nils-herrmann/clean_scirepeval huggingface dataset:
Active Estimation for Jump Markov Linear Systems

Jump Markov Linear Systems are convenient models for systems that exhibit both continuous dynamics and discrete mode changes. Estimating the hybrid discrete-continuous state of these systems is important for control and fault detection. Existing solutions for hybrid estimation approximate the belief state by maintaining a subset of the possible discrete mode sequences. This approximation can cause the estimator to lose track of the true mode sequence when the effects of discrete mode changes are subtle. In this paper, we present a method for active hybrid estimation, where control inputs can be designed to discriminate between possible mode sequences. By probing the system for the purposes of estimation, such a sequence of control inputs can greatly reduce the probability of losing the true mode sequence compared to a nominal control sequence. Furthermore, by using a constrained finite horizon optimization formulation, we are able to guarantee that a given control task is achieved, while optimally detecting the hybrid state. In order to achieve this, we present three main contributions. First, we develop a method by which a sequence of control inputs is designed in order to discriminate optimally between a finite number of linear dynamic system models. These control inputs minimize a novel, tractable upper bound on the probability of model selection error. Second, we extend this approach to develop an active estimation method for Jump Markov Linear Systems by relating the probability of model selection error to the probability of losing the true mode sequence. Finally, we make this method tractable using a principled pruning technique. Simulation results show that the new method applied to an aircraft fault detection problem significantly decreases the probability of a hybrid estimator losing the true mode sequence.
CDStore: Toward Reliable, Secure, and Cost-Efficient Cloud Storage via Convergent Dispersal

CDStore is a unified, multicloud storage solution for users to outsource backup data with reliability, security, and cost-efficiency guarantees. CDStore builds on an augmented secret-sharing scheme called convergent dispersal, which supports deduplication by using deterministic, content-derived hashes as input to secret sharing. CDStore's design is presented here, with an emphasis on how it combines convergent dispersal with two-stage deduplication to achieve both bandwidth and storage savings while robustly diverting side-channel attacks (launched by malicious users on the client side). A cost analysis shows that CDStore yields significant savings over baseline cloud storage solutions.
Lexicon-based Sentiment Analysis for Persian Text

The vast information related to products and services available online, of both objective and subjective nature, can be used to provide contextualized suggestions and guidance to possible new customers. User feedback and comments left on different shopping websites, portals and social media have become a valuable resource, and text analysis methods have become an invaluable tool to process this kind of data. A lot of business use-cases have applied sentiment analysis in order to gauge people’s response to a service or product, or to support customers with reaching a decision when choosing such a product. Although methods and techniques in this area abound, the majority only address a handful of natural languages at best. In this paper, we describe a lexiconbased sentiment analysis method designed around the Persian language. An evaluation of the developed GATE pipeline shows an encouraging overall accuracy of up to 69%.
Algorithms for Semantic Segmentation of Multispectral Remote Sensing Imagery using Deep Learning

Deep convolutional neural networks (DCNNs) have been used to achieve state-of-the-art performance on many computer vision tasks (e.g., object recognition, object detection, semantic segmentation) thanks to a large repository of annotated image data. Large labeled datasets for other sensor modalities, e.g., multispectral imagery (MSI), are not available due to the large cost and manpower required. In this paper, we adapt state-of-the-art DCNN frameworks in computer vision for semantic segmentation for MSI imagery. To overcome label scarcity for MSI data, we substitute real MSI for generated synthetic MSI in order to initialize a DCNN framework. We evaluate our network initialization scheme on the new RIT-18 dataset that we present in this paper. This dataset contains very-high resolution MSI collected by an unmanned aircraft system. The models initialized with synthetic imagery were less prone to over-fitting and provide a state-of-the-art baseline for future work.
VCCFinder: Finding Potential Vulnerabilities in Open-Source Projects to Assist Code Audits

Despite the security community's best effort, the number of serious vulnerabilities discovered in software is increasing rapidly. In theory, security audits should find and remove the vulnerabilities before the code ever gets deployed. However, due to the enormous amount of code being produced, as well as a the lack of manpower and expertise, not all code is sufficiently audited. Thus, many vulnerabilities slip into production systems. A best-practice approach is to use a code metric analysis tool, such as Flawfinder, to flag potentially dangerous code so that it can receive special attention. However, because these tools have a very high false-positive rate, the manual effort needed to find vulnerabilities remains overwhelming. In this paper, we present a new method of finding potentially dangerous code in code repositories with a significantly lower false-positive rate than comparable systems. We combine code-metric analysis with metadata gathered from code repositories to help code review teams prioritize their work. The paper makes three contributions. First, we conducted the first large-scale mapping of CVEs to GitHub commits in order to create a vulnerable commit database. Second, based on this database, we trained a SVM classifier to flag suspicious commits. Compared to Flawfinder, our approach reduces the amount of false alarms by over 99 % at the same level of recall. Finally, we present a thorough quantitative and qualitative analysis of our approach and discuss lessons learned from the results. We will share the database as a benchmark for future research and will also provide our analysis tool as a web service.
Sequential Event Prediction with Association Rules

We consider a supervised learning problem in which data are revealed sequentially and the goal is to determine what will next be revealed. In the context of this problem, algorithms based on association rules have a distinct advantage over classical statistical and machine learning methods; however, there has not previously been a theoretical foundation established for using association rules in supervised learning. We present two simple algorithms that incorporate association rules, and provide generalization guarantees on these algorithms based on algorithmic stability analysis from statistical learning theory. We include a discussion of the strict minimum support threshold often used in association rule mining, and introduce an “adjusted confidence” measure that provides a weaker minimum support condition that has advantages over the strict minimum support. The paper brings together ideas from statistical learning theory, association rule mining and Bayesian analysis.
Visual Analysis of Eye State and Head Pose for Driver Alertness Monitoring

This paper presents visual analysis of eye state and head pose (HP) for continuous monitoring of alertness of a vehicle driver. Most existing approaches to visual detection of nonalert driving patterns rely either on eye closure or head nodding angles to determine the driver drowsiness or distraction level. The proposed scheme uses visual features such as eye index (EI), pupil activity (PA), and HP to extract critical information on nonalertness of a vehicle driver. EI determines if the eye is open, half closed, or closed from the ratio of pupil height and eye height. PA measures the rate of deviation of the pupil center from the eye center over a time period. HP finds the amount of the driver's head movements by counting the number of video segments that involve a large deviation of three Euler angles of HP, i.e., nodding, shaking, and tilting, from its normal driving position. HP provides useful information on the lack of attention, particularly when the driver's eyes are not visible due to occlusion caused by large head movements. A support vector machine (SVM) classifies a sequence of video segments into alert or nonalert driving events. Experimental results show that the proposed scheme offers high classification accuracy with acceptably low errors and false alarms for people of various ethnicity and gender in real road driving conditions.
RetroVisor : Nested Virtualization for Multi IaaS VM Availability

Nested virtualization  provides an extra layer of virtualization to enhance security with fairly reasonable performance impact. Usercentric vision of cloud computing gives a high-level of control on the whole infrastructure , such as untrusted dom0 [3, 4]. This paper introduces RetroVisor, a security architecture to seamlessly run a virtual machine (VM) on multiple hypervisors simultaneously. We argue that this approach delivers high-availability and provides strong guarantees on multi IaaS infrastructures. The user can perform detection and remediation against potential hypervisors weaknesses, unexpected behaviors and exploits.
A Cheap Linear Attention Mechanism with Fast Lookups and Fixed-Size Representations

The softmax content-based attention mechanism has proven to be very beneficial in many applications of recurrent neural networks. Nevertheless it suffers from two major computational limitations. First, its computations for an attention lookup scale linearly in the size of the attended sequence. Second, it does not encode the sequence into a fixed-size representation but instead requires to memorize all the hidden states. These two limitations restrict the use of the softmax attention mechanism to relatively small-scale applications with short sequences and few lookups per sequence. In this work we introduce a family of linear attention mechanisms designed to overcome the two limitations listed above. We show that removing the softmax non-linearity from the traditional attention formulation yields constant-time attention lookups and fixed-size representations of the attended sequences. These properties make these linear attention mechanisms particularly suitable for large-scale applications with extreme query loads, realtime requirements and memory constraints. Early experiments on a question answering task show that these linear mechanisms yield significantly better accuracy results than no attention, but obviously worse than their softmax alternative.
Parallel matrix factorization for low-rank tensor completion

Higher-order low-rank tensors naturally arise in many applications including hyperspectral data recovery, video inpainting, seismic data reconstruction, and so on. We propose a new model to recover a low-rank tensor by simultaneously performing low-rank matrix factorizations to the all-mode matricizations of the underlying tensor. An alternating minimization algorithm is applied to solve the model, along with two adaptive rank-adjusting strategies when the exact rank is not known. Phase transition plots reveal that our algorithm can recover a variety of synthetic low-rank tensors from significantly fewer samples than the compared methods, which include a matrix completion method applied to tensor recovery and two state-of-the-art tensor completion methods. Further tests on real-world data show similar advantages. Although our model is non-convex, our algorithm performs consistently throughout the tests and give better results than the compared methods, some of which are based on convex models. In addition, the global convergence of our algorithm can be established in the sense that the gradient of Lagrangian function converges to zero.
Comparing four approaches for technical debt identification

Software systems accumulate technical debt (TD) when short-term goals in software development are traded for long-term goals (e.g., quick-and-dirty implementation to reach a release date versus a well-refactored implementation that supports the long-term health of the project). Some forms of TD accumulate over time in the form of source code that is difficult to work with and exhibits a variety of anomalies. A number of source code analysis techniques and tools have been proposed to potentially identify the code-level debt accumulated in a system. What has not yet been studied is if using multiple tools to detect TD can lead to benefits, that is, if different tools will flag the same or different source code components. Further, these techniques also lack investigation into the symptoms of TD “interest” that they lead to. To address this latter question, we also investigated whether TD, as identified by the source code analysis techniques, correlates with interest payments in the form of increased defect- and change-proneness. Comparing the results of different TD identification approaches to understand their commonalities and differences and to evaluate their relationship to indicators of future TD “interest.” We selected four different TD identification techniques (code smells, automatic static analysis issues, grime buildup, and Modularity violations) and applied them to 13 versions of the Apache Hadoop open source software project. We collected and aggregated statistical measures to investigate whether the different techniques identified TD indicators in the same or different classes and whether those classes in turn exhibited high interest (in the form of a large number of defects and higher change-proneness). The outputs of the four approaches have very little overlap and are therefore pointing to different problems in the source code. Dispersed Coupling and Modularity violations were co-located in classes with higher defect-proneness. We also observed a strong relationship between Modularity violations and change-proneness. Our main contribution is an initial overview of the TD landscape, showing that different TD techniques are loosely coupled and therefore indicate problems in different locations of the source code. Moreover, our proxy interest indicators (change- and defect-proneness) correlate with only a small subset of TD indicators.
Architecture-based approach to reliability assessment of software systems

With the growing emphasis on reuse software development process moves toward component based software design As a result there is a need for modeling ap proaches that are capable of considering the architecture of the software and es timating the reliability by taking into account the interactions between the com ponents the utilization of the components and the reliabilities of the components and of their interfaces with other components This paper details the state of the architecture based approach to reliability assessment of component based software and describes how it can be used to examine software behavior right from the de sign stage to implementation and nal deployment First the common requirements of the architecture based models are identi ed and the classi cation is proposed Then the key models in each class are described in detail and the relation among them is discussed A critical analysis of underlying assumptions limitations and applicability of these models is provided which should be helpful in determining the directions for future research
Phinding Phish: An Evaluation of Anti-Phishing Toolbars

There are currently dozens of freely available tools to combat phishing and other web-based scams, many of which are web browser extensions that warn users when they are browsing a suspected phishing site. We developed an automated test bed for testing antiphishing tools. We used 200 verified phishing URLs from two sources and 516 legitimate URLs to test the effectiveness of 10 popular anti-phishing tools. Only one tool was able to consistently identify more than 90% of phishing URLs correctly; however, it also incorrectly identified 42% of legitimate URLs as phish. The performance of the other tools varied considerably depending on the source of the phishing URLs. Of these remaining tools, only one correctly identified over 60% of phishing URLs from both sources. Performance also changed significantly depending on the freshness of the phishing URLs tested. Thus we demonstrate that the source of phishing URLs and the freshness of the URLs tested can significantly impact the results of anti-phishing tool testing. We also demonstrate that many of the tools we tested were vulnerable to simple exploits. In this paper we describe our anti-phishing tool test bed, summarize our findings, and offer observations about the effectiveness of these tools as well as ways they might be improved.
A Low-Voltage Mobility-Based Frequency Reference for Crystal-Less ULP Radios

The design of a 100 kHz frequency reference based on the electron mobility in a MOS transistor is presented. The proposed low-voltage low-power circuit requires no off-chip components, making it suitable for application in wireless sensor networks (WSN). After a single-point calibration, the spread of its output frequency is less than 1.1% (3sigma) over the temperature range from -22degC to 85degC . Fabricated in a baseline 65 nm CMOS technology, the frequency reference circuit occupies 0.11 mm2 and draws 34 muA from a 1.2 V supply at room temperature.
KASLR is Dead: Long Live KASLR

Modern operating system kernels employ address space layout randomization (ASLR) to prevent control-flow hijacking attacks and code-injection attacks. While kernel security relies fundamentally on preventing access to address information, recent attacks have shown that the hardware directly leaks this information. Strictly splitting kernel space and user space has recently been proposed as a theoretical concept to close these side channels. However, this is not trivially possible due to architectural restrictions of the x86 platform. In this paper we present KAISER, a system that overcomes limitations of x86 and provides practical kernel address isolation. We implemented our proof-of-concept on top of the Linux kernel, closing all hardware side channels on kernel address information. KAISER enforces a strict kernel and user space isolation such that the hardware does not hold any information about kernel addresses while running in user mode. We show that KAISER protects against double page fault attacks, prefetch side-channel attacks, and TSX-based side-channel attacks. Finally, we demonstrate that KAISER has a runtime overhead of only 0.28%.
An Improved Microcontroller-Based Sensorless Brushless DC (BLDC) Motor Drive for Automotive Applications

The direct back-electromotive-force (EMF) detection method previously described in a sensorless brushless dc (BLDC) motor-drive system ( 2002, pp. 33-38) synchronously samples the motor back EMF during the pulsewidth-modulation (PWM) off time without the need to sense or reconstruct the motor neutral. Since this direct back-EMF-sensing scheme requires a minimum PWM off time to sample the back-EMF signal, the duty cycle is limited to something less than 100%. In this paper, an improved direct back-EMF detection scheme that samples the motor back EMF synchronously during either the PWM on time or the PWM off time is proposed to overcome the problem. In this paper, some techniques for automotive applications, such as motor-rotation detection, and current sensing are proposed as well. Experimental results are presented
Cell phones: modern man's nemesis?

Over the past decade, the use of mobile phones has increased significantly. However, with every technological development comes some element of health concern, and cell phones are no exception. Recently, various studies have highlighted the negative effects of cell phone exposure on human health, and concerns about possible hazards related to cell phone exposure have been growing. This is a comprehensive, up-to-the-minute overview of the effects of cell phone exposure on human health. The types of cell phones and cell phone technologies currently used in the world are discussed in an attempt to improve the understanding of the technical aspects, including the effect of cell phone exposure on the cardiovascular system, sleep and cognitive function, as well as localized and general adverse effects, genotoxicity potential, neurohormonal secretion and tumour induction. The proposed mechanisms by which cell phones adversely affect various aspects of human health, and male fertility in particular, are explained, and the emerging molecular techniques and approaches for elucidating the effects of mobile phone radiation on cellular physiology using high-throughput screening techniques, such as metabolomics and microarrays, are discussed. A novel study is described, which is looking at changes in semen parameters, oxidative stress markers and sperm DNA damage in semen samples exposed in vitro to cell phone radiation.
Tangible views for information visualization

In information visualization, interaction is commonly carried out by using traditional input devices, and visual feedback is usually given on desktop displays. By contrast, recent advances in interactive surface technology suggest combining interaction and display functionality in a single device for a more direct interaction. With our work, we contribute to the seamless integration of interaction and display devices and introduce new ways of visualizing and directly interacting with information. Rather than restricting the interaction to the display surface alone, we explicitly use the physical three-dimensional space above it for natural interaction with multiple displays. For this purpose, we introduce tangible views as spatially aware lightweight displays that can be interacted with by moving them through the physical space on or above a tabletop display's surface. Tracking the 3D movement of tangible views allows us to control various parameters of a visualization with more degrees of freedom. Tangible views also facilitate making multiple -- previously virtual -- views physically "graspable". In this paper, we introduce a number of interaction and visualization patterns for tangible views that constitute the vocabulary for performing a variety of common visualization tasks. Several implemented case studies demonstrate the usefulness of tangible views for widely used information visualization approaches and suggest the high potential of this novel approach to support interaction with complex visualizations.
Mitigating the imitation of aggressive behaviors by changing children's attitudes about media violence.

A sample of 169 first- and third-grade children, selected because of their high exposure to television violence, was randomly divided into an experimental and a control group. Over the course of 2 years, the experimental subjects were exposed to two treatments designed to reduce the likelihood of their imitating the aggressive behaviors they observed on TV. The control group received comparable neutral treatments. By the end of the second year, the experimental subjects were rated as significantly less aggressive by their peers, and the relation between violence viewing and aggressiveness was diminished in the experimental group.