nils-herrmann/clean_scirepeval huggingface dataset:
Security trade-offs in Cloud storage systems

Securing software systems, is of paramount importance today. This is especially true for cloud systems, as they can be attacked nearly anytime from everywhere over the Internet. Ensuring security in general, however, typically has a negative impact on performance, usability, and increases the system’s complexity. For cloud systems, which are built for high performance and availability as well as elastic scalability, security, therefore, may annihilate many of these original quality properties. Consequently, security, especially for cloud systems, can be understood as a trade-off problem where security mechanisms, applied to protect the system from specific threats and to achieve specific security goals, trade in security for other quality properties. For Cloud Storage Systems (CSS)—i.e., distributed storage systems that replicate data over a cluster of nodes in order to manage huge data amounts, highly volatile query load (elastic scalability), and extraordinary needs for availability and fault-tolerance—this trade-off problem is particularly prominent. Already, the different original quality properties of CSS cannot be provided at the same time and, thus, lead to fundamental trade-offs such as the trade-off between consistency, availability, and partition tolerance (see, e.g.: [53]). The piggybacked trade-offs coming from considering security as an additionally wanted quality property of such systems lead to further trade-offs that must be decided and managed. In this thesis, we focus on the trade-offs between security and performance in CSS. In order to not contradict the original design goals of CSS, a sensible management of these trade-offs in CSS requires a high degree of understanding of the relationships between security and performance in the security mechanisms of a specific CSS. Otherwise, this can lead to a badly configured CSS which is a security risk or consumes a lot of resources unnecessarily. This thesis, hence, aims at enhancing the understanding of the trade-offs between security and performance in CSS as well as at improving the management of these trade-offs in such systems. For this, we present three independent contributions in this thesis. The first contribution intends to improve the overall understanding of security in and security requirements for CSS. We present two reference usage models that support a security engineer in understanding the general usage of cloud storage
Improving Video Game Development: Facilitating Heterogeneous Team Collaboration through Flexible Software Processes

Based on our observations in the Austrian video game software development practices we identified a lack of systematic processes and method support and inefficient collaboration between various involved disciplines, i.e. engineers and artists. Video game development includes heterogeneous disciplines, e.g. creative arts, game and content design, and software. Nevertheless, improvement of team collaboration and process support is an ongoing challenge to enable a comprehensive view on game development projects. Lessons learned from software engineering practices can help game developers to increase game development processes within a heterogeneous environment. Based on a state of the practice survey in the Austrian games industry, this paper presents (a) first results with focus on process and method support and (b) suggests a candidate flexible process approach based on Scrum to improve video game development and team collaboration. The results showed (a) a trend to highly flexible software processes involving various disciplines and (b) identified the suggested flexible process approach as feasible and useful for project application.
SGD-QN: Careful Quasi-Newton Stochastic Gradient Descent

TheSGD-QN algorithm is a stochastic gradient descent algorithm that m akes careful use of secondorder information and splits the parameter update into inde pendently scheduled components. Thanks to this design, SGD-QN iterates nearly as fast as a first-order stochastic gradient escent but requires less iterations to achieve the same accuracy. This algorith m won the “Wild Track” of the first PASCAL Large Scale Learning Challenge (Sonnenburg et al., 2008 ).
ScaleGraph: A high-performance library for billion-scale graph analytics

Recently, large-scale graph analytics has become a very popular topic owing to the emergence of gigantic graphs whose number of vertices and edges is in millions, billions or even trillions. Many graph analytics libraries and frameworks have been proposed with various computational models and programming languages to deal with such graphs. X10 programming language is a PGAS language that aims at both software performance and programmer's productivity. We introduce ScaleGraph library developed using X10 programming to illustrate the use of X10 for large-scale graph analytics. ScaleGraph library provides XPregel framework that is inspired by Google's Pregel computation model, serving as a building block for implementing graph kernels. We also optimized X10 runtime in some parts such as collective communication and memory management. We evaluated the performance and scalability of ScaleGraph libraries. The result shows that most graph kernels have good performance and scalability. ScaleGraph library is 9.4 times faster than Giraph in the experiment of PageRank with 16 machine nodes. To the best of our knowledge, ScaleGraph is the first X10-based library to address performance, scalability and productivity issues in dealing with large-scale graph analytics.
Health worker motivation in Africa: the role of non-financial incentives and human resource management tools

There is a serious human resource crisis in the health sector in developing countries, particularly in Africa. One of the challenges is the low motivation of health workers. Experience and the evidence suggest that any comprehensive strategy to maximize health worker motivation in a developing country context has to involve a mix of financial and non-financial incentives. This study assesses the role of non-financial incentives for motivation in two cases, in Benin and Kenya.



The study design entailed semi-structured qualitative interviews with doctors and nurses from public, private and NGO facilities in rural areas. The selection of health professionals was the result of a layered sampling process. In Benin 62 interviews with health professionals were carried out; in Kenya 37 were obtained. Results from individual interviews were backed up with information from focus group discussions. For further contextual information, interviews with civil servants in the Ministry of Health and at the district level were carried out. The interview material was coded and quantitative data was analysed with SPSS software.



The study shows that health workers overall are strongly guided by their professional conscience and similar aspects related to professional ethos. In fact, many health workers are demotivated and frustrated precisely because they are unable to satisfy their professional conscience and impeded in pursuing their vocation due to lack of means and supplies and due to inadequate or inappropriately applied human resources management (HRM) tools. The paper also indicates that even some HRM tools that are applied may adversely affect the motivation of health workers.



The findings confirm the starting hypothesis that non-financial incentives and HRM tools play an important role with respect to increasing motivation of health professionals. Adequate HRM tools can uphold and strengthen the professional ethos of doctors and nurses. This entails acknowledging their professionalism and addressing professional goals such as recognition, career development and further qualification. It must be the aim of human resources management/quality management (HRM/QM) to develop the work environment so that health workers are enabled to meet their personal and the organizational goals.
A near-optimal algorithm for shortest paths among curved obstacles in the plane

We propose an algorithm for the problem of computing shortest paths among curved obstacles in the plane. If the obstacles have O(n) description complexity, then the algorithm runs in O(n log n) time plus a term dependent on the properties of the boundary arcs. Specifically, if the arcs allow a certain kind of bisector intersection to be computed in constant time, or even in O(log n) time, then the running time of the overall algorithm is O(n log n). If the arcs support only constant-time tangent, intersection, and length queries, as is customarily assumed, then the algorithm computes an approximate shortest path, with relative error ε, in time O(n log n + n log 1/ε). In fact, the algorithm computes an approximate shortest path map, a data structure with O(n log n) size, that allows it to report the (approximate) length of a shortest path from a fixed source point to any query point in the plane in O(log n) time.
An improved traffic signs recognition and tracking method for driver assistance system

We introduce a new computer vision based system for robust traffic sign recognition and tracking. Such a system presents a vital support for driver assistance in an intelligent automotive. Firstly, a color based segmentation method is applied to generate traffic sign candidate regions. Secondly, the HoG features are extracted to encode the detected traffic signs and then generating the feature vector. This vector is used as an input to an SVM classifier to identify the traffic sign class. Finally, a tracking method based on optical flow is performed to ensure a continuous capture of the recognized traffic sign while accelerating the execution time. Our method affords high precision rates under different challenging conditions.
Anomaly detection of malicious users' behaviors for web applications based on web logs

With more and more online services developed into web applications, security problems based on web applications become more serious now. Most intrusion detection systems are based on every single request to find the cyber-attack instead of users' behaviors, and these systems can only protect web application from known vulnerability rather than some zero-day attacks. In order to detect newly developed attacks, we analyze web logs from web servers and define users' behaviors to divide them into normal and malicious ones. The result shows that by using the feature of web resources to define users' behaviors, a higher accuracy rate and lower false alarm rate of intrusion detection can be obtained.
Learning Possibilistic Logic Theories from Default Rules

We introduce a setting for learning possibilistic logic theories from defaults of the form “if alpha then typically beta”. We first analyse this problem from the point of view of machine learning theory, determining the VC dimension of possibilistic stratifications as well as the complexity of the associated learning problems, after which we present a heuristic learning algorithm that can easily scale to thousands of defaults. An important property of our approach is that it is inherently able to handle noisy and conflicting sets of defaults. Among others, this allows us to learn possibilistic logic theories from crowdsourced data and to approximate propositional Markov logic networks using heuristic MAP solvers. We present experimental results that demonstrate the effectiveness of this approach.
Residual vs. Inception vs. Classical Networks for Low-Resolution Face Recognition

When analyzing surveillance footage, low-resolution face recognition is still a challenging task. While high-resolution face recognition experienced impressive improvements by Convolutional Neural Network (CNN) approaches, the benefit to low-resolution face recognition remains unclear as only few work has been done in this area. This paper adapts three popular high-resolution CNN designs to the low-resolution (LR) domain to find the most suitable architecture. Namely, the classical AlexNet/VGG architecture, Google’s inception architecture and Microsoft’s residual architecture are considered. While the inception and residual concept have been proven to be useful for very deep networks, it is shown in our case that shallower networks than for high-resolution recognition are sufficient. This leads to an advantage of the classical network architecture. Final evaluation on a downscaled version of the public YouTube Faces Database indicates a comparable performance to the high-resolution domain. Results with faces extracted from the SoBiS surveillance dataset indicate a superior performance of the trained networks in the LR domain.
Implementation of WiFi-based single phase smart meter for Internet of Things (IoT)

With the advent of Internet and computational era, not only opportunity to send and receive data between humans, but also among the devices without human control over it. This is known as Internet of Things (IoTs) concept which can be applied for solving the growing issue of power/energy management. A solution is a cheap and easy to implement and manage energy monitoring system for our daily usage of electric power. In order to overcome the human errors, manual labor and cost reducing in energy consumption with more efficiency for the power management system, in this paper, we focus mainly on IoT's energy monitoring. The proposed design is to implement a very low cost wireless sensor network and protocol for smart energy and web application capable of automatically reading the unit and sending the data automatically for the power users to view their current energy meter reading. By using this system, the users will be aware of the electricity usage in his/her home to reduce the power wastage and cost of consumption. The system consists of a digital energy meter, ESP8266 WiFi module and web applications for management system. The ESP8266 WiFi module will be embedded into the meter and implement the TCP/IP protocol for the communications between the meter and web application. The experimental results show that the proposed system works very well with efficiency, and it is feasible to implement in practical applications for very low cost-build automatic energy meter reading.
Multi-View Unsupervised Feature Selection with Adaptive Similarity and View Weight

With the advent of multi-view data, multi-view learning has become an important research direction in both machine learning and data mining. Considering the difficulty of obtaining labeled data in many real applications, we focus on the multi-view unsupervised feature selection problem. Traditional approaches all characterize the similarity by fixed and pre-defined graph Laplacian in each view separately and ignore the underlying common structures across different views. In this paper, we propose an algorithm named Multi-view Unsupervised Feature Selection with Adaptive Similarity and View Weight (ASVW) to overcome the above mentioned problems. Specifically, by leveraging the learning mechanism to characterize the common structures adaptively, we formulate the objective function by a common graph Laplacian across different views, together with the sparse <inline-formula><tex-math notation="LaTeX">$\ell _{2,p}$</tex-math> <alternatives><inline-graphic xlink:href="hou-ieq1-2681670.gif"/></alternatives></inline-formula>-norm constraint designed for feature selection. We develop an efficient algorithm to address the non-smooth minimization problem and prove that the algorithm will converge. To validate the effectiveness of ASVW, comparisons are made with some benchmark methods on real-world datasets. We also evaluate our method in the real sports action recognition task. The experimental results demonstrate the effectiveness of our proposed algorithm.
Motion planning for urban autonomous driving using Bézier curves and MPC

This paper presents a real-time motion planning scheme for urban autonomous driving that will be deployed as a basis for cooperative maneuvers defined in the European project AutoNet2030. We use a path-velocity decomposition approach to separate the motion planning problem into a path planning problem and a velocity planning problem. The path planner first generates a collision-free piecewise linear path and then uses quintic Bézier curves to smooth the path with C2 continuity. A derive-free optimization technique Subplex is used to further smooth the curvature of the path in a best-effort basis. The velocity planner generates an optimal velocity profile along the reference path using Model Predictive Control (MPC), taking into account user preferences and cooperative maneuver requirements. Simulation results are presented to validate the approach, with special focus on the flexibility, cooperative-awareness and efficiency of the algorithms.
Local Gaussian Process Regression for Real Time Online Model Learning

Learning in real-time applications, e.g., online approximation of the inverse dynamics model for model-based robot control, requires fast online regression techniques. Inspired by local learning, we propose a method to speed up standard Gaussian process regression (GPR) with local GP models (LGP). The training data is partitioned in local regions, for each an individual GP model is trained. The prediction for a query point is performed by weighted estimation using nearby local models. Unlike other GP approximations, such as mixtures of experts, we use a distance based measure for partitioning of the data and weighted prediction. The proposed method achieves online learning and prediction in real-time. Comparisons with other non-parametric regression methods show that LGP has higher accuracy than LWPR and close to the performance of standard GPR and ν-SVR.
On Covering a Solid Sphere with Concentric Spheres in ${\mathbb Z}^3$

We show that a digital sphere, constructed by the circular sweep of a digital semicircle (generatrix) around its diameter, consists of some holes (absentee-voxels), which appear on its spherical surface of revolution. This incompleteness calls for a proper characterization of the absentee-voxels whose restoration will yield a complete spherical surface without any holes. In this paper, we present a characterization of such absentee-voxels using certain techniques of digital geometry and show that their count varies quadratically with the radius of the semicircular generatrix. Next, we design an algorithm to fill these absentee-voxels so as to generate a spherical surface of revolution, which is more realistic from the viewpoint of visual perception. We further show that covering a solid sphere by a set of complete spheres also results in an asymptotically larger count of absentees, which is cubic in the radius of the sphere. The characterization and generation of complete solid spheres without any holes can also be accomplished in a similar fashion. We furnish test results to substantiate our theoretical findings.
The network weather service: a distributed resource performance forecasting service for metacomputing

The goal of the Network Weather Service is to provide accurate forecasts of dy namically changing performance characteristics from a distributed set of metacom puting resources Providing a ubiquitous service that can both track dynamic per formance changes and remain stable in spite of them requires adaptive programming techniques an architectural design that supports extensibility and internal abstrac tions that can be implemented e ciently and portably In this paper we describe the current implementation of the NWS for Unix and TCP IP sockets and provide examples of its performance monitoring and forecasting capabilities
Using Genetic Algorithm to Solve Game of Go-Moku

Genetic algorithm is a stochastic parallel beam search that can be applied to many typical search problems. This paper describes a genetic algorithmic approach to a problem in artificial intelligence. During the process of evolution, the environment cooperates with the population by continuously making itself friendlier so as to lower the evolutionary pressure. Evaluations show the performance of this approach seems considerably effective in solving this type of board games. Game-playing programs are often described as being a combination of search and
Isolation of radial glial cells by fluorescent-activated cell sorting reveals a neuronal lineage.

The developing central nervous system of vertebrates contains an abundant cell type designated radial glial cells. These cells are known as guiding cables for migrating neurons, while their role as precursor cells is less clear. Since radial glial cells express a variety of astroglial characteristics and differentiate as astrocytes after completing their guidance function, they have been considered as part of the glial lineage. Using fluorescence-activated cell sorting, we show here that radial glial cells also are neuronal precursors and only later, after neurogenesis, do they shift towards an exclusive generation of astrocytes. These results thus demonstrate a novel function for radial glial cells, namely their ability to generate two major cell types found in the nervous system, neurons and astrocytes.
Conceptual modeling and development of an intelligent agent-assisted decision support system for anti-money laundering

Criminal elements in today’s technology-driven society are using every means available at their disposal to launder the proceeds from their illegal activities. In response, international anti-money laundering (AML) efforts are being made. The events of September 11, 2001, highlighted the need for more sophisticated AML and anti-terrorist financing programs across the industry and nation. In the wake of this, regulators are focusing on the role that technology can play in compliance with laws and ultimately in law enforcement. Banks will have to employ or enhance AML tools and technology to satisfy rising regulatory expectations. While many AML solutions have been in place for some time within the banks, they are faced with the challenge of adapting to the ever-changing risks and methods related to money laundering. In order to provide support for AML decisions, we have formulated an AML conceptual model by following Simon [Simon, H. A. (1977). The new science of management decision. Englewood Cliffs, NJ: Prentice-Hall] decision-making process model. Based on this model, a novel and open multi-agent AML system prototype has been designed and developed. Intelligent agents with their properties of autonomy, reactivity, and proactivity are well suited for dynamic, ill-structured, and complex ML prevention controls. The advanced architecture is able to provide more adaptive, intelligent, and flexible solution for AML. This paper is the first attempt at intelligent agent financial application in the AML domain, with a decision-making/problem-solving process model, an innovative agent-based architecture, and a prototype system.
Cache-Oblivious R-Trees

We develop a cache-oblivious data structure for storing a set S of N axis-aligned rectangles in the plane, such that all rectangles in S intersecting a query rectangle or point can be found efficiently. Our structure is an axis-aligned bounding-box hierarchy and as such it is the first cache-oblivious R-tree with provable performance guarantees. If no point in the plane is contained in more than a constant number of rectangles in S, we can construct, for any constant ε, a structure that answers a rectangle query using $O(\sqrt{N/B}+T/B)$ memory transfers and a point query using O((N/B) ε ) memory transfers, where T is the number of reported rectangles and B is the block size of memory transfers between any two levels of a multilevel memory hierarchy. We also develop a variant of our structure that achieves the same performance on input sets with arbitrary overlap among the rectangles. The rectangle query bound matches the bound of the best known linear-space cache-aware structure.