Miracle-dz/UniFAR-FTUs huggingface dataset:
A Definition of General Weighted Fairness and its Support in Explicit Rate Switch Algorithms

In this paper we give a general definition of weighted fairness and show how this can achieve various fairness definitions, such as those mentioned in the ATM Forum TM 4.0 Specifications. We discuss how a pricing policy can be mapped to general weighted (GW) fairness. The GW fairness can be achieved by calculating the $ExcessFairshare$ (weighted fairshare of the left over bandwidth) for each VC. We show how a switch algorithm can be modified to support the GW fairness by using the $ExcessFairshare$. We use ERICA+ as an example switch algorithm and show how it can be modified to achieve the general fairness. Simulations results are presented to demonstrate that the modified switch algorithm achieves GW fairness. An analytical proof for convergence of the modified ERICA+ algorithm is given in the appendix.
Fairness Comparison of FAST TCP and TCP Reno

In networking, it is often required to quantify by how much one protocol is fairer than another and how certain parameter setting and/or protocol enhancements improve fairness. This paper provides a framework to evaluate the fairness of various protocols in a general telecommunications network. Within this framework, there are two key components: (1) a benchmark and (2) a single dimension metric. We suggest to use the max-min fairness bandwidth allocation as the benchmark and the Euclidean distance between any bandwidth allocation under any protocol and the max-min bandwidth allocation as the metric. Explicitly, we provide a method to compare the fairness of two sets of bandwidth allocation under two different protocols for a given network by using this metric. On the basis of this new framework, we evaluate the fairness of FAST TCP and TCP Reno relative to the max-min fairness criteria. The distance between the max-min fair allocation and allocations based on each of the two protocols is measured using the Euclidian norm. We derive explicit expressions for these distances for a general network and compare the fairness of these two protocols by using their corresponding utility functions. Finally, we numerically demonstrate how this method can be applied to compare the fairness of FAST TCP and TCP Reno for a “Parking Lot” linear network and for the NSFNET Backbone network. In addition to merely a comparison between protocols, such numerical results can provide guidelines for better choice of parameters to make a protocol fairer in a given scenario.
Using High-Speed WANs and Network Data Caches to Enable Remote and Distributed Visualization

The most troublesome issue with “tera-scale” visualization is the sheer amount of data tha be stored, processed, transmitted, visualized, and then rendered. To varying degrees, each the visualization pipeline is sensitive to data volume -increase the total amount of data, a overall throughput correspondingly diminishes. We will describe a prototype application, demonstrated at the SC99 Conference in a Research Exhibit, which aims to address som problems that accompany tera-scale visualization. The emphasis is upon parallel storage, and distributed rendering, and parallel communication over high-speed WANs. Our applica used to perform interactive visualization of large-scale data sets using a combination of res including parallel network-based data caches, parallel software rendering engines, paral work communication channels, and a viewer that may be deployed on the desktop or in an sive or high-resolution display environment. The application is an example of a usefu created by integrating emerging technologies.
The Feedback Capacity of the First-Order Moving Average Gaussian Channel

The feedback capacity of the stationary Gaussian additive noise channel has been open, except for the case where the noise is white. Here we obtain the closed-form feedback capacity of the first-order moving average additive Gaussian noise channel. Specifically, the channel is given by Yi = Xi + Zi, i = 1, 2, . , where the input {Xi} satisfies a power constraint and the noise {Zi} is a first-order moving average Gaussian process defined by Zi = αUi−1 +Ui, |α| < 1, with white Gaussian innovation {Ui}i=0. We show that the feedback capacity of this channel is − log x0, where x0 is the unique positive root of the equation ρ x2 = (1 − x2)(1 − |α|x)2, and ρ is the ratio of the average input power per transmission to the variance of the noise innovation Ui. Paralleling the simple linear signalling scheme by Schalkwijk and Kailath for the additive white Gaussian noise channel, the optimal transmitter sends a real-valued information-bearing signal at the beginning of communication, then subsequently processes the feedback noise process through a simple linear stationary first-order autoregressive filter to help the receiver decode the information. The resulting decoding error decays doublyexponentially in the duration of the communication. This feedback capacity of the first-order moving average Gaussian channel is very similar, in form, to the best known achievable rate for the first-order autoregressive Gaussian noise channel studied by Butman, Wolfowitz, and Tiernan, although the optimality of the latter is yet to be established.
The Degrees of Freedom of Compute-and-Forward

We analyze the asymptotic behavior of compute-and-forward relay networks in the regime of high signal-to-noise ratios. We consider a section of such a network consisting of K transmitters and K relays. The aim of the relays is to reliably decode an invertible function of the messages sent by the transmitters. An upper bound on the capacity of this system can be obtained by allowing full cooperation among the transmitters and among the relays, transforming the network into a K × K multiple-input multiple-output (MIMO) channel. The number of degrees of freedom of compute-and-forward is hence at most K. In this paper, we analyze the degrees of freedom achieved by the lattice coding implementation of compute-and-forward proposed recently by Nazer and Gastpar. We show that this lattice implementation achieves at most 2/(1+1/K) ≤ 2 degrees of freedom, thus exhibiting a very different asymptotic behavior than the MIMO upper bound. This raises the question if this gap of the lattice implementation to the MIMO upper bound is inherent to compute-and-forward in general. We answer this question in the negative by proposing a novel compute-and-forward implementation achieving K degrees of freedom.
A Definition and a Test for Human-Level Artificial Intelligence

Although AI research aims to build human-level artificial intelligence, it was not clearly defined. Furthermore, many tests for HLAI have been proposed, but those are not practical and thus are not used in evaluating AI research. We conjecture that learning from others' experience with the language is the essential characteristic that distinguishes human intelligence from the rest. Humans can update the behavior policy with verbal descriptions as if they had experienced it first-hand. We present a classification of intelligence according to how individual agents learn and propose a definition and a test for HLAI. The main idea is that language acquisition without explicit rewards can be a sufficient test for HLAI. We built a simulated environment to conduct this test practically, and we hope other researchers can use it to facilitate the research on HLAI.
Uncovering social network Sybils in the wild

Sybil accounts are fake identities created to unfairly increase the power or resources of a single malicious user. Researchers have long known about the existence of Sybil accounts in online communities such as file-sharing systems, but they have not been able to perform large-scale measurements to detect them or measure their activities. In this article, we describe our efforts to detect, characterize, and understand Sybil account activity in the Renren Online Social Network (OSN). We use ground truth provided by Renren Inc. to build measurement-based Sybil detectors and deploy them on Renren to detect more than 100,000 Sybil accounts. Using our full dataset of 650,000 Sybils, we examine several aspects of Sybil behavior. First, we study their link creation behavior and find that contrary to prior conjecture, Sybils in OSNs do not form tight-knit communities. Next, we examine the fine-grained behaviors of Sybils on Renren using clickstream data. Third, we investigate behind-the-scenes collusion between large groups of Sybils. Our results reveal that Sybils with no explicit social ties still act in concert to launch attacks. Finally, we investigate enhanced techniques to identify stealthy Sybils. In summary, our study advances the understanding of Sybil behavior on OSNs and shows that Sybils can effectively avoid existing community-based Sybil detectors. We hope that our results will foster new research on Sybil detection that is based on novel types of Sybil features.
Scanning and Sequential Decision Making for Multidimensional Data - Part II: The Noisy Case

We investigate the problem of scanning and prediction (ldquoscandiction,rdquo for short) of multidimensional data arrays. This problem arises in several aspects of image and video processing, such as predictive coding, for example, where an image is compressed by coding the error sequence resulting from scandicting it. Thus, it is natural to ask what is the optimal method to scan and predict a given image, what is the resulting minimum prediction loss, and whether there exist specific scandiction schemes which are universal in some sense. Specifically, we investigate the following problems: first, modeling the data array as a random field, we wish to examine whether there exists a scandiction scheme which is independent of the field's distribution, yet asymptotically achieves the same performance as if this distribution were known. This question is answered in the affirmative for the set of all spatially stationary random fields and under mild conditions on the loss function. We then discuss the scenario where a nonoptimal scanning order is used, yet accompanied by an optimal predictor, and derive bounds on the excess loss compared to optimal scanning and prediction. This paper is the first part of a two-part paper on sequential decision making for multidimensional data. It deals with clean, noiseless data arrays. The second part deals with noisy data arrays, namely, with the case where the decision maker observes only a noisy version of the data, yet it is judged with respect to the original, clean data.
Compression Models 2 . Related Work Most relevant to our

Spam filtering poses a special problem in text categorization, of which the defining characteristic is that filters face an active adversary, which constantly attempts to evade filtering. Since spam evolves continuously and most practical applications are based on online user feedback, the task calls for fast, incremental and robust learning algorithms. In this paper, we investigate a novel approach to spam filtering based on adaptive statistical data compression models. The nature of these models allows them to be employed as probabilistic text classifiers based on character-level or binary sequences. By modeling messages as sequences, tokenization and other error-prone preprocessing steps are omitted altogether, resulting in a method that is very robust. The models are also fast to construct and incrementally updateable. We evaluate the filtering performance of two different compression algorithms; dynamic Markov compression and prediction by partial matching. The results of our extensive empirical evaluation indicate that compression models outperform currently established spam filters, as well as a number of methods proposed in previous studies.
2 An End-to-End System for Embedded Parallel DSLs

Domain-specific languages raise the level of abstraction in software development. While it is evident that programmers can more easily reason about very high-level programs, the same holds for compilers only if the compiler has an accurate model of the application domain and the underlying target platform. Since mapping high-level, general-purpose languages to modern, heterogeneous hardware is becoming increasingly difficult, DSLs are an attractive way to capitalize on improved hardware performance, precisely by making the compiler reason on a higher level. Implementing efficient DSL compilers is a daunting task however, and support for building performance-oriented DSLs is urgently needed. To this end, we present the Delite Framework, an extensible toolkit that drastically simplifies building embedded DSLs and compiling DSL programs for execution on heterogeneous hardware. We discuss several building blocks in some detail and present experimental results for the OptiML machine-learning DSL implemented on top of Delite.
Finite-time analysis of approximate policy iteration for the linear quadratic regulator

Datacenter disaggregation provides numerous bene � ts to both the datacenter operator and the application designer. However switching from the server-centric model to a dis-aggregated model requires developing new programming abstractions that can achieve high performance while ben-e � ting from the greater elasticity. To explore the limits of datacenter disaggregation, we study an application area that near-maximally bene � ts from current server-centric datacenters: dense linear algebra. We build NumPyWren, a system for linear algebra built on a disaggregated serverless programming model, and LAmbdaPACK, a companion domain-speci � c language designed for serverless execution of highly parallel linear algebra algorithms. We show that, for a number of linear algebra algorithms such as matrix multiply, singular value decomposition, Cholesky decomposition, and QR decomposition, NumPyWren’s performance (completion time) is within a factor of 2 of optimized server-centric MPI implementations, and has up to 15 % greater compute e � - ciency (total CPU-hours), while providing fault tolerance.
Features for Ground Texture Based Localization-A Survey Supplementary Material

In order to find the best parameter settings of the evaluated methods, we extract features from synthetically transformed images using varying parameter settings, and evaluate the obtained results based on a few selected key performance metrics. A set of 3 non-overlapping images have been selected for each of the 6 examined texture types (fine asphalt, coarse asphalt, carpet, concrete, tiles, and wood). This results in a number of 18 evaluated images, which are selected from a training set that is not included during testing. Each image is synthetically transformed as for testing (overall 72 synthetic transformations are tested), which results in 1296 pairs of reference and transformed images. For keypoint detector parameter optimization, we introduce another performance metric. Adjusted repeatability is a derived metric that combines the conventional repeatability metric of Mikolajczyk et al. [12] and our ambiguity score:
Automated Human Activity Recognition by Colliding Bodies Optimization-based Optimal Feature Selection with Recurrent Neural Network

In smart healthcare, Human Activity Recognition (HAR) is considered to be an efficient model in pervasive computation from sensor readings. The Ambient Assisted Living (AAL) in the home or community helps the people in providing independent care and enhanced living quality. However, many AAL models were restricted using many factors that include computational cost and system complexity. Moreover, the HAR concept has more relevance because of its applications. Hence, this paper tempts to implement the HAR system using deep learning with the data collected from smart sensors that are publicly available in the UC Irvine Machine Learning Repository (UCI). The proposed model involves three processes: (1) Data collection, (b) Optimal feature selection, (c) Recognition. The data gathered from the benchmark repository is initially subjected to optimal feature selection that helps to select the most significant features. The proposed optimal feature selection is based on a new meta-heuristic algorithm called Colliding Bodies Optimization (CBO). An objective function derived by the recognition accuracy is used for accomplishing the optimal feature selection. Here, the deep learning model called Recurrent Neural Network (RNN) is used for activity recognition. The proposed model on the concerned benchmark dataset outperforms existing learning methods, providing high performance compared to the conventional models.
mS2GD: Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting

We propose a mini-batching scheme for improving the theoretical complexity and practical performance of semi-stochastic gradient descent applied to the problem of minimizing a strongly convex composite function represented as the sum of an average of a large number of smooth convex functions, and simple nonsmooth convex function. Our method first performs a deterministic step (computation of the gradient of the objective function at the starting point), followed by a large number of stochastic steps. The process is repeated a few times with the last iterate becoming the new starting point. The novelty of our method is in introduction of mini-batching into the computation of stochastic steps. In each step, instead of choosing a single function, we sample $b$ functions, compute their gradients, and compute the direction based on this. We analyze the complexity of the method and show that the method benefits from two speedup effects. First, we prove that as long as $b$ is below a certain threshold, we can reach predefined accuracy with less overall work than without mini-batching. Second, our mini-batching scheme admits a simple parallel implementation, and hence is suitable for further acceleration by parallelization.
Martingale representation processes and applications in the market viability under information flow expansion

When the martingale representation property holds, we call any local martingale which realizes the representation a representation process . There are two properties of the representation process which can greatly facilitate the computations under the martingale representation property . On the one hand, the representation process is not unique and there always exists a representation process which is locally bounded and has pathwise orthogonal components outside of a predictable thin set. On the other hand, the jump measure of a representation process satisfies the finite predictable constraint , which implies the martingale projection property . In this paper, we give a detailed account of these properties. As application, we will prove that, under the martingale representation property , the full viability of an expansion of market information flow implies the drift multiplier assumption .
2 0 Ju n 20 06 Sparse SOS Relaxations for Minimizing Functions that are Summation of Small Polynomials

This paper discusses how to find the global minimum and minimizers of functions that are given as the summation of small polynomials. Some sparse SOS relaxations are proposed. We analyze the computational complexity and the quality of lower bounds, and give the algorithm. Some numerical implementations of testing problems and randomly generated polynomials are given. We also show the applications of this problem in solving polynomial systems derived from nonlinear differential equations and sensor network localization.
Southeast Europe Journal of Soft Computing Available online:

Food Recognition is an essential topic in the area of computer of its target applications is to avoid achieving a cashier at the dining place. In this paper, we investigate the application of Deep Transfer Learning for food recognition. We fine-tune three well learning models namely; AlexNet, GoogleNet, and Vgg16. The fine tuning procedure depends on removing the last three layers of each model and adds another five
Digested Information as an Information Theoretic Motivation for Social Interaction

Within a universal agent-world interaction framework, based on Information Theory and Causal Bayesian Networks, we demonstrate how every agent that needs to acquire relevant information in regard to its strategy selection will automatically inject part of this information back into the environment. We introduce the concept of 'Digested Information' which both quantifies, and explains this phenomenon. Based on the properties of digested information, especially the high density of relevant information in other agents actions, we outline how this could motivate the development of low level social interaction mechanisms, such as the ability to detect other agents
Grassmannian beamforming for MIMO amplify-and-forward relaying

In this paper, we consider the beamforming code-book design problem for the half-duplex MIMO amplify-and-forward relay channel with Rayleigh fading. The analysis is divided into two steps. First, we present the optimal beamforming scheme with full channel state information (CSI) and derive the optimal source and relay beamforming vectors. Next, we consider the beamforming problem with receiver CSI only and provide a beamforming vector quantization scheme. Based on the statistics of the optimal beamforming vectors, we show that Grassmannian codebooks minimize the upper bound for SNR loss caused by quantization, and therefore these codebooks are appropriate choices for quantizing the optimal beamforming vectors. The efficiency of the Grassmannian codebooks is verified by simulation results.
A New Dynamic Building Health Monitoring Method Based on the Hilbert-Huang Transform

The theoretical solution for a Multiple-Degree-Of-Freedom (MDOF) structure is composed of a combination of several individual modes. When we demonstrate the actual response directly on the time-frequency spectrum, the energy distribution is usually concentrated at some range of frequencies, with the temporal variations of each band clearly shown. Those variations in the frequencies are the actual structure performances even though those “modal behaviors” are not coming from the theoretical solutions. Using the Hilbert-Huang Transform (HHT) the modal behaviors can be obtained easily by directly reading the time-frequency spectrum. To find Structural-Health-Monitoring (SHM) information, some numerical steps are found helpful. Including the signal enhancement skills, the time-frequency domain amplification function (T.F.AF), the modal temporal variation curve (MTVC) and the instantaneous frequency application. We develop a new method called the HHT SHM method; both experiment measurement and building observation are used to show the performance and method-validation. The procedures are described as follows. The original signal is first transferred into the wave-propagating properties, the T.F. AF. The T.F. AF can give dynamic parameter results through all phases in an earthquake event. After adopting useful modal information from the T.F. AF, which is called the MTVC. The MTVC contains detailed SHM information that enables observers to read the modal behavior. This is a new way to explore SHM information from actual records. These MTVCs can be used to explore the vivid differences in structure healthy conditions that might be ignored by other observers.
Complexity Curve : a Graphical Measure of Data Complexity and Classifier Performance Supplementary document S 2 : Evaluating Classifier Performance with Generalisation Curves

We discussed the role of data complexity measures in the evaluation of classification algorithms performance. Knowing characteristics of benchmark data sets it is possible to check which algorithms perform well in the context of scarce data. To fully utilise this information, we present a graphical performance measure called generalisation curve. It is based on learning curve concept and allows to compare the learning process of different algorithms while controlling the variance of the data. To demonstrate its validity we apply it to a set of popular algorithms. We show that the analysis of generalisation curves points to important properties of the learning algorithms and benchmark data sets, which were previously suggested in the literature.