of the information about the software which is to be developed is available during the early stages of development. The information that is to be gathered for various attributes of software needs to be subjective which otherwise leads to imprecision and uncertainty. Inaccurate estimation of the software effort and schedule leads to financial loses and also delays in project deadline. In this paper, we present the use of soft computing technique to build a suitable model which improves the process of effort estimation. To do so, various parameters of Constructive Cost Model (COCOMO) II are fuzzified that leads to reliable and accurate estimates of effort. The results show that the value of Magnitude of Relative Error (MRE) obtained by applying fuzzy logic is quite lower than MRE obtained from algorithmic model. By analyzing the results further it is observed that Gaussian Membership Function (gaussmf) performs better than Triangular Membership Function (trimf) and Trapezoidal Membership Function (trapmf) as the transition from one interval to another is quite smoother. Here varying number of COCOMO II inputs are fuzzified with these membership functions. The validation of the experiment is carried on COCOMO public dataset.
Current Stress Minimization of Dual-Active-Bridge DC–DC Converter Within the Whole Operating Range

Current stress minimization is one of the most important challenges for the steady-state studies of dual-active-bridge (DAB) dc–dc converter. Triple-phase-shift (TPS) control can minimize the current stress to the utmost extent, and a segmented analytical method-based TPS (SA-TPS) control has been proposed in this paper to minimize the current stress of DAB within the whole operating range. The full model of DAB with TPS control was classified into 12 operating modes by a complete operating mode classification method. On the basis of the analytical expressions of transmission power and current stress in different modes, the current stress minimization issue was transferred to inequality constraints’ problems in different operating modes. The mathematical derivation process of the SA-TPS control method is described in detail, and the analytical expressions of current stress minimization results as a function of voltage conversion ratio and transmission power have been derived by solving the forward and reverse power transmission processes, respectively. The operating mode selection principle and the regularities of minimization results varying with transmission power and voltage conversion ratio have been explored. Experiments under different conditions have been implemented on a laboratory prototype to validate the correctness and effectiveness of the proposed SA-TPS control method.
Data Quality in mHealth

mHealth enables remote health monitoring and facilitates communication and data exchange between health providers and patients, but comes at the cost of reduced data quality. Loss of quality can be multifaceted and includes an increase in missing data and a higher level of uncertainty in data compared to clinical data acquisition. This short review discusses emerging strategies to cope with challenges in keeping mHealth systems that are used by lay users reliable.
A Robust Algorithm for Classification and Rejection of NLOS Signals in Narrowband Ultrasonic Localization Systems

This paper develops a novel algorithm for classifying and rejecting non-line-of-sight ultrasonic signals for use in indoor positioning and measurement systems. The algorithm consists of two parts, an initial probability estimate based upon signal amplitude followed by an iteratively reweighed least squares regression that utilizes the probabilities in the weight update step. This method allows for reflections to be identified and rejected while simultaneously calculating the position of a receiving node. The algorithm has been tested on a data collected during experiments designed to generate many challenging specular reflections. The experiments were conducted with transducers in a variety of different positions, and amplitude-modulated ultrasonic signals were used with four different envelopes. The algorithm was capable of correctly classifying over 96% of narrowband signals with severe multipath effect at low computational cost.
An Analytical Model of CSMA/CA Performance For Periodic Broadcast Scheme

In highly-mobile environments such as Vehicle Ad Hoc Networks (VANETs) or Flying Ad Hoc Networks (FANETs), the status messages that contain information on speed, position, and direction should be transmitted periodically by each vehicle in the network to its neighbors to support situation-based traffic applications. Many analytical models have been proposed to evaluate the MAC layer performance of status message transmission based on the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) protocol. However, they either fail to consider lifetime of status messages or assume a random arrival of status messages. In addition, all these models are based on the assumption that distribution of MAC layer states is unchanged with the time, which is not applicable in the periodic broadcast schemes. In this paper, we propose an analytical model where constant arrival intervals and lifetime expiration of messages are simultaneously considered. Moreover, we discard the assumption of unchanged states distribution by introducing a pure death process. The proposed model is verified with simulation data obtained by NS2, and results showed that proposed model can accurately capture the reception probability, collision probability, and discard probability of periodic broadcast messages.
Surface syntax of English

This book is the first attempt to describe the syntax of Contemporary English exclusively in terms of dependencies (most American works on the subject being in terms of phrase structure, or constituency). The three main features of it are: (1) a fully formal presentation, (2) a reasonably complete coverage of English surface syntax, and (3) an exposition oriented towards human readers (rather than computers). The book can be recommended for several categories of readers: specialists in English syntax, linguists interested in general and theoretical syntax, computational linguists, researchers in related fields (including psychology and artificial intelligence) concerned with automatic processing (both synthesis and analysis) of English texts.
Security-Constrained Unit Commitment Considering Large-Scale Compressed Air Energy Storage (CAES) Integrated With Wind Power Generation

Environmental concerns and depletion of nonrenewable resources has made great interest towards renewable energy resources. Cleanness and high potential are factors that caused fast growth of wind energy. However, the stochastic nature of wind energy makes the presence of energy storage systems (ESS) in wind integrated power systems, inevitable. Due to capability of being used in large-scale systems and the lower capital cost, compressed air energy storage (CAES) is one of the favorable storage systems. This paper proposes a model for security-constrained unit commitment (SCUC) with integration of large-scale CAES and wind generation. The SCUC problem is formulated as a mixed-integer linear program (MIP) in which a lossless dc representation of transmission network is used and it has been solved by CPLEX solver using General Algebraic Modeling System (GAMS) optimization package. The IEEE RTS 96-bus systems is used to validate the performance of the proposed method.
Analysis and Synthesis of Structured Variations in 3D Geometries

In recent years, the use of 3D digital content becomes widespread in various industrial and scientific domains. However, content creation still remains a costly task as extensive manual work is often required. As such, one of the core research topics in computer graphics is to accelerate the content creation process. In this dissertation, we investigate the benefit of utilizing shape structure in creating different categories of digital content. To speed up the design process of 3D geometries, instead of dealing with individual shape separately, we propose to process structured variations, which are different models sharing certain key structural information. A geometry processing framework of structured variations consists of the analysis of structure from a collection of model variations, and the synthesis of novel shapes. We propose algorithms for three common types of digital content in computer graphics, which include facade textures, procedural modeling output, and 3D reconstruction point clouds from multiview stereo or scanning. There is a high demand in high quality and customized facade textures, especially in urban design, 3D cities or games. We introduce a framework to create structured variations of building facades via structure-aware editing. Our framework deals with irregular facade layouts, which are common in practice. To automatically create a large number of structured variations, a suitable technique is procedural modeling, which generates models by means of computer programs or procedures. However, the connection between the procedures and generated models is not explicit and it is usually difficult to modify the underlying procedures to generate a set of models customized to certain design intent. We present a framework which allows a user to interactively manipulate the set of generated models. Finally, a cost-effective method to generate digital content is to digitalize the real world. Nonetheless, the reconstructed point clouds are often noisy and contain missing data. We analyze point clouds of buildings to detect structural relations amongst building elements by means of template fitting. Once detected, these information can be used to improve the reconstruction output or to synthesize novel models via structural-aware editing.
A Performance Prediction Framework for Irregular Applications

Predicting performance of applications is an important requirement for many goals – choosing future procurements or upgrades, selecting specific optimization/implementation, requesting and allocating resources, and others. Irregular access patterns, commonly seen in many compute-intensive and data-intensive applications, pose many challenges in estimating overall execution time of applications, including, but not limit to, cache behavior. While much work exists on analysis of cache behavior with regular accesses, relatively little attention has been paid to irregular codes. In this paper, we aim to predict execution time of irregular applications on different hardware configurations, with emphasis on analyzing cache behavior with varying size of the cache and the number of nodes. Cache performance of irregular computations is highly input-dependent. Based on the sparse matrix view of irregular computation as well as the cache locality analysis, we propose a novel sampling approach named Adaptive Stratified Row sampling – this method is capable of generating a representative sample that delivers cache performance similar to the original input. On top of our sampling method, we incorporate reuse distance analysis to accommodate different cache configurations with high efficiency. Besides, we modify SKOPE, a code skeleton framework, to predict the execution time for irregular applications with the predicted cache performance. The results show that our approaches keep average error rates under 6% in predicting L1 cache miss rate for different cache configurations. The average error rates of predicting execution time for sequential and parallel scenarios are under 5% and 15%, respectively.
Inductance Analysis in Inductive Double-Layer Energy Harvesters

The inductive electromagnetic (EM) energy harvesters, capable to change its geometry under the pressure of some external force, have been considered in this paper. Regarding their ability to increase its total inductance, the sign of mutual inductance have been analyzed in order to propose the most promising design of those harvesters. The performance comparison of two double-layer inductors with variable geometry has been presented. The total inductance modeling was performed using partial inductance approach, while the simulation framework has been developed under the MATLAB environment.
Perceptual watermarks for digital images and video : Identification and protection of multimedia information

The growth of new imaging technologies has created a need for techniques that can be used for copyright protection of digital images and video. One approach for copyright protection is to introduce an invisible signal, known as a digital watermark, into an image or video sequence. In this paper, we describe digital watermarking techniques, known as perceptually based watermarks, that are designed to exploit aspects of the human visual system in order to provide a transparent (invisible), yet robust watermark. In the most general sense, any watermarking technique that attempts to incorporate an invisible mark into an image is perceptually based. However, in order to provide transparency and robustness to attack, two conflicting requirements from a signal processing perspective, more sophisticated use of perceptual information in the watermarking process is required. We will describe watermarking techniques ranging from simple schemes which incorporate common-sense rules in using perceptual information in the watermarking process, to more elaborate schemes which adapt to local image characteristics based on more formal perceptual models. This review is not meant to be exhaustive ; its aim is to provide the reader with an understanding of how the techniques have been evolving as the requirements and applications become better defined.
Cooperative Buffer-Aided Relaying Using Full-Duplex Relays With Flow Control

In this paper, we investigate buffer-aided cooperative relaying schemes using full-duplex (FD) relay nodes with flow control. We show that incorporating various transmission options combined with proper successive interference cancellation can better exploit the opportunism of fading channels, leading to improved achievable rates. We analytically derive the optimal policy for node selection and link scheduling. To deal with the delay incurred at relay nodes, we propose flow control using a threshold-based policy on relays’ queues. For performance analysis, we develop a novel technique to derive a delay estimate of the controlled queues with general arrival and service statistics. Numerical results show that the proposed scheme achieves higher throughputs than conventional relaying schemes without FD relaying, and that the proposed estimate closely approximates the actual delay. For the delay-constrained case, our scheme is shown to achieve an improved tradeoff between throughput and delay.
Towards a Gliding Robotic Dolphin: Design, Modeling, and Experiments

This paper presents the mechatronic design and implementation of a gliding robotic dolphin. To pursue both high maneuverability and long endurance simultaneously, the gliding robotic dolphin novelly integrates propulsion modes of real dolphins and traditional underwater gliders, through importing a practical buoyancy-driven mechanism on the basis of a bio-inspired robotic dolphin. The hybrid mechatronic design for actual application environments is first holistically provided. In comparison with traditional underwater gliders, the robotic dolphin particularly possesses a pair of controllable flippers and a flatten flukes, which can effectively assist in regulating the gliding attitude. Consequently, a full-state dynamic model with particular consideration of these controllable fins for three-dimensional (3-D) gliding motion is established. Meanwhile, two typical controllers are built to realize these two propulsive modes, e.g., an active disturbance rejection control-based controller for the gliding motion and a central pattern generator-based controller for dolphin-like swimming. Numerical simulations are conducted to analyze 3-D gliding motions of the robotic dolphin as well as the gliding maneuvers based on the controllable fins. Finally, extensive experiments involving gently gliding motion and several dolphin-like propulsive modes illustrate the great locomotion ability of the developed gliding robotic dolphin and also validate the effectiveness of the formulated dynamic model. These hybrid motion modes offer promising prospects of robot applications in complex deep-sea conditions.
An Improved Hyperspectral Pansharpening Algorithm Based on Optimized Injection Model

The hyperspectral pansharpening is a significant preprocessing technology in hyperspectral images application. A new optimized injection model-based hyperspectral pansharpening algorithm is proposed in this paper. Compared with the traditional pansharpening methods, the algorithm achieves two major improvements: 1) the total injected spatial information is obtained by integrating the spatial components of hyperspectral (HS) and panchromatic (PAN) images by PCA transformation; and 2) the gain matrix proposed in this paper is composed of two factors which constraint spectral and spatial distortions respectively. Specifically, the morphological open-closing operation and Laplacian of Gaussian enhancement scheme are used for denoising the interpolated HS and PAN images, respectively. Then, the spatial components of the denoised HS and PAN images are respectively extracted by the morphological gradient operation and homomorphic filtering. The PCA transform is applied to the results to obtain the first principal component served as total spatial details. The total spatial information weighted by the gain matrix is finally combined with the interpolated HS images to generate the pan-sharpened images, in which a new gain matrix is constructed to minimize the spectral and spatial distortions. The extensive experiments have demonstrated the potential of the proposed method in balancing spectral preservation and spatial sharpness.