nils-herrmann/clean_scirepeval huggingface dataset:
Animal Bodies , Colonial Subjects : ( Re ) Locating Animality in Decolonial Thought

In this paper, I argue that animal domestication, speciesism, and other modern human-animal interactions in North America are possible because of and through the erasure of Indigenous bodies and the emptying of Indigenous lands for settler-colonial expansion. That is, we cannot address animal oppression or talk about animal liberation without naming and subsequently dismantling settler colonialism and white supremacy as political machinations that require the simultaneous exploitation and/or erasure of animal and Indigenous bodies. I begin by re-framing animality as a politics of space to suggest that animal bodies are made intelligible in the settler imagination on stolen, colonized, and re-settled Indigenous lands. Thinking through Andrea Smith’s logics of white supremacy, I then re-center anthropocentrism as a racialized and speciesist site of settler coloniality to re-orient decolonial thought toward animality. To critique the ways in which Indigenous bodies and epistemologies are at stake in neoliberal re-figurings of animals as settler citizens, I reject the colonial politics of recognition developed in Sue Donaldson and Will Kymlicka’s recent monograph, Zoopolis: A Political Theory of Animal Rights ( 2011) because it militarizes settler-colonial infrastructures of subjecthood and governmentality. I then propose a decolonized animal ethic that finds legitimacy in Indigenous cosmologies to argue that decolonization can only be reified through a totalizing disruption of those power apparatuses (i.e., settler colonialism, anthropocentrism, white supremacy, and neoliberal pluralism) that lend the settler state sovereignty, normalcy, and futurity insofar as animality is a settler-colonial particularity.
A Novel Tri-State Binary Phase Detector

In this paper a phase detector is introduced which has a similar phase detector response as the Alexander phase detector. Both the Alexander and proposed phase detector are analyzed with respect to their robustness. The analysis shows that the novel phase detector is more robust against process non-idealities than the Alexander, with a 75% reduction in the variation of static phase offsets. The proposed phase detector also consumes less power and requires less area. A CDR circuit which implements the proposed phase detector was designed and fabricated in a 0.18mum six metal layer standard CMOS process. The fabricated CDR circuit can lock to pseudo-random bit sequences (PRBS) up to 231 - 1 at data rates from 5 - 6.25Gb/s. For a PRBS of 231 - 1 at 6.25Gb/s the measured rms jitter and peak-to-peak jitter were 1.7ps and 11ps.
Assessment of catastrophic risk using Bayesian network constructed from domain knowledge and spatial data.

Prediction of natural disasters and their consequences is difficult due to the uncertainties and complexity of multiple related factors. This article explores the use of domain knowledge and spatial data to construct a Bayesian network (BN) that facilitates the integration of multiple factors and quantification of uncertainties within a consistent system for assessment of catastrophic risk. A BN is chosen due to its advantages such as merging multiple source data and domain knowledge in a consistent system, learning from the data set, inference with missing data, and support of decision making. A key advantage of our methodology is the combination of domain knowledge and learning from the data to construct a robust network. To improve the assessment, we employ spatial data analysis and data mining to extend the training data set, select risk factors, and fine-tune the network. Another major advantage of our methodology is the integration of an optimal discretizer, informative feature selector, learners, search strategies for local topologies, and Bayesian model averaging. These techniques all contribute to a robust prediction of risk probability of natural disasters. In the flood disaster's study, our methodology achieved a better probability of detection of high risk, a better precision, and a better ROC area compared with other methods, using both cross-validation and prediction of catastrophic risk based on historic data. Our results suggest that BN is a good alternative for risk assessment and as a decision tool in the management of catastrophic risk.
Insole plantar pressure systems in the gait analysis of post-stroke rehabilitation

This article presents a review of the application of insole plantar pressure sensor system in recognition and analysis of the hemiplegic gait in stroke patients. Based on the review, tailor made 3D insoles for plantar pressure measurement were designed and fabricated. The function is to compare with that of conventional flat insoles. Tailor made 3D contour of the insole can improve the contact between insole and foot and enable sampling plantar pressure at a high reproducibility.
New prototype of hybrid 3D-biometric facial recognition system

In the last decades, a lot of 3D face recognition techniques have been proposed. They can be divided into three parts, holistic matching techniques, feature-based techniques and hybrid techniques. In this paper, a hybrid technique is used, where, a prototype of a new hybrid face recognition technique depends on 3D face scan images are designed, simulated and implemented. Some geometric rules are used for analyzing and mapping the face. Image processing is used to get the twodimensional values of predetermined and specific facial points, software programming is used to perform a three-dimensional coordinates of the predetermined points and to calculate several geometric parameter ratios and relations. Neural network technique is used for processing the calculated geometric parameters and then performing facial recognition. The new design is not affected by variant pose, illumination and expression and has high accurate level compared with the 2D analysis. Moreover, the proposed algorithm is of higher performance than latest’s published biometric recognition algorithms in terms of cost, confidentiality of results, and availability of design tools.
Strategy as a portfolio of real options.

In financial terms, a business strategy is much more like a series of options than like a single projected cash flow. Executing a strategy almost always involves making a sequence of major decisions. Some actions are taken immediately while others are deliberately deferred so that managers can optimize their choices as circumstances evolve. While executives readily grasp the analogy between strategy and real options, until recently the mechanics of option pricing was so complex that few companies found it practical to use when formulating strategy. But advances in both computing power and our understanding of option pricing over the last 20 years now make it feasible to apply real-options thinking to strategic decision making. To analyze a strategy as a portfolio of related real options, this article exploits a framework presented by the author in "Investment Opportunities as Real Options: Getting Started on the Numbers" (HBR July-August 1998). That article explained how to get from discounted-cash-flow value to option value for a typical project; in other words, it was about reaching a number. This article extends that framework, exploring how, once you've worked out the numbers, you can use option pricing to improve decision making about the sequence and timing of a portfolio of strategic investments. Timothy Luehrman shows executives how to plot their strategies in two-dimensional "option space," giving them a way to "draw" a strategy in terms that are neither wholly strategic nor wholly financial, but some of both. Such pictures inject financial discipline and new insight into how a company's future opportunities can be actively cultivated and harvested.
Characterizing avian survival along a rural‐to‐urban land use gradient

Many avian species persist in human-dominated landscapes; however, little is known about the demographic consequences of urbanization in these populations. Given that urban habitats introduce novel benefits (e.g., anthropogenic resources) and pressures (e.g., mortality risks), conflicting mechanisms have been hypothesized to drive the dynamics of urban bird populations. Top-down processes such as predation predict reduced survivorship in suburban and urban habitats, whereas bottom-up processes, such as increased resource availability, predict peak survival in suburban habitats. In this study, we use mark–recapture data of seven focal species encountered between 2000 and 2012 to test hypotheses about the processes that regulate avian survival along an urbanization gradient in greater Washington, D.C., USA. American Robin, Gray Catbird, Northern Cardinal, and Song Sparrow exhibited peak survival at intermediate and upper portions of the rural-to-urban gradient; this pattern supports the hypothesis that bottom-up processes (e.g., resource availability) can drive patterns of avian survival in some species. In contrast, Carolina Chickadee showed no response and Carolina and House Wren showed a slightly negative response to urban land cover. These contrasting results underscore the need for comparative studies documenting the mechanisms that drive demography and how those factors differentially affect urban adapted and urban avoiding species.
How textual quality of online reviews affect classification performance: a case of deep learning sentiment analysis

Cognitive computing is an interdisciplinary research field that simulates human thought processes in a computerized model. One application for cognitive computing is sentiment analysis on online reviews, which reflects opinions and attitudes toward products and services experienced by consumers. A high level of classification performance facilitates decision making for both consumers and firms. However, while much effort has been made to propose advanced classification algorithms to improve the performance, the importance of the textual quality of the data has been ignored. This research explores the impact of two influential textual features, namely the word count and review readability, on the performance of sentiment classification. We apply three representative deep learning techniques, namely SRN, LSTM, and CNN, to sentiment analysis tasks on a benchmark movie reviews dataset. Multiple regression models are further employed for statistical analysis. Our findings show that the dataset with reviews having a short length and high readability could achieve the best performance compared with any other combinations of the levels of word count and readability and that controlling the review length is more effective for garnering a higher level of accuracy than increasing the readability. Based on these findings, a practical application, i.e., a text evaluator or a website plug-in for text evaluation, can be developed to provide a service of review editorials and quality control for crowd-sourced review websites. These findings greatly contribute to generating more valuable reviews with high textual quality to better serve sentiment analysis and decision making.
DVB-S2 full cross-layer design for QoS provision

The second generation of the Digital Video Broadcasting standard for Satellite transmission, DVB-S2, is the evolution of the highly successful DVB-S satellite distribution technology. DVB-S2 has benefited from the latest progress in channel coding and modulation such as Low Density Parity Check Codes and higher order constellations to achieve performance that approaches Shannon¿s theoretical limit. We present a cross-layer design for Quality-of-Service (QoS) provision of interactive services, which is not specified in the standard. Our cross-layer approach exploits the satellite channel characteristics of space-time correlation via a cross-layer queueing architecture and an adaptive cross-layer scheduling policy. We show that our approach not only allows system load control but also rate adaptation to channel conditions and traffic demands on the coverage area. We also present the extension of our cross-layer design for mobile gateways focusing on the railway scenario. We illustrate the trade-off between system-wide and individual throughput by means of simulation, and that this trade-off could be a key metric in measuring the service level of DVB-S2 Broadband Service.
A 2.6 nW, 0.45 V Temperature-Compensated Subthreshold CMOS Voltage Reference

A voltage reference circuit operating with all transistors biased in weak inversion, providing a mean reference voltage of 257.5 mV, has been fabricated in 0.18 m CMOS technology. The reference voltage can be approximated by the difference of transistor threshold voltages at room temperature. Accurate subthreshold design allows the circuit to work at room temperature with supply voltages down to 0.45 V and an average current consumption of 5.8 nA. Measurements performed over a set of 40 samples showed an average temperature coefficient of 165 ppm/ C with a standard deviation of 100 ppm/ C, in a temperature range from 0 to 125°C. The mean line sensitivity is ≈0.44%/V, for supply voltages ranging from 0.45 to 1.8 V. The power supply rejection ratio measured at 30 Hz and simulated at 10 MHz is lower than -40 dB and -12 dB, respectively. The active area of the circuit is ≈0.043mm2.
On Iris Spoofing Using Print Attack

Human iris contains rich textural information which serves as the key information for biometric identifications. It is very unique and one of the most accurate biometric modalities. However, spoofing techniques can be used to obfuscate or impersonate identities and increase the risk of false acceptance or false rejection. This paper revisits iris recognition with spoofing attacks and analyzes their effect on the recognition performance. Specifically, print attack with contact lens variations is used as the spoofing mechanism. It is observed that print attack and contact lens, individually and in conjunction, can significantly change the inter-personal and intra-personal distributions and thereby increase the possibility to deceive the iris recognition systems. The paper also presents the IIITD iris spoofing database, which contains over 4800 iris images pertaining to over 100 individuals with variations due to contact lens, sensor, and print attack. Finally, the paper also shows that cost effective descriptor approaches may help in counter-measuring spooking attacks.
Approaches to Participation in Urban Planning Theories

Raine Mäntysalo The purpose of this article is to make an overview of postWWII urban planning theories from the point of view of participation. How have the ideas of public accountability, deliberative democracy and involvement of special interests developed from one theory to another? The urban planning theories examined are rational-comprehensive planning theory, advocacy planning theory, incrementalist planning theory and the two branches of communicative planning theory: planning as consensus-seeking and planning as management of conflicts.
Vocabulary Learning Strategies in an ESP Context

The paper focuses on vocabulary learning strategies as a subcategory of language learning strategies and their instruction within the ESP context at the Faculty of Maritime Studies and Transport in Portorož. Vocabulary strategy instruction will be implemented at our faculty as part of a broader PhD research into the effect of language learning strategy instruction on strategy use and subject-specific and general language acquisition. Additional variables that will be taken into consideration are language proficiency, motivation and learning styles of the students. The introductory section in which the situation that triggered my PhD research is presented is followed by a theoretical introduction to the concept of language and vocabulary learning strategies. The aspects that the paper focuses on are the central role of lexis within ESP, vocabulary learning strategy taxonomies, and the presentation of research studies made in the examined field to date. The final section presents the explicit vocabulary learning strategy instruction model. In the conclusion, some implications for teaching can be found.  Slovensko društvo uèiteljev tujega strokovnega jezika.
Balance as bias : global warming and the US prestige press $

This paper demonstrates that US prestige-press coverage of global warming from 1988 to 2002 has contributed to a significant divergence of popular discourse from scientific discourse. This failed discursive translation results from an accumulation of tactical media responses and practices guided by widely accepted journalistic norms. Through content analysis of US prestige press— meaning the New York Times, the Washington Post, the Los Angeles Times, and the Wall Street Journal—this paper focuses on the norm of balanced reporting, and shows that the prestige press’s adherence to balance actually leads to biased coverage of both anthropogenic contributions to global warming and resultant action. r 2003
Impulsivity, Compulsivity, and Top-Down Cognitive Control

Impulsivity is the tendency to act prematurely without foresight. Behavioral and neurobiological analysis of this construct, with evidence from both animal and human studies, defines several dissociable forms depending on distinct cortico-striatal substrates. One form of impulsivity depends on the temporal discounting of reward, another on motor or response disinhibition. Impulsivity is commonly associated with addiction to drugs from different pharmacological classes, but its causal role in human addiction is unclear. We characterize in neurobehavioral and neurochemical terms a rodent model of impulsivity based on premature responding in an attentional task. Evidence is surveyed that high impulsivity on this task precedes the escalation subsequently of cocaine self-administration behavior, and also a tendency toward compulsive cocaine-seeking and to relapse. These results indicate that the vulnerability to stimulant addiction may depend on an impulsivity endophenotype. Implications of these findings for the etiology, development, and treatment of drug addiction are considered.
Robust Convolutional Neural Networks under Adversarial Noise

Recent studies have shown that Convolutional Neural Networks (CNNs) are vulnerable to a small perturbation of input called “adversarial examples”. In this work, we propose a new feedforward CNN that improves robustness in the presence of adversarial noise. Our model uses stochastic additive noise added to the input image and to the CNN models. The proposed model operates in conjunction with a CNN trained with standard backpropagation algorithm. In particular, convolution, max-pooling, and ReLU layers are modified to benefit from the noise model. Our model is parameterized by only a mean and variance per pixel which simplifies computations and makes our method scalable to a deep architecture. The proposed model outperforms the standard CNN by 13.12% on ImageNet and 7.37% on CIFAR-10 under adversarial noise at the expense of 0.28% of accuracy drop when used in the original dataset – with no added noise.
Novel applications of BCI technology: Psychophysiological optimization of working conditions in industry

Brain Computer Interface (BCI) research advanced for more than forty years, providing a rich variety of sophisticated data analysis methods. Yet, most BCI studies have been restricted to the laboratory with controlled and undisturbed environment. BCI research was aiming at developing tools for communication and control. Recently, BCI research has broadened to explore novel applications for improved man-machine interaction. In the present study, we investigated the option to employ neurotechnology in an industrial environment for the psychophysiological optimization of working conditions in such settings. Our findings suggest that it is possible to use BCI-related analysis techniques to qualify responses of an operator by assessing the depth of cognitive processing on the basis of neuronal correlates of behaviourally relevant measures. This could lead to assistive technologies helping to avoid accidents in working environments by designing a collaborative workspace in which the environment takes into account the actual cognitive mental state of the operator.
Reconfigurable manufacturing systems: Key to future manufacturing

Presented in this article is a review of manufacturing techniques and introduction of reconfigurable manufacturing systems; a new paradigm in manufacturing which is designed for rapid adjustment of production capacity and functionality, in response to new market conditions. A definition of reconfigurable manufacturing systems is outlined and an overview of available manufacturing techniques, their key drivers and enablers, and their impacts, achievements and limitations is presented. A historical review of manufacturing from the point-of-view of the major developments in the market, technology and sciences issues affecting manufacturing is provided. The new requirements for manufacturing are discussed and characteristics of reconfigurable manufacturing systems and their key role in future manufacturing are explained. The paper is concluded with a brief review of specific technologies and research issues related to RMSs.
Design-oriented compact models for CNTFETs

This paper deals with the compact modeling of an emerging technology: the carbon nanotube field-effect transistor (CNTFET). The paper proposed two design-oriented compact models, the first one for CNTFET with a classical behavior (MOSFET-like CNTFET), and the second one for CNTFET with an ambipolar behavior (Schottky-barrier CNTFET). Both models have been compared with exact numerical simulations and then implemented in VHDL-AMS
Short segment pedicle screw instrumentation and augmentation vertebroplasty in lumbar burst fractures: an experience

To assess the efficacy and feasibility of vertebroplasty and posterior short-segment pedicle screw fixation for the treatment of traumatic lumbar burst fractures. Short-segment pedicle screw instrumentation is a well described technique to reduce and stabilize thoracic and lumbar spine fractures. It is relatively a easy procedure but can only indirectly reduce a fractured vertebral body, and the means of augmenting the anterior column are limited. Hardware failure and a loss of reduction are recognized complications caused by insufficient anterior column support. Patients with traumatic lumbar burst fractures without neurologic deficits were included. After a short segment posterior reduction and fixation, bilateral transpedicular reduction of the endplate was performed using a balloon, and polymethyl methacrylate cement was injected. Pre-operative and post-operative central and anterior heights were assessed with radiographs and MRI. Sixteen patients underwent this procedure, and a substantial reduction of the endplates could be achieved with the technique. All patients recovered uneventfully, and the neurologic examination revealed no deficits. The post-operative radiographs and magnetic resonance images demonstrated a good fracture reduction and filling of the bone defect without unwarranted bone displacement. The central and anterior height of the vertebral body could be restored to 72 and 82% of the estimated intact height, respectively. Complications were cement leakage in three cases without clinical implications and one superficial wound infection. Posterior short-segment pedicle fixation in conjunction with balloon vertebroplasty seems to be a feasible option in the management of lumbar burst fractures, thereby addressing all the three columns through a single approach. Although cement leakage occurred but had no clinical consequences or neurological deficit.