nils-herrmann/clean_scirepeval huggingface dataset:
Through-the-Wall Sensing of Personnel Using Passive Bistatic WiFi Radar at Standoff Distances

In this paper, we investigate the feasibility of uncooperatively and covertly detecting people moving behind walls using passive bistatic WiFi radar at standoff distances. A series of experiments was conducted which involved personnel targets moving inside a building within the coverage area of a WiFi access point. These targets were monitored from outside the building using a 2.4-GHz passive multistatic receiver, and the data were processed offline to yield range and Doppler information. The results presented show the first through-the-wall (TTW) detections of moving personnel using passive WiFi radar. The measured Doppler shifts agree with those predicted by bistatic theory. Further analysis of the data revealed that the system is limited by the signal-to-interference ratio (SIR), and not the signal-to-noise ratio. We have also shown that a new interference suppression technique based on the CLEAN algorithm can improve the SIR by approximately 19 dB. These encouraging initial findings demonstrate the potential for using passive WiFi radar as a low-cost TTW detection sensor with widespread applicability.
Classes of Multiagent Q-learning Dynamics with epsilon-greedy Exploration

Q-learning in single-agent environments is known to converge in the limit given sufficient exploration. The same algorithm has been applied, with some success, in multiagent environments, where traditional analysis techniques break down. Using established dynamical systems methods, we derive and study an idealization of Q-learning in 2-player 2-action repeated general-sum games. In particular, we address the discontinuous case of -greedy exploration and use it as a proxy for value-based algorithms to highlight a contrast with existing results in policy search. Analogously to previous results for gradient ascent algorithms, we provide a complete catalog of the convergence behavior of the -greedy Q-learning algorithm by introducing new subclasses of these games. We identify two subclasses of Prisoner’s Dilemma-like games where the application of Q-learning with -greedy exploration results in higher-than-Nash average payoffs for some initial conditions.
Algorithmic Nuggets in Content Delivery

This paper "peeks under the covers" at the subsystems that provide the basic functionality of a leading content delivery network. Based on our experiences in building one of the largest distributed systems in the world, we illustrate how sophisticated algorithmic research has been adapted to balance the load between and within server clusters, manage the caches on servers, select paths through an overlay routing network, and elect leaders in various contexts. In each instance, we first explain the theory underlying the algorithms, then introduce practical considerations not captured by the theoretical models, and finally describe what is implemented in practice. Through these examples, we highlight the role of algorithmic research in the design of complex networked systems. The paper also illustrates the close synergy that exists between research and industry where research ideas cross over into products and product requirements drive future research.
Theory of Mind for a Humanoid Robot

One of the fundamental social skills for humans is a theory of other minds. This set of skills allows us to attribute beliefs, goals, and desires to other individuals. To take part in normal human social dynamics, a robot must not only know about the properties of objects, but also the properties of animate agents in the world. This research project attempts to implement basic social skills on a humanoid robot using models of social development in both normal and autistic children.
Structural Data De-anonymization: Quantification, Practice, and Implications

In this paper, we study the quantification, practice, and implications of structural data (e.g., social data, mobility traces) De-Anonymization (DA). First, we address several open problems in structural data DA by quantifying perfect and (1-ε)-perfect structural data DA}, where ε is the error tolerated by a DA scheme. To the best of our knowledge, this is the first work on quantifying structural data DA under a general data model, which closes the gap between structural data DA practice and theory. Second, we conduct the first large-scale study on the de-anonymizability of 26 real world structural datasets, including Social Networks (SNs), Collaborations Networks, Communication Networks, Autonomous Systems, and Peer-to-Peer networks. We also quantitatively show the conditions for perfect and (1-ε)-perfect DA of the 26 datasets. Third, following our quantification, we design a practical and novel single-phase cold start Optimization based DA} (ODA) algorithm. Experimental analysis of ODA shows that about 77.7% - 83.3% of the users in Gowalla (.2M users and 1M edges) and 86.9% - 95.5% of the users in Google+ (4.7M users and 90.8M edges) are de-anonymizable in different scenarios, which implies optimization based DA is implementable and powerful in practice. Finally, we discuss the implications of our DA quantification and ODA and provide some general suggestions for future secure data publishing.
Volley: Automated Data Placement for Geo-Distributed Cloud Services

As cloud services grow to span more and more globally distributed datacenters, there is an increasingly urgent need for automated mechanisms to place application data across these datacenters. This placement must deal with business constraints such as WAN bandwidth costs and datacenter capacity limits, while also minimizing user-perceived latency. The task of placement is further complicated by the issues of shared data, data inter-dependencies, application changes and user mobility. We document these challenges by analyzing month-long traces from Microsoft's Live Messenger and Live Mesh, two large-scale commercial cloud services.
Analysis of a new topology of flexible PCB winding for slotless BLDC machines

The slotless BLDC motor is one of the most efficient motor in the fractional horse power range. However, the demand for higher performance, incessantly growing, asks for break-through improvements. A solution to achieve better performances is to use windings printed on a flexible PCB, allowing more elaborated winding shapes and topologies. In this paper, a new winding topology is introduced and compared to three other PCB windings. An analytical magnetic model is presented, and validated by FEM. The comparison shows that the new topology achieves a motor power density 10% higher than the best existing PCB winding. Further, this gain reaches 40% when comparing with the winding shape and topology currently used in the classical windings made of copper wire, suggesting a consequent breakthrough in the performances achievable in the fractional horse power range.
Multiresolution Segmentation : an optimization approach for high quality multi-scale image segmentation

A necessary prerequisite for object oriented image processing is successful image segmentation. The approach presented in this paper aims for an universal high-quality solution applicable and adaptable to many problems and data types. As each image analysis problem deals with structures of a certain spatial scale, the average image objects size must be free adaptable to the scale of interest. This is achieved by a general segmentation algorithm based on homogeneity definitions in combination with local and global optimization techniques. A scale parameter is used to control the average image object size. Different homogeneity criteria for image objects based on spectral and/or spatial information are developed and compared.
Web-Based Virtual Learning Environments: A Research Framework and a Preliminary Assessment of Effectiveness in Basic IT Skills Training

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Agent-Based and Individual-Based Modeling: A Practical Introduction

agent-based models of geographical systems PDF architecture-based design of multi-agent systems PDF agent-based models of the economy from theories to applications PDF new trends in agent based complex automated negotiations PDF churchills secret agent a novel based on a true story PDF agent-based models quantitative applications in the social sciences PDF the complexity of cooperation agent-based models of competition and collaboration PDF party competition an agent-based model princeton studies in complexity PDF modeling for decision support in network-based services the application of quantitative modeling to service science lecture notes in business information processing volume 42 PDF
Memory-Efficient Adaptive Optimization for Large-Scale Learning

Adaptive gradient-based optimizers such as AdaGrad and Adam are among the methods of choice in modern machine learning. These methods maintain second-order statistics of each parameter, thus doubling the memory footprint of the optimizer. In behemoth-size applications, this memory overhead restricts the size of the model being used as well as the number of examples in a mini-batch. We describe a novel, simple, and flexible adaptive optimization method with sublinear memory cost that retains the benefits of per-parameter adaptivity while allowing for larger models and mini-batches. We give convergence guarantees for our method and demonstrate its effectiveness in training very large deep models.
Revenue Management Under a General Discrete Choice Model of Consumer Behavior

C choice behavior, such as buy-up and buy-down, is an important phenomenon in a wide range of revenue management contexts. Yet most revenue management methodologies ignore this phenomenon— or at best approximate it in a heuristic way. In this paper, we provide an exact and quite general analysis of this problem. Specifically, we analyze a single-leg reserve management problem in which the buyers’ choice behavior is modeled explicitly. The choice model is very general, simply specifying the probability of purchase for each fare product as a function of the set of fare products offered. The control problem is to decide which subset of fare products to offer at each point in time. We show that the optimal policy for this problem has a quite simple form. Namely, it consists of identifying an ordered family of “efficient” subsets S1 Sm, and at each point in time opening one of these sets Sk, where the optimal index k is increasing in the remaining capacity x and decreasing in the remaining time. That is, the more capacity (or less time) available, the further the optimal set is along this sequence. We also show that the optimal policy is a nested allocation policy if and only if the sequence of efficient sets is nested, that is S1 ⊆ S2 ⊆ · · · ⊆ Sm. Moreover, we give a characterization of when nesting by fare order is optimal. We also develop an estimation procedure for this setting based on the expectation-maximization (EM) method that jointly estimates arrival rates and choice model parameters when no-purchase outcomes are unobservable. Numerical results are given to illustrate both the model and estimation procedure.
Adapting the Tesseract open source OCR engine for multilingual OCR

We describe efforts to adapt the Tesseract open source OCR engine for multiple scripts and languages. Effort has been concentrated on enabling generic multi-lingual operation such that negligible customization is required for a new language beyond providing a corpus of text. Although change was required to various modules, including physical layout analysis, and linguistic post-processing, no change was required to the character classifier beyond changing a few limits. The Tesseract classifier has adapted easily to Simplified Chinese. Test results on English, a mixture of European languages, and Russian, taken from a random sample of books, show a reasonably consistent word error rate between 3.72% and 5.78%, and Simplified Chinese has a character error rate of only 3.77%.
Grassmannian beamforming for multiple-input multiple-output wireless systems

Multiple-input multiple-output (MIMO) wireless systems provide capacity much larger than that provided by traditional single-input single-output (SISO) wireless systems. Beamforming is a low complexity technique that increases the receive signal-to-noise ratio (SNR), however, it requires channel knowledge. Since in practice channel knowledge at the transmitter is difficult to realize, we propose a technique where the receiver designs the beamforming vector and sends it to the transmitter by transmitting a label in a finite set, or codebook, of beamforming vectors. A codebook design method for quantized versions of maximum ratio transmission, equal gain transmission, and generalized selection diversity with maximum ratio combining at the receiver is presented. The codebook design criterion exploits the quantization problem’s relationship with Grassmannian line packing. Systems using the beamforming codebooks are shown to have a diversity order of the product of the number of transmit and the number of receive antennas. Monte Carlo simulations compare the performance of systems using this new codebook method with the performance of systems using previously proposed quantized and unquantized systems.
Wideband Compact CPW-Fed Circularly Polarized Antenna for Universal UHF RFID Reader

A compact circularly polarized slot antenna is proposed in this communication, which has a much wider operation bandwidth compared to the antennas with similar sizes. This antenna is designed for universal ultrahigh frequency (UHF) radio-frequency identification (RFID) reader applications. The antenna is coplanar waveguide (CPW) fed by an L-shaped feeding line. To achieve good impedance matching and broadband CP operation, two L-shaped strip lines are inserted into the circular slot in the ground plane. The measured 10-dB return loss bandwidth is 380 MHz (618-998 MHz, 47.0% centered at 808 MHz). The measured 3-dB axial ratio bandwidth is about 332 MHz (791-1123 MHz, 34.7% centered at 957 MHz). The overall size of the antenna is 120 × 120 × 0.8 mm3.
Application Design for Wearable Computing

The confluence of decades of computer science and computer engineering research in multimodal interaction (e.g., speech and gesture recognition), machine learning (e.g., classification and feature extraction), software (e.g., web browsers, distributed agents), electronics (e.g., energy-efficient microprocessors, head-mounted displays), design methodology in user-centered design, and rapid prototyping have enabled a new class of computers—wearable computers. The lecture takes the viewpoint of a potential designer or researcher in wearable computing. Designing wearable computers requires attention to many different factors because of the computer’s closeness to the body and its use while performing other tasks. For the purposes of discussion, we have created the UCAMP framework, which consists of the following factors: user, corporal, attention, manipulation, and perception. Each of these factors and their importance is described. A number of example prototypes developed by the authors, as well as by other researchers, are used to illustrate these concepts. Wearable computers have established their first foothold in several application domains, such as vehicle and aircraft maintenance and manufacturing, inspection, language translation, and other areas. The lecture continues by describing the next step in the evolution of wearable computers, namely, context awareness. Context-aware computing takes into account a user’s state and surroundings, and the mobile computer modifies its behavior based on this information. A user’s context can be quite rich, consisting of attributes such as physical location, physiological state, personal history, daily behavioral patterns, and so forth. If a human assistant were given such context, he or she would make decisions in a proactive fashion, anticipating user needs, and acting as a proactive assistant. The goal is to enable mobile computers to play an analogous role, exploiting context information to significantly reduce demands on human attention. Context-aware intelligent agents can deliver relevant information when a user needs that information. These data make possible many exciting new applications, such as augmented reality, context-aware collaboration, and augmented manufacturing. The combined studies and research reported in this lecture suggest a number of useful guidelines for designing wearable computing devices. Also included with the guidelines is a list of questions that designers should consider when beginning to design a wearable computer. The research directions section emphasizes remaining challenges and trends in the areas of user interface, modalities of interaction, and wearable cognitive augmentation. Finally, we summarize the most important challenges and conclude with a projection of future directions in wearable computing. KEyWoRDS wearable computers, wearability design, user interface, manipulation devices, interaction modalities, context-aware computing, cognitive augmentation vi
Optimal single image capture for motion deblurring

Deblurring images of moving objects captured from a traditional camera is an ill-posed problem due to the loss of high spatial frequencies in the captured images. Techniques have attempted to engineer the motion point spread function (PSF) by either making it invertible using coded exposure, or invariant to motion by moving the camera in a specific fashion. We address the problem of optimal single image capture strategy for best deblurring performance. We formulate the problem of optimal capture as maximizing the signal to noise ratio (SNR) of the deconvolved image given a scene light level. As the exposure time increases, the sensor integrates more light, thereby increasing the SNR of the captured signal. However, for moving objects, larger exposure time also results in more blur and hence more deconvolution noise. We compare the following three single image capture strategies: (a) traditional camera, (b) coded exposure camera, and (c) motion invariant photography, as well as the best exposure time for capture by analyzing the rate of increase of deconvolution noise with exposure time. We analyze which strategy is optimal for known/unknown motion direction and speed and investigate how the performance degrades for other cases. We present real experimental results by simulating the above capture strategies using a high speed video camera.
Automated Test Data Generation Using an Iterative Relaxation Method

An important problem that arises in path oriented testing is the generation of test data that causes a program to follow a given path. In this paper, we present a novel program execution based approach using an iterative relaxation method to address the above problem. In this method, test data generation is initiated with an arbitrarily chosen input from a given domain. This input is then iteratively refined to obtain an input on which all the branch predicates on the given path evaluate to the desired outcome. In each iteration the program statements relevant to the evaluation of each branch predicate on the path are executed, and a set of linear constraints is derived. The constraints are then solved to obtain the increments for the input. These increments are added to the current input to obtain the input for the next iteration. The relaxation technique used in deriving the constraints provides feedback on the amount by which each input variable should be adjusted for the branches on the path to evaluate to the desired outcome.When the branch conditions on a path are linear functions of input variables, our technique either finds a solution for such paths in one iteration or it guarantees that the path is infeasible. In contrast, existing execution based approaches may require an unacceptably large number of iterations for relatively long paths because they consider only one input variable and one branch predicate at a time and use backtracking. When the branch conditions on a path are nonlinear functions of input variables, though it may take more then one iteration to derive a desired input, the set of constraints to be solved in each iteration is linear and is solved using Gaussian elimination. This makes our technique practical and suitable for automation.
On label dependence and loss minimization in multi-label classification

Most of the multi-label classification (MLC) methods proposed in recent years intended to exploit, in one way or the other, dependencies between the class labels. Comparing to simple binary relevance learning as a baseline, any gain in performance is normally explained by the fact that this method is ignoring such dependencies. Without questioning the correctness of such studies, one has to admit that a blanket explanation of that kind is hiding many subtle details, and indeed, the underlying mechanisms and true reasons for the improvements reported in experimental studies are rarely laid bare. Rather than proposing yet another MLC algorithm, the aim of this paper is to elaborate more closely on the idea of exploiting label dependence, thereby contributing to a better understanding of MLC. Adopting a statistical perspective, we claim that two types of label dependence should be distinguished, namely conditional and marginal dependence. Subsequently, we present three scenarios in which the exploitation of one of these types of dependence may boost the predictive performance of a classifier. In this regard, a close connection with loss minimization is established, showing that the benefit of exploiting label dependence does also depend on the type of loss to be minimized. Concrete theoretical results are presented for two representative loss functions, namely the Hamming loss and the subset 0/1 loss. In addition, we give an overview of state-of-the-art decomposition algorithms for MLC and we try to reveal the reasons for their effectiveness. Our conclusions are supported by carefully designed experiments on synthetic and benchmark data.
Machine Learning for the Internet of Things Security: A Systematic Review

Internet of things (IoT) is nowadays one of the fastest growing technologies for both private and business purposes. Due to a big number of IoT devices and their rapid introduction to the market, security of things and their services is often not at the expected level. Recently, machine learning algorithms, techniques, and methods are used in research papers to enhance IoT security. In this paper, we systematically review the state-of-the art to classify the research on machine learning for the IoT security. We analysed the primary studies, identify types of studies and publication fora. Next, we have extracted all machine learning algorithms and techniques described in primary studies, and identified the most used ones to tackle IoT security issues. We classify the research into three main categories (intrusion detection, authentication and other) and describe the primary studies in detail to analyse existing relevant works and propose topics for future research.
The evolution of cooperation.

Cooperation in organisms, whether bacteria or primates, has been a difficulty for evolutionary theory since Darwin. On the assumption that interactions between pairs of individuals occur on a probabilistic basis, a model is developed based on the concept of an evolutionarily stable strategy in the context of the Prisoner's Dilemma game. Deductions from the model, and the results of a computer tournament show how cooperation based on reciprocity can get started in an asocial world, can thrive while interacting with a wide range of other strategies, and can resist invasion once fully established. Potential applications include specific aspects of territoriality, mating, and disease.