nils-herrmann/clean_scirepeval huggingface dataset:
StackGuard: Automatic Adaptive Detection and Prevention of Buffer-Overflow Attacks

This paper presents a systematic solution to the persistent problem of buffer overflow attacks. Buffer overflow attacks gained notoriety in 1988 as part of the Morris Worm incident on the Internet. While it is fairly simple to fix individual buffer overflow vulnerabilities, buffer overflow attacks continue to this day. Hundreds of attacks have been discovered, and while most of the obvious vulnerabilities have now been patched, more sophisticated buffer overflow attacks continue to emerge. We describe StackGuard: a simple compiler technique that virtually eliminates buffer overflow vulnerabilities with only modest performance penalties. Privileged programs that are recompiled with the StackGuard compiler extension no longer yield control to the attacker, but rather enter a fail-safe state. These programs require no source code changes at all, and are binary-compatible with existing operating systems and libraries. We describe the compiler technique (a simple patch to gcc), as well as a set of variations on the technique that tradeoff between penetration resistance and performance. We present experimental results of both the penetration resistance and the performance impact of this technique. This research is partially supported by DARPA contracts F3060296-1-0331 and F30602-96-1-0302. Ryerson Polytechnic University
Internet of Things: a New Application for Intelligent Traffic Monitoring System

Constructing an intelligent traffic monitoring system firstly depends on automatic identification for vehicles. At present, automatic identification technology based on image and vehicle license plate is going to fall in the trap due to its low recognition rate and affection by adverse weather. Thus it is necessary to apply new technologies to solve this problem, and technologies based on Internet of Things provide a new approach for it. In this paper, we explored this issue and proposed a feasible scheme. At first, we took global unique EPC code as identity identification of vehicles in stead of vehicle license plate and utilized RFID reader to read EPC code by RF electromagnetic wave, which completely solved the problem of no all-weather operations. Secondly, we obtained positioning information of vehicles by using GPS technology. Thirdly, because GPRS provides high-speed wireless IP services for mobile users, fully supports the TCP/IP, we took wireless GPRS scheme to transmit data of mobile objects. The realization of automatic detection and transmission of data provided a fundamental guarantee for constructing an intelligent traffic monitoring system. And then, we designed and discussed in turn its network architecture, data flow analysis, hardware logic structure, software flow, as well as its intelligent decision-making module. Research and design show that it is feasible and inexpensive to construct an intelligent traffic monitoring system based on Internet of Things, and the intelligent traffic monitoring system based on Internet of Things has a number of advantages such low cost, high reliability, never affected by adverse weather, all weather operations etc. Therefore, it will have a broad applying
Localization and Tracking of Implantable Biomedical Sensors

Implantable sensor systems are effective tools for biomedical diagnosis, visualization and treatment of various health conditions, attracting the interest of researchers, as well as healthcare practitioners. These systems efficiently and conveniently provide essential data of the body part being diagnosed, such as gastrointestinal (temperature, pH, pressure) parameter values, blood glucose and pressure levels and electrocardiogram data. Such data are first transmitted from the implantable sensor units to an external receiver node or network and then to a central monitoring and control (computer) unit for analysis, diagnosis and/or treatment. Implantable sensor units are typically in the form of mobile microrobotic capsules or implanted stationary (body-fixed) units. In particular, capsule-based systems have attracted significant research interest recently, with a variety of applications, including endoscopy, microsurgery, drug delivery and biopsy. In such implantable sensor systems, one of the most challenging problems is the accurate localization and tracking of the microrobotic sensor unit (e.g., robotic capsule) inside the human body. This article presents a literature review of the existing localization and tracking techniques for robotic implantable sensor systems with their merits and limitations and possible solutions of the proposed localization methods. The article also provides a brief discussion on the connection and cooperation of such techniques with wearable biomedical sensor systems.
Mondor's disease of penis: a forgotten disease.

Mondor's disease of penis is an uncommon genital condition involving the penile sulcus in a circumferential manner. Although this disorder is almost always self limited, it is associated with considerable psychological stress and sexual disharmony.



All patients attending the sexually transmitted disease clinic during 1991-2003 were examined for evidence of Mondor's disease of penis. Detailed history and clinical findings were recorded in a specially designed proforma. Histopathological examination and staining with CD31 and CD34 monoclonal antibodies was also done in 11 patients.



18 out of 1296 patients attending the sexually transmitted diseases (STD) clinic during the study period were found to have penile Mondor's disease, giving an incidence of 1.39%. 17 patients had history of one or more episodes of STDs. Histopathological specimens showed prominent vessels with plump endothelial cells and thickened blood vessel walls. The occasional vessel showed complete occlusion of its lumen.



In our study we did not find any evidence of lymphatic involvement. Non-lymphatic vessels, mainly veins, were predominantly involved. In our opinion Mondor's phlebitis of penis or Mondor's disease of penis are better terms to describe the condition rather than non-venereal sclerosing lymphangitis.
Firewall Policy Advisor for Anomaly Discovery and Rule Editing

Firewalls are core elements in network security. However, managing firewall rules, especially for enterprize networks, has become complex and error-prone. Firewall filtering rules have to be carefully written and organized in order to correctly implement the security policy. In addition, inserting or modifying a filtering rule requires thorough analysis of the relationship between this rule and other rules in order to determine the proper order of this rule and commit the updates. In this paper, we present a set of techniques and algorithms that provide (1) automatic discovery of firewall policy anomalies to reveal rule conflicts and potential problems in legacy firewalls, and (2) anomaly-free policy editing for rule insertion, removal and modification. This is implemented in a user-friendly tool called “Firewall Policy Advisor.” The Firewall Policy Advisor significantly simplifies the management of any generic firewall policy written as filtering rules, while minimizing network vulnerability due to firewall rule misconfiguration.
Articulated pose estimation with flexible mixtures-of-parts

We describe a method for human pose estimation in static images based on a novel representation of part models. Notably, we do not use articulated limb parts, but rather capture orientation with a mixture of templates for each part. We describe a general, flexible mixture model for capturing contextual co-occurrence relations between parts, augmenting standard spring models that encode spatial relations. We show that such relations can capture notions of local rigidity. When co-occurrence and spatial relations are tree-structured, our model can be efficiently optimized with dynamic programming. We present experimental results on standard benchmarks for pose estimation that indicate our approach is the state-of-the-art system for pose estimation, outperforming past work by 50% while being orders of magnitude faster.
PerfAugur: Robust diagnostics for performance anomalies in cloud services

Cloud platforms involve multiple independently developed components, often executing on diverse hardware configurations and across multiple data centers. This complexity makes tracking various key performance indicators (KPIs) and manual diagnosing of anomalies in system behavior both difficult and expensive. In this paper, we describe PerfAugur, an automated system for mining service logs to identify anomalies and help formulate data-driven hypotheses. PerfAugur includes a suite of efficient mining algorithms for detecting significant anomalies in system behavior, along with potential explanations for such anomalies, without the need for an explicit supervision signal. We perform extensive experimental evaluation using both synthetic and real-life data sets, and present detailed case studies showing the impact of this technology on operations of the Windows Azure Service.
Coding, Analysis, Interpretation, and Recognition of Facial Expressions

We describe a computer vision system for observing facial motion by using anoptimal estimationoptical flow method coupled with geometric, physical and motion-based dynamic models describing the facial structure. Our method produces a reliable parametric representation of the face's independent muscle action groups, as well as an accurate estimate of facial motion. Previous efforts at analysis of facial expression have been based on the Facial Action Coding System (FACS), a representation developed in order to allow human psychologists to code expression from static pictures. To avoid use of this heuristic coding scheme, we have used our computer vision system to probabilistically characterize facial motion and muscle activation in an experimental population, thus deriving a new, more accurate representation of human facial expressions that we call FACS +. Finally, we show how this method can be used for coding, analysis, interpretation, and recognition of facial expressions.
Strategies for distant speech recognitionin reverberant environments

Reverberation and noise are known to severely affect the automatic speech recognition (ASR) performance of speech recorded by distant microphones. Therefore, we must deal with reverberation if we are to realize high-performance hands-free speech recognition. In this paper, we review a recognition system that we developed at our laboratory to deal with reverberant speech. The system consists of a speech enhancement (SE) front-end that employs long-term linear prediction-based dereverberation followed by noise reduction. We combine our SE front-end with an ASR back-end that uses neural networks for acoustic and language modeling. The proposed system achieved top scores on the ASR task of the REVERB challenge. This paper describes the different technologies used in our system and presents detailed experimental results that justify our implementation choices and may provide hints for designing distant ASR systems.
0.18-V input charge pump with forward body biasing in startup circuit using 65nm CMOS

In this paper, a 0.18-V input three-stage charge pump circuit applying forward body bias is proposed. In the developed charge pump, all the MOSFETs are forward body biased by using the inter-stage/output voltages. By applying the proposed charge pump as the startup in the boost converter, the lower kick-up input voltage of the boost converter can be achieved. To verify the circuit characteristics, four test circuits have been implemented by using 65nm CMOS process. The measured available output current of the proposed charge pump under 0.18-V input voltage can be improved more than 150%. In addition, the boost converter can successfully been boosted from 0.18-V input to the 0.74-V output under 6mA output current. The proposed circuit is suitable for extremely low voltage applications such as harvesting energy sources.
Automated Driving in Uncertain Environments: Planning With Interaction and Uncertain Maneuver Prediction

Automated driving requires decision making in dynamic and uncertain environments. The uncertainty from the prediction originates from the noisy sensor data and from the fact that the intention of human drivers cannot be directly measured. This problem is formulated as a partially observable Markov decision process (POMDP) with the intended route of the other vehicles as hidden variables. The solution of the POMDP is a policy determining the optimal acceleration of the ego vehicle along a preplanned path. Therefore, the policy is optimized for the most likely future scenarios resulting from an interactive, probabilistic motion model for the other vehicles. Considering possible future measurements of the surrounding cars allows the autonomous car to incorporate the estimated change in future prediction accuracy in the optimal policy. A compact representation results in a low-dimensional state-space. Thus, the problem can be solved online for varying road layouts and number of vehicles. This is done with a point-based solver in an anytime fashion on a continuous state-space. Our evaluation is threefold: At first, the convergence of the algorithm is evaluated and it is shown how the convergence can be improved with an additional search heuristic. Second, we show various planning scenarios to demonstrate how the introduction of different considered uncertainties results in more conservative planning. At the end, we show online simulations for the crossing of complex (unsignalized) intersections. We can demonstrate that our approach performs nearly as good as with full prior information about the intentions of the other vehicles and clearly outperforms reactive approaches.
A Domain-Specific Language for Software-Defined Radio

Software-defined radio (SDR) is a demanding domain; realworld wireless protocols require high data rates and low latency. Existing SDR platforms, typically based on FPGAs, provide the necessary substrate for meeting these requirements, but the high-level tools available to program them are not capable of fully exploiting the underlying hardware to meet rigorous performance requirements. Ziria [11] demonstrated that a high-level language can compete in this demanding space, but its design was ad-hoc and overly influenced by the needs of the compiler writer since its surface language does double duty as the compiler’s intermediate language. We present a re-formulation of Ziria’s surface language that includes a new type system that allows this language, which is effectful, to elaborate into a pure, monadic language where effects such as input/output and reference manipulation can be distinguished purely by type. This reformulation and its elaboration into a core language is embodied in a new compiler for Ziria, kzc. By choosing an appropriate type system, awkward syntactic distinctions currently made by Ziria can be eliminated, although our new implementation maintains source compatibility with the original compiler due to a large body of existing Ziria code (a full 802.11 physical layer implementation). Our contribution is a description of the surface language, its type system, and its elaboration into a core language. We also show that far from being limited to the SDR domain, the constructs built-in to Ziria are applicable to other resource-constrained domains that require high-speed data processing.
An Integrated Semantic Web Service Discovery and Composition Framework

In this paper we present a theoretical analysis of graph-based service composition in terms of its dependency with service discovery. Driven by this analysis we define a composition framework by means of integration with fine-grained I/O service discovery that enables the generation of a graph-based composition which contains the set of services that are semantically relevant for an input-output request. The proposed framework also includes an optimal composition search algorithm to extract the best composition from the graph minimising the length and the number of services, and different graph optimisations to improve the scalability of the system. A practical implementation used for the empirical analysis is also provided. This analysis proves the scalability and flexibility of our proposal and provides insights on how integrated composition systems can be designed in order to achieve good performance in real scenarios for the web.
The pursuit of happiness: time, money, and social connection.

Does thinking about time, rather than money, influence how effectively individuals pursue personal happiness? Laboratory and field experiments revealed that implicitly activating the construct of time motivates individuals to spend more time with friends and family and less time working-behaviors that are associated with greater happiness. In contrast, implicitly activating money motivates individuals to work more and socialize less, which (although productive) does not increase happiness. Implications for the relative roles of time versus money in the pursuit of happiness are discussed.
An Algorithm for Differential File Comparison

The programdiff reports differences between tw o files, expressed as a minimal list of line changes to bring either file into agreement with the other . Diff has been engineered to mak e efficient use of time and space on typical inputs that arise in vetting v ersion-to-version changes in computer-maintained or computer-generated documents. T ime and space usage are observed to v ary about as the sum of the file lengths on real data, although they are known to vary as the product of the file lengths in the worst case. The central algorithm of diff solves the ‘longest common subsequence problem’ to find the lines that do not change between files. Practical ef ficiency is gained by attending only to certain critical ‘candidate’ matches between the files, the breaking of which would shorten the longest subsequence common to some pair of initial segments of the two files. Various techniques of hashing, presorting into equi valence classes, merging by binary search, and dynamic storage allocation are used to obtain good performance.  The programdiff creates a list of what lines of one file ha ve to be changed to bring it into agreement with a second file or vice versa. It is based on ideas from se veral sources[1,2,7,8]. As an example of its work, consider the two files, listed horizontally for brevity: a b c d e f g w a b x y z e It is easy to see that the first file can be made into the second by the follo wing prescription, in which an imaginary line 0 is understood at the beginning of each: append after line 0: w, change lines 3 through 4, which were: c d into: x y z, delete lines 6 through 7, which were: f g. Going the other way , the first file can be made from the second this way: delete line 1, which was: w, change lines 4 through 6, which were: x y z into: c d, append after line 7: f g. Delete, change and append are the only operations a vailable to diff . It indicates them by 1-letter
Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation

Named Entity Disambiguation (NED) refers to the task of resolving multiple named entity mentions in a document to their correct references in a knowledge base (KB) (e.g., Wikipedia). In this paper, we propose a novel embedding method specifically designed for NED. The proposed method j intly maps words and entities into the same continuous vector space. We extend the skip-gram model by using two models. The KB graph modellearns the relatedness of entities using the link structure of the KB, whereas the anchor context model aims to align vectors such that similar words and entities occur close to one another in the vector space by leveraging KB anchors and their context words. By combining contexts based on the proposed embedding with standard NED features, we achieved state-of-the-art accuracy of 93.1% on the standard CoNLL dataset and 85.2% on the TAC 2010 dataset. Our code and pre-trained vectors will be made available online.
All-at-once Optimization for Coupled Matrix and Tensor Factorizations

Joint analysis of data from multiple sources has the potential to improve our understanding of the underlying structures in complex data sets. For instance, in restaurant recommendation systems, recommendations can be based on rating histories of customers. In addition to rating histories, customers’ social networks (e.g., Facebook friendships) and restaurant categories information (e.g., Thai or Italian) can also be used to make better recommendations. The task of fusing data, however, is challenging since data sets can be incomplete and heterogeneous, i.e., data consist of both matrices, e.g., the person by person social network matrix or the restaurant by category matrix, and higher-order tensors, e.g., the “ratings” tensor of the form restaurant by meal by person. In this paper, we are particularly interested in fusing data sets with the goal of capturing their underlying latent structures. We formulate this problem as a coupled matrix and tensor factorization (CMTF) problem where heterogeneous data sets are modeled by fitting outer-product models to higher-order tensors and matrices in a coupled manner. Unlike traditional approaches solving this problem using alternating algorithms, we propose an all-at-once optimization approach called CMTF-OPT (CMTF-OPTimization), which is a gradient-based optimization approach for joint analysis of matrices and higher-order tensors. We also extend the algorithm to handle coupled incomplete data sets. Using numerical experiments, we demonstrate that the proposed all-at-once approach is more accurate than the alternating least squares approach.
Connected component-based technique for automatic extraction of road centerline in high resolution satellite images

In remote sensing analysis, automatic extraction of road network from satellite or aerial images can be a most needed approach for efficient road database creation, refinement, and updating. In this paper, a method for automatic extraction of road centerline from high resolution satellite image is proposed. The proposed work consists of three stages: segmentation of road region, connected component-based operations used to extract the connected road component from segmented road region, and removal of unwanted non-road pixels using morphological operation. The proposed algorithm is implemented on various satellite images, and the results are given in this work. The performance of the proposed method is evaluated by comparing the results with ground truth road map as reference data, and performance measures such as completeness, correctness, and quality are calculated. The average value of completeness, correctness, and quality of various images are 90%, 96%, and 87%, respectively. These measures prove that the proposed work yields road network very closer to reference road map. The proposed method yields very good result for noisy image also, and it proved that the proposed method is insensitive for noise. Performance measures of the proposed work are compared with other methods, and this comparison proves that the proposed method yields very good results than other methods.