nils-herrmann/clean_scirepeval huggingface dataset:
Analyzing and Comparing the Protection Quality of Security Enhanced Operating Systems

Host compromise is a serious computer security problem today. To better protect hosts, several Mandatory Access Control systems, such as Security Enhanced Linux (SELinux) and AppArmor, have been introduced. In this paper we propose an approach to analyze and compare the quality of protection offered by these different MAC systems. We introduce the notion of vulnerability surfaces under attack scenarios as the measurement of protection quality, and implement a tool called VulSAN for computing such vulnerability surfaces. In VulSAN, we encode security policies, system states, and system rules using logic programs. Given an attack scenario, VulSAN computes a host attack graph and the vulnerability surface. We apply our approach to compare SELinux and AppArmor policies in several Linux distributions and discuss the results. Our tool can also be used by Linux system administrators as a system hardening tool. Because of its ability to analyze SELinux as well as AppArmor policies, it can be used for most enterprise Linux distributions and home user distributions.
Interworking of DSRC and Cellular Network Technologies for V2X Communications: A Survey

Vehicle-to-anything (V2X) communications refer to information exchange between a vehicle and various elements of the intelligent transportation system (ITS), including other vehicles, pedestrians, Internet gateways, and transport infrastructure (such as traffic lights and signs). The technology has a great potential of enabling a variety of novel applications for road safety, passenger infotainment, car manufacturer services, and vehicle traffic optimization. Today, V2X communications is based on one of two main technologies: dedicated short-range communications (DSRC) and cellular networks. However, in the near future, it is not expected that a single technology can support such a variety of expected V2X applications for a large number of vehicles. Hence, interworking between DSRC and cellular network technologies for efficient V2X communications is proposed. This paper surveys potential DSRC and cellular interworking solutions for efficient V2X communications. First, we highlight the limitations of each technology in supporting V2X applications. Then, we review potential DSRC-cellular hybrid architectures, together with the main interworking challenges resulting from vehicle mobility, such as vertical handover and network selection issues. In addition, we provide an overview of the global DSRC standards, the existing V2X research and development platforms, and the V2X products already adopted and deployed in vehicles by car manufactures, as an attempt to align academic research with automotive industrial activities. Finally, we suggest some open research issues for future V2X communications based on the interworking of DSRC and cellular network technologies.
Autonomous dynamic honeypot routing mechanism for mitigating DDoS attacks in DMZ

DDOS attacks generate flooding traffic from multiple sources towards selected nodes and cause obstruction in flow of legitimate information within a network. If the victim node is a server in DMZ requiring fast information processing, the entire network operation stops. We use various lines of honeypot based defence against such attacks. The first line of defence detects the presence of attacks and tags attack flows in real time. The work in this paper concentrates on the next line of defence, where a model for autonomous dynamic honeypot routing has been proposed in response to identified attack flows. We propose the automatic generation of adequate server nodes to service client requests and honeypots to interact with attackers in contained manner. The judicious mixture of servers and honeypots in DMZ at different time intervals provide stable network functionality even in the attacked network. We validate the effectiveness of the approach with modelling on Internet type topology and simulation in ns-2 on a Linux platform.
Techniques of EMG signal analysis: detection, processing, classification and applications

Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardware Chip (EHW) development, and modern human computer interaction. EMG signals acquired from muscles require advanced methods for detection, decomposition, processing, and classification. The purpose of this paper is to illustrate the various methodologies and algorithms for EMG signal analysis to provide efficient and effective ways of understanding the signal and its nature. We further point up some of the hardware implementations using EMG focusing on applications related to prosthetic hand control, grasp recognition, and human computer interaction. A comparison study is also given to show performance of various EMG signal analysis methods. This paper provides researchers a good understanding of EMG signal and its analysis procedures. This knowledge will help them develop more powerful, flexible, and efficient applications.
Big data needs approximate computing: technical perspective

以容忍。还有一点让人印象深刻: 对于不同的程序,这些提升和能 耗降低并不需要使用不同的加速 器实现方式——它们通过相同的 近似加速器得到,那些加速器可 用于所有程序中所有类型的可近 似代码区。 除了文中描述的之外,通过让 近似计算引擎在明显低得多的电压 下运行,还能节约更多的能源。业 界增长的真正引擎——登纳德缩放 (Dennard scaling)——允许晶体 管减少尺寸,同时消耗更少的能源, 得到更高的性能。但是登纳德缩放 正趋近极限。设备的尺寸已经缩减 到了原子级别。而且,在不牺牲设 备可靠性的情况下,无法继续显著 降低电压。新技术会带来不错的能 耗性能,但由于需要利用冗余来补 偿它们的不可靠性,这一优势被抵 消了。因此,对于传统的交易系统 而言,未来的技术不大可能大幅提 升其能耗性能。然而,对于那些生 成近似结果的系统(比如作者描述 的系统),需要的冗余可能有所不 同且要简单得多。对于这些系统类 型,低电压运行是可行的。 未 来, 当 我 们 使 用 缩 小 的 CMOS 和后硅技术,利用海量计算 处理世上产生的海量数据时,近似 计算似乎是我们最有可能取得成功 的希望。下面的文章就是为实现此 目标所需开展的工作的一个很好的 例子。
Adversarial Regression with Multiple Learners

Despite the considerable success enjoyed by machine learning techniques in practice, numerous studies demonstrated that many approaches are vulnerable to attacks. An important class of such attacks involves adversaries changing features at test time to cause incorrect predictions. Previous investigations of this problem pit a single learner against an adversary. However, in many situations an adversary’s decision is aimed at a collection of learners, rather than specifically targeted at each independently. We study the problem of adversarial linear regression with multiple learners. We approximate the resulting game by exhibiting an upper bound on learner loss functions, and show that the resulting game has a unique symmetric equilibrium. We present an algorithm for computing this equilibrium, and show through extensive experiments that equilibrium models are significantly more robust than conventional regularized linear regression.
Botnet detection techniques: review, future trends, and issues

In recent years, the Internet has enabled access to widespread remote services in the distributed computing environment; however, integrity of data transmission in the distributed computing platform is hindered by a number of security issues. For instance, the botnet phenomenon is a prominent threat to Internet security, including the threat of malicious codes. The botnet phenomenon supports a wide range of criminal activities, including distributed denial of service (DDoS) attacks, click fraud, phishing, malware distribution, spam emails, and building machines for illegitimate exchange of information/materials. Therefore, it is imperative to design and develop a robust mechanism for improving the botnet detection, analysis, and removal process. Currently, botnet detection techniques have been reviewed in different ways; however, such studies are limited in scope and lack discussions on the latest botnet detection techniques. This paper presents a comprehensive review of the latest state-of-the-art techniques for botnet detection and figures out the trends of previous and current research. It provides a thematic taxonomy for the classification of botnet detection techniques and highlights the implications and critical aspects by qualitatively analyzing such techniques. Related to our comprehensive review, we highlight future directions for improving the schemes that broadly span the entire botnet detection research field and identify the persistent and prominent research challenges that remain open.
Constructing C 1 surfaces of arbitrary topology using biquadratic and bicubic splines

Given a bivariate mesh of points, a C 1 surface of corresponding genus and connectedness is constructed. Most of the surface is parametrized by a biquadratic spline whose control points are obtained by refining the input mesh via corner cutting. The remaining mesh regions are parametrized by bicubic patches in Bernstein-Bezier form. The construction can be extended to rational patches and to interpolate at the vertices of the input mesh. t Department of Computer Science, Purdue University, W-Lafayette IN 47907 Supported by NSF grant CCR-9211322
Sequence-to-Sequence RNNs for Text Summarization

In this work, we cast text summarization as a sequence-to-sequence problem and apply the attentional encoder-decoder RNN that has been shown to be successful for Machine Translation (Bahdanau et al. (2014)). Our experiments show that the proposed architecture significantly outperforms the state-of-the art model of Rush et al. (2015) on the Gigaword dataset without any additional tuning. We also propose additional extensions to the standard architecture, which we show contribute to further improvement in performance.
Embedding Temporal Network via Neighborhood Formation

Given the rich real-life applications of network mining as well as the surge of representation learning in recent years, network embedding has become the focal point of increasing research interests in both academic and industrial domains. Nevertheless, the complete temporal formation process of networks characterized by sequential interactive events between nodes has yet seldom been modeled in the existing studies, which calls for further research on the so-called temporal network embedding problem. In light of this, in this paper, we introduce the concept of neighborhood formation sequence to describe the evolution of a node, where temporal excitation effects exist between neighbors in the sequence, and thus we propose a Hawkes process based Temporal Network Embedding (HTNE) method. HTNE well integrates the Hawkes process into network embedding so as to capture the influence of historical neighbors on the current neighbors. In particular, the interactions of low-dimensional vectors are fed into the Hawkes process as base rate and temporal influence, respectively. In addition, attention mechanism is also integrated into HTNE to better determine the influence of historical neighbors on current neighbors of a node. Experiments on three large-scale real-life networks demonstrate that the embeddings learned from the proposed HTNE model achieve better performance than state-of-the-art methods in various tasks including node classification, link prediction, and embedding visualization. In particular, temporal recommendation based on arrival rate inferred from node embeddings shows excellent predictive power of the proposed model.
Self-Assembling Machine

This letter describes dynamic self-assembly of two-component rotors floating at the interface between liquid and air into simple, reconfigurable mechanical systems ~‘‘machines’’!. The rotors are powered by an external, rotating magnetic field, and their positions within the interface are controlled by:~i! repulsive hydrodynamic interactions between them and ~ii ! by localized magnetic fields produced by an array of small electromagnets located below the plane of the interface. The mechanical functions of the machines depend on the spatiotemporal sequence of activation of the electromagnets.
Crafting Integrated Multichannel Retailing Strategies

Multichannel retailing is the set of activities involved in selling merchandise or services to consumers through more than one channel. Multichannel retailers dominate today's retail landscape. While there are many benefits of operating multiple channels, these retailers also face many challenges. In this article, we discuss the key issues concerning multichannel retailing, including the motivations and constraints of going multichannel, the challenges of crafting multichannel retailing strategies and opportunities for creating synergies across channels, key retail mix decisions facing multichannel retailers, and the dynamics of multichannel retailing. We synthesize current knowledge drawn from the academic literature and industry practice, and discuss potential directions for future research.
A Novel Approach for Addressing Wandering Off Elderly Using Low Cost Passive RFID Tags

Wandering (e.g. elopement) by elderly persons at acute hospitals and nursing homes poses a significant problem to providing patient care as such incidents can lead to injury and even accidental morbidity. These problems are particularly serious given aging populations around the world. While various technologies exit (such as door alarms), they are expensive and the reliability of such systems have not been evaluated in the past. In this article we propose two novel methods for a very low cost solution to address the problem of wandering off patients using body worn low cost passive Radio Frequency Identification (RFID) tags using phase based measurements. Our approach requires no modification to the air interface protocols, firmware or hardware. Results from extensive experiments show that: i) the proposed algorithms can accurately identify whether a person is moving into or out of, for example, a room; and ii) it can be implemented in real-time to develop a low cost wandering off alarm.
How useful are your comments?: analyzing and predicting youtube comments and comment ratings

An analysis of the social video sharing platform YouTube reveals a high amount of community feedback through comments for published videos as well as through meta ratings for these comments. In this paper, we present an in-depth study of commenting and comment rating behavior on a sample of more than 6 million comments on 67,000 YouTube videos for which we analyzed dependencies between comments, views, comment ratings and topic categories. In addition, we studied the influence of sentiment expressed in comments on the ratings for these comments using the SentiWordNet thesaurus, a lexical WordNet-based resource containing sentiment annotations. Finally, to predict community acceptance for comments not yet rated, we built different classifiers for the estimation of ratings for these comments. The results of our large-scale evaluations are promising and indicate that community feedback on already rated comments can help to filter new unrated comments or suggest particularly useful but still unrated comments.
Security and usability: the gap in real-world online banking

Online banking is one of the most sensitive tasks performed by general Internet users. Most traditional banks now offer online banking services, and strongly encourage customers to do online banking with 'peace of mind.' Although banks heavily advertise an apparent '100% online security guarantee,' typically the fine print makes this conditional on users fulfilling certain security requirements. We examine some of these requirements as set by major Canadian banks, in terms of security and usability. We opened personal checking accounts at the five largest Canadian banks, and one online-only bank. We found that many security requirements are too difficult for regular users to follow, and believe that some marketing-related messages about safety and security actually mislead users. We are also interested in what kind of computer systems people really use for online banking, and whether users satisfy common online banking requirements. Our survey of 123 technically advanced users from a university environment strongly supports our view of an emerging gap between banks' expectations (or at least what their written customer policy agreements imply) and users' actions related to security requirements of online banking. Our participants, being more security-aware than the general population, arguably makes our results best-case regarding what can be expected from regular users. Yet most participants failed to satisfy common security requirements, implying most online banking customers do not (or cannot) follow banks' stated end-user security requirements and guidelines. The survey also sheds light on the security settings of systems used for sensitive online transactions. This work is intended to spur a discussion on real-world system security and user responsibilities, in a scenario where everyday users are heavily encouraged to perform critical tasks over the Internet, despite the continuing absence of appropriate tools to do so.
Novel Density-Based Clustering Algorithms for Uncertain Data

Density-based techniques seem promising for handling data uncertainty in uncertain data clustering. Nevertheless, some issues have not been addressed well in existing algorithms. In this paper, we firstly propose a novel density-based uncertain data clustering algorithm, which improves upon existing algorithms from the following two aspects: (1) it employs an exact method to compute the probability that the distance between two uncertain objects is less than or equal to a boundary value, instead of the sampling-based method in previous work; (2) it introduces new definitions of core object probability and direct reachability probability, thus reducing the complexity and avoiding sampling. We then further improve the algorithm by using a novel assignment strategy to ensure that every object will be assigned to the most appropriate cluster. Experimental results show the superiority of our proposed algorithms over existing ones.
Statics and Dynamics of Continuum Robots With General Tendon Routing and External Loading

Tendons are a widely used actuation strategy for continuum robots that enable forces and moments to be transmitted along the robot from base-mounted actuators. Most prior robots have used tendons routed in straight paths along the robot. However, routing tendons through general curved paths within the robot offers potential advantages in reshaping the workspace and enabling a single section of the robot to achieve a wider variety of desired shapes. In this paper, we provide a new model for the statics and dynamics of robots with general tendon routing paths that is derived by coupling the classical Cosserat-rod and Cosserat-string models. This model also accounts for general external loading conditions and includes traditional axially routed tendons as a special case. The advantage of the usage of this coupled model for straight-tendon robots is that it accounts for the distributed wrenches that tendons apply along the robot. We show that these are necessary to consider when the robot is subjected to out-of-plane external loads. Our experimental results demonstrate that the coupled model matches experimental tip positions with an error of 1.7% of the robot length, in a set of experiments that include both straight and nonstraight routing cases, with both point and distributed external loads.
Efficient SIMD code generation for runtime alignment and length conversion

When generating codes for today's multimedia extensions, one of the major challenges is to deal with memory alignment issues. While hand programming still yields best performing SIMD codes, it is both time consuming and error prone. Compiler technology has greatly improved, including techniques that simdize loops with misaligned accesses by automatically rearranging mis-aligned memory streams in registers. Current techniques are applicable to runtime alignments, but they aggressively reduce the alignment overhead only when all alignments are known at compile time. This paper presents two major enhancements to the state of the art, improving both performance and coverage. First, we propose a novel technique to simdize loops with runtime alignment nearly as efficiently as those with compile-time misalignment. Runtime alignment is pervasive in real applications because it is either part of the algorithms, or it is an artifact of the compiler's inability to extract accurate alignment information from complex applications. Second, we incorporate length conversion operations, e.g., conversions between data of different sizes, into the alignment handling framework. Length conversions are pervasive in multimedia applications where mixed integer types are often used. Supporting length conversion can greatly improve the coverage of simdizable loops. Experimental results indicate that our runtime alignment technique achieves a 19% to 32% speedup increase over prior art for a benchmark stressing the impact of misaligned data. We also demonstrate speedup factors of up to 8.11 for real benchmarks over sequential execution.
Efficient Object Instance Search Using Fuzzy Objects Matching

Recently, global features aggregated from local convolutional features of the convolutional neural network have shown to be much more effective in comparison with hand-crafted features for image retrieval. However, the global feature might not effectively capture the relevance between the query object and reference images in the object instance search task, especially when the query object is relatively small and there exist multiple types of objects in reference images. Moreover, the object instance search requires to localize the object in the reference image, which may not be achieved through global representations. In this paper, we propose a Fuzzy Objects Matching (FOM) framework to effectively and efficiently capture the relevance between the query object and reference images in the dataset. In the proposed FOM scheme, object proposals are utilized to detect the potential regions of the query object in reference images. To achieve high search efficiency, we factorize the feature matrix of all the object proposals from one reference image into the product of a set of fuzzy objects and sparse codes. In addition, we refine the feature of the generated fuzzy objects according to its neighborhood in the feature space to generate more robust representation. The experimental results demonstrate that the proposed FOM framework significantly outperforms the state-of-theart methods in precision with less memory and computational cost on three public datasets. The task of object instance search, is to retrieve all the images containing a specific object query and localize the query object in the reference images. It has received a sustained attention over the last decade, leading to many object instance search systems (Meng et al. 2010; Jiang, Meng, and Yuan 2012; Jiang et al. 2015; Tolias, Avrithis, and Jégou 2013; Tao et al. 2014; Razavian et al. 2014a; 2014b; Tolias, Sicre, and Jégou 2016; Meng et al. 2016; Bhattacharjee et al. 2016b; 2016a; Mohedano et al. 2016; Cao et al. 2016; Wu et al. 2016). Since the query object only occupies a small part of an image, the global representation may not be effective to capture the relevance between the query object with reference image. Therefore, the relevance between the query object and one reference image is not determined by the overall similarity between the query and the reference image.