Miracle-dz/UniFAR-FTUs huggingface dataset:
Toward a Unified Framework for Analysis of Multi-RAT Heterogeneous Wireless Networks

The increased penetration of different radio access technologies (RATs) and the growing trend towards their convergence necessitates the investigation of wireless heterogeneous networks (HetNets) from coverage and capacity perspective. This paper develops a unified framework for signal-to-interference-plus-noise ratio and rate coverage analysis of multi-RAT HetNets, with each RAT employing either a contention-free or a contention-based channel access strategy. The proposed framework adopts tools from stochastic geometry, with the location of APs and mobile users modeled through independent Poisson point processes (PPPs). We specifically focus on a two-RAT scenario (i.e., cellular and Wi-Fi), where for multi-tier Wi-Fi RAT, with contention-based channel access like CSMA/CA, the location dependent distribution of interfering APs has been approximated through a homogeneous PPP. Moreover, by using some simple yet realistic set of assumptions, the distance to nearest active AP has been defined which results in simplified expressions. The medium access probability for a random and a tagged AP under a multi-tier Wi-Fi RAT has also been derived and discussed. By keeping in view the tremendous effect of temporal domain on overall network performance, the stable queue probability has been derived by assuming a non-saturated traffic model. The results have been validated through extensive simulations and compared with existing approaches. Some useful insights have also been presented that shed light on design and analysis of multi-RAT HetNets and provide motivation for further research in this direction.
A percolation-based recovery mechanism for bot infected P2P cloud

Execution of any recovery procedure in compromised nodes of a Cloud should aim at covering as many nodes as possible in a given time. The authors propose an innovative approach using the concept of percolation centrality to spread the execution of a recovery procedure across adjacent nodes in a P2PCloud. Compromised networks can be large since botnets and malware infections use the advantage of the internet to spread rapidly. Using percolation centrality to choose a node from where to begin, the recovery procedure runs in parallel in different nodes which can be reached from a chosen node and spreads fast.
Georeferencing places from collective human descriptions using place graphs

Place descriptions in everyday communication or in online text provide a rich source of spatial knowledge about places. Such descriptions typically consist of references to places and spatial relationships between them. An important step to utilize such knowledge in information systems is georeferencing the referred places. Beside place name disambiguation, another challenge is that a significant proportion of place references in such descriptions are not official place names indexed by gazetteers, thus cannot be resolved easily. This paper presents a novel approach for georeferencing places from collective descriptions using place graphs, regardless of whether they are referred to by gazetteered names or not. The approach leverages spatial relation models for approximate locating and matching. Different models are proposed and evaluated using several metrics.
A novel approach for segmentation of human metaphase chromosome images using region based active contours

The chromosomes are the genetic information carries. A healthy human being has 46 chromosomes. Any alteration in either the number of chromosomes or the structure of chromosomes in a human being is diagnosed as a genetic defect. To uncover the genetic defects the metaphase chromosomes are imaged and analyzed. The metaphase chromosome images often contain intensity inhomogeneity that makes the image segmentation task difficult. The difficulties caused by intensity inhomogeneity can be resolved by using region based active contours techniques. These techniques uses the local intensity values of the nearby regions of the objects and find the approximate intensity values along both sides of the contour. In the proposed work a segmentation technique has been proposed to segment the objects present in the human metaphase chromosome images using region based active contours. The proposed technique has been quite efficient from prospective of number of objects segmented. The method has been tested on Advanced Digital Imaging Research (ADIR) dataset. The experimental results have shown quite good performance.
Consistify: preserving correctness and SLA under weak consistency

While executing a client application, comprising a sequence of one or more storage operations (read, write, etc.), replicated datastores provide the option of tuning per-operation client-centric consistency settings, Weaker consistency settings allow client applications to execute with lower observed latency due to less coordination among the replica, improving throughput. To enable a client application running on a geo-replicated datastore to meet stringent SLA (Service Level Agreement) deadlines, i.e., latency thresholds provided in the SLA, users apply weak client-centric consistency settings. Strong consistency settings may result in higher latency due to coordination among greater number of replicas. However, since a weak consistency setting requires only a partial quorum of replicas to coordinate, some of the replicas may be overwritten by conflicting updates from concurrent clients, which, in turn, may result in violation of correctness conditions specified by the developer. Such correctness conditions are provided as postconditions that impose restrictions on the values observed by the clients. Enforcing correctness conditions under weak consistency settings on geo-replicated datastores from the client application code is a tedious and challenging task. The task is even more complicated when considering application-level SLA deadlines. Deadline (latency) is one of the most important SLA parameters for optimizing client-centric performance. To this end, we present Consistify, a novel framework, that acts as an interface layer between client applications and underlying data-stores, and enables client applications to execute with weak per-operation consistency settings, while simultaneously respecting: 1) the correctness conditions, specified using logical predicates, and 2) the SLA deadline. Using benchmark workloads, we experimentally demonstrate that Consistify outperforms the state-of-the-art systems, namely QUELA. For several other systems that provide flexible consistency settings, namely Bolt-on causal system, SIEVE, and Cure, we provide a comparison of the throughput and latency observed with Consistify against the numbers reported with those systems in prior papers.
Grading Prenatal Hydronephrosis from Ultrasound Imaging Using Deep Convolutional Neural Networks

We evaluate the performance of a Deep Convolutional Neural Network in grading the severity of prenatal hydronephrosis (PHN), one of the most common congenital urological anomalies, from renal ultrasound images. We present results on a variety of classification tasks based on clinically defined grades of severity, including predictions of whether or not an ultrasound image represents a case that is at high risk for further complications requiring surgical intervention with approximately 80% accuracy. The prediction rates obtained by the model are well beyond the rates of agreement among trained clinicians, suggesting that this work can lead to a useful diagnostic aid.
Finite-time boundedness of Markovain jump nonlinear systems with incomplete information

ABSTRACT This paper deals with the finite-time bounded control problem of a class of uncertain Markovian jump nonlinear systems with partially unknown transition probabilities and norm-bounded disturbances. The purpose of the paper is to derive a stochastic finite-time controller such that the resulting nominal or uncertain stochastic nonlinear model is stochastically finite-time bounded by applying stochastic analysis techniques and free connection weighting matrix approach. We also provide stochastic finite-time stability and stochastic finite-time boundedness criteria of the class of nominal or uncertain stochastic nonlinear dynamics. Moreover, the derived criteria can be simplified into linear matrix inequalities (LMIs), which could be solved by employing Matlab LMI toolbox. Finally, the effectiveness of proposed approaches is demonstrated by a practical example.
The Approximability of Assortment Optimization Under Ranking Preferences

The main contribution of this paper is to provide best-possible approximability bounds for assortment planning under a general choice model, where customer choices are modeled through an arbitrary distribution over ranked lists of their preferred products, subsuming most random utility choice models of interest. From a technical perspective, we show how to relate this optimization problem to the computational task of detecting large independent sets in graphs, allowing us to argue that general ranking preferences are extremely hard to approximate with respect to various problem parameters. These findings are complemented by a number of approximation algorithms that attain essentially best-possible factors, proving that our hardness results are tight up to lower-order terms. Surprisingly, our results imply that a simple and widely studied policy, known as revenue-ordered assortments, achieves the best possible performance guarantee with respect to the price parameters.
The Mixed Methods Appraisal Tool (MMAT) version 2018 for information professionals and researchers

INTRODUCTION: Appraising the quality of studies included in systematic reviews combining qualitative and quantitative evidence is challenging. To address this challenge, a critical appraisal tool was developed: the Mixed Methods Appraisal Tool (MMAT). The aim of this paper is to present the enhancements made to the MMAT. DEVELOPMENT: The MMAT was initially developed in 2006 based on a literature review on systematic reviews combining qualitative and quantitative evidence. It was subject to pilot and interrater reliability testing. A revised version of the MMAT was developed in 2018 based on the results from usefulness testing, a literature review on critical appraisal tools and a modified e-Delphi study with methodological experts to identify core criteria. TOOL DESCRIPTION: The MMAT assesses the quality of qualitative, quantitative, and mixed methods studies. It focuses on methodological criteria and includes five core quality criteria for each of the following five categories of study designs: (a) qualitative, (b) randomized controlled, (c) nonrandomized, (d) quantitative descriptive, and (e) mixed methods. CONCLUSION: The MMAT is a unique tool that can be used to appraise the quality of different study designs. Also, by limiting to core criteria, the MMAT can provide a more efficient appraisal.
Command-filtered sensor-based backstepping controller for small unmanned aerial vehicles with actuator dynamics

ABSTRACT In this study, a command-filtered sensor-based backstepping controller is proposed for small unmanned aerial vehicles (UAVs) with actuator dynamics. The command filter is introduced to prompt the virtual control law to be limited in a certain range and the corresponding state to subsequently be restricted to a certain area. When using the sensor-based backstepping recursive method, precise models of the UAVs are not required because the controller is not sensitive to the external disturbance. The actuator dynamics are compensated without prior knowledge of the mathematical model of the executing agency. Besides, a robust compensator is developed for the virtual control law of the first subsystem of the UAV, which shows strong robustness against the uncertainties of the aerodynamic coefficients and external disturbances. Moreover, the closed-loop system is proven stable in the sense that the signals are bounded. A numerical simulation is carried out to verify the effectiveness of the developed controller.
Surface-Based GICP

In this paper we present an extension of the Generalized ICP algorithm for the registration of point clouds for use in lidar-based SLAM applications. As opposed to the plane-to-plane cost function, which assumes that each point set is locally planar, we propose to incorporate additional information on the underlying surface into the GICP process. Doing so, we are able to deal better with the artefacts that are typically present in lidar point clouds, including an inhomogeneous and sparse point density, noise and missing data. Experiments on lidar sequences of the KITTI benchmark demonstrate that we are able to substantially reduce the positional error compared to the original GICP algorithm.
Homophily and Social Norms in Experimental Network Formation Games

Field studies of networks have uncovered a preference to befriend people we perceive as similar according to some dimensions of our identity (“homophily”). Lab studies of network formation games have found that adherence to social norms of reciprocity and inequity aversion are also drivers of network choices. No study so far has attempted to investigate the role of both homophily and social norms in a controlled environment. At the beginning of our experiment, each player fills in a personal profile. Each player then views the profile of all other players and expresses a degree of perceived similarity between his/her profile and the profile of the other player. At this point, a repeated network formation game ensues. We find that: (1) potential homophily considerations triggered by the profile rating task did not measurably change the players’ behavior compared to the baseline; (2) reciprocity plays a significant role in the formulation of the players’ strategies, in particular lowering the probability that the player naively best responds to the network observed in the previous period. We speculate that reciprocation of past choices might be a more “available” aid in strategy-formulation than considerations related to the similarity of the other players.
Playing a Game or Making a Decision? Methodological Issues in the Measurement of Distributional Preferences

In terms of role assignment and informational characteristics, different contexts have been used when measuring distributional preferences. This could be problematic as contextual variance may inadvertently muddle the measurement process. We use a within-subjects design and systemically vary role assignment as well as the way information is displayed to subjects when measuring distributional preferences in resource allocation tasks as well as proper games. Specifically we examine choice behavior in the contexts of role certainty, role uncertainty, decomposed games, and matrix games. Results show that there is large heterogeneity in the choices people make when deciding how to allocate resources between themselves and some other person under different contextual frames. For instance, people make more prosocial choices under role uncertainty as compared to role certainty. Furthermore, altering the way information is displayed given a particular situation can have a more dramatic effect on choice behavior than altering the situation itself. That is, depending on how information is displayed, people may behave as if they would perform a non-strategic decision making task when in fact they are playing a proper game characterized by strategic interdependence.
Distributed symmetry-breaking with improved vertex-averaged complexity

We study the distributed message-passing model in which a communication network is represented by a graph G = (V, E). Usually, the measure of complexity that is considered in this model is the worst-case complexity, which is the largest number of rounds performed by a vertex ν ∈ V. While often this is a reasonable measure, in some occasions it does not express sufficiently well the actual performance of the algorithm. For example, an execution in which one processor performs r rounds, and all the rest perform significantly less rounds than r, has the same running time as an execution in which all processors perform the same number of rounds r. On the other hand, the latter execution is less efficient in several respects, such as energy efficiency, task execution efficiency, local-neighborhood efficiency and simulation efficiency. Consequently, a more appropriate measure is required in these cases. Recently, the vertex-averaged complexity was proposed by [13]. In this measure, the running time is the worst-case average of rounds over the number of vertices. Feuilloley [13] showed that leader-election admits an algorithm with significantly better vertex-averaged complexity than worst-case complexity. On the other hand, for O(1)-coloring of rings, the worst-case and vertex-averaged complexities are the same. This complexity is Θ (log* n) [13]. It remained open whether the vertex-averaged complexity of symmetry-breaking in general graphs can be better than the worst-case complexity. In this paper we devise symmetry-breaking algorithms with significantly improved vertex-averaged complexity for both general graphs, as well as specific graph families. Some algorithms of ours have significantly better vertex-averaged complexity than the best-possible worst case complexity. In particular, for general graphs, we devise an O(a)-forests-decomposition algorithm with a vertex-averaged complexity of O(1) rounds, where the arboricity a is the minimum number of forests that the graph's edges can be partitioned into. In the worst-case, this requires Ω(log n) rounds [10]. In addition, for graphs with constant arboricity a, we compute (Δ + 1)-vertex-coloring, Maximal Independent Set, Maximal Matching and (2Δ - 1)-edge-coloring, deterministically, with O (log* n) vertex-averaged complexity. The best known deterministic algorithms for (Δ + 1)-coloring have time complexity [MATH HERE] in the worst case [3,14], and the best known Maximal Independent Set and Maximal Matching algorithms on these graphs have worst-case complexity at least [MATH HERE] [10, 18]. In addition to deterministic algorithms, we devise randomized algorithms, in which the vertex-averaged bounds hold with high probability. In particular, we show that (Δ + 1)-coloring of general graphs requires O(1) vertex-averaged complexity, with high probability. This is in contrast to the worst case complexity, which is Ω (log* n) even on rings [19].
Observer-based H∞ control with finite frequency specifications for discrete-time T–S fuzzy systems

ABSTRACT This paper deals with the design problem of observer-based control in finite frequency (FF) domain for discrete-time Takagi–Sugeno (T–S) fuzzy systems. With the aid of Generalised Kalman–Yakubovi–Popov (GKYP) lemma, new conditions guaranteeing the FF performance of the T–S fuzzy systems are derived in terms of Linear Matrix Inequalities. This study reduces the conservativeness of the existing design of the entire frequency range and includes it as special case. In the end, some examples are provided to illustrate the effectiveness of the proposed strategy.
Outage Analysis of User Pairing Algorithm for Full-Duplex Cellular Networks

In a full-duplex (FD) cellular network, a base station transmits data to the downlink (DL) user and receives data from uplink (UL) users at the same time; thereby the interference from UL users to DL users occurs. One of the possible solutions to reduce this interuser interference in the FD cellular network is user pairing, which pairs a DL user with a UL user so that they use the same radio resource at the same time. In this paper, we consider a user pairing problem to minimize outage probability and formulate it as a nonconvex optimization problem. As a solution, we design a low-complexity user pairing algorithm, which first controls the UL transmit power to minimize the interuser interference and then allows the DL user having a worse signal quality to choose first its UL user giving less interference to minimize the outage probability. Then, we perform theoretical outage analysis of the FD cellular network on the basis of stochastic geometry and analyze the performance of the user pairing algorithm. Results show that the proposed user pairing significantly decreases the interuser interference and thus improves the DL outage performance while satisfying the requirement of UL signal-to-interference-plus-noise ratio, compared to the conventional HD mode and a random pairing. We also reveal that there is a fundamental tradeoff between the DL outage and UL outage according to the user pairing strategy (e.g., throughput maximization or outage minimization) in the FD cellular network.
Pooled Mining Makes Selfish Mining Tricky

Bitcoin, the first successful cryptocurrency, uses the blockchain structure and PoW mechanism to generate blocks. PoW makes an adversary difficult to control the network until she retains over 50% of the hashrate of the total network. Another cryptocurrency, Ethereum, also uses this mechanism and it did not make problem before. In PoW research, however, several attack strategies are studied. In this paper, we researched selfish mining in the pooled mining environment and found the pooled mining exposes mining information of the block which adversary is mining to the random miners. Using this leaked information, other miners can exploit the selfish miner. At the same time, the adversary loses revenue than when she does honest mining. Because of the existence of our counter method, the adversary with pooled mining cannot do selfish mining easily on Bitcoin or blockchains using PoW.
NOVN: named-object based virtual network architecture

This paper presents NOVN: a novel network virtualization framework aimed at providing an efficient and low overhead solution for deploying Virtual Networks (VNs) based on the concept of named-objects. Name based communication paradigms, realized by separating object names and network addresses through a logically centralized globally distributed Name Resolution Service (NRS), can be used to build a natural and efficient architecture for virtual networks. The proposed VN framework exploits the name based abstraction to create customized networking for distributed services which benefit from an awareness of network topology and routing. A specific example of edge cloud computing is presented in which VN's are used to realize "application specific routing" (ASR) for efficiently connecting users with cloud resources. Experimental results are presented for validation of the proposed VN architecture using a software router implementation running on the ORBIT testbed. The results validate the feasibility of the named-object approach, showing minimal VN processing, control overhead, and latency. The results also validate application aware ASR routing functionality for an example latency constrained edge cloud service scenario.