nils-herrmann/clean_scirepeval huggingface dataset:
Complex event processing for reactive security monitoring in virtualized computer systems

The number of security incidents in computer systems is steadily increasing, despite intrusion detection and prevention mechanisms deployed as countermeasures. Many existing intrusion detection and prevention systems struggle to keep up with new threats posed by zero-day attacks and/or have serious performance impacts through extensive monitoring, questioning their effectiveness in most real-life scenarios. In this paper, we present a new approach for reactive security monitoring in a virtualized computer environment based on minimally-intrusive dynamic sensors deployed vertically across virtualization layers and horizontally within a virtual machine instance. The sensor streams are analyzed using a novel federation of complex event processing engines and an optimized query index to maximize the performance of continuous queries, and the results of the analysis are used to trigger appropriate actions on different virtualization layers in response to detected security anomalies. Furthermore, a novel event store that supports fast event logging is utilized for offline analysis of collected historical data. Experiments show that the proposed system can execute tens of thousands of complex, stateful detection rules simultaneously and trigger actions efficiently and with low latency.
Memory access scheduling

The bandwidth and latency of a memory system are strongly dependent on the manner in which accesses interact with the “3-D” structure of banks, rows, and columns characteristic of contemporary DRAM chips. There is nearly an order of magnitude difference in bandwidth between successive references to different columns within a row and different rows within a bank. This paper introduces memory access scheduling, a technique that improves the performance of a memory system by reordering memory references to exploit locality within the 3-D memory structure. Conservative reordering, in which the first ready reference in a sequence is performed, improves bandwidth by 40% for traces from five media benchmarks. Aggressive reordering, in which operations are scheduled to optimize memory bandwidth, improves bandwidth by 93% for the same set of applications. Memory access scheduling is particularly important for media processors where it enables the processor to make the most efficient use of scarce memory bandwidth.
ChoiceNet: Robust Learning by Revealing Output Correlations

In this paper, we focus on the supervised learning problem with corrupted training data. We assume that the training dataset is generated from a mixture of a target distribution and other unknown distributions. We estimate the quality of each data by revealing the correlation between the generated distribution and the target distribution. To this end, we present a novel framework referred to here as ChoiceNet that can robustly infer the target distribution in the presence of inconsistent data. We demonstrate that the proposed framework is applicable to both classification and regression tasks. ChoiceNet is evaluated in comprehensive experiments, where we show that it constantly outperforms existing baseline methods in the handling of noisy data. Particularly, ChoiceNet is successfully applied to autonomous driving tasks where it learns a safe driving policy from a dataset with mixed qualities. In the classification task, we apply the proposed method to the MNIST and CIFAR-10 datasets and it shows superior performances in terms of robustness to noisy labels.
Optimizing 2D Gabor Filters for Iris Recognition

The randomness and richness present in the iris texture make the 2D Gabor filter bank analysis a suitable technique to be used for iris recognition systems. To accurately characterize complex texture structures using 2D Gabor filters it is necessary to use multiple sets of parameters of this type of filters. This paper proposes a technique of optimizing multiple sets of 2D Gabor filter parameters to gradually enhance the accuracy of an iris recognition system. The proposed methodology is suitable to be applied on both near infrared and visible spectrum iris images. To illustrate the efficiency of the filter bank design technique, UBIRISv1 database was used for benchmarking.
"It's alive, it's magic, it's in love with you": opportunities, challenges and open questions for actuated interfaces

Actuated Interfaces are receiving a great deal of interest from the research community. The field can now present a range of point designs, illustrating the potential design space of Actuated Interfaces. However, despite the increasing interest in Actuated Interfaces, the research carried out is nevertheless primarily preoccupied with the technical challenges and potential application areas, rather than how users actually approach, experience, interpret and understand Actuated Interfaces. Based on three case studies, investigating how people experience Actuated Interfaces, we point to; magic, movement and ambiguity as fruitful perspectives for understanding users' experiences with Actuated Interfaces. The three perspectives are employed to reflect upon opportunities and challenges, as well as point to open questions and relevant areas for future research for Actuated Interfaces.
Learning Face Representation from Scratch

Pushing by big data and deep convolutional neural network (CNN), the performance of face recognition is becoming comparable to human. Using private large scale training datasets, several groups achieve very high performance on LFW, i.e., 97% to 99%. While there are many open source implementations of CNN, none of large scale face dataset is publicly available. The current situation in the field of face recognition is that data is more important than algorithm. To solve this problem, this paper proposes a semi-automatical way to collect face images from Internet and builds a large scale dataset containing about 10,000 subjects and 500,000 images, called CASIAWebFace. Based on the database, we use a 11-layer CNN to learn discriminative representation and obtain state-of-theart accuracy on LFW and YTF. The publication of CASIAWebFace will attract more research groups entering this field and accelerate the development of face recognition in the wild.
An efficient selection of HOG feature for SVM classification of vehicle

Support Vector Machine (SVM) classifier with Histogram of Oriented Gradients (HOG) feature become one of the most popular techniques used for vehicle detection in recent years. And the computing time of SVM is a main obstacle to get real time implementation which is important for Advanced Driver Assistance Systems (ADAS) applications. One of the effective ways to reduce the computing complexity of SVM is to reduce the dimension of HOG feature. In this paper, we examine the effect of the number of HOG bins on the vehicle detection and the symmetric characteristics of HOG feature of vehicle. And we successfully demonstrate the speed-up of SVM classifier for vehicle detection by about three times while maintaining the detection performance.
Time Series Based LSTMModel to Predict Air Pollutant ’ s Concentration for Prominent Cities in India

Air pollution has been a major concern in India since last few years. Rapid industrialization has led to tremendous increase in air pollution. Growing air pollution has led to an increase in health related ailments like stroke, heart disease, lung cancer etc. To monitor air pollution, particularly in major pollution prone cities like Delhi, the government of India has installed pollutant’s measuring sensors. The sensors regularly monitors the level of air pollutants like PM2.5, PM10, CO, NO2, SO2, O3. However, capturing current pollutants concentration is not helpful in decreasing/avoiding pollution exposure. This paper proposes a deep learning based LSTM model to predict future air pollutant’s concentration. We formulate the problem of predicting pollutant’s concentration as a time series based problem where current pollutants level are dependent on previous pollutant, meteorological, traffic data, festivals and national holidays information. We create a sample dataset of historic pollutant levels, meteorological and traffic data, and identify discriminatory features to predict pollutants concentration for the next # of hours. Our empirical analysis reveals that certain meteorological features (temperature, pressure, humidity, wind speed, wind direction, UV Index, cloud cover, rain), traffic information, festivals information can be used to forecast pollutants. We conduct a series of experiments to validate the proposed solution approach and present evidences to demonstrate the effectiveness of proposed framework with average RMSE of pollutants is less than 15 for next 12 hour prediction, less than 8 for next 6 hour prediction and less than 5 for next hour prediction. We compare our model accuracy with real time data fetched from CPCB (Central pollution Control Board).
Soil Property Prediction: An Extreme Learning Machine Approach

In this paper, we propose a method for predicting functional properties of soil samples from a number of measurable spatial and spectral features of those samples. Our method is based on Savitzky-Golay filter for preprocessing and a relatively recent evolution of single hiddenlayer feed-forward network (SLFN) learning technique called extreme learning machine (ELM) for prediction. We tested our method with Africa Soil Property Prediction dataset, and observed that the results were promising.
Simplified feeding for a modified printed Yagi antenna

A modified printed Yagi antenna with a simplified feeding mechanism is proposed. In the new design, the driver dipole is fed by a transmission line formed by two parallel strips printed on the opposite sides of the dielectric substrate. The parallel strips are connected to a microstrip line with a truncated ground plane. This simplified feeding structure results in the reduction of the transmission line length, and, consequently, the radiation losses. The analysis of the modified Yagi antenna is performed numerically using commercial FEM and FD-TD software.
Hand gesture recognition using depth data

A method is presented for recognizing hand gestures by using a sequence of real-time depth image data acquired by an active sensing hardware. Hand posture and motion information extracted from a video is represented in a gesture space which consists of a number of aspects including hand shape, location and motion information. In this space, it is shown to be possible to recognize many types of gestures. Experimental results are shown to validate our approach and characteristics of our approach are discussed.
Social-driven Internet of Connected Objects

Internet evolution has been recently related with some aspect of user empowerment, mostly in terms of content distribution, and this has been ultimately accelerated by the fast-paced introduction and expansion of wireless technologies. Hence, the Internet should start to be seen as a communications infrastructure able to support the integration of a myriad of embedded and personal wireless objects. This way a future Internet will support the interaction between users’ social, physical and virtual sphere. This position paper aims to raise some discussion about the technology required to ensure an efficient interaction between the physical, social and virtual worlds by extending the Internet by means of interconnected objects. Namely, it is argued that an efficient interaction between the physical, social and virtual worlds requires the development of a data-centric architecture based on IP-driven opportunisitc networking able to make useful data available to people when and where they really need it, augmenting their social and environmental awareness.
Social Interaction and Stock-Market Participation

We investigate the idea that stock-market participation is influenced by social interaction. We build a simple model in which any given “social” investor finds it more attractive to invest in the market when the participation rate among his peers is higher. The model predicts higher participation rates among social investors than among “nonsocials”. It also admits the possibility of multiple social equilibria. We then test the theory using data from the Health and Retirement Study. Social households—defined as those who interact with their neighbors, or who attend church—are indeed substantially more likely to invest in the stock market than non-social households, controlling for other factors like wealth, race, education and risk tolerance. Moreover, consistent with a peereffects story, the impact of sociability is stronger in states where stock-market participation rates are higher. We are grateful to the National Science Foundation for research support, and to Michael Kremer, David Laibson, Michael Morris, Lara Tiedens, Tuomo Vuolteenaho and Ezra Zuckerman for their comments and suggestions. A special thanks to Jun Liu for some very helpful modeling tips.
Hybrid Position / Force Control of Manipulators 1

A new conceptually simple approach to controlling compliant motions of a robot manipulator is presented. The "hybrid" technique described combines force and torque information with positional data to satisfy simultaneous position and force trajectory constraints specified in a convenient task related coordinate system. Analysis, simulation, and experiments are used to evaluate the controller's ability to execute trajectories using feedback from a force sensing wrist and from position sensors found in the manipulator joints. The results show that the method achieves stable, accurate control of force and position trajectories for a variety of test conditions.
A capability-based security approach to manage access control in the Internet of Things

Resource and information protection plays a relevant role in distributed systems like the ones present in the Internet of Things (IoT). Authorization frameworks like RBAC and ABAC do not provide scalable, manageable, effective, and efficient mechanisms to support distributed systems with many interacting services and are not able to effectively support the dynamicity and scaling needs of IoT contexts that envisage a potentially unbound number of sensors, actuators and related resources, services and subjects, as well as a more relevance of short-lived, unplanned and dynamic interaction patterns. Furthermore, as more end-users start using smart devices (e.g. smart phones, smart home appliances, etc.) the need to have more scalable, manageable, understandable and easy to use access control mechanisms increases. This paper describes a capability based access control system that enterprises, or even individuals, can use to manage their own access control processes to services and information. The proposed mechanism supports rights delegation and a more sophisticated access control customization. The proposed approach is being developed within the European FP7 IoT@Work project to manage access control to some of the project’s services deployed in the shop floor.
Learning Compact Appearance Representation for Video-based Person Re-Identification

This paper presents a novel approach for video-based person re-identification using multiple Convolutional Neural Networks (CNNs). Unlike previous work, we intend to extract a compact yet discriminative appearance representation from several frames rather than the whole sequence. Specifically, given a video, the representative frames are selected based on the walking profile of consecutive frames. A multiple CNN architecture incorporated with feature pooling is proposed to learn and compile the features of the selected representative frames into a compact description about the pedestrian for identification. Experiments are conducted on benchmark datasets to demonstrate the superiority of the proposed method over existing person re-identification approaches.
Bi-directional LSTM Recurrent Neural Network for Chinese Word Segmentation

Recurrent neural network(RNN) has been broadly applied to natural language processing(NLP) problems. This kind of neural network is designed for modeling sequential data and has been testified to be quite efficient in sequential tagging tasks. In this paper, we propose to use bi-directional RNN with long short-term memory(LSTM) units for Chinese word segmentation, which is a crucial preprocess task for modeling Chinese sentences and articles. Classical methods focus on designing and combining hand-craft features from context, whereas bi-directional LSTM network(BLSTM) does not need any prior knowledge or pre-designing, and it is expert in keeping the contextual information in both directions. Experiment result shows that our approach gets stateof-the-art performance in word segmentation on both traditional Chinese datasets and simplified Chinese datasets.
Model-based dynamic gait generation for a leg-wheel transformable robot

We report on the model-based approach to dynamic trotting and pronking gait generation for a leg-wheel transformable robot. The rolling spring-loaded inverted pendulum (R-SLIP) model served as the template for robot locomotion by programming the robot's motion according to the stable fixed-point trajectories of the model. Two strategies are developed to match the robot leg motions to the virtual model leg: First, using the two active degrees of freedom on each leg to simulate the passive spring effect of the R-SLIP model leg. Second, installing a torsion spring on the leg-wheel module to render the leg-wheel morphology identical to that of the model leg. This model-based approach to dynamic behavior generation for the robot is experimentally evaluated. The robot can successfully generate an R-SLIP-like stable pronking gait with a flight phase.
Compensation of magnetic disturbances improves inertial and magnetic sensing of human body segment orientation

This paper describes a complementary Kalman filter design to estimate orientation of human body segments by fusing gyroscope, accelerometer, and magnetometer signals from miniature sensors. Ferromagnetic materials or other magnetic fields near the sensor module disturb the local earth magnetic field and, therefore, the orientation estimation, which impedes many (ambulatory) applications. In the filter, the gyroscope bias error, orientation error, and magnetic disturbance error are estimated. The filter was tested under quasi-static and dynamic conditions with ferromagnetic materials close to the sensor module. The quasi-static experiments implied static positions and rotations around the three axes. In the dynamic experiments, three-dimensional rotations were performed near a metal tool case. The orientation estimated by the filter was compared with the orientation obtained with an optical reference system Vicon. Results show accurate and drift-free orientation estimates. The compensation results in a significant difference (p<0.01) between the orientation estimates with compensation of magnetic disturbances in comparison to no compensation or only gyroscopes. The average static error was 1.4/spl deg/ (standard deviation 0.4) in the magnetically disturbed experiments. The dynamic error was 2.6/spl deg/ root means square.
Market-Oriented Cloud Computing: Vision, Hype, and Reality for Delivering IT Services as Computing Utilities

presents a 21st century vision of computing; identifies various computing paradigms promising to deliver the vision of computing utilities; defines Cloud computing and provides the architecture for creating market-oriented Clouds by leveraging technologies such as VMs; provides thoughts on market-based resource management strategies that encompass both customer-driven service management and computational risk management to sustain SLA-oriented resource allocation; presents some representative Cloud platforms especially those developed in industries along with our current work towards realising market-oriented resource allocation of Clouds by leveraging the 3rd generation Aneka enterprise Grid technology; reveals our early thoughts on interconnecting Clouds for dynamically creating an atmospheric computing environment along with pointers to future community research; and concludes with the need for convergence of competing IT paradigms for delivering our 21st century vision.
Overview of the Lucy Project: Dynamic Stabilization of a Biped Powered by Pneumatic Artificial Muscles

This paper gives an overview of the Lucy project. What is special is that the biped is not actuated with the classical electrical drives, but with pleated pneumatic artificial muscles. In an antagonistic setup of such muscles both the torque and the compliance are controllable. From human walking there is evidence that joint compliance plays an important role in energy-efficient walking and running. To be able to walk at different walking speeds and step lengths, a trajectory generator and joint trajectory tracking controller are combined. The first generates dynamically stable trajectories based on the objective locomotion parameters which can be changed from step to step. The joint trajectory tracking unit controls the pressure inside the muscles so the desired motion is followed. It is based on a computed torque model and takes the torque– angle relation of the antagonistic muscle setup into account. With this strategy the robot is able to walk at a speed up to 0.15 m/s. A compliance controller is developed to reduce the energy consumption by combining active trajectory control with the exploitation of the natural dynamics. A mathematical formulation was developed to find an optimal compliance setting depending on the desired trajectory and physical properties of the system. This strategy is experimentally evaluated on a single pendulum structure and not implemented on the real robot because the walking speed of the robot is currently too slow. At the end a discussion is given about the pros and cons of building a pneumatic biped, and the control architecture used.
Architecting for DevOps and Continuous Deployment

Development and Operations (DevOps) in the context of Continuous Deployment (CD) have emerged as an attractive software development movement, which tries to establish a strong connection between development and operations teams. CD is defined as the ability to quickly put new releases into production. We believe that DevOps/CD brings new challenges for architects, which considerably impacts both on their (architectural) design decisions and their organizational responsibilities. We assert that there is an important and urgent need of sufficient research work to gain a deep understanding of how DevOps/CD adoption can influence architecting, architectural decision-making processes and their outcomes in an organization. This PhD research is aimed at understanding and addressing new challenges for designing architectures for supporting DevOps in the context of CD.