Miracle-dz/UniFAR-FTUs huggingface dataset:
Locator/Identifier Split Network Based Cross-Layer Roaming Mechanism

Based on the analysis of 802.11 link-layer handoff and representative cross-layer mechanisms supporting mobility, this paper proposes a Locator/Identifier Split Network Based Cross-Layer Roaming Mechanism (LISN-CLRM) with the Layer 2 and Layer 3 handoff coupling organically. Theoretical analysis shows that the total delay of this mechanism is much smaller than MIPv6, and is 77.5 ms smaller than SMOS[1]. By pre-registration on Layer 2, our mechanism effectively reduces the handoff delay and overhead and basically meets the demand of real-time business.
Real-time DRAM throughput guarantees for latency sensitive mixed QoS MPSoCs

The trend towards integration is leading to the design of multi- and many-core platforms that accommodate processing tiles (requestors) with different memory requirements. Such platforms require a memory controller capable of providing low-latency best-effort (BE) service for some requestors and guaranteed throughput (GT) for others. Although there are realtime controllers that support the concept of different traffic classes, they do not efficiently handle scenarios with multiple BE and GT requestors. We propose a memory controller that tackles this problem, providing low latency for BE requestors and real-time guarantees for GT ones. We support the guarantees with a formal timing analysis. Our experiments confirm that our approach enforces tight guarantees for GT requestors, while simultaneously reducing the latency of BE ones by up to 67%, when compared with a baseline memory controller.
Postprocessing for Iterative Differentially Private Algorithms

Iterative algorithms for differential privacy run for a fixed number of iterations, where each iteration learns some information from data and produces an intermediate output. However, the algorithm only releases the output of the last iteration, and from which the accuracy of algorithm is judged. In this paper, we propose a post-processing algorithm that seeks to improve the accuracy by incorporating the knowledge on the data contained in intermediate outputs.
Advancing Cyber Resilience Analysis with Performance-Based Metrics from Infrastructure Assessments

Cyber resilience is becoming increasingly recognized as a critical component of comprehensive cybersecurity practices. Current cyber resilience assessment approaches are primarily qualitative methods, making validation of their resilience analyses and enhancement recommendations difficult, if not impossible. The evolution of infrastructure resilience assessment methods has paralleled that of their cyber counterparts. However, the development of performance-based assessment methods has shown promise for overcoming the validation challenge for infrastructure systems. This article describes a hybrid infrastructure resilience assessment approach that combines both qualitative analysis techniques with performance-based metrics. The qualitative component enables identification of system features that limit resilience, and the quantitative metrics can be used to evaluate and confirm the effectiveness of proposed mitigation options. The authors propose adaptation of this methodology for cyber resilience analysis. A case study is presented to demonstrate how the approach could be applied to a hypothetical system. INTRODUCTION AND BACKGROUND Cybersecurity is generally acknowledged as a critical priority within the national, homeland, and business security communities. This sentiment has been echoed at the highest levels of the U.S. government, with President Obama (2009) stating that “cyber threat is one of the most serious economic and national security challenges we face as a nation. ” Fortunately, the concept of cybersecurity is not new to the academic and research communities. Eric D. Vugrin Sandia National Laboratories, USA Jennifer Turgeon Sandia National Laboratories, USA
Mining Trajectory Data for Discovering Communities of Moving Objects

Recent advances on tracking technologies enable the collection of spatio-temporal data in the form of trajectories. The analysis of such data can convey knowledge in prominent applications, and mining groups of moving objects turns out to be a valuable mean to model their movement. Existing approaches pay particular attention in groups where objects are close and move together or follow similar trajectories by assuming that movement cannot change over time. Instead, we observe that groups can be of interest also when objects are spatially distant and have di↵erent but inter-related movements: objects can start from di↵erent places and join together to move towards a common location. To take into account inter-related movements, we have to analyze the objects jointly, follow their respective movements and consider changes of movements over time. Motivated by this, we introduce the notion ofcommunitiesand propose a computational solution to discover them. The method is structured in three steps. The first step performs a feature extraction technique to elicit the inter-related movements between the objects. The second one leverages a tree-structure in order to group objects with similar inter-related movements. In the third step, these groupings are used to mine communities as groups of objects which exhibit inter-related movements over time. We evaluate our approach on real data-sets and compare it with existing algorithms.
Conditioning prices on search behaviour

We consider a market in which firms can partially observe each consumer's search behavior in the market. In our main model, a firm knows whether a consumer is visiting it for the first time or whether she is returning after a previous visit. Firms have an incentive to offer a lower price on a first visit than a return visit, so that new consumers are offered a "buy-now" discount. The ability to offer such discounts acts to raise all prices in the market. If firms cannot commit to their buy-later price, in many cases firms make "exploding" offers, and consumers never return to a previously sampled firm. Likewise, if firms must charge the same price to all consumers, regardless of search history, we show that they sometimes have the incentive to make exploding offers. We also consider other ways in which firms could use information about search behaviour to determine their prices.
Installation Error Dynamic Correction Based on Quasi-Linear Model

In order to suppress the installation errors of photoelectric payload and increase the precision of target location using photoelectric payload equipped on the aerial mobile platform, a new method of calibrating fixing errors dynamically is proposed based on quasi-linear model. The method analyzes the relationship between measurement data and true value and comes to a quasi-linear conclusion between them under some conditions. The new method is used to real-time dynamically calibrating installation errors of photoelectric payload, which improves the precision of target location effectively. Both the simulation and the experiment prove the validity of the method. It is easy to calculate and be put into practice. Furthermore, the new method could meet the real-time need and is of high value to application.
A Query by Humming System that Learns from Experience

Query-by-Humming (QBH) systems transcribe a sung or hummed query and search for related musical themes in a database, returning the most similar themes. Since it is not possible to predict all individual singer profiles before system deployment, a robust QBH system should be able to adapt to different singers after deployment. Currently deployed systems do not have this capability. We describe a new QBH system that learns from user provided feedback on the search results, letting the system improve while deployed, after only a few queries. This is made possible by a trainable note segmentation system, an easily parameterized singer error model and a straight-forward genetic algorithm. Results show significant improvement in performance given only ten example queries from a particular user.
On Adaptation to Climate Change and Food Security: a Micro-perspective from Ethiopia

We examine the driving forces behind farmers’ decis ion to adapt to climate change, and the impact of adaptation on far mers’ food production. We investigate whether there are differences in the fo od production functions of farm households that adapted and those that did not adap t. We estimate a simultaneous equations model with endogenous switching to accoun t for the heterogeneity in the decision to adapt or not, and for unobservable char acte istics of farmers and their farm. We compare the expected food production under the actual and counterfactual cases that the farm household adapted or not to cli mate change. We find that the group of farm households that adapted has systemati cally different characteristics than the group of farm households that did not adapt. Th e relationship between production and average temperature is inverted U-shaped for farm households that adapted, while it is U-shaped for farm households that did not adapt, and vice versa in the case of precipitation. We find that adaptation i creases food production, however, the impact of adaptation on food production is smaller for the farm households that actually did adapt than for the farm households that did not ada pt in the counterfactual case that they adapted.
Scale Invariance, Bounded Rationality and Non-Equilibrium Economics

We study a class of heterogeneous agent-based models which are based on a basic set of principles, and the most fundamental operations of an economic system: trade and product transformations. A basic guiding principle is scale invariance, which means that the dynamics of the economy should not depend on the units used to measure the different products. We develop the idea of a "near-equilibrium" expansion which allow us to study the dynamics of fluctuations around economic equilibrium. This is similar to the familiar "perturbation theory" studied in many areas of physics. We study some simple models of both centralized and decentralized markets. We show the relaxation to equilibrium when appropriate. More interestingly, we study a simple model of a decentralized market that shows a spontaneous transition into a monetary phase. We use mean field theory analysis to provide a statistical interpretation of the monetary phase. Furthermore, we show that such phase can be dynamically unstable. Finally, we study some simple centralized financial markets, one of which shows a speculative bubble and a crash.
Unsupervised email vector space model (UEVSM)

There are many challenges of grouping email messages. One of the challenging issues of email message group is to pinpoint when the clustering process accumulates accurate and sufficient information for grouping is archived. In this work, an unsupervised machine learning technique has been developed based on unsupervised clustering method (UCM). The proposed unsupervised clustering method is new and different from other existing UCMs such as: email evolving clustering method.
Taxi Driver Assistant – A Proposal for a Recommendation System

Taxi is a flexible way of transportation; however, with the fast growth of urban areas, the task of fi nding new passenger quickly becomes a challenging task for ta xi drivers. In this work, we propose a recommendati on system to assist taxi drivers searching for the nex t pick-up. With an inference engine based on a naïv e Bayesian classifier, we analyze taxi-GPS traces collected in Lisbon, Portugal. An exploratory and predictive an alysis is performed to better understand the apparent randomn ess of taxi movements, and the impact of features’ variation, namely, temporal periods, driver strateg ies or cell sizes, on the classifier.
Norges Teknisk-naturvitenskapelige Universitet Spatial Modelling of Temperature and Humidity Using Systems of Stochastic Partial Differential Equations Spatial Modelling of Temperature and Humidity Using Systems of Stochastic Partial Differential Equations

This work is motivated by constructing a weather simulator for precipitation. Temperature and humidity are two of the most important driving forces of precipitation , and the strategy is to have a stochastic model for temperature and humidity, and use a deterministic model to go from these variables to precipitation. Temperature and humidity are empirically positively correlated. Generally speaking, if variables are empirically dependent, then multivariate models should be considered. In this work we model humidity and temperature in southern Norway. We want to construct bivariate Gaussian random fields (GRFs) based on this dataset. The aim of our work is to use the bivariate GRFs to capture both the dependence structure between humidity and temperature as well as their spatial dependencies. One important feature for the dataset is that the humidity and temperature are not necessarily observed at the same locations. Both univariate and bivariate spatial models are fitted and compared. For modeling and inference the SPDE approach for univariate models and the systems of SPDEs approach for multivariate models have been used. To evaluate the performance of the difference between the univariate and bivari-ate models, we compare predictive performance using some commonly used scoring rules: mean absolute error, mean-square error and continuous ranked probability score. The results illustrate that we can capture strong positive correlation between the temperature and the humidity. Furthermore, the results also agree with the physical or empirical knowledge. At the end, we conclude that using the bivariate GRFs to model this dataset is superior to the approach with independent univari-ate GRFs both when evaluating point predictions and for quantifying prediction uncertainty.
ACYCLIC LIST EDGE COLORING OF PLANAR GRAPHS

A proper edge coloring of a graph is said to be acyclic if any cycle is colored with at least three colors. The acyclic chromatic index, denoted a ′ (G), is the least number of colors required for an acyclic edge coloring of G. An edge-list L of a graph G is a mapping that assigns a finite set of positive integers to each edge
Ramsey properties of random discrete structures

We study thresholds for Ramsey properties of random discrete structures. In particular, we determine the threshold for Rado's theorem for solutions of partition regular systems of equations in random subsets of the integers and we prove the 1-statement of the conjectured threshold for Ramsey's theorem for random hypergraphs. Those results were conjectured by Rodl and Rucinski and similar results were obtained independently by Conlon and Gowers. © 2010 Wiley Periodicals, Inc. Random Struct. Alg. , 2010
After Sandy Hook Elementary: A Year in the Gun Control Debate on Twitter

The mass shooting at Sandy Hook elementary school on December 14, 2012 catalyzed a year of active debate and legislation on gun control in the United States. Social media hosted an active public discussion where people expressed their support and opposition to a variety of issues surrounding gun legislation. In this paper, we show how a content-based analysis of Twitter data can provide insights and understanding into this debate. We estimate the relative support and opposition to gun control measures, along with a topic analysis of each camp by analyzing over 70 million gun-related tweets from 2013. We focus on spikes in conversation surrounding major events related to guns throughout the year. Our general approach can be applied to other important public health and political issues to analyze the prevalence and nature of public opinion.
Learning Parametric Inverse Dynamics Models from multiple conditions for fast adaptive computed torque control

In this paper, we propose a novel approach for learning an inverse dynamics model of a serial-link robot only from data to be suitable for achieving computed torque control under unknown conditions, i.e., adaptive computed torque control. In our approach, we first collect a varied set of data from the robot under multiple conditions, each of which is constructed by putting loads on the body of the robot or giving the robot tools and bags to imitate real environmental situations. Then, a subspace representation that contains the various inverse dynamics models is extracted from the data as the Parametric Inverse Dynamics Model (PIDM) which is composed of the basis functions and weight coefficients. Using the PIDM, fast adaptive computed torque control under an unknown condition can be efficiently performed by automatically adjusting the weight coefficients of the basis functions unlike solving a high-dimensional learning problem as previous studies. To validate our approach, we applied the proposed method for the problem of adaptive computed torque control on trajectory-tracking tasks with an 7 DoF anthropomorphic manipulator and demonstrated the effectiveness. As a result, our approach achieved fast adaptation of computed torque control for the manipulator (within two seconds) even under unknown conditions such as holding and mounting unknown objects.
Habitat loss and connectivity of reserve networks in probability approaches to reserve design

Quantitative methods for the design of reserve networks often select over-dispersed reserves, and consequently a number of species extinctions can be expected in such reserves, especially if unprotected surrounding habitat is lost. A novel approach that deals with this problem is presented by considering factors such as habitat quality and spatial configuration of reserves during the selection process. Species-specific effects of habitat loss and fragmentation, together with habitat composition, are taken into account when computing species probabilities of local occurrence. Sites are then chosen to represent all species with a given target probability. The method is applied to a dataset of butterflies and moths from the Creuddyn Peninsula, North Wales, UK, which includes species with various responses to habitat quality and configuration. The results show that the resulting level of reserve clustering will depend on the number of species for which the spatial configuration plays an important role (at the scale under consideration), and on the pattern and amount of habitat loss that is expected to follow around the reserves. The method represents a step towards taking better into account species persistence when selecting reserve networks in a changing world.
Cross-Layer Optimization for UWB-Based AD HOC Networks

In this paper, we consider a UWB-based ad hoc network and study how to maximize data rate utility for a group of communication sessions. We formulate the data rate utility problem into a nonlinear programming (NLP) problem through a cross-layer approach by taking into consideration of scheduling and power control at link layer and routing at network layer. The main contribution of this paper is the development of a solution procedure based on branch-and-bound framework. We employ a powerful optimization technique called reformulation linearization technique (RLT) to obtain a linear relaxation, which is a key component required in the branch-and-bound framework. Numerical results demonstrate the importance of cross-layer approach and offer some important insights
Validation of a French Version of the Impact of Event Scale-Revised

Objective: This report presents a French translation and validation of the Impact of Event Scale-Revised (IES-R) in a population of women exposed to a natural disaster during or preceding pregnancy. Method: A total of 223 francophone women who were either pregnant at the time of the 1998 ice storm or who became pregnant shortly thereafter completed the IES-R and other questionnaires 6 months after the disaster. Results: The French IES-R has good internal consistency, with alpha coefficients ranging from 0.81 to 0.93 for its 3 subscales and total score. The test–retest reliability of the scale, although examined with another sample, proved to be satisfactory, with correlation coefficients ranging from 0.71 to 0.76 for its 3 subscales and total score. Its convergent validity with perceived life threat and general psychiatric symptoms was judged to be marginally acceptable. Finally, a principal components analysis was conducted and a 3-factor solution, which explained 56% of the variance, was retained: a hyperarousal factor (7 items), an avoidance factor (6 items), and an intrusion factor (6 items). Conclusions: The French version of the IES-R has satisfactory internal validity and test–retest reliability. Further, the factor structure of the translation was similar to the proposed theoretical structure of the IES-R.
Design of weighted order statistic filters using the perceptron algorithm

In this paper, we observe that the design of optimal weighted order statistic(WOS) filters under the mean absolute error criterion can be thought of as a two-class linear classification problem. Based on this observation, the perceptron algorithm is applied to design WOS filters. It is shown, through experiments, that the perceptron algorithm can find optimal or near optimal WOS filters in practical situations.
Function length as a tool for malware classification

The proliferation of malware is a serious threat to computer and information systems throughout the world. Anti-malware companies are continually challenged to identify and counter new malware as it is released into the wild. In attempts to speed up this identification and response, many researchers have examined ways to efficiently automate classification of malware as it appears in the environment. In this paper, we present a fast, simple and scalable method of classifying Trojans based only on the lengths of their functions. Our results indicate that function length may play a significant role in classifying malware, and, combined with other features, may result in a fast, inexpensive and scalable method of malware classification.
Word-based Japanese typed dependency parsing with grammatical function analysis

We present a novel scheme for wordbased Japanese typed dependency parser which integrates syntactic structure analysis and grammatical function analysis such as predicate-argument structure analysis. Compared to bunsetsu-based dependency parsing, which is predominantly used in Japanese NLP, it provides a natural way of extracting syntactic constituents, which is useful for downstream applications such as statistical machine translation. It also makes it possible to jointly decide dependency and predicate-argument structure, which is usually implemented as two separate steps. We convert an existing treebank to the new dependency scheme and report parsing results as a baseline for future research. We achieved a better accuracy for assigning function labels than a predicate-argument structure analyzer by using grammatical functions as dependency label.
Extension of t-subnorms on complete lattices via retractions

In general, the task of extending functions is not simple. It is necessary, in most cases, to impose some properties to the function and their domain. As, in particular, t-subnorms (t-subconorms) are functions, it is pertinent to question under what conditions a t-subnorm (t-subconorm) can be extended from a sublattice M to a lattice L. We present an answer to this question considering a more relaxed definition of sublattices, defined using retractions where the sublattice is not necessarily a subset. In addition, we prove some results related to these extensions with relevant concepts, such as De Morgan triples and automorphisms.