nils-herrmann/clean_scirepeval huggingface dataset:
Localized forgery detection in hyperspectral document images

Hyperspectral imaging is emerging as a promising technology to discover patterns that are otherwise hard to identify with regular cameras. Recent research has shown the potential of hyperspectral image analysis to automatically distinguish visually similar inks. However, a major limitation of prior work is that automatic distinction only works when the number of inks to be distinguished is known a priori and their relative proportions in the inspected image are roughly equal. This research work aims at addressing these two problems. We show how anomaly detection combined with unsupervised clustering can be used to handle cases where the proportions of pixels belonging to the two inks are highly unbalanced. We have performed experiments on the publicly available UWA Hyperspectral Documents dataset. Our results show that INFLO anomaly detection algorithm is able to best distinguish inks for highly unbalanced ink proportions.
An IoT-Oriented Data Storage Framework in Cloud Computing Platform

The Internet of Things (IoT) has provided a promising opportunity to build powerful industrial systems and applications by leveraging the growing ubiquity of Radio Frequency IDentification (RFID) and wireless sensors devices. Benefiting from RFID and sensor network technology, common physical objects can be connected, and are able to be monitored and managed by a single system. Such a network brings a series of challenges for data storage and processing in a cloud platform. IoT data can be generated quite rapidly, the volume of data can be huge and the types of data can be various. In order to address these potential problems, this paper proposes a data storage framework not only enabling efficient storing of massive IoT data, but also integrating both structured and unstructured data. This data storage framework is able to combine and extend multiple databases and Hadoop to store and manage diverse types of data collected by sensors and RFID readers. In addition, some components are developed to extend the Hadoop to realize a distributed file repository, which is able to process massive unstructured files efficiently. A prototype system based on the proposed framework is also developed to illustrate the framework's effectiveness.
Sampling cube: a framework for statistical olap over sampling data

Sampling is a popular method of data collection when it is impossible or too costly to reach the entire population. For example, television show ratings in the United States are gathered from a sample of roughly 5,000 households. To use the results effectively, the samples are further partitioned in a multidimensional space based on multiple attribute values. This naturally leads to the desirability of OLAP (Online Analytical Processing) over sampling data. However, unlike traditional data, sampling data is inherently uncertain, i.e., not representing the full data in the population. Thus, it is desirable to return not only query results but also the confidence intervals indicating the reliability of the results. Moreover, a certain segment in a multidimensional space may contain none or too few samples. This requires some additional analysis to return trustable results.
 In this paper we propose a Sampling Cube framework, which efficiently calculates confidence intervals for any multidimensional query and uses the OLAP structure to group similar segments to increase sampling size when needed. Further, to handle high dimensional data, a Sampling Cube Shell method is proposed to effectively reduce the storage requirement while still preserving query result quality.
Ethical Challenges in Data-Driven Dialogue Systems

The use of dialogue systems as a medium for human-machine interaction is an increasingly prevalent paradigm. A growing number of dialogue systems use conversation strategies that are learned from large datasets. There are well documented instances where interactions with these system have resulted in biased or even offensive conversations due to the data-driven training process. Here, we highlight potential ethical issues that arise in dialogue systems research, including: implicit biases in data-driven systems, the rise of adversarial examples, potential sources of privacy violations, safety concerns, special considerations for reinforcement learning systems, and reproducibility concerns. We also suggest areas stemming from these issues that deserve further investigation. Through this initial survey, we hope to spur research leading to robust, safe, and ethically sound dialogue systems.
Imbalanced classification using support vector machine ensemble

Imbalanced data sets often have detrimental effects on the performance of a conventional support vector machine (SVM). To solve this problem, we adopt both strategies of modifying the data distribution and adjusting the classifier. Both minority and majority classes are resampled to increase the generalization ability. For minority class, an one-class support vector machine model combined with synthetic minority oversampling technique is used to oversample the support vector instances. For majority class, we propose a new method to decompose the majority class into clusters and remove two clusters using a distance measure to lessen the effect of outliers. The remaining clusters are used to build an SVM ensemble with the oversampled minority patterns, the SVM ensemble can achieve better performance by considering potentially suboptimal solutions. Experimental results on benchmark data sets are provided to illustrate the effectiveness of the proposed method.
Agent-Based Service Composition in Cloud Computing

In a Cloud-computing environment, consumers, brokers, and service providers interact to achieve their individual purposes. In this regard, service providers offer a pool of resources wrapped as web services, which should be composed by broker agents to provide a single virtualized service to Cloud consumers. In this study, an agent-based test bed for simulating Cloud-computing environments is developed. Each Cloud participant is represented by an agent, whose behavior is defined by means of colored Petri nets. The relationship between web services and service providers is modeled using object Petri nets. Both Petri net formalisms are combined to support a design methodology for defining concurrent and parallel service choreographies. This results in the creation of a dynamic agent-based service composition algorithm. The simulation results indicate that service composition is achieved with a linear time complexity despite dealing with interleaving choreographies and synchronization of heterogeneous services.
Resource allocation for device-to-device communications underlaying LTE-advanced networks

The Long Term Evolution-Advanced (LTEAdvanced) networks are being developed to provide mobile broadband services for the fourth generation (4G) cellular wireless systems. Deviceto- device (D2D) communications is a promising technique to provide wireless peer-to-peer services and enhance spectrum utilization in the LTE-Advanced networks. In D2D communications, the user equipments (UEs) are allowed to directly communicate between each other by reusing the cellular resources rather than using uplink and downlink resources in the cellular mode when communicating via the base station. However, enabling D2D communications in a cellular network poses two major challenges. First, the interference caused to the cellular users by D2D devices could critically affect the performances of the cellular devices. Second, the minimum quality-of-service (QoS) requirements of D2D communications need to be guaranteed. In this article, we introduce a novel resource allocation scheme (i.e. joint resource block scheduling and power control) for D2D communications in LTE-Advanced networks to maximize the spectrum utilization while addressing the above challenges. First, an overview of LTE-Advanced networks, and architecture and signaling support for provisioning of D2D communications in these networks are described. Furthermore, research issues and the current state-of-the-art of D2D communications are discussed. Then, a resource allocation scheme based on a column generation method is proposed for D2D communications. The objective is to maximize the spectrum utilization by finding the minimum transmission length in terms of time slots for D2D links while protecting the cellular users from harmful interference and guaranteeing the QoS of D2D links. The performance of this scheme is evaluated through simulations.
A modified two-section UWB Wilkinson power divider

A novel Wilkinson power divider with modification of the traditional two-section Wilkinson power divider by adding one open stub on each branch is proposed. This design method is validated by building a divider working in the UWB band (3.1 GHz - 10.6 GHz). It is better in performance, but is about 10% smaller than the traditional three-section power divider and spares one resistor. The simulation and measurement results of the modified divider show equal power split, a good insertion loss of less than 1.1 dB, a good return loss of all ports of less than - 10 dB, and a good isolation is less than -11 dB over the UWB band.
A vision-based robotic grasping system using deep learning for 3D object recognition and pose estimation

Pose estimation of object is one of the key problems for the automatic-grasping task of robotics. In this paper, we present a new vision-based robotic grasping system, which can not only recognize different objects but also estimate their poses by using a deep learning model, finally grasp them and move to a predefined destination. The deep learning model demonstrates strong power in learning hierarchical features which greatly facilitates the recognition mission. We apply the Max-pooling Convolutional Neural Network (MPCNN), one of the most popular deep learning models, in this system, and assign different poses of objects as different classes in MPCNN. Besides, a new object detection method is also presented to overcome the disadvantage of the deep learning model. We have built a database comprised of 5 objects with different poses and illuminations for experimental performance evaluation. The experimental results demonstrate that our system can achieve high accuracy on object recognition as well as pose estimation. And the vision-based robotic system can grasp objects successfully regardless of different poses and illuminations.
System for multimodal biometric recognition based on finger knuckle and finger vein using feature-level fusion and k-support vector machine classifier

In this study, the authors propose a multimodal biometric system by combining the finger knuckle and finger vein images at feature-level fusion using fractional firefly (FFF) optimisation. Biometric characteristics, like finger knuckle and finger vein are unique and secure. Initially, the features are extracted from the finger knuckle and finger vein images using repeated line tracking method. Then, a newly developed method of feature-level fusion using FFF optimisation is used. This method is utilised to find out the optimal weight score to fuse the extracted feature sets of finger knuckle and finger vein images. Thus, the recognition is carried out by the fused feature set using layered k-SVM (k-support vector machine) which is newly developed by combining the layered SVM classifier and k-neural network classifier. The experimental results are evaluated and the performance is analysed with false acceptance ratio, false rejection ratio and accuracy. The outcome of the proposed FFF optimisation system obtains a higher accuracy of 96%.
Concepts for dynamic obstacle avoidance and their extended application in underground navigation

An effective approach to dynamic obstacle avoidance for mobile robots is described in this paper. The main concepts in this approach include local Polar Object Chart (POC) for defining possible ways out, obstacle definition, detour mode, anchor for preventing getting lost, steering delimiting and velocity delimiting. The presented concepts and methods were tested and verified both by simulations and experiments on a developed prototype industrial Automated Guided Vehicle (AGV). This paper also presents the extended application of some of the concepts in underground navigation.
A Balanced-to-Balanced Power Divider With Arbitrary Power Division

In this paper, a balanced-to-balanced power divider is proposed, for the first time, with arbitrary power division, which can be regarded as the balanced form of a Gysel power divider. The constraint rules are provided for its mixed-mode and single-ended S-parameters. The six-port network is analyzed by simplifying it to two-port networks with other ports matched at the central frequency. Its critical characteristic impedances are then calculated analytically by our derived equations according to the desired differential-mode power division ratio. The maximum achievable power division ratio is 1:4.692 when the characteristic impedances are limited within the realizable range of 20-120 Ω. The impacts of several freely selected design parameters on the operating bandwidth are explored numerically. A prototype is realized by microstrip lines and lumped resistors with the power division ratio of 1:32. The balanced-to-balanced performances of unequal power division, low differential-mode insertion loss, good suppression of common-mode noises, and mode conventions have been demonstrated by the simulated and measured results of the balanced-to-balanced power divider prototype.
A Large-Scale Study of Mobile Web App Security

Mobile apps that use an embedded web browser, or mobile web apps, make up 85% of the free apps on the Google Play store. The security concerns for developing mobile web apps go beyond just those for developing traditional web apps or mobile apps. In this paper we develop scalable analyses for finding several classes of vulnerabilities in mobile web apps and analyze a large dataset of 998,286 mobile web apps, representing a complete snapshot of all of the free mobile web apps on the Google Play store as of June 2014. We find that 28% of the studied apps have at least one vulnerability. We explore the severity of these vulnerabilities and identify trends in the vulnerable apps. We find that severe vulnerabilities are present across the entire Android app ecosystem, even in popular apps and libraries. Finally, we offer several changes to the Android APIs to mitigate these vulnerabilities.
Analysis and Performance of a Smart Antenna for 2.45-GHz Single-Anchor Indoor Positioning

This paper presents the theoretical analysis and the experimental evaluation of a new switched beam antenna designed to operate at 2.45 GHz. The antenna enables direction of arrival estimation using six directional planar elements arranged to form a platonic solid geometry. It also supports polarization diversity, and it is suitable for single-anchor indoor positioning applications. We adopt the Cramer-Rao bound to study the estimation accuracy of the proposed antenna in absolute 2-D target positioning using received signal strength measurements. First, we describe the design principles for the radiators, we provide an extensive characterization of the switched antenna prototype, and we discuss positioning applications. We then report experimental data that support the results of the theoretical analysis and show consistency between theoretical expectation and the measurements. Finally, we discuss results from proof-of-concept operative indoor positioning example, showing an average localization error as low as 1.7 m.
Utility-maximizing event stream suppression

Complex Event Processing (CEP) has emerged as a technology for monitoring event streams in search of user specified event patterns. When a CEP system is deployed in sensitive environments the user may wish to mitigate leaks of private information while ensuring that useful nonsensitive patterns are still reported. In this paper we consider how to suppress events in a stream to reduce the disclosure of sensitive patterns while maximizing the detection of nonsensitive patterns. We first formally define the problem of utility-maximizing event suppression with privacy preferences, and analyze its computational hardness. We then design a suite of real-time solutions to solve this problem. Our first solution optimally solves the problem at the event-type level. The second solution, at the event-instance level, further optimizes the event-type level solution by exploiting runtime event distributions using advanced pattern match cardinality estimation techniques. Our user study and experimental evaluation over both real-world and synthetic event streams show that our algorithms are effective in maximizing utility yet still efficient enough to offer near real-time system responsiveness.
Integrated control of a multi-fingered hand and arm using proximity sensors on the fingertips

In this study, we propose integrated control of a robotic hand and arm using only proximity sensing from the fingertips. An integrated control scheme for the fingers and for the arm enables quick control of the position and posture of the arm by placing the fingertips adjacent to the surface of an object to be grasped. The arm control scheme enables adjustments based on errors in hand position and posture that would be impossible to achieve by finger motions alone, thus allowing the fingers to grasp an object in a laterally symmetric grasp. This can prevent grasp failures such as a finger pushing the object out of the hand or knocking the object over. Proposed control of the arm and hand allowed correction of position errors on the order of several centimeters. For example, an object on a workbench that is in an uncertain positional relation with the robot, with an inexpensive optical sensor such as a Kinect, which only provides coarse image data, would be sufficient for grasping an object.
Low-k interconnect stack with a novel self-aligned via patterning process for 32nm high volume manufacturing

Interconnect process features are described for a 32nm high performance logic technology. Lower-k, yet highly manufacturable, Carbon-Doped Oxide (CDO) dielectric layers are introduced on this technology at three layers to address the demand for ever lower metal line capacitance. The pitches have been aggressively scaled to meet the expectation for density, and the metal resistance and electromigration performance have been carefully balanced to meet the high reliability requirements while maintaining the lowest possible resistance. A new patterning scheme has been used to limit any patterning damage to the lower-k ILD and address the increasingly difficult problem of via-to-metal shorting at these very tight pitches. The interconnect stack has a thick Metal-9 layer to provide a low resistance path for the power and I/O routing that has been carefully scaled to maintain a low resistance. The combined interconnect stack provides high density, performance, and reliability, and supports a Pb-free 32nm process.
Online fuzzy c means

Clustering streaming data presents the problem of not having all the data available at one time. Further, the total size of the data may be larger than will fit in the available memory of a typical computer. If the data is very large, it is a challenge to apply fuzzy clustering algorithms to get a partition in a timely manner. In this paper, we present an online fuzzy clustering algorithm which can be used to cluster streaming data, as well as very large data sets which might be treated as streaming data. Results on several large volumes of magnetic resonance images show that the new algorithm produces partitions which are very close to what you could get if you clustered all the data at one time. So, the algorithm is an accurate approach for online clustering.
A Low Cost Eeg Based Bci Prosthetic Using Motor Imagery

Brain Computer Interfaces (BCI) provide the opportunity to control external devices using the brain ElectroEncephaloGram (EEG) signals. In this paper we propose two software framework in order to control a 5 degree of freedom robotic and prosthetic hand. Results are presented where an Emotiv Cognitive Suite (i.e. the 1 st framework) combined with an embedded software system (i.e. an open source Arduino board) is able to control the hand through character input associated with the taught actions of the suite. This system provides evidence of the feasibility of brain signals being a viable approach to controlling the chosen prosthetic. Results are then presented in the second framework. This latter one allowed for the training and classification of EEG signals for motor imagery tasks. When analysing the system, clear visual representations of the performance and accuracy are presented in the results using a confusion matrix, accuracy measurement and a feedback bar signifying signal strength. Experiments with various acquisition datasets were carried out and with a critical evaluation of the results given. Finally depending on the classification of the brain signal a Python script outputs the driving command to the Arduino to control the prosthetic. The proposed architecture performs overall good results for the design and implementation of economically convenient BCI and prosthesis.
A Model of Capital and Crises

We develop a model in which the capital of the intermediary sector plays a critical role in determining asset prices. The model is cast within a dynamic general equilibrium economy, and the role for intermediation is derived endogenously based on optimal contracting considerations. Low intermediary capital reduces the risk-bearing capacity of the marginal investor. We show how this force helps to explain patterns during financial crises. The model replicates the observed rise during crises in Sharpe ratios, conditional volatility, correlation in price movements of assets held by the intermediary sector, and fall in riskless interest rates.
Towards detection of bad habits by fusing smartphone and smartwatch sensors

Recently, there has been a growing interest in the research community about using wrist-worn devices, such as smartwatches for human activity recognition, since these devices are equipped with various sensors such as an accelerometer and a gyroscope. Similarly, smartphones are already being used for activity recognition. In this paper, we study the fusion of a wrist-worn device (smartwatch) and a smartphone for human activity recognition. We evaluate these two devices for their strengths and weaknesses in recognizing various daily physical activities. We use three classifiers to recognize 13 different activities, such as smoking, eating, typing, writing, drinking coffee, giving a talk, walking, jogging, biking, walking upstairs, walking downstairs, sitting, and standing. Some complex activities, such as smoking, eating, drinking coffee, giving a talk, writing, and typing cannot be recognized with a smartphone in the pocket position alone. We show that the combination of a smartwatch and a smartphone recognizes such activities with a reasonable accuracy. The recognition of such complex activities can enable well-being applications for detecting bad habits, such as smoking, missing a meal, and drinking too much coffee. We also show how to fuse information from these devices in an energy-efficient way by using low sampling rates. We make our dataset publicly available in order to make our work reproducible.