nils-herrmann/clean_scirepeval huggingface dataset:
Feature deduction and ensemble design of intrusion detection systems

Current intrusion detection systems (IDS) examine all data features to detect intrusion or misuse patterns. Some of the features may be redundant or contribute little (if anything) to the detection process. The purpose of this study is to identify important input features in building an IDS that is computationally efficient and effective. We investigated the performance of two feature selection algorithms involving Bayesian networks (BN) and Classification and Regression Trees (CART) and an ensemble of BN and CART. Empirical results indicate that significant input feature selection is important to design an IDS that is lightweight, efficient and effective for real world detection systems. Finally, we propose an hybrid architecture for combining different feature selection algorithms for real world intrusion detection.
Towards deep learning with segregated dendrites

Deep learning has led to significant advances in artificial intelligence, in part, by adopting strategies motivated by neurophysiology. However, it is unclear whether deep learning could occur in the real brain. Here, we show that a deep learning algorithm that utilizes multi-compartment neurons might help us to understand how the neocortex optimizes cost functions. Like neocortical pyramidal neurons, neurons in our model receive sensory information and higher-order feedback in electrotonically segregated compartments. Thanks to this segregation, neurons in different layers of the network can coordinate synaptic weight updates. As a result, the network learns to categorize images better than a single layer network. Furthermore, we show that our algorithm takes advantage of multilayer architectures to identify useful higher-order representations-the hallmark of deep learning. This work demonstrates that deep learning can be achieved using segregated dendritic compartments, which may help to explain the morphology of neocortical pyramidal neurons.
Hotplug or Ballooning: A Comparative Study on Dynamic Memory Management Techniques for Virtual Machines

In virtualization environments, static memory allocation for virtual machines (VMs) can lead to severe service level agreement (SLA) violations or inefficient use of memory. Dynamic memory allocation mechanisms such as ballooning and memory hotplug were proposed to handle the dynamics of memory demands. However, these mechanisms so far have not been quantitatively or comparatively studied. In this paper, we first develop a runtime system called U-tube, which provides a framework to adopt memory hotplug or ballooning for dynamic memory allocation. We then implement fine-grained memory hotplug in Xen. We demonstrate the effectiveness of U-tube for dynamic memory management through two case studies: dynamic memory balancing and memory overcommitment. With these two case studies, we make a quantitative comparison between memory hotplug and ballooning. The experiments show that there is no absolute winner for different scenarios. Our findings can be very useful for practitioners to choose the suitable dynamic memory management techniques in different scenarios.
Grounding Strategic Conversation: Using Negotiation Dialogues to Predict Trades in a Win-Lose Game

This paper describes a method that predicts which trades players execute during a winlose game. Our method uses data collected from chat negotiations of the game The Settlers of Catan and exploits the conversation to construct dynamically a partial model of each player’s preferences. This in turn yields equilibrium trading moves via principles from game theory. We compare our method against four baselines and show that tracking how preferences evolve through the dialogue and reasoning about equilibrium moves are both crucial to success.
Design and control of a second-generation hyper-redundant mechanism

We present a refined, second-generation design, construction and integration, of a compact hyper-redundant snakelike robot, called "Woodstock." This robot has substantial advantages over our previous design iteration, "Snoopy," in terms of cost and performance. The robot is composed of six actuated universal joints which are serially chained to construct a twelve degrees of freedom snake-like robot optimized for strength and compactness. Any joint in the robot is strong enough to produce a torque that is capable of cantilevering the entire robot. This paper also presents the low-level system- control architecture, which is based on a high-speed RS-485 data bus; this allows the entire system to be operated with only two power and two data wires. The system is controlled from a remote computer on a wireless network and can also run over the Internet.
Attribute reduction in decision-theoretic rough set models

Rough set theory can be applied to rule induction. There are two different types of classification rules, positive and boundary rules, leading to different decisions and consequences. They can be distinguished not only from the syntax measures such as confidence, coverage and generality, but also the semantic measures such as decision-monotocity, cost and risk. The classification rules can be evaluated locally for each individual rule, or globally for a set of rules. Both the two types of classification rules can be generated from, and interpreted by, a decision-theoretic model, which is a probabilistic extension of the Pawlak rough set model. As an important concept of rough set theory, an attribute reduct is a subset of attributes that are jointly sufficient and individually necessary for preserving a particular property of the given information table. This paper addresses attribute reduction in decision-theoretic rough set models regarding different classification properties, such as: decisionmonotocity, confidence, coverage, generality and cost. It is important to note that many of these properties can be truthfully reflected by a single measure c in the Pawlak rough set model. On the other hand, they need to be considered separately in probabilistic models. A straightforward extension of the c measure is unable to evaluate these properties. This study provides a new insight into the problem of attribute reduction.
Like: The Discourse Particle and Semantics

Using data from interviews with high school students, I first adduce evidence that lends support to Schourup’s (1985) claim that the United States English adolescent hedge like is a discourse particle signalling a possible slight mismatch between words and meaning. Such a particle would generally be included in a grammar in a post-compositional pragmatic component, but, surprisingly, like also affects basic semantic attributes. These include both truth-conditions and the weak/strong distinction—though only in existential there and sluicing sentences. I argue that the differential behaviour of like in various constructions selecting weak NP’s stems from the restricted free variable it introduces, a variable which only there and sluicing require. This variable is available for binding, quantifier interpretation and other syntactic-semantic processes, yet is pragmatically conditioned. Indeed, I show that, due to its formal properties, like can be interpreted only during the assignment of model-theoretic denotations to expressions, along the lines of Lasersohn’s (1999) pragmatic haloes. These results support the idea that weak/strong is not a unitary distinction and suggest that the various components of grammars must be organized to allow information from pragmatic/discourse elements to affect basic compositional semantics.
Usefulness of the Medical Research Council (MRC) dyspnoea scale as a measure of disability in patients with chronic obstructive pulmonary disease.

Methods of classifying chronic obstructive pulmonary disease (COPD) depend largely upon spirometric measurements but disability is only weakly related to measurements of lung function. With the increased use of pulmonary rehabilitation, a need has been identified for a simple and standardised method of categorising disability in COPD. This study examined the validity of the Medical Research Council (MRC) dyspnoea scale for this purpose.



One hundred patients with COPD were recruited from an outpatient pulmonary rehabilitation programme. Assessments included the MRC dyspnoea scale, spirometric tests, blood gas tensions, a shuttle walking test, and Borg scores for perceived breathlessness before and after exercise. Health status was assessed using the St George's Respiratory Questionnaire (SGRQ) and Chronic Respiratory Questionnaire (CRQ). The Nottingham Extended Activities of Daily Living (EADL) score and Hospital Anxiety and Depression (HAD) score were also measured.



Of the patients studied, 32 were classified as having MRC grade 3 dyspnoea, 34 MRC grade 4 dyspnoea, and 34 MRC grade 5 dyspnoea. Patients with MRC grades 1 and 2 dyspnoea were not included in the study. There was a significant association between MRC grade and shuttle distance, SGRQ and CRQ scores, mood state and EADL. Forced expiratory volume in one second (FEV1) was not associated with MRC grade. Multiple logistic regression showed that the determinants of disability appeared to vary with the level of disability. Between MRC grades 3 and 4 the significant covariates were exercise performance, SGRQ and depression score, whilst between grades 4 and 5 exercise performance and age were the major determinants.



The MRC dyspnoea scale is a simple and valid method of categorising patients with COPD in terms of their disability that could be used to complement FEV1 in the classification of COPD severity.
A passive technique for fingerprinting wireless devices with Wired-side Observations

In this paper, we introduce GTID, a technique that passively fingerprints wireless devices and their types from the wired backbone. GTID exploits the heterogeneity of devices, which is a function of different device hardware compositions and variations in devices' clock skew. We use statistical techniques to create unique, reproducible device and device type signatures that represent time variant behavior in network traffic and use artificial neural networks (ANNs) to classify devices and device types. We demonstrate the efficacy of our technique on both an isolated testbed and a live campus network (during peak hours) using a corpus of 27 devices representing a wide range of device classes. We collected more than 100 GB of traffic captures for ANN training and classification. We assert that for any fingerprinting technique to be practical, it must be able to detect previously unseen devices (i.e., devices for which no stored signature is available) and must be able to withstand various attacks. GTID is the first fingerprinting technique to detect previously unseen devices and to illustrate its resilience under various attacker models. We measure the performance of GTID by considering accuracy, recall, and processing time and illustrate how it can be used to complement existing authentication systems and to detect counterfeit devices.
Covariate Shift in Hilbert Space: A Solution via Sorrogate Kernels

Covariate shift is an unconventional learning scenario in which training and testing data have different distributions. A general principle to solve the problem is to make the training data distribution similar to that of the test domain, such that classifiers computed on the former generalize well to the latter. Current approaches typically target on sample distributions in the input space, however, for kernel-based learning methods, the algorithm performance depends directly on the geometry of the kernel-induced feature space. Motivated by this, we propose to match data distributions in the Hilbert space, which, given a pre-defined empirical kernel map, can be formulated as aligning kernel matrices across domains. In particular, to evaluate similarity of kernel matrices defined on arbitrarily different samples, the novel concept of surrogate kernel is introduced based on the Mercer’s theorem. Our approach caters the model adaptation specifically to kernel-based learning mechanism, and demonstrates promising results on several real-world applications. Proceedings of the 30 th International Conference on Machine Learning, Atlanta, Georgia, USA, 2013.  W&CP volume 28.
Cold Fusion: Training Seq2Seq Models Together with Language Models

Sequence-to-sequence (Seq2Seq) models with attention have excelled at tasks which involve generating natural language sentences such as machine translation, image captioning and speech recognition. Performance has further been improved by leveraging unlabeled data, often in the form of a language model. In this work, we present the Cold Fusion method, which leverages a pre-trained language model during training, and show its effectiveness on the speech recognition task. We show that Seq2Seq models with Cold Fusion are able to better utilize language information enjoying i) faster convergence and better generalization, and ii) almost complete transfer to a new domain while using less than 10% of the labeled training data.
Policy ratification

It is not sufficient to merely check the syntax of new policies before they are deployed in a system; policies need to be analyzed for their interactions with each other and with their local environment. That is, policies need to go through a ratification process. We believe policy ratification becomes an essential part of system management as the number of policies in the system increases and as the system administration becomes more decentralized. In this paper, we focus on the basic tasks involved in policy ratification. To a large degree, these basic tasks can be performed independent of policy model and language and require little domain-specific knowledge. We present algorithms from constraint, linear, and logic programming disciplines to help perform ratification tasks. We provide an algorithm to efficiently assign priorities to the policies based on relative policy preferences indicated by policy administrators. Finally, with an example, we show how these algorithms have been integrated with our policy system to provide feedback to a policy administrator regarding potential interactions of policies with each other and with their deployment environment.
A New Method for Automatic Sleep Stage Classification

Traditionally, automatic sleep stage classification is quite a challenging task because of the difficulty in translating open-textured standards to mathematical models and the limitations of handcrafted features. In this paper, a new system for automatic sleep stage classification is presented. Compared with existing sleep stage methods, our method can capture the sleep information hidden inside electroencephalography (EEG) signals and automatically extract features from raw data. To translate open sleep stage standards into machine rules recognized by computers, a new model named fast discriminative complex-valued convolutional neural network (FDCCNN) is proposed to extract features from raw EEG data and classify sleep stages. The new model combines complex-valued backpropagation and the Fisher criterion. It can learn discriminative features and overcome the negative effect of imbalance dataset. More importantly, the orthogonal decision boundaries for the real and imaginary parts of a complex-valued convolutional neuron are proven. A speed-up algorithm is proposed to reduce computational workload and yield improvements of over an order of magnitude compared to the normal convolution algorithm. The classification performances of handcrafted features and different convolutional neural networks are compared with that of the FDCCNN. The total accuracy and kappa coefficient of the proposed method are 92% and 0.84, respectively. Experiment results demonstrated that the performance of our system is comparable to those of human experts.
Game-Theoretic Analysis of DDoS Attacks Against Bitcoin Mining Pools

One of the unique features of the digital currency Bitcoin is that new cash is introduced by so-called miners carrying out resourceintensive proof-of-work operations. To increase their chances of obtaining freshly minted bitcoins, miners typically join pools to collaborate on the computations. However, intense competition among mining pools has recently manifested in two ways. Miners may invest in additional computing resources to increase the likelihood of winning the next mining race. But, at times, a more sinister tactic is also employed: a mining pool may trigger a costly distributed denial-of-service (DDoS) attack to lower the expected success outlook of a competing mining pool. We explore the trade-off between these strategies with a series of game-theoretical models of competition between two pools of varying sizes. We consider differences in costs of investment and attack, as well as uncertainty over whether a DDoS attack will succeed. By characterizing the game’s equilibria, we can draw a number of conclusions. In particular, we find that pools have a greater incentive to attack large pools than small ones. We also observe that larger mining pools have a greater incentive to attack than smaller ones.
Influence of Power Delivery Timing on the Energetics and Biomechanics of Humans Wearing a Hip Exoskeleton

A broad goal in the field of powered lower limb exoskeletons is to reduce the metabolic cost of walking. Ankle exoskeletons have successfully achieved this goal by correctly timing a plantarflexor torque during late stance phase. Hip exoskeletons have the potential to assist with both flexion and extension during walking gait, but the optimal timing for maximally reducing metabolic cost is unknown. The focus of our study was to determine the best assistance timing for applying hip assistance through a pneumatic exoskeleton on human subjects. Ten non-impaired subjects walked with a powered hip exoskeleton, and both hip flexion and extension assistance were separately provided at different actuation timings using a simple burst controller. The largest average across-subject reduction in metabolic cost for hip extension was at 90% of the gait cycle (just prior to heel contact) and for hip flexion was at 50% of the gait cycle; this resulted in an 8.4 and 6.1% metabolic reduction, respectively, compared to walking with the unpowered exoskeleton. However, the ideal timing for both flexion and extension assistance varied across subjects. When selecting the assistance timing that maximally reduced metabolic cost for each subject, average metabolic cost for hip extension was 10.3% lower and hip flexion was 9.7% lower than the unpowered condition. When taking into account user preference, we found that subject preference did not correlate with metabolic cost. This indicated that user feedback was a poor method of determining the most metabolically efficient assistance power timing. The findings of this study are relevant to developers of exoskeletons that have a powered hip component to assist during human walking gait.
A Wideband Dual-Polarized Antenna for LTE700/GSM850/GSM900 Applications

A novel resonator-loaded wideband dual-polarized antenna is proposed for LTE700/GSM850/GSM900 applications. Four single-polarized miniaturized antenna elements are placed with square arrangement upon the reflector for dual polarization. One pair of parallel elements is for +45° polarization, and the other pair is for –45° polarization. The impedance bandwidth is greatly improved loaded with a resonator. Experimentally, the antenna exhibits a wide impedance bandwidth of 37.5% ranging from 0.67 to 0.98 GHz for |<inline-formula> <tex-math notation="LaTeX">$S$</tex-math></inline-formula><sub>11</sub>|<–15 dB, high isolation of more than 40 dB, and stable gain of around 9.5 dBi over the whole operating band.
Dynamic Highway-Node Routing

We introduce a dynamic technique for fast route planning in large road networks. For the first time, it is possible to handle the practically relevant scenarios that arise in present-day navigation systems: When an edge weight changes (e.g., due to a traffic jam), we can update the preprocessed information in 2–40 ms allowing subsequent fast queries in about one millisecond on average. When we want to perform only a single query, we can skip the comparatively expensive update step and directly perform a prudent query that automatically takes the changed situation into account. If the overall cost function changes (e.g., due to a different vehicle type), recomputing the preprocessed information takes typically less than two minutes. The foundation of our dynamic method is a new static approach that generalises and combines several previous speedup techniques. It has outstandingly low memory requirements of only a few bytes per node.
Interpreting Neural Networks to Improve Politeness Comprehension

We present an interpretable neural network approach to predicting and understanding politeness in natural language requests. Our models are based on simple convolutional neural networks directly on raw text, avoiding any manual identification of complex sentiment or syntactic features, while performing better than such feature-based models from previous work. More importantly, we use the challenging task of politeness prediction as a testbed to next present a much-needed understanding of what these successful networks are actually learning. For this, we present several network visualizations based on activation clusters, first derivative saliency, and embedding space transformations, helping us automatically identify several subtle linguistics markers of politeness theories. Further, this analysis reveals multiple novel, high-scoring politeness strategies which, when added back as new features, reduce the accuracy gap between the original featurized system and the neural model, thus providing a clear quantitative interpretation of the success of these neural networks.
Performance Analysis of Modified LLC Resonant Converter

In this paper a modified form of the most efficient resonant LLC series parallel converter configuration is proposed. The proposed system comprises of an additional LC circuit synchronized with the existing resonant tank of LLC configuration (LLC-LC configuration). With the development of power electronics devices, resonant converters have been proved to be more efficient than conventional converters as they employ soft switching technique. Among the three basic configurations of resonant converter, Series Resonant Converter (SRC), Parallel Resonant Converter (PRC) and Series Parallel Resonant Converter (SPRC), the LLC configuration under SPRC is proved to be most efficient providing narrow switching frequency range for wide range of load variation, improved efficiency and providing ZVS capability even under no load condition. The modified LLC configuration i.e., LLC-LC configuration offers better efficiency as well as better output voltage and gain. The efficiency tends to increase with increase in input voltage and hence these are suitable for high input voltage operation. The simulation and analysis has been done for full bridge configuration of the switching circuit and the results are presented