nils-herrmann/clean_scirepeval huggingface dataset:
Model predictive control of autonomous mobility-on-demand systems

In this paper we present a model predictive control (MPC) approach to optimize vehicle scheduling and routing in an autonomous mobility-on-demand (AMoD) system. In AMoD systems, robotic, self-driving vehicles transport customers within an urban environment and are coordinated to optimize service throughout the entire network. Specifically, we first propose a novel discrete-time model of an AMoD system and we show that this formulation allows the easy integration of a number of real-world constraints, e.g., electric vehicle charging constraints. Second, leveraging our model, we design a model predictive control algorithm for the optimal coordination of an AMoD system and prove its stability in the sense of Lyapunov. At each optimization step, the vehicle scheduling and routing problem is solved as a mixed integer linear program (MILP) where the decision variables are binary variables representing whether a vehicle will 1) wait at a station, 2) service a customer, or 3) rebalance to another station. Finally, by using real-world data, we show that the MPC algorithm can be run in real-time for moderately-sized systems and outperforms previous control strategies for AMoD systems.
A Joint Reconstruction and Segmentation Method for Limited-Angle X-Ray Tomography

Limited-angle computed tomography (CT) is common in industrial applications, where incomplete projection data can cause artifacts. For objects made from homogeneous materials, we propose a joint reconstruction and segmentation method that performs joint image reconstruction and segmentation directly on the projection data. We describe an alternating minimization algorithm to solve the resulting optimization problem, and we modify the primal-dual hybrid gradient algorithm for the non-convex piecewise constant Mumford-Shah model, which is a popular approximation model in biomedical image segmentation. The effectiveness of the proposed approach is validated by simulation and by application to actual micro-CT data sets.
From Instance-level Constraints to Space-Level Constraints: Making the Most of Prior Knowledge in Data Clustering

We present an improved method for clustering in the presence of very limited supervisory information, given as pairwise instance constraints. By allowing instance-level constraints to have spacelevel inductive implications, we are able to successfully incorporate constraints for a wide range of data set types. Our method greatly improves on the previously studied constrained -means algorithm, generally requiring less than half as many constraints to achieve a given accuracy on a range of real-world data, while also being more robust when over-constrained. We additionally discuss an active learning algorithm which increases the value of constraints even further.
Stochastic Dynamics for Video Infilling.

In this paper, we introduce a stochastic generation framework (SDVI) to infill long intervals in video sequences. To enhance the temporal resolution, video interpolation aims to produce transitional frames for a short interval between every two frames. Video Infilling, however, aims to complete long intervals in a video sequence. Our framework models the infilling as a constrained stochastic generation process and sequentially samples dynamics from the inferred distribution. SDVI consists of two parts: (1) a bi-directional constraint propagation to guarantee the spatial-temporal coherency among frames, (2) a stochastic sampling process to generate dynamics from the inferred distributions. Experimental results show that SDVI can generate clear and varied sequences. Moreover, motions in the generated sequence are realistic and able to transfer smoothly from the referenced start frame to the end frame.
An Energy Efficiency Feature Survey of the Intel Haswell Processor

The recently introduced Intel Xeon E5-1600 v3 and E5-2600 v3 series processors -- codenamed Haswell-EP -- implement major changes compared to their predecessors. Among these changes are integrated voltage regulators that enable individual voltages and frequencies for every core. In this paper we analyze a number of consequences of this development that are of utmost importance for energy efficiency optimization strategies such as dynamic voltage and frequency scaling (DVFS) and dynamic concurrency throttling (DCT). This includes the enhanced RAPL implementation and its improved accuracy as it moves from modeling to actual measurement. Another fundamental change is that every clock speed above AVX frequency -- including nominal frequency -- is opportunistic and unreliable, which vastly decreases performance predictability with potential effects on scalability. Moreover, we characterize significantly changed p-state transition behavior, and determine crucial memory performance data.
Dynamic circular work-stealing deque

The non-blocking work-stealing algorithm of Arora, Blumofe, and Plaxton (henceforth ABP work-stealing) is on its way to becoming the multiprocessor load balancing technology of choice in both industry and academia. This highly efficient scheme is based on a collection of array-based double-ended queues (deques) with low cost synchronization among local and stealing processes. Unfortunately, the algorithm's synchronization protocol is strongly based on the use of fixed size arrays, which are prone to overflows, especially in the multiprogrammed environments for which they are designed. We present a work-stealing deque that does not have the overflow problem.The only ABP-style work-stealing algorithm that eliminates the overflow problem is the list-based one presented by Hendler, Lev and Shavit. Their algorithm indeed deals with the overflow problem, but it is complicated, and introduces a trade-off between the space and time complexity, due to the extra work required to maintain the list.Our new algorithm presents a simple lock-free work-stealing deque, which stores the elements in a cyclic array that can grow when it overflows. The algorithm has no limit other than integer overflow (and the system's memory size) on the number of elements that may be on the deque, and the total memory required is linear in the number of elements in the deque.
Inferring the source of encrypted HTTP connections

We examine the effectiveness of two traffic analysis techniques for identifying encrypted HTTP streams. The techniques are based upon classification algorithms, identifying encrypted traffic on the basis of similarities to features in a library of known profiles. We show that these profiles need not be collected immediately before the encrypted stream; these methods can be used to identify traffic observed both well before and well after the library is created. We give evidence that these techniques will exhibit the scalability necessary to be effective on the Internet. We examine several methods of actively countering the techniques, and we find that such countermeasures are effective, but at a significant increase in the size of the traffic stream. Our claims are substantiated by experiments and simulation on over 400,000 traffic streams we collected from 2,000 distinct web sites during a two month period.
Supervised Locally Linear Embedding

Locally linear embedding (LLE) is a recently proposed method for unsupervised nonlinear dimensionality reduction. It has a number of attractive features: it does not require an iterative algorithm, and just a few parameters need to be set. Two extensions of LLE to supervised feature extraction were independently proposed by the authors of this paper. Here, both methods are unified in a common framework and applied to a number of benchmark data sets. Results show that they perform very well on high-dimensional data which exhibits a manifold structure.
Hand Rehabilitation Learning System With an Exoskeleton Robotic Glove

This paper presents a hand rehabilitation learning system, the SAFE Glove, a device that can be utilized to enhance the rehabilitation of subjects with disabilities. This system is able to learn fingertip motion and force for grasping different objects and then record and analyze the common movements of hand function including grip and release patterns. The glove is then able to reproduce these movement patterns in playback fashion to assist a weakened hand to accomplish these movements, or to modulate the assistive level based on the user's or therapist's intent for the purpose of hand rehabilitation therapy. Preliminary data have been collected from healthy hands. To demonstrate the glove's ability to manipulate the hand, the glove has been fitted on a wooden hand and the grasping of various objects was performed. To further prove that hands can be safely driven by this haptic mechanism, force sensor readings placed between each finger and the mechanism are plotted. These experimental results demonstrate the potential of the proposed system in rehabilitation therapy.
Robotic origami folding

Origami, the human art of paper sculpture, is a fresh challenge for the field of robotic manipulation, and provides a concrete example for many difficult and general manipulation problems. This thesis will present some initial results, including the world’s first origami-folding robot, some new theorems about foldability, definition of a simple class of origami for which I have designed a complete automatic planner, analysis of the kinematics of more complicated folds, and some observations about the configuration spaces of compound spherical closed chains.
Automatic Domain Adaptation for Parsing

Current statistical parsers tend to perform well only on their training domain and nearby genres. While strong performance on a few related domains is sufficient for many situations, it is advantageous for parsers to be able to generalize to a wide variety of domains. When parsing document collections involving heterogeneous domains (e.g. the web), the optimal parsing model for each document is typically not obvious. We study this problem as a new task —multiple source parser adaptation. Our system trains on corpora from many different domains. It learns not only statistics of those domains but quantitative measures of domain differences and how those differences affect parsing accuracy. Given a specific target text, the resulting system proposes linear combinations of parsing models trained on the source corpora. Tested across six domains, our system outperforms all non-oracle baselines including the best domain-independent parsing model. Thus, we are able to demonstrate the value of customizing parsing models to specific domains.
Recurrent Neural Networks for Short-Term Load Forecasting

A short-term load forecast is the prediction of the consumption of resources in a distribution network in the near future. The supplied resource can be of any kind, such as electricity in power grids or telephone service in telecommunication networks. An accurate forecast of the demand is of utmost importance for the planning of facilities, optimization of day-to-day operations, and an effective management of the available resources. In the context of energy and telecommunication networks, the load data are usually represented as real-valued time series characterized by strong temporal dependencies and seasonal patterns. We begin by reviewing several methods that have been adopted in the past years for the task of short-term load forecast and we highlight their main advantages and limitations. We then introduce the framework of recurrent neural networks, a particular class of artificial neural networks specialized in the processing of sequential/temporal data. We explain how recurrent neural networks can be an effective tool for prediction, especially in those cases where the extent of the time dependencies is unknown a-priori.
LANMAR: landmark routing for large scale wireless ad hoc networks with group mobility

In this paper, we present a novel routing protocol for wireless ad hoc networks - Landmark Ad Hoc Routing (LANMAR). LANMAR combines the features of Fisheye State Routing (FSR) and Landmark routing. The key novelty is the use of landmarks for each set of nodes which move as a group (e.g., a team of co-workers at a convention or a tank battalion in the battlefield) in order to reduce routing update overhead. Like in FSR, nodes exchange link state only with their neighbors. Routes within Fisheye scope are accurate, while routes to remote groups of nodes are "summarized" by the corresponding landmarks. A packet directed to a remote destination initially aims at the Landmark; as it gets closer to destination it eventually switches to the accurate route provided by Fisheye. Simulation experiments show that LANMAR provides efficient and scalable routing in large, mobile, ad hoc environments in which group mobility applies.
Adaptive Speculative Processing of Out-of-Order Event Streams

Distributed event-based systems are used to detect meaningful events with low latency in high data-rate event streams that occur in surveillance, sports, finances, etc. However, both known approaches to dealing with the predominant out-of-order event arrival at the distributed detectors have their shortcomings: buffering approaches introduce latencies for event ordering, and stream revision approaches may result in system overloads due to unbounded retraction cascades.
 This article presents an adaptive speculative processing technique for out-of-order event streams that enhances typical buffering approaches. In contrast to other stream revision approaches developed so far, our novel technique encapsulates the event detector, uses the buffering technique to delay events but also speculatively processes a portion of it, and adapts the degree of speculation at runtime to fit the available system resources so that detection latency becomes minimal.
 Our technique outperforms known approaches on both synthetical data and real sensor data from a realtime locating system (RTLS) with several thousands of out-of-order sensor events per second. Speculative buffering exploits system resources and reduces latency by 40% on average.
Substrate switching noise analysis and layout/circuit considerations in monolithic power converters

A monolithic power converter has the analog control circuit and the switching power stage integrated on the same silicon die. The noise generated by the switching power stage can affect the sensitive analog circuits through the shared common substrate and degrade the system performance. Although much literature has been published about the substrate noise generated by digital circuits in mixed-signal systems, the analysis methods and results may not apply for monolithic power converters. Monolithic power converters use different process technologies, have much higher noise peak voltages, and have noise that is synchronized to the pulse-width modulator (PWM) switching period. To handle strong switching noise that exists in monolithic power converters, analysis of the sources of the switching noise, the noise paths, and the noise effects on analog circuitry is necessary. The noise effects on the PWM control circuit are explained in this paper, and a circuit and layout design guide helps to improve the substrate switching noise immunity. The experimental results show the noise immunity improvements.
Semantic Motion Segmentation Using Dense CRF Formulation

While the literature has been fairly dense in the areas of scene understanding and semantic labeling there have been few works that make use of motion cues to embellish semantic performance and vice versa. In this paper, we address the problem of semantic motion segmentation, and show how semantic and motion priors augments performance. We propose an algorithm that jointly infers the semantic class and motion labels of an object. Integrating semantic, geometric and optical flow based constraints into a dense CRF-model we infer both the object class as well as motion class, for each pixel. We found improvement in performance using a fully connected CRF as compared to a standard clique-based CRFs. For inference, we use a Mean Field approximation based algorithm. Our method outperforms recently proposed motion detection algorithms and also improves the semantic labeling compared to the state-of-the-art Automatic Labeling Environment algorithm on the challenging KITTI dataset especially for object classes such as pedestrians and cars that are critical to an outdoor robotic navigation scenario.
A Solution to Forecast Demand Using Long Short-Term Memory Recurrent Neural Networks for Time Series Forecasting

This study focuses on predicting demand based on data collected which spans across many periods. To help our client build a solution to forecast demand effectively, we developed a model using Long Short-Term Memory (LSTM) Networks, a type of Recurrent Neural Network, to estimate demand based on historical patterns. While there may be many available models for dealing with a time series problem, the LSTM model is relatively new and highly sophisticated to its counterpart. By comparing this study which works excellently for sequential learning, to the other existing models and techniques, we are now closer to solving at least one of many complications apparent across industries. The study becomes even more important for supply chain professionals, especially those in the purchase department, as they can now rely on a highly accurate model instead of basing their forecasts purely on intuition and recent customer behavior. Using data from the M3-Competition, which is a competition conducted by the International Institute of Forecasters, we develop a working framework to help our client compare their existing models (feedforward neural network and exponential smoothing model) with our LSTM model.
FINDING TIME QUANTUM OF ROUND ROBIN CPU SCHEDULING ALGORITHM IN GENERAL COMPUTING SYSTEMS USING INTEGER PROGRAMMING

In Round Robin Scheduling, the time quantum is fixed and then processes are scheduled such that no process get CPU time more than one time quantum in one go. If time quantum is too large, the response time of the processes is too much which may not be tolerated in interactive environment. If time quantum is too small, it causes unnecessarily frequent context switch leading to more overheads resulting in less throughput. In this paper, a method using integer programming has been proposed to solve equations that decide a value that is neither too large nor too small such that every process has reasonable response time and the throughput of the system is not decreased due to unnecessarily context switches.
Coordinated Control of the Energy Router-Based Smart Home Energy Management System

Home area energy networks will be an essential part of the future Energy Internet in terms of energy saving, demand-side management and stability improvement of the distribution network, while an energy router will be the perfect choice to serve as an intelligent and multi-functional energy interface between the home area energy network and power grid. This paper elaborates on the design, analysis and implementation of coordinated control of the low-voltage energy router-based smart home energy management system (HEMS). The main contribution of this paper is to develop a novel solution to make the energy router technically feasible and practical for the HEMS to make full use of the renewable energy sources (RESs), while maintaining “operational friendly and beneficial” to the power grid. The behaviors of the energy router-based HEMS in correlation with the power grid are investigated, then the coordinated control scheme composed of a reference voltage and current compensation strategy and a fuzzy logic control-based power management strategy is developed. The system model is built on the MATLAB/Simulink platform, simulation results have demonstrated that the presented control scheme is a strong performer in making full use of the RES generations for the HEMS while maintaining the operational stability of the whole system, as well as in collaboration with the power grid to suppress the impact of RES output fluctuations and load consumption variations.
Advanced automated SQL injection attacks and defensive mechanisms

SQL Injection vulnerabilities still exist even after almost two decades that it first appeared. In spite of numerous prevention methodologies being used today, web applications still tend to be vulnerable to SQL injection attacks. Technology has improved drastically over the past few years and computers have certainly brought a great impact on our lifestyle. The computer applications and their usage over the web are myriad. It is quite evident that the in the near future, the usage of computers would relatively be higher than what we are witnessing today. A wide variety of data such as credit information, military data, human communication data, and countless types of data is shared over the far-flung computer networks. As the usage and reliability on computers increase, the threat to sensitive data likewise increases. The challenges with the cyber security when dealing with sensitive information is now a nightmare. To help understand the threats and the severity of exploits deployed, the paper provides proof of concepts for exploits carried out to compromise web applications and how the databases are exploited using the SQL injection methodologies. The SQL injection vulnerabilities in the web applications are surprisingly very vast and this is definitely is a huge security threat to personal data of people that is stored on web. In this paper, the methods used in information gathering, how the security is breached, and how payloads are used to exploit web applications are explained using the Kali Linux. In addition, an analysis is carried out on how the websites are comprised. Advanced methods on how to defend SQL injections are briefly justified. For the readers to understand better, a real time scenario of a penetration tester and a database server is set up with a few suppositions, and the commands that dodge the security characteristics and manipulate the databases are explicated.
Early Steps Towards Web Scale Information Extraction with LODIE

SPRING 2015 55 Extracting information from a gigantic data source such as the web has been considered a major research challenge, and over the years many different approaches (Etzioni et al. 2004; Banko et al. 2007; Carlson et al. 2010; Freedman and Ramshaw 2011; Nakashole, Theobald, and Weikum 2011) have been proposed. Nevertheless, the current state of the art has mainly addressed tasks for which resources for training are available (for example, the TAP ontology in the paper by Etzioni and colleagues [2004]) or use generic patterns to extract generic facts (for example, Banko et al. [2007]; OpenCalais.com). The limited availability of resources for training has so far prevented the study of the generalized use of large-scale resources to port to specific user information needs. The linked open data information-extraction (LODIE) project focuses on the study of IE models and algorithms able to perform efficient user-centered web-scale learning by exploiting linked open data (LOD). In this article we will highlight the initial steps of the LODIE project, focusing on a specific IE task, wrapper induction (WI), which consists of automatically learning wrappers for uniform web pages, that is, pages from one website, usually generated with the same script and all describing the same type of entity. We show results on the WI task, exploiting linked data obtained from DBpedia as learning material. Linked data is a recom-