nils-herrmann/clean_scirepeval huggingface dataset:
Distributed Method of Multiplier for Coupled Lagrangian Problems: A Control Approach

In this paper, we propose a method for solving the distributed optimization problem in which the objective function is the sum of separable convex functions with linear constraints. In our approach, the primal variable is partially updated to make the Method of Multiplier algorithm distributed which is based on the suitable scaling of constraint matrix. The algorithm is then viewed as a dynamical system the convergence analysis of which is done using the passivity concepts of nonlinear control theory. The convexity of the function is related to the passivity of the non-linear functions which is in feedback with the positive real linear system.
Automatic generation of buffer overflow attack signatures: an approach based on program behavior models

Buffer overflows have become the most common target for network-based attacks. They are also the primary mechanism used by worms and other forms of automated attacks. Although many techniques have been developed to prevent server compromises due to buffer overflows, these defenses still lead to server crashes. When attacks occur repeatedly, as is common with automated attacks, these protection mechanisms lead to repeated restarts of the victim application, rendering its service unavailable. To overcome this problem, we develop a new approach that can learn the characteristics of a particular attack, and filter out future instances of the same attack or its variants. By doing so, our approach significantly increases the availability of servers subjected to repeated attacks. The approach is fully automatic, does not require source code, and has low runtime overheads. In our experiments, it was effective against most attacks, and did not produce any false positives
A transmission control scheme for media access in sensor networks

We study the problem of media access control in the novel regime of sensor networks, where unique application behavior and tight constraints in computation power, storage, energy resources, and radio technology have shaped this design space to be very different from that found in traditional mobile computing regime. Media access control in sensor networks must not only be energy efficient but should also allow fair bandwidth allocation to the infrastructure for all nodes in a multihop network. We propose an adaptive rate control mechanism aiming to support these two goals and find that such a scheme is most effective in achieving our fairness goal while being energy efficient for both low and high duty cycle of network traffic.
The ArchC Architecture Description Language and Tools

This paper presents an architecture description language (ADL) called ArchC, which is an open-source SystemC-based language that is specialized for processor architecture description. Its main goal is to provide enough information, at the right level of abstraction, in order to allow users to explore and verify new architectures, by automatically generating software tools like simulators and co-verification interfaces. ’s key features are a storage-based co-verification mechanism that automatically checks the consistency of a refined ArchC model against a reference (functional) description, memory hierarchy modeling capability, the possibility of integration with other SystemC IPs and the automatic generation of high-level SystemC simulators and assemblers. We have used ArchC to synthesize both functional and cycle-based simulators for the MIPS and Intel 8051 processors, as well as functional models of architectures like SPARC V8, TMS320C62x, XScale and PowerPC.
Project Adam: Building an Efficient and Scalable Deep Learning Training System

Large deep neural network models have recently demonstrated state-of-the-art accuracy on hard visual recognition tasks. Unfortunately such models are extremely time consuming to train and require large amount of compute cycles. We describe the design and implementation of a distributed system called Adam comprised of commodity server machines to train such models that exhibits world-class performance, scaling and task accuracy on visual recognition tasks. Adam achieves high efficiency and scalability through whole system co-design that optimizes and balances workload computation and communication. We exploit asynchrony throughout the system to improve performance and show that it additionally improves the accuracy of trained models. Adam is significantly more efficient and scalable than was previously thought possible and used 30x fewer machines to train a large 2 billion connection model to 2x higher accuracy in comparable time on the ImageNet 22,000 category image classification task than the system that previously held the record for this benchmark. We also show that task accuracy improves with larger models. Our results provide compelling evidence that a distributed systems-driven approach to deep learning using current training algorithms is worth pursuing.
Stemming of Amharic Words for Information Retrieval

This paper presents a stemmer for processing document and query words to facilitate searching databases of Amharic text. An iterative stemmer has been developed that involves the removal of both prefixes and suffixes and that also takes account of letter inconsistency and reiterative verb forms. Application of the stemmer to a test file of 1221 words suggested that appropriate stems were generated for ca. 95 per cent of them, with only limited overstemming and understemming. Lit&Ling 17/1 001-017 FINAL 23/7/02 3:33 pm Page 1
A novel secure hash algorithm for public key digital signature schemes

Hash functions are the most widespread among all cryptographic primitives, and are currently used in multiple cryptographic schemes and in security protocols. This paper presents a new Secure Hash Algorithm called (SHA-192). It uses a famous secure hash algorithm given by the National Institute of Standard and Technology (NIST).The basic design of SHA192 is to have the output length of 192.The SHA-192 has been designed to satisfy the different level of enhanced security and to resist the advanced SHA attacks. The security analysis of the SHA-192 is compared to the old one given by NIST and gives more security and excellent results as shown in our discussion. In this paper the digital signature algorithm which is given by NIST has been modified using the proposed algorithms SHA-192. Using proposed SHA-192 hash algorithm a new digital signature schemes is also proposed. The SHA-192 can be used in many applications such s public key cryptosystem, digital signcryption, message authentication code, random generator and in security architecture of upcoming wireless devices like software defined radio etc.
Semi-supervised Coupled Dictionary Learning for Person Re-identification

The desirability of being able to search for specific persons in surveillance videos captured by different cameras has increasingly motivated interest in the problem of person re-identification, which is a critical yet under-addressed challenge in multi-camera tracking systems. The main difficulty of person re-identification arises from the variations in human appearances from different camera views. In this paper, to bridge the human appearance variations across cameras, two coupled dictionaries that relate to the gallery and probe cameras are jointly learned in the training phase from both labeled and unlabeled images. The labeled training images carry the relationship between features from different cameras, and the abundant unlabeled training images are introduced to exploit the geometry of the marginal distribution for obtaining robust sparse representation. In the testing phase, the feature of each target image from the probe camera is first encoded by the sparse representation and then recovered in the feature space spanned by the images from the gallery camera. The features of the same person from different cameras are similar following the above transformation. Experimental results on publicly available datasets demonstrate the superiority of our method.
Implementation of multiport dc-dc converter-based Solid State Transformer in smart grid system

A solid-state transformer (SST) would be at least as efficient as a conventional version but would provide other benefits as well, particularly as renewable power sources become more widely used. Among its more notable strong points are on-demand reactive power support for the grid, better power quality, current limiting, management of distributed storage devices and a dc bus. Most of the nation's power grid currently operates one way - power flows from the utility to the consumer - and traditional transformers simply change voltage from one level to another. But smart transformers, based on power semiconductor switches, are more versatile. Not only can they change voltage levels, but also can effectively control the power flow in both directions. The development of a Solid State Transformer (SST) that incorporates a DC-DC multiport converter to integrate both photovoltaic (PV) power generation and battery energy storage is presented in this paper. The DC-DC stage is based on a quad active-bridge (QAB) converter which not only provides isolation for the load, but also for the PV and storage. The AC-DC stage is implemented with a pulse-width-modulated (PWM) single phase rectifier. A novel technique that complements the SISO controller by taking into account the cross coupling characteristics of the QAB converter is also presented herein. Cascaded SISO controllers are designed for the AC-DC stage. The QAB demanded power is calculated at the QAB controls and then fed into the rectifier controls in order to minimize the effect of the interaction between the two SST stages. The dynamic performance of the designed control loops based on the proposed control strategies are verified through extensive simulation of the SST average and switching models.
From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions

We propose to use the visual denotations of linguistic expressions (i.e. the set of images they describe) to define novel denotational similarity metrics, which we show to be at least as beneficial as distributional similarities for two tasks that require semantic inference. To compute these denotational similarities, we construct a denotation graph, i.e. a subsumption hierarchy over constituents and their denotations, based on a large corpus of 30K images and 150K descriptive captions.
A Low-Bandwidth Network File System

Users rarely consider running network file systems over slow or wide-area networks, as the performance would be unacceptable and the bandwidth consumption too high. Nonetheless, efficient remote file access would often be desirable over such networks---particularly when high latency makes remote login sessions unresponsive. Rather than run interactive programs such as editors remotely, users could run the programs locally and manipulate remote files through the file system. To do so, however, would require a network file system that consumes less bandwidth than most current file systems.This paper presents LBFS, a network file system designed for low-bandwidth networks. LBFS exploits similarities between files or versions of the same file to save bandwidth. It avoids sending data over the network when the same data can already be found in the server's file system or the client's cache. Using this technique in conjunction with conventional compression and caching, LBFS consumes over an order of magnitude less bandwidth than traditional network file systems on common workloads.
CYRUS: towards client-defined cloud storage

Public cloud storage has recently surged in popularity. However, cloud storage providers (CSPs) today offer fairly rigid services, which cannot be customized to meet individual users' needs. We propose a distributed, client-defined architecture that integrates multiple autonomous CSPs into one unified cloud and allows individual clients to specify their desired performance levels and share files. We design, implement, and deploy CYRUS (Client-defined privacY-protected Reliable cloUd Service), a practical system that realizes this architecture. CYRUS ensures user privacy and reliability by scattering files into smaller pieces across multiple CSPs, so that no one CSP can read users' data. We develop an algorithm that sets reliability and privacy parameters according to user needs and selects CSPs from which to download user data so as to minimize latency. To accommodate multiple autonomous clients, we allow clients to upload simultaneous file updates and detect conflicts after the fact from the client. We finally evaluate the performance of a CYRUS prototype that connects to four popular commercial CSPs in both lab testbeds and user trials, and discuss CYRUS's implications for the cloud storage market.
Synthetic on-line signature generation. Part I: Methodology and algorithms

The theoretical framework and algorithms of a novel method for the generation of synthetic on-line signatures are presented. This model-based approach combines the spectral analysis of real signatures with the Kinematic Theory of rapid human movements in order to generate totally synthetic specimens. Two different algorithms are also described in order to produce duplicated samples from the synthetic master signatures, so that the generation scheme as a whole is able to produce in a complete automatic fashion huge synthetic databases. Typical examples of synthetic specimens are presented to highlight their human-like appearance. The validation protocol and the test results are presented and discussed in a companion paper.
Voting models in random networks

A crucial problem of Social Sciences is under what conditions agreement, or disagreement, emerge in a network of interacting agents. This topic has application in many contexts, including business and marketing decisions, with potential impact on information and technological networks. In this paper we consider a particular model of interaction between a group of individuals connected through a network of acquaintances. In the first model, a node waits an exponentially time with parameter one, and when it expires it chooses one of its neighbors' at random and adopts its decision. In the second one, the node chooses the opinion which is the most adopted by its neighbors (hence, majority rule). We show how different updating rule of the agent' state lead to different emerging patterns, namely, agreement and disagreement. In addition, in the case of agreement, we provide bounds on the time to convergence for various types of graphs.
Speeding Up Automatic Hyperparameter Optimization of Deep Neural Networks by Extrapolation of Learning Curves

Deep neural networks (DNNs) show very strong performance on many machine learning problems, but they are very sensitive to the setting of their hyperparameters. Automated hyperparameter optimization methods have recently been shown to yield settings competitive with those found by human experts, but their widespread adoption is hampered by the fact that they require more computational resources than human experts. Humans have one advantage: when they evaluate a poor hyperparameter setting they can quickly detect (after a few steps of stochastic gradient descent) that the resulting network performs poorly and terminate the corresponding evaluation to save time. In this paper, we mimic the early termination of bad runs using a probabilistic model that extrapolates the performance from the first part of a learning curve. Experiments with a broad range of neural network architectures on various prominent object recognition benchmarks show that our resulting approach speeds up state-of-the-art hyperparameter optimization methods for DNNs roughly twofold, enabling them to find DNN settings that yield better performance than those chosen by human experts.
Coordination and Geometric Optimization via Distributed Dynamical Systems

Emerging applications for networked and cooperative robots motivate the study of motion coordination for groups of agents. For example, it is envisioned that groups of agents will perform a variety of useful tasks including surveillance, exploration, and environmental monitoring. This paper deals with basic interactions among mobile agents such as “move away from the closest other agent” or “move toward the furthest vertex of your own Voronoi polygon.” These simple interactions amount to distributed dynamical systems because their implementation requires only minimal information about neighboring agents. We characterize the close relationship between these distributed dynamical systems and the disk-covering and sphere-packing cost functions from geometric optimization. Our main results are: (i) we characterize the smoothness properties of these geometric cost functions, (ii) we show that the interaction laws are variations of the nonsmooth gradient of the cost functions, and (iii) we establish various asymptotic convergence properties of the laws. The technical approach relies on concepts from computational geometry, nonsmooth analysis, and nonsmooth stability theory.
Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur's entropy

The objective of image segmentation is to extract meaningful objects. A meaningful segmentation selects the proper threshold values to optimize a criterion using entropy. The conventional multilevel thresholding methods are efficient for bi-level thresholding. However, they are computationally expensive when extended to multilevel thresholding since they exhaustively search the optimal thresholds to optimize the objective functions. To overcome this problem, two successful swarm-intelligence-based global optimization algorithms, cuckoo search (CS) algorithm and wind driven optimization (WDO) for multilevel thresholding using Kapur’s entropy has been employed. For this purpose, best solution as fitness function is achieved through CS and WDO algorithm using Kapur’s entropy for optimal multilevel thresholding. A new approach of CS andWDO algorithm is used for selection of optimal threshold value. This algorithm is used to obtain the best solution or best fitness value from the initial random threshold values, and to evaluate the quality of a solution, correlation function is used. Experimental results have been examined on standard set of satellite images using various numbers of thresholds. The results based on Kapur’s entropy reveal that CS, ELR-CS and WDO method can be accurately and efficiently used in multilevel thresholding problem.
The Potential Energy of an Autoencoder

Autoencoders are popular feature learning models, that are conceptually simple, easy to train and allow for efficient inference. Recent work has shown how certain autoencoders can be associated with an energy landscape, akin to negative log-probability in a probabilistic model, which measures how well the autoencoder can represent regions in the input space. The energy landscape has been commonly inferred heuristically, by using a training criterion that relates the autoencoder to a probabilistic model such as a Restricted Boltzmann Machine (RBM). In this paper we show how most common autoencoders are naturally associated with an energy function, independent of the training procedure, and that the energy landscape can be inferred analytically by integrating the reconstruction function of the autoencoder. For autoencoders with sigmoid hidden units, the energy function is identical to the free energy of an RBM, which helps shed light onto the relationship between these two types of model. We also show that the autoencoder energy function allows us to explain common regularization procedures, such as contractive training, from the perspective of dynamical systems. As a practical application of the energy function, a generative classifier based on class-specific autoencoders is presented.
Theta*: Any-Angle Path Planning on Grids

Grids with blocked and unblocked cells are often used to represent terrain in robotics and video games. However, paths formed by grid edges can be longer than true shortest paths in the terrain since their headings are artificially constrained. We present two new correct and complete anyangle path-planning algorithms that avoid this shortcoming. Basic Theta* and Angle-Propagation Theta* are both variants of A* that propagate information along grid edges without constraining paths to grid edges. Basic Theta* is simple to understand and implement, fast and finds short paths. However, it is not guaranteed to find true shortest paths.  Theta* achieves a better worst-case complexity per vertex expansion than Basic Theta* by propagating angle ranges when it expands vertices, but is more complex, not as fast and finds slightly longer paths. We refer to Basic Theta* and Angle-Propagation Theta* collectively as Theta*. Theta* has unique properties, which we analyze in detail. We show experimentally that it finds shorter paths than both A* with post-smoothed paths and Field D* (the only other version of A* we know of that propagates information along grid edges without constraining paths to grid edges) with a runtime comparable to that of A* on grids. Finally, we extend Theta* to grids that contain unblocked cells with non-uniform traversal costs and introduce variants of Theta* which provide different tradeoffs between path length and runtime.
Affordance detection of tool parts from geometric features

As robots begin to collaborate with humans in everyday workspaces, they will need to understand the functions of tools and their parts. To cut an apple or hammer a nail, robots need to not just know the tool's name, but they must localize the tool's parts and identify their functions. Intuitively, the geometry of a part is closely related to its possible functions, or its affordances. Therefore, we propose two approaches for learning affordances from local shape and geometry primitives: 1) superpixel based hierarchical matching pursuit (S-HMP); and 2) structured random forests (SRF). Moreover, since a part can be used in many ways, we introduce a large RGB-Depth dataset where tool parts are labeled with multiple affordances and their relative rankings. With ranked affordances, we evaluate the proposed methods on 3 cluttered scenes and over 105 kitchen, workshop and garden tools, using ranked correlation and a weighted F-measure score [26]. Experimental results over sequences containing clutter, occlusions, and viewpoint changes show that the approaches return precise predictions that could be used by a robot. S-HMP achieves high accuracy but at a significant computational cost, while SRF provides slightly less accurate predictions but in real-time. Finally, we validate the effectiveness of our approaches on the Cornell Grasping Dataset  for detecting graspable regions, and achieve state-of-the-art performance.
Motion Planning for Humanoid Robots

Humanoid robotics hardware and control techniques have advanced rapidly during the last five years. Presently, several companies have announced the commercial availability of various humanoid robot prototypes. In order to improve the autonomy and overall functionality of these robots, reliable sensors, safety mechanisms, and general integrated software tools and techniques are needed. We believe that the development of practical motion planning algorithms and obstacle avoidance software for humanoid robots represents an important enabling technology. This paper gives an overview of some of our recent efforts to develop motion planning methods for humanoid robots for application tasks involving navigation, object grasping and manipulation, footstep placement, and dynamically-stable full-body motions. We show experimental results obtained by implementations running within a simulation environment as well as on actual humanoid robot hardware.