nils-herrmann/clean_scirepeval huggingface dataset:
A New Dominance Relation-Based Evolutionary Algorithm for Many-Objective Optimization

Many-objective optimization has posed a great challenge to the classical Pareto dominance-based multiobjective evolutionary algorithms (MOEAs). In this paper, an evolutionary algorithm based on a new dominance relation is proposed for many-objective optimization. The proposed evolutionary algorithm aims to enhance the convergence of the recently suggested nondominated sorting genetic algorithm III by exploiting the fitness evaluation scheme in the MOEA based on decomposition, but still inherit the strength of the former in diversity maintenance. In the proposed algorithm, the nondominated sorting scheme based on the introduced new dominance relation is employed to rank solutions in the environmental selection phase, ensuring both convergence and diversity. The proposed algorithm is evaluated on a number of well-known benchmark problems having 3-15 objectives and compared against eight state-of-the-art algorithms. The extensive experimental results show that the proposed algorithm can work well on almost all the test functions considered in this paper, and it is compared favorably with the other many-objective optimizers. Additionally, a parametric study is provided to investigate the influence of a key parameter in the proposed algorithm.
Achieving scalability and expressiveness in an Internet-scale event notification service

This paper describes the design of <italic>S</italic>IENA, an Internet-scale event notification middleware service for distributed event-based applications deployed over wide-area networks. <italic>S</italic>IENA is responsible for <italic>selecting</italic> the notifications that are of interest to clients (as expressed in client subscriptions) and then <italic>delivering</italic> those notifications to the clients via access points. The key design challenge for <italic>S</italic>IENA is maximizing <italic>expressiveness</italic> in the selection mechanism without sacrificing <italic>scalability</italic> of the delivery mechanism. This paper focuses on those aspects of the design of <italic>S</italic>IENA that fundamentally impact scalability and expressiveness. In particular, we describe <italic>S</italic>IENA's data model for notifications, the covering relations that formally define the semantics of the data model, the distributed architectures we have studied for <italic>S</italic>IENA's implementation, and the processing strategies we developed to exploit the covering relations for optimizing the routing of notifications.
Graph Rigidity and Distributed Formation Stabilization of Multi-Vehicle Systems

In this paper, we provide a graph theoretical framework that allows us to formally define formations of multiple vehicles and the issues arising in uniqueness of graph realizations and its connection to stability of formations. The notion of graph rigidity is crucial in identifying the shape variables of a formation and an appropriate potential function associated with the formation. This allows formulation of meaningful optimization or nonlinear control problems for formation stabilization/tacking, in addition to formal representation of split, rejoin, and reconfiguration maneuvers for multi-vehicle formations. We introduce an algebra that consists of performing some basic operations on graphs which allow creation of larger rigidby-construction graphs by combining smaller rigid subgraphs. This is particularly useful in performing and representing rejoin/split maneuvers of multiple formations in a distributed fashion.
Extracting information nuggets from disaster- Related messages in social media

Microblogging sites such as Twitter can play a vital role in spreading information during “natural” or man-made disasters. But the volume and velocity of tweets posted during crises today tend to be extremely high, making it hard for disaster-affected communities and professional emergency responders to process the information in a timely manner. Furthermore, posts tend to vary highly in terms of their subjects and usefulness; from messages that are entirely off-topic or personal in nature, to messages containing critical information that augments situational awareness. Finding actionable information can accelerate disaster response and alleviate both property and human losses. In this paper, we describe automatic methods for extracting information from microblog posts. Specifically, we focus on extracting valuable “information nuggets”, brief, self-contained information items relevant to disaster response. Our methods leverage machine learning methods for classifying posts and information extraction. Our results, validated over one large disaster-related dataset, reveal that a careful design can yield an effective system, paving the way for more sophisticated data analysis and visualization systems.
Analysing the Relationship between Risk and Trust

Among the various human factors impinging upon making a decision in an uncertain environment, risk and trust are surely crucial ones. Several models for trust have been proposed in the literature but few explicitly take risk into account. This paper analyses the relationship between the two concepts by first looking at how a decision is made to enter into a transaction based on the risk information. We then draw a model of the invested fraction of the capital function of a decision surface. We finally define a model of trust composed of a reliability trust as the probability of transaction success and a decision trust derived from the decision surface.
An Efficient Mutual Authentication and Access Control Scheme for Wireless Sensor Networks in Healthcare

Wireless sensor networks (WSNs) will play an active role in the 21th Century Healthcare IT to reduce the healthcare cost and improve the quality of care. The protection of data confidentiality and patient privacy are the most critical requirements for the ubiquitous use of WSNs in healthcare environments. This requires a secure and lightweight user authentication and access control. Symmetric key based access control is not suitable for WSNs in healthcare due to dynamic network topology, mobility, and stringent resource constraints. In this paper, we propose a secure, lightweight public key based security scheme, Mutual Authentication and Access Control based on Elliptic curve cryptography (MAACE). MAACE is a mutual authentication protocol where a healthcare professional can authenticate to an accessed node (a PDA or medical sensor) and vice versa. This is to ensure that medical data is not exposed to an unauthorized person. On the other hand, it ensures that medical data sent to healthcare professionals did not originate from a malicious node. MAACE is more scalable and requires less memory compared to symmetric key-based schemes. Furthermore, it is much more lightweight than other public key-based schemes. Security analysis and performance evaluation results are presented and compared to existing schemes to show advantages of the proposed scheme.
A segmented topic model based on the two-parameter Poisson-Dirichlet process

Documents come naturally with structure: a section contains paragraphs which itself contains sentences; a blog page contains a sequence of comments and links to related blogs. Structure, of course, implies something about shared topics. In this paper we take the simplest form of structure, a document consisting of multiple segments, as the basis for a new form of topic model. To make this computationally feasible, and to allow the form of collapsed Gibbs sampling that has worked well to date with topic models, we use the marginalized posterior of a two-parameter Poisson-Dirichlet process (or Pitman-Yor process) to handle the hierarchical modelling. Experiments using either paragraphs or sentences as segments show the method significantly outperforms standard topic models on either whole document or segment, and previous segmented models, based on the held-out perplexity measure.
Image segmentation evaluation: A survey of unsupervised methods

Image segmentation is an important processing step in many image, video and computer vision applications. Extensive research has been done in creating many different approaches and algorithms for image segmentation, but it is still difficult to assess whether one algorithm produces more accurate segmentations than another, whether it be for a particular image or set of images, or more generally, for a whole class of images. To date, the most common method for evaluating the effectiveness of a segmentation method is subjective evaluation, in which a human visually compares the image segmentation results for separate segmentation algorithms, which is a tedious process and inherently limits the depth of evaluation to a relatively small number of segmentation comparisons over a predetermined set of images. Another common evaluation alternative is supervised evaluation, in which a segmented image is compared against a manuallysegmented or pre-processed reference image. Evaluation methods that require user assistance, such as subjective evaluation and supervised evaluation, are infeasible in many vision applications, so unsupervised methods are necessary. Unsupervised evaluation enables the objective comparison of both different segmentation methods and different parameterizations of a single method, without requiring human visual comparisons or comparison with a manually-segmented or pre-processed reference image. Additionally, unsupervised methods generate results for individual images and images whose characteristics may not be known until evaluation time. Unsupervised methods are crucial to real-time segmentation evaluation, and can furthermore enable self-tuning of algorithm parameters based on evaluation results. In this paper, we examine the unsupervised objective evaluation methods that have been proposed in the literature. An extensive evaluation of these methods are presented. The advantages and shortcomings of the underlying design mechanisms in these methods are discussed and analyzed through analytical evaluation and empirical evaluation. Finally, possible future directions for research in unsupervised evaluation are proposed.
Enabling Public Auditability and Data Dynamics for Storage Security in Cloud Computing

Cloud Computing has been envisioned as the next-generation architecture of IT Enterprise. It moves the application software and databases to the centralized large data centers, where the management of the data and services may not be fully trustworthy. This unique paradigm brings about many new security challenges, which have not been well understood. This work studies the problem of ensuring the integrity of data storage in Cloud Computing. In particular, we consider the task of allowing a third party auditor (TPA), on behalf of the cloud client, to verify the integrity of the dynamic data stored in the cloud. The introduction of TPA eliminates the involvement of the client through the auditing of whether his data stored in the cloud are indeed intact, which can be important in achieving economies of scale for Cloud Computing. The support for data dynamics via the most general forms of data operation, such as block modification, insertion, and deletion, is also a significant step toward practicality, since services in Cloud Computing are not limited to archive or backup data only. While prior works on ensuring remote data integrity often lacks the support of either public auditability or dynamic data operations, this paper achieves both. We first identify the difficulties and potential security problems of direct extensions with fully dynamic data updates from prior works and then show how to construct an elegant verification scheme for the seamless integration of these two salient features in our protocol design. In particular, to achieve efficient data dynamics, we improve the existing proof of storage models by manipulating the classic Merkle Hash Tree construction for block tag authentication. To support efficient handling of multiple auditing tasks, we further explore the technique of bilinear aggregate signature to extend our main result into a multiuser setting, where TPA can perform multiple auditing tasks simultaneously. Extensive security and performance analysis show that the proposed schemes are highly efficient and provably secure.
SARFID on drone: Drone-based UHF-RFID tag localization

This paper presents a localization system for objects tagged with UHF-RFID passive tags, where the reader is attached to a drone. The system implements the phase-based SARFID technique to locate static tags with respect to a UHF-RFID reader attached to a commercial drone. The reader antenna trajectory is achieved through a Global Positioning System. The bi-dimensional tag position is estimated with centimeter order accuracy. Only one reader antenna is required, without any reference tag.
A natural language approach to automated cryptanalysis of two-time pads

While keystream reuse in stream ciphers and one-time pads has been a well known problem for several decades, the risk to real systems has been underappreciated. Previous techniques have relied on being able to accurately guess words and phrases that appear in one of the plaintext messages, making it far easier to claim that "an attacker would never be able to do that." In this paper, we show how an adversary can automatically recover messages encrypted under the same keystream if only the type of each message is known (e.g. an HTML page in English). Our method, which is related to HMMs, recovers the most probable plaintext of this type by using a statistical language model and a dynamic programming algorithm. It produces up to 99% accuracy on realistic data and can process ciphertexts at 200ms per byte on a $2,000 PC. To further demonstrate the practical effectiveness of the method, we show that our tool can recover documents encrypted by Microsoft Word 2002 .
D3S: Debugging Deployed Distributed Systems

Testing large-scale distributed systems is a challenge, because some errors manifest themselves only after a distributed sequence of events that involves machine and network failures. DS is a checker that allows developers to specify predicates on distributed properties of a deployed system, and that checks these predicates while the system is running. When DS finds a problem it produces the sequence of state changes that led to the problem, allowing developers to quickly find the root cause. Developers write predicates in a simple and sequential programming style, while DS checks these predicates in a distributed and parallel manner to allow checking to be scalable to large systems and fault tolerant. By using binary instrumentation, DS works transparently with legacy systems and can change predicates to be checked at runtime. An evaluation with 5 deployed systems shows that DS can detect non-trivial correctness and performance bugs at runtime and with low performance overhead (less than 8%).
Harvesting Wireless Power: Survey of Energy-Harvester Conversion Efficiency in Far-Field, Wireless Power Transfer Systems

The idea of wireless power transfer (WPT) has been around since the inception of electricity. In the late 19th century, Nikola Tesla described the freedom to transfer energy between two points without the need for a physical connection to a power source as an "all-surpassing importance to man". A truly wireless device, capable of being remotely powered, not only allows the obvious freedom of movement but also enables devices to be more compact by removing the necessity of a large battery. Applications could leverage this reduction in size and weight to increase the feasibility of concepts such as paper-thin, flexible displays, contact-lens-based augmented reality, and smart dust, among traditional point-to-point power transfer applications. While several methods of wireless power have been introduced since Tesla's work, including near-field magnetic resonance and inductive coupling, laser-based optical power transmission, and far-field RF/microwave energy transmission, only RF/microwave and laser-based systems are truly long-range methods. While optical power transmission certainly has merit, its mechanisms are outside of the scope of this article and will not be discussed.
Trivariate solid T-spline construction from boundary triangulations with arbitrary genus topology

A comprehensive scheme is described to construct rational trivariate solid T-splines from boundary triangulations with arbitrary topology. To extract the topology of the input geometry, we first compute a smooth harmonic scalar field defined over the mesh, and saddle points are extracted to determine the topology. By dealing with the saddle points, a polycube whose topology is equivalent to the input geometry is built, and it serves as the parametric domain for the trivariate T-spline. A polycube mapping is then used to build a one-to-one correspondence between the input triangulation and the polycube boundary. After that, we choose the deformed octree subdivision of the polycube as the initial T-mesh, and make it valid through pillowing, quality improvement and applying templates to handle extraordinary nodes and partial extraordinary nodes. The T-spline that is obtained is C2-continuous everywhere over the boundary surface except for the local region surrounding polycube corner nodes. The efficiency and robustness of the presented technique are demonstrated with several applications in isogeometric analysis.
Addressing appearance change in outdoor robotics with adversarial domain adaptation

Appearance changes due to weather and seasonal conditions represent a strong impediment to the robust implementation of machine learning systems in outdoor robotics. While supervised learning optimises a model for the training domain, it will deliver degraded performance in application domains that underlie distributional shifts caused by these changes. Traditionally, this problem has been addressed via the collection of labelled data in multiple domains or by imposing priors on the type of shift between both domains. We frame the problem in the context of unsupervised domain adaptation and develop a framework for applying adversarial techniques to adapt popular, state-of-the-art network architectures with the additional objective to align features across domains. Moreover, as adversarial training is notoriously unstable, we first perform an extensive ablation study, adapting many techniques known to stabilise generative adversarial networks, and evaluate on a surrogate classification task with the same appearance change. The distilled insights are applied to the problem of free-space segmentation for motion planning in autonomous driving.
Wear unleveling: improving NAND flash lifetime by balancing page endurance

Flash memory cells typically undergo a few thousand Program/Erase (P/E) cycles before they wear out. However, the programming strategy of flash devices and process variations cause some flash cells to wear out significantly faster than others. This paper studies this variability on two commercial devices, acknowledges its unavoidability, figures out how to identify the weakest cells, and introduces a wear unbalancing technique that let the strongest cells relieve the weak ones in order to lengthen the overall lifetime of the device. Our technique periodically skips or relieves the weakest pages whenever a flash block is programmed. Relieving the weakest pages can lead to a lifetime extension of up to 60% for a negligible memory and storage overhead, while minimally affecting (sometimes improving) the write performance. Future technology nodes will bring larger variance to page endurance, increasing the need for techniques similar to the one proposed in this work.
Using Educational Data Mining Methods to Study the Impact of Virtual Classroom in E-Learning

In the past few years, Iranian universities have embarked to use e-learning tools and technologies to extend and improve their educational services. After a few years of conducting e-learning programs a debate took place within the executives and managers of the e-learning institutes concerning which activities are of the most influence on the learning progress of online students. This research is aimed to investigate the impact of a number of e-learning activities on the students’ learning development. The results show that participation in virtual classroom sessions has the most substantial impact on the students’ final grades. This paper presents the process of applying data mining methods to the web usage records of students’ activities in a virtual learning environment. The main idea is to rank the learning activities based on their importance in order to improve students’ performance by focusing on the most important ones.
End-to-end learning for lane keeping of self-driving cars

Lane keeping is an important feature for self-driving cars. This paper presents an end-to-end learning approach to obtain the proper steering angle to maintain the car in the lane. The convolutional neural network (CNN) model takes raw image frames as input and outputs the steering angles accordingly. The model is trained and evaluated using the comma.ai dataset, which contains the front view image frames and the steering angle data captured when driving on the road. Unlike the traditional approach that manually decomposes the autonomous driving problem into technical components such as lane detection, path planning and steering control, the end-to-end model can directly steer the vehicle from the front view camera data after training. It learns how to keep in lane from human driving data. Further discussion of this end-to-end approach and its limitation are also provided.
An open-source research kit for the da Vinci® Surgical System

We present a telerobotics research platform that provides complete access to all levels of control via open-source electronics and software. The electronics employs an FPGA to enable a centralized computation and distributed I/O architecture in which all control computations are implemented in a familiar development environment (Linux PC) and low-latency I/O is performed over an IEEE-1394a (FireWire) bus at speeds up to 400 Mbits/sec. The mechanical components are obtained from retired first-generation da Vinci ® Surgical Systems. This system is currently installed at 11 research institutions, with additional installations underway, thereby creating a research community around a common open-source hardware and software platform.
A NewWay of Using the Interactive Whiteboard in a High School Physics Classroom : A Case Study

In recent decades, the interactive whiteboard (IWB) has become a relatively common educational tool in Western schools. The IWB is essentially a large touch screen, that enables the user to interact with digital content in ways that are not possible with an ordinary computer-projector-canvas setup. However, the unique possibilities of IWBs are rarely leveraged to enhance teaching and learning beyond the primary school level. This is particularly noticeable in high school physics. We describe how a high school physics teacher learned to use an IWB in a new way, how she planned and implemented a lesson on the topic of orbital motion of planets, and what tensions arose in the process. We used an ethnographic approach to account for the teacher’s and involved students’ perspectives throughout the process of teacher preparation, lesson planning, and the implementation of the lesson. To interpret the data, we used the conceptual framework of activity theory. We found that an entrenched culture of traditional white/blackboard use in physics instruction interferes with more technologically innovative and more student-centered instructional approaches that leverage the IWB’s unique instructional potential. Furthermore, we found that the teacher’s confidence in the mastery of the IWB plays a crucial role in the teacher’s willingness to transfer agency within the lesson to the students.