Miracle-dz/UniFAR-FTUs huggingface dataset:
Low Complexity Zero Forcing Detector Based on Newton-Schultz Iterative Algorithm for Massive MIMO Systems

Linear detectors, such as zero forcing (ZF) and minimum mean-square error (MMSE), can achieve near-optimum performance due to the favorable channel propagation of massive multiple-input multiple-output systems. However, these detectors employ, in general, exact matrix inversion, which is computationally complex for such huge systems. Thus, in order to avoid the computation of the exact matrix inversion of ZF or MMSE, Newton–Schultz iterative (NSI) algorithm for obtaining an approximate matrix inversion is proposed, which yields similar performance of the exact matrix inversion. Besides, in order to further accelerate the convergence rate and reduce the complexity, we propose a novel initial matrix inversion solution for NSI algorithm based on Tchebychev polynomial, which is much closer to the final exact matrix inverse than the traditional initial matrix inversion solutions. Simulation results show that NSI algorithm with the proposed initial matrix inversion solution achieves the near-optimum ZF performance in just two iterations. Finally, the band matrix concept is employed in order to reduce the computational complexity from <inline-formula><tex-math notation="LaTeX">$O\left(N_T^3 \right)$</tex-math></inline-formula> to <inline-formula><tex-math notation="LaTeX">$O\left(N_T^2 \right)$</tex-math></inline-formula>, especially when the number of transmitting antennas, <inline-formula><tex-math notation="LaTeX">$N_T$</tex-math></inline-formula>, is large.
Waveform Design for Fair Wireless Power Transfer With Multiple Energy Harvesting Devices

In this paper, we study the waveform design in the wireless power transfer (WPT) system with multiple receivers. In the multi-receiver WPT system, due to the severe power attenuation of RF signals according to the distance and the heterogeneity in the energy harvesting capability, there exists severe unfairness in energy harvesting among receivers at different distances from the transmitter and/or different energy harvesting capabilities. Hence, alleviating unfairness in energy harvesting among receivers is one of the critical challenges in the multi-receiver WPT system. To tackle this challenge in a systematic way, we consider the fairness in our waveform design applying the $\alpha $ -proportional fairness. With the analysis of the rectenna circuit, we derive the output dc voltage and power of the rectifier in closed forms. Thereby, we formulate an optimization problem to design the waveform for fair WPT. The problem is shown to be a non-convex optimization problem, which is hard to solve in general. However, with proper approximations, we convert it into a convex optimization problem that can be solved easily and obtain the waveform for fair WPT. Numerical results show that our designed waveform makes receivers harvest energy fairly and can control the degree of the fairness easily.
On the Statistical Properties of Constant Envelope Quantizers

Transmit signals that have constant magnitudes and discrete phases can decrease the power consumption of future wireless communication systems. To obtain signals fulfilling that property, constant envelope quantizers (CEQs) have to be deployed in the system. However, the increased power efficiency comes at the cost of degraded system performance. To mitigate this performance loss with conventional digital filters, e.g., Wiener Filter precoders, the statistical signal properties at the CEQ have to be known. In this context, we study the effect of the CEQ on the statistical signal properties and derive closed-form expressions of the correlation factors at the CEQ with complex-valued Gaussian input signals.
Generalized PID Synchronization of Higher Order Nonlinear Systems With a Recursive Lyapunov Approach

This paper investigates the problem of synchronization for nonlinear systems. Following a Lyapunov approach, we first study the global synchronization of nonlinear systems in the canonical control form with both distributed proportional-derivative and proportional-integral-derivative control actions of any order. To do so, we develop a constructive methodology and generate in an iterative way inequality constraints on the coupling matrices that guarantee the solvability of the problem or, in a dual form, provide the nonlinear weights on the coupling links between the agents such that the network synchronizes. The same methodology allows us to include a possible distributed integral action of any order to enhance the rejection of heterogeneous disturbances. The considered approach does not require any dynamic cancellation, thus preserving the original nonlinear dynamics of the agents. The results are then extended to linear and nonlinear systems admitting a canonical control transformation. Numerical simulations validate the theoretical results.
Distributed Resource Allocation and Computation Offloading in Fog and Cloud Networks With Non-Orthogonal Multiple Access

Fog computing, complementary to cloud computing, has recently emerged as a promising solution that extends the computing infrastructure from the cloud center to the network edge. By offloading computational applications to the network edge, fog computing could support delay-sensitive applications and reliable access to nearby users. However, with the growing number of demands from various applications, fog computing may be overwhelmed and it may result in significant performance degradation. Thus, to process applications efficiently, we propose an integrated fog and cloud computing (FCC) approach, where users can offload a series of applications to nearby fog nodes (FNs) or cloud center cooperatively. Nevertheless, due to the constrained computing, storage, and radio resources, how to perform resource allocation to achieve an optimal and stable performance is an important problem. To address this issue, we focus on multiple resource allocation problem in a general system, which consists of multi-user, multi-FN, and a cloud center. In addition, to reduce offloading transmission latency and release the constraint of limited radio resource, non-orthogonal multiple access, which enables multiple users to transmit data to the same FN for offloading tasks on the same spectrum resource, is introduced into the proposed FCC approach. To this end, we formulate joint offloading decision, user scheduling, and resource allocation problem as an optimization problem that aims at minimizing the total system cost of energy as well as the delay of users. Furthermore, we decouple the original problem and transform it into a convex problem. Finally, we develop alternating direction method of multipliers based algorithms to solve the optimization problem in a distributed and efficient way. Simulation results show that the proposed approach achieves better performance compared with existing schemes.
Peer-to-Peer Gradient Topologies in Networks With Churn

We investigate the network topology convergence in a peer-to-peer (P2P) network system, where the goal of the system is to maximize live-streaming performance. The P2P system constructs a gradient overlay topology, characterized by a directed graph, where each node prefers neighbors containing higher utility values such that paths of increasing utilities emerge in the network topology. The gradient overlay network is built using gossiping and a preference function that samples nodes from a uniform random peer sampling service. Conditions for convergence to a gradient topology is derived, including the expected convergence time, and a threshold on the churn rate is provided for a gradient topology to emerge. Finally, a live-streaming video distribution experiment illustrates the benefits of constructing and utilizing the gradient topology for information dissemination in P2P systems.
Mean-Square Stabilization Over Gaussian Finite-State Markov Channels

This paper studies the mean-square stabilization problem of discrete-time linear time-invariant systems over Gaussian finite-state Markov channels, which suffer from both signal-to-noise ratio constraint and correlated channel fading modeled by a Markov process. The existence of a fundamental limitation for mean-square stabilization is first established. Then, sufficient stabilization conditions under a time-division multiple access (TDMA) communication scheme are derived in terms of the stability of a Markov jump linear system. Moreover, we present a necessary and sufficient condition for mean-square stabilization of 2-D systems controlled over power-constrained Markov lossy channels. Furthermore, improved sufficient stabilization conditions are derived based on an adaptive TDMA communication scheme for general high-dimensional systems, which achieves a larger stabilization region than the TDMA communication scheme.
A Mutual Subsidy Mechanism for a Seasonal Product Supply Chain Channel under Double Price Regulation

In this paper, we consider the decision-making and coordination mechanism of a seasonal product supply chain channel over a period consisting of a low season and a high season, where the price of the final product is strictly regulated, and the raw material is subject to a price cap regulation during the high season. The optimal decisions and profits under decentralized and centralized cases are derived for the low season and the high season. Our research indicates that, under the double price regulation, the manufacturer’s raw material order in the channel usually is not completely fulfilled during the high season and the order during the low season is usually small. Based on the results, we propose a mutual subsidy mechanism, in order to encourage the supplier to supply more raw material during the high season and to encourage the manufacturer to order more raw material during the low season.The theoretical results indicate that the mutual subsidy mechanism can effectively coordinate the seasonal product supply chain channel under the double price regulation, which is further verified by a numerical example. Moreover, the negotiation skills of the supplier and the manufacturer are also considered under the mutual subsidy mechanism, so that the spillover profit can be allocated on a win–win basis for both sides.
The Death of System Administration

I come to bury system administration, not to praise it. The evil that well intentioned, clever geeks do lives after them; The good is oft interred with their bones (and their previous jobs); So let it be with system administration. The noble devops Hath told you sysadmins were ambitious but insufficiently so: If it were so, it was a grievous fault; And grievously hath system administrators answer'd it. Here, under leave of software engineers and the rest, — For we are an honorable people; So are they all, all honorable sysadmins, — Come I to speak in sysadmins's funeral. It was my friend, faithful and just to me and to all of us… —with apologies to Bill Shakespeare W e are in the final days of system administration. I'm as nostalgic as many of you are. But I hope it is the same nostalgia that causes some of us old folks to fetishize terrible, unsafe, inefficient cars from the 1960s—a nostalgia borne of history but with no designs on the future. I hope we can cherish our shared history and accomplishments and then put them to rest in favor of a future where computers work for us rather than us for them. I dream of a future where we stop feeding the machines with human blood. In the Beginning…ISPs I grew up as a sysadmin first at a university and then at an Internet Service Provider (AS2901) during the 1990s. This is just what we did at the dawn of the Internet age. ISPs were a fantastic place to learn. We were all working massively beyond our capabilities and knowledge, gleefully skating over the edge of competence. At my ISP, we ran Sendmail (didn't you? ). Everyone was still configured as an open relay, of course. One of the first things I did when I started was teach a class in locking down Sendmail. I didn't know how to lock down Sendmail. Obviously, I had to learn sendmail.cf syntax, m4, and at least a little bit about what the heck these spammers were doing with our mail systems. And I needed to teach a class on these things. In three days. Those were heady days. Everyone did some of everything. The networking staff ran their own name servers, BIND 4 on AIX and FreeBSD. The systems staff often ran our own cable, and sometimes terminated it for ourselves, badly with plenty …
Characterization and Detection of Malicious Behavior on the Web

Title of dissertation: CHARACTERIZATION AND DETECTION OF MALICIOUS BEHAVIOR ON THE WEB Srijan Kumar, Doctor of Philosophy, 2017 Dissertation directed by: Professor V.S. Subrahmanian Department of Computer Science Web platforms enable unprecedented speed and ease in transmission of knowledge, and allow users to communicate and shape opinions. However, the safety, usability and reliability of these platforms is compromised by the prevalence of online malicious behavior – for example 40% of users have experienced online harassment. This is present in the form of malicious users, such as trolls, sockpuppets and vandals, and misinformation, such as hoaxes and fraudulent reviews. This thesis presents research spanning two aspects of malicious behavior: characterization of their behavioral properties, and development of algorithms and models for detecting them. We characterize the behavior of malicious users and misinformation in terms of their activity, temporal frequency of actions, network connections to other entities, linguistic properties of how they write, and community feedback received from others. We find several striking characteristics of malicious behavior that are very distinct from those of benign behavior. For instance, we find that vandals and fraudulent reviewers are faster in their actions compared to benign editors and reviewers, respectively. Hoax articles are long pieces of plain text that are less coherent and created by more recent editors, compared to non-hoax articles. We find that sockpuppets are created that vary in their deceptiveness (i.e., whether they pretend to be different users) and their supportiveness (i.e., if they support arguments of other sockpuppets controlled by the same user). We create a suite of feature based and graph based algorithms to efficiently detect malicious from benign behavior. We first create the first vandal early warning system that accurately predicts vandals using very few edits. Next, based on the properties of Wikipedia articles, we develop a supervised machine learning classifier to predict whether an article is a hoax, and another that predicts whether a pair of accounts belongs to the same user, both with very high accuracy. We develop a graph-based decluttering algorithm that iteratively removes suspicious edges that malicious users use to masquerade as benign users, which outperforms existing graph algorithms to detect trolls. And finally, we develop an efficient graph-based algorithm to assess the fairness of all reviewers, reliability of all ratings, and goodness of all products, simultaneously, in a rating network, and incorporate penalties for suspicious behavior. Overall, in this thesis, we develop a suite of five models and algorithms to accurately identify and predict several distinct types of malicious behavior – namely, vandals, hoaxes, sockpuppets, trolls and fraudulent reviewers – in multiple web platforms. The analysis leading to the algorithms develops an interpretable understanding of malicious behavior on the web. CHARACTERIZATION AND DETECTION OF MALICIOUS BEHAVIOR ON THE WEB
Erasure Codes for Storage Systems: A Brief Primer

Storage systems have grown to the point where failures are inevitable, and those who design systems must plan ahead so that precious data is not lost when failures occur. The core technology for protecting data from failures is erasure coding, which has a rich 50+ year history stemming communication systems, and as such, can be confusing to the storage systems community. In this article, I present a primer on erasure coding as it applies to storage systems, and I summarize some recent research on erasure coding.
Predictive Cruise Control Using High-Definition Map and Real Vehicle Implementation

The development of high-definition (HD) maps has enabled predictive cruise control (PCC) systems to access additional road and traffic information. This study provides a novel control scheme of PCC, which utilizes HD map information. To minimize fuel consumption, the problem of the PCC is formulated as a nonlinear model predictive control, and the derivation and implementation of the fast solver are discussed. Then, a novel shift-map is proposed to define the different working regions to allow the application of the proposed PCC system. The use of the real-time HD map is discussed, and the proposed control scheme is evaluated through simulation and experimental tests. The total fuel-savings rates obtained with the PCC system and factory-installed ACC system over a 370 km route were compared. An average fuel-savings rate of as high as 8.73% can be obtained by the proposed PCC system.
Object sequences: encoding categorical and spatial information for a yes/no visual question answering task

The task of visual question answering (VQA) has gained wide popularity in recent times. Effectively solving the VQA task requires the understanding of both the visual content in the image and the language information associated with the text-based question. In this study, the authors propose a novel method of encoding the visual information (categorical and spatial object information) of all the objects present in the image into a sequential format, which is called an object sequence. These object sequences can then be suitably processed by a neural network. They experiment with multiple techniques for obtaining a joint embedding from the visual features (in the form of object sequences) and language-based features obtained from the question. They also provide a detailed analysis on the performance of a neural network architecture using object sequences, on the Oracle task of GuessWhat dataset (a Yes / No VQA task) and benchmark it against the baseline.
Machine Learning Aided Context-Aware Self-Healing Management for Ultra Dense Networks With QoS Provisions

The self-organizing network is envisioned as a key technology to future wireless networks, especially for densely deployed small cell scenarios. Self-healing (SH) is an essential functionality to allow the networks to automatically detect and compensate for cell outages, which typically occur when unexpected network failures arise. In this paper, reaping the benefits of machine learning, we propose a novel SH framework in ultra dense small cell networks for meeting the demands of low-cost and fast network operation, quality of service (QoS), and energy efficiency. The proposed SH scheme comprises small cell outage detection (SCOD) and small cell outage compensation (SCOC) to enable self-healing in ultra dense small cell networks. Based on the context information of the partial key performance indicator (KPI) statistics, we propose a novel SCOD algorithm to detect the outage by applying support vector data description (SVDD) approach. The SCOD algorithm detects a small cell outage efficiently considering two situations: KPIs available situation and non-KPIs available situation. Furthermore, in order to compensate the small cell outage, SCOC is formulated as a network utility maximization problem to optimally compensate for the outaged zone in small cell network. A distributed compensation algorithm with low computational complexity is developed to balance the load of small cell networks, considering the QoS provision for users. Simulation results demonstrate that the proposed SH scheme can detect the small cell outage efficiently and can achieve an optimized QoS performance when compensating for the detected small cell outage.
Security-Aware Proportional Fairness Resource Allocation for Cognitive Heterogeneous Networks

Cognitive radio and heterogeneous wireless networks are important candidate techniques for the fifth generation (5G) communication systems. On the other hand, propagation properties of radio channels in a physical layer are exploited to design efficient secure transmission schemes for 5G wireless networks. In this work, a security-aware joint power and subchannel allocation problem based on the inter-network cooperation is investigated at cognitive heterogeneous networks via imperfect spectral sensing. The security-aware joint power and subchannel allocation is subject to constraints in available subchannels, proportional fairness secrecy transmission rates among secondary mobile terminals (MTs), total interference power threshold, and total power for each secondary MT. In order to determine the impact of imperfect spectral sensing to resource allocation, we first represent the interference power at primary base station in terms of the misdetection probability and false alarm probability. Then, the security-aware power and subchannel allocation problem with inter-network cooperation is formulated as a biconvex optimization problem, and an optimal security-aware power and subchannel allocation algorithm is proposed utilizing the dual decomposition method. Compared to the heuristic power and subchannel allocation algorithms, numerical simulation results show that the proposed algorithms improve the total secrecy throughput and guarantee the proportional fairness among different secondary MTs.
ACE: Availability-Aware CloudSim Extension

In the interconnected globe where service delivery is the success measure, cloud high availability (HA) is an indispensable area for enterprises. An HA-aware cloud system provides different approaches to handle the outages. This includes geo-redundancy, failover schemes, and HA-aware placement solutions. However, using real-cloud platforms to model HA-aware approaches is hindered by the configuration settings. To this end, simulation tools, such as CloudSim, can be used to evaluate HA solutions and a cloud resiliency against failures. CloudSim allows implementing of scheduling policies, but it does not support HA properties. This paper provides availability-aware CloudSim extension (ACE). ACE extends CloudSim with a graphical and textual modeling to ensure simplicity and reusability of cloud scenarios. ACE has added HA-aware modeling (HA metrics and failure/redundancy/interdependency models) and HA-aware scheduling (HA-aware placements, failover, repair, and load balancing policies) into CloudSim. With ACE, the creation of cloud scenarios is facilitated, and multiple HA-aware deployment solutions can be evaluated under different stochastic and deterministic events. ACE can assess the impact of different redundancy/failure models, and other performance policies to extract HA-aware lessons. In this paper, ACE is assessed on a cloud application to evaluate different redundancy/failure models and provide availability analysis of the HA-aware placement solution.