nils-herrmann/clean_scirepeval huggingface dataset:
Information security culture: A management perspective

Information technology has become an integral part of modern life. Today, the use of information permeates every aspect of both business and private lives. Most organizations need information systems to survive and prosper and thus need to be serious about protecting their information assets. Many of the processes needed to protect these information assets are, to a large extent, dependent on human cooperated behavior. Employees, whether intentionally or through negligence, often due to a lack of knowledge, are the greatest threat to information security. It has become widely accepted that the establishment of an organizational sub-culture of information security is key to managing the human factors involved in information security. This paper briefly examines the generic concept of corporate culture and then borrows from the management and economical sciences to present a conceptual model of information security culture. The presented model incorporates the concept of elasticity from the economical sciences in order to show how various variables in an information security culture influence each other. The purpose of the presented model is to facilitate conceptual thinking and argumentation about information security culture. a 2009
Distributional Semantic Models of Attribute Meaning in Adjectives and Nouns

Attributes such as size, weight or color are at the core of conceptualization, i.e., the formal representation of entities or events in the real world. In natural language, formal attributes find their counterpart in attribute nouns which can be used in order to generalize over individual properties (e.g., big or small in case of size, blue or red in case of color). In order to ascribe such properties to entities or events, adjectivenoun phrases are a very frequent linguistic pattern (e.g., a blue shirt, a big lion). In these constructions, attribute meaning is conveyed only implicitly, i.e., without being overtly realized at the phrasal surface. This thesis is about modeling attribute meaning in adjectives and nouns in a distributional semantics framework. This implies the acquisition of meaning representations for adjectives, nouns and their phrasal combination from corpora of natural language text in an unsupervised manner, without tedious handcrafting or manual annotation efforts. These phrase representations can be used to predict implicit attribute meaning from adjective-noun phrases – a problem which will be referred to as attribute selection throughout this thesis. The approach to attribute selection proposed in this thesis is framed in structured distributional models. We model adjective and noun meanings as distinct semantic vectors in the same semantic space spanned by attributes as dimensions of meaning. Based on these word representations, we make use of vector composition operations in order to construct a phrase representation from which the most prominent attribute(s) being expressed in the compositional semantics of the adjective-noun phrase can be selected by means of an unsupervised selection function. This approach not only accounts for the linguistic principle of compositionality that underlies adjective-noun phrases, but also avoids inherent sparsity issues that result from the fact that the relationship between an adjective, a noun and a particular attribute is rarely explicitly observed in corpora. The attribute models developed in this thesis aim at a reconciliation of the conflict between specificity and sparsity in distributional semantic models. For this purpose, we compare various instantiations of attribute models capitalizing on pattern-based and dependency-based distributional information as well as attribute-specific latent topics induced from a weakly supervised adaptation of Latent Dirichlet Allocation. Moreover, we propose a novel framework of distributional enrichment in order to enhance structured vector representations by incorporating additional lexical information from complementary distributional sources. In applying distributional enrichment to distributional attribute models, we follow the idea to augment structured
Successive Interference Cancellation in Heterogeneous Networks

At present, operators address the explosive growth of mobile data demand by densification of the cellular network so as to reduce the transmitter-receiver distance and to achieve higher spectral efficiency. Due to such network densification and the intense proliferation of wireless devices, modern wireless networks are interference-limited, which motivates the use of interference mitigation and coordination techniques. In this work, we develop a statistical framework to evaluate the performance of multi-tier heterogeneous networks with successive interference cancellation (SIC) capabilities, accounting for the computational complexity of the cancellation scheme and relevant network related parameters such as random location of the access points (APs) and mobile users, and the characteristics of the wireless propagation channel. We explicitly model the consecutive events of canceling interferers and we derive the success probability to cancel the n-th strongest signal and to decode the signal of interest after n cancellations. When users are connected to the AP which provides the maximum average received signal power, the analysis indicates that the performance gains of SIC diminish quickly with n and the benefits are modest for realistic values of the signal-to-interference ration (SIR). We extend the statistical model to include several association policies where distinct gains of SIC are expected: (i) maximum instantaneous SIR association, (ii) minimum load association, and (iii) range expansion. Numerical results show the effectiveness of SIC for the considered association policies. This work deepens the understanding of SIC by defining the achievable gains for different association policies in multi-tier heterogeneous networks.
A Wavelet Tour of Signal Processing, 2nd Edition

Bargaining with reading habit is no need. Reading is not kind of something sold that you can take or not. It is a thing that will change your life to life better. It is the thing that will give you many things around the world and this universe, in the real world and here after. As what will be given by this a wavelet tour of signal processing 2nd edition, how can you bargain with the thing that has many benefits for you?
Confocal Microwave Imaging for Breast Cancer Detection: Delay-Multiply-and-Sum Image Reconstruction Algorithm

A new image reconstruction algorithm, termed as delay-multiply-and-sum (DMAS), for breast cancer detection using an ultra-wideband confocal microwave imaging technique is proposed. In DMAS algorithm, the backscattered signals received from numerical breast phantoms simulated using the finite-difference time-domain method are time shifted, multiplied in pair, and the products are summed to form a synthetic focal point. The effectiveness of the DMAS algorithm is shown by applying it to backscattered signals received from a variety of numerical breast phantoms. The reconstructed images illustrate improvement in identification of embedded malignant tumors over the delay-and-sum algorithm. Successful detection and localization of tumors as small as 2 mm in diameter are also demonstrated.
Human iris recognition in post-mortem subjects: Study and database

This paper presents a unique study of post-mortem human iris recognition and the first known to us database of near-infrared and visible-light iris images of deceased humans collected up to almost 17 days after death. We used four different iris recognition methods to analyze the dynamics of iris quality decay in short-term comparisons (samples collected up to 60 hours after death) and long-term comparisons (for samples acquired up to 407 hours after demise). This study shows that post-mortem iris recognition is possible and occasionally works even 17 days after death. These conclusions contradict a promulgated rumor that iris is unusable shortly after decease. We make this dataset publicly available to let others verify our findings and to research new aspects of this important and unfamiliar topic. We are not aware of any earlier papers offering post-mortem human iris images and such comprehensive analysis employing four different matchers.
"I just wanted to tell you that loperamide WILL WORK": a web-based study of extra-medical use of loperamide.

Many websites provide a means for individuals to share their experiences and knowledge about different drugs. Such User-Generated Content (UGC) can be a rich data source to study emerging drug use practices and trends. This study examined UGC on extra-medical use of loperamide among illicit opioid users.



A website that allows for the free discussion of illicit drugs and is accessible for public viewing was selected for analysis. Web-forum posts were retrieved using web crawlers and retained in a local text database. The database was queried to extract posts with a mention of loperamide and relevant brand/slang terms. Over 1290 posts were identified. A random sample of 258 posts was coded using NVivo to identify intent, dosage, and side-effects of loperamide use.



There has been an increase in discussions related to loperamide's use by non-medical opioid users, especially in 2010-2011 Loperamide was primarily discussed as a remedy to alleviate a broad range of opioid withdrawal symptoms, and was sometimes referred to as "poor man's" methadone. Typical doses ranged 70-100mg per day, much higher than an indicated daily dose of 16mg.



This study suggests that loperamide is being used extra-medically to self-treat opioid withdrawal symptoms. There is a growing demand among people who are opioid dependent for drugs to control withdrawal symptoms, and loperamide appears to fit that role. The study also highlights the potential of the Web as a "leading edge" data source in identifying emerging drug use practices.
Spring Gravity Compensation Using the Noncircular Pulley and Cable For the Less-Spring Design

This work proposed a new method for a gravity compensation using the noncircular pulley, cable driving, and spring balancer. The noncircular pulley is arranged in the front of one transmission chain. When the mechanism apply this method for the gravity compensation of some multi-DOF and multilink manipulator, it can be achieved that a perfect gravity balance and less number of springs. To obtain the parameter and arrangement of the spring and transmission, the potential energy related to the transmission structure is discussed. Based on the invariance of all the potential energy of a static balance mechanism, some design rules and cases are presented to get the transmission function. In this study, the synthesis of the noncircular pulley is demonstrated according to the established transmission function. At last, the example of gravity compensation for two-DOF yaw-pitch mechanism is presented.
Environmental data mining and modeling based on machine learning algorithms and geostatistics

The paper presents some contemporary approaches to the spatial environmental data analysis, processing and presentation. The main topics are concentrated on the decision–oriented problems of environmental and pollution spatial data mining and modelling: valorisation and representativity of data with the help of exploratory data analysis, topological, statistical and fractal measures of monitoring networks, spatial predictions and classifications, probabilistic and risk mapping, development and application of conditional stochastic simulation models. The set of tools used consists of machine learning algorithms (MLA) – Multilayer Perceptron, General Regression Neural Networks, Probabilistic Neural Networks, Radial Basis Function Networks, Support Vector Machines and Support Vector Regression, and recently developed geostatistical predictive and simulation models. The innovative part of the report deals with integrated/hybrid models, including ML Residuals Kriging/Cokriging predictions, ML Residuals Simulated Annealing/Sequential Gaussian simulations. The objective of the integrated models is twofold: from one side ML algorithms efficiently solve problems of spatial non-stationarity, which are difficult for geostatistical approach; from another side geostatistical tools are widely and successfully applied to characterise the performance of the ML algorithms, analysing the quality and quantity of the spatially structured information extracted from data by ML. Moreover, mixture of ML data driven and geostatistical model based approaches are attractive for decision-making process.
A variable stiffness soft robotic gripper with low-melting-point alloy

In this paper, we present the design, fabrication and the function of a soft actuator embedded with Low-Melting-Point Alloy (LMPA). By melting the metal via joule heating, the phase of the metal transformed from the solid state to the liquid state, by which the stiffness of the actuator changed over nearly an order of magnitude. Through a reheating-recrystallizing circle, the actuator can self-healing and recovered from the crack state. The melting speed under different electrical currents was measured. Besides, two experiments were conducted to investigate the self-healing property and stiffness enhancement of the actuator. The LMPA could be melted within 40 seconds. By using three actuators embedding with LMPA, we implemented a soft gripper with variable mechanical stiffness. Experimental results show that the actuator not only has the self-healing property but also enhance the mechanical stiffness compared to the no-LMPA actuator (control). While the LMPA was under solid (recrystallized) state, the bending stiffness of the actuator increased up to 3.5 times over that of the control; while the pull-off force of the gripper increased 6.5 times. The LMPA provide the soft robots with capacities of variable mechanical stiffness, and the self-healing provide a more robust capability for the system.
A novel wireless power transfer for in-motion EV/PHEV charging

Wireless power transfer (WPT) is a convenient, safe, and autonomous means for electric and plug-in hybrid electric vehicle charging that has seen rapid growth in recent years for stationary applications. WPT does not require bulky contacts, plugs, and wires, is not affected by dirt or weather conditions, and is as efficient as conventional charging systems. When applied in-motion, WPT additionally relives range anxiety, adds further convenience, reduces battery size, and may help increase the battery life through charge sustaining approach. This study summarizes some of the recent activities of Oak Ridge National Laboratory (ORNL) in WPT charging of EV and PHEV's inmotion. Laboratory experimental results that highlight the wireless transfer of power to a moving receiver coil as it passes a pair of transmit coils and investigation of results of insertion loss due to roadway surfacing materials. Some of the experimental lessons learned are also included in this study.
Collision avoidance and path planning of multiple UAVs using flyable paths in 3D

This paper discusses path planning of a Micro Air Vehicle (UAV) with collision avoidance in a three dimensional space. The 3D space is chosen as an urban environment where the buildings are sparsely located. The process is divided into two phases: (i) Route planning where the buildings are isolated to form a route, which is followed by (ii) Path planning in which simple Dubins paths are employed to follow through the way-points generated by the route planner. The first phase creates a network of waypoints in between the buildings which meets the minimum distance required for safe manoeuvre. Delaunay triangulation is used to isolate the buildings from each other and to produce safe waypoints between them. Once the waypoints are assured safe to pass through, the path planner uses Dubins path to produce the flyable and safe paths.
Predicate learning in neural systems: Discovering latent generative structures

Humans learn complex latent structures from their environments (e.g., natural language, mathematics, music, social hierarchies). In cognitive science and cognitive neuroscience, models that infer higher-order structures from sensory or first-order representations have been proposed to account for the complexity and flexibility of human behavior. But how do the structures that these models invoke arise in neural systems in the first place? To answer this question, we explain how a system can learn latent representational structures (i.e., predicates) from experience with wholly unstructured data. During the process of predicate learning, an artificial neural network exploits the naturally occurring dynamic properties of distributed computing across neuronal assemblies in order to learn predicates, but also to combine them compositionally, two computational aspects which appear to be necessary for human behavior as per formal theories in multiple domains. We describe how predicates can be combined generatively using neural oscillations to achieve human-like extrapolation and compositionality in an artificial neural network. The ability to learn predicates from experience, to represent structures compositionally, and to extrapolate to unseen data offers an inroads to understanding and modeling the most complex human behaviors.
Learning Styles and Learning Spaces : A Review of the Multidisciplinary Application of Experiential Learning Theory in Higher Education

Using the concept of learning style and learning space to describe the interface between learners and the learning environment in higher education, in this chapter we review studies addressing how learning style information and the experiential learning model have been applied to improve teaching and learning in sixteen different academic fields and professions. Studies suggest that experiential learning affords educators a way to design and implement teaching and learning strategies conducive to creating a learning environment beneficial for both faculty and students with possibilities for institutional wide dissemination of its core principles and practices. Drawing from the findings generated from the studies, we propose guiding principles for creating growth promoting learning spaces throughout higher education.
Aletheia - An Advanced Document Layout and Text Ground-Truthing System for Production Environments

Large-scale digitisation has led to a number of new possibilities with regard to adaptive and learning based methods in the field of Document Image Analysis and OCR. For ground truth production of large corpora, however, there is still a gap in terms of productivity. Ground truth is not only crucial for training and evaluation at the development stage of tools but also for quality assurance in the scope of production workflows for digital libraries. This paper describes Aletheia, an advanced system for accurate and yet cost-effective ground truthing of large amounts of documents. It aids the user with a number of automated and semi-automated tools which were partly developed and improved based on feedback from major libraries across Europe and from their digitisation service providers which are using the tool in a production environment. Novel features are, among others, the support of top-down ground truthing with sophisticated split and shrink tools as well as bottom-up ground truthing supporting the aggregation of lower-level elements to more complex structures. Special features have been developed to support working with the complexities of historical documents. The integrated rules and guidelines validator, in combination with powerful correction tools, enable efficient production of highly accurate ground truth.
A 12-Step Sensorless Drive for Brushless DC Motors Based on Back-EMF Differences

This paper presents a new 12-step sensorless drive for brushless dc motor based on back-electromotive force (back-EMF) differences, which are estimated from the disturbance observer (DOB) structure. Availability of rotor position information with a resolution of 30° and commutation without phase shift are the advantages of using the back-EMF differences. Unlike the conventional methods that obtain part of the back-EMF signal from the manipulation of three phase voltages, the proposed DOB structure can access the entire back-EMF profile. By designing a proper low-pass filter in the DOB structure, the back-EMF difference can be well detected with lower sensor noise. Compensation of delay commutation introduced by the filter is also discussed. Experiment results showed that the proposed sensorless drive is capable of running the motor in lower speed, and performs better under different speed and load torque as compared with the conventional method.
Mechatronic model of a novel slotless permanent magnet DC-motor with air gap winding design

This paper presents a mechatronic model of a novel slotless permanent magnet DC-motor with air gap winding. Besides technical advantages of this type of motor like high power density, high torque, very low weight and high efficiency, the motor design allows a very precise and efficient modelling with limited effort. A nonlinear model of magnetic field density can be extracted from a detailed nonlinear FE-model build in ANSYS/Maxwell, approximated by Fourier series and then used to model driving torque and back EMF, representing the coupling between electrical and mechanical subsystems. Analytically founded numerical models for driving torque and back EMF will be given. Real geometry of the phase winding is taken into account to improve model accuracy. The electrical subsystem will be described as coupled three phase system, whose parameters can also be extracted from the nonlinear FE-model with high accuracy. Together with a mechanical model of the rotor a MATLAB/Simulink model is build and extended by models of the hall sensors to detect rotor position and commutation logic to control the HEX-Bridge during operation. Finally, results of a complex simulation model, based on the parameters of the prototype of a wheel-hub motor, implementing the new motor design, are getting shown. Simulation results compare very well to measured data. Simulation time is very short due to the efficient approximation of magnetic flux density.
Sparse Malicious False Data Injection Attacks and Defense Mechanisms in Smart Grids

This paper discusses malicious false data injection attacks on the wide area measurement and monitoring system in smart grids. First, methods of constructing sparse stealth attacks are developed for two typical scenarios: 1) random attacks in which arbitrary measurements can be compromised; and 2) targeted attacks in which specified state variables are modified. It is already demonstrated that stealth attacks can always exist if the number of compromised measurements exceeds a certain value. In this paper, it is found that random undetectable attacks can be accomplished by modifying only a much smaller number of measurements than this value. It is well known that protecting the system from malicious attacks can be achieved by making a certain subset of measurements immune to attacks. An efficient greedy search algorithm is then proposed to quickly find this subset of measurements to be protected to defend against stealth attacks. It is shown that this greedy algorithm has almost the same performance as the brute-force method, but without the combinatorial complexity. Third, a robust attack detection method is discussed. The detection method is designed based on the robust principal component analysis problem by introducing element-wise constraints. This method is shown to be able to identify the real measurements, as well as attacks even when only partial observations are collected. The simulations are conducted based on IEEE test systems.