nils-herrmann/clean_scirepeval huggingface dataset:
A Formal Model for a System's Attack Surface

Practical software security metrics and measurements are essential to the development of secure software [18]. In this paper, we propose to use a software system’s attack surface measurement as an indicator of the system’s security; the larger the attack surface, the more insecure the system. We formalize the notion of a system’s attack surface using an I/O automata model of the system [15] and define a quantitative measure of the attack surface in terms of three kinds of resources used in attacks on the system: methods, channels, and data. We demonstrate the feasibility of our approach by measuring the attack surfaces of two open source FTP daemons and two IMAP servers. Software developers can use our attack surface measurement method in the software development process and software consumers can use the method in their decision making process. This research was sponsored by the US Army Research Office (ARO) under contract no. DAAD190210389, SAP Labs, LLC under award no. 1010751, and the Software Engineering Institute. The views and conclusions contained in this document are those of the author and should not be interpreted as representing the official policies, either expressed or implied, of any sponsoring institution, the U.S. government or any other entity.
Deep End2End Voxel2Voxel Prediction

Over the last few years deep learning methods have emerged as one of the most prominent approaches for video analysis. However, so far their most successful applications have been in the area of video classification and detection, i.e., problems involving the prediction of a single class label or a handful of output variables per video. Furthermore, while deep networks are commonly recognized as the best models to use in these domains, there is a widespread perception that in order to yield successful results they often require time-consuming architecture search, manual tweaking of parameters and computationally intensive preprocessing or post-processing methods. In this paper we challenge these views by presenting a deep 3D convolutional architecture trained end to end to perform voxel-level prediction, i.e., to output a variable at every voxel of the video. Most importantly, we show that the same exact architecture can be used to achieve competitive results on three widely different voxel-prediction tasks: video semantic segmentation, optical flow estimation, and video coloring. The three networks learned on these problems are trained from raw video without any form of preprocessing and their outputs do not require post-processing to achieve outstanding performance. Thus, they offer an efficient alternative to traditional and much more computationally expensive methods in these video domains.
Production-run software failure diagnosis via hardware performance counters

Sequential and concurrency bugs are widespread in deployed software. They cause severe failures and huge financial loss during production runs. Tools that diagnose production-run failures with low overhead are needed. The state-of-the-art diagnosis techniques use software instrumentation to sample program properties at run time and use off-line statistical analysis to identify properties most correlated with failures. Although promising, these techniques suffer from high run-time overhead, which is sometimes over 100%, for concurrency-bug failure diagnosis and hence are not suitable for production-run usage.
 We present PBI, a system that uses existing hardware performance counters to diagnose production-run failures caused by sequential and concurrency bugs with low overhead. PBI is designed based on several key observations. First, a few widely supported performance counter events can reflect a wide variety of common software bugs and can be monitored by hardware with almost no overhead. Second, the counter overflow interrupt supported by existing hardware and operating systems provides a natural and effective mechanism to conduct event sampling at user level. Third, the noise and non-determinism in interrupt delivery complements well with statistical processing.
 We evaluate PBI using 13 real-world concurrency and sequential bugs from representative open-source server, client, and utility programs, and 10 bugs from a widely used software-testing benchmark. Quantitatively, PBI can effectively diagnose failures caused by these bugs with a small overhead that is never higher than 10%. Qualitatively, PBI does not require any change to software and presents a novel use of existing hardware performance counters.
libtissue - implementing innate immunity

In a previous paper the authors argued the case for incorporating ideas from innate immunity into artificial immune systems (AISs) and presented an outline for a conceptual framework for such systems. A number of key general properties observed in the biological innate and adaptive immune systems were highlighted, and how such properties might be instantiated in artificial systems was discussed in detail. The next logical step is to take these ideas and build a software system with which AISs with these properties can be implemented and experimentally evaluated. This paper reports on the results of that step - the libtissue system.
Bricolage versus breakthrough : distributed and embedded agency in technology entrepreneurship

We develop a perspective on technology entrepreneurship as involving agency that is distributed across different kinds of actors. Each actor becomes involved with a technology, and, in the process, generates inputs that result in the transformation of an emerging technological path. The steady accumulation of inputs to a technological path generates a momentum that enables and constrains the activities of distributed actors. In other words, agency is not only distributed, but it is embedded as well. We explicate this perspective through a comparative study of processes underlying the emergence of wind turbines in Denmark and in United States. Through our comparative study, we flesh out “bricolage” and “breakthrough” as contrasting approaches to the engagement of actors in shaping technological paths.
Simulation Framework of Wireless Sensor Network (WSN) Using MATLAB/SIMULINK Software

A wireless sensor network consists of spatially distributed autonomous sensors to cooperatively monitor physical or environmental conditions, such as temperature, sound, vibration, pressure, motion or pollutants. The development of wireless sensor networks was motivated by military applications such as battlefield surveillance. They are now used in many industrial and civilian application areas, including industrial process monitoring and control, machine health monitoring, environment and habitat monitoring, healthcare applications, home automation, and traffic control [1-2].
Immune System Based Intrusion Detection System

The threats and intrusions in IT systems can basically be compared to human diseases with the difference that the human body has an effective way to deal with them, what still need to be designed for IT systems. The human immune system (HIS) can detect and defend against yet unseen intruders, is distributed, adaptive and multilayered to name only a few of its features. Our immune system incorporates a powerful and diverse set of characteristics which are very interesting to use in the design of Intrusion Detection Systems (IDS). The authors propose therefore a hybrid intrusion detection system which combines host based and network based components but giving the focus to the host based intrusion detection as it is similar to the HIS. The proposed intrusion detection system will use the concepts of the artificial immune systems (AIS) which is a promising biologically inspired computing model based on the HIS. This paper presents an intrusion detection system based on the model of the human immune system and which will use the artificial immune systems paradigm. Furthermore the paper will also introduce some yet unused AIS concepts that can be applied to improve the effectiveness of IDS.
Syntactic ambiguity resolution in discourse: modeling the effects of referential context and lexical frequency.

Sentences with temporarily ambiguous reduced relative clauses (e.g., The actress selected by the director believed that...) were preceded by discourse contexts biasing a main clause or a relative clause. Eye movements in the disambiguating region (by the director) revealed that, in the relative clause biasing contexts, ambiguous reduced relatives were no more difficult to process than unambiguous reduced relatives or full (unreduced) relatives. Regression analyses demonstrated that the effects of discourse context at the point of ambiguity (e.g., selected) interacted with the past participle frequency of the ambiguous verb. Reading times were modeled using a constraint-based competition framework in which multiple constraints are immediately integrated during parsing and interpretation. Simulations suggested that this framework reconciles the superficially conflicting results in the literature on referential context effects on syntactic ambiguity resolution.
Cognitive Architecture and Instructional Design

Cognitive load theory has been designed to provide guidelines intended to assist in the presentation of information in a manner that encourages learner activities that optimize intellectual performance. The theory assumes a limited capacity working memory that includes partially independent subcomponents to deal with auditory/verbal material and visual/2or 3-dimensional information as well as an effectively unlimited long-term memory, holding schemas that vary in their degree of automation. These structures and functions of human cognitive architecture have been used to design a variety of novel instructional procedures based on the assumption that working memory load should be reduced and schema construction encouraged. This paper reviews the theory and the instructional designs generated by it.
BotMiner: Clustering Analysis of Network Traffic for Protocol- and Structure-Independent Botnet Detection

Botnets are now the key platform for many Internet attacks, such as spam, distributed denial-of-service (DDoS), identity theft, and phishing. Most of the current botnet detection approaches work only on specific botnet command and control (C&C) protocols (e.g., IRC) and structures (e.g., centralized), and can become ineffective as botnets change their C&C techniques. In this paper, we present a general detection framework that is independent of botnet C&C protocol and structure, and requires noa priori knowledge of botnets (such as captured bot binaries and hence the botnet signatures, and C&C server names/addresses). We start from the definition and essential properties of botnets. We define a botnet as acoordinated groupof malware instances that arecontrolled via C&C communication channels. The essential properties of a botnet are that the bots communicate with some C&C servers/peers, perform malicious activities, and do so in a similar or correlated way. Accordingly, our detection framework clusters similar communication traffic and similar malicious traffic, and performs cross cluster correlation to identify the hosts that share both similar communication patterns and similar malicious activity patterns. These hosts are thus bots in the monitored network. We have implemented our BotMiner prototype system and evaluated it using many real network traces. The results show that it can detect real-world botnets ( IRC-based, HTTP-based, and P2P botnets including Nugache and Storm worm), and has a very low false positive rate.
Pathology , pseudopathology , and the Dark Triad of personality

The Dark Triad traits (i.e., psychopathy, Machiavellianism, and narcissism) have traditionally been viewed as undesirable and pathological. In contrast, an evolutionary perspective suggests that traits like these might be pseudopathologies; traits that society actively dislikes in that they pose a threat to the collective good. We examined (N = 290) how the Dark Triad traits related to intrapersonal (i.e., behavioral dysfunction), quasibehavioral (i.e., reactive and proactive aggression), and interpersonal (i.e., communal and exchange orientation) factors. Psychopathy predicted high rates of behavioral dysregulation and both forms of aggression. Psychopathy and Machiavellianism showed an aversion towards communalism but an exchange orientation to social relationships. Lastly, individual differences in the Dark Triad traits accounted for part (5–22%) of the sex differences in social strategies and aggression. The theoretical implications of these findings are discussed in, and in support of, an evolutionary paradigm.
Uptane: Security and Customizability of Software Updates for Vehicles

A widely accepted premise is that complex software frequently contains bugs that can be remotely exploited by attackers. When this software is on an electronic control unit (ECU) in a vehicle, exploitation of these bugs can have life or death consequences. Since software for vehicles is likely to proliferate and grow more complex in time, the number of exploitable vulnerabilities will increase. As a result, manufacturers are keenly aware of the need to quickly and efficiently deploy updates so that software vulnerabilities can be remedied as soon as possible.
QuadricSLAM: Dual Quadrics From Object Detections as Landmarks in Object-Oriented SLAM

In this letter, we use two-dimensional (2-D) object detections from multiple views to simultaneously estimate a 3-D quadric surface for each object and localize the camera position. We derive a simultaneous localization and mapping (SLAM) formulation that uses dual quadrics as 3-D landmark representations, exploiting their ability to compactly represent the size, position and orientation of an object, and show how 2-D object detections can directly constrain the quadric parameters via a novel geometric error formulation. We develop a sensor model for object detectors that addresses the challenge of partially visible objects, and demonstrate how to jointly estimate the camera pose and constrained dual quadric parameters in factor graph based SLAM with a general perspective camera.
Variance Stabilization for Noisy+Estimate Combination in Iterative Poisson Denoising

We denoise Poisson images with an iterative algorithm that progressively improves the effectiveness of variance-stabilizing transformations (VST) for Gaussian denoising filters. At each iteration, a combination of the Poisson observations with the denoised estimate from the previous iteration is treated as scaled Poisson data and filtered through a VST scheme. Due to the slight mismatch between a true scaled Poisson distribution and this combination, a special exact unbiased inverse is designed. We present an implementation of this approach based on the BM3D Gaussian denoising filter. With a computational cost at worst twice that of the noniterative scheme, the proposed algorithm provides significantly better quality, particularly at low signal-to-noise ratio, outperforming much costlier state-of-the-art alternatives.
Design and simulation of a 1 to 14 GHz broadband electromagnetic compatibility DRGH antenna

This work presents the design and simulation of a 1-14 GHz double ridged guide horn antenna (DRGH) with coaxial input feed section. This antenna, due to the large frequency bands required by standards, is suitable to be used in electromagnetic compatibility (EMC) testing. The horn-antenna model analyzed dates back to the early 1970s when J. L. Kerr suggested the use of a feed horn launcher whose dimensions where found experimentally. Although this type of horn has become the preferred test antenna of EMC testing for the 1-18 GHz range, which has been widely used for over four decades, no explanation of the effect of the launcher dimensions and the ridges shape into the flared section on the antenna parameters was found in the open literature. To investigate this phenomenon in detail, the entire horn has been modeled, including the coaxial feed using a time domain method. The simulations indicate that deficiencies in the radiation pattern start to appear at frequencies above 6 GHz and it starts to split at frequencies above 12 GHz. In spite those problems, the designed antenna can work well up to 14 GHz.
Supply Chain Management ( SCM ) and Organizational Key Factors for Successful Implementation of Enterprise Resource Planning ( ERP ) Systems

This paper reports some findings of an on-going research into ERP implementation issues. A part of the research, which this paper reports, consists of reviewing several cases of successful implementations of ERP systems. The analysis of all these cases revealed that the critical factors for successful implementation of ERP systems fall into two main categories: technological and organizational factors. However, the organizational factors seem to be more important than the technological ones as far as the successful implementation of ERP systems under SCM is concerned.
Fast and Concurrent RDF Queries with RDMA-Based Distributed Graph Exploration

Many public knowledge bases are represented and stored as RDF graphs, where users can issue structured queries on such graphs using SPARQL. With massive queries over large and constantly growing RDF data, it is imperative that an RDF graph store should provide low latency and high throughput for concurrent query processing. However, prior systems still experience high perquery latency over large datasets and most prior designs have poor resource utilization such that each query is processed in sequence. We present Wukong, a distributed graph-based RDF store that leverages RDMA-based graph exploration to provide highly concurrent and low-latency queries over large data sets. Wukong is novel in three ways. First, Wukong provides an RDMA-friendly distributed key/value store that provides differentiated encoding and fine-grained partitioning of graph data to reduce RDMA transfers. Second, Wukong leverages full-history pruning to avoid the cost of expensive final join operations, based on the observation that the cost of one-sided RDMA operations is largely oblivious to the payload size to a certain extent. Third, countering conventional wisdom of preferring migration of execution over data, Wukong seamlessly combines data migration for low latency and execution distribution for high throughput by leveraging the low latency and high throughput of onesided RDMA operations, and proposes a worker-obliger model for efficient load balancing. Evaluation on a 6-node RDMA-capable cluster shows that Wukong significantly outperforms state-of-the-art systems like TriAD and Trinity.RDF for both latency and throughput, usually at the scale of orders of magnitude. Short for Sun Wukong, who is known as the Monkey King and is a main character in the Chinese classical novel “Journey to the West”. Since Wukong is known for his extremely fast speed (21,675 kilometers in one somersault) and the ability to fork himself to do massive multi-tasking, we term our system as Wukong. The source code and a brief instruction on how to install Wukong is available at http://ipads.se.sjtu.edu.cn/projects/wukong.
Cancer chemoprevention with dietary phytochemicals

Chemoprevention refers to the use of agents to inhibit, reverse or retard tumorigenesis. Numerous phytochemicals derived from edible plants have been reported to interfere with a specific stage of the carcinogenic process. Many mechanisms have been shown to account for the anticarcinogenic actions of dietary constituents, but attention has recently been focused on intracellular-signalling cascades as common molecular targets for various chemopreventive phytochemicals.
Implementation of optimal control for ball and beam system

The ball and beam system is a widely used tool for learning classical as well as advanced control techniques. The system with highly non linear characteristics is an excellent tool to represent unstable systems. The paper presents an optimal control strategy for controlling the position of ball on the beam. The system is an open loop unstable system. The control problem is a challenging one as the ball position continuously changes with the beam angle. The control strategy includes PID control for both ball position control and servo position control. The control parameters are tuned using Genetic Algorithm. The results are verified experimentally using real time ball and beam system.
Distance Transforms of Sampled Functions

We describe linear-time algorithms for solving a class of problems that involve transforming a cost function on a grid using spatial information. These problems can be viewed as a generalization of classical distance transforms of binary images, where the binary image is replaced by an arbitrary function on a grid. Alternatively they can be viewed in terms of the minimum convolution of two functions, which is an important operation in grayscale morphology. A consequence of our techniques is a simple and fast method for computing the Euclidean distance transform of a binary image. Our algorithms are also applicable to Viterbi decoding, belief propagation, and optimal control.
Graphene plasmonics for terahertz to mid-infrared applications.

In recent years, we have seen a rapid progress in the field of graphene plasmonics, motivated by graphene's unique electrical and optical properties, tunability, long-lived collective excitation and its extreme light confinement. Here, we review the basic properties of graphene plasmons: their energy dispersion, localization and propagation, plasmon-phonon hybridization, lifetimes and damping pathways. The application space of graphene plasmonics lies in the technologically significant, but relatively unexploited terahertz to mid-infrared regime. We discuss emerging and potential applications, such as modulators, notch filters, polarizers, mid-infrared photodetectors, and mid-infrared vibrational spectroscopy, among many others.
Control of hybrid AC/DC microgrid involving energy storage, renewable energy and pulsed loads

This paper proposes the coordinated control of a hybrid AC/DC power system with renewable energy source, energy storages and critical loads. The hybrid microgrid consists of both AC and DC sides. A synchronous generator and a PV farm supply power to the system's AC and DC sides, respectively. A bidirectional fully controlled AC/DC converter with active and reactive power decoupling technique is used to link the AC bus with the DC bus while regulating the system voltage and frequency. A DC/DC boost converter with a maximum power point tracking (MPPT) function is implemented to maximize the energy generation from the PV farm. Current controlled bidirectional DC/DC converters are applied to connect each lithium-ion battery bank to the DC bus. Lithium-ion battery banks act as energy storage devices that serve to increase the system stability by absorbing or injecting power to the grid as ancillary services. The proposed system can function in both grid-connected mode and islanding mode. Power electronic converters with different control strategies are analyzed in both modes in detail. Simulation results in MATLAB Simulink verify that the proposed topology is coordinated for power management in both AC and DC sides under critical loads with high efficiency, reliability, and robustness under both grid-connected and islanding modes.