nils-herrmann/clean_scirepeval huggingface dataset:
On the controller placement for designing a distributed SDN control layer

The software-defined network (SDN) advocates a centralized network control, where a controller manages a network from a global view of the network. Large SDN networks may consist of multiple controllers or controller domains that distribute the network management between them, where each controller has a logically centralized but physically distributed vision of the network. In this context, a key challenge faced by providers is to define a scalable control network that exploits the benefits of SDN when used in conjunction with efficient management strategies. Most of the control layer models proposed are not concerned with controller scalability, because they assume that commercial controllers are scalable in terms of capacity (quantity of flows processed per second). However, it has been demonstrated that overloads and long propagation delays among controllers and controllers-switches can lead to a long response time of the controllers, affecting their ability to respond to network events in a very short time and reducing the reliability of communication. In this work we define the principles for designing a scalable control layer for SDN, and show the desired control layer characteristics that optimize the management of the network. We address these principles from the perspective of the controller placement problem. For this purpose we improve and evaluate our previous approach, the algorithm called k-Critical. K-Critical discovers the minimum number of controllers and their location to create a robust control topology that deals robustly with failures and balances the load among the selected controllers. The results demonstrate the effectiveness of our solution by comparing it with other controller placement solutions.
A soft robotic exomusculature glove with integrated sEMG sensing for hand rehabilitation

Stroke affects 750,000 people annually, and 80% of stroke survivors are left with weakened limbs and hands. Repetitive hand movement is often used as a rehabilitation technique in order to regain hand movement and strength. In order to facilitate this rehabilitation, a robotic glove was designed to aid in the movement and coordination of gripping exercises. This glove utilizes a cable system to open and close a patients hand. The cables are actuated by servomotors, mounted in a backpack weighing 13.2lbs including battery power sources. The glove can be controlled in terms of finger position and grip force through switch interface, software program, or surface myoelectric (sEMG) signal. The primary control modes of the system provide: active assistance, active resistance and a preprogrammed mode. This project developed a working prototype of the rehabilitative robotic glove which actuates the fingers over a full range of motion across one degree-of-freedom, and is capable of generating a maximum 15N grip force.
Symbolic Dynamic Programming for Discrete and Continuous State MDPs

Many real-world decision-theoretic planning problems can be naturally modeled with discrete and continuous state Markov decision processes (DC-MDPs). While previous work has addressed automated decision-theoretic planning for DCMDPs, optimal solutions have only been defined so far for limited settings, e.g., DC-MDPs having hyper-rectangular piecewise linear value functions. In this work, we extend symbolic dynamic programming (SDP) techniques to provide optimal solutions for a vastly expanded class of DCMDPs. To address the inherent combinatorial aspects of SDP, we introduce the XADD — a continuous variable extension of the algebraic decision diagram (ADD) — that maintains compact representations of the exact value function. Empirically, we demonstrate an implementation of SDP with XADDs on various DC-MDPs, showing the first optimal automated solutions to DCMDPs with linear and nonlinear piecewise partitioned value functions and showing the advantages of constraint-based pruning for XADDs.
On-line novelty detection by recursive dynamic principal component analysis and gas sensor arrays under drift conditions

Leakage detection is a common chemical-sensing application. Leakage detection by thresholds on a single sensor signal suffers from important drawbacks when sensors show drift effects or when they are affected by other long-term cross sensitivities. In this paper, we present an adaptive method based on a recursive dynamic principal component analysis (RDPCA) algorithm that models the relationships between the sensors in the array and their past history. In normal conditions, a certain variance distribution characterizes sensor signals, however, in the presence of a new source of variance the PCA decomposition changes drastically. In order to prevent the influence of sensor drift, the model is adaptive, and it is calculated in a recursive manner with minimum computational effort. The behavior of this technique is studied with synthetic and real signals arising by oil vapor leakages in an air compressor. Results clearly demonstrate the efficiency of the proposed method
Tracking deformable objects with unscented Kalman filtering and geometric active contours

Geometric active contours represented as the zero level sets of the graph of a surface have been used very successfully to segment static images. However, tracking involves estimating the global motion of the object and its local deformations as functions of time. Some attempts have been made to use geometric active contours for tracking, but most of these minimize the energy at each frame and do not utilize the temporal coherency of the motion or the deformation. Recently, particle filters for geometric active contours were used for tracking deforming objects. However, the method is computationally very expensive since it requires a large number of particles to approximate the state density. In the present work, we propose to use the unscented Kalman filter together with geometric active contours to track deformable objects in a computationally efficient manner
An improved ID3 decision tree algorithm

Decision tree is an important method for both induction research and data mining, which is mainly used for model classification and prediction. ID3 algorithm is the most widely used algorithm in the decision tree so far. Through illustrating on the basic ideas of decision tree in data mining, in this paper, the shortcoming of ID3's inclining to choose attributes with many values is discussed, and then a new decision tree algorithm combining ID3 and Association Function(AF) is presented. The experiment results show that the proposed algorithm can overcome ID3's shortcoming effectively and get more reasonable and effective rules
An Experiment to Assess Different Defect Detection Methods for Software Requirements Inspections

Software requirements specifications (SRS) are usually validated by inspections, in which several reviewers read all or part of the specification and search for defects. We hypothesize that diflerent methods for conducting these searches may have significantly diflerent rat es of success. Using a controlled experiment, we show that a Scenario-based detection method, in which each reviewer executes a specific procedure to discover a particular class of defects has a higher defect detection rate than either Ad Hoc or Checklist methods. We describe the design, execution, and analysis of the expem”ment so others may reproduce it and test our results for diflerent kinds of software developments and different populations of software engineers.
Attribute Relationship Evaluation Methodology for Big Data Security

There has been an increasing interest in big data and big data security with the development of network technology and cloud computing. However, big data is not an entirely new technology but an extension of data mining. In this paper, we describe the background of big data, data mining and big data features, and propose attribute selection methodology for protecting the value of big data. Extracting valuable information is the main goal of analyzing big data which need to be protected. Therefore, relevance between attributes of a dataset is a very important element for big data analysis. We focus on two things. Firstly, attribute relevance in big data is a key element for extracting information. In this perspective, we studied on how to secure a big data through protecting valuable information inside. Secondly, it is impossible to protect all big data and its attributes. We consider big data as a single object which has its own attributes. We assume that a attribute which have a higher relevance is more important than other attributes.
Lifted Rule Injection for Relation Embeddings

Methods based on representation learning currently hold the state-of-the-art in many natural language processing and knowledge base inference tasks. Yet, a major challenge is how to efficiently incorporate commonsense knowledge into such models. A recent approach regularizes relation and entity representations by propositionalization of first-order logic rules. However, propositionalization does not scale beyond domains with only few entities and rules. In this paper we present a highly efficient method for incorporating implication rules into distributed representations for automated knowledge base construction. We map entity-tuple embeddings into an approximately Boolean space and encourage a partial ordering over relation embeddings based on implication rules mined from WordNet. Surprisingly, we find that the strong restriction of the entity-tuple embedding space does not hurt the expressiveness of the model and even acts as a regularizer that improves generalization. By incorporating few commonsense rules, we achieve an increase of 2 percentage points mean average precision over a matrix factorization baseline, while observing a negligible increase in runtime.
Struggling or exploring?: disambiguating long search sessions

Web searchers often exhibit directed search behaviors such as navigating to a particular Website. However, in many circumstances they exhibit different behaviors that involve issuing many queries and visiting many results. In such cases, it is not clear whether the user's rationale is to intentionally explore the results or whether they are struggling to find the information they seek. Being able to disambiguate between these types of long search sessions is important for search engines both in performing retrospective analysis to understand search success, and in developing real-time support to assist searchers. The difficulty of this challenge is amplified since many of the characteristics of exploration (e.g., multiple queries, long duration) are also observed in sessions where people are struggling. In this paper, we analyze struggling and exploring behavior in Web search using log data from a commercial search engine. We first compare and contrast search behaviors along a number dimensions, including query dynamics during the session. We then build classifiers that can accurately distinguish between exploring and struggling sessions using behavioral and topical features. Finally, we show that by considering the struggling/exploring prediction we can more accurately predict search satisfaction.
Adaptive Seeding in Social Networks

The algorithmic challenge of maximizing information diffusion through word-of-mouth processes in social networks has been heavily studied in the past decade. While there has been immense progress and an impressive arsenal of techniques has been developed, the algorithmic frameworks make idealized assumptions regarding access to the network that can often result in poor performance of state-of-the-art techniques. In this paper we introduce a new framework which we call Adaptive Seeding. The framework is a two-stage stochastic optimization model designed to leverage the potential that typically lies in neighboring nodes of arbitrary samples of social networks. Our main result is an algorithm which provides a constant factor approximation to the optimal adaptive policy for any influence function in the Triggering model.
Hidden style in the city: an analysis of geolocated airbnb rental images in ten major cities

In this article, we analyze geolocated Airbnb rental images in ten major cities. Airbnb is a hallmark institution in the sharing economy, allowing anyone with a bed and shelter to act like a micro-hotel, i.e. a bed-and-breakfast for other travelers. Travelers often spend less on Airbnb rentals than hotels and get a residential experience in a new place.
 Since hosts advertise their rentals on Airbnb, the site has a wealth of residential interior images from all over the world: from rural Africa to downtown Manhattan. As part of an ongoing project, we have downloaded over 200,000 images posted on Airbnb to ask: how do people decorate their homes in different locales? Do they use certain colors, or have a certain ornate or simple style?
 Here, we test ten major metropolitan areas using image rating responses from Mechanical Turk as well as automated image color predominance routines to investigate geographical differences in interior styles. We find overarching indicators of globalization and a lack of local culture in the case of color, but that different neighborhoods within cities have different levels or ornateness when decorating their properties. The results of this research can also help to identify the kinds of interiors that are more pleasant in the eyes of customers.
Social-network analysis using topic models

In this paper, we discuss how we can extend probabilistic topic models to analyze the relationship graph of popular social-network data, so that we can group or label the edges and nodes in the graph based on their topic similarity. In particular, we first apply the well-known Latent Dirichlet Allocation (LDA) model and its existing variants to the graph-labeling task and argue that the existing models do not handle popular nodes (nodes with many incoming edges) in the graph very well. We then propose possible extensions to this model to deal with popular nodes. Our experiments show that the proposed extensions are very effective in labeling popular nodes, showing significant improvements over the existing methods. Our proposed methods can be used for providing, for instance, more relevant friend recommendations within a social network.
LOOP: Logic-Oriented Opaque Predicate Detection in Obfuscated Binary Code

Opaque predicates have been widely used to insert superfluous branches for control flow obfuscation. Opaque predicates can be seamlessly applied together with other obfuscation methods such as junk code to turn reverse engineering attempts into arduous work. Previous efforts in detecting opaque predicates are far from mature. They are either ad hoc, designed for a specific problem, or have a considerably high error rate. This paper introduces LOOP, a Logic Oriented Opaque Predicate detection tool for obfuscated binary code. Being different from previous work, we do not rely on any heuristics; instead we construct general logical formulas, which represent the intrinsic characteristics of opaque predicates, by symbolic execution along a trace. We then solve these formulas with a constraint solver. The result accurately answers whether the predicate under examination is opaque or not. In addition, LOOP is obfuscation resilient and able to detect previously unknown opaque predicates. We have developed a prototype of LOOP and evaluated it with a range of common utilities and obfuscated malicious programs. Our experimental results demonstrate the efficacy and generality of LOOP. By integrating LOOP with code normalization for matching metamorphic malware variants, we show that LOOP is an appealing complement to existing malware defenses.
Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization

Responses generated by neural conversational models tend to lack informativeness and diversity. We present a novel adversarial learning method, called Adversarial Information Maximization (AIM) model, to address these two related but distinct problems. To foster response diversity, we leverage adversarial training that allows distributional matching of synthetic and real responses. To improve informativeness, we explicitly optimize a variational lower bound on pairwise mutual information between query and response. Empirical results from automatic and human evaluations demonstrate that our methods significantly boost informativeness and diversity.
Axial flux PM machine design with optimum magnet shape for constant power region capability

Unlike radial flux machines, slotted axial flux machine has a particular airgap flux distribution which is a function of the machine diameter. Due to the rectangular slot geometry, stator teeth present a trapezoidal geometry with small tooth width close to the shaft, increasing as the diameter becomes larger. This fact introduces an uneven airgap flux distribution if a constant flux source, such as rectangular PM, is utilized to magnetize the machine. As a result, flux density over the stator tooth becomes irregular, inductance parameters are a function of the stator diameter and saliency varies according to machine load. All these effects degrade machine power capability for low and rated load. In this paper, a novel axial flux PM machine with tangential magnetization is presented. An analytic and numerical study is carried out to consider stator tooth geometry and its effect over machine saliency ratio.
Psychosocial health among young victims and offenders of direct and indirect bullying.

To assess the association between bullying (both directly and indirectly) and indicators of psychosocial health for boys and girls separately.



A school-based questionnaire survey of bullying, depression, suicidal ideation, and delinquent behavior.



Primary schools in Amsterdam, The Netherlands.



A total of 4811 children aged 9 to 13.



Depression and suicidal ideation are common outcomes of being bullied in both boys and girls. These associations are stronger for indirect than direct bullying. After correction, direct bullying had a significant effect on depression and suicidal ideation in girls, but not in boys. Boy and girl offenders of bullying far more often reported delinquent behavior. Bullying others directly is a much greater risk factor for delinquent behavior than bullying others indirectly. This was true for both boys and girls. Boy and girl offenders of bullying also more often reported depressive symptoms and suicidal ideation. However, after correction for both sexes only a significant association still existed between bullying others directly and suicidal ideation.



The association between bullying and psychosocial health differs notably between girls and boys as well as between direct and indirect forms of bullying. Interventions to stop bullying must pay attention to these differences to enhance effectiveness.
MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking

In the recent past, the computer vision community has developed centralized benchmarks for the performance evaluation of a variety of tasks, including generic object and pedestrian detection, 3D reconstruction, optical flow, single-object short-term tracking, and stereo estimation. Despite potential pitfalls of such benchmarks, they have proved to be extremely helpful to advance the state of the art in the respective area. Interestingly, there has been rather limited work on the standardization of quantitative benchmarks for multiple target tracking. One of the few exceptions is the well-known PETS dataset [20], targeted primarily at surveillance applications. Despite being widely used, it is often applied inconsistently, for example involving using different subsets of the available data, different ways of training the models, or differing evaluation scripts. This paper describes our work toward a novel multiple object tracking benchmark aimed to address such issues. We discuss the challenges of creating such a framework, collecting existing and new data, gathering state-of-the-art methods to be tested on the datasets, and finally creating a unified evaluation system. With MOTChallenge we aim to pave the way toward a unified evaluation framework for a more meaningful quantification of multi-target tracking.
Low Q electrically small linear and elliptical polarized spherical dipole antennas

Electrically small antennas are generally presumed to exhibit high impedance mismatch (high VSWR), low efficiency, high quality factor (Q); and, therefore, narrow operating bandwidth. For an electric or magnetic dipole antenna, there is a fundamental lower bound for the quality factor that is determined as a function of the antenna's occupied physical volume. In this paper, the quality factor of a resonant, electrically small electric dipole is minimized by allowing the antenna geometry to utilize the occupied spherical volume to the greatest extent possible. A self-resonant, electrically small electric dipole antenna is presented that exhibits an impedance near 50 Ohms, an efficiency in excess of 95% and a quality factor that is within 1.5 times the fundamental lower bound at a value of ka less than 0.27. Through an arrangement of the antenna's wire geometry, the electrically small dipole's polarization is converted from linear to elliptical (with an axial ratio of 3 dB), resulting in a further reduction in the quality factor. The elliptically polarized, electrically small antenna exhibits an impedance near 50 Ohms, an efficiency in excess of 95% and it has an omnidirectional, figure-eight radiation pattern.
Design and development of a compressor-embedded pneumatic-driven musculoskeletal humanoid

Although pneumatic robots are expected to be physically friendly to humans and human-environments, large and heavy air sources and reservoir tanks are a problem to build a self-contained pneumatic robot. This paper proposes a compressor-embedded pneumatic-driven humanoid system consisting of a very small distributed compressors and hollow bones as air reservoir tanks as well as the structural parts. Musculoskeletal systems have possibility of doing dynamic motions using physical elasticity of muscles and tendons, coupled-driven systems of multi-articular muscles, and so on. We suppose a pneumatic driven flexible spine will be contribute to dynamic motions as well as physical adaptivity to environments. This paper presents the concept, design, and implementation of the compressor-embedded pneumatic-driven musculoskeletal humanoid robot named “buEnwa.” We have developed the pneumatic robot which embeds very small compressors and reservoir tanks, and has a multi-joint spine in which physically elastic elements such as rubber bands are attached, and the coupled-driving system of the spine and the shoulder. This paper also shows preliminary experiments of the real robot.