nils-herrmann/clean_scirepeval huggingface dataset:
Detecting Novel Network Intrusions Using Bayes Estimators

From the first appearance of network attacks, the internet worm, to the most recent one in which the servers of several famous e-business companies were paralyzed for several hours, causing huge financial losses, network-based attacks have been increasing in frequency and severity. As a powerful weapon to protect networks, intrusion detection has been gaining a lot of attention. Traditionally, intrusion detection techniques are classified into two broad categories: misuse detection and anomaly detection. Misuse detection aims to detect well-known attacks as well as slight variations of them, by characterizing the rules that govern these attacks. Due to its nature, misuse detection has low false alarms but it is unable to detect any attacks that lie beyond its knowledge. Anomaly detection is designed to capture any deviations from the established profiles of users and systems normal behavior pattern. Although in principle, anomaly detection has the ability to detect new attacks, in practice this is far from easy. Anomaly detection has the potential to generate too many false alarms, and it is very time consuming and labor expensive to sift true intrusions from the false alarms. As new network attacks emerge, the need for intrusion detection systems to detect novel attacks becomes pressing. As we stated before, this is one of the hardest tasks to accomplish, since no knowledge about the novel attacks is available. However, if we view the problem from another angle, we can find a solution. Attacks do something that is different from normal activities: if we have comprehensive knowledge about normal activities and their normal deviations, then all activities ∗This work has been funded by AFRL Rome Labs under the contract F 30602-00-2-0512. †All the authors are at George Mason University, Center for Secure Information Systems Fairfax, VA 22303
XRCE at SemEval-2016 Task 5: Feedbacked Ensemble Modeling on Syntactico-Semantic Knowledge for Aspect Based Sentiment Analysis

This paper presents our contribution to the SemEval 2016 task 5: Aspect-Based Sentiment Analysis. We have addressed Subtask 1 for the restaurant domain, in English and French, which implies opinion target expression detection, aspect category and polarity classification. We describe the different components of the system, based on composite models combining sophisticated linguistic features with Machine Learning algorithms, and report the results obtained for both languages.
A practical guide to controlled experiments of software engineering tools with human participants

Empirical studies, often in the form of controlled experiments, have been widely adopted in software engineering research as a way to evaluate the merits of new software engineering tools. However, controlled experiments involving human participants actually using new tools are still rare, and when they are conducted, some have serious validity concerns. Recent research has also shown that many software engineering researchers view this form of tool evaluation as too risky and too difficult to conduct, as they might ultimately lead to inconclusive or negative results. In this paper, we aim both to help researchers minimize the risks of this form of tool evaluation, and to increase their quality, by offering practical methodological guidance on designing and running controlled experiments with developers. Our guidance fills gaps in the empirical literature by explaining, from a practical perspective, options in the recruitment and selection of human participants, informed consent, experimental procedures, demographic measurements, group assignment, training, the selecting and design of tasks, the measurement of common outcome variables such as success and time on task, and study debriefing. Throughout, we situate this guidance in the results of a new systematic review of the tool evaluations that were published in over 1,700 software engineering papers published from 2001 to 2011.
Different tumor microenvironments contain functionally distinct subsets of macrophages derived from Ly6C(high) monocytes.

Tumor-associated macrophages (TAM) form a major component of the tumor stroma. However, important concepts such as TAM heterogeneity and the nature of the monocytic TAM precursors remain speculative. Here, we show for the first time that mouse mammary tumors contained functionally distinct subsets of TAMs and provide markers for their identification. Furthermore, in search of the TAM progenitors, we show that the tumor-monocyte pool almost exclusively consisted of Ly6C(hi)CX(3)CR1(low) monocytes, which continuously seeded tumors and renewed all nonproliferating TAM subsets. Interestingly, gene and protein profiling indicated that distinct TAM populations differed at the molecular level and could be classified based on the classic (M1) versus alternative (M2) macrophage activation paradigm. Importantly, the more M2-like TAMs were enriched in hypoxic tumor areas, had a superior proangiogenic activity in vivo, and increased in numbers as tumors progressed. Finally, it was shown that the TAM subsets were poor antigen presenters, but could suppress T-cell activation, albeit by using different suppressive mechanisms. Together, our data help to unravel the complexities of the tumor-infiltrating myeloid cell compartment and provide a rationale for targeting specialized TAM subsets, thereby optimally "re-educating" the TAM compartment.
Understanding the Dark Side of Domain Parking

Domain parking is a booming business with millions of dollars in revenues. However, it is also among the least regulated: parked domains have been routinely found to connect to illicit online activities even though the roles they play there have never been clarified. In this paper, we report the first systematic study on this “dark side” of domain parking based upon a novel infiltration analysis on domains hosted by major parking services. The idea here is to control the traffic sources (crawlers) of the domain parking ecosystem, some of its start nodes (parked domains) and its end nodes (advertisers and traffic buyers) and then “connect the dots”, delivering our own traffic to our end nodes across our own start nodes with other monetization entities (parking services, ad networks, etc) in-between. This provided us a unique observation of the whole monetization process and over one thousand seed redirection chains where some ends were under our control. From those chains, we were able to confirm the presence of click fraud, traffic spam and traffic stealing. To further understand the scope and magnitude of this threat, we extracted a set of salient features from those seed chains and utilized them to detect illicit activities on 24 million monetization chains we collected from leading parking services over 5.5 months. This study reveals the pervasiveness of those illicit monetization activities, parties responsible for them and the revenues they generate which approaches 40% of the total revenue for some parking services. Our findings point to an urgent need for a better regulation of domain parking.
A coupled HMM for audio-visual speech recognition

In recent years several speech recognition systems that use visual together with audio information showed significant increase in performance over the standard speech recognition systems. The use of visual features is justified by both the bimodality of the speech generation and by the need of features that are invariant to acoustic noise perturbation. The audio-visual speech recognition system presented in this paper introduces a novel audio-visual fusion technique that uses a coupled hidden Markov model (HMM). The statistical properties of the coupled-HMM allow us to model the state asynchrony of the audio and visual observations sequences while still preserving their natural correlation over time. The experimental results show that the coupled HMM outperforms the multistream HMM in audio visual speech recognition.
Embedding Identity and Interest for Social Networks

Network embedding fills the gap of applying tuple-based data mining models to networked datasets through learning latent representations or embeddings. However, it may not be likely to associate latent embeddings with physical meanings just as the name, latent embedding, literally suggests. Hence, models built on embeddings may not be interpretable. In this paper, we thus propose to learn identity embeddings and interest embeddings, where user identity includes demographic and a liation information, and interest is demonstrated by activities or topics users are interested in. With identity and interest information, we can make data mining models not only more interpretable, but also more accurate, which is demonstrated on three real-world social networks in link prediction and multi-task classification.
Quantifying the strength and form of sexual selection on men ' s traits ☆

☆ JH was funded by NERC and a University Royal Socie by an AIB grant awarded to DAP. ⁎ Corresponding author.  E-mail address: dap27@psu.edu (D.A. Puts). 1090-5138/$ – see front matter  (2013), http://dx.doi.org/10.1016/j.evolhum Article history: Initial receipt 13 March 2013 Final revision received 30 May 2013 Available online xxxx
Initial over-the-air performance assessment of ranging and clock synchronization using radio frequency signal exchange

In this paper we demonstrate results of a technique for synchronizing clocks and estimating ranges between a pair of RF transceivers. The technique uses a periodic exchange of ranging waveforms between two transceivers along with sophisticated delay estimation and tracking. The technique was implemented on wireless testbed transceivers with independent clocks and tested over-the-air in stationary and moving configurations. The technique achieved ~10ps synchronization accuracy and 2.1mm range deviation, using A two-channel oscilloscope and tape measure as truth sources. The timing resolution attained is three orders of magnitude better than the inverse signal bandwidth of the ranging waveform (50MHz⇒ 6m resolution), and is within a small fraction of the carrier wavelength (915MHz⇒ 327mm wavelength). We discuss how this result is consistent with the Weiss-Weinstein bound and cite new applications enabled by this technique.
NLML - a Markup Language to Describe the Unlimited English Grammar

In this paper we present NLML (Natural Language Markup Language), a markup language to describe the syntactic and semantic structure of any grammatically correct English expression. At first the related works are analyzed to demonstrate the necessity of the NLML: simple form, easy management and direct storage. Then the description of the English grammar with NLML is introduced in details in three levels: sentences (with different complexities, voices, moods, and tenses), clause (relative clause and noun clause) and phrase (noun phrase, verb phrase, prepositional phrase, adjective phrase, adverb phrase and predicate phrase). At last the application fields of the NLML in NLP are shown with two typical examples: NLOJM (Natural Language Object Modal in Java) and NLDB (Natural Language Database).
Bézier curve based path planning for autonomous vehicle in urban environment

This paper presents a Bézier curve based path planner which enables the anti-collision behavior of an electronic car. The anti-collision system is a fundamental module in the architecture. The path tracking implementation uses pure pursuit algorithm. The anti-collision system based on laser scanner data consists of estimating the trajectories and behavior of surrounding objects, and a Bézier curve based path planner. Experimental results are presented showing the effectiveness of the overall navigation control system.
Pipelined Compaction for the LSM-Tree

Write-optimized data structures like Log-Structured Merge-tree (LSM-tree) and its variants are widely used in key-value storage systems like Big Table and Cassandra. Due to deferral and batching, the LSM-tree based storage systems need background compactions to merge key-value entries and keep them sorted for future queries and scans. Background compactions play a key role on the performance of the LSM-tree based storage systems. Existing studies about the background compaction focus on decreasing the compaction frequency, reducing I/Os or confining compactions on hot data key-ranges. They do not pay much attention to the computation time in background compactions. However, the computation time is no longer negligible, and even the computation takes more than 60% of the total compaction time in storage systems using flash based SSDs. Therefore, an alternative method to speedup the compaction is to make good use of the parallelism of underlying hardware including CPUs and I/O devices. In this paper, we analyze the compaction procedure, recognize the performance bottleneck, and propose the Pipelined Compaction Procedure (PCP) to better utilize the parallelism of CPUs and I/O devices. Theoretical analysis proves that PCP can improve the compaction bandwidth. Furthermore, we implement PCP in real system and conduct extensive experiments. The experimental results show that the pipelined compaction procedure can increase the compaction bandwidth and storage system throughput by 77% and 62% respectively.
Bringing Machine Learning and Compositional Semantics Together

Computational semantics has long been seen as a field divided between logical and statistical approaches, but this divide is rapidly eroding, with the development of statistical models that learn compositional semantic theories from corpora and databases. This paper presents a simple discriminative learning framework for defining such models and relating them to logical theories. Within this framework, we discuss the task of learning to map utterances to logical forms (semantic parsing) and the task of learning from denotations with logical forms as latent variables. We also consider models that use distributed (e.g., vector) representations rather than logical ones, showing that these can be seen as part of the same overall framework for understanding meaning and structural complexity.
Feature extraction using Restricted Boltzmann Machine for stock price prediction

Recently, many different types of artificial neural networks (ANNs) have been applied to forecast stock price and good performance is obtained. However, most of these models use only a small number of features as input and there may not be enough information to make prediction due to the complexity of stock market. If having a larger number of features, the run time of training would be increased and the generalization performance would be deteriorated due to the curse of dimension. Therefore, an effective tool to extract highly discriminative low-dimensional features from the high-dimensional raw input would be a great help in improving the generalization performance of the regression model. Restricted Boltzmann Machine (RBM) is a new type of machine learning tool with strong power of representation, which has been utilized as the feature extractor in a large variety of classification problems. In this paper, we use the RBM to extract discriminative low-dimensional features from raw data with dimension up to 324, and then use the extracted features as the input of Support Vector Machine (SVM) for regression. Experimental results indicate that our approach for stock price prediction has great improvement in terms of low forecasting errors compared with SVM using raw data.
Non-constrained Blood Pressure Monitoring Using ECG and PPG for Personal Healthcare

Blood pressure (BP) is one of the important vital signs that need to be monitored for personal healthcare. Arterial blood pressure (BP) was estimated from pulse transit time (PTT) and PPG waveform. PTT is a time interval between an R-wave of electrocardiography (ECG) and a photoplethysmography (PPG) signal. This method does not require an aircuff and only a minimal inconvenience of attaching electrodes and LED/photo detector sensors on a subject. PTT computed between the ECG R-wave and the maximum first derivative PPG was strongly correlated with systolic blood pressure (SBP) (R = −0.712) compared with other PTT values, and the diastolic time proved to be appropriate for estimation diastolic blood pressure (DBP) (R = −0.764). The percent errors of SBP using the individual regression line (4–11%) were lower than those using the regression line obtained from all five subjects (9–14%). On the other hand, the DBP estimation did not show much difference between the individual regression (4–10%) and total regression line (6–10%). Our developed device had a total size of 7 × 13.5 cm and was operated by single 3-V battery. Biosignals can be measured for 72 h continuously without external interruptions. Through a serial network communication, an external personal computer can monitor measured waveforms in real time. Our proposed method can be used for non-constrained, thus continuous BP monitoring for the purpose of personal healthcare.
Enhanced privacy of a remote data integrity-checking protocol for secure cloud storage

Remote data integrity checking (RDIC) enables a server to prove to an auditor the integrity of a stored file. It is a useful technology for remote storage such as cloud storage. The auditor could be a party other than the data owner; hence, an RDIC proof is based usually on publicly available information. To capture the need of data privacy against an untrusted auditor, Hao et al. formally defined “privacy against third party verifiers” as one of the security requirements and proposed a protocol satisfying this definition. However, we observe that all existing protocols with public verifiability supporting data update, including Hao et al.’s proposal, require the data owner to publish some meta-data related to the stored data. We show that the auditor can tell whether or not a client has stored a specific file and link various parts of those files based solely on the published meta-data in Hao et al.’s protocol. In other words, the notion “privacy against third party verifiers” is not sufficient in protecting data privacy, and hence, we introduce “zero-knowledge privacy” to ensure the third party verifier learns nothing about the client’s data from all available information. We enhance the privacy of Hao et al.’s protocol, develop a prototype to evaluate the performance and perform experiment to demonstrate the practicality of our proposal.
Learning to Generate Images of Outdoor Scenes from Attributes and Semantic Layouts

Automatic image synthesis research has been rapidly growing with deep networks getting more and more expressive. In the last couple of years, we have observed images of digits, indoor scenes, birds, chairs, etc. being automatically generated. The expressive power of image generators have also been enhanced by introducing several forms of conditioning variables such as object names, sentences, bounding box and key-point locations. In this work, we propose a novel deep conditional generative adversarial network architecture that takes its strength from the semantic layout and scene attributes integrated as conditioning variables. We show that our architecture is able to generate realistic outdoor scene images under different conditions, e.g. daynight, sunny-foggy, with clear object boundaries.
Microstructure of a spatial map in the entorhinal cortex

The ability to find one's way depends on neural algorithms that integrate information about place, distance and direction, but the implementation of these operations in cortical microcircuits is poorly understood. Here we show that the dorsocaudal medial entorhinal cortex (dMEC) contains a directionally oriented, topographically organized neural map of the spatial environment. Its key unit is the ‘grid cell’, which is activated whenever the animal's position coincides with any vertex of a regular grid of equilateral triangles spanning the surface of the environment. Grids of neighbouring cells share a common orientation and spacing, but their vertex locations (their phases) differ. The spacing and size of individual fields increase from dorsal to ventral dMEC. The map is anchored to external landmarks, but persists in their absence, suggesting that grid cells may be part of a generalized, path-integration-based map of the spatial environment.
Incentivizing Users for Balancing Bike Sharing Systems

Bike sharing systems have been recently adopted by a growing number of cities as a new means of transportation offering citizens a flexible, fast and green alternative for mobility. Users can pick up or drop off the bicycles at a station of their choice without prior notice or time planning. This increased flexibility comes with the challenge of unpredictable and fluctuating demand as well as irregular flow patterns of the bikes. As a result, these systems can incur imbalance problems such as the unavailability of bikes or parking docks at stations. In this light, operators deploy fleets of vehicles which re-distribute the bikes in order to guarantee a desirable service level. Can we engage the users themselves to solve the imbalance problem in bike sharing systems? In this paper, we address this question and present a crowdsourcing mechanism that incentivizes the users in the bike repositioning process by providing them with alternate choices to pick or return bikes in exchange for monetary incentives. We design the complete architecture of the incentives system which employs optimal pricing policies using the approach of regret minimization in online learning. We investigate the incentive compatibility of our mechanism and extensively evaluate it through simulations based on data collected via a survey study. Finally, we deployed the proposed system through a smartphone app among users of a large scale bike sharing system operated by a public transport company, and we provide results from this experimental deployment. To our knowledge, this is the first dynamic incentives system for bikes re-distribution ever deployed in a realworld bike sharing system.
Ku-band sidearm orthomode transducer manufactured by additive layer manufacturing

This paper investigates the use of additive layer manufacturing (ALM) for waveguide components based on two Ku-band sidearm orthomode transducers (OMT). The advantages and disadvantages of the ALM manufacturing regarding RF waveguide components are discussed and measurement results are compared to those of an equal OMT manufactured by conventional techniques. The paper concludes with an outlook to the capability of advanced manufacturing techniques for RF space applications as well as ongoing development activities.
Design of the robot-cub (iCub) head

This paper describes the design of a robot head, developed in the framework of the RobotCub project. This project goals consists on the design and construction of a humanoid robotic platform, the iCub, for studying human cognition. The final platform would be approximately 90 cm tall, with 23 kg and with a total number of 53 degrees of freedom. For its size, the iCub is the most complete humanoid robot currently being designed, in terms of kinematic complexity. The eyes can also move, as opposed to similarly sized humanoid platforms. Specifications are made based on biological anatomical and behavioral data, as well as tasks constraints. Different concepts for the neck design (flexible, parallel and serial solutions) are analyzed and compared with respect to the specifications. The eye structure and the proprioceptive sensors are presented, together with some discussion of preliminary work on the face design