nils-herrmann/clean_scirepeval huggingface dataset:
Pyrosequencing Reveals High-Temperature Cellulolytic Microbial Consortia in Great Boiling Spring after In Situ Lignocellulose Enrichment

To characterize high-temperature cellulolytic microbial communities, two lignocellulosic substrates, ammonia fiber-explosion-treated corn stover and aspen shavings, were incubated at average temperatures of 77 and 85°C in the sediment and water column of Great Boiling Spring, Nevada. Comparison of 109,941 quality-filtered 16S rRNA gene pyrosequences (pyrotags) from eight enrichments to 37,057 quality-filtered pyrotags from corresponding natural samples revealed distinct enriched communities dominated by phylotypes related to cellulolytic and hemicellulolytic Thermotoga and Dictyoglomus, cellulolytic and sugar-fermenting Desulfurococcales, and sugar-fermenting and hydrogenotrophic Archaeoglobales. Minor enriched populations included close relatives of hydrogenotrophic Thermodesulfobacteria, the candidate bacterial phylum OP9, and candidate archaeal groups C2 and DHVE3. Enrichment temperature was the major factor influencing community composition, with a negative correlation between temperature and richness, followed by lignocellulosic substrate composition. This study establishes the importance of these groups in the natural degradation of lignocellulose at high temperatures and suggests that a substantial portion of the diversity of thermophiles contributing to consortial cellulolysis may be contained within lineages that have representatives in pure culture.
Adaptive RED : An Algorithm for Increasing the Robustness of RED ’ s Active Queue Management

The RED active queuemanagement algorithmallows network operatorsto simultaneouslyachieve high throughput and low averagedelay. However, the resulting average queuelength is quite sensiti ve to the level of congestion andto theREDparameter settings,andis thereforenotpredictable in advance. Delay being a major componentof thequalityof servicedeliveredto their customers, network operatorswould naturallylike to have a rougha priori estimateof the averagedelaysin their congestedrouters;to achieve suchpredictableaveragedelayswith RED would requireconstanttuning of the parametersto adjustto currenttraffic conditions. Our goal in this paperis to solve this problemwith minimal changesto the overall RED algorithm. To do so,we revisit theAdaptiveREDproposalof Fenget al. from 1997 [6, 7]. We make several algorithmicmodificationsto this proposal,while leaving thebasicideaintact,andthenevaluateits performanceusingsimulation.Wefind thatthis revisedversionof AdaptiveRED,whichcanbeimplemented asasimpleextensionwithin REDrouters,removesthesensitivity to parametersthat affect RED’s performanceand canreliablyachieve aspecifiedtargetaveragequeuelength in a wide variety of traffic scenarios.Basedon extensi ve simulations,we believe that Adaptive RED is sufficiently robustfor deploymentin routers.
Uncovering social network sybils in the wild

Sybil accounts are fake identities created to unfairly increase the power or resources of a single user. Researchers have long known about the existence of Sybil accounts in online communities such as file-sharing systems, but have not been able to perform large scale measurements to detect them or measure their activities. In this paper, we describe our efforts to detect, characterize and understand Sybil account activity in the Renren online social network (OSN). We use ground truth provided by Renren Inc. to build measurement based Sybil account detectors, and deploy them on Renren to detect over 100,000 Sybil accounts. We study these Sybil accounts, as well as an additional 560,000 Sybil accounts caught by Renren, and analyze their link creation behavior. Most interestingly, we find that contrary to prior conjecture, Sybil accounts in OSNs do not form tight-knit communities. Instead, they integrate into the social graph just like normal users. Using link creation timestamps, we verify that the large majority of links between Sybil accounts are created accidentally, unbeknownst to the attacker. Overall, only a very small portion of Sybil accounts are connected to other Sybils with social links. Our study shows that existing Sybil defenses are unlikely to succeed in today's OSNs, and we must design new techniques to effectively detect and defend against Sybil attacks.
Mosaic: quantifying privacy leakage in mobile networks

With the proliferation of online social networking (OSN) and mobile devices, preserving user privacy has become a great challenge. While prior studies have directly focused on OSN services, we call attention to the privacy leakage in mobile network data. This concern is motivated by two factors. First, the prevalence of OSN usage leaves identifiable digital footprints that can be traced back to users in the real-world. Second, the association between users and their mobile devices makes it easier to associate traffic to its owners. These pose a serious threat to user privacy as they enable an adversary to attribute significant portions of data traffic including the ones with NO identity leaks to network users' true identities. To demonstrate its feasibility, we develop the Tessellation methodology. By applying Tessellation on traffic from a cellular service provider (CSP), we show that up to 50% of the traffic can be attributed to the names of users. In addition to revealing the user identity, the reconstructed profile, dubbed as "mosaic," associates personal information such as political views, browsing habits, and favorite apps to the users. We conclude by discussing approaches for preventing and mitigating the alarming leakage of sensitive user information.
MUTO: the modular unified tagging ontology

Tagging has become a popular indexing method within the last years and can be considered one of the cornerstones of the Social Web. Several ontologies have been developed that aim to formally describe tagging and folksonomies in order to improve their interoperability and processability. However, each of these ontologies covers different aspects of the domain; finding the right ontology for a certain purpose and aligning it correctly with other ontologies is difficult. This paper critically reviews available tagging ontologies and presents a unified vocabulary that combines the 'best of' these ontologies in one consistent schema. A central goal was to ensure high concept reuse by simultaneously avoiding redundancies. A modular design was chosen to reduce complexity and prevent inconsistencies. It groups the different elements of tagging and separates advanced concepts from the core ontology. Key design decisions are justified and modeling alternatives are discussed, not only to explain the unified ontology but also to contribute to a better understanding of the conceptual space of tagging and folksonomies.
BVFG An Unbiased Detector of Curvilinear Structures

The extraction of curvilinear structures is an important low-level operation in computer vision that has many applications. Most existing operators use a simple model for the line that is to be extracted, i.e., they do not take into account the surroundings of a line. This leads to the undesired consequence that the line will be extracted in the wrong position whenever a line with different lateral contrast is extracted. In contrast, the algorithm proposed in this paper uses an explicit model for lines and their surroundings. By analyzing the scale-space behaviour of a model line profile, it is shown how the bias that is induced by asymmetrical lines can be removed. Furthermore, the algorithm not only returns the precise sub-pixel line position, but also the width of the line for each line point, also with sub-pixel accuracy.
No "power" struggles: coordinated multi-level power management for the data center

Power delivery, electricity consumption, and heat management are becoming key challenges in data center environments. Several past solutions have individually evaluated different techniques to address separate aspects of this problem, in hardware and software, and at local and global levels. Unfortunately, there has been no corresponding work on coordinating all these solutions. In the absence of such coordination, these solutions are likely to interfere with one another, in unpredictable (and potentially dangerous) ways. This paper seeks to address this problem. We make two key contributions. First, we propose and validate a power management solution that coordinates different individual approaches. Using simulations based on 180 server traces from nine different real-world enterprises, we demonstrate the correctness, stability, and efficiency advantages of our solution. Second, using our unified architecture as the base, we perform a detailed quantitative sensitivity analysis and draw conclusions about the impact of different architectures, implementations, workloads, and system design choices.
A soft robotic approach to robust and dexterous grasping

In this paper, we present a compliant robotic gripper, Edgy-2, with 4-DOF dexterity, enabling four grasping modes: parallel grasping, power grasping, finger-tip pinch and fully-actuated grasping. The roboticfinger is based on soft-rigid-hybrid structures, combining fiber-reinforced soft pneumatic actuators with rigid joints, which exhibit reliable structural rigidity and grasping robustness while maintaining excellent adaptability and inherent compliance. With both grasping dexterity and grasping robustness, the Edgy-2 achieves excellent grasping reliability with various daily objects, from a fragile cherry to a 2 kg water bottled water. The relationship of design parameters and grasping strength is presented with analytical models. The performance of a prototype Edgy-2 is verified by dedicated experiments. The proposed hybrid dexterous grasping approach can be easily extended into different end-effector designs according to application requirements. The proposed mechanism for grasping provides excellent human-robot interaction safety and reliability.
Comparative study of rectangular and circular microstrip patch antennas in X band

A comparative performance study of rectangular and circular shape microstrip patch antennas at an X band frequency is presented in this paper. The resonant frequency is chosen at 10 GHz which is suitable for a variety of wireless applications. CST Microwave Studio is used as the software environment to design and compare the performance of the antennas. It is found that the rectangular patch antenna shows about 3.0 dB higher return loss than the return loss of circular patch antenna. In addition, the rectangular patch antenna has an improved VSWR value of 1.18 than that of the circular patch with VSWR 1.27. However, circular patch antenna offers about 8% higher bandwidth and nearly 2.0dB less side lobe power than that of rectangular patch antenna.
Relative phase information for detecting human speech and spoofed speech

The detection of human and spoofed (synthetic/converted) speech has started to receive more attention. In this study, relative phase information extracted from a Fourier spectrum is used to detect human and spoofed speech. Because original/natural phase information is almost entirely lost in spoofed speech using current synthesis/conversion techniques, a modified group delay based feature, the frequency derivative of the phase spectrum, has been shown effective for detecting human speech and spoofed speech. The modified group delay based phase contains both the magnitude spectrum and phase information. Therefore, the relative phase information, which contains only phase information, is expected to achieve a better spoofing detection performance. In this study, the relative phase information is also combined with the Mel-Frequency Cepstral Coefficient (MFCC) and modified group delay. The proposed method was evaluated using the “ASVspoof 2015: Automatic Speaker Verification Spoofing and Countermeasures Challenge” dataset. The results show that the proposed relative phase information significantly outperforms the MFCC and modified group delay. The equal error rate (EER) was reduced from 1.74% of MFCC, 0.83% of modified group delay to 0.013% of relative phase. By combining the relative phase with MFCC and modified group delay, the EER was reduced to 0.002%.
Synthetic aperture radar interferometry

Synthetic aperture radar interferometry is an imaging technique for measuring the topography of a surface, its changes over time, and other changes in the detailed characteristic of the surface. By exploiting the phase of the coherent radar signal, interferometry has transformed radar remote sensing from a largely interpretive science to a quantitative tool, with applications in cartography, geodesy, land cover characterization, and natural hazards. This paper reviews the techniques of interferometry, systems and limitations, and applications in a rapidly growing area of science and engineering.
An implementation of the 802.1AE MAC Security Standard for in-car networks

The continuous increase in complexity in automotive electronics has led to cars that include up to 80 Electronic Control Units (ECUs). As a consequence, in-car networks are currently up to their limit in terms of data load, flexibility and bandwidth. The Ethernet backbone is thus considered as the best performing solution. On the other hand, the growing interconnection of cars with the external world requires high security standards in order to prevent safety risks for car passengers. The IEEE 802.1AE MAC Security Standard (MACSec) solves the security issues of Ethernet networks by providing confidentiality, authenticity and integrity of data. This paper presents an efficient hardware implementation of the MACSec standard for the automotive world. The system was synthesized on a Stratix V FPGA and on a 28nm standard-cell CMOS technology. In terms of maximum throughput, the FPGA results in 1.1 Gbps while the standard-cell technology reaches 3.9 Gbps. The FPGA implementation occupies 4.5% of the Adaptive Logic Modules (ALMs) while the standard-cell one gives a 285 kgate size. The proposed architecture represents a suitable implementation for a low area and high-performance solution as usually required by in-car network controllers.
A Semi-Supervised Approach for Kernel-Based Temporal Clustering

Temporal clustering refers to the partitioning of a time series into multiple nonoverlapping segments that belong to k temporal clusters, in such a way that segments in the same cluster are more similar to each other than to those in other clusters. Temporal clustering is a fundamental task in many fields, such as computer animation, computer vision, health care, and robotics. The applications of temporal clustering in those areas are diverse, and include human-motion imitation and recognition, emotion analysis, human activity segmentation, automated rehabilitation exercise analysis, and human-computer interaction. However, temporal clustering using a completely unsupervised method may not produce satisfactory results. Similar to regular clustering, temporal clustering also benefits from some expert knowledge that may be available. The type of approach that utilizes a small amount of knowledge to “guide” the clustering process is known as “semi-supervised clustering.” Semi-supervised temporal clustering is a strategy in which extra knowledge, in the form of pairwise constraints, is incorporated into the temporal data to help with the partitioning problem. This thesis proposes a process to adapt and transform two kernel-based methods into semi-supervised temporal clustering methods. The proposed process is exclusive to kernel-based clustering methods, and is based on two concepts. First, it uses the idea of instance-level constraints, in the form of must-link and cannot-link, to supervise the clustering methods. Second, it uses a dynamic-programming method to search for the optimal temporal clusters. The proposed process is applied to two algorithms, aligned cluster analysis (ACA) and spectral clustering. To validate the advantages of the proposed temporal semi-supervised clustering methods, a comparative analysis was performed, using the original versions of the algorithm and another semi-supervised temporal cluster. This evaluation was conducted with both synthetic data and two real-world applications. The first application includes two naturalistic audio-visual human emotion datasets, and the second application focuses on human-motion segmentation. Results show substantial improvements in accuracy, with minimal supervision, compared to unsupervised and other temporal semi-supervised approaches, without compromising time performance.
Anarchy, stability, and utopia: creating better matchings

Historically, the analysis of matching has centered on designing algorithms to produce stable matchings as well as on analyzing the incentive compatibility of matching mechanisms. Less attention has been paid to questions related to the social welfare of stable matchings in cardinal utility models. We examine the loss in social welfare that arises from requiring matchings to be stable, the natural equilibrium concept under individual rationality. While this loss can be arbitrarily bad under general preferences, when there is some structure to the underlying graph corresponding to natural conditions on preferences, we prove worst case bounds on the price of anarchy. Surprisingly, under simple distributions of utilities, the average case loss turns out to be significantly smaller than the worst-case analysis would suggest. Furthermore, we derive conditions for the existence of approximately stable matchings that are also close to socially optimal, demonstrating that adding small switching costs can make socially (near-)optimal matchings stable. Our analysis leads to several concomitant results of interest on the convergence of decentralized partner-switching algorithms, and on the impact of heterogeneity of tastes on social welfare.
The Evolution of Learning : An Experiment in Genetic Connectionism

This paper explores how an evolutionary process can produce systems that learn. A general framework for the evolution of learning is outlined, and is applied to the task of evolving mechanisms suitable for supervised learning in single-layer neural networks. Dynamic properties of a network’s information-processing capacity are encoded genetically, and these properties are subjected to selective pressure based on their success in producing adaptive behavior in diverse environments. As a result of selection and genetic recombination, various successful learning mechanisms evolve, including the well-known delta rule. The effect of environmental diversity on the evolution of learning is investigated, and the role of different kinds of emergent phenomena in genetic and connectionist systems is discussed. In D. S. Touretzky, J. L. Elman, T. J. Sejnowski & G. E. Hinton (eds.), : Morgan Kaufmann.
Beyond Independent Relevance: Methods and Evaluation Metrics for Subtopic Retrieval

We present a non-traditional retrieval problem we call subtopic retrieval. The subtopic retrieval problem is concerned with finding documents that cover many different subtopics of a query topic. In such a problem, the utility of a document in a ranking is dependent on other documents in the ranking, violating the assumption of independent relevance which is assumed in most traditional retrieval methods. Subtopic retrieval poses challenges for evaluating performance, as well as for developing effective algorithms. We propose a framework for evaluating subtopic retrieval which generalizes the traditional precision and recall metrics by accounting for intrinsic topic difficulty as well as redundancy in documents. We propose and systematically evaluate several methods for performing subtopic retrieval using statistical language models and a maximal marginal relevance (MMR) ranking strategy. A mixture model combined with query likelihood relevance ranking is shown to modestly outperform a baseline relevance ranking on a data set used in the TREC interactive track.
DRAM errors in the wild: a large-scale field study

Errors in dynamic random access memory (DRAM) are a common form of hardware failure in modern compute clusters. Failures are costly both in terms of hardware replacement costs and service disruption. While a large body of work exists on DRAM in laboratory conditions, little has been reported on real DRAM failures in large production clusters. In this paper, we analyze measurements of memory errors in a large fleet of commodity servers over a period of 2.5 years. The collected data covers multiple vendors, DRAM capacities and technologies, and comprises many millions of DIMM days.
 The goal of this paper is to answer questions such as the following: How common are memory errors in practice? What are their statistical properties? How are they affected by external factors, such as temperature and utilization, and by chip-specific factors, such as chip density, memory technology and DIMM age?
 We find that DRAM error behavior in the field differs in many key aspects from commonly held assumptions. For example, we observe DRAM error rates that are orders of magnitude higher than previously reported, with 25,000 to 70,000 errors per billion device hours per Mbit and more than 8% of DIMMs affected by errors per year. We provide strong evidence that memory errors are dominated by hard errors, rather than soft errors, which previous work suspects to be the dominant error mode. We find that temperature, known to strongly impact DIMM error rates in lab conditions, has a surprisingly small effect on error behavior in the field, when taking all other factors into account. Finally, unlike commonly feared, we don't observe any indication that newer generations of DIMMs have worse error behavior.
The Exact Security of Digital Signatures - HOw to Sign with RSA and Rabin

We describe an RSA-based signing scheme which combines essentially optimal e ciency with attractive security properties. Signing takes one RSA decryption plus some hashing, veri cation takes one RSA encryption plus some hashing, and the size of the signature is the size of the modulus. Assuming the underlying hash functions are ideal, our schemes are not only provably secure, but are so in a tight way| an ability to forge signatures with a certain amount of computational resources implies the ability to invert RSA (on the same size modulus) with about the same computational e ort. Furthermore, we provide a second scheme which maintains all of the above features and in addition provides message recovery. These ideas extend to provide schemes for Rabin signatures with analogous properties; in particular their security can be tightly related to the hardness of factoring. Department of Computer Science and Engineering, Mail Code 0114, University of California at San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA. E-mail: mihir@cs.ucsd.edu ; Web page: http://www-cse.ucsd.edu/users/mihir y Department of Computer Science, University of California at Davis, Davis, CA 95616, USA.
Multilabel Subject-Based Classification of Poetry

Oftentimes, the question “what is this poem about?” has no trivial answer, regardless of length, style, author, or context in which the poem is found. We propose a simple system of multi-label classification of poems based on their subjects following the categories and subcategories as laid out by the Poetry Foundation. We make use of a model that combines the methodologies of tf-idf and Latent Dirichlet Allocation for feature extraction, and a Support Vector Machine model for the classification task. We determine how likely it is for our models to correctly classify each poem they read into one or more main categories and subcategories. Our contribution is, thus, a new method to automatically classify poetry given a set and various subsets of categories.